-
-FR Legends APK 0.3.3.1: The Ultimate Drifting Game for Android
-If you are a fan of drifting and racing games, you might have heard of FR Legends, one of the most popular and realistic drifting games for mobile devices. FR Legends is a game that lets you experience the thrill of drifting with customizable cars, realistic physics, various game modes, online multiplayer, stunning graphics and sound, and more.
-In this article, we will tell you everything you need to know about FR Legends APK 0.3.3.1, the latest version of this amazing game that you can download and install on your Android device for free.
-fr legends apk 0.3.3.1 Download — https://jinyurl.com/2uNM2c
What is FR Legends?
-FR Legends is a game developed by TWIN TURBO TECH CO., LTD, a company based in China that specializes in creating high-quality racing games for mobile platforms.
-FR Legends stands for Front-engine, Rear-wheel drive Legend cars, which are the type of cars that you can drive and customize in this game.
-FR Legends is a game that focuses on drifting, which is a driving technique where the driver intentionally oversteers the car to make it slide sideways while maintaining control and speed.
-Drifting is not only fun and challenging, but also a form of art and expression that requires skill and creativity.
-FR Legends is a game that lets you unleash your inner drifter and show off your style and skills to other players around the world.
-What are the features of FR Legends APK 0.3.3.1?
-Customizable cars
-One of the best features of FR Legends is that you can customize your car to suit your preferences and personality.
-You can choose from a variety of cars that are inspired by real-life models such as Toyota AE86, Nissan S13, BMW E30, Mazda RX
-7, and more.
-You can also modify your car's engine, suspension, tires, brakes, exhaust, turbo, transmission, and more to improve its performance and handling.
-fr legends apk 0.3.3.1 download
-Moreover, you can change your car's appearance by changing its color, paint, decals, stickers, body kits, spoilers, wheels, lights, and more to make it look unique and cool.
-Realistic physics
-Another great feature of FR Legends is that it has realistic physics that simulate the behavior of the car and the environment.
-You can feel the weight, speed, traction, inertia, and gravity of your car as you drift and race on different tracks and terrains.
-You can also see the smoke, sparks, dust, and skid marks that your car leaves behind as you slide and burn rubber.
-The game also has dynamic weather and lighting effects that change the atmosphere and visibility of the tracks.
-Various game modes
-FR Legends has various game modes that you can choose from depending on your mood and preference.
-You can play the Career mode, where you can start from the bottom and work your way up to become a legendary drifter. You can compete in different events and challenges that test your skills and earn coins and reputation.
-You can play the Free mode, where you can practice your drifting skills and explore the tracks without any pressure or limitations. You can also customize your car and settings to your liking.
-You can play the Arcade mode, where you can enjoy a casual and fun drifting experience with simple controls and objectives. You can also unlock new cars and tracks as you progress.
-Online multiplayer
-FR Legends also has an online multiplayer feature that lets you connect with other players around the world and challenge them to drift battles.
-You can join or create a room with up to 8 players and choose a track and a game mode to play. You can also chat with other players and make friends or rivals.
-You can compete in two types of drift battles: Tandem and Solo. In Tandem battles, you have to follow or lead another player's car as closely as possible while drifting. In Solo battles, you have to score higher than your opponent by drifting better and faster.
-Stunning graphics and sound
-FR Legends also has stunning graphics and sound that make the game more immersive and realistic.
-The game has 3D graphics that are detailed and smooth. The cars look authentic and have different animations and effects. The tracks look diverse and have different scenery and landmarks. The game also has a dynamic camera that follows your car from different angles.
-The game also has realistic sound effects that match the action on the screen. You can hear the engine roar, the tires screech, the turbo whistle, the exhaust pop, and more. You can also hear the crowd cheer, the announcer commentate, and the music play in the background.
-How to download and install FR Legends APK 0.3.3.1?
-Step 1: Download the APK file from a trusted source
-To download FR Legends APK 0.3.3.1, you need to find a trusted source that provides the latest version of the file. You can use Google or any other search engine to find such sources.
-One of the sources that we recommend is [FR Legends APK 0.3.3.1 Download], which is a website that offers safe and fast downloads of various APK files for Android devices.
-Step 2: Enable unknown sources on your device
-To install FR Legends APK 0.3.3.1, you need to enable unknown sources on your device. This is because FR Legends APK 0.3.3.1 is not available on the Google Play Store, so you need to allow your device to install apps from other sources.
-To enable unknown sources on your device, follow these steps:
-
-Go to Settings > Security > Unknown sources
-Toggle on the switch or check the box to enable unknown sources
-A warning message will appear. Tap OK or Confirm to proceed
-
-Step 3: Locate and install the APK file
-To locate and install FR Legends APK 0.3.3.1, follow these steps:
-
-Go to your device's file manager or download manager
-Find the FR Legends APK 0.3.3.1 file that you downloaded in step 1. Tap on the file to open it
-A pop-up message will appear. Tap Install to start the installation process
-Wait for the installation to finish. It may take a few seconds or minutes depending on your device and internet speed
-
-Step 4: Launch the game and enjoy
-To launch the game and enjoy, follow these steps:
-
-Go to your device's app drawer or home screen
-Find the FR Legends icon and tap on it to open the game
-Grant the necessary permissions and accept the terms and conditions
-Choose your language and region
-Create your profile and customize your settings
-Start playing and drifting with FR Legends APK 0.3.3.1
-
-How to play FR Legends APK 0.3.3.1?
-Choose your car and customize it
-To choose your car and customize it, follow these steps:
-
-Tap on the Garage icon on the main menu
-Swipe left or right to browse through the available cars
-Tap on the car that you want to drive and customize
-Tap on the Customize icon on the bottom right corner
-Swipe left or right to access different categories of customization such as Engine, Suspension, Body, Paint, Decals, etc.
-Tap on the category that you want to modify and select the option that you want to apply
-You can preview the changes on your car by tapping on the Preview icon on the top right corner
-You can also rotate, zoom, and move your car by using your fingers on the screen
-When you are satisfied with your customization, tap on the Save icon on the top left corner
-You can also buy new cars or parts with coins that you earn by playing the game
-
-Select a game mode and a track
-To select a game mode and a track, follow these steps:
-
-Tap on the Play icon on the main menu
-Swipe left or right to choose between Career mode, Free mode, or Arcade mode
-Tap on the mode that you want to play
-Swipe left or right to choose between different tracks such as Ebisu, Meihan, Sekia Hills, etc.
-Tap on the track that you want to play
-You can also change the weather, time of day, and difficulty level by tapping on the icons on the bottom of the screen
-When you are ready, tap on the Start icon on the top right corner
-
-Control your car with simple gestures
-To control your car with simple gestures, follow these steps:
-
-To accelerate, press and hold the gas pedal on the right side of the screen
-To brake, press and hold the brake pedal on the left side of the screen
-To steer, swipe left or right on the steering wheel on the bottom center of the screen
-To drift, swipe up or down on the handbrake lever on the right side of the screen
-To change the camera angle, tap on the camera icon on the top left corner of the screen
-To pause the game, tap on the pause icon on the top right corner of the screen
-
-Earn coins and reputation by drifting and racing
-To earn coins and reputation by drifting and racing, follow these steps:
-
-When you are playing a game mode, you will see a score meter on the top center of the screen that shows your current score and combo
-You can increase your score and combo by drifting, overtaking, following, or leading other cars
-You can also perform tricks such as donuts, 360s, wall taps, etc. to earn extra points
-The longer and better you drift, the higher your score and combo will be
-However, if you crash, spin out, or stop drifting, your score and combo will reset
-At the end of each game mode, you will see a summary screen that shows your total score, coins earned, reputation earned, and rank achieved
-You can use coins to buy new cars or parts in the garage
-You can use reputation to unlock new tracks and events in the career mode
-You can also compare your scores and ranks with other players on the leaderboard
-
-Conclusion
-FR Legends APK 0.3.3.1 is a game that lets you experience the thrill of drifting with customizable cars, realistic physics, various game modes, online multiplayer, stunning graphics and sound, and more.
-If you are a fan of drifting and racing games, you should definitely download and install FR Legends APK 0.3.3.1 on your Android device for free.
-You will not regret it as you will have hours of fun and excitement with this amazing game.
-So what are you waiting for? Download FR Legends APK 0.3.3.1 now and start drifting like a legend!
-FAQs
-Q: Is FR Legends APK 0.3.3.1 safe to download and install?
-A: Yes, FR Legends APK 0.3.3.1 is safe to download and install as long as you get it from a trusted source such as [FR Legends APK 0.3.3.1 Download]. However, you should always scan any APK file with an antivirus software before installing it on your device.
-Q: Is FR Legends APK 0.3.3.1 compatible with my device?
-A: FR Legends APK 0.3.3.1 is compatible with most Android devices that have Android 4.1 or higher version installed. However, some devices may have performance issues or bugs due to different hardware specifications or software versions.
-Q: How can I update FR Legends APK 0.3.3.1?
-A: To update FR Legends APK 0.3.3.1, you need to download and install the latest version of the file from a trusted source such as [FR Legends APK 0.3.3.1 Download]. You may also need to uninstall the previous version of the game before installing the new one.
-Q: How can I contact the developer of FR Legends?
-A: You can contact the developer of FR Legends by sending an email to frlegends@twinturbo.co or by visiting their official website at https://www.twinturbo.co/.
-Q: How can I support the development of FR Legends?
-A: You can support the development of FR Legends by rating and reviewing the game on Google Play Store or any other app store that you downloaded it from.
401be4b1e0AudioCraft — MOS
- {% endblock %}
-
-
-
-
AudioCraft — MOS
- {% block content %}{% endblock %}
-
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diff --git a/spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/align_trans.py b/spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/align_trans.py
deleted file mode 100644
index ab5f1df002bc19556ae8a75cabf56310084785a9..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/align_trans.py
+++ /dev/null
@@ -1,304 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Mon Apr 24 15:43:29 2017
-@author: zhaoy
-"""
-import numpy as np
-import cv2
-
-# from scipy.linalg import lstsq
-# from scipy.ndimage import geometric_transform # , map_coordinates
-
-from models.mtcnn.mtcnn_pytorch.src.matlab_cp2tform import get_similarity_transform_for_cv2
-
-# reference facial points, a list of coordinates (x,y)
-REFERENCE_FACIAL_POINTS = [
- [30.29459953, 51.69630051],
- [65.53179932, 51.50139999],
- [48.02519989, 71.73660278],
- [33.54930115, 92.3655014],
- [62.72990036, 92.20410156]
-]
-
-DEFAULT_CROP_SIZE = (96, 112)
-
-
-class FaceWarpException(Exception):
- def __str__(self):
- return 'In File {}:{}'.format(
- __file__, super.__str__(self))
-
-
-def get_reference_facial_points(output_size=None,
- inner_padding_factor=0.0,
- outer_padding=(0, 0),
- default_square=False):
- """
- Function:
- ----------
- get reference 5 key points according to crop settings:
- 0. Set default crop_size:
- if default_square:
- crop_size = (112, 112)
- else:
- crop_size = (96, 112)
- 1. Pad the crop_size by inner_padding_factor in each side;
- 2. Resize crop_size into (output_size - outer_padding*2),
- pad into output_size with outer_padding;
- 3. Output reference_5point;
- Parameters:
- ----------
- @output_size: (w, h) or None
- size of aligned face image
- @inner_padding_factor: (w_factor, h_factor)
- padding factor for inner (w, h)
- @outer_padding: (w_pad, h_pad)
- each row is a pair of coordinates (x, y)
- @default_square: True or False
- if True:
- default crop_size = (112, 112)
- else:
- default crop_size = (96, 112);
- !!! make sure, if output_size is not None:
- (output_size - outer_padding)
- = some_scale * (default crop_size * (1.0 + inner_padding_factor))
- Returns:
- ----------
- @reference_5point: 5x2 np.array
- each row is a pair of transformed coordinates (x, y)
- """
- # print('\n===> get_reference_facial_points():')
-
- # print('---> Params:')
- # print(' output_size: ', output_size)
- # print(' inner_padding_factor: ', inner_padding_factor)
- # print(' outer_padding:', outer_padding)
- # print(' default_square: ', default_square)
-
- tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
- tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
-
- # 0) make the inner region a square
- if default_square:
- size_diff = max(tmp_crop_size) - tmp_crop_size
- tmp_5pts += size_diff / 2
- tmp_crop_size += size_diff
-
- # print('---> default:')
- # print(' crop_size = ', tmp_crop_size)
- # print(' reference_5pts = ', tmp_5pts)
-
- if (output_size and
- output_size[0] == tmp_crop_size[0] and
- output_size[1] == tmp_crop_size[1]):
- # print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size))
- return tmp_5pts
-
- if (inner_padding_factor == 0 and
- outer_padding == (0, 0)):
- if output_size is None:
- # print('No paddings to do: return default reference points')
- return tmp_5pts
- else:
- raise FaceWarpException(
- 'No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
-
- # check output size
- if not (0 <= inner_padding_factor <= 1.0):
- raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
-
- if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0)
- and output_size is None):
- output_size = tmp_crop_size * \
- (1 + inner_padding_factor * 2).astype(np.int32)
- output_size += np.array(outer_padding)
- # print(' deduced from paddings, output_size = ', output_size)
-
- if not (outer_padding[0] < output_size[0]
- and outer_padding[1] < output_size[1]):
- raise FaceWarpException('Not (outer_padding[0] < output_size[0]'
- 'and outer_padding[1] < output_size[1])')
-
- # 1) pad the inner region according inner_padding_factor
- # print('---> STEP1: pad the inner region according inner_padding_factor')
- if inner_padding_factor > 0:
- size_diff = tmp_crop_size * inner_padding_factor * 2
- tmp_5pts += size_diff / 2
- tmp_crop_size += np.round(size_diff).astype(np.int32)
-
- # print(' crop_size = ', tmp_crop_size)
- # print(' reference_5pts = ', tmp_5pts)
-
- # 2) resize the padded inner region
- # print('---> STEP2: resize the padded inner region')
- size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
- # print(' crop_size = ', tmp_crop_size)
- # print(' size_bf_outer_pad = ', size_bf_outer_pad)
-
- if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
- raise FaceWarpException('Must have (output_size - outer_padding)'
- '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
-
- scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
- # print(' resize scale_factor = ', scale_factor)
- tmp_5pts = tmp_5pts * scale_factor
- # size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
- # tmp_5pts = tmp_5pts + size_diff / 2
- tmp_crop_size = size_bf_outer_pad
- # print(' crop_size = ', tmp_crop_size)
- # print(' reference_5pts = ', tmp_5pts)
-
- # 3) add outer_padding to make output_size
- reference_5point = tmp_5pts + np.array(outer_padding)
- tmp_crop_size = output_size
- # print('---> STEP3: add outer_padding to make output_size')
- # print(' crop_size = ', tmp_crop_size)
- # print(' reference_5pts = ', tmp_5pts)
-
- # print('===> end get_reference_facial_points\n')
-
- return reference_5point
-
-
-def get_affine_transform_matrix(src_pts, dst_pts):
- """
- Function:
- ----------
- get affine transform matrix 'tfm' from src_pts to dst_pts
- Parameters:
- ----------
- @src_pts: Kx2 np.array
- source points matrix, each row is a pair of coordinates (x, y)
- @dst_pts: Kx2 np.array
- destination points matrix, each row is a pair of coordinates (x, y)
- Returns:
- ----------
- @tfm: 2x3 np.array
- transform matrix from src_pts to dst_pts
- """
-
- tfm = np.float32([[1, 0, 0], [0, 1, 0]])
- n_pts = src_pts.shape[0]
- ones = np.ones((n_pts, 1), src_pts.dtype)
- src_pts_ = np.hstack([src_pts, ones])
- dst_pts_ = np.hstack([dst_pts, ones])
-
- # #print(('src_pts_:\n' + str(src_pts_))
- # #print(('dst_pts_:\n' + str(dst_pts_))
-
- A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
-
- # #print(('np.linalg.lstsq return A: \n' + str(A))
- # #print(('np.linalg.lstsq return res: \n' + str(res))
- # #print(('np.linalg.lstsq return rank: \n' + str(rank))
- # #print(('np.linalg.lstsq return s: \n' + str(s))
-
- if rank == 3:
- tfm = np.float32([
- [A[0, 0], A[1, 0], A[2, 0]],
- [A[0, 1], A[1, 1], A[2, 1]]
- ])
- elif rank == 2:
- tfm = np.float32([
- [A[0, 0], A[1, 0], 0],
- [A[0, 1], A[1, 1], 0]
- ])
-
- return tfm
-
-
-def warp_and_crop_face(src_img,
- facial_pts,
- reference_pts=None,
- crop_size=(96, 112),
- align_type='smilarity'):
- """
- Function:
- ----------
- apply affine transform 'trans' to uv
- Parameters:
- ----------
- @src_img: 3x3 np.array
- input image
- @facial_pts: could be
- 1)a list of K coordinates (x,y)
- or
- 2) Kx2 or 2xK np.array
- each row or col is a pair of coordinates (x, y)
- @reference_pts: could be
- 1) a list of K coordinates (x,y)
- or
- 2) Kx2 or 2xK np.array
- each row or col is a pair of coordinates (x, y)
- or
- 3) None
- if None, use default reference facial points
- @crop_size: (w, h)
- output face image size
- @align_type: transform type, could be one of
- 1) 'similarity': use similarity transform
- 2) 'cv2_affine': use the first 3 points to do affine transform,
- by calling cv2.getAffineTransform()
- 3) 'affine': use all points to do affine transform
- Returns:
- ----------
- @face_img: output face image with size (w, h) = @crop_size
- """
-
- if reference_pts is None:
- if crop_size[0] == 96 and crop_size[1] == 112:
- reference_pts = REFERENCE_FACIAL_POINTS
- else:
- default_square = False
- inner_padding_factor = 0
- outer_padding = (0, 0)
- output_size = crop_size
-
- reference_pts = get_reference_facial_points(output_size,
- inner_padding_factor,
- outer_padding,
- default_square)
-
- ref_pts = np.float32(reference_pts)
- ref_pts_shp = ref_pts.shape
- if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
- raise FaceWarpException(
- 'reference_pts.shape must be (K,2) or (2,K) and K>2')
-
- if ref_pts_shp[0] == 2:
- ref_pts = ref_pts.T
-
- src_pts = np.float32(facial_pts)
- src_pts_shp = src_pts.shape
- if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
- raise FaceWarpException(
- 'facial_pts.shape must be (K,2) or (2,K) and K>2')
-
- if src_pts_shp[0] == 2:
- src_pts = src_pts.T
-
- # #print('--->src_pts:\n', src_pts
- # #print('--->ref_pts\n', ref_pts
-
- if src_pts.shape != ref_pts.shape:
- raise FaceWarpException(
- 'facial_pts and reference_pts must have the same shape')
-
- if align_type is 'cv2_affine':
- tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
- # #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm))
- elif align_type is 'affine':
- tfm = get_affine_transform_matrix(src_pts, ref_pts)
- # #print(('get_affine_transform_matrix() returns tfm=\n' + str(tfm))
- else:
- tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
- # #print(('get_similarity_transform_for_cv2() returns tfm=\n' + str(tfm))
-
- # #print('--->Transform matrix: '
- # #print(('type(tfm):' + str(type(tfm)))
- # #print(('tfm.dtype:' + str(tfm.dtype))
- # #print( tfm
-
- face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
-
- return face_img, tfm
diff --git a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/diffusionmodules/model.py b/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 2746d74c16cd9a7a418487599399cdea8dc1bbac..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,835 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-
-from ldm.util import instantiate_from_config
-from ldm.modules.attention import LinearAttention
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
- """
- This matches the implementation in Denoising Diffusion Probabilistic Models:
- From Fairseq.
- Build sinusoidal embeddings.
- This matches the implementation in tensor2tensor, but differs slightly
- from the description in Section 3.5 of "Attention Is All You Need".
- """
- assert len(timesteps.shape) == 1
-
- half_dim = embedding_dim // 2
- emb = math.log(10000) / (half_dim - 1)
- emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
- emb = emb.to(device=timesteps.device)
- emb = timesteps.float()[:, None] * emb[None, :]
- emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
- if embedding_dim % 2 == 1: # zero pad
- emb = torch.nn.functional.pad(emb, (0,1,0,0))
- return emb
-
-
-def nonlinearity(x):
- # swish
- return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
- return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
- if self.with_conv:
- x = self.conv(x)
- return x
-
-
-class Downsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=2,
- padding=0)
-
- def forward(self, x):
- if self.with_conv:
- pad = (0,1,0,1)
- x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
- x = self.conv(x)
- else:
- x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
- return x
-
-
-class ResnetBlock(nn.Module):
- def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
- dropout, temb_channels=512):
- super().__init__()
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
-
- self.norm1 = Normalize(in_channels)
- self.conv1 = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if temb_channels > 0:
- self.temb_proj = torch.nn.Linear(temb_channels,
- out_channels)
- self.norm2 = Normalize(out_channels)
- self.dropout = torch.nn.Dropout(dropout)
- self.conv2 = torch.nn.Conv2d(out_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- self.conv_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- else:
- self.nin_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x, temb):
- h = x
- h = self.norm1(h)
- h = nonlinearity(h)
- h = self.conv1(h)
-
- if temb is not None:
- h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
- h = self.norm2(h)
- h = nonlinearity(h)
- h = self.dropout(h)
- h = self.conv2(h)
-
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- x = self.conv_shortcut(x)
- else:
- x = self.nin_shortcut(x)
-
- return x+h
-
-
-class LinAttnBlock(LinearAttention):
- """to match AttnBlock usage"""
- def __init__(self, in_channels):
- super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b,c,h,w = q.shape
- q = q.reshape(b,c,h*w)
- q = q.permute(0,2,1) # b,hw,c
- k = k.reshape(b,c,h*w) # b,c,hw
- w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
- w_ = w_ * (int(c)**(-0.5))
- w_ = torch.nn.functional.softmax(w_, dim=2)
-
- # attend to values
- v = v.reshape(b,c,h*w)
- w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
- h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
- h_ = h_.reshape(b,c,h,w)
-
- h_ = self.proj_out(h_)
-
- return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla"):
- assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
- print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
- if attn_type == "vanilla":
- return AttnBlock(in_channels)
- elif attn_type == "none":
- return nn.Identity(in_channels)
- else:
- return LinAttnBlock(in_channels)
-
-
-class Model(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = self.ch*4
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- self.use_timestep = use_timestep
- if self.use_timestep:
- # timestep embedding
- self.temb = nn.Module()
- self.temb.dense = nn.ModuleList([
- torch.nn.Linear(self.ch,
- self.temb_ch),
- torch.nn.Linear(self.temb_ch,
- self.temb_ch),
- ])
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- skip_in = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- if i_block == self.num_res_blocks:
- skip_in = ch*in_ch_mult[i_level]
- block.append(ResnetBlock(in_channels=block_in+skip_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x, t=None, context=None):
- #assert x.shape[2] == x.shape[3] == self.resolution
- if context is not None:
- # assume aligned context, cat along channel axis
- x = torch.cat((x, context), dim=1)
- if self.use_timestep:
- # timestep embedding
- assert t is not None
- temb = get_timestep_embedding(t, self.ch)
- temb = self.temb.dense[0](temb)
- temb = nonlinearity(temb)
- temb = self.temb.dense[1](temb)
- else:
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](
- torch.cat([h, hs.pop()], dim=1), temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
- def get_last_layer(self):
- return self.conv_out.weight
-
-
-class Encoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
- **ignore_kwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.in_ch_mult = in_ch_mult
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))# vanilla attention
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # end
- self.norm_out = Normalize(block_in)# GroupNorm
- self.conv_out = torch.nn.Conv2d(block_in,
- 2*z_channels if double_z else z_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # timestep embedding
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class Decoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
- attn_type="vanilla", **ignorekwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
- self.give_pre_end = give_pre_end
- self.tanh_out = tanh_out
-
- # compute in_ch_mult, block_in and curr_res at lowest res
- in_ch_mult = (1,)+tuple(ch_mult)
- block_in = ch*ch_mult[self.num_resolutions-1]
- curr_res = resolution // 2**(self.num_resolutions-1)
- self.z_shape = (1,z_channels,curr_res,curr_res)
- print("Working with z of shape {} = {} dimensions.".format(
- self.z_shape, np.prod(self.z_shape)))
-
- # z to block_in
- self.conv_in = torch.nn.Conv2d(z_channels,
- block_in,
- kernel_size=3,
- stride=1,
- padding=1)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, z):
- #assert z.shape[1:] == self.z_shape[1:]
- self.last_z_shape = z.shape
-
- # timestep embedding
- temb = None
-
- # z to block_in
- h = self.conv_in(z)
-
- # middle
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](h, temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- if self.give_pre_end:
- return h
-
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- if self.tanh_out:
- h = torch.tanh(h)
- return h
-
-
-class SimpleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, *args, **kwargs):
- super().__init__()
- self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
- ResnetBlock(in_channels=in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=2 * in_channels,
- out_channels=4 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=4 * in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- nn.Conv2d(2*in_channels, in_channels, 1),
- Upsample(in_channels, with_conv=True)])
- # end
- self.norm_out = Normalize(in_channels)
- self.conv_out = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- for i, layer in enumerate(self.model):
- if i in [1,2,3]:
- x = layer(x, None)
- else:
- x = layer(x)
-
- h = self.norm_out(x)
- h = nonlinearity(h)
- x = self.conv_out(h)
- return x
-
-
-class UpsampleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
- ch_mult=(2,2), dropout=0.0):
- super().__init__()
- # upsampling
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- block_in = in_channels
- curr_res = resolution // 2 ** (self.num_resolutions - 1)
- self.res_blocks = nn.ModuleList()
- self.upsample_blocks = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- res_block = []
- block_out = ch * ch_mult[i_level]
- for i_block in range(self.num_res_blocks + 1):
- res_block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- self.res_blocks.append(nn.ModuleList(res_block))
- if i_level != self.num_resolutions - 1:
- self.upsample_blocks.append(Upsample(block_in, True))
- curr_res = curr_res * 2
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # upsampling
- h = x
- for k, i_level in enumerate(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks + 1):
- h = self.res_blocks[i_level][i_block](h, None)
- if i_level != self.num_resolutions - 1:
- h = self.upsample_blocks[k](h)
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class LatentRescaler(nn.Module):
- def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
- super().__init__()
- # residual block, interpolate, residual block
- self.factor = factor
- self.conv_in = nn.Conv2d(in_channels,
- mid_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
- self.attn = AttnBlock(mid_channels)
- self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
-
- self.conv_out = nn.Conv2d(mid_channels,
- out_channels,
- kernel_size=1,
- )
-
- def forward(self, x):
- x = self.conv_in(x)
- for block in self.res_block1:
- x = block(x, None)
- x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
- x = self.attn(x)
- for block in self.res_block2:
- x = block(x, None)
- x = self.conv_out(x)
- return x
-
-
-class MergedRescaleEncoder(nn.Module):
- def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True,
- ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- intermediate_chn = ch * ch_mult[-1]
- self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
- z_channels=intermediate_chn, double_z=False, resolution=resolution,
- attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
- out_ch=None)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
- mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.encoder(x)
- x = self.rescaler(x)
- return x
-
-
-class MergedRescaleDecoder(nn.Module):
- def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
- dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- tmp_chn = z_channels*ch_mult[-1]
- self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
- resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
- ch_mult=ch_mult, resolution=resolution, ch=ch)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
- out_channels=tmp_chn, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Upsampler(nn.Module):
- def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
- super().__init__()
- assert out_size >= in_size
- num_blocks = int(np.log2(out_size//in_size))+1
- factor_up = 1.+ (out_size % in_size)
- print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
- self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
- out_channels=in_channels)
- self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
- attn_resolutions=[], in_channels=None, ch=in_channels,
- ch_mult=[ch_mult for _ in range(num_blocks)])
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Resize(nn.Module):
- def __init__(self, in_channels=None, learned=False, mode="bilinear"):
- super().__init__()
- self.with_conv = learned
- self.mode = mode
- if self.with_conv:
- print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
- raise NotImplementedError()
- assert in_channels is not None
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=4,
- stride=2,
- padding=1)
-
- def forward(self, x, scale_factor=1.0):
- if scale_factor==1.0:
- return x
- else:
- x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
- return x
-
-class FirstStagePostProcessor(nn.Module):
-
- def __init__(self, ch_mult:list, in_channels,
- pretrained_model:nn.Module=None,
- reshape=False,
- n_channels=None,
- dropout=0.,
- pretrained_config=None):
- super().__init__()
- if pretrained_config is None:
- assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
- self.pretrained_model = pretrained_model
- else:
- assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
- self.instantiate_pretrained(pretrained_config)
-
- self.do_reshape = reshape
-
- if n_channels is None:
- n_channels = self.pretrained_model.encoder.ch
-
- self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
- self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
- stride=1,padding=1)
-
- blocks = []
- downs = []
- ch_in = n_channels
- for m in ch_mult:
- blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
- ch_in = m * n_channels
- downs.append(Downsample(ch_in, with_conv=False))
-
- self.model = nn.ModuleList(blocks)
- self.downsampler = nn.ModuleList(downs)
-
-
- def instantiate_pretrained(self, config):
- model = instantiate_from_config(config)
- self.pretrained_model = model.eval()
- # self.pretrained_model.train = False
- for param in self.pretrained_model.parameters():
- param.requires_grad = False
-
-
- @torch.no_grad()
- def encode_with_pretrained(self,x):
- c = self.pretrained_model.encode(x)
- if isinstance(c, DiagonalGaussianDistribution):
- c = c.mode()
- return c
-
- def forward(self,x):
- z_fs = self.encode_with_pretrained(x)
- z = self.proj_norm(z_fs)
- z = self.proj(z)
- z = nonlinearity(z)
-
- for submodel, downmodel in zip(self.model,self.downsampler):
- z = submodel(z,temb=None)
- z = downmodel(z)
-
- if self.do_reshape:
- z = rearrange(z,'b c h w -> b (h w) c')
- return z
-
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192.py
deleted file mode 100644
index 6d83441720cf20224ab6d2d790285fc204e4dffe..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td_hm_res50_4xb16-150e_deepfashion2_long_sleeved_dress_256x192.py
+++ /dev/null
@@ -1,172 +0,0 @@
-_base_ = [
- '../../../_base_/default_runtime.py',
- '../../../_base_/datasets/deepfashion2.py'
-]
-
-default_hooks = dict(checkpoint=dict(save_best='PCK', rule='greater'))
-
-resume = False # 断点恢复
-load_from = None # 模型权重加载
-train_cfg = dict(by_epoch=True, max_epochs=150, val_interval=10) # 训练轮数，测试间隔
-param_scheduler = [
- dict( # warmup策略
- type='LinearLR',
- begin=0,
- end=500,
- start_factor=0.001,
- by_epoch=False),
- dict( # scheduler
- type='MultiStepLR',
- begin=0,
- end=150,
- milestones=[100, 130],
- gamma=0.1,
- by_epoch=True)
-]
-optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005)) # 优化器和学习率
-auto_scale_lr = dict(base_batch_size=512) # 根据batch_size自动缩放学习率
-
-backend_args = dict(backend='local') # 数据加载后端设置，默认从本地硬盘加载
-dataset_type = 'DeepFashion2Dataset' # 数据集类名 DeepFashionDataset
-data_mode = 'topdown' # 算法结构类型，用于指定标注信息加载策略
-data_root = 'data/deepfashion2/' # 数据存放路径
-# 定义数据编解码器，用于生成target和对pred进行解码，同时包含了输入图片和输出heatmap尺寸等信息
-codec = dict(
- type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
-
-train_pipeline = [
- dict(type='LoadImage'),
- dict(type='GetBBoxCenterScale'),
- dict(type='RandomFlip', direction='horizontal'),
- dict(
- type='RandomBBoxTransform',
- shift_prob=0,
- rotate_factor=60,
- scale_factor=(0.75, 1.25)),
- dict(type='TopdownAffine', input_size=codec['input_size']),
- dict(type='GenerateTarget', encoder=codec),
- dict(type='PackPoseInputs')
-]
-val_pipeline = [ # 测试时数据增强
- dict(type='LoadImage', backend_args=backend_args), # 加载图片
- dict(type='GetBBoxCenterScale'), # 根据bbox获取center和scale
- dict(type='TopdownAffine', input_size=codec['input_size']), # 根据变换矩阵更新目标数据
- dict(type='PackPoseInputs') # 对target进行打包用于训练
-]
-train_dataloader = dict( # 训练数据加载
- batch_size=16, # 批次大小
- num_workers=6, # 数据加载进程数
- persistent_workers=True, # 在不活跃时维持进程不终止，避免反复启动进程的开销
- sampler=dict(type='DefaultSampler', shuffle=True), # 采样策略，打乱数据
- dataset=dict(
- type=dataset_type, # 数据集类名
- data_root=data_root, # 数据集路径
- data_mode=data_mode, # 算法类型
- ann_file='train/deepfashion2_long_sleeved_dress.json', # 标注文件路径
- data_prefix=dict(img='train/image/'), # 图像路径
- pipeline=train_pipeline # 数据流水线
- ))
-val_dataloader = dict(
- batch_size=16,
- num_workers=6,
- persistent_workers=True, # 在不活跃时维持进程不终止，避免反复启动进程的开销
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False), # 采样策略，不进行打乱
- dataset=dict(
- type=dataset_type, # 数据集类名
- data_root=data_root, # 数据集路径
- data_mode=data_mode, # 算法类型
- ann_file='validation/deepfashion2_long_sleeved_dress.json', # 标注文件路径
- data_prefix=dict(img='validation/image/'), # 图像路径
- test_mode=True, # 测试模式开关
- pipeline=val_pipeline # 数据流水线
- ))
-test_dataloader = val_dataloader # 默认情况下不区分验证集和测试集，用户根据需要来自行定义
-
-channel_cfg = dict(
- num_output_channels=294,
- dataset_joints=294,
- dataset_channel=[
- [
- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
- 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
- 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
- 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
- 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
- 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102,
- 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,
- 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,
- 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,
- 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
- 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
- 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,
- 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,
- 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,
- 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
- 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,
- 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,
- 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258,
- 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,
- 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284,
- 285, 286, 287, 288, 289, 290, 291, 292, 293
- ],
- ],
- inference_channel=[
- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
- 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
- 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
- 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
- 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
- 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
- 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
- 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
- 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
- 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
- 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
- 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
- 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
- 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
- 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
- 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
- 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
- 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
- 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
- 290, 291, 292, 293
- ])
-
-model = dict(
- type='TopdownPoseEstimator', # 模型结构决定了算法流程
- data_preprocessor=dict( # 数据归一化和通道顺序调整，作为模型的一部分
- type='PoseDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True),
- backbone=dict(
- type='ResNet',
- depth=50,
- init_cfg=dict(
- type='Pretrained', # 预训练参数，只加载backbone权重用于迁移学习
- checkpoint='torchvision://resnet50')),
- head=dict( # 模型头部
- type='HeatmapHead',
- in_channels=2048,
- out_channels=channel_cfg['num_output_channels'],
- # deconv_out_channels=None,
- loss=dict(type='KeypointMSELoss', use_target_weight=True), # 损失函数
- decoder=codec), # 解码器，将heatmap解码成坐标值
- test_cfg=dict(
- flip_test=True, # 开启测试时水平翻转集成
- flip_mode='heatmap', # 对heatmap进行翻转
- shift_heatmap=True, # 对翻转后的结果进行平移提高精度
- ))
-
-val_evaluator = [
- dict(type='PCKAccuracy', thr=0.2),
- dict(type='AUC'),
- dict(type='EPE'),
-]
-test_evaluator = val_evaluator # 默认情况下不区分验证集和测试集，用户根据需要来自行定义
-
-visualizer = dict(
- vis_backends=[dict(type='LocalVisBackend'),
- dict(type='WandbVisBackend')])
diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnest50.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnest50.py
deleted file mode 100644
index 51c90e86f468edccc3de3b0e7cd783548d220db4..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnest50.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# model settings
-model = dict(
- type='ImageClassifier',
- backbone=dict(
- type='ResNeSt',
- depth=50,
- num_stages=4,
- out_indices=(3, ),
- style='pytorch'),
- neck=dict(type='GlobalAveragePooling'),
- head=dict(
- type='LinearClsHead',
- num_classes=1000,
- in_channels=2048,
- loss=dict(
- type='LabelSmoothLoss',
- label_smooth_val=0.1,
- num_classes=1000,
- reduction='mean',
- loss_weight=1.0),
- topk=(1, 5),
- cal_acc=False),
- train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
-)
diff --git a/spaces/AUST001/True-GPT4/app.py b/spaces/AUST001/True-GPT4/app.py
deleted file mode 100644
index e5968d08cdb70fbf3caa1d2593820c9e5032ac3e..0000000000000000000000000000000000000000
--- a/spaces/AUST001/True-GPT4/app.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from pickle import NONE
-import numpy as np
-import cv2
-import urllib.request
-import openai
-import gradio as gr
-import random
-import poe
-
-client = None
-user_contexts = {}
-
-def get_assistant_response(user_question, context):
- global client
- context.append({"role": "user", "content": user_question})
- for chunk in client.send_message("beaver", context): # capybara
- pass
- # print(chunk["text"])
- assistant_response = chunk["text"]
- context.append({"role": "assistant", "content": assistant_response})
- client.send_chat_break("beaver") # capybara
- return assistant_response
-
-def generate_image_url(prompt):
- response = openai.Image.create(
- prompt=prompt,
- n=1, # 生成1张图片
- size="512x512", # 图像大小
- )
- image_url = response["data"][0]["url"]
- return image_url
-
-def greet(user_id, api_key, user_question, clear_history):
- global client
- if len(api_key)>5:
- client = poe.Client(api_key)
- global user_contexts
- if user_id not in user_contexts:
- user_contexts[user_id] = [
- {"role": "system", "content": "你是一个聪明的AI助手。请参考对话记录，回答用户的最后一个问题，无需做多余的解释，更不要强调对话历史的事情"},
- {"role": "user", "content": "你会说中文吗？"},
- {"role": "assistant", "content": "是的，我可以说中文。"}
- ]
-
- context = user_contexts[user_id]
-
- if clear_history:
- context = [
- {"role": "system", "content": "你是一个聪明的AI助手。请参考对话记录，回答用户的最后一个问题，无需做多余的解释，更不要强调对话历史的事情"},
- {"role": "user", "content": "你会说中文吗？"},
- {"role": "assistant", "content": "是的，我可以说中文。"}
- ]
- user_contexts[user_id] = context
- return '清空成功', '保持聊天记录', np.ones((5,5))
- else:
- # 如果user提问包含生成图像的特定指令（这里我们使用“生成图片：”作为示例）
- if user_question.startswith("生成图片：") or user_question.startswith("生成图片:"):
- image_prompt = user_question[5:] # 提取用于生成图片的文本
- image_url = generate_image_url(image_prompt)
- resp = urllib.request.urlopen(image_url)
- image = np.asarray(bytearray(resp.read()), dtype="uint8")
- image = cv2.imdecode(image, cv2.IMREAD_COLOR)
- image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- # return image
- return '', '图片已生成', image
- get_assistant_response(user_question, context)
- prompt = ""
-
- for item in context[3:]:
- prompt += item["role"] + ": " + item["content"] + "\n"
- return '', prompt, np.ones((5,5))
-
-demo = gr.Interface(
- fn=greet,
- inputs=[
- gr.Textbox(lines=1, label='请输入用户ID', placeholder='请输入用户ID'),
- gr.Textbox(lines=1, label='请输入你的专属密钥', placeholder='请输入你的专属密钥'),
- gr.Textbox(lines=15, label='请输入问题', placeholder='请输入您的问题'),
- gr.Checkbox(label='清空聊天记录', default=False)
- ],
- outputs=[
- gr.Textbox(lines=1, label='聊天记录状态', placeholder='等待清空聊天记录'),
- gr.Textbox(lines=23, label='AI回答', placeholder='等待AI回答')
- ],
- title="True GPT4",
- description="""
-1.使用说明：
-请输入您的问题，AI助手会给出回答。
-支持连续对话，可以记录对话历史。
-重新开始对话勾选清空聊天记录，输出清空成功表示重新开启对话。
-2.特别警告：
-为了防止用户数据混乱，请自定义用户ID。
-理论上如果被别人知道自己的ID，那么别人可以查看自己的历史对话，对此你可以选择在对话结束后清除对话记录。
-3.作者的GPT4网页导航网站链接如下：http://aust001.pythonanywhere.com/ -> 专属密钥进群获取
- """
-)
-
-if __name__ == "__main__":
- demo.launch()
\ No newline at end of file
diff --git a/spaces/Acapellas/vocalinstrumentalremover/README.md b/spaces/Acapellas/vocalinstrumentalremover/README.md
deleted file mode 100644
index 7356c6289837664d2f60ce646f599a5afa089980..0000000000000000000000000000000000000000
--- a/spaces/Acapellas/vocalinstrumentalremover/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: null
-emoji: ⚡
-colorFrom: red
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: true
-duplicated_from: null
-python_version: 3.9.13
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/toggleswitch.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/toggleswitch.js
deleted file mode 100644
index 145a30fed867f658b7aaec0c8fc47d9fb306af97..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/toggleswitch.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import ToggleSwitch from './gameobjects/shape/toggleswitch/ToggleSwitch.js';
-export default ToggleSwitch;
\ No newline at end of file
diff --git a/spaces/Aishwini/myfirstaigen/README.md b/spaces/Aishwini/myfirstaigen/README.md
deleted file mode 100644
index 01fbc692191a09ebba73ab28736e80be83eea188..0000000000000000000000000000000000000000
--- a/spaces/Aishwini/myfirstaigen/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Myfirstaigen
-emoji: ⚡
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Aki004/herta-so-vits/onnxexport/model_onnx.py b/spaces/Aki004/herta-so-vits/onnxexport/model_onnx.py
deleted file mode 100644
index e28bae95ec1e53aa05d06fc784ff86d55f228d60..0000000000000000000000000000000000000000
--- a/spaces/Aki004/herta-so-vits/onnxexport/model_onnx.py
+++ /dev/null
@@ -1,335 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.attentions as attentions
-import modules.commons as commons
-import modules.modules as modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-import utils
-from modules.commons import init_weights, get_padding
-from vdecoder.hifigan.models import Generator
-from utils import f0_to_coarse
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(
- modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
- gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- # print(x.shape,x_lengths.shape)
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- out_channels,
- hidden_channels,
- kernel_size,
- n_layers,
- gin_channels=0,
- filter_channels=None,
- n_heads=None,
- p_dropout=None):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
- self.f0_emb = nn.Embedding(256, hidden_channels)
-
- self.enc_ = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
-
- def forward(self, x, x_mask, f0=None, z=None):
- x = x + self.f0_emb(f0).transpose(1, 2)
- x = self.enc_(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + z * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class F0Decoder(nn.Module):
- def __init__(self,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- spk_channels=0):
- super().__init__()
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.spk_channels = spk_channels
-
- self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
- self.decoder = attentions.FFT(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
- self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
-
- def forward(self, x, norm_f0, x_mask, spk_emb=None):
- x = torch.detach(x)
- if spk_emb is not None:
- x = x + self.cond(spk_emb)
- x += self.f0_prenet(norm_f0)
- x = self.prenet(x) * x_mask
- x = self.decoder(x * x_mask, x_mask)
- x = self.proj(x) * x_mask
- return x
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- n_speakers,
- sampling_rate=44100,
- **kwargs):
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
- self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
-
- self.enc_p = TextEncoder(
- inter_channels,
- hidden_channels,
- filter_channels=filter_channels,
- n_heads=n_heads,
- n_layers=n_layers,
- kernel_size=kernel_size,
- p_dropout=p_dropout
- )
- hps = {
- "sampling_rate": sampling_rate,
- "inter_channels": inter_channels,
- "resblock": resblock,
- "resblock_kernel_sizes": resblock_kernel_sizes,
- "resblock_dilation_sizes": resblock_dilation_sizes,
- "upsample_rates": upsample_rates,
- "upsample_initial_channel": upsample_initial_channel,
- "upsample_kernel_sizes": upsample_kernel_sizes,
- "gin_channels": gin_channels,
- }
- self.dec = Generator(h=hps)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
- self.f0_decoder = F0Decoder(
- 1,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- spk_channels=gin_channels
- )
- self.emb_uv = nn.Embedding(2, hidden_channels)
- self.predict_f0 = False
-
- def forward(self, c, f0, mel2ph, uv, noise=None, g=None):
-
- decoder_inp = F.pad(c, [0, 0, 1, 0])
- mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
- c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H]
-
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- g = g.unsqueeze(0)
- g = self.emb_g(g).transpose(1, 2)
- x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
- x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
-
- if self.predict_f0:
- lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
- norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
- pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
- f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g, f0=f0)
- return o
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/shanghainese.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/shanghainese.py
deleted file mode 100644
index cb29c24a08d2e406e8399cf7bc9fe5cb43cb9c61..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/shanghainese.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('zaonhe')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('A', 'ᴇ'),
- ('B', 'bi'),
- ('C', 'si'),
- ('D', 'di'),
- ('E', 'i'),
- ('F', 'ᴇf'),
- ('G', 'dʑi'),
- ('H', 'ᴇtɕʰ'),
- ('I', 'ᴀi'),
- ('J', 'dʑᴇ'),
- ('K', 'kʰᴇ'),
- ('L', 'ᴇl'),
- ('M', 'ᴇm'),
- ('N', 'ᴇn'),
- ('O', 'o'),
- ('P', 'pʰi'),
- ('Q', 'kʰiu'),
- ('R', 'ᴀl'),
- ('S', 'ᴇs'),
- ('T', 'tʰi'),
- ('U', 'ɦiu'),
- ('V', 'vi'),
- ('W', 'dᴀbɤliu'),
- ('X', 'ᴇks'),
- ('Y', 'uᴀi'),
- ('Z', 'zᴇ')
-]]
-
-
-def _number_to_shanghainese(num):
- num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
- return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
-
-
-def number_to_shanghainese(text):
- return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
-
-
-def latin_to_ipa(text):
- for regex, replacement in _latin_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def shanghainese_to_ipa(text):
- text = number_to_shanghainese(text.upper())
- text = converter.convert(text).replace('-','').replace('$',' ')
- text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
- text = re.sub(r'[、；：]', '，', text)
- text = re.sub(r'\s*，\s*', ', ', text)
- text = re.sub(r'\s*。\s*', '. ', text)
- text = re.sub(r'\s*？\s*', '? ', text)
- text = re.sub(r'\s*！\s*', '! ', text)
- text = re.sub(r'\s*$', '', text)
- return text
diff --git a/spaces/Aloento/9Nine-PITS/text/paddle_zh.py b/spaces/Aloento/9Nine-PITS/text/paddle_zh.py
deleted file mode 100644
index 6838855b263cb2e3c81b3d69d56949574cd73fbe..0000000000000000000000000000000000000000
--- a/spaces/Aloento/9Nine-PITS/text/paddle_zh.py
+++ /dev/null
@@ -1,115 +0,0 @@
-from text.frontend.zh_frontend import Frontend
-
-frontend = Frontend()
-
-pu_symbols = ['!', '?', '…', ",", "."]
-replacements = [
- (u"yu", u"u:"), (u"ü", u"u:"), (u"v", u"u:"),
- (u"yi", u"i"), (u"you", u"ㄧㄡ"), (u"y", u"i"),
- (u"wu", u"u"), (u"wong", u"ㄨㄥ"), (u"w", u"u"),
-]
-
-table = [
- # special cases
- (u"ju", u"ㄐㄩ"), (u"qu", u"ㄑㄩ"), (u"xu", u"ㄒㄩ"),
- (u"zhi", u"ㄓ"), (u"chi", u"ㄔ"), (u"shi", u"ㄕ"), (u"ri", u"ㄖ"),
- (u"zi", u"ㄗ"), (u"ci", u"ㄘ"), (u"si", u"ㄙ"),
- (u"r5", u"ㄦ"),
-
- # initials
- (u"b", u"ㄅ"), (u"p", u"ㄆ"), (u"m", u"ㄇ"), (u"f", u"ㄈ"),
- (u"d", u"ㄉ"), (u"t", u"ㄊ"), (u"n", u"ㄋ"), (u"l", u"ㄌ"),
- (u"g", u"ㄍ"), (u"k", u"ㄎ"), (u"h", u"ㄏ"),
- (u"j", u"ㄐ"), (u"q", u"ㄑ"), (u"x", u"ㄒ"),
- (u"zh", u"ㄓ"), (u"ch", u"ㄔ"), (u"sh", u"ㄕ"), (u"r", u"ㄖ"),
- (u"z", u"ㄗ"), (u"c", u"ㄘ"), (u"s", u"ㄙ"),
-
- # finals
- (u"i", u"ㄧ"), (u"u", u"ㄨ"), (u"u:", u"ㄩ"),
- (u"a", u"ㄚ"), (u"o", u"ㄛ"), (u"e", u"ㄜ"), (u"ê", u"ㄝ"),
- (u"ai", u"ㄞ"), (u"ei", u"ㄟ"), (u"ao", u"ㄠ"), (u"ou", u"ㄡ"),
- (u"an", u"ㄢ"), (u"en", u"ㄣ"), (u"ang", u"ㄤ"), (u"eng", u"ㄥ"),
- (u"er", u"ㄦ"),
- (u"ia", u"ㄧㄚ"), (u"io", u"ㄧㄛ"), (u"ie", u"ㄧㄝ"), (u"iai", u"ㄧㄞ"),
- (u"iao", u"ㄧㄠ"), (u"iu", u"ㄧㄡ"), (u"ian", u"ㄧㄢ"),
- (u"in", u"ㄧㄣ"), (u"iang", u"ㄧㄤ"), (u"ing", u"ㄧㄥ"),
- (u"ua", u"ㄨㄚ"), (u"uo", u"ㄨㄛ"), (u"uai", u"ㄨㄞ"),
- (u"ui", u"ㄨㄟ"), (u"uan", u"ㄨㄢ"), (u"un", u"ㄨㄣ"),
- (u"uang", u"ㄨㄤ"), (u"ong", u"ㄨㄥ"),
- (u"u:e", u"ㄩㄝ"), (u"u:an", u"ㄩㄢ"), (u"u:n", u"ㄩㄣ"), (u"iong", u"ㄩㄥ"),
-
- # tones
- (u"1", u"ˉ"), (u"2", u"ˊ"),
- (u"3", u"ˇ"), (u"4", u"ˋ"),
- (u"5", u"˙"),
-]
-
-table.sort(key=lambda pair: len(pair[0]), reverse=True)
-replacements.extend(table)
-
-zh_dict = [i.strip() for i in open("text/zh_dict.dict").readlines()]
-zh_dict = {i.split("\t")[0]: i.split("\t")[1] for i in zh_dict}
-
-reversed_zh_dict = {}
-all_zh_phones = set()
-for k, v in zh_dict.items():
- reversed_zh_dict[v] = k
- [all_zh_phones.add(i) for i in v.split(" ")]
-
-
-def bopomofo(pinyin):
- """
- Convert a pinyin string to Bopomofo
- The optional tone info must be given as a number suffix, eg: 'ni3'
- """
-
- pinyin = pinyin.lower()
- for pair in replacements:
- pinyin = pinyin.replace(pair[0], pair[1])
-
- return pinyin
-
-
-def phones_to_pinyins(phones):
- pinyins = ''
- accu_ph = []
- for ph in phones:
- accu_ph.append(ph)
- if ph not in all_zh_phones:
- assert len(accu_ph) == 1
- pinyins += ph
- accu_ph = []
- elif " ".join(accu_ph) in reversed_zh_dict.keys():
- pinyins += " " + reversed_zh_dict[" ".join(accu_ph)]
- accu_ph = []
- if not accu_ph == []:
- print(accu_ph)
- return pinyins.strip()
-
-
-def pu_symbol_replace(data):
- chinaTab = ['！', '？', "…", "，", "。", '、', "..."]
- englishTab = ['!', '?', "…", ",", ".", ",", "…"]
- for index in range(len(chinaTab)):
- if chinaTab[index] in data:
- data = data.replace(chinaTab[index], englishTab[index])
- return data
-
-
-def zh_to_bopomofo(text):
- phones = zh_to_phonemes(text)
- pinyins = phones_to_pinyins(phones)
- bopomofos = bopomofo(pinyins)
- return bopomofos.replace(" ", "").replace("#", " ")
-
-
-def pinyin_to_bopomofo(pinyin):
- bopomofos = bopomofo(pinyin)
- return bopomofos.replace(" ", "").replace("#", " ").replace("%", "% ")
-
-
-def zh_to_phonemes(text):
- # 替换标点为英文标点
- text = pu_symbol_replace(text)
- phones = frontend.get_phonemes(text)[0]
- return phones
diff --git a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/data_utils.py b/spaces/Alycer/VITS-Umamusume-voice-synthesizer/data_utils.py
deleted file mode 100644
index e9246c6c8f2ff3c37a7f8529ea1593c7f80f887e..0000000000000000000000000000000000000000
--- a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/data_utils.py
+++ /dev/null
@@ -1,393 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-
-import commons
-from mel_processing import spectrogram_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import text_to_sequence, cleaned_text_to_sequence
-
-
-class TextAudioLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
- def __init__(self, audiopaths_and_text, hparams):
- self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
- self.text_cleaners = hparams.text_cleaners
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 190)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_and_text)
- self._filter()
-
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_and_text_new = []
- lengths = []
- for audiopath, text in self.audiopaths_and_text:
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_and_text_new.append([audiopath, text])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.audiopaths_and_text = audiopaths_and_text_new
- self.lengths = lengths
-
- def get_audio_text_pair(self, audiopath_and_text):
- # separate filename and text
- audiopath, text = audiopath_and_text[0], audiopath_and_text[1]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- return (text, spec, wav)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text):
- if self.cleaned_text:
- text_norm = cleaned_text_to_sequence(text)
- else:
- text_norm = text_to_sequence(text, self.text_cleaners)
- if self.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
- def __getitem__(self, index):
- return self.get_audio_text_pair(self.audiopaths_and_text[index])
-
- def __len__(self):
- return len(self.audiopaths_and_text)
-
-
-class TextAudioCollate():
- """ Zero-pads model inputs and targets
- """
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text and aduio
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, ids_sorted_decreasing
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths
-
-
-"""Multi speaker version"""
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
- def __init__(self, audiopaths_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- self.text_cleaners = hparams.text_cleaners
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 190)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- for audiopath, sid, text in self.audiopaths_sid_text:
- audiopath = "E:/uma_voice/" + audiopath
- if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
- audiopaths_sid_text_new.append([audiopath, sid, text])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, text = audiopath_sid_text[0], audiopath_sid_text[1], audiopath_sid_text[2]
- text = self.get_text(text)
- spec, wav = self.get_audio(audiopath)
- sid = self.get_sid(sid)
- return (text, spec, wav, sid)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if os.path.exists(spec_filename):
- spec = torch.load(spec_filename)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text):
- if self.cleaned_text:
- text_norm = cleaned_text_to_sequence(text)
- else:
- text_norm = text_to_sequence(text, self.text_cleaners)
- if self.add_blank:
- text_norm = commons.intersperse(text_norm, 0)
- text_norm = torch.LongTensor(text_norm)
- return text_norm
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- if self.return_ids:
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, ids_sorted_decreasing
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i+1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [bucket[idx] for idx in ids_bucket[j*self.batch_size:(j+1)*self.batch_size]]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid+1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py
deleted file mode 100644
index 83cb37dc1e35caa65fa69fcedb2c9c83c61681f2..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py
+++ /dev/null
@@ -1,290 +0,0 @@
-from typing import Callable, List, Optional, Union
-
-import torch
-
-from ...models import UNet2DModel
-from ...schedulers import CMStochasticIterativeScheduler
-from ...utils import (
- is_accelerate_available,
- is_accelerate_version,
- logging,
- randn_tensor,
- replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-EXAMPLE_DOC_STRING = """
- Examples:
- ```py
- >>> import torch
-
- >>> from diffusers import ConsistencyModelPipeline
-
- >>> device = "cuda"
- >>> # Load the cd_imagenet64_l2 checkpoint.
- >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
- >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
- >>> pipe.to(device)
-
- >>> # Onestep Sampling
- >>> image = pipe(num_inference_steps=1).images[0]
- >>> image.save("cd_imagenet64_l2_onestep_sample.png")
-
- >>> # Onestep sampling, class-conditional image generation
- >>> # ImageNet-64 class label 145 corresponds to king penguins
- >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
- >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")
-
- >>> # Multistep sampling, class-conditional image generation
- >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
- >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
- >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
- >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
- ```
-"""
-
-
-class ConsistencyModelPipeline(DiffusionPipeline):
- r"""
- Pipeline for unconditional or class-conditional image generation.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
- implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
- Args:
- unet ([`UNet2DModel`]):
- A `UNet2DModel` to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
- compatible with [`CMStochasticIterativeScheduler`].
- """
-
- def __init__(self, unet: UNet2DModel, scheduler: CMStochasticIterativeScheduler) -> None:
- super().__init__()
-
- self.register_modules(
- unet=unet,
- scheduler=scheduler,
- )
-
- self.safety_checker = None
-
- def enable_model_cpu_offload(self, gpu_id=0):
- r"""
- Offload all models to CPU to reduce memory usage with a low impact on performance. Moves one whole model at a
- time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs.
- Memory savings are lower than using `enable_sequential_cpu_offload`, but performance is much better due to the
- iterative execution of the `unet`.
- """
- if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
- from accelerate import cpu_offload_with_hook
- else:
- raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
- device = torch.device(f"cuda:{gpu_id}")
-
- if self.device.type != "cpu":
- self.to("cpu", silence_dtype_warnings=True)
- torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
-
- hook = None
- for cpu_offloaded_model in [self.unet]:
- _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
- if self.safety_checker is not None:
- _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)
-
- # We'll offload the last model manually.
- self.final_offload_hook = hook
-
- def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):
- shape = (batch_size, num_channels, height, width)
- if isinstance(generator, list) and len(generator) != batch_size:
- raise ValueError(
- f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
- f" size of {batch_size}. Make sure the batch size matches the length of the generators."
- )
-
- if latents is None:
- latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
- else:
- latents = latents.to(device=device, dtype=dtype)
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
- return latents
-
- # Follows diffusers.VaeImageProcessor.postprocess
- def postprocess_image(self, sample: torch.FloatTensor, output_type: str = "pil"):
- if output_type not in ["pt", "np", "pil"]:
- raise ValueError(
- f"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"
- )
-
- # Equivalent to diffusers.VaeImageProcessor.denormalize
- sample = (sample / 2 + 0.5).clamp(0, 1)
- if output_type == "pt":
- return sample
-
- # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
- sample = sample.cpu().permute(0, 2, 3, 1).numpy()
- if output_type == "np":
- return sample
-
- # Output_type must be 'pil'
- sample = self.numpy_to_pil(sample)
- return sample
-
- def prepare_class_labels(self, batch_size, device, class_labels=None):
- if self.unet.config.num_class_embeds is not None:
- if isinstance(class_labels, list):
- class_labels = torch.tensor(class_labels, dtype=torch.int)
- elif isinstance(class_labels, int):
- assert batch_size == 1, "Batch size must be 1 if classes is an int"
- class_labels = torch.tensor([class_labels], dtype=torch.int)
- elif class_labels is None:
- # Randomly generate batch_size class labels
- # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
- class_labels = torch.randint(0, self.unet.config.num_class_embeds, size=(batch_size,))
- class_labels = class_labels.to(device)
- else:
- class_labels = None
- return class_labels
-
- def check_inputs(self, num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps):
- if num_inference_steps is None and timesteps is None:
- raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied.")
-
- if num_inference_steps is not None and timesteps is not None:
- logger.warning(
- f"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
- " `timesteps` will be used over `num_inference_steps`."
- )
-
- if latents is not None:
- expected_shape = (batch_size, 3, img_size, img_size)
- if latents.shape != expected_shape:
- raise ValueError(f"The shape of latents is {latents.shape} but is expected to be {expected_shape}.")
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- @torch.no_grad()
- @replace_example_docstring(EXAMPLE_DOC_STRING)
- def __call__(
- self,
- batch_size: int = 1,
- class_labels: Optional[Union[torch.Tensor, List[int], int]] = None,
- num_inference_steps: int = 1,
- timesteps: List[int] = None,
- generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
- latents: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: int = 1,
- ):
- r"""
- Args:
- batch_size (`int`, *optional*, defaults to 1):
- The number of images to generate.
- class_labels (`torch.Tensor` or `List[int]` or `int`, *optional*):
- Optional class labels for conditioning class-conditional consistency models. Not used if the model is
- not class-conditional.
- num_inference_steps (`int`, *optional*, defaults to 1):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- timesteps (`List[int]`, *optional*):
- Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
- timesteps are used. Must be in descending order.
- generator (`torch.Generator`, *optional*):
- A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
- generation deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor is generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generated image. Choose between `PIL.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
- callback (`Callable`, *optional*):
- A function that calls every `callback_steps` steps during inference. The function is called with the
- following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function is called. If not specified, the callback is called at
- every step.
-
- Examples:
-
- Returns:
- [`~pipelines.ImagePipelineOutput`] or `tuple`:
- If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
- returned where the first element is a list with the generated images.
- """
- # 0. Prepare call parameters
- img_size = self.unet.config.sample_size
- device = self._execution_device
-
- # 1. Check inputs
- self.check_inputs(num_inference_steps, timesteps, latents, batch_size, img_size, callback_steps)
-
- # 2. Prepare image latents
- # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
- sample = self.prepare_latents(
- batch_size=batch_size,
- num_channels=self.unet.config.in_channels,
- height=img_size,
- width=img_size,
- dtype=self.unet.dtype,
- device=device,
- generator=generator,
- latents=latents,
- )
-
- # 3. Handle class_labels for class-conditional models
- class_labels = self.prepare_class_labels(batch_size, device, class_labels=class_labels)
-
- # 4. Prepare timesteps
- if timesteps is not None:
- self.scheduler.set_timesteps(timesteps=timesteps, device=device)
- timesteps = self.scheduler.timesteps
- num_inference_steps = len(timesteps)
- else:
- self.scheduler.set_timesteps(num_inference_steps)
- timesteps = self.scheduler.timesteps
-
- # 5. Denoising loop
- # Multistep sampling: implements Algorithm 1 in the paper
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- for i, t in enumerate(timesteps):
- scaled_sample = self.scheduler.scale_model_input(sample, t)
- model_output = self.unet(scaled_sample, t, class_labels=class_labels, return_dict=False)[0]
-
- sample = self.scheduler.step(model_output, t, sample, generator=generator)[0]
-
- # call the callback, if provided
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, sample)
-
- # 6. Post-process image sample
- image = self.postprocess_image(sample, output_type=output_type)
-
- # Offload last model to CPU
- if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
- self.final_offload_hook.offload()
-
- if not return_dict:
- return (image,)
-
- return ImagePipelineOutput(images=image)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/_base_/models/cascade_mask_rcnn_uniformer_fpn.py b/spaces/Andy1621/uniformer_image_detection/configs/_base_/models/cascade_mask_rcnn_uniformer_fpn.py
deleted file mode 100644
index 18678e98f24dfa9f6c2c4a753308d6eecd308124..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/_base_/models/cascade_mask_rcnn_uniformer_fpn.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# model settings
-model = dict(
- type='CascadeRCNN',
- pretrained=None,
- backbone=dict(
- type='UniFormer',
- embed_dim=[64, 128, 320, 512],
- layers=[3, 4, 8, 3],
- head_dim=64,
- mlp_ratio=4.,
- qkv_bias=True,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.2),
- neck=dict(
- type='FPN',
- in_channels=[64, 128, 320, 512],
- out_channels=256,
- num_outs=5),
- rpn_head=dict(
- type='RPNHead',
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
- roi_head=dict(
- type='CascadeRoIHead',
- num_stages=3,
- stage_loss_weights=[1, 0.5, 0.25],
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- bbox_head=[
- dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=True,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
- loss_weight=1.0)),
- dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.05, 0.05, 0.1, 0.1]),
- reg_class_agnostic=True,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
- loss_weight=1.0)),
- dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.033, 0.033, 0.067, 0.067]),
- reg_class_agnostic=True,
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
- ],
- mask_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- mask_head=dict(
- type='FCNMaskHead',
- num_convs=4,
- in_channels=256,
- conv_out_channels=256,
- num_classes=80,
- loss_mask=dict(
- type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
- # model training and testing settings
- train_cfg = dict(
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=0,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_across_levels=False,
- nms_pre=2000,
- nms_post=2000,
- max_per_img=2000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=[
- dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- mask_size=28,
- pos_weight=-1,
- debug=False),
- dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.6,
- neg_iou_thr=0.6,
- min_pos_iou=0.6,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- mask_size=28,
- pos_weight=-1,
- debug=False),
- dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.7,
- min_pos_iou=0.7,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- mask_size=28,
- pos_weight=-1,
- debug=False)
- ]),
- test_cfg = dict(
- rpn=dict(
- nms_across_levels=False,
- nms_pre=1000,
- nms_post=1000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100,
- mask_thr_binary=0.5)))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/chase_db1.py b/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/chase_db1.py
deleted file mode 100644
index 298594ea925f87f22b37094a2ec50e370aec96a0..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/_base_/datasets/chase_db1.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# dataset settings
-dataset_type = 'ChaseDB1Dataset'
-data_root = 'data/CHASE_DB1'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-img_scale = (960, 999)
-crop_size = (128, 128)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=img_scale,
- # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
-]
-
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- train=dict(
- type='RepeatDataset',
- times=40000,
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/training',
- ann_dir='annotations/training',
- pipeline=train_pipeline)),
- val=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline))
diff --git a/spaces/Annotation-AI/segment-similarthings/README.md b/spaces/Annotation-AI/segment-similarthings/README.md
deleted file mode 100644
index c5958a1dcab4a3997c268385ca6766c1f112cc7d..0000000000000000000000000000000000000000
--- a/spaces/Annotation-AI/segment-similarthings/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Segment Similarthings
-emoji: 📈
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/table.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/table.py
deleted file mode 100644
index 17409f2ee8df322a5ac115d1d0ff0c2d2aa11c4e..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/table.py
+++ /dev/null
@@ -1,1002 +0,0 @@
-from dataclasses import dataclass, field, replace
-from typing import (
- TYPE_CHECKING,
- Dict,
- Iterable,
- List,
- NamedTuple,
- Optional,
- Sequence,
- Tuple,
- Union,
-)
-
-from . import box, errors
-from ._loop import loop_first_last, loop_last
-from ._pick import pick_bool
-from ._ratio import ratio_distribute, ratio_reduce
-from .align import VerticalAlignMethod
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .padding import Padding, PaddingDimensions
-from .protocol import is_renderable
-from .segment import Segment
-from .style import Style, StyleType
-from .text import Text, TextType
-
-if TYPE_CHECKING:
- from .console import (
- Console,
- ConsoleOptions,
- JustifyMethod,
- OverflowMethod,
- RenderableType,
- RenderResult,
- )
-
-
-@dataclass
-class Column:
- """Defines a column within a ~Table.
-
- Args:
- title (Union[str, Text], optional): The title of the table rendered at the top. Defaults to None.
- caption (Union[str, Text], optional): The table caption rendered below. Defaults to None.
- width (int, optional): The width in characters of the table, or ``None`` to automatically fit. Defaults to None.
- min_width (Optional[int], optional): The minimum width of the table, or ``None`` for no minimum. Defaults to None.
- box (box.Box, optional): One of the constants in box.py used to draw the edges (see :ref:`appendix_box`), or ``None`` for no box lines. Defaults to box.HEAVY_HEAD.
- safe_box (Optional[bool], optional): Disable box characters that don't display on windows legacy terminal with *raster* fonts. Defaults to True.
- padding (PaddingDimensions, optional): Padding for cells (top, right, bottom, left). Defaults to (0, 1).
- collapse_padding (bool, optional): Enable collapsing of padding around cells. Defaults to False.
- pad_edge (bool, optional): Enable padding of edge cells. Defaults to True.
- expand (bool, optional): Expand the table to fit the available space if ``True``, otherwise the table width will be auto-calculated. Defaults to False.
- show_header (bool, optional): Show a header row. Defaults to True.
- show_footer (bool, optional): Show a footer row. Defaults to False.
- show_edge (bool, optional): Draw a box around the outside of the table. Defaults to True.
- show_lines (bool, optional): Draw lines between every row. Defaults to False.
- leading (bool, optional): Number of blank lines between rows (precludes ``show_lines``). Defaults to 0.
- style (Union[str, Style], optional): Default style for the table. Defaults to "none".
- row_styles (List[Union, str], optional): Optional list of row styles, if more than one style is given then the styles will alternate. Defaults to None.
- header_style (Union[str, Style], optional): Style of the header. Defaults to "table.header".
- footer_style (Union[str, Style], optional): Style of the footer. Defaults to "table.footer".
- border_style (Union[str, Style], optional): Style of the border. Defaults to None.
- title_style (Union[str, Style], optional): Style of the title. Defaults to None.
- caption_style (Union[str, Style], optional): Style of the caption. Defaults to None.
- title_justify (str, optional): Justify method for title. Defaults to "center".
- caption_justify (str, optional): Justify method for caption. Defaults to "center".
- highlight (bool, optional): Highlight cell contents (if str). Defaults to False.
- """
-
- header: "RenderableType" = ""
- """RenderableType: Renderable for the header (typically a string)"""
-
- footer: "RenderableType" = ""
- """RenderableType: Renderable for the footer (typically a string)"""
-
- header_style: StyleType = ""
- """StyleType: The style of the header."""
-
- footer_style: StyleType = ""
- """StyleType: The style of the footer."""
-
- style: StyleType = ""
- """StyleType: The style of the column."""
-
- justify: "JustifyMethod" = "left"
- """str: How to justify text within the column ("left", "center", "right", or "full")"""
-
- vertical: "VerticalAlignMethod" = "top"
- """str: How to vertically align content ("top", "middle", or "bottom")"""
-
- overflow: "OverflowMethod" = "ellipsis"
- """str: Overflow method."""
-
- width: Optional[int] = None
- """Optional[int]: Width of the column, or ``None`` (default) to auto calculate width."""
-
- min_width: Optional[int] = None
- """Optional[int]: Minimum width of column, or ``None`` for no minimum. Defaults to None."""
-
- max_width: Optional[int] = None
- """Optional[int]: Maximum width of column, or ``None`` for no maximum. Defaults to None."""
-
- ratio: Optional[int] = None
- """Optional[int]: Ratio to use when calculating column width, or ``None`` (default) to adapt to column contents."""
-
- no_wrap: bool = False
- """bool: Prevent wrapping of text within the column. Defaults to ``False``."""
-
- _index: int = 0
- """Index of column."""
-
- _cells: List["RenderableType"] = field(default_factory=list)
-
- def copy(self) -> "Column":
- """Return a copy of this Column."""
- return replace(self, _cells=[])
-
- @property
- def cells(self) -> Iterable["RenderableType"]:
- """Get all cells in the column, not including header."""
- yield from self._cells
-
- @property
- def flexible(self) -> bool:
- """Check if this column is flexible."""
- return self.ratio is not None
-
-
-@dataclass
-class Row:
- """Information regarding a row."""
-
- style: Optional[StyleType] = None
- """Style to apply to row."""
-
- end_section: bool = False
- """Indicated end of section, which will force a line beneath the row."""
-
-
-class _Cell(NamedTuple):
- """A single cell in a table."""
-
- style: StyleType
- """Style to apply to cell."""
- renderable: "RenderableType"
- """Cell renderable."""
- vertical: VerticalAlignMethod
- """Cell vertical alignment."""
-
-
-class Table(JupyterMixin):
- """A console renderable to draw a table.
-
- Args:
- *headers (Union[Column, str]): Column headers, either as a string, or :class:`~rich.table.Column` instance.
- title (Union[str, Text], optional): The title of the table rendered at the top. Defaults to None.
- caption (Union[str, Text], optional): The table caption rendered below. Defaults to None.
- width (int, optional): The width in characters of the table, or ``None`` to automatically fit. Defaults to None.
- min_width (Optional[int], optional): The minimum width of the table, or ``None`` for no minimum. Defaults to None.
- box (box.Box, optional): One of the constants in box.py used to draw the edges (see :ref:`appendix_box`), or ``None`` for no box lines. Defaults to box.HEAVY_HEAD.
- safe_box (Optional[bool], optional): Disable box characters that don't display on windows legacy terminal with *raster* fonts. Defaults to True.
- padding (PaddingDimensions, optional): Padding for cells (top, right, bottom, left). Defaults to (0, 1).
- collapse_padding (bool, optional): Enable collapsing of padding around cells. Defaults to False.
- pad_edge (bool, optional): Enable padding of edge cells. Defaults to True.
- expand (bool, optional): Expand the table to fit the available space if ``True``, otherwise the table width will be auto-calculated. Defaults to False.
- show_header (bool, optional): Show a header row. Defaults to True.
- show_footer (bool, optional): Show a footer row. Defaults to False.
- show_edge (bool, optional): Draw a box around the outside of the table. Defaults to True.
- show_lines (bool, optional): Draw lines between every row. Defaults to False.
- leading (bool, optional): Number of blank lines between rows (precludes ``show_lines``). Defaults to 0.
- style (Union[str, Style], optional): Default style for the table. Defaults to "none".
- row_styles (List[Union, str], optional): Optional list of row styles, if more than one style is given then the styles will alternate. Defaults to None.
- header_style (Union[str, Style], optional): Style of the header. Defaults to "table.header".
- footer_style (Union[str, Style], optional): Style of the footer. Defaults to "table.footer".
- border_style (Union[str, Style], optional): Style of the border. Defaults to None.
- title_style (Union[str, Style], optional): Style of the title. Defaults to None.
- caption_style (Union[str, Style], optional): Style of the caption. Defaults to None.
- title_justify (str, optional): Justify method for title. Defaults to "center".
- caption_justify (str, optional): Justify method for caption. Defaults to "center".
- highlight (bool, optional): Highlight cell contents (if str). Defaults to False.
- """
-
- columns: List[Column]
- rows: List[Row]
-
- def __init__(
- self,
- *headers: Union[Column, str],
- title: Optional[TextType] = None,
- caption: Optional[TextType] = None,
- width: Optional[int] = None,
- min_width: Optional[int] = None,
- box: Optional[box.Box] = box.HEAVY_HEAD,
- safe_box: Optional[bool] = None,
- padding: PaddingDimensions = (0, 1),
- collapse_padding: bool = False,
- pad_edge: bool = True,
- expand: bool = False,
- show_header: bool = True,
- show_footer: bool = False,
- show_edge: bool = True,
- show_lines: bool = False,
- leading: int = 0,
- style: StyleType = "none",
- row_styles: Optional[Iterable[StyleType]] = None,
- header_style: Optional[StyleType] = "table.header",
- footer_style: Optional[StyleType] = "table.footer",
- border_style: Optional[StyleType] = None,
- title_style: Optional[StyleType] = None,
- caption_style: Optional[StyleType] = None,
- title_justify: "JustifyMethod" = "center",
- caption_justify: "JustifyMethod" = "center",
- highlight: bool = False,
- ) -> None:
-
- self.columns: List[Column] = []
- self.rows: List[Row] = []
- self.title = title
- self.caption = caption
- self.width = width
- self.min_width = min_width
- self.box = box
- self.safe_box = safe_box
- self._padding = Padding.unpack(padding)
- self.pad_edge = pad_edge
- self._expand = expand
- self.show_header = show_header
- self.show_footer = show_footer
- self.show_edge = show_edge
- self.show_lines = show_lines
- self.leading = leading
- self.collapse_padding = collapse_padding
- self.style = style
- self.header_style = header_style or ""
- self.footer_style = footer_style or ""
- self.border_style = border_style
- self.title_style = title_style
- self.caption_style = caption_style
- self.title_justify: "JustifyMethod" = title_justify
- self.caption_justify: "JustifyMethod" = caption_justify
- self.highlight = highlight
- self.row_styles: Sequence[StyleType] = list(row_styles or [])
- append_column = self.columns.append
- for header in headers:
- if isinstance(header, str):
- self.add_column(header=header)
- else:
- header._index = len(self.columns)
- append_column(header)
-
- @classmethod
- def grid(
- cls,
- *headers: Union[Column, str],
- padding: PaddingDimensions = 0,
- collapse_padding: bool = True,
- pad_edge: bool = False,
- expand: bool = False,
- ) -> "Table":
- """Get a table with no lines, headers, or footer.
-
- Args:
- *headers (Union[Column, str]): Column headers, either as a string, or :class:`~rich.table.Column` instance.
- padding (PaddingDimensions, optional): Get padding around cells. Defaults to 0.
- collapse_padding (bool, optional): Enable collapsing of padding around cells. Defaults to True.
- pad_edge (bool, optional): Enable padding around edges of table. Defaults to False.
- expand (bool, optional): Expand the table to fit the available space if ``True``, otherwise the table width will be auto-calculated. Defaults to False.
-
- Returns:
- Table: A table instance.
- """
- return cls(
- *headers,
- box=None,
- padding=padding,
- collapse_padding=collapse_padding,
- show_header=False,
- show_footer=False,
- show_edge=False,
- pad_edge=pad_edge,
- expand=expand,
- )
-
- @property
- def expand(self) -> bool:
- """Setting a non-None self.width implies expand."""
- return self._expand or self.width is not None
-
- @expand.setter
- def expand(self, expand: bool) -> None:
- """Set expand."""
- self._expand = expand
-
- @property
- def _extra_width(self) -> int:
- """Get extra width to add to cell content."""
- width = 0
- if self.box and self.show_edge:
- width += 2
- if self.box:
- width += len(self.columns) - 1
- return width
-
- @property
- def row_count(self) -> int:
- """Get the current number of rows."""
- return len(self.rows)
-
- def get_row_style(self, console: "Console", index: int) -> StyleType:
- """Get the current row style."""
- style = Style.null()
- if self.row_styles:
- style += console.get_style(self.row_styles[index % len(self.row_styles)])
- row_style = self.rows[index].style
- if row_style is not None:
- style += console.get_style(row_style)
- return style
-
- def __rich_measure__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> Measurement:
- max_width = options.max_width
- if self.width is not None:
- max_width = self.width
- if max_width < 0:
- return Measurement(0, 0)
-
- extra_width = self._extra_width
- max_width = sum(
- self._calculate_column_widths(
- console, options.update_width(max_width - extra_width)
- )
- )
- _measure_column = self._measure_column
-
- measurements = [
- _measure_column(console, options.update_width(max_width), column)
- for column in self.columns
- ]
- minimum_width = (
- sum(measurement.minimum for measurement in measurements) + extra_width
- )
- maximum_width = (
- sum(measurement.maximum for measurement in measurements) + extra_width
- if (self.width is None)
- else self.width
- )
- measurement = Measurement(minimum_width, maximum_width)
- measurement = measurement.clamp(self.min_width)
- return measurement
-
- @property
- def padding(self) -> Tuple[int, int, int, int]:
- """Get cell padding."""
- return self._padding
-
- @padding.setter
- def padding(self, padding: PaddingDimensions) -> "Table":
- """Set cell padding."""
- self._padding = Padding.unpack(padding)
- return self
-
- def add_column(
- self,
- header: "RenderableType" = "",
- footer: "RenderableType" = "",
- *,
- header_style: Optional[StyleType] = None,
- footer_style: Optional[StyleType] = None,
- style: Optional[StyleType] = None,
- justify: "JustifyMethod" = "left",
- vertical: "VerticalAlignMethod" = "top",
- overflow: "OverflowMethod" = "ellipsis",
- width: Optional[int] = None,
- min_width: Optional[int] = None,
- max_width: Optional[int] = None,
- ratio: Optional[int] = None,
- no_wrap: bool = False,
- ) -> None:
- """Add a column to the table.
-
- Args:
- header (RenderableType, optional): Text or renderable for the header.
- Defaults to "".
- footer (RenderableType, optional): Text or renderable for the footer.
- Defaults to "".
- header_style (Union[str, Style], optional): Style for the header, or None for default. Defaults to None.
- footer_style (Union[str, Style], optional): Style for the footer, or None for default. Defaults to None.
- style (Union[str, Style], optional): Style for the column cells, or None for default. Defaults to None.
- justify (JustifyMethod, optional): Alignment for cells. Defaults to "left".
- vertical (VerticalAlignMethod, optional): Vertical alignment, one of "top", "middle", or "bottom". Defaults to "top".
- overflow (OverflowMethod): Overflow method: "crop", "fold", "ellipsis". Defaults to "ellipsis".
- width (int, optional): Desired width of column in characters, or None to fit to contents. Defaults to None.
- min_width (Optional[int], optional): Minimum width of column, or ``None`` for no minimum. Defaults to None.
- max_width (Optional[int], optional): Maximum width of column, or ``None`` for no maximum. Defaults to None.
- ratio (int, optional): Flexible ratio for the column (requires ``Table.expand`` or ``Table.width``). Defaults to None.
- no_wrap (bool, optional): Set to ``True`` to disable wrapping of this column.
- """
-
- column = Column(
- _index=len(self.columns),
- header=header,
- footer=footer,
- header_style=header_style or "",
- footer_style=footer_style or "",
- style=style or "",
- justify=justify,
- vertical=vertical,
- overflow=overflow,
- width=width,
- min_width=min_width,
- max_width=max_width,
- ratio=ratio,
- no_wrap=no_wrap,
- )
- self.columns.append(column)
-
- def add_row(
- self,
- *renderables: Optional["RenderableType"],
- style: Optional[StyleType] = None,
- end_section: bool = False,
- ) -> None:
- """Add a row of renderables.
-
- Args:
- *renderables (None or renderable): Each cell in a row must be a renderable object (including str),
- or ``None`` for a blank cell.
- style (StyleType, optional): An optional style to apply to the entire row. Defaults to None.
- end_section (bool, optional): End a section and draw a line. Defaults to False.
-
- Raises:
- errors.NotRenderableError: If you add something that can't be rendered.
- """
-
- def add_cell(column: Column, renderable: "RenderableType") -> None:
- column._cells.append(renderable)
-
- cell_renderables: List[Optional["RenderableType"]] = list(renderables)
-
- columns = self.columns
- if len(cell_renderables) < len(columns):
- cell_renderables = [
- *cell_renderables,
- *[None] * (len(columns) - len(cell_renderables)),
- ]
- for index, renderable in enumerate(cell_renderables):
- if index == len(columns):
- column = Column(_index=index)
- for _ in self.rows:
- add_cell(column, Text(""))
- self.columns.append(column)
- else:
- column = columns[index]
- if renderable is None:
- add_cell(column, "")
- elif is_renderable(renderable):
- add_cell(column, renderable)
- else:
- raise errors.NotRenderableError(
- f"unable to render {type(renderable).__name__}; a string or other renderable object is required"
- )
- self.rows.append(Row(style=style, end_section=end_section))
-
- def add_section(self) -> None:
- """Add a new section (draw a line after current row)."""
-
- if self.rows:
- self.rows[-1].end_section = True
-
- def __rich_console__(
- self, console: "Console", options: "ConsoleOptions"
- ) -> "RenderResult":
-
- if not self.columns:
- yield Segment("\n")
- return
-
- max_width = options.max_width
- if self.width is not None:
- max_width = self.width
-
- extra_width = self._extra_width
- widths = self._calculate_column_widths(
- console, options.update_width(max_width - extra_width)
- )
- table_width = sum(widths) + extra_width
-
- render_options = options.update(
- width=table_width, highlight=self.highlight, height=None
- )
-
- def render_annotation(
- text: TextType, style: StyleType, justify: "JustifyMethod" = "center"
- ) -> "RenderResult":
- render_text = (
- console.render_str(text, style=style, highlight=False)
- if isinstance(text, str)
- else text
- )
- return console.render(
- render_text, options=render_options.update(justify=justify)
- )
-
- if self.title:
- yield from render_annotation(
- self.title,
- style=Style.pick_first(self.title_style, "table.title"),
- justify=self.title_justify,
- )
- yield from self._render(console, render_options, widths)
- if self.caption:
- yield from render_annotation(
- self.caption,
- style=Style.pick_first(self.caption_style, "table.caption"),
- justify=self.caption_justify,
- )
-
- def _calculate_column_widths(
- self, console: "Console", options: "ConsoleOptions"
- ) -> List[int]:
- """Calculate the widths of each column, including padding, not including borders."""
- max_width = options.max_width
- columns = self.columns
- width_ranges = [
- self._measure_column(console, options, column) for column in columns
- ]
- widths = [_range.maximum or 1 for _range in width_ranges]
- get_padding_width = self._get_padding_width
- extra_width = self._extra_width
- if self.expand:
- ratios = [col.ratio or 0 for col in columns if col.flexible]
- if any(ratios):
- fixed_widths = [
- 0 if column.flexible else _range.maximum
- for _range, column in zip(width_ranges, columns)
- ]
- flex_minimum = [
- (column.width or 1) + get_padding_width(column._index)
- for column in columns
- if column.flexible
- ]
- flexible_width = max_width - sum(fixed_widths)
- flex_widths = ratio_distribute(flexible_width, ratios, flex_minimum)
- iter_flex_widths = iter(flex_widths)
- for index, column in enumerate(columns):
- if column.flexible:
- widths[index] = fixed_widths[index] + next(iter_flex_widths)
- table_width = sum(widths)
-
- if table_width > max_width:
- widths = self._collapse_widths(
- widths,
- [(column.width is None and not column.no_wrap) for column in columns],
- max_width,
- )
- table_width = sum(widths)
- # last resort, reduce columns evenly
- if table_width > max_width:
- excess_width = table_width - max_width
- widths = ratio_reduce(excess_width, [1] * len(widths), widths, widths)
- table_width = sum(widths)
-
- width_ranges = [
- self._measure_column(console, options.update_width(width), column)
- for width, column in zip(widths, columns)
- ]
- widths = [_range.maximum or 0 for _range in width_ranges]
-
- if (table_width < max_width and self.expand) or (
- self.min_width is not None and table_width < (self.min_width - extra_width)
- ):
- _max_width = (
- max_width
- if self.min_width is None
- else min(self.min_width - extra_width, max_width)
- )
- pad_widths = ratio_distribute(_max_width - table_width, widths)
- widths = [_width + pad for _width, pad in zip(widths, pad_widths)]
-
- return widths
-
- @classmethod
- def _collapse_widths(
- cls, widths: List[int], wrapable: List[bool], max_width: int
- ) -> List[int]:
- """Reduce widths so that the total is under max_width.
-
- Args:
- widths (List[int]): List of widths.
- wrapable (List[bool]): List of booleans that indicate if a column may shrink.
- max_width (int): Maximum width to reduce to.
-
- Returns:
- List[int]: A new list of widths.
- """
- total_width = sum(widths)
- excess_width = total_width - max_width
- if any(wrapable):
- while total_width and excess_width > 0:
- max_column = max(
- width for width, allow_wrap in zip(widths, wrapable) if allow_wrap
- )
- second_max_column = max(
- width if allow_wrap and width != max_column else 0
- for width, allow_wrap in zip(widths, wrapable)
- )
- column_difference = max_column - second_max_column
- ratios = [
- (1 if (width == max_column and allow_wrap) else 0)
- for width, allow_wrap in zip(widths, wrapable)
- ]
- if not any(ratios) or not column_difference:
- break
- max_reduce = [min(excess_width, column_difference)] * len(widths)
- widths = ratio_reduce(excess_width, ratios, max_reduce, widths)
-
- total_width = sum(widths)
- excess_width = total_width - max_width
- return widths
-
- def _get_cells(
- self, console: "Console", column_index: int, column: Column
- ) -> Iterable[_Cell]:
- """Get all the cells with padding and optional header."""
-
- collapse_padding = self.collapse_padding
- pad_edge = self.pad_edge
- padding = self.padding
- any_padding = any(padding)
-
- first_column = column_index == 0
- last_column = column_index == len(self.columns) - 1
-
- _padding_cache: Dict[Tuple[bool, bool], Tuple[int, int, int, int]] = {}
-
- def get_padding(first_row: bool, last_row: bool) -> Tuple[int, int, int, int]:
- cached = _padding_cache.get((first_row, last_row))
- if cached:
- return cached
- top, right, bottom, left = padding
-
- if collapse_padding:
- if not first_column:
- left = max(0, left - right)
- if not last_row:
- bottom = max(0, top - bottom)
-
- if not pad_edge:
- if first_column:
- left = 0
- if last_column:
- right = 0
- if first_row:
- top = 0
- if last_row:
- bottom = 0
- _padding = (top, right, bottom, left)
- _padding_cache[(first_row, last_row)] = _padding
- return _padding
-
- raw_cells: List[Tuple[StyleType, "RenderableType"]] = []
- _append = raw_cells.append
- get_style = console.get_style
- if self.show_header:
- header_style = get_style(self.header_style or "") + get_style(
- column.header_style
- )
- _append((header_style, column.header))
- cell_style = get_style(column.style or "")
- for cell in column.cells:
- _append((cell_style, cell))
- if self.show_footer:
- footer_style = get_style(self.footer_style or "") + get_style(
- column.footer_style
- )
- _append((footer_style, column.footer))
-
- if any_padding:
- _Padding = Padding
- for first, last, (style, renderable) in loop_first_last(raw_cells):
- yield _Cell(
- style,
- _Padding(renderable, get_padding(first, last)),
- getattr(renderable, "vertical", None) or column.vertical,
- )
- else:
- for (style, renderable) in raw_cells:
- yield _Cell(
- style,
- renderable,
- getattr(renderable, "vertical", None) or column.vertical,
- )
-
- def _get_padding_width(self, column_index: int) -> int:
- """Get extra width from padding."""
- _, pad_right, _, pad_left = self.padding
- if self.collapse_padding:
- if column_index > 0:
- pad_left = max(0, pad_left - pad_right)
- return pad_left + pad_right
-
- def _measure_column(
- self,
- console: "Console",
- options: "ConsoleOptions",
- column: Column,
- ) -> Measurement:
- """Get the minimum and maximum width of the column."""
-
- max_width = options.max_width
- if max_width < 1:
- return Measurement(0, 0)
-
- padding_width = self._get_padding_width(column._index)
-
- if column.width is not None:
- # Fixed width column
- return Measurement(
- column.width + padding_width, column.width + padding_width
- ).with_maximum(max_width)
- # Flexible column, we need to measure contents
- min_widths: List[int] = []
- max_widths: List[int] = []
- append_min = min_widths.append
- append_max = max_widths.append
- get_render_width = Measurement.get
- for cell in self._get_cells(console, column._index, column):
- _min, _max = get_render_width(console, options, cell.renderable)
- append_min(_min)
- append_max(_max)
-
- measurement = Measurement(
- max(min_widths) if min_widths else 1,
- max(max_widths) if max_widths else max_width,
- ).with_maximum(max_width)
- measurement = measurement.clamp(
- None if column.min_width is None else column.min_width + padding_width,
- None if column.max_width is None else column.max_width + padding_width,
- )
- return measurement
-
- def _render(
- self, console: "Console", options: "ConsoleOptions", widths: List[int]
- ) -> "RenderResult":
- table_style = console.get_style(self.style or "")
-
- border_style = table_style + console.get_style(self.border_style or "")
- _column_cells = (
- self._get_cells(console, column_index, column)
- for column_index, column in enumerate(self.columns)
- )
- row_cells: List[Tuple[_Cell, ...]] = list(zip(*_column_cells))
- _box = (
- self.box.substitute(
- options, safe=pick_bool(self.safe_box, console.safe_box)
- )
- if self.box
- else None
- )
- _box = _box.get_plain_headed_box() if _box and not self.show_header else _box
-
- new_line = Segment.line()
-
- columns = self.columns
- show_header = self.show_header
- show_footer = self.show_footer
- show_edge = self.show_edge
- show_lines = self.show_lines
- leading = self.leading
-
- _Segment = Segment
- if _box:
- box_segments = [
- (
- _Segment(_box.head_left, border_style),
- _Segment(_box.head_right, border_style),
- _Segment(_box.head_vertical, border_style),
- ),
- (
- _Segment(_box.foot_left, border_style),
- _Segment(_box.foot_right, border_style),
- _Segment(_box.foot_vertical, border_style),
- ),
- (
- _Segment(_box.mid_left, border_style),
- _Segment(_box.mid_right, border_style),
- _Segment(_box.mid_vertical, border_style),
- ),
- ]
- if show_edge:
- yield _Segment(_box.get_top(widths), border_style)
- yield new_line
- else:
- box_segments = []
-
- get_row_style = self.get_row_style
- get_style = console.get_style
-
- for index, (first, last, row_cell) in enumerate(loop_first_last(row_cells)):
- header_row = first and show_header
- footer_row = last and show_footer
- row = (
- self.rows[index - show_header]
- if (not header_row and not footer_row)
- else None
- )
- max_height = 1
- cells: List[List[List[Segment]]] = []
- if header_row or footer_row:
- row_style = Style.null()
- else:
- row_style = get_style(
- get_row_style(console, index - 1 if show_header else index)
- )
- for width, cell, column in zip(widths, row_cell, columns):
- render_options = options.update(
- width=width,
- justify=column.justify,
- no_wrap=column.no_wrap,
- overflow=column.overflow,
- height=None,
- )
- lines = console.render_lines(
- cell.renderable,
- render_options,
- style=get_style(cell.style) + row_style,
- )
- max_height = max(max_height, len(lines))
- cells.append(lines)
-
- row_height = max(len(cell) for cell in cells)
-
- def align_cell(
- cell: List[List[Segment]],
- vertical: "VerticalAlignMethod",
- width: int,
- style: Style,
- ) -> List[List[Segment]]:
- if header_row:
- vertical = "bottom"
- elif footer_row:
- vertical = "top"
-
- if vertical == "top":
- return _Segment.align_top(cell, width, row_height, style)
- elif vertical == "middle":
- return _Segment.align_middle(cell, width, row_height, style)
- return _Segment.align_bottom(cell, width, row_height, style)
-
- cells[:] = [
- _Segment.set_shape(
- align_cell(
- cell,
- _cell.vertical,
- width,
- get_style(_cell.style) + row_style,
- ),
- width,
- max_height,
- )
- for width, _cell, cell, column in zip(widths, row_cell, cells, columns)
- ]
-
- if _box:
- if last and show_footer:
- yield _Segment(
- _box.get_row(widths, "foot", edge=show_edge), border_style
- )
- yield new_line
- left, right, _divider = box_segments[0 if first else (2 if last else 1)]
-
- # If the column divider is whitespace also style it with the row background
- divider = (
- _divider
- if _divider.text.strip()
- else _Segment(
- _divider.text, row_style.background_style + _divider.style
- )
- )
- for line_no in range(max_height):
- if show_edge:
- yield left
- for last_cell, rendered_cell in loop_last(cells):
- yield from rendered_cell[line_no]
- if not last_cell:
- yield divider
- if show_edge:
- yield right
- yield new_line
- else:
- for line_no in range(max_height):
- for rendered_cell in cells:
- yield from rendered_cell[line_no]
- yield new_line
- if _box and first and show_header:
- yield _Segment(
- _box.get_row(widths, "head", edge=show_edge), border_style
- )
- yield new_line
- end_section = row and row.end_section
- if _box and (show_lines or leading or end_section):
- if (
- not last
- and not (show_footer and index >= len(row_cells) - 2)
- and not (show_header and header_row)
- ):
- if leading:
- yield _Segment(
- _box.get_row(widths, "mid", edge=show_edge) * leading,
- border_style,
- )
- else:
- yield _Segment(
- _box.get_row(widths, "row", edge=show_edge), border_style
- )
- yield new_line
-
- if _box and show_edge:
- yield _Segment(_box.get_bottom(widths), border_style)
- yield new_line
-
-
-if __name__ == "__main__": # pragma: no cover
- from pip._vendor.rich.console import Console
- from pip._vendor.rich.highlighter import ReprHighlighter
- from pip._vendor.rich.table import Table as Table
-
- from ._timer import timer
-
- with timer("Table render"):
- table = Table(
- title="Star Wars Movies",
- caption="Rich example table",
- caption_justify="right",
- )
-
- table.add_column(
- "Released", header_style="bright_cyan", style="cyan", no_wrap=True
- )
- table.add_column("Title", style="magenta")
- table.add_column("Box Office", justify="right", style="green")
-
- table.add_row(
- "Dec 20, 2019",
- "Star Wars: The Rise of Skywalker",
- "$952,110,690",
- )
- table.add_row("May 25, 2018", "Solo: A Star Wars Story", "$393,151,347")
- table.add_row(
- "Dec 15, 2017",
- "Star Wars Ep. V111: The Last Jedi",
- "$1,332,539,889",
- style="on black",
- end_section=True,
- )
- table.add_row(
- "Dec 16, 2016",
- "Rogue One: A Star Wars Story",
- "$1,332,439,889",
- )
-
- def header(text: str) -> None:
- console.print()
- console.rule(highlight(text))
- console.print()
-
- console = Console()
- highlight = ReprHighlighter()
- header("Example Table")
- console.print(table, justify="center")
-
- table.expand = True
- header("expand=True")
- console.print(table)
-
- table.width = 50
- header("width=50")
-
- console.print(table, justify="center")
-
- table.width = None
- table.expand = False
- table.row_styles = ["dim", "none"]
- header("row_styles=['dim', 'none']")
-
- console.print(table, justify="center")
-
- table.width = None
- table.expand = False
- table.row_styles = ["dim", "none"]
- table.leading = 1
- header("leading=1, row_styles=['dim', 'none']")
- console.print(table, justify="center")
-
- table.width = None
- table.expand = False
- table.row_styles = ["dim", "none"]
- table.show_lines = True
- table.leading = 0
- header("show_lines=True, row_styles=['dim', 'none']")
- console.print(table, justify="center")
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py
deleted file mode 100644
index 18e5f0720c568db4ef0c97b59688b5e7866df606..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_101_FPN_200ep_LSJ.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .mask_rcnn_R_101_FPN_100ep_LSJ import (
- dataloader,
- lr_multiplier,
- model,
- optimizer,
- train,
-)
-
-train.max_iter *= 2 # 100ep -> 200ep
-
-lr_multiplier.scheduler.milestones = [
- milestone * 2 for milestone in lr_multiplier.scheduler.milestones
-]
-lr_multiplier.scheduler.num_updates = train.max_iter
diff --git a/spaces/BBrother/Pandora/README.md b/spaces/BBrother/Pandora/README.md
deleted file mode 100644
index 6281bc037c577f88e9b951d852198560990a730e..0000000000000000000000000000000000000000
--- a/spaces/BBrother/Pandora/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Pandora
-emoji: 🐢
-colorFrom: green
-colorTo: yellow
-sdk: docker
-pinned: false
-app_port: 8018
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benson/text-generation/Examples/Descargar Counter Strike 1.3.md b/spaces/Benson/text-generation/Examples/Descargar Counter Strike 1.3.md
deleted file mode 100644
index eb084329115a4450c5305501505653fc482cbf78..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Counter Strike 1.3.md
+++ /dev/null
@@ -1,81 +0,0 @@
-
-Descargar Counter Strike 1.3: Un juego clásico de FPS
-Si eres un fan de los juegos de disparos en primera persona (FPS), es posible que hayas oído hablar de Counter Strike, uno de los juegos FPS más populares e influyentes jamás creados. Pero ¿sabías que hay una versión más antigua de Counter Strike que todavía se juega por muchos jugadores de todo el mundo? Se llama Counter Strike 1.3, y es un juego clásico que puedes descargar y disfrutar en tu PC.
-descargar counter strike 1.3 Download → https://bltlly.com/2v6KHW
En este artículo, le diremos todo lo que necesita saber sobre Counter Strike 1.3, incluyendo lo que es, por qué debe descargarlo y cómo descargarlo. También responderemos algunas preguntas frecuentes sobre este juego al final del artículo.
- ¿Qué es Counter Strike 1.3?
-Counter Strike 1.3 es un juego multijugador FPS que fue lanzado en 2001 como un mod para Half-Life, otro popular juego de FPS por Valve Corporation. Counter Strike 1.3 es la tercera actualización importante del mod original de Counter Strike, que se lanzó por primera vez en 1999.
- Historia y características de Counter Strike 1.3
-Counter Strike fue creado por dos desarrolladores independientes, Minh Le y Jess Cliffe, que querían hacer un juego FPS realista y basado en equipos que simulara escenarios de guerra modernos. Usaron el motor Half-Life para crear sus propios mapas, armas, personajes y mecánica de juego.
-Counter Strike rápidamente se convirtió en un éxito entre los fans de FPS, especialmente aquellos que disfrutaron de los partidos multijugador en línea. Valve Corporation se dio cuenta del potencial del mod y contrató a Le y Cliffe para trabajar en la versión oficial de Counter Strike, que fue lanzado en 2000.
-
-Counter Strike 1.3 fue una de las actualizaciones más significativas del juego, ya que introdujo muchas nuevas características y mejoras, como:
-
-Nuevos mapas, como de_dust2, cs_italy, cs_office, de_train y de_inferno
-Nuevas armas, como el Galil, FAMAS, USP, Glock-18, y el Escudo Táctico
-
-Nuevos comandos y opciones, tales como comunicación de voz, modo espectador, recarga automática, compra automática y equilibrio de equipo automático
-Nuevos gráficos y sonidos, como texturas mejoradas, modelos, animaciones, efectos y música
-Nuevo sistema anti-trucos, como Válvula Anti-Cheat (VAC)
-
- El modo de juego y modos de Counter Strike 1.3
-El modo de juego de Counter Strike 1.3 se basa en dos equipos opuestos: los terroristas y los antiterroristas. Cada equipo tiene diferentes objetivos dependiendo del mapa y el modo de juego que esté jugando.
-El modo de juego más común es Bomb Defusal, donde los terroristas tienen que colocar una bomba en uno de los dos sitios designados y defenderla hasta que explote, mientras que los antiterroristas tienen que evitar que lo hagan o desactivar la bomba si se planta.
-Otro modo de juego popular es Rescate de rehenes, donde los terroristas tienen que proteger a un grupo de rehenes en su base, mientras que los antiterroristas tienen que rescatarlos y llevarlos a una zona segura.
-Otros modos de juego incluyen Escolta VIP, donde los Antiterroristas tienen que escoltar a un jugador VIP a un punto de extracción mientras que los Terroristas tienen que asesinarlo; Asesinato, donde los Terroristas tienen que matar a un objetivo específico mientras que los Antiterroristas tienen que protegerlo; y Deathmatch, donde los jugadores pueden elegir cualquier arma y reaparecer después de morir, y el equipo con más muertes gana.
-El modo de juego de Counter Strike 1.3 es rápido, táctico y basado en habilidades. Los jugadores tienen que utilizar varias armas, equipos y estrategias para lograr sus objetivos y eliminar a sus enemigos. Los jugadores también tienen que administrar su dinero, salud, armadura y munición, ya que son limitados y afectan su rendimiento.
-
- ¿Por qué descargar Counter Strike 1.3?
-Counter Strike 1.3 es un clásico juego de FPS que tiene muchos beneficios y ventajas para los jugadores que aman este género. Estas son algunas de las razones por las que deberías descargar Counter Strike 1.3:
- Los beneficios y ventajas de Counter Strike 1.3
-
-Counter Strike 1.3 es un juego divertido y adictivo que puede proporcionar horas de entretenimiento y desafío. Puedes jugar con tus amigos o con otros jugadores online, y disfrutar de la emoción de competir en diferentes escenarios y modos.
-Counter Strike 1.3 es un juego que puede mejorar tus habilidades y reflejos. Puede aprender a apuntar, disparar, moverse, comunicarse y cooperar con sus compañeros de equipo, y desarrollar sus habilidades de pensamiento estratégico y resolución de problemas.
-Counter Strike 1.3 es un juego que puede satisfacer su nostalgia y curiosidad. Puedes experimentar la versión original de Counter Strike que lo inició todo, y ver cómo evolucionó con los años. También puede compararlo con las versiones más recientes de Counter Strike, como Counter Strike: Source y Counter Strike: Global Offensive.
-Counter Strike 1.3 es un juego que es fácil de descargar e instalar. No necesitas un PC potente o una conexión a Internet de alta velocidad para jugar a este juego, ya que tiene bajos requisitos del sistema y tamaño de archivo. También puede encontrar muchas fuentes y guías sobre cómo descargar Counter Strike 1.3 en línea.
-
- Los desafíos y desventajas de Counter Strike 1.3
-Sin embargo, Counter Strike 1.3 no es un juego perfecto, y también tiene algunos retos y desventajas que debes tener en cuenta antes de descargarlo. Estos son algunos de los problemas que puede encontrar con Counter Strike 1.3:
-
-Counter Strike 1.3 es un juego antiguo que tiene gráficos y sonidos obsoletos. Usted puede encontrar el juego visualmente poco atractivo o aburrido en comparación con los modernos juegos FPS que tienen gráficos y sonidos más realistas e inmersivos.
-
-Counter Strike 1.3 es un juego que tiene una curva de aprendizaje pronunciada y una comunidad competitiva. Puedes encontrar el juego difícil o frustrante para jugar, especialmente si eres nuevo en el juego o si te enfrentas a jugadores más experimentados o expertos que pueden dominarte fácilmente.
-Counter Strike 1.3 es un juego que requiere actualizaciones y mantenimiento constantes. Es posible que tenga que descargar parches o mods para solucionar algunos de los problemas o mejorar algunas de las características del juego, o para mantenerse al día con las últimas versiones o tendencias del juego.
-
- Cómo descargar Counter Strike 1.3?
-Si está interesado en descargar Counter Strike 1.3, tendrá que seguir algunos pasos y requisitos para garantizar un proceso de instalación sin problemas y con éxito. Estas son algunas de las cosas que necesitas saber sobre la descarga de Counter Strike 1.3:
- Los requisitos y la compatibilidad de Counter Strike 1.3
-Antes de descargar Counter Strike 1.3, debe asegurarse de que su PC cumple con los requisitos mínimos del sistema para ejecutar el juego. Aquí están las especificaciones que necesita:
-
-Sistema operativo Windows XP/Vista/7/8/10 Procesador Pentium III 500 MHz o equivalente Memoria 96 MB de RAM Gráficos 16 MB tarjeta de vídeo Almacenamiento 500 MB de espacio disponible Tarjeta de sonido Tarjeta de sonido compatible con DirectX RedConexión a Internet de banda ancha
-
-También debe asegurarse de que tiene Half-Life instalado en su PC, ya que Counter Strike 1.3 es un mod para Half-Life y requiere que se ejecute. Puedes comprar Half-Life en Steam u otras plataformas online, o usar tu propio CD-ROM si tienes uno.
- Las fuentes y pasos de la descarga de Counter Strike 1.3
-
-
-Puedes descargar Counter Strike 1.3 de Steam, la plataforma oficial para juegos de Valve Corporation. Deberás crear una cuenta de Steam e instalar el cliente de Steam en tu PC, luego buscar Counter Strike 1.3 en la tienda de Steam y hacer clic en el botón de descarga. Steam instalará y actualizará el juego automáticamente.
-Puede descargar Counter Strike 1.3 de CS-Download, un sitio web que proporciona descargas gratuitas y seguras de las versiones y mods de Counter Strike. Usted tendrá que visitar el sitio web y haga clic en el enlace de descarga para Counter Strike 1.3, a continuación, siga las instrucciones y solicitudes para instalar el juego en su PC.
-Puede descargar Counter Strike 1.3 de FilePlanet, un sitio web que alberga varios archivos y descargas de juegos y software. Deberá visitar el sitio web y buscar Counter Strike 1.3, luego elegir el archivo que coincida con su sistema y preferencias, y haga clic en el botón de descarga. A continuación, tendrá que extraer y ejecutar el archivo para instalar el juego en su PC.
-
-Después de haber descargado Counter Strike 1.3, puede iniciar el juego desde su escritorio o menú de inicio, y disfrutar jugando con sus amigos u otros jugadores en línea.
- Conclusión
-Resumen y recomendaciones
-Counter Strike 1.3 es un clásico juego de FPS que puedes descargar y jugar en tu PC. Es un juego multijugador que enfrenta a dos equipos de Terroristas y Antiterroristas entre sí en varios mapas y modos. Es un juego que tiene muchas características y beneficios, como un juego divertido y adictivo, mejora de habilidades, satisfacción por la nostalgia y fácil instalación. También es un juego que tiene algunos desafíos y desventajas, tales como gráficos anticuados, errores y fallas, curva de aprendizaje empinada, y actualizaciones constantes.
-
- Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre Counter Strike 1.3:
-
-¿Está libre Counter Strike 1.3? ¿Cómo puedo mejorar mis habilidades en Counter Strike 1.3? ¿Cuáles son algunos de los mejores mapas en Counter Strike 1.3? 64aa2da5cf\n",
- "
\n",
- "
Stable Diffusion 2.1 \n",
- " \n",
- "
Model used: v2-1_768-ema-pruned.ckpt
\n",
- " Running on
{\"GPU 🔥\" if torch.cuda.is_available() else \"CPU 🥶\"} \n",
- "
\n",
- "
\n",
- "
Space by:
\n",
- "
Enjoying this app? Please consider supporting me
\n",
- "
\n",
- "
\n",
- "
\n",
- "
-
-namespace detectron2 {
-
-at::Tensor box_iou_rotated_cpu(
- const at::Tensor& boxes1,
- const at::Tensor& boxes2);
-
-#ifdef WITH_CUDA
-at::Tensor box_iou_rotated_cuda(
- const at::Tensor& boxes1,
- const at::Tensor& boxes2);
-#endif
-
-// Interface for Python
-// inline is needed to prevent multiple function definitions when this header is
-// included by different cpps
-inline at::Tensor box_iou_rotated(
- const at::Tensor& boxes1,
- const at::Tensor& boxes2) {
- assert(boxes1.device().is_cuda() == boxes2.device().is_cuda());
- if (boxes1.device().is_cuda()) {
-#ifdef WITH_CUDA
- return box_iou_rotated_cuda(boxes1, boxes2);
-#else
- AT_ERROR("Not compiled with GPU support");
-#endif
- }
-
- return box_iou_rotated_cpu(boxes1, boxes2);
-}
-
-} // namespace detectron2
diff --git a/spaces/CVPR/LIVE/thrust/thrust/cmake/thrust-config.cmake b/spaces/CVPR/LIVE/thrust/thrust/cmake/thrust-config.cmake
deleted file mode 100644
index 467579d1d0b8e273b562ff7f6eb01fc31c901d10..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/cmake/thrust-config.cmake
+++ /dev/null
@@ -1,652 +0,0 @@
-#
-# find_package(Thrust) config file.
-#
-# Provided by NVIDIA under the same license as the associated Thrust library.
-#
-# Reply-To: Allison Vacanti
-#
-# *****************************************************************************
-# ** The following is a short reference to using Thrust from CMake. **
-# ** For more details, see the README.md in the same directory as this file. **
-# *****************************************************************************
-#
-# # General Usage:
-# find_package(Thrust REQUIRED CONFIG)
-# thrust_create_target(Thrust [options])
-# target_link_libraries(some_project_lib Thrust)
-#
-# # Create default target with: HOST=CPP DEVICE=CUDA
-# thrust_create_target(TargetName)
-#
-# # Create target with: HOST=CPP DEVICE=TBB
-# thrust_create_target(TargetName DEVICE TBB)
-#
-# # Create target with: HOST=TBB DEVICE=OMP
-# thrust_create_target(TargetName HOST TBB DEVICE OMP)
-#
-# # Create CMake cache options THRUST_[HOST|DEVICE]_SYSTEM and configure a
-# # target from them. This allows these systems to be changed by developers at
-# # configure time, per build.
-# thrust_create_target(TargetName FROM_OPTIONS
-# [HOST_OPTION ] # Optionally rename the host system option
-# [DEVICE_OPTION ] # Optionally rename the device system option
-# [HOST_OPTION_DOC ] # Optionally change the cache label
-# [DEVICE_OPTION_DOC ] # Optionally change the cache label
-# [HOST ] # Optionally change the default backend
-# [DEVICE ] # Optionally change the default backend
-# [ADVANCED] # Optionally mark options as advanced
-# )
-#
-# # Use a custom TBB, CUB, and/or OMP
-# # (Note that once set, these cannot be changed. This includes COMPONENT
-# # preloading and lazy lookups in thrust_create_target)
-# find_package(Thrust REQUIRED)
-# thrust_set_CUB_target(MyCUBTarget) # MyXXXTarget contains an existing
-# thrust_set_TBB_target(MyTBBTarget) # interface to XXX for Thrust to use.
-# thrust_set_OMP_target(MyOMPTarget)
-# thrust_create_target(ThrustWithMyCUB DEVICE CUDA)
-# thrust_create_target(ThrustWithMyTBB DEVICE TBB)
-# thrust_create_target(ThrustWithMyOMP DEVICE OMP)
-#
-# # Create target with HOST=CPP DEVICE=CUDA and some advanced flags set
-# thrust_create_target(TargetName
-# IGNORE_DEPRECATED_CPP_DIALECT # Silence build warnings about deprecated compilers and C++ standards
-# IGNORE_DEPRECATED_CPP_11 # Only silence deprecation warnings for C++11
-# IGNORE_DEPRECATED_COMPILER # Only silence deprecation warnings for old compilers
-# IGNORE_CUB_VERSION # Skip configure-time and compile-time CUB version checks
-# )
-#
-# # Test if a particular system has been loaded. ${var_name} is set to TRUE or
-# # FALSE to indicate if "system" is found.
-# thrust_is_system_found( )
-# thrust_is_cuda_system_found()
-# thrust_is_tbb_system_found()
-# thrust_is_omp_system_found()
-# thrust_is_cpp_system_found()
-#
-# # Define / update THRUST_${system}_FOUND flags in current scope
-# thrust_update_system_found_flags()
-#
-# # View verbose log with target and dependency information:
-# $ cmake . --log-level=VERBOSE (CMake 3.15.7 and above)
-#
-# # Print debugging output to status channel:
-# thrust_debug_internal_targets()
-# thrust_debug_target(TargetName "${THRUST_VERSION}")
-
-cmake_minimum_required(VERSION 3.15)
-
-################################################################################
-# User variables and APIs. Users can rely on these:
-#
-
-# Advertise system options:
-set(THRUST_HOST_SYSTEM_OPTIONS
- CPP OMP TBB
- CACHE INTERNAL "Valid Thrust host systems."
-)
-set(THRUST_DEVICE_SYSTEM_OPTIONS
- CUDA CPP OMP TBB
- CACHE INTERNAL "Valid Thrust device systems"
-)
-
-# Workaround cmake issue #20670 https://gitlab.kitware.com/cmake/cmake/-/issues/20670
-set(THRUST_VERSION ${${CMAKE_FIND_PACKAGE_NAME}_VERSION} CACHE INTERNAL "")
-set(THRUST_VERSION_MAJOR ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_MAJOR} CACHE INTERNAL "")
-set(THRUST_VERSION_MINOR ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_MINOR} CACHE INTERNAL "")
-set(THRUST_VERSION_PATCH ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_PATCH} CACHE INTERNAL "")
-set(THRUST_VERSION_TWEAK ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_TWEAK} CACHE INTERNAL "")
-set(THRUST_VERSION_COUNT ${${CMAKE_FIND_PACKAGE_NAME}_VERSION_COUNT} CACHE INTERNAL "")
-
-function(thrust_create_target target_name)
- thrust_debug("Assembling target ${target_name}. Options: ${ARGN}" internal)
- set(options
- ADVANCED
- FROM_OPTIONS
- IGNORE_CUB_VERSION_CHECK
- IGNORE_DEPRECATED_COMPILER
- IGNORE_DEPRECATED_CPP_11
- IGNORE_DEPRECATED_CPP_DIALECT
- )
- set(keys
- DEVICE
- DEVICE_OPTION
- DEVICE_OPTION_DOC
- HOST
- HOST_OPTION
- HOST_OPTION_DOC
- )
- cmake_parse_arguments(TCT "${options}" "${keys}" "" ${ARGN})
- if (TCT_UNPARSED_ARGUMENTS)
- message(AUTHOR_WARNING
- "Unrecognized arguments passed to thrust_create_target: "
- ${TCT_UNPARSED_ARGUMENTS}
- )
- endif()
-
- # Check that the main Thrust internal target is available
- # (functions have global scope, targets have directory scope, so this
- # might happen)
- if (NOT TARGET Thrust::Thrust)
- message(AUTHOR_WARNING
- "The `thrust_create_target` function was called outside the scope of the "
- "thrust targets. Call find_package again to recreate targets."
- )
- endif()
-
- _thrust_set_if_undefined(TCT_HOST CPP)
- _thrust_set_if_undefined(TCT_DEVICE CUDA)
- _thrust_set_if_undefined(TCT_HOST_OPTION THRUST_HOST_SYSTEM)
- _thrust_set_if_undefined(TCT_DEVICE_OPTION THRUST_DEVICE_SYSTEM)
- _thrust_set_if_undefined(TCT_HOST_OPTION_DOC "Thrust host system.")
- _thrust_set_if_undefined(TCT_DEVICE_OPTION_DOC "Thrust device system.")
-
- if (NOT TCT_HOST IN_LIST THRUST_HOST_SYSTEM_OPTIONS)
- message(FATAL_ERROR
- "Requested HOST=${TCT_HOST}; must be one of ${THRUST_HOST_SYSTEM_OPTIONS}")
- endif()
-
- if (NOT TCT_DEVICE IN_LIST THRUST_DEVICE_SYSTEM_OPTIONS)
- message(FATAL_ERROR
- "Requested DEVICE=${TCT_DEVICE}; must be one of ${THRUST_DEVICE_SYSTEM_OPTIONS}")
- endif()
-
- if (TCT_FROM_OPTIONS)
- _thrust_create_cache_options(
- ${TCT_HOST} ${TCT_DEVICE}
- ${TCT_HOST_OPTION} ${TCT_DEVICE_OPTION}
- ${TCT_HOST_OPTION_DOC} ${TCT_DEVICE_OPTION_DOC}
- ${TCT_ADVANCED}
- )
- set(TCT_HOST ${${TCT_HOST_OPTION}})
- set(TCT_DEVICE ${${TCT_DEVICE_OPTION}})
- thrust_debug("Current option settings:" internal)
- thrust_debug(" - ${TCT_HOST_OPTION}=${TCT_HOST}" internal)
- thrust_debug(" - ${TCT_DEVICE_OPTION}=${TCT_DEVICE}" internal)
- endif()
-
- _thrust_find_backend(${TCT_HOST} REQUIRED)
- _thrust_find_backend(${TCT_DEVICE} REQUIRED)
-
- # We can just create an INTERFACE IMPORTED target here instead of going
- # through _thrust_declare_interface_alias as long as we aren't hanging any
- # Thrust/CUB include paths on ${target_name}.
- add_library(${target_name} INTERFACE IMPORTED)
- target_link_libraries(${target_name}
- INTERFACE
- Thrust::${TCT_HOST}::Host
- Thrust::${TCT_DEVICE}::Device
- )
-
- # This would be nice to enforce, but breaks when using old cmake + new
- # compiler, since cmake doesn't know what features the new compiler version
- # supports.
- # Leaving this here as a reminder not to add it back. Just let the
- # compile-time checks in thrust/detail/config/cpp_dialect.h handle it.
- #
- # if (NOT TCT_IGNORE_DEPRECATED_CPP_DIALECT)
- # if (TCT_IGNORE_DEPRECATED_CPP_11)
- # target_compile_features(${target_name} INTERFACE cxx_std_11)
- # else()
- # target_compile_features(${target_name} INTERFACE cxx_std_14)
- # endif()
- # endif()
-
- if (TCT_IGNORE_DEPRECATED_CPP_DIALECT)
- target_compile_definitions(${target_name} INTERFACE "THRUST_IGNORE_DEPRECATED_CPP_DIALECT")
- endif()
-
- if (TCT_IGNORE_DEPRECATED_CPP_11)
- target_compile_definitions(${target_name} INTERFACE "THRUST_IGNORE_DEPRECATED_CPP_11")
- endif()
-
- if (TCT_IGNORE_DEPRECATED_COMPILER)
- target_compile_definitions(${target_name} INTERFACE "THRUST_IGNORE_DEPRECATED_COMPILER")
- endif()
-
- if (TCT_IGNORE_CUB_VERSION_CHECK)
- target_compile_definitions(${target_name} INTERFACE "THRUST_IGNORE_CUB_VERSION_CHECK")
- else()
- if (("${TCT_HOST}" STREQUAL "CUDA" OR "${TCT_DEVICE}" STREQUAL "CUDA") AND
- (NOT THRUST_VERSION VERSION_EQUAL THRUST_CUB_VERSION))
- message(FATAL_ERROR
- "The version of CUB found by CMake is not compatible with this release of Thrust. "
- "CUB is now included in the CUDA Toolkit, so you no longer need to use your own checkout of CUB. "
- "Pass IGNORE_CUB_VERSION_CHECK to thrust_create_target to ignore. "
- "(CUB ${THRUST_CUB_VERSION}, Thrust ${THRUST_VERSION})."
- )
- endif()
- endif()
-
- thrust_debug_target(${target_name} "Thrust ${THRUST_VERSION}" internal)
-endfunction()
-
-function(thrust_is_system_found system var_name)
- if (TARGET Thrust::${system})
- set(${var_name} TRUE PARENT_SCOPE)
- else()
- set(${var_name} FALSE PARENT_SCOPE)
- endif()
-endfunction()
-
-function(thrust_is_cpp_system_found var_name)
- thrust_is_system_found(CPP ${var_name})
- set(${var_name} ${${var_name}} PARENT_SCOPE)
-endfunction()
-
-function(thrust_is_cuda_system_found var_name)
- thrust_is_system_found(CUDA ${var_name})
- set(${var_name} ${${var_name}} PARENT_SCOPE)
-endfunction()
-
-function(thrust_is_tbb_system_found var_name)
- thrust_is_system_found(TBB ${var_name})
- set(${var_name} ${${var_name}} PARENT_SCOPE)
-endfunction()
-
-function(thrust_is_omp_system_found var_name)
- thrust_is_system_found(OMP ${var_name})
- set(${var_name} ${${var_name}} PARENT_SCOPE)
-endfunction()
-
-# Since components are loaded lazily, this will refresh the
-# THRUST_${component}_FOUND flags in the current scope.
-# Alternatively, check system states individually using the
-# thrust_is_system_found functions.
-macro(thrust_update_system_found_flags)
- set(THRUST_FOUND TRUE)
- thrust_is_system_found(CPP THRUST_CPP_FOUND)
- thrust_is_system_found(CUDA THRUST_CUDA_FOUND)
- thrust_is_system_found(TBB THRUST_TBB_FOUND)
- thrust_is_system_found(OMP THRUST_OMP_FOUND)
-endmacro()
-
-function(thrust_debug msg)
- # Use the VERBOSE channel when called internally
- # Run `cmake . --log-level=VERBOSE` to view.
- if ("${ARGN}" STREQUAL "internal")
- # If CMake is too old to know about the VERBOSE channel, just be silent.
- # Users reproduce much the same output on the STATUS channel by using:
- # thrust_create_target(Thrust [...])
- # thrust_debug_internal_targets()
- # thrust_debug_target(Thrust)
- if (CMAKE_VERSION VERSION_GREATER_EQUAL "3.15.7")
- set(channel VERBOSE)
- else()
- return()
- endif()
- else()
- set(channel STATUS)
- endif()
-
- message(${channel} "Thrust: ${msg}")
-endfunction()
-
-# Print details of the specified target.
-function(thrust_debug_target target_name version)
- if (NOT TARGET ${target_name})
- return()
- endif()
-
- set(is_internal "${ARGN}")
-
- if (version)
- set(version "(${version})")
- endif()
-
- thrust_debug("TargetInfo: ${target_name}: ${version}" ${is_internal})
-
- function(_thrust_print_prop_if_set target_name prop)
- get_target_property(value ${target_name} ${prop})
- if (value)
- thrust_debug("TargetInfo: ${target_name} > ${prop}: ${value}" ${is_internal})
- endif()
- endfunction()
-
- function(_thrust_print_imported_prop_if_set target_name prop)
- get_target_property(imported ${target_name} IMPORTED)
- get_target_property(type ${target_name} TYPE)
- if (imported AND NOT ${type} STREQUAL "INTERFACE_LIBRARY")
- _thrust_print_prop_if_set(${target_name} ${prop})
- endif()
- endfunction()
-
- _thrust_print_prop_if_set(${target_name} ALIASED_TARGET)
- _thrust_print_prop_if_set(${target_name} IMPORTED)
- _thrust_print_prop_if_set(${target_name} INTERFACE_COMPILE_DEFINITIONS)
- _thrust_print_prop_if_set(${target_name} INTERFACE_COMPILE_FEATURES)
- _thrust_print_prop_if_set(${target_name} INTERFACE_COMPILE_OPTIONS)
- _thrust_print_prop_if_set(${target_name} INTERFACE_INCLUDE_DIRECTORIES)
- _thrust_print_prop_if_set(${target_name} INTERFACE_LINK_DEPENDS)
- _thrust_print_prop_if_set(${target_name} INTERFACE_LINK_DIRECTORIES)
- _thrust_print_prop_if_set(${target_name} INTERFACE_LINK_LIBRARIES)
- _thrust_print_prop_if_set(${target_name} INTERFACE_LINK_OPTIONS)
- _thrust_print_prop_if_set(${target_name} INTERFACE_SYSTEM_INCLUDE_DIRECTORIES)
- _thrust_print_prop_if_set(${target_name} INTERFACE_THRUST_HOST)
- _thrust_print_prop_if_set(${target_name} INTERFACE_THRUST_DEVICE)
- _thrust_print_imported_prop_if_set(${target_name} IMPORTED_LOCATION)
- _thrust_print_imported_prop_if_set(${target_name} IMPORTED_LOCATION_DEBUG)
- _thrust_print_imported_prop_if_set(${target_name} IMPORTED_LOCATION_RELEASE)
-endfunction()
-
-function(thrust_debug_internal_targets)
- function(_thrust_debug_backend_targets backend version)
- thrust_debug_target(Thrust::${backend} "${version}")
- thrust_debug_target(Thrust::${backend}::Host "${version}")
- thrust_debug_target(Thrust::${backend}::Device "${version}")
- endfunction()
-
- thrust_debug_target(Thrust::Thrust "${THRUST_VERSION}")
-
- _thrust_debug_backend_targets(CPP "Thrust ${THRUST_VERSION}")
-
- _thrust_debug_backend_targets(CUDA "CUB ${THRUST_CUB_VERSION}")
- thrust_debug_target(CUB::CUB "${THRUST_CUB_VERSION}")
-
- _thrust_debug_backend_targets(TBB "${THRUST_TBB_VERSION}")
- thrust_debug_target(TBB:tbb "${THRUST_TBB_VERSION}")
-
- _thrust_debug_backend_targets(OMP "${THRUST_OMP_VERSION}")
- thrust_debug_target(OpenMP::OpenMP_CXX "${THRUST_OMP_VERSION}")
-endfunction()
-
-################################################################################
-# Internal utilities. Subject to change.
-#
-
-function(_thrust_set_if_undefined var)
- if (NOT DEFINED ${var})
- set(${var} ${ARGN} PARENT_SCOPE)
- endif()
-endfunction()
-
-function(_thrust_declare_interface_alias alias_name ugly_name)
- # 1) Only IMPORTED and ALIAS targets can be placed in a namespace.
- # 2) When an IMPORTED library is linked to another target, its include
- # directories are treated as SYSTEM includes.
- # 3) nvcc will automatically check the CUDA Toolkit include path *before* the
- # system includes. This means that the Toolkit Thrust will *always* be used
- # during compilation, and the include paths of an IMPORTED Thrust::Thrust
- # target will never have any effect.
- # 4) This behavior can be fixed by setting the property NO_SYSTEM_FROM_IMPORTED
- # on EVERY target that links to Thrust::Thrust. This would be a burden and a
- # footgun for our users. Forgetting this would silently pull in the wrong thrust!
- # 5) A workaround is to make a non-IMPORTED library outside of the namespace,
- # configure it, and then ALIAS it into the namespace (or ALIAS and then
- # configure, that seems to work too).
- add_library(${ugly_name} INTERFACE)
- add_library(${alias_name} ALIAS ${ugly_name})
-endfunction()
-
-# Create cache options for selecting the user/device systems with ccmake/cmake-gui.
-function(_thrust_create_cache_options host device host_option device_option host_doc device_doc advanced)
- thrust_debug("Creating system cache options: (advanced=${advanced})" internal)
- thrust_debug(" - Host Option=${host_option} Default=${host} Doc='${host_doc}'" internal)
- thrust_debug(" - Device Option=${device_option} Default=${device} Doc='${device_doc}'" internal)
- set(${host_option} ${host} CACHE STRING "${host_doc}")
- set_property(CACHE ${host_option} PROPERTY STRINGS ${THRUST_HOST_SYSTEM_OPTIONS})
- set(${device_option} ${device} CACHE STRING "${device_doc}")
- set_property(CACHE ${device_option} PROPERTY STRINGS ${THRUST_DEVICE_SYSTEM_OPTIONS})
- if (advanced)
- mark_as_advanced(${host_option} ${device_option})
- endif()
-endfunction()
-
-# Create Thrust::${backend}::Host and Thrust::${backend}::Device targets.
-# Assumes that `Thrust::${backend}` and `_Thrust_${backend}` have been created
-# by _thrust_declare_interface_alias and configured to bring in system
-# dependency interfaces (including Thrust::Thrust).
-function(_thrust_setup_system backend)
- set(backend_target_alias "Thrust::${backend}")
-
- if (backend IN_LIST THRUST_HOST_SYSTEM_OPTIONS)
- set(host_target "_Thrust_${backend}_Host")
- set(host_target_alias "Thrust::${backend}::Host")
- if (NOT TARGET ${host_target_alias})
- _thrust_declare_interface_alias(${host_target_alias} ${host_target})
- target_compile_definitions(${host_target} INTERFACE
- "THRUST_HOST_SYSTEM=THRUST_HOST_SYSTEM_${backend}")
- target_link_libraries(${host_target} INTERFACE ${backend_target_alias})
- set_property(TARGET ${host_target} PROPERTY INTERFACE_THRUST_HOST ${backend})
- set_property(TARGET ${host_target} APPEND PROPERTY COMPATIBLE_INTERFACE_STRING THRUST_HOST)
- thrust_debug_target(${host_target_alias} "" internal)
- endif()
- endif()
-
- if (backend IN_LIST THRUST_DEVICE_SYSTEM_OPTIONS)
- set(device_target "_Thrust_${backend}_Device")
- set(device_target_alias "Thrust::${backend}::Device")
- if (NOT TARGET ${device_target_alias})
- _thrust_declare_interface_alias(${device_target_alias} ${device_target})
- target_compile_definitions(${device_target} INTERFACE
- "THRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_${backend}")
- target_link_libraries(${device_target} INTERFACE ${backend_target_alias})
- set_property(TARGET ${device_target} PROPERTY INTERFACE_THRUST_DEVICE ${backend})
- set_property(TARGET ${device_target} APPEND PROPERTY COMPATIBLE_INTERFACE_STRING THRUST_DEVICE)
- thrust_debug_target(${device_target_alias} "" internal)
- endif()
- endif()
-endfunction()
-
-# Use the provided cub_target for the CUDA backend. If Thrust::CUDA already
-# exists, this call has no effect.
-function(thrust_set_CUB_target cub_target)
- if (NOT TARGET Thrust::CUDA)
- thrust_debug("Setting CUB target to ${cub_target}" internal)
- # Workaround cmake issue #20670 https://gitlab.kitware.com/cmake/cmake/-/issues/20670
- set(THRUST_CUB_VERSION ${CUB_VERSION} CACHE INTERNAL "CUB version used by Thrust")
- _thrust_declare_interface_alias(Thrust::CUDA _Thrust_CUDA)
- target_link_libraries(_Thrust_CUDA INTERFACE Thrust::Thrust ${cub_target})
- thrust_debug_target(${cub_target} "${THRUST_CUB_VERSION}" internal)
- thrust_debug_target(Thrust::CUDA "CUB ${THRUST_CUB_VERSION}" internal)
- _thrust_setup_system(CUDA)
- endif()
-endfunction()
-
-# Use the provided tbb_target for the TBB backend. If Thrust::TBB already
-# exists, this call has no effect.
-function(thrust_set_TBB_target tbb_target)
- if (NOT TARGET Thrust::TBB)
- thrust_debug("Setting TBB target to ${tbb_target}" internal)
- # Workaround cmake issue #20670 https://gitlab.kitware.com/cmake/cmake/-/issues/20670
- set(THRUST_TBB_VERSION ${TBB_VERSION} CACHE INTERNAL "TBB version used by Thrust")
- _thrust_declare_interface_alias(Thrust::TBB _Thrust_TBB)
- target_link_libraries(_Thrust_TBB INTERFACE Thrust::Thrust ${tbb_target})
- thrust_debug_target(${tbb_target} "${THRUST_TBB_VERSION}" internal)
- thrust_debug_target(Thrust::TBB "${THRUST_TBB_VERSION}" internal)
- _thrust_setup_system(TBB)
- endif()
-endfunction()
-
-# Use the provided omp_target for the OMP backend. If Thrust::OMP already
-# exists, this call has no effect.
-function(thrust_set_OMP_target omp_target)
- if (NOT TARGET Thrust::OMP)
- thrust_debug("Setting OMP target to ${omp_target}" internal)
- # Workaround cmake issue #20670 https://gitlab.kitware.com/cmake/cmake/-/issues/20670
- set(THRUST_OMP_VERSION ${OpenMP_CXX_VERSION} CACHE INTERNAL "OpenMP version used by Thrust")
- _thrust_declare_interface_alias(Thrust::OMP _Thrust_OMP)
- target_link_libraries(_Thrust_OMP INTERFACE Thrust::Thrust ${omp_target})
- thrust_debug_target(${omp_target} "${THRUST_OMP_VERSION}" internal)
- thrust_debug_target(Thrust::OMP "${THRUST_OMP_VERSION}" internal)
- _thrust_setup_system(OMP)
- endif()
-endfunction()
-
-function(_thrust_find_CPP required)
- if (NOT TARGET Thrust::CPP)
- thrust_debug("Generating CPP targets." internal)
- _thrust_declare_interface_alias(Thrust::CPP _Thrust_CPP)
- target_link_libraries(_Thrust_CPP INTERFACE Thrust::Thrust)
- thrust_debug_target(Thrust::CPP "Thrust ${THRUST_VERSION}" internal)
- _thrust_setup_system(CPP)
- endif()
-endfunction()
-
-# This must be a macro instead of a function to ensure that backends passed to
-# find_package(Thrust COMPONENTS [...]) have their full configuration loaded
-# into the current scope. This provides at least some remedy for CMake issue
-# #20670 -- otherwise variables like CUB_VERSION, etc won't be in the caller's
-# scope.
-macro(_thrust_find_CUDA required)
- if (NOT TARGET Thrust::CUDA)
- thrust_debug("Searching for CUB ${required}" internal)
- find_package(CUB CONFIG
- ${_THRUST_QUIET_FLAG}
- ${required}
- NO_DEFAULT_PATH # Only check the explicit HINTS below:
- HINTS
- "${_THRUST_INCLUDE_DIR}/dependencies/cub" # Source layout
- "${_THRUST_INCLUDE_DIR}" # Install layout
- )
-
- if (TARGET CUB::CUB)
- thrust_set_CUB_target(CUB::CUB)
- else()
- thrust_debug("CUB not found!" internal)
- endif()
- endif()
-endmacro()
-
-# This must be a macro instead of a function to ensure that backends passed to
-# find_package(Thrust COMPONENTS [...]) have their full configuration loaded
-# into the current scope. This provides at least some remedy for CMake issue
-# #20670 -- otherwise variables like TBB_VERSION, etc won't be in the caller's
-# scope.
-macro(_thrust_find_TBB required)
- if(NOT TARGET Thrust::TBB)
- thrust_debug("Searching for TBB ${required}" internal)
- # Swap in a temporary module path to make sure we use our FindTBB.cmake
- set(_THRUST_STASH_MODULE_PATH "${CMAKE_MODULE_PATH}")
- set(CMAKE_MODULE_PATH "${_THRUST_CMAKE_DIR}")
-
- # Push policy CMP0074 to silence warnings about TBB_ROOT being set. This
- # var is used unconventionally in this FindTBB.cmake module.
- # Someday we'll have a suitable TBB cmake configuration and can avoid this.
- cmake_policy(PUSH)
- cmake_policy(SET CMP0074 OLD)
- set(THRUST_TBB_ROOT "" CACHE PATH "Path to the root of the TBB installation.")
- if (TBB_ROOT AND NOT THRUST_TBB_ROOT)
- message(
- "Warning: TBB_ROOT is set. "
- "Thrust uses THRUST_TBB_ROOT to avoid issues with CMake Policy CMP0074. "
- "Please set this variable instead when using Thrust with TBB."
- )
- endif()
- set(TBB_ROOT "${THRUST_TBB_ROOT}")
- set(_THRUST_STASH_TBB_ROOT "${TBB_ROOT}")
-
- find_package(TBB
- ${_THRUST_QUIET_FLAG}
- ${required}
- )
-
- cmake_policy(POP)
- set(TBB_ROOT "${_THRUST_STASH_TBB_ROOT}")
- set(CMAKE_MODULE_PATH "${_THRUST_STASH_MODULE_PATH}")
-
- if (TARGET TBB::tbb)
- thrust_set_TBB_target(TBB::tbb)
- else()
- thrust_debug("TBB not found!" internal)
- endif()
- endif()
-endmacro()
-
-# Wrap the OpenMP flags for CUDA targets
-function(thrust_fixup_omp_target omp_target)
- get_target_property(opts ${omp_target} INTERFACE_COMPILE_OPTIONS)
- if (opts MATCHES "\\$<\\$:([^>]*)>")
- target_compile_options(${omp_target} INTERFACE
- $<$,$>:-Xcompiler=${CMAKE_MATCH_1}>
- )
- endif()
-endfunction()
-
-# This must be a macro instead of a function to ensure that backends passed to
-# find_package(Thrust COMPONENTS [...]) have their full configuration loaded
-# into the current scope. This provides at least some remedy for CMake issue
-# #20670 -- otherwise variables like OpenMP_CXX_VERSION, etc won't be in the caller's
-# scope.
-macro(_thrust_find_OMP required)
- if (NOT TARGET Thrust::OMP)
- thrust_debug("Searching for OMP ${required}" internal)
- find_package(OpenMP
- ${_THRUST_QUIET_FLAG}
- ${_THRUST_REQUIRED_FLAG_OMP}
- COMPONENTS CXX
- )
-
- if (TARGET OpenMP::OpenMP_CXX)
- thrust_fixup_omp_target(OpenMP::OpenMP_CXX)
- thrust_set_OMP_target(OpenMP::OpenMP_CXX)
- else()
- thrust_debug("OpenMP::OpenMP_CXX not found!" internal)
- endif()
- endif()
-endmacro()
-
-# This must be a macro instead of a function to ensure that backends passed to
-# find_package(Thrust COMPONENTS [...]) have their full configuration loaded
-# into the current scope. This provides at least some remedy for CMake issue
-# #20670 -- otherwise variables like CUB_VERSION, etc won't be in the caller's
-# scope.
-macro(_thrust_find_backend backend required)
- # Unfortunately, _thrust_find_${backend}(req) is not valid CMake syntax. Hence
- # why this function exists.
- if ("${backend}" STREQUAL "CPP")
- _thrust_find_CPP("${required}")
- elseif ("${backend}" STREQUAL "CUDA")
- _thrust_find_CUDA("${required}")
- elseif ("${backend}" STREQUAL "TBB")
- _thrust_find_TBB("${required}")
- elseif ("${backend}" STREQUAL "OMP")
- _thrust_find_OMP("${required}")
- else()
- message(FATAL_ERROR "_thrust_find_backend: Invalid system: ${backend}")
- endif()
-endmacro()
-
-################################################################################
-# Initialization. Executed inside find_package(Thrust) call.
-#
-
-if (${CMAKE_FIND_PACKAGE_NAME}_FIND_QUIETLY)
- set(_THRUST_QUIET ON CACHE INTERNAL "Quiet mode enabled for Thrust find_package calls.")
- set(_THRUST_QUIET_FLAG "QUIET" CACHE INTERNAL "")
-else()
- unset(_THRUST_QUIET CACHE)
- unset(_THRUST_QUIET_FLAG CACHE)
-endif()
-
-set(_THRUST_CMAKE_DIR "${CMAKE_CURRENT_LIST_DIR}" CACHE INTERNAL "Location of thrust-config.cmake")
-
-# Internal target that actually holds the Thrust interface. Used by all other Thrust targets.
-if (NOT TARGET Thrust::Thrust)
- _thrust_declare_interface_alias(Thrust::Thrust _Thrust_Thrust)
- # Strip out the 'thrust/cmake/' from '[thrust_include_path]/thrust/cmake/':
- get_filename_component(_THRUST_INCLUDE_DIR "../.." ABSOLUTE BASE_DIR "${_THRUST_CMAKE_DIR}")
- set(_THRUST_INCLUDE_DIR "${_THRUST_INCLUDE_DIR}"
- CACHE INTERNAL "Location of thrust headers."
- )
- target_include_directories(_Thrust_Thrust INTERFACE "${_THRUST_INCLUDE_DIR}")
- thrust_debug_target(Thrust::Thrust "${THRUST_VERSION}" internal)
-endif()
-
-# Handle find_package COMPONENT requests:
-foreach(component ${${CMAKE_FIND_PACKAGE_NAME}_FIND_COMPONENTS})
- if (NOT component IN_LIST THRUST_HOST_SYSTEM_OPTIONS AND
- NOT component IN_LIST THRUST_DEVICE_SYSTEM_OPTIONS)
- message(FATAL_ERROR "Invalid component requested: '${component}'")
- endif()
-
- unset(req)
- if (${CMAKE_FIND_PACKAGE_NAME}_FIND_REQUIRED_${component})
- set(req "REQUIRED")
- endif()
-
- thrust_debug("Preloading COMPONENT '${component}' ${req}" internal)
- _thrust_find_backend(${component} "${req}")
-endforeach()
-
-thrust_update_system_found_flags()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/mr/pool.h b/spaces/CVPR/LIVE/thrust/thrust/mr/pool.h
deleted file mode 100644
index 322e4312f0ec25e3e0d7f4e7db384b55c2de13ef..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/mr/pool.h
+++ /dev/null
@@ -1,505 +0,0 @@
-/*
- * Copyright 2018 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*! \file pool.h
- * \brief A caching and pooling memory resource adaptor which uses a single upstream resource for memory allocation,
- * and embeds bookkeeping information in allocated blocks.
- */
-
-#pragma once
-
-#include
-
-#include
-
-#include
-#include
-#include
-
-#include
-
-namespace thrust
-{
-namespace mr
-{
-
-/** \addtogroup memory_resources Memory Resources
- * \ingroup memory_management_classes
- * \{
- */
-
-/*! A memory resource adaptor allowing for pooling and caching allocations from \p Upstream, using memory allocated
- * from it for both blocks then allocated to the user and for internal bookkeeping of the cached memory.
- *
- * On a typical memory resource, calls to \p allocate and \p deallocate actually allocate and deallocate memory. Pooling
- * memory resources only allocate and deallocate memory from an external resource (the upstream memory resource) when
- * there's no suitable memory currently cached; otherwise, they use memory they have acquired beforehand, to make
- * memory allocation faster and more efficient.
- *
- * The non-disjoint version of the pool resource uses a single upstream memory resource. Every allocation is larger than
- * strictly necessary to fulfill the end-user's request, because it needs to account for the memory overhead of tracking
- * the memory blocks and chunks inside those same memory regions. Nevertheless, this version should be more memory-efficient
- * than the \p disjoint_unsynchronized_pool_resource, because it doesn't need to allocate additional blocks of memory
- * from a separate resource, which in turn would necessitate the bookkeeping overhead in the upstream resource.
- *
- * This version requires that memory allocated from Upstream is accessible from device. It supports smart references,
- * meaning that the non-managed CUDA resource, returning a device-tagged pointer, will work, but will be much less
- * efficient than the disjoint version, which wouldn't need to touch device memory at all, and therefore wouldn't need
- * to transfer it back and forth between the host and the device whenever an allocation or a deallocation happens.
- *
- * \tparam Upstream the type of memory resources that will be used for allocating memory blocks
- */
-template
-class unsynchronized_pool_resource THRUST_FINAL
- : public memory_resource,
- private validator
-{
-public:
- /*! Get the default options for a pool. These are meant to be a sensible set of values for many use cases,
- * and as such, may be tuned in the future. This function is exposed so that creating a set of options that are
- * just a slight departure from the defaults is easy.
- */
- static pool_options get_default_options()
- {
- pool_options ret;
-
- ret.min_blocks_per_chunk = 16;
- ret.min_bytes_per_chunk = 1024;
- ret.max_blocks_per_chunk = static_cast(1) << 20;
- ret.max_bytes_per_chunk = static_cast(1) << 30;
-
- ret.smallest_block_size = THRUST_MR_DEFAULT_ALIGNMENT;
- ret.largest_block_size = static_cast(1) << 20;
-
- ret.alignment = THRUST_MR_DEFAULT_ALIGNMENT;
-
- ret.cache_oversized = true;
-
- ret.cached_size_cutoff_factor = 16;
- ret.cached_alignment_cutoff_factor = 16;
-
- return ret;
- }
-
- /*! Constructor.
- *
- * \param upstream the upstream memory resource for allocations
- * \param options pool options to use
- */
- unsynchronized_pool_resource(Upstream * upstream, pool_options options = get_default_options())
- : m_upstream(upstream),
- m_options(options),
- m_smallest_block_log2(detail::log2_ri(m_options.smallest_block_size)),
- m_pools(upstream),
- m_allocated(),
- m_oversized(),
- m_cached_oversized()
- {
- assert(m_options.validate());
-
- pool p = { block_descriptor_ptr(), 0 };
- m_pools.resize(detail::log2_ri(m_options.largest_block_size) - m_smallest_block_log2 + 1, p);
- }
-
- // TODO: C++11: use delegating constructors
-
- /*! Constructor. The upstream resource is obtained by calling \p get_global_resource.
- *
- * \param options pool options to use
- */
- unsynchronized_pool_resource(pool_options options = get_default_options())
- : m_upstream(get_global_resource()),
- m_options(options),
- m_smallest_block_log2(detail::log2_ri(m_options.smallest_block_size)),
- m_pools(get_global_resource()),
- m_allocated(),
- m_oversized(),
- m_cached_oversized()
- {
- assert(m_options.validate());
-
- pool p = { block_descriptor_ptr(), 0 };
- m_pools.resize(detail::log2_ri(m_options.largest_block_size) - m_smallest_block_log2 + 1, p);
- }
-
- /*! Destructor. Releases all held memory to upstream.
- */
- ~unsynchronized_pool_resource()
- {
- release();
- }
-
-private:
- typedef typename Upstream::pointer void_ptr;
- typedef typename thrust::detail::pointer_traits::template rebind::other char_ptr;
-
- struct block_descriptor;
- struct chunk_descriptor;
- struct oversized_block_descriptor;
-
- typedef typename thrust::detail::pointer_traits::template rebind::other block_descriptor_ptr;
- typedef typename thrust::detail::pointer_traits::template rebind::other chunk_descriptor_ptr;
- typedef typename thrust::detail::pointer_traits::template rebind::other oversized_block_descriptor_ptr;
-
- struct block_descriptor
- {
- block_descriptor_ptr next;
- };
-
- struct chunk_descriptor
- {
- std::size_t size;
- chunk_descriptor_ptr next;
- };
-
- // this was originally a forward list, but I made it a doubly linked list
- // because that way deallocation when not caching is faster and doesn't require
- // traversal of a linked list (it's still a forward list for the cached list,
- // because allocation from that list already traverses)
- //
- // TODO: investigate whether it's better to have this be a doubly-linked list
- // with fast do_deallocate when !m_options.cache_oversized, or to have this be
- // a forward list and require traversal in do_deallocate
- //
- // I assume that it is better this way, but the additional pointer could
- // potentially hurt? these are supposed to be oversized and/or overaligned,
- // so they are kinda memory intensive already
- struct oversized_block_descriptor
- {
- std::size_t size;
- std::size_t alignment;
- oversized_block_descriptor_ptr prev;
- oversized_block_descriptor_ptr next;
- oversized_block_descriptor_ptr next_cached;
- };
-
- struct pool
- {
- block_descriptor_ptr free_list;
- std::size_t previous_allocated_count;
- };
-
- typedef thrust::host_vector<
- pool,
- allocator
- > pool_vector;
-
- Upstream * m_upstream;
-
- pool_options m_options;
- std::size_t m_smallest_block_log2;
-
- pool_vector m_pools;
- chunk_descriptor_ptr m_allocated;
- oversized_block_descriptor_ptr m_oversized;
- oversized_block_descriptor_ptr m_cached_oversized;
-
-public:
- /*! Releases all held memory to upstream.
- */
- void release()
- {
- // reset the buckets
- for (std::size_t i = 0; i < m_pools.size(); ++i)
- {
- thrust::raw_reference_cast(m_pools[i]).free_list = block_descriptor_ptr();
- thrust::raw_reference_cast(m_pools[i]).previous_allocated_count = 0;
- }
-
- // deallocate memory allocated for the buckets
- while (detail::pointer_traits::get(m_allocated))
- {
- chunk_descriptor_ptr alloc = m_allocated;
- m_allocated = thrust::raw_reference_cast(*m_allocated).next;
-
- void_ptr p = static_cast(
- static_cast(
- static_cast(alloc)
- ) - thrust::raw_reference_cast(*alloc).size
- );
- m_upstream->do_deallocate(p, thrust::raw_reference_cast(*alloc).size + sizeof(chunk_descriptor), m_options.alignment);
- }
-
- // deallocate cached oversized/overaligned memory
- while (detail::pointer_traits::get(m_oversized))
- {
- oversized_block_descriptor_ptr alloc = m_oversized;
- m_oversized = thrust::raw_reference_cast(*m_oversized).next;
-
- void_ptr p = static_cast(
- static_cast(
- static_cast(alloc)
- ) - thrust::raw_reference_cast(*alloc).size
- );
- m_upstream->do_deallocate(p, thrust::raw_reference_cast(*alloc).size + sizeof(oversized_block_descriptor), thrust::raw_reference_cast(*alloc).alignment);
- }
-
- m_cached_oversized = oversized_block_descriptor_ptr();
- }
-
- THRUST_NODISCARD virtual void_ptr do_allocate(std::size_t bytes, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT) THRUST_OVERRIDE
- {
- bytes = (std::max)(bytes, m_options.smallest_block_size);
- assert(detail::is_power_of_2(alignment));
-
- // an oversized and/or overaligned allocation requested; needs to be allocated separately
- if (bytes > m_options.largest_block_size || alignment > m_options.alignment)
- {
- if (m_options.cache_oversized)
- {
- oversized_block_descriptor_ptr ptr = m_cached_oversized;
- oversized_block_descriptor_ptr * previous = &m_cached_oversized;
- while (detail::pointer_traits::get(ptr))
- {
- oversized_block_descriptor desc = *ptr;
- bool is_good = desc.size >= bytes && desc.alignment >= alignment;
-
- // if the size is bigger than the requested size by a factor
- // bigger than or equal to the specified cutoff for size,
- // allocate a new block
- if (is_good)
- {
- std::size_t size_factor = desc.size / bytes;
- if (size_factor >= m_options.cached_size_cutoff_factor)
- {
- is_good = false;
- }
- }
-
- // if the alignment is bigger than the requested one by a factor
- // bigger than or equal to the specified cutoff for alignment,
- // allocate a new block
- if (is_good)
- {
- std::size_t alignment_factor = desc.alignment / alignment;
- if (alignment_factor >= m_options.cached_alignment_cutoff_factor)
- {
- is_good = false;
- }
- }
-
- if (is_good)
- {
- if (previous != &m_cached_oversized)
- {
- oversized_block_descriptor previous_desc = **previous;
- previous_desc.next_cached = desc.next_cached;
- **previous = previous_desc;
- }
- else
- {
- m_cached_oversized = desc.next_cached;
- }
-
- desc.next_cached = oversized_block_descriptor_ptr();
- *ptr = desc;
-
- return static_cast(
- static_cast(
- static_cast(ptr)
- ) - desc.size
- );
- }
-
- previous = &thrust::raw_reference_cast(*ptr).next_cached;
- ptr = *previous;
- }
- }
-
- // no fitting cached block found; allocate a new one that's just up to the specs
- void_ptr allocated = m_upstream->do_allocate(bytes + sizeof(oversized_block_descriptor), alignment);
- oversized_block_descriptor_ptr block = static_cast(
- static_cast(
- static_cast(allocated) + bytes
- )
- );
-
- oversized_block_descriptor desc;
- desc.size = bytes;
- desc.alignment = alignment;
- desc.prev = oversized_block_descriptor_ptr();
- desc.next = m_oversized;
- desc.next_cached = oversized_block_descriptor_ptr();
- *block = desc;
- m_oversized = block;
-
- if (detail::pointer_traits::get(desc.next))
- {
- oversized_block_descriptor next = *desc.next;
- next.prev = block;
- *desc.next = next;
- }
-
- return allocated;
- }
-
- // the request is NOT for oversized and/or overaligned memory
- // allocate a block from an appropriate bucket
- std::size_t bytes_log2 = thrust::detail::log2_ri(bytes);
- std::size_t bucket_idx = bytes_log2 - m_smallest_block_log2;
- pool & bucket = thrust::raw_reference_cast(m_pools[bucket_idx]);
-
- bytes = static_cast(1) << bytes_log2;
-
- // if the free list of the bucket has no elements, allocate a new chunk
- // and split it into blocks pushed to the free list
- if (!detail::pointer_traits::get(bucket.free_list))
- {
- std::size_t n = bucket.previous_allocated_count;
- if (n == 0)
- {
- n = m_options.min_blocks_per_chunk;
- if (n < (m_options.min_bytes_per_chunk >> bytes_log2))
- {
- n = m_options.min_bytes_per_chunk >> bytes_log2;
- }
- }
- else
- {
- n = n * 3 / 2;
- if (n > (m_options.max_bytes_per_chunk >> bytes_log2))
- {
- n = m_options.max_bytes_per_chunk >> bytes_log2;
- }
- if (n > m_options.max_blocks_per_chunk)
- {
- n = m_options.max_blocks_per_chunk;
- }
- }
-
- std::size_t descriptor_size = (std::max)(sizeof(block_descriptor), m_options.alignment);
- std::size_t block_size = bytes + descriptor_size;
- block_size += m_options.alignment - block_size % m_options.alignment;
- std::size_t chunk_size = block_size * n;
-
- void_ptr allocated = m_upstream->do_allocate(chunk_size + sizeof(chunk_descriptor), m_options.alignment);
- chunk_descriptor_ptr chunk = static_cast(
- static_cast(
- static_cast(allocated) + chunk_size
- )
- );
-
- chunk_descriptor desc;
- desc.size = chunk_size;
- desc.next = m_allocated;
- *chunk = desc;
- m_allocated = chunk;
-
- for (std::size_t i = 0; i < n; ++i)
- {
- block_descriptor_ptr block = static_cast(
- static_cast(
- static_cast(allocated) + block_size * i + bytes
- )
- );
-
- block_descriptor desc;
- desc.next = bucket.free_list;
- *block = desc;
- bucket.free_list = block;
- }
- }
-
- // allocate a block from the front of the bucket's free list
- block_descriptor_ptr block = bucket.free_list;
- bucket.free_list = thrust::raw_reference_cast(*block).next;
- return static_cast(
- static_cast(
- static_cast(block)
- ) - bytes
- );
- }
-
- virtual void do_deallocate(void_ptr p, std::size_t n, std::size_t alignment = THRUST_MR_DEFAULT_ALIGNMENT) THRUST_OVERRIDE
- {
- n = (std::max)(n, m_options.smallest_block_size);
- assert(detail::is_power_of_2(alignment));
-
- // verify that the pointer is at least as aligned as claimed
- assert(reinterpret_cast(detail::pointer_traits::get(p)) % alignment == 0);
-
- // the deallocated block is oversized and/or overaligned
- if (n > m_options.largest_block_size || alignment > m_options.alignment)
- {
- oversized_block_descriptor_ptr block = static_cast(
- static_cast(
- static_cast(p) + n
- )
- );
-
- oversized_block_descriptor desc = *block;
-
- if (m_options.cache_oversized)
- {
- desc.next_cached = m_cached_oversized;
- *block = desc;
- m_cached_oversized = block;
-
- return;
- }
-
- if (!detail::pointer_traits::get(desc.prev))
- {
- assert(m_oversized == block);
- m_oversized = desc.next;
- }
- else
- {
- oversized_block_descriptor prev = *desc.prev;
- assert(prev.next == block);
- prev.next = desc.next;
- *desc.prev = prev;
- }
-
- if (detail::pointer_traits::get(desc.next))
- {
- oversized_block_descriptor next = *desc.next;
- assert(next.prev == block);
- next.prev = desc.prev;
- *desc.next = next;
- }
-
- m_upstream->do_deallocate(p, desc.size + sizeof(oversized_block_descriptor), desc.alignment);
-
- return;
- }
-
- // push the block to the front of the appropriate bucket's free list
- std::size_t n_log2 = thrust::detail::log2_ri(n);
- std::size_t bucket_idx = n_log2 - m_smallest_block_log2;
- pool & bucket = thrust::raw_reference_cast(m_pools[bucket_idx]);
-
- n = static_cast(1) << n_log2;
-
- block_descriptor_ptr block = static_cast(
- static_cast(
- static_cast(p) + n
- )
- );
-
- block_descriptor desc;
- desc.next = bucket.free_list;
- *block = desc;
- bucket.free_list = block;
- }
-};
-
-/*! \}
- */
-
-} // end mr
-} // end thrust
-
diff --git a/spaces/CVPR/WALT/mmdet/datasets/__init__.py b/spaces/CVPR/WALT/mmdet/datasets/__init__.py
deleted file mode 100644
index 9b18b30a258c32283cbfc03ba01781a19fd993c1..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/datasets/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
-from .cityscapes import CityscapesDataset
-from .coco import CocoDataset
-from .custom import CustomDataset
-from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
- RepeatDataset)
-from .deepfashion import DeepFashionDataset
-from .lvis import LVISDataset, LVISV1Dataset, LVISV05Dataset
-from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
-from .utils import (NumClassCheckHook, get_loading_pipeline,
- replace_ImageToTensor)
-from .voc import VOCDataset
-from .wider_face import WIDERFaceDataset
-from .xml_style import XMLDataset
-
-__all__ = [
- 'CustomDataset', 'XMLDataset', 'CocoDataset', 'DeepFashionDataset',
- 'VOCDataset', 'CityscapesDataset', 'LVISDataset', 'LVISV05Dataset',
- 'LVISV1Dataset', 'GroupSampler', 'DistributedGroupSampler',
- 'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
- 'ClassBalancedDataset', 'WIDERFaceDataset', 'DATASETS', 'PIPELINES',
- 'build_dataset', 'replace_ImageToTensor', 'get_loading_pipeline',
- 'NumClassCheckHook'
-]
diff --git a/spaces/CVPR/WALT/mmdet/models/roi_heads/mask_heads/__init__.py b/spaces/CVPR/WALT/mmdet/models/roi_heads/mask_heads/__init__.py
deleted file mode 100644
index 9c8a76a76f275287006a5e9bbb52b9de962b627c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/roi_heads/mask_heads/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from .coarse_mask_head import CoarseMaskHead
-from .fcn_mask_head import FCNMaskHead
-from .fcn_occmask_head import FCNOccMaskHead
-from .feature_relay_head import FeatureRelayHead
-from .fused_semantic_head import FusedSemanticHead
-from .global_context_head import GlobalContextHead
-from .grid_head import GridHead
-from .htc_mask_head import HTCMaskHead
-from .mask_point_head import MaskPointHead
-from .maskiou_head import MaskIoUHead
-from .scnet_mask_head import SCNetMaskHead
-from .scnet_semantic_head import SCNetSemanticHead
-
-__all__ = [
- 'FCNMaskHead', 'FCNOccMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead',
- 'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead',
- 'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead'
-]
diff --git a/spaces/CVPR/drawings-to-human/static/_app/immutable/chunks/index-bcf2726a.js b/spaces/CVPR/drawings-to-human/static/_app/immutable/chunks/index-bcf2726a.js
deleted file mode 100644
index 2d47b275bdcb23c7324444798fdc9687822aeb28..0000000000000000000000000000000000000000
--- a/spaces/CVPR/drawings-to-human/static/_app/immutable/chunks/index-bcf2726a.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/CVPR/monoscene_lite/helpers.py b/spaces/CVPR/monoscene_lite/helpers.py
deleted file mode 100644
index 332c837e19404c0fd96fc7905423404d3a415bae..0000000000000000000000000000000000000000
--- a/spaces/CVPR/monoscene_lite/helpers.py
+++ /dev/null
@@ -1,336 +0,0 @@
-import numpy as np
-import torch
-import fusion
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-
-def read_calib(calib_path):
- """
- Modify from https://github.com/utiasSTARS/pykitti/blob/d3e1bb81676e831886726cc5ed79ce1f049aef2c/pykitti/utils.py#L68
- :param calib_path: Path to a calibration text file.
- :return: dict with calibration matrices.
- """
- calib_all = {}
- with open(calib_path, "r") as f:
- for line in f.readlines():
- if line == "\n":
- break
- key, value = line.split(":", 1)
- calib_all[key] = np.array([float(x) for x in value.split()])
-
- # reshape matrices
- calib_out = {}
- # 3x4 projection matrix for left camera
- calib_out["P2"] = calib_all["P2"].reshape(3, 4)
- calib_out["Tr"] = np.identity(4) # 4x4 matrix
- calib_out["Tr"][:3, :4] = calib_all["Tr"].reshape(3, 4)
- return calib_out
-
-
-def vox2pix(cam_E, cam_k,
- vox_origin, voxel_size,
- img_W, img_H,
- scene_size):
- """
- compute the 2D projection of voxels centroids
-
- Parameters:
- ----------
- cam_E: 4x4
- =camera pose in case of NYUv2 dataset
- =Transformation from camera to lidar coordinate in case of SemKITTI
- cam_k: 3x3
- camera intrinsics
- vox_origin: (3,)
- world(NYU)/lidar(SemKITTI) cooridnates of the voxel at index (0, 0, 0)
- img_W: int
- image width
- img_H: int
- image height
- scene_size: (3,)
- scene size in meter: (51.2, 51.2, 6.4) for SemKITTI and (4.8, 4.8, 2.88) for NYUv2
-
- Returns
- -------
- projected_pix: (N, 2)
- Projected 2D positions of voxels
- fov_mask: (N,)
- Voxels mask indice voxels inside image's FOV
- pix_z: (N,)
- Voxels'distance to the sensor in meter
- """
- # Compute the x, y, z bounding of the scene in meter
- vol_bnds = np.zeros((3,2))
- vol_bnds[:,0] = vox_origin
- vol_bnds[:,1] = vox_origin + np.array(scene_size)
-
- # Compute the voxels centroids in lidar cooridnates
- vol_dim = np.ceil((vol_bnds[:,1]- vol_bnds[:,0])/ voxel_size).copy(order='C').astype(int)
- xv, yv, zv = np.meshgrid(
- range(vol_dim[0]),
- range(vol_dim[1]),
- range(vol_dim[2]),
- indexing='ij'
- )
- vox_coords = np.concatenate([
- xv.reshape(1,-1),
- yv.reshape(1,-1),
- zv.reshape(1,-1)
- ], axis=0).astype(int).T
-
- # Project voxels'centroid from lidar coordinates to camera coordinates
- cam_pts = fusion.TSDFVolume.vox2world(vox_origin, vox_coords, voxel_size)
- cam_pts = fusion.rigid_transform(cam_pts, cam_E)
-
- # Project camera coordinates to pixel positions
- projected_pix = fusion.TSDFVolume.cam2pix(cam_pts, cam_k)
- pix_x, pix_y = projected_pix[:, 0], projected_pix[:, 1]
-
- # Eliminate pixels outside view frustum
- pix_z = cam_pts[:, 2]
- fov_mask = np.logical_and(pix_x >= 0,
- np.logical_and(pix_x < img_W,
- np.logical_and(pix_y >= 0,
- np.logical_and(pix_y < img_H,
- pix_z > 0))))
-
-
- return torch.from_numpy(projected_pix), torch.from_numpy(fov_mask), torch.from_numpy(pix_z)
-
-
-
-def get_grid_coords(dims, resolution):
- """
- :param dims: the dimensions of the grid [x, y, z] (i.e. [256, 256, 32])
- :return coords_grid: is the center coords of voxels in the grid
- """
-
- g_xx = np.arange(0, dims[0] + 1)
- g_yy = np.arange(0, dims[1] + 1)
- sensor_pose = 10
- g_zz = np.arange(0, dims[2] + 1)
-
- # Obtaining the grid with coords...
- xx, yy, zz = np.meshgrid(g_xx[:-1], g_yy[:-1], g_zz[:-1])
- coords_grid = np.array([xx.flatten(), yy.flatten(), zz.flatten()]).T
- coords_grid = coords_grid.astype(np.float)
-
- coords_grid = (coords_grid * resolution) + resolution / 2
-
- temp = np.copy(coords_grid)
- temp[:, 0] = coords_grid[:, 1]
- temp[:, 1] = coords_grid[:, 0]
- coords_grid = np.copy(temp)
-
- return coords_grid
-
-def get_projections(img_W, img_H):
- scale_3ds = [2, 4]
- data = {}
- for scale_3d in scale_3ds:
- scene_size = (51.2, 51.2, 6.4)
- vox_origin = np.array([0, -25.6, -2])
- voxel_size = 0.2
-
- calib = read_calib("calib.txt")
- cam_k = calib["P2"][:3, :3]
- T_velo_2_cam = calib["Tr"]
-
- # compute the 3D-2D mapping
- projected_pix, fov_mask, pix_z = vox2pix(
- T_velo_2_cam,
- cam_k,
- vox_origin,
- voxel_size * scale_3d,
- img_W,
- img_H,
- scene_size,
- )
-
- data["projected_pix_{}".format(scale_3d)] = projected_pix
- data["pix_z_{}".format(scale_3d)] = pix_z
- data["fov_mask_{}".format(scale_3d)] = fov_mask
- return data
-
-
-def majority_pooling(grid, k_size=2):
- result = np.zeros(
- (grid.shape[0] // k_size, grid.shape[1] // k_size, grid.shape[2] // k_size)
- )
- for xx in range(0, int(np.floor(grid.shape[0] / k_size))):
- for yy in range(0, int(np.floor(grid.shape[1] / k_size))):
- for zz in range(0, int(np.floor(grid.shape[2] / k_size))):
-
- sub_m = grid[
- (xx * k_size) : (xx * k_size) + k_size,
- (yy * k_size) : (yy * k_size) + k_size,
- (zz * k_size) : (zz * k_size) + k_size,
- ]
- unique, counts = np.unique(sub_m, return_counts=True)
- if True in ((unique != 0) & (unique != 255)):
- # Remove counts with 0 and 255
- counts = counts[((unique != 0) & (unique != 255))]
- unique = unique[((unique != 0) & (unique != 255))]
- else:
- if True in (unique == 0):
- counts = counts[(unique != 255)]
- unique = unique[(unique != 255)]
- value = unique[np.argmax(counts)]
- result[xx, yy, zz] = value
- return result
-
-
-def draw(
- voxels,
- # T_velo_2_cam,
- # vox_origin,
- fov_mask,
- # img_size,
- # f,
- voxel_size=0.4,
- # d=7, # 7m - determine the size of the mesh representing the camera
-):
-
- fov_mask = fov_mask.reshape(-1)
- # Compute the voxels coordinates
- grid_coords = get_grid_coords(
- [voxels.shape[0], voxels.shape[1], voxels.shape[2]], voxel_size
- )
-
-
- # Attach the predicted class to every voxel
- grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T
-
- # Get the voxels inside FOV
- fov_grid_coords = grid_coords[fov_mask, :]
-
- # Get the voxels outside FOV
- outfov_grid_coords = grid_coords[~fov_mask, :]
-
- # Remove empty and unknown voxels
- fov_voxels = fov_grid_coords[
- (fov_grid_coords[:, 3] > 0) & (fov_grid_coords[:, 3] < 255), :
- ]
- # print(np.unique(fov_voxels[:, 3], return_counts=True))
- outfov_voxels = outfov_grid_coords[
- (outfov_grid_coords[:, 3] > 0) & (outfov_grid_coords[:, 3] < 255), :
- ]
-
- # figure = mlab.figure(size=(1400, 1400), bgcolor=(1, 1, 1))
- colors = np.array(
- [
- [0,0,0],
- [100, 150, 245],
- [100, 230, 245],
- [30, 60, 150],
- [80, 30, 180],
- [100, 80, 250],
- [255, 30, 30],
- [255, 40, 200],
- [150, 30, 90],
- [255, 0, 255],
- [255, 150, 255],
- [75, 0, 75],
- [175, 0, 75],
- [255, 200, 0],
- [255, 120, 50],
- [0, 175, 0],
- [135, 60, 0],
- [150, 240, 80],
- [255, 240, 150],
- [255, 0, 0],
- ]
- ).astype(np.uint8)
-
- pts_colors = [f'rgb({colors[int(i)][0]}, {colors[int(i)][1]}, {colors[int(i)][2]})' for i in fov_voxels[:, 3]]
- out_fov_colors = [f'rgb({colors[int(i)][0]//3*2}, {colors[int(i)][1]//3*2}, {colors[int(i)][2]//3*2})' for i in outfov_voxels[:, 3]]
- pts_colors = pts_colors + out_fov_colors
-
- fov_voxels = np.concatenate([fov_voxels, outfov_voxels], axis=0)
- x = fov_voxels[:, 0].flatten()
- y = fov_voxels[:, 1].flatten()
- z = fov_voxels[:, 2].flatten()
- # label = fov_voxels[:, 3].flatten()
- fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z,mode='markers',
- marker=dict(
- size=2,
- color=pts_colors, # set color to an array/list of desired values
- # colorscale='Viridis', # choose a colorscale
- opacity=1.0,
- symbol='square'
- ))])
- fig.update_layout(
- scene = dict(
- aspectmode='data',
- xaxis = dict(
- backgroundcolor="rgb(255, 255, 255)",
- gridcolor="black",
- showbackground=True,
- zerolinecolor="black",
- nticks=4,
- visible=False,
- range=[-1,55],),
- yaxis = dict(
- backgroundcolor="rgb(255, 255, 255)",
- gridcolor="black",
- showbackground=True,
- zerolinecolor="black",
- visible=False,
- nticks=4, range=[-1,55],),
- zaxis = dict(
- backgroundcolor="rgb(255, 255, 255)",
- gridcolor="black",
- showbackground=True,
- zerolinecolor="black",
- visible=False,
- nticks=4, range=[-1,7],),
- bgcolor="black",
- ),
-
- )
-
- # fig = px.scatter_3d(
- # fov_voxels,
- # x=fov_voxels[:, 0], y="y", z="z", color="label")
- # Draw occupied inside FOV voxels
- # plt_plot_fov = mlab.points3d(
- # fov_voxels[:, 0],
- # fov_voxels[:, 1],
- # fov_voxels[:, 2],
- # fov_voxels[:, 3],
- # colormap="viridis",
- # scale_factor=voxel_size - 0.05 * voxel_size,
- # mode="cube",
- # opacity=1.0,
- # vmin=1,
- # vmax=19,
- # )
-
- # # Draw occupied outside FOV voxels
- # plt_plot_outfov = mlab.points3d(
- # outfov_voxels[:, 0],
- # outfov_voxels[:, 1],
- # outfov_voxels[:, 2],
- # outfov_voxels[:, 3],
- # colormap="viridis",
- # scale_factor=voxel_size - 0.05 * voxel_size,
- # mode="cube",
- # opacity=1.0,
- # vmin=1,
- # vmax=19,
- # )
-
-
-
- # plt_plot_fov.glyph.scale_mode = "scale_by_vector"
- # plt_plot_outfov.glyph.scale_mode = "scale_by_vector"
-
- # plt_plot_fov.module_manager.scalar_lut_manager.lut.table = colors
-
- # outfov_colors = colors
- # outfov_colors[:, :3] = outfov_colors[:, :3] // 3 * 2
- # plt_plot_outfov.module_manager.scalar_lut_manager.lut.table = outfov_colors
-
- # mlab.show()
- return fig
\ No newline at end of file
diff --git a/spaces/CVPR/regionclip-demo/detectron2/checkpoint/catalog.py b/spaces/CVPR/regionclip-demo/detectron2/checkpoint/catalog.py
deleted file mode 100644
index 9a85736754a0de4550df96c22f38fc515bd02d71..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/checkpoint/catalog.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-
-from detectron2.utils.file_io import PathHandler, PathManager
-
-
-class ModelCatalog(object):
- """
- Store mappings from names to third-party models.
- """
-
- S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron"
-
- # MSRA models have STRIDE_IN_1X1=True. False otherwise.
- # NOTE: all BN models here have fused BN into an affine layer.
- # As a result, you should only load them to a model with "FrozenBN".
- # Loading them to a model with regular BN or SyncBN is wrong.
- # Even when loaded to FrozenBN, it is still different from affine by an epsilon,
- # which should be negligible for training.
- # NOTE: all models here uses PIXEL_STD=[1,1,1]
- # NOTE: Most of the BN models here are no longer used. We use the
- # re-converted pre-trained models under detectron2 model zoo instead.
- C2_IMAGENET_MODELS = {
- "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
- "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
- "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
- "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
- "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
- "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl",
- "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl",
- }
-
- C2_DETECTRON_PATH_FORMAT = (
- "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl" # noqa B950
- )
-
- C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival"
- C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival"
-
- # format: {model_name} -> part of the url
- C2_DETECTRON_MODELS = {
- "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW", # noqa B950
- "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I", # noqa B950
- "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7", # noqa B950
- "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ", # noqa B950
- "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB", # noqa B950
- "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC", # noqa B950
- "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT", # noqa B950
- "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI", # noqa B950
- "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q", # noqa B950
- "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao", # noqa B950
- "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L", # noqa B950
- "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179", # noqa B950
- "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2", # noqa B950
- }
-
- @staticmethod
- def get(name):
- if name.startswith("Caffe2Detectron/COCO"):
- return ModelCatalog._get_c2_detectron_baseline(name)
- if name.startswith("ImageNetPretrained/"):
- return ModelCatalog._get_c2_imagenet_pretrained(name)
- raise RuntimeError("model not present in the catalog: {}".format(name))
-
- @staticmethod
- def _get_c2_imagenet_pretrained(name):
- prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX
- name = name[len("ImageNetPretrained/") :]
- name = ModelCatalog.C2_IMAGENET_MODELS[name]
- url = "/".join([prefix, name])
- return url
-
- @staticmethod
- def _get_c2_detectron_baseline(name):
- name = name[len("Caffe2Detectron/COCO/") :]
- url = ModelCatalog.C2_DETECTRON_MODELS[name]
- if "keypoint_rcnn" in name:
- dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS
- else:
- dataset = ModelCatalog.C2_DATASET_COCO
-
- if "35998355/rpn_R-50-C4_1x" in name:
- # this one model is somehow different from others ..
- type = "rpn"
- else:
- type = "generalized_rcnn"
-
- # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`.
- url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format(
- prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset
- )
- return url
-
-
-class ModelCatalogHandler(PathHandler):
- """
- Resolve URL like catalog://.
- """
-
- PREFIX = "catalog://"
-
- def _get_supported_prefixes(self):
- return [self.PREFIX]
-
- def _get_local_path(self, path, **kwargs):
- logger = logging.getLogger(__name__)
- catalog_path = ModelCatalog.get(path[len(self.PREFIX) :])
- logger.info("Catalog entry {} points to {}".format(path, catalog_path))
- return PathManager.get_local_path(catalog_path, **kwargs)
-
- def _open(self, path, mode="r", **kwargs):
- return PathManager.open(self._get_local_path(path), mode, **kwargs)
-
-
-PathManager.register_handler(ModelCatalogHandler())
diff --git a/spaces/Cam-Brazy/BearTest/app.py b/spaces/Cam-Brazy/BearTest/app.py
deleted file mode 100644
index 7e09844c443b734531bc271c130fd7dedbae97c2..0000000000000000000000000000000000000000
--- a/spaces/Cam-Brazy/BearTest/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-
-__all__ = ["learn", "classify_image", "categories", "image", "label", "examples", "intf"]
-
-learn = load_learner('export.pkl')
-
-categories = ('Black', 'Grizzly', 'Teddy')
-
-def classify_image(inp):
- pred,idx,probs = learn.predict(inp)
- return dict(zip(categories, map(float, probs)))
-
-
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ["grizzly.jpg", "teddy.jpg"]
-
-iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-iface.launch(inline=False)
diff --git a/spaces/Chaitanya01/InvestingPlatform/alerts.py b/spaces/Chaitanya01/InvestingPlatform/alerts.py
deleted file mode 100644
index ed4b5ef00f075b1176e3da60888dae65430ebbc8..0000000000000000000000000000000000000000
--- a/spaces/Chaitanya01/InvestingPlatform/alerts.py
+++ /dev/null
@@ -1,85 +0,0 @@
-from distutils.command.sdist import sdist
-from numpy import tri
-import pandas as pd
-import json, requests
-import slack, time
-from datetime import datetime
-# from bs4 import BeautifulSoup
-from config import *
-def get_yahoo_finance_quote(symbol):
- # Get the symbol quote from yahoo finance, we are using Beautiful Soup for scraping
- URL = f"https://finance.yahoo.com/quote/{symbol}"
- headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
- page = requests.get(URL, headers = headers)
- soup = BeautifulSoup(page.text, "html.parser")
- price = soup.find('div',{'class':'D(ib) Mend(20px)'}).find_all('fin-streamer')[0].text
- return float(price.replace(",",""))
-def get_cnbc_data(symbol):
- ticker = symbol.replace(" ","")
- if ticker == "NASDAQ":
- ticker = "NDX"
- elif ticker == "NIFTY50":
- ticker = ".NSEI"
- # Get the symbol quote from yahoo finance, we are using Beautiful Soup for scraping
- df = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/1Y.json?symbol={ticker}").json()["barData"]["priceBars"])
- # df_1D = pd.DataFrame(requests.get(f"https://ts-api.cnbc.com/harmony/app/charts/1D.json?symbol={ticker}").json()["barData"]["priceBars"])
- df["datetime"] = pd.to_datetime(df['tradeTimeinMills'],unit='ms')
- df["close"] = df["close"].astype(float)
- # df_1D["close"] = df_1D["close"].astype(float)
- df.set_index("datetime",inplace = True)
- dma200 = (df["close"].rolling(200).mean()).iloc[-1]
- close = (df["close"].iloc[-1])
- return dma200, close
-
-client = slack.WebClient(token = SLACK_TOKEN)
-
-while True:
- df = pd.read_csv('watchlist.csv')
- df.set_index("Symbol",inplace = True)
- # df_crypto = pd.DataFrame(json.loads(requests.get("https://ftx.com/api/markets").text)["result"])
- # df_crypto = df_crypto[df_crypto["quoteCurrency"].isin(["USD","USDT"])]
- # df_crypto.set_index("name",inplace = True)
-
- if len(df)>0:
- req_df_price = df[df["status"] == "Pending"]
- req_df_dma = df[df["dma_status"] == "Pending"]
- for symbol in req_df_price.index:
- if symbol in ["SPX","US 2Y","US 5Y","US 10Y","US 30Y","HYG","LQD","NASDAQ","VIX","NIFTY50"]:
- dma200, ltp= get_cnbc_data(symbol)
- # else:
- # ltp = df_crypto.loc[symbol]["last"]
- trigger_level = req_df_price.loc[symbol]["Trigger"]
- triggered = 0
-
- if req_df_price.loc[symbol]["view_type"] == "Above":
- if trigger_level<=ltp:
- triggered = 1
- elif req_df_price.loc[symbol]["view_type"] == "Below":
- if trigger_level>=ltp:
- triggered = 1
-
- if triggered == 1:
- df.at[symbol,"status"] = "Triggered"
- client.chat_postMessage(channel = f"#{df.loc[symbol]['alert_type'].lower()}_signal",
- text = f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} {symbol} is {df.loc[symbol]['view_type']} {trigger_level} at {ltp}")
- for symbol in req_df_dma.index:
- dma_check = req_df_dma.loc[symbol]["dma200"]
- if dma_check == False:
- continue
- triggered_dma200 = 0
- dma200, ltp= get_cnbc_data(symbol)
- print(dma200)
- if req_df_dma.loc[symbol]["dma200_view_type"] == "Above":
- if dma200<=ltp:
- triggered_dma200 = 1
- elif req_df_dma.loc[symbol]["dma200_view_type"] == "Below":
- if dma200>=ltp:
- triggered_dma200 = 1
-
- if triggered_dma200 == 1:
- df.at[symbol,"dma_status"] = "Triggered"
- client.chat_postMessage(channel = f"#{df.loc[symbol]['alert_type'].lower()}_signal",
- text = f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} {symbol} is {df.loc[symbol]['dma200_view_type']} DMA200 at {ltp}")
- df.to_csv("watchlist.csv")
- # Recheck again after 60 minutes
- time.sleep(60*60)
\ No newline at end of file
diff --git a/spaces/ChandraMohanNayal/AutoGPT/run_continuous.bat b/spaces/ChandraMohanNayal/AutoGPT/run_continuous.bat
deleted file mode 100644
index 812aa01c1c5506c452665610c0e9e83a17c426f2..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/run_continuous.bat
+++ /dev/null
@@ -1,3 +0,0 @@
-@echo off
-set argument=--continuous
-call run.bat %argument%
diff --git a/spaces/ChillyFaze/runwayml-stable-diffusion-v1-5/README.md b/spaces/ChillyFaze/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index a7b31fa0ad1c091d37961ae68f2bb4e0a2f6b0e5..0000000000000000000000000000000000000000
--- a/spaces/ChillyFaze/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 🌖
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ClearLove443/Robby-chatbot/modules/history.py b/spaces/ClearLove443/Robby-chatbot/modules/history.py
deleted file mode 100644
index 1ee0583dfbffd322eb7a0d0dfbb65048488e1718..0000000000000000000000000000000000000000
--- a/spaces/ClearLove443/Robby-chatbot/modules/history.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import streamlit as st
-from streamlit_chat import message
-
-class ChatHistory:
-
- def __init__(self):
- self.history = st.session_state.get("history", [])
- st.session_state["history"] = self.history
-
- def default_greeting(self):
- return "Hey Robby ! 👋"
-
- def default_prompt(self, topic):
- return f"Hello ! Ask me anything about {topic} 🤗"
-
- def initialize_user_history(self):
- st.session_state["user"] = [self.default_greeting()]
-
- def initialize_assistant_history(self, uploaded_file):
- st.session_state["assistant"] = [self.default_prompt(uploaded_file.name)]
-
- def initialize(self, uploaded_file):
- if "assistant" not in st.session_state:
- self.initialize_assistant_history(uploaded_file)
- if "user" not in st.session_state:
- self.initialize_user_history()
-
- def reset(self, uploaded_file):
- st.session_state["history"] = []
-
- self.initialize_user_history()
- self.initialize_assistant_history(uploaded_file)
- st.session_state["reset_chat"] = False
-
- def append(self, mode, message):
- st.session_state[mode].append(message)
-
- def generate_messages(self, container):
- if st.session_state["assistant"]:
- with container:
- for i in range(len(st.session_state["assistant"])):
- message(
- st.session_state["user"][i],
- is_user=True,
- key=f"history_{i}_user",
- avatar_style="big-smile",
- )
- message(st.session_state["assistant"][i], key=str(i), avatar_style="thumbs")
-
- def load(self):
- if os.path.exists(self.history_file):
- with open(self.history_file, "r") as f:
- self.history = f.read().splitlines()
-
- def save(self):
- with open(self.history_file, "w") as f:
- f.write("\n".join(self.history))
diff --git a/spaces/Cletrason/Cletrason-toad-mario-movie/app_text_to_video.py b/spaces/Cletrason/Cletrason-toad-mario-movie/app_text_to_video.py
deleted file mode 100644
index b592201171989117d8c3cc1ef2c4522b0c30363f..0000000000000000000000000000000000000000
--- a/spaces/Cletrason/Cletrason-toad-mario-movie/app_text_to_video.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import gradio as gr
-from model import Model
-import os
-from hf_utils import get_model_list
-
-on_huggingspace = os.environ.get("SPACE_AUTHOR_NAME") == "PAIR"
-
-examples = [
- ["an astronaut waving the arm on the moon"],
- ["a sloth surfing on a wakeboard"],
- ["an astronaut walking on a street"],
- ["a cute cat walking on grass"],
- ["a horse is galloping on a street"],
- ["an astronaut is skiing down the hill"],
- ["a gorilla walking alone down the street"],
- ["a gorilla dancing on times square"],
- ["A panda dancing dancing like crazy on Times Square"],
-]
-
-
-def create_demo(model: Model):
-
- with gr.Blocks() as demo:
- with gr.Row():
- gr.Markdown('## Text2Video-Zero: Video Generation')
- with gr.Row():
- gr.HTML(
- """
-
-
- Description: Simply input any textual prompt to generate videos right away and unleash your creativity and imagination! You can also select from the examples below. For performance purposes, our current preview release allows to generate up to 16 frames, which can be configured in the Advanced Options.
-
-
- """)
-
- with gr.Row():
- with gr.Column():
- model_name = gr.Dropdown(
- label="Model",
- choices=get_model_list(),
- value="dreamlike-art/dreamlike-photoreal-2.0",
- )
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button(label='Run')
- with gr.Accordion('Advanced options', open=False):
- watermark = gr.Radio(["Picsart AI Research", "Text2Video-Zero",
- "None"], label="Watermark", value='Picsart AI Research')
-
- if on_huggingspace:
- video_length = gr.Slider(
- label="Video length", minimum=8, maximum=16, step=1)
- else:
- video_length = gr.Number(
- label="Video length", value=8, precision=0)
- chunk_size = gr.Slider(
- label="Chunk size", minimum=2, maximum=16, value=12 if on_huggingspace else 8, step=1, visible=not on_huggingspace)
-
- motion_field_strength_x = gr.Slider(
- label='Global Translation $\delta_{x}$', minimum=-20, maximum=20, value=12, step=1)
- motion_field_strength_y = gr.Slider(
- label='Global Translation $\delta_{y}$', minimum=-20, maximum=20, value=12, step=1)
-
- t0 = gr.Slider(label="Timestep t0", minimum=0,
- maximum=49, value=44, step=1)
- t1 = gr.Slider(label="Timestep t1", minimum=0,
- maximum=49, value=47, step=1)
-
- n_prompt = gr.Textbox(
- label="Optional Negative Prompt", value='')
- with gr.Column():
- result = gr.Video(label="Generated Video")
-
- inputs = [
- prompt,
- model_name,
- motion_field_strength_x,
- motion_field_strength_y,
- t0,
- t1,
- n_prompt,
- chunk_size,
- video_length,
- watermark,
- ]
-
- gr.Examples(examples=examples,
- inputs=inputs,
- outputs=result,
- fn=model.process_text2video,
- run_on_click=False,
- cache_examples=on_huggingspace,
- )
-
- run_button.click(fn=model.process_text2video,
- inputs=inputs,
- outputs=result,)
- return demo
diff --git a/spaces/CofAI/chat/g4f/Provider/Providers/helpers/theb.py b/spaces/CofAI/chat/g4f/Provider/Providers/helpers/theb.py
deleted file mode 100644
index 71cfd23ff34768092e4dbe3ff6b719a946dceebb..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat/g4f/Provider/Providers/helpers/theb.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import json
-import sys
-from re import findall
-from curl_cffi import requests
-
-config = json.loads(sys.argv[1])
-prompt = config['messages'][-1]['content']
-
-headers = {
- 'authority': 'chatbot.theb.ai',
- 'accept': 'application/json, text/plain, */*',
- 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
- 'content-type': 'application/json',
- 'origin': 'https://chatbot.theb.ai',
- 'referer': 'https://chatbot.theb.ai/',
- 'sec-ch-ua': '"Google Chrome";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-platform': '"macOS"',
- 'sec-fetch-dest': 'empty',
- 'sec-fetch-mode': 'cors',
- 'sec-fetch-site': 'same-origin',
- 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',
-}
-
-json_data = {
- 'prompt': prompt,
- 'options': {}
-}
-
-def format(chunk):
- try:
- completion_chunk = findall(r'content":"(.*)"},"fin', chunk.decode())[0]
- print(completion_chunk, flush=True, end='')
-
- except Exception as e:
- print(f'[ERROR] an error occured, retrying... | [[{chunk.decode()}]]', flush=True)
- return
-
-while True:
- try:
- response = requests.post('https://chatbot.theb.ai/api/chat-process',
- headers=headers, json=json_data, content_callback=format, impersonate='chrome110')
-
- exit(0)
-
- except Exception as e:
- print('[ERROR] an error occured, retrying... |', e, flush=True)
- continue
\ No newline at end of file
diff --git a/spaces/Cong723/gpt-academic-public/docs/WithFastapi.md b/spaces/Cong723/gpt-academic-public/docs/WithFastapi.md
deleted file mode 100644
index 188b52716485f15e528772c6454ee7839ced4406..0000000000000000000000000000000000000000
--- a/spaces/Cong723/gpt-academic-public/docs/WithFastapi.md
+++ /dev/null
@@ -1,43 +0,0 @@
-# Running with fastapi
-
-We currently support fastapi in order to solve sub-path deploy issue.
-
-1. change CUSTOM_PATH setting in `config.py`
-
-``` sh
-nano config.py
-```
-
-2. Edit main.py
-
-```diff
- auto_opentab_delay()
- - demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
- + demo.queue(concurrency_count=CONCURRENT_COUNT)
-
- - # 如果需要在二级路径下运行
- - # CUSTOM_PATH, = get_conf('CUSTOM_PATH')
- - # if CUSTOM_PATH != "/":
- - # from toolbox import run_gradio_in_subpath
- - # run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
- - # else:
- - # demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
-
- + 如果需要在二级路径下运行
- + CUSTOM_PATH, = get_conf('CUSTOM_PATH')
- + if CUSTOM_PATH != "/":
- + from toolbox import run_gradio_in_subpath
- + run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
- + else:
- + demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
-
-if __name__ == "__main__":
- main()
-```
-
-
-3. Go!
-
-``` sh
-python main.py
-```
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/requests.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/requests.py
deleted file mode 100644
index d16552c0a9535e1c0bd7f701987301681832eba5..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/requests.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from starlette.requests import HTTPConnection as HTTPConnection # noqa: F401
-from starlette.requests import Request as Request # noqa: F401
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-10c5655a.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-10c5655a.js
deleted file mode 100644
index 83a87c2d9e76c02d19bf8b84abf4d6f598f3215b..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-10c5655a.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as U,e as G,s as H,N as S,k as R,O as K,K as o,p as B,M as C,o as T,ap as E,Q as k,aw as N,z,v as A,A as M,x as D,a1 as W,B as X,am as Y,P as Z,R as y,E as p,ae as x,h as O,j as P,q as $,r as ee,t as Q,F}from"./index-1d65707a.js";/* empty css */import{B as le}from"./Button-f155035a.js";import{B as ne}from"./BlockTitle-dee077e8.js";import"./Info-7c6961ef.js";function ie(e){let n;return{c(){n=Z(e[5])},m(i,a){B(i,n,a)},p(i,a){a&32&&y(n,i[5])},d(i){i&&M(n)}}}function ae(e){let n,i,a,m,_,s,c,f,d,r,g;return m=new ne({props:{show_label:e[7],info:e[6],$$slots:{default:[ie]},$$scope:{ctx:e}}}),{c(){n=S("div"),i=S("div"),a=S("label"),R(m.$$.fragment),_=K(),s=S("input"),c=K(),f=S("input"),o(a,"for",e[8]),o(s,"data-testid","number-input"),o(s,"type","number"),o(s,"min",e[1]),o(s,"max",e[2]),o(s,"step",e[3]),s.disabled=e[4],o(s,"class","svelte-1cl284s"),o(i,"class","head svelte-1cl284s"),o(n,"class","wrap svelte-1cl284s"),o(f,"type","range"),o(f,"id",e[8]),o(f,"name","cowbell"),o(f,"min",e[1]),o(f,"max",e[2]),o(f,"step",e[3]),f.disabled=e[4],o(f,"class","svelte-1cl284s")},m(l,u){B(l,n,u),C(n,i),C(i,a),T(m,a,null),C(i,_),C(i,s),E(s,e[0]),B(l,c,u),B(l,f,u),E(f,e[0]),d=!0,r||(g=[k(s,"input",e[12]),k(s,"blur",e[10]),k(s,"pointerup",e[9]),k(f,"change",e[13]),k(f,"input",e[13]),k(f,"pointerup",e[9])],r=!0)},p(l,[u]){const v={};u&128&&(v.show_label=l[7]),u&64&&(v.info=l[6]),u&65568&&(v.$$scope={dirty:u,ctx:l}),m.$set(v),(!d||u&2)&&o(s,"min",l[1]),(!d||u&4)&&o(s,"max",l[2]),(!d||u&8)&&o(s,"step",l[3]),(!d||u&16)&&(s.disabled=l[4]),u&1&&N(s.value)!==l[0]&&E(s,l[0]),(!d||u&2)&&o(f,"min",l[1]),(!d||u&4)&&o(f,"max",l[2]),(!d||u&8)&&o(f,"step",l[3]),(!d||u&16)&&(f.disabled=l[4]),u&1&&E(f,l[0])},i(l){d||(z(m.$$.fragment,l),d=!0)},o(l){A(m.$$.fragment,l),d=!1},d(l){l&&(M(n),M(c),M(f)),D(m),r=!1,W(g)}}}let te=0;function ue(e,n,i){let{value:a=0}=n,{value_is_output:m=!1}=n,{minimum:_=0}=n,{maximum:s=100}=n,{step:c=1}=n,{disabled:f=!1}=n,{label:d}=n,{info:r=void 0}=n,{show_label:g}=n;const l=`range_id_${te++}`,u=X();function v(){u("change",a),m||u("input")}Y(()=>{i(11,m=!1)});function h(b){u("release",a)}const j=()=>{u("release",a),i(0,a=Math.min(Math.max(a,_),s))};function q(){a=N(this.value),i(0,a)}function w(){a=N(this.value),i(0,a)}return e.$$set=b=>{"value"in b&&i(0,a=b.value),"value_is_output"in b&&i(11,m=b.value_is_output),"minimum"in b&&i(1,_=b.minimum),"maximum"in b&&i(2,s=b.maximum),"step"in b&&i(3,c=b.step),"disabled"in b&&i(4,f=b.disabled),"label"in b&&i(5,d=b.label),"info"in b&&i(6,r=b.info),"show_label"in b&&i(7,g=b.show_label)},e.$$.update=()=>{e.$$.dirty&1&&v()},[a,_,s,c,f,d,r,g,l,h,j,m,q,w]}class se extends U{constructor(n){super(),G(this,n,ue,ae,H,{value:0,value_is_output:11,minimum:1,maximum:2,step:3,disabled:4,label:5,info:6,show_label:7})}}function me(e){let n,i,a,m,_,s;const 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_={};m&16&&(_.visible=a[4]),m&4&&(_.elem_id=a[2]),m&8&&(_.elem_classes=a[3]),m&128&&(_.container=a[7]),m&256&&(_.scale=a[8]),m&512&&(_.min_width=a[9]),m&2161763&&(_.$$scope={dirty:m,ctx:a}),n.$set(_)},i(a){i||(z(n.$$.fragment,a),i=!0)},o(a){A(n.$$.fragment,a),i=!1},d(a){D(n,a)}}}function _e(e,n,i){let{elem_id:a=""}=n,{elem_classes:m=[]}=n,{visible:_=!0}=n,{value:s=0}=n,{label:c="Slider"}=n,{info:f=void 0}=n,{container:d=!0}=n,{scale:r=null}=n,{min_width:g=void 0}=n,{minimum:l}=n,{maximum:u}=n,{step:v}=n,{mode:h}=n,{show_label:j}=n,{loading_status:q}=n,{value_is_output:w=!1}=n;function b(t){s=t,i(0,s)}function I(t){w=t,i(1,w)}function J(t){F.call(this,e,t)}function L(t){F.call(this,e,t)}function V(t){F.call(this,e,t)}return e.$$set=t=>{"elem_id"in t&&i(2,a=t.elem_id),"elem_classes"in t&&i(3,m=t.elem_classes),"visible"in t&&i(4,_=t.visible),"value"in t&&i(0,s=t.value),"label"in t&&i(5,c=t.label),"info"in t&&i(6,f=t.info),"container"in t&&i(7,d=t.container),"scale"in 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-//# sourceMappingURL=index-10c5655a.js.map
diff --git a/spaces/Dagfinn1962/stablediffusion-articlera/main.css b/spaces/Dagfinn1962/stablediffusion-articlera/main.css
deleted file mode 100644
index 285a682553c850e5d5b6c68d11b93f1bcfdb9ae1..0000000000000000000000000000000000000000
--- a/spaces/Dagfinn1962/stablediffusion-articlera/main.css
+++ /dev/null
@@ -1,58 +0,0 @@
-
-
-body {
- background-color: #6262d1;
- width: 100%;
- color: #FFFFFF;
-}
-
-h3 {
- color: #FFFFFF;
- text-align: center;
- font-family: verdana;
- font-size: 24px;
- border: 1px solid #FFFFFF;
- border-radius: 10px;
-}
-
-p {
- font-family: verdana;
- font-size: 14px;
-}
-
-label {
- font-family: verdana;
- color: #000000;
- font-weight: 700;
- font-size: 14px;
-
-}
-
-gr.Textbox {
- font-family: verdana;
- background-color: #6262d1;
- color: #000000;
- font-weight: 700;
- font-size: 14px;
- border-radius: 6px;
-}
-
-gr.Botton {
- font-family: verdana;
- background-color: #6262d1;
- color: #FFFFFF;
- font-weight: 700;
- font-size: 14px;
-
- border-radius: 6px;
-}
-
-a a:active a.hover
- {
- font-family: verdana;
- color: #572430;
- text-decoration: none;
- font-weight: 700;
- font-size: 14px;
-
-}
\ No newline at end of file
diff --git a/spaces/DaleChen/AutoGPT/tests/smoke_test.py b/spaces/DaleChen/AutoGPT/tests/smoke_test.py
deleted file mode 100644
index 1b9d643fc21f3703384a2bb4f2bd1d725f4dd418..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/tests/smoke_test.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Smoke test for the autogpt package."""
-import os
-import subprocess
-import sys
-
-import pytest
-
-from autogpt.commands.file_operations import delete_file, read_file
-
-
-@pytest.mark.integration_test
-def test_write_file() -> None:
- """
- Test case to check if the write_file command can successfully write 'Hello World' to a file
- named 'hello_world.txt'.
-
- Read the current ai_settings.yaml file and store its content.
- """
- env_vars = {"MEMORY_BACKEND": "no_memory", "TEMPERATURE": "0"}
- ai_settings = None
- if os.path.exists("ai_settings.yaml"):
- with open("ai_settings.yaml", "r") as f:
- ai_settings = f.read()
- os.remove("ai_settings.yaml")
-
- try:
- if os.path.exists("hello_world.txt"):
- # Clean up any existing 'hello_world.txt' file before testing.
- delete_file("hello_world.txt")
- # Prepare input data for the test.
- input_data = """write_file-GPT
-an AI designed to use the write_file command to write 'Hello World' into a file named "hello_world.txt" and then use the task_complete command to complete the task.
-Use the write_file command to write 'Hello World' into a file named "hello_world.txt".
-Use the task_complete command to complete the task.
-Do not use any other commands.
-
-y -5
-EOF"""
- command = f"{sys.executable} -m autogpt"
-
- # Execute the script with the input data.
- process = subprocess.Popen(
- command,
- stdin=subprocess.PIPE,
- shell=True,
- env={**os.environ, **env_vars},
- )
- process.communicate(input_data.encode())
-
- # Read the content of the 'hello_world.txt' file created during the test.
- content = read_file("hello_world.txt")
- finally:
- if ai_settings:
- # Restore the original ai_settings.yaml file.
- with open("ai_settings.yaml", "w") as f:
- f.write(ai_settings)
-
- # Check if the content of the 'hello_world.txt' file is equal to 'Hello World'.
- assert content == "Hello World", f"Expected 'Hello World', got {content}"
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/zind_dataset.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/zind_dataset.py
deleted file mode 100644
index 20258763fcfa6bc130e33a3889d5e88018d4708e..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/zind_dataset.py
+++ /dev/null
@@ -1,138 +0,0 @@
-"""
-@Date: 2021/09/22
-@description:
-"""
-import os
-import json
-import math
-import numpy as np
-
-from dataset.communal.read import read_image, read_label, read_zind
-from dataset.communal.base_dataset import BaseDataset
-from utils.logger import get_logger
-from preprocessing.filter import filter_center, filter_boundary, filter_self_intersection
-from utils.boundary import calc_rotation
-
-
-class ZindDataset(BaseDataset):
- def __init__(self, root_dir, mode, shape=None, max_wall_num=0, aug=None, camera_height=1.6, logger=None,
- split_list=None, patch_num=256, keys=None, for_test_index=None,
- is_simple=True, is_ceiling_flat=False, vp_align=False):
- # if keys is None:
- # keys = ['image', 'depth', 'ratio', 'id', 'corners', 'corner_heat_map', 'object']
- super().__init__(mode, shape, max_wall_num, aug, camera_height, patch_num, keys)
- if logger is None:
- logger = get_logger()
- self.root_dir = root_dir
- self.vp_align = vp_align
-
- data_dir = os.path.join(root_dir)
- img_dir = os.path.join(root_dir, 'image')
-
- pano_list = read_zind(partition_path=os.path.join(data_dir, f"zind_partition.json"),
- simplicity_path=os.path.join(data_dir, f"room_shape_simplicity_labels.json"),
- data_dir=data_dir, mode=mode, is_simple=is_simple, is_ceiling_flat=is_ceiling_flat)
-
- if for_test_index is not None:
- pano_list = pano_list[:for_test_index]
- if split_list:
- pano_list = [pano for pano in pano_list if pano['id'] in split_list]
- self.data = []
- invalid_num = 0
- for pano in pano_list:
- if not os.path.exists(pano['img_path']):
- logger.warning(f"{pano['img_path']} not exists")
- invalid_num += 1
- continue
-
- if not filter_center(pano['corners']):
- # logger.warning(f"{pano['id']} camera center not in layout")
- # invalid_num += 1
- continue
-
- if self.max_wall_num >= 10:
- if len(pano['corners']) < self.max_wall_num:
- invalid_num += 1
- continue
- elif self.max_wall_num != 0 and len(pano['corners']) != self.max_wall_num:
- invalid_num += 1
- continue
-
- if not filter_boundary(pano['corners']):
- logger.warning(f"{pano['id']} boundary cross")
- invalid_num += 1
- continue
-
- if not filter_self_intersection(pano['corners']):
- logger.warning(f"{pano['id']} self_intersection")
- invalid_num += 1
- continue
-
- self.data.append(pano)
-
- logger.info(
- f"Build dataset mode: {self.mode} max_wall_num: {self.max_wall_num} valid: {len(self.data)} invalid: {invalid_num}")
-
- def __getitem__(self, idx):
- pano = self.data[idx]
- rgb_path = pano['img_path']
- label = pano
- image = read_image(rgb_path, self.shape)
-
- if self.vp_align:
- # Equivalent to vanishing point alignment step
- rotation = calc_rotation(corners=label['corners'])
- shift = math.modf(rotation / (2 * np.pi) + 1)[0]
- image = np.roll(image, round(shift * self.shape[1]), axis=1)
- label['corners'][:, 0] = np.modf(label['corners'][:, 0] + shift)[0]
-
- output = self.process_data(label, image, self.patch_num)
- return output
-
-
-if __name__ == "__main__":
- import numpy as np
- from PIL import Image
-
- from tqdm import tqdm
- from visualization.boundary import draw_boundaries, draw_object
- from visualization.floorplan import draw_floorplan
- from utils.boundary import depth2boundaries, calc_rotation
- from utils.conversion import uv2xyz
- from models.other.init_env import init_env
-
- init_env(123)
-
- modes = ['val']
- for i in range(1):
- for mode in modes:
- print(mode)
- mp3d_dataset = ZindDataset(root_dir='../src/dataset/zind', mode=mode, aug={
- 'STRETCH': False,
- 'ROTATE': False,
- 'FLIP': False,
- 'GAMMA': False
- })
- # continue
- # save_dir = f'../src/dataset/zind/visualization/{mode}'
- # if not os.path.isdir(save_dir):
- # os.makedirs(save_dir)
-
- bar = tqdm(mp3d_dataset, ncols=100)
- for data in bar:
- # if data['id'] != '1079_pano_18':
- # continue
- bar.set_description(f"Processing {data['id']}")
- boundary_list = depth2boundaries(data['ratio'], data['depth'], step=None)
-
- pano_img = draw_boundaries(data['image'].transpose(1, 2, 0), boundary_list=boundary_list, show=True)
- # Image.fromarray((pano_img * 255).astype(np.uint8)).save(
- # os.path.join(save_dir, f"{data['id']}_boundary.png"))
- # draw_object(pano_img, heat_maps=data['object_heat_map'], depth=data['depth'],
- # size=data['object_size'], show=True)
- # pass
- #
- floorplan = draw_floorplan(uv2xyz(boundary_list[0])[..., ::2], show=True,
- marker_color=None, center_color=0.2)
- # Image.fromarray((floorplan.squeeze() * 255).astype(np.uint8)).save(
- # os.path.join(save_dir, f"{data['id']}_floorplan.png"))
diff --git a/spaces/Datasculptor/DescriptionGPT/detic/data/datasets/lvis_22k_categories.py b/spaces/Datasculptor/DescriptionGPT/detic/data/datasets/lvis_22k_categories.py
deleted file mode 100644
index 9525f0873d68d84dd691979c32eaadd7860f59fe..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/DescriptionGPT/detic/data/datasets/lvis_22k_categories.py
+++ /dev/null
@@ -1 +0,0 @@
-CATEGORIES = [{'name': 'aerosol_can', 'id': 1, 'frequency': 'c', 'synset': 'aerosol.n.02'}, {'name': 'air_conditioner', 'id': 2, 'frequency': 'f', 'synset': 'air_conditioner.n.01'}, {'name': 'airplane', 'id': 3, 'frequency': 'f', 'synset': 'airplane.n.01'}, {'name': 'alarm_clock', 'id': 4, 'frequency': 'f', 'synset': 'alarm_clock.n.01'}, {'name': 'alcohol', 'id': 5, 'frequency': 'c', 'synset': 'alcohol.n.01'}, {'name': 'alligator', 'id': 6, 'frequency': 'c', 'synset': 'alligator.n.02'}, {'name': 'almond', 'id': 7, 'frequency': 'c', 'synset': 'almond.n.02'}, {'name': 'ambulance', 'id': 8, 'frequency': 'c', 'synset': 'ambulance.n.01'}, {'name': 'amplifier', 'id': 9, 'frequency': 'c', 'synset': 'amplifier.n.01'}, {'name': 'anklet', 'id': 10, 'frequency': 'c', 'synset': 'anklet.n.03'}, {'name': 'antenna', 'id': 11, 'frequency': 'f', 'synset': 'antenna.n.01'}, {'name': 'apple', 'id': 12, 'frequency': 'f', 'synset': 'apple.n.01'}, {'name': 'applesauce', 'id': 13, 'frequency': 'r', 'synset': 'applesauce.n.01'}, {'name': 'apricot', 'id': 14, 'frequency': 'r', 'synset': 'apricot.n.02'}, {'name': 'apron', 'id': 15, 'frequency': 'f', 'synset': 'apron.n.01'}, {'name': 'aquarium', 'id': 16, 'frequency': 'c', 'synset': 'aquarium.n.01'}, {'name': 'arctic_(type_of_shoe)', 'id': 17, 'frequency': 'r', 'synset': 'arctic.n.02'}, {'name': 'armband', 'id': 18, 'frequency': 'c', 'synset': 'armband.n.02'}, {'name': 'armchair', 'id': 19, 'frequency': 'f', 'synset': 'armchair.n.01'}, {'name': 'armoire', 'id': 20, 'frequency': 'r', 'synset': 'armoire.n.01'}, {'name': 'armor', 'id': 21, 'frequency': 'r', 'synset': 'armor.n.01'}, {'name': 'artichoke', 'id': 22, 'frequency': 'c', 'synset': 'artichoke.n.02'}, {'name': 'trash_can', 'id': 23, 'frequency': 'f', 'synset': 'ashcan.n.01'}, {'name': 'ashtray', 'id': 24, 'frequency': 'c', 'synset': 'ashtray.n.01'}, {'name': 'asparagus', 'id': 25, 'frequency': 'c', 'synset': 'asparagus.n.02'}, {'name': 'atomizer', 'id': 26, 'frequency': 'c', 'synset': 'atomizer.n.01'}, {'name': 'avocado', 'id': 27, 'frequency': 'f', 'synset': 'avocado.n.01'}, {'name': 'award', 'id': 28, 'frequency': 'c', 'synset': 'award.n.02'}, {'name': 'awning', 'id': 29, 'frequency': 'f', 'synset': 'awning.n.01'}, {'name': 'ax', 'id': 30, 'frequency': 'r', 'synset': 'ax.n.01'}, {'name': 'baboon', 'id': 31, 'frequency': 'r', 'synset': 'baboon.n.01'}, {'name': 'baby_buggy', 'id': 32, 'frequency': 'f', 'synset': 'baby_buggy.n.01'}, {'name': 'basketball_backboard', 'id': 33, 'frequency': 'c', 'synset': 'backboard.n.01'}, {'name': 'backpack', 'id': 34, 'frequency': 'f', 'synset': 'backpack.n.01'}, {'name': 'handbag', 'id': 35, 'frequency': 'f', 'synset': 'bag.n.04'}, {'name': 'suitcase', 'id': 36, 'frequency': 'f', 'synset': 'bag.n.06'}, {'name': 'bagel', 'id': 37, 'frequency': 'c', 'synset': 'bagel.n.01'}, {'name': 'bagpipe', 'id': 38, 'frequency': 'r', 'synset': 'bagpipe.n.01'}, {'name': 'baguet', 'id': 39, 'frequency': 'r', 'synset': 'baguet.n.01'}, {'name': 'bait', 'id': 40, 'frequency': 'r', 'synset': 'bait.n.02'}, {'name': 'ball', 'id': 41, 'frequency': 'f', 'synset': 'ball.n.06'}, {'name': 'ballet_skirt', 'id': 42, 'frequency': 'r', 'synset': 'ballet_skirt.n.01'}, {'name': 'balloon', 'id': 43, 'frequency': 'f', 'synset': 'balloon.n.01'}, {'name': 'bamboo', 'id': 44, 'frequency': 'c', 'synset': 'bamboo.n.02'}, {'name': 'banana', 'id': 45, 'frequency': 'f', 'synset': 'banana.n.02'}, {'name': 'Band_Aid', 'id': 46, 'frequency': 'c', 'synset': 'band_aid.n.01'}, {'name': 'bandage', 'id': 47, 'frequency': 'c', 'synset': 'bandage.n.01'}, {'name': 'bandanna', 'id': 48, 'frequency': 'f', 'synset': 'bandanna.n.01'}, {'name': 'banjo', 'id': 49, 'frequency': 'r', 'synset': 'banjo.n.01'}, {'name': 'banner', 'id': 50, 'frequency': 'f', 'synset': 'banner.n.01'}, {'name': 'barbell', 'id': 51, 'frequency': 'r', 'synset': 'barbell.n.01'}, {'name': 'barge', 'id': 52, 'frequency': 'r', 'synset': 'barge.n.01'}, {'name': 'barrel', 'id': 53, 'frequency': 'f', 'synset': 'barrel.n.02'}, {'name': 'barrette', 'id': 54, 'frequency': 'c', 'synset': 'barrette.n.01'}, {'name': 'barrow', 'id': 55, 'frequency': 'c', 'synset': 'barrow.n.03'}, {'name': 'baseball_base', 'id': 56, 'frequency': 'f', 'synset': 'base.n.03'}, {'name': 'baseball', 'id': 57, 'frequency': 'f', 'synset': 'baseball.n.02'}, {'name': 'baseball_bat', 'id': 58, 'frequency': 'f', 'synset': 'baseball_bat.n.01'}, {'name': 'baseball_cap', 'id': 59, 'frequency': 'f', 'synset': 'baseball_cap.n.01'}, {'name': 'baseball_glove', 'id': 60, 'frequency': 'f', 'synset': 'baseball_glove.n.01'}, {'name': 'basket', 'id': 61, 'frequency': 'f', 'synset': 'basket.n.01'}, {'name': 'basketball', 'id': 62, 'frequency': 'c', 'synset': 'basketball.n.02'}, {'name': 'bass_horn', 'id': 63, 'frequency': 'r', 'synset': 'bass_horn.n.01'}, {'name': 'bat_(animal)', 'id': 64, 'frequency': 'c', 'synset': 'bat.n.01'}, {'name': 'bath_mat', 'id': 65, 'frequency': 'f', 'synset': 'bath_mat.n.01'}, {'name': 'bath_towel', 'id': 66, 'frequency': 'f', 'synset': 'bath_towel.n.01'}, {'name': 'bathrobe', 'id': 67, 'frequency': 'c', 'synset': 'bathrobe.n.01'}, {'name': 'bathtub', 'id': 68, 'frequency': 'f', 'synset': 'bathtub.n.01'}, {'name': 'batter_(food)', 'id': 69, 'frequency': 'r', 'synset': 'batter.n.02'}, {'name': 'battery', 'id': 70, 'frequency': 'c', 'synset': 'battery.n.02'}, {'name': 'beachball', 'id': 71, 'frequency': 'r', 'synset': 'beach_ball.n.01'}, {'name': 'bead', 'id': 72, 'frequency': 'c', 'synset': 'bead.n.01'}, {'name': 'bean_curd', 'id': 73, 'frequency': 'c', 'synset': 'bean_curd.n.01'}, {'name': 'beanbag', 'id': 74, 'frequency': 'c', 'synset': 'beanbag.n.01'}, {'name': 'beanie', 'id': 75, 'frequency': 'f', 'synset': 'beanie.n.01'}, {'name': 'bear', 'id': 76, 'frequency': 'f', 'synset': 'bear.n.01'}, {'name': 'bed', 'id': 77, 'frequency': 'f', 'synset': 'bed.n.01'}, {'name': 'bedpan', 'id': 78, 'frequency': 'r', 'synset': 'bedpan.n.01'}, {'name': 'bedspread', 'id': 79, 'frequency': 'f', 'synset': 'bedspread.n.01'}, {'name': 'cow', 'id': 80, 'frequency': 'f', 'synset': 'beef.n.01'}, {'name': 'beef_(food)', 'id': 81, 'frequency': 'f', 'synset': 'beef.n.02'}, {'name': 'beeper', 'id': 82, 'frequency': 'r', 'synset': 'beeper.n.01'}, {'name': 'beer_bottle', 'id': 83, 'frequency': 'f', 'synset': 'beer_bottle.n.01'}, {'name': 'beer_can', 'id': 84, 'frequency': 'c', 'synset': 'beer_can.n.01'}, {'name': 'beetle', 'id': 85, 'frequency': 'r', 'synset': 'beetle.n.01'}, {'name': 'bell', 'id': 86, 'frequency': 'f', 'synset': 'bell.n.01'}, {'name': 'bell_pepper', 'id': 87, 'frequency': 'f', 'synset': 'bell_pepper.n.02'}, {'name': 'belt', 'id': 88, 'frequency': 'f', 'synset': 'belt.n.02'}, {'name': 'belt_buckle', 'id': 89, 'frequency': 'f', 'synset': 'belt_buckle.n.01'}, {'name': 'bench', 'id': 90, 'frequency': 'f', 'synset': 'bench.n.01'}, {'name': 'beret', 'id': 91, 'frequency': 'c', 'synset': 'beret.n.01'}, {'name': 'bib', 'id': 92, 'frequency': 'c', 'synset': 'bib.n.02'}, {'name': 'Bible', 'id': 93, 'frequency': 'r', 'synset': 'bible.n.01'}, {'name': 'bicycle', 'id': 94, 'frequency': 'f', 'synset': 'bicycle.n.01'}, {'name': 'visor', 'id': 95, 'frequency': 'f', 'synset': 'bill.n.09'}, {'name': 'billboard', 'id': 96, 'frequency': 'f', 'synset': 'billboard.n.01'}, {'name': 'binder', 'id': 97, 'frequency': 'c', 'synset': 'binder.n.03'}, {'name': 'binoculars', 'id': 98, 'frequency': 'c', 'synset': 'binoculars.n.01'}, {'name': 'bird', 'id': 99, 'frequency': 'f', 'synset': 'bird.n.01'}, {'name': 'birdfeeder', 'id': 100, 'frequency': 'c', 'synset': 'bird_feeder.n.01'}, {'name': 'birdbath', 'id': 101, 'frequency': 'c', 'synset': 'birdbath.n.01'}, {'name': 'birdcage', 'id': 102, 'frequency': 'c', 'synset': 'birdcage.n.01'}, {'name': 'birdhouse', 'id': 103, 'frequency': 'c', 'synset': 'birdhouse.n.01'}, {'name': 'birthday_cake', 'id': 104, 'frequency': 'f', 'synset': 'birthday_cake.n.01'}, {'name': 'birthday_card', 'id': 105, 'frequency': 'r', 'synset': 'birthday_card.n.01'}, {'name': 'pirate_flag', 'id': 106, 'frequency': 'r', 'synset': 'black_flag.n.01'}, {'name': 'black_sheep', 'id': 107, 'frequency': 'c', 'synset': 'black_sheep.n.02'}, {'name': 'blackberry', 'id': 108, 'frequency': 'c', 'synset': 'blackberry.n.01'}, {'name': 'blackboard', 'id': 109, 'frequency': 'f', 'synset': 'blackboard.n.01'}, {'name': 'blanket', 'id': 110, 'frequency': 'f', 'synset': 'blanket.n.01'}, {'name': 'blazer', 'id': 111, 'frequency': 'c', 'synset': 'blazer.n.01'}, {'name': 'blender', 'id': 112, 'frequency': 'f', 'synset': 'blender.n.01'}, {'name': 'blimp', 'id': 113, 'frequency': 'r', 'synset': 'blimp.n.02'}, {'name': 'blinker', 'id': 114, 'frequency': 'f', 'synset': 'blinker.n.01'}, {'name': 'blouse', 'id': 115, 'frequency': 'f', 'synset': 'blouse.n.01'}, {'name': 'blueberry', 'id': 116, 'frequency': 'f', 'synset': 'blueberry.n.02'}, {'name': 'gameboard', 'id': 117, 'frequency': 'r', 'synset': 'board.n.09'}, {'name': 'boat', 'id': 118, 'frequency': 'f', 'synset': 'boat.n.01'}, {'name': 'bob', 'id': 119, 'frequency': 'r', 'synset': 'bob.n.05'}, {'name': 'bobbin', 'id': 120, 'frequency': 'c', 'synset': 'bobbin.n.01'}, {'name': 'bobby_pin', 'id': 121, 'frequency': 'c', 'synset': 'bobby_pin.n.01'}, {'name': 'boiled_egg', 'id': 122, 'frequency': 'c', 'synset': 'boiled_egg.n.01'}, {'name': 'bolo_tie', 'id': 123, 'frequency': 'r', 'synset': 'bolo_tie.n.01'}, {'name': 'deadbolt', 'id': 124, 'frequency': 'c', 'synset': 'bolt.n.03'}, {'name': 'bolt', 'id': 125, 'frequency': 'f', 'synset': 'bolt.n.06'}, {'name': 'bonnet', 'id': 126, 'frequency': 'r', 'synset': 'bonnet.n.01'}, {'name': 'book', 'id': 127, 'frequency': 'f', 'synset': 'book.n.01'}, {'name': 'bookcase', 'id': 128, 'frequency': 'c', 'synset': 'bookcase.n.01'}, {'name': 'booklet', 'id': 129, 'frequency': 'c', 'synset': 'booklet.n.01'}, {'name': 'bookmark', 'id': 130, 'frequency': 'r', 'synset': 'bookmark.n.01'}, {'name': 'boom_microphone', 'id': 131, 'frequency': 'r', 'synset': 'boom.n.04'}, {'name': 'boot', 'id': 132, 'frequency': 'f', 'synset': 'boot.n.01'}, {'name': 'bottle', 'id': 133, 'frequency': 'f', 'synset': 'bottle.n.01'}, {'name': 'bottle_opener', 'id': 134, 'frequency': 'c', 'synset': 'bottle_opener.n.01'}, {'name': 'bouquet', 'id': 135, 'frequency': 'c', 'synset': 'bouquet.n.01'}, {'name': 'bow_(weapon)', 'id': 136, 'frequency': 'r', 'synset': 'bow.n.04'}, {'name': 'bow_(decorative_ribbons)', 'id': 137, 'frequency': 'f', 'synset': 'bow.n.08'}, {'name': 'bow-tie', 'id': 138, 'frequency': 'f', 'synset': 'bow_tie.n.01'}, {'name': 'bowl', 'id': 139, 'frequency': 'f', 'synset': 'bowl.n.03'}, {'name': 'pipe_bowl', 'id': 140, 'frequency': 'r', 'synset': 'bowl.n.08'}, {'name': 'bowler_hat', 'id': 141, 'frequency': 'c', 'synset': 'bowler_hat.n.01'}, {'name': 'bowling_ball', 'id': 142, 'frequency': 'r', 'synset': 'bowling_ball.n.01'}, {'name': 'box', 'id': 143, 'frequency': 'f', 'synset': 'box.n.01'}, {'name': 'boxing_glove', 'id': 144, 'frequency': 'r', 'synset': 'boxing_glove.n.01'}, {'name': 'suspenders', 'id': 145, 'frequency': 'c', 'synset': 'brace.n.06'}, {'name': 'bracelet', 'id': 146, 'frequency': 'f', 'synset': 'bracelet.n.02'}, {'name': 'brass_plaque', 'id': 147, 'frequency': 'r', 'synset': 'brass.n.07'}, {'name': 'brassiere', 'id': 148, 'frequency': 'c', 'synset': 'brassiere.n.01'}, {'name': 'bread-bin', 'id': 149, 'frequency': 'c', 'synset': 'bread-bin.n.01'}, {'name': 'bread', 'id': 150, 'frequency': 'f', 'synset': 'bread.n.01'}, {'name': 'breechcloth', 'id': 151, 'frequency': 'r', 'synset': 'breechcloth.n.01'}, {'name': 'bridal_gown', 'id': 152, 'frequency': 'f', 'synset': 'bridal_gown.n.01'}, {'name': 'briefcase', 'id': 153, 'frequency': 'c', 'synset': 'briefcase.n.01'}, {'name': 'broccoli', 'id': 154, 'frequency': 'f', 'synset': 'broccoli.n.01'}, {'name': 'broach', 'id': 155, 'frequency': 'r', 'synset': 'brooch.n.01'}, {'name': 'broom', 'id': 156, 'frequency': 'c', 'synset': 'broom.n.01'}, {'name': 'brownie', 'id': 157, 'frequency': 'c', 'synset': 'brownie.n.03'}, {'name': 'brussels_sprouts', 'id': 158, 'frequency': 'c', 'synset': 'brussels_sprouts.n.01'}, {'name': 'bubble_gum', 'id': 159, 'frequency': 'r', 'synset': 'bubble_gum.n.01'}, {'name': 'bucket', 'id': 160, 'frequency': 'f', 'synset': 'bucket.n.01'}, {'name': 'horse_buggy', 'id': 161, 'frequency': 'r', 'synset': 'buggy.n.01'}, {'name': 'bull', 'id': 162, 'frequency': 'c', 'synset': 'bull.n.11'}, {'name': 'bulldog', 'id': 163, 'frequency': 'c', 'synset': 'bulldog.n.01'}, {'name': 'bulldozer', 'id': 164, 'frequency': 'r', 'synset': 'bulldozer.n.01'}, {'name': 'bullet_train', 'id': 165, 'frequency': 'c', 'synset': 'bullet_train.n.01'}, {'name': 'bulletin_board', 'id': 166, 'frequency': 'c', 'synset': 'bulletin_board.n.02'}, {'name': 'bulletproof_vest', 'id': 167, 'frequency': 'r', 'synset': 'bulletproof_vest.n.01'}, {'name': 'bullhorn', 'id': 168, 'frequency': 'c', 'synset': 'bullhorn.n.01'}, {'name': 'bun', 'id': 169, 'frequency': 'f', 'synset': 'bun.n.01'}, {'name': 'bunk_bed', 'id': 170, 'frequency': 'c', 'synset': 'bunk_bed.n.01'}, {'name': 'buoy', 'id': 171, 'frequency': 'f', 'synset': 'buoy.n.01'}, {'name': 'burrito', 'id': 172, 'frequency': 'r', 'synset': 'burrito.n.01'}, {'name': 'bus_(vehicle)', 'id': 173, 'frequency': 'f', 'synset': 'bus.n.01'}, {'name': 'business_card', 'id': 174, 'frequency': 'c', 'synset': 'business_card.n.01'}, {'name': 'butter', 'id': 175, 'frequency': 'f', 'synset': 'butter.n.01'}, {'name': 'butterfly', 'id': 176, 'frequency': 'c', 'synset': 'butterfly.n.01'}, {'name': 'button', 'id': 177, 'frequency': 'f', 'synset': 'button.n.01'}, {'name': 'cab_(taxi)', 'id': 178, 'frequency': 'f', 'synset': 'cab.n.03'}, {'name': 'cabana', 'id': 179, 'frequency': 'r', 'synset': 'cabana.n.01'}, {'name': 'cabin_car', 'id': 180, 'frequency': 'c', 'synset': 'cabin_car.n.01'}, {'name': 'cabinet', 'id': 181, 'frequency': 'f', 'synset': 'cabinet.n.01'}, {'name': 'locker', 'id': 182, 'frequency': 'r', 'synset': 'cabinet.n.03'}, {'name': 'cake', 'id': 183, 'frequency': 'f', 'synset': 'cake.n.03'}, {'name': 'calculator', 'id': 184, 'frequency': 'c', 'synset': 'calculator.n.02'}, {'name': 'calendar', 'id': 185, 'frequency': 'f', 'synset': 'calendar.n.02'}, {'name': 'calf', 'id': 186, 'frequency': 'c', 'synset': 'calf.n.01'}, {'name': 'camcorder', 'id': 187, 'frequency': 'c', 'synset': 'camcorder.n.01'}, {'name': 'camel', 'id': 188, 'frequency': 'c', 'synset': 'camel.n.01'}, {'name': 'camera', 'id': 189, 'frequency': 'f', 'synset': 'camera.n.01'}, {'name': 'camera_lens', 'id': 190, 'frequency': 'c', 'synset': 'camera_lens.n.01'}, {'name': 'camper_(vehicle)', 'id': 191, 'frequency': 'c', 'synset': 'camper.n.02'}, {'name': 'can', 'id': 192, 'frequency': 'f', 'synset': 'can.n.01'}, {'name': 'can_opener', 'id': 193, 'frequency': 'c', 'synset': 'can_opener.n.01'}, {'name': 'candle', 'id': 194, 'frequency': 'f', 'synset': 'candle.n.01'}, {'name': 'candle_holder', 'id': 195, 'frequency': 'f', 'synset': 'candlestick.n.01'}, {'name': 'candy_bar', 'id': 196, 'frequency': 'r', 'synset': 'candy_bar.n.01'}, {'name': 'candy_cane', 'id': 197, 'frequency': 'c', 'synset': 'candy_cane.n.01'}, {'name': 'walking_cane', 'id': 198, 'frequency': 'c', 'synset': 'cane.n.01'}, {'name': 'canister', 'id': 199, 'frequency': 'c', 'synset': 'canister.n.02'}, {'name': 'canoe', 'id': 200, 'frequency': 'c', 'synset': 'canoe.n.01'}, {'name': 'cantaloup', 'id': 201, 'frequency': 'c', 'synset': 'cantaloup.n.02'}, {'name': 'canteen', 'id': 202, 'frequency': 'r', 'synset': 'canteen.n.01'}, {'name': 'cap_(headwear)', 'id': 203, 'frequency': 'f', 'synset': 'cap.n.01'}, {'name': 'bottle_cap', 'id': 204, 'frequency': 'f', 'synset': 'cap.n.02'}, {'name': 'cape', 'id': 205, 'frequency': 'c', 'synset': 'cape.n.02'}, {'name': 'cappuccino', 'id': 206, 'frequency': 'c', 'synset': 'cappuccino.n.01'}, {'name': 'car_(automobile)', 'id': 207, 'frequency': 'f', 'synset': 'car.n.01'}, {'name': 'railcar_(part_of_a_train)', 'id': 208, 'frequency': 'f', 'synset': 'car.n.02'}, {'name': 'elevator_car', 'id': 209, 'frequency': 'r', 'synset': 'car.n.04'}, {'name': 'car_battery', 'id': 210, 'frequency': 'r', 'synset': 'car_battery.n.01'}, {'name': 'identity_card', 'id': 211, 'frequency': 'c', 'synset': 'card.n.02'}, {'name': 'card', 'id': 212, 'frequency': 'c', 'synset': 'card.n.03'}, {'name': 'cardigan', 'id': 213, 'frequency': 'c', 'synset': 'cardigan.n.01'}, {'name': 'cargo_ship', 'id': 214, 'frequency': 'r', 'synset': 'cargo_ship.n.01'}, {'name': 'carnation', 'id': 215, 'frequency': 'r', 'synset': 'carnation.n.01'}, {'name': 'horse_carriage', 'id': 216, 'frequency': 'c', 'synset': 'carriage.n.02'}, {'name': 'carrot', 'id': 217, 'frequency': 'f', 'synset': 'carrot.n.01'}, {'name': 'tote_bag', 'id': 218, 'frequency': 'f', 'synset': 'carryall.n.01'}, {'name': 'cart', 'id': 219, 'frequency': 'c', 'synset': 'cart.n.01'}, {'name': 'carton', 'id': 220, 'frequency': 'c', 'synset': 'carton.n.02'}, {'name': 'cash_register', 'id': 221, 'frequency': 'c', 'synset': 'cash_register.n.01'}, {'name': 'casserole', 'id': 222, 'frequency': 'r', 'synset': 'casserole.n.01'}, {'name': 'cassette', 'id': 223, 'frequency': 'r', 'synset': 'cassette.n.01'}, {'name': 'cast', 'id': 224, 'frequency': 'c', 'synset': 'cast.n.05'}, {'name': 'cat', 'id': 225, 'frequency': 'f', 'synset': 'cat.n.01'}, {'name': 'cauliflower', 'id': 226, 'frequency': 'f', 'synset': 'cauliflower.n.02'}, {'name': 'cayenne_(spice)', 'id': 227, 'frequency': 'c', 'synset': 'cayenne.n.02'}, {'name': 'CD_player', 'id': 228, 'frequency': 'c', 'synset': 'cd_player.n.01'}, {'name': 'celery', 'id': 229, 'frequency': 'f', 'synset': 'celery.n.01'}, {'name': 'cellular_telephone', 'id': 230, 'frequency': 'f', 'synset': 'cellular_telephone.n.01'}, {'name': 'chain_mail', 'id': 231, 'frequency': 'r', 'synset': 'chain_mail.n.01'}, {'name': 'chair', 'id': 232, 'frequency': 'f', 'synset': 'chair.n.01'}, {'name': 'chaise_longue', 'id': 233, 'frequency': 'r', 'synset': 'chaise_longue.n.01'}, {'name': 'chalice', 'id': 234, 'frequency': 'r', 'synset': 'chalice.n.01'}, {'name': 'chandelier', 'id': 235, 'frequency': 'f', 'synset': 'chandelier.n.01'}, {'name': 'chap', 'id': 236, 'frequency': 'r', 'synset': 'chap.n.04'}, {'name': 'checkbook', 'id': 237, 'frequency': 'r', 'synset': 'checkbook.n.01'}, {'name': 'checkerboard', 'id': 238, 'frequency': 'r', 'synset': 'checkerboard.n.01'}, {'name': 'cherry', 'id': 239, 'frequency': 'c', 'synset': 'cherry.n.03'}, {'name': 'chessboard', 'id': 240, 'frequency': 'r', 'synset': 'chessboard.n.01'}, {'name': 'chicken_(animal)', 'id': 241, 'frequency': 'c', 'synset': 'chicken.n.02'}, {'name': 'chickpea', 'id': 242, 'frequency': 'c', 'synset': 'chickpea.n.01'}, {'name': 'chili_(vegetable)', 'id': 243, 'frequency': 'c', 'synset': 'chili.n.02'}, {'name': 'chime', 'id': 244, 'frequency': 'r', 'synset': 'chime.n.01'}, {'name': 'chinaware', 'id': 245, 'frequency': 'r', 'synset': 'chinaware.n.01'}, {'name': 'crisp_(potato_chip)', 'id': 246, 'frequency': 'c', 'synset': 'chip.n.04'}, {'name': 'poker_chip', 'id': 247, 'frequency': 'r', 'synset': 'chip.n.06'}, {'name': 'chocolate_bar', 'id': 248, 'frequency': 'c', 'synset': 'chocolate_bar.n.01'}, {'name': 'chocolate_cake', 'id': 249, 'frequency': 'c', 'synset': 'chocolate_cake.n.01'}, {'name': 'chocolate_milk', 'id': 250, 'frequency': 'r', 'synset': 'chocolate_milk.n.01'}, {'name': 'chocolate_mousse', 'id': 251, 'frequency': 'r', 'synset': 'chocolate_mousse.n.01'}, {'name': 'choker', 'id': 252, 'frequency': 'f', 'synset': 'choker.n.03'}, {'name': 'chopping_board', 'id': 253, 'frequency': 'f', 'synset': 'chopping_board.n.01'}, {'name': 'chopstick', 'id': 254, 'frequency': 'f', 'synset': 'chopstick.n.01'}, {'name': 'Christmas_tree', 'id': 255, 'frequency': 'f', 'synset': 'christmas_tree.n.05'}, {'name': 'slide', 'id': 256, 'frequency': 'c', 'synset': 'chute.n.02'}, {'name': 'cider', 'id': 257, 'frequency': 'r', 'synset': 'cider.n.01'}, {'name': 'cigar_box', 'id': 258, 'frequency': 'r', 'synset': 'cigar_box.n.01'}, {'name': 'cigarette', 'id': 259, 'frequency': 'f', 'synset': 'cigarette.n.01'}, {'name': 'cigarette_case', 'id': 260, 'frequency': 'c', 'synset': 'cigarette_case.n.01'}, {'name': 'cistern', 'id': 261, 'frequency': 'f', 'synset': 'cistern.n.02'}, {'name': 'clarinet', 'id': 262, 'frequency': 'r', 'synset': 'clarinet.n.01'}, {'name': 'clasp', 'id': 263, 'frequency': 'c', 'synset': 'clasp.n.01'}, {'name': 'cleansing_agent', 'id': 264, 'frequency': 'c', 'synset': 'cleansing_agent.n.01'}, {'name': 'cleat_(for_securing_rope)', 'id': 265, 'frequency': 'r', 'synset': 'cleat.n.02'}, {'name': 'clementine', 'id': 266, 'frequency': 'r', 'synset': 'clementine.n.01'}, {'name': 'clip', 'id': 267, 'frequency': 'c', 'synset': 'clip.n.03'}, {'name': 'clipboard', 'id': 268, 'frequency': 'c', 'synset': 'clipboard.n.01'}, {'name': 'clippers_(for_plants)', 'id': 269, 'frequency': 'r', 'synset': 'clipper.n.03'}, {'name': 'cloak', 'id': 270, 'frequency': 'r', 'synset': 'cloak.n.02'}, {'name': 'clock', 'id': 271, 'frequency': 'f', 'synset': 'clock.n.01'}, {'name': 'clock_tower', 'id': 272, 'frequency': 'f', 'synset': 'clock_tower.n.01'}, {'name': 'clothes_hamper', 'id': 273, 'frequency': 'c', 'synset': 'clothes_hamper.n.01'}, {'name': 'clothespin', 'id': 274, 'frequency': 'c', 'synset': 'clothespin.n.01'}, {'name': 'clutch_bag', 'id': 275, 'frequency': 'r', 'synset': 'clutch_bag.n.01'}, {'name': 'coaster', 'id': 276, 'frequency': 'f', 'synset': 'coaster.n.03'}, {'name': 'coat', 'id': 277, 'frequency': 'f', 'synset': 'coat.n.01'}, {'name': 'coat_hanger', 'id': 278, 'frequency': 'c', 'synset': 'coat_hanger.n.01'}, {'name': 'coatrack', 'id': 279, 'frequency': 'c', 'synset': 'coatrack.n.01'}, {'name': 'cock', 'id': 280, 'frequency': 'c', 'synset': 'cock.n.04'}, {'name': 'cockroach', 'id': 281, 'frequency': 'r', 'synset': 'cockroach.n.01'}, {'name': 'cocoa_(beverage)', 'id': 282, 'frequency': 'r', 'synset': 'cocoa.n.01'}, {'name': 'coconut', 'id': 283, 'frequency': 'c', 'synset': 'coconut.n.02'}, {'name': 'coffee_maker', 'id': 284, 'frequency': 'f', 'synset': 'coffee_maker.n.01'}, {'name': 'coffee_table', 'id': 285, 'frequency': 'f', 'synset': 'coffee_table.n.01'}, {'name': 'coffeepot', 'id': 286, 'frequency': 'c', 'synset': 'coffeepot.n.01'}, {'name': 'coil', 'id': 287, 'frequency': 'r', 'synset': 'coil.n.05'}, {'name': 'coin', 'id': 288, 'frequency': 'c', 'synset': 'coin.n.01'}, {'name': 'colander', 'id': 289, 'frequency': 'c', 'synset': 'colander.n.01'}, {'name': 'coleslaw', 'id': 290, 'frequency': 'c', 'synset': 'coleslaw.n.01'}, {'name': 'coloring_material', 'id': 291, 'frequency': 'r', 'synset': 'coloring_material.n.01'}, {'name': 'combination_lock', 'id': 292, 'frequency': 'r', 'synset': 'combination_lock.n.01'}, {'name': 'pacifier', 'id': 293, 'frequency': 'c', 'synset': 'comforter.n.04'}, {'name': 'comic_book', 'id': 294, 'frequency': 'r', 'synset': 'comic_book.n.01'}, {'name': 'compass', 'id': 295, 'frequency': 'r', 'synset': 'compass.n.01'}, {'name': 'computer_keyboard', 'id': 296, 'frequency': 'f', 'synset': 'computer_keyboard.n.01'}, {'name': 'condiment', 'id': 297, 'frequency': 'f', 'synset': 'condiment.n.01'}, {'name': 'cone', 'id': 298, 'frequency': 'f', 'synset': 'cone.n.01'}, {'name': 'control', 'id': 299, 'frequency': 'f', 'synset': 'control.n.09'}, {'name': 'convertible_(automobile)', 'id': 300, 'frequency': 'r', 'synset': 'convertible.n.01'}, {'name': 'sofa_bed', 'id': 301, 'frequency': 'r', 'synset': 'convertible.n.03'}, {'name': 'cooker', 'id': 302, 'frequency': 'r', 'synset': 'cooker.n.01'}, {'name': 'cookie', 'id': 303, 'frequency': 'f', 'synset': 'cookie.n.01'}, {'name': 'cooking_utensil', 'id': 304, 'frequency': 'r', 'synset': 'cooking_utensil.n.01'}, {'name': 'cooler_(for_food)', 'id': 305, 'frequency': 'f', 'synset': 'cooler.n.01'}, {'name': 'cork_(bottle_plug)', 'id': 306, 'frequency': 'f', 'synset': 'cork.n.04'}, {'name': 'corkboard', 'id': 307, 'frequency': 'r', 'synset': 'corkboard.n.01'}, {'name': 'corkscrew', 'id': 308, 'frequency': 'c', 'synset': 'corkscrew.n.01'}, {'name': 'edible_corn', 'id': 309, 'frequency': 'f', 'synset': 'corn.n.03'}, {'name': 'cornbread', 'id': 310, 'frequency': 'r', 'synset': 'cornbread.n.01'}, {'name': 'cornet', 'id': 311, 'frequency': 'c', 'synset': 'cornet.n.01'}, {'name': 'cornice', 'id': 312, 'frequency': 'c', 'synset': 'cornice.n.01'}, {'name': 'cornmeal', 'id': 313, 'frequency': 'r', 'synset': 'cornmeal.n.01'}, {'name': 'corset', 'id': 314, 'frequency': 'c', 'synset': 'corset.n.01'}, {'name': 'costume', 'id': 315, 'frequency': 'c', 'synset': 'costume.n.04'}, {'name': 'cougar', 'id': 316, 'frequency': 'r', 'synset': 'cougar.n.01'}, {'name': 'coverall', 'id': 317, 'frequency': 'r', 'synset': 'coverall.n.01'}, {'name': 'cowbell', 'id': 318, 'frequency': 'c', 'synset': 'cowbell.n.01'}, {'name': 'cowboy_hat', 'id': 319, 'frequency': 'f', 'synset': 'cowboy_hat.n.01'}, {'name': 'crab_(animal)', 'id': 320, 'frequency': 'c', 'synset': 'crab.n.01'}, {'name': 'crabmeat', 'id': 321, 'frequency': 'r', 'synset': 'crab.n.05'}, {'name': 'cracker', 'id': 322, 'frequency': 'c', 'synset': 'cracker.n.01'}, {'name': 'crape', 'id': 323, 'frequency': 'r', 'synset': 'crape.n.01'}, {'name': 'crate', 'id': 324, 'frequency': 'f', 'synset': 'crate.n.01'}, {'name': 'crayon', 'id': 325, 'frequency': 'c', 'synset': 'crayon.n.01'}, {'name': 'cream_pitcher', 'id': 326, 'frequency': 'r', 'synset': 'cream_pitcher.n.01'}, {'name': 'crescent_roll', 'id': 327, 'frequency': 'c', 'synset': 'crescent_roll.n.01'}, {'name': 'crib', 'id': 328, 'frequency': 'c', 'synset': 'crib.n.01'}, {'name': 'crock_pot', 'id': 329, 'frequency': 'c', 'synset': 'crock.n.03'}, {'name': 'crossbar', 'id': 330, 'frequency': 'f', 'synset': 'crossbar.n.01'}, {'name': 'crouton', 'id': 331, 'frequency': 'r', 'synset': 'crouton.n.01'}, {'name': 'crow', 'id': 332, 'frequency': 'c', 'synset': 'crow.n.01'}, {'name': 'crowbar', 'id': 333, 'frequency': 'r', 'synset': 'crowbar.n.01'}, {'name': 'crown', 'id': 334, 'frequency': 'c', 'synset': 'crown.n.04'}, {'name': 'crucifix', 'id': 335, 'frequency': 'c', 'synset': 'crucifix.n.01'}, {'name': 'cruise_ship', 'id': 336, 'frequency': 'c', 'synset': 'cruise_ship.n.01'}, {'name': 'police_cruiser', 'id': 337, 'frequency': 'c', 'synset': 'cruiser.n.01'}, {'name': 'crumb', 'id': 338, 'frequency': 'f', 'synset': 'crumb.n.03'}, {'name': 'crutch', 'id': 339, 'frequency': 'c', 'synset': 'crutch.n.01'}, {'name': 'cub_(animal)', 'id': 340, 'frequency': 'c', 'synset': 'cub.n.03'}, {'name': 'cube', 'id': 341, 'frequency': 'c', 'synset': 'cube.n.05'}, {'name': 'cucumber', 'id': 342, 'frequency': 'f', 'synset': 'cucumber.n.02'}, {'name': 'cufflink', 'id': 343, 'frequency': 'c', 'synset': 'cufflink.n.01'}, {'name': 'cup', 'id': 344, 'frequency': 'f', 'synset': 'cup.n.01'}, {'name': 'trophy_cup', 'id': 345, 'frequency': 'c', 'synset': 'cup.n.08'}, {'name': 'cupboard', 'id': 346, 'frequency': 'f', 'synset': 'cupboard.n.01'}, {'name': 'cupcake', 'id': 347, 'frequency': 'f', 'synset': 'cupcake.n.01'}, {'name': 'hair_curler', 'id': 348, 'frequency': 'r', 'synset': 'curler.n.01'}, {'name': 'curling_iron', 'id': 349, 'frequency': 'r', 'synset': 'curling_iron.n.01'}, {'name': 'curtain', 'id': 350, 'frequency': 'f', 'synset': 'curtain.n.01'}, {'name': 'cushion', 'id': 351, 'frequency': 'f', 'synset': 'cushion.n.03'}, {'name': 'cylinder', 'id': 352, 'frequency': 'r', 'synset': 'cylinder.n.04'}, {'name': 'cymbal', 'id': 353, 'frequency': 'r', 'synset': 'cymbal.n.01'}, {'name': 'dagger', 'id': 354, 'frequency': 'r', 'synset': 'dagger.n.01'}, {'name': 'dalmatian', 'id': 355, 'frequency': 'r', 'synset': 'dalmatian.n.02'}, {'name': 'dartboard', 'id': 356, 'frequency': 'c', 'synset': 'dartboard.n.01'}, {'name': 'date_(fruit)', 'id': 357, 'frequency': 'r', 'synset': 'date.n.08'}, {'name': 'deck_chair', 'id': 358, 'frequency': 'f', 'synset': 'deck_chair.n.01'}, {'name': 'deer', 'id': 359, 'frequency': 'c', 'synset': 'deer.n.01'}, {'name': 'dental_floss', 'id': 360, 'frequency': 'c', 'synset': 'dental_floss.n.01'}, {'name': 'desk', 'id': 361, 'frequency': 'f', 'synset': 'desk.n.01'}, {'name': 'detergent', 'id': 362, 'frequency': 'r', 'synset': 'detergent.n.01'}, {'name': 'diaper', 'id': 363, 'frequency': 'c', 'synset': 'diaper.n.01'}, {'name': 'diary', 'id': 364, 'frequency': 'r', 'synset': 'diary.n.01'}, {'name': 'die', 'id': 365, 'frequency': 'r', 'synset': 'die.n.01'}, {'name': 'dinghy', 'id': 366, 'frequency': 'r', 'synset': 'dinghy.n.01'}, {'name': 'dining_table', 'id': 367, 'frequency': 'f', 'synset': 'dining_table.n.01'}, {'name': 'tux', 'id': 368, 'frequency': 'r', 'synset': 'dinner_jacket.n.01'}, {'name': 'dish', 'id': 369, 'frequency': 'f', 'synset': 'dish.n.01'}, {'name': 'dish_antenna', 'id': 370, 'frequency': 'c', 'synset': 'dish.n.05'}, {'name': 'dishrag', 'id': 371, 'frequency': 'c', 'synset': 'dishrag.n.01'}, {'name': 'dishtowel', 'id': 372, 'frequency': 'f', 'synset': 'dishtowel.n.01'}, {'name': 'dishwasher', 'id': 373, 'frequency': 'f', 'synset': 'dishwasher.n.01'}, {'name': 'dishwasher_detergent', 'id': 374, 'frequency': 'r', 'synset': 'dishwasher_detergent.n.01'}, {'name': 'dispenser', 'id': 375, 'frequency': 'f', 'synset': 'dispenser.n.01'}, {'name': 'diving_board', 'id': 376, 'frequency': 'r', 'synset': 'diving_board.n.01'}, {'name': 'Dixie_cup', 'id': 377, 'frequency': 'f', 'synset': 'dixie_cup.n.01'}, {'name': 'dog', 'id': 378, 'frequency': 'f', 'synset': 'dog.n.01'}, {'name': 'dog_collar', 'id': 379, 'frequency': 'f', 'synset': 'dog_collar.n.01'}, {'name': 'doll', 'id': 380, 'frequency': 'f', 'synset': 'doll.n.01'}, {'name': 'dollar', 'id': 381, 'frequency': 'r', 'synset': 'dollar.n.02'}, {'name': 'dollhouse', 'id': 382, 'frequency': 'r', 'synset': 'dollhouse.n.01'}, {'name': 'dolphin', 'id': 383, 'frequency': 'c', 'synset': 'dolphin.n.02'}, {'name': 'domestic_ass', 'id': 384, 'frequency': 'c', 'synset': 'domestic_ass.n.01'}, {'name': 'doorknob', 'id': 385, 'frequency': 'f', 'synset': 'doorknob.n.01'}, {'name': 'doormat', 'id': 386, 'frequency': 'c', 'synset': 'doormat.n.02'}, {'name': 'doughnut', 'id': 387, 'frequency': 'f', 'synset': 'doughnut.n.02'}, {'name': 'dove', 'id': 388, 'frequency': 'r', 'synset': 'dove.n.01'}, {'name': 'dragonfly', 'id': 389, 'frequency': 'r', 'synset': 'dragonfly.n.01'}, {'name': 'drawer', 'id': 390, 'frequency': 'f', 'synset': 'drawer.n.01'}, {'name': 'underdrawers', 'id': 391, 'frequency': 'c', 'synset': 'drawers.n.01'}, {'name': 'dress', 'id': 392, 'frequency': 'f', 'synset': 'dress.n.01'}, {'name': 'dress_hat', 'id': 393, 'frequency': 'c', 'synset': 'dress_hat.n.01'}, {'name': 'dress_suit', 'id': 394, 'frequency': 'f', 'synset': 'dress_suit.n.01'}, {'name': 'dresser', 'id': 395, 'frequency': 'f', 'synset': 'dresser.n.05'}, {'name': 'drill', 'id': 396, 'frequency': 'c', 'synset': 'drill.n.01'}, {'name': 'drone', 'id': 397, 'frequency': 'r', 'synset': 'drone.n.04'}, {'name': 'dropper', 'id': 398, 'frequency': 'r', 'synset': 'dropper.n.01'}, {'name': 'drum_(musical_instrument)', 'id': 399, 'frequency': 'c', 'synset': 'drum.n.01'}, {'name': 'drumstick', 'id': 400, 'frequency': 'r', 'synset': 'drumstick.n.02'}, {'name': 'duck', 'id': 401, 'frequency': 'f', 'synset': 'duck.n.01'}, {'name': 'duckling', 'id': 402, 'frequency': 'c', 'synset': 'duckling.n.02'}, {'name': 'duct_tape', 'id': 403, 'frequency': 'c', 'synset': 'duct_tape.n.01'}, {'name': 'duffel_bag', 'id': 404, 'frequency': 'f', 'synset': 'duffel_bag.n.01'}, {'name': 'dumbbell', 'id': 405, 'frequency': 'r', 'synset': 'dumbbell.n.01'}, {'name': 'dumpster', 'id': 406, 'frequency': 'c', 'synset': 'dumpster.n.01'}, {'name': 'dustpan', 'id': 407, 'frequency': 'r', 'synset': 'dustpan.n.02'}, {'name': 'eagle', 'id': 408, 'frequency': 'c', 'synset': 'eagle.n.01'}, {'name': 'earphone', 'id': 409, 'frequency': 'f', 'synset': 'earphone.n.01'}, {'name': 'earplug', 'id': 410, 'frequency': 'r', 'synset': 'earplug.n.01'}, {'name': 'earring', 'id': 411, 'frequency': 'f', 'synset': 'earring.n.01'}, {'name': 'easel', 'id': 412, 'frequency': 'c', 'synset': 'easel.n.01'}, {'name': 'eclair', 'id': 413, 'frequency': 'r', 'synset': 'eclair.n.01'}, {'name': 'eel', 'id': 414, 'frequency': 'r', 'synset': 'eel.n.01'}, {'name': 'egg', 'id': 415, 'frequency': 'f', 'synset': 'egg.n.02'}, {'name': 'egg_roll', 'id': 416, 'frequency': 'r', 'synset': 'egg_roll.n.01'}, {'name': 'egg_yolk', 'id': 417, 'frequency': 'c', 'synset': 'egg_yolk.n.01'}, {'name': 'eggbeater', 'id': 418, 'frequency': 'c', 'synset': 'eggbeater.n.02'}, {'name': 'eggplant', 'id': 419, 'frequency': 'c', 'synset': 'eggplant.n.01'}, {'name': 'electric_chair', 'id': 420, 'frequency': 'r', 'synset': 'electric_chair.n.01'}, {'name': 'refrigerator', 'id': 421, 'frequency': 'f', 'synset': 'electric_refrigerator.n.01'}, {'name': 'elephant', 'id': 422, 'frequency': 'f', 'synset': 'elephant.n.01'}, {'name': 'elk', 'id': 423, 'frequency': 'c', 'synset': 'elk.n.01'}, {'name': 'envelope', 'id': 424, 'frequency': 'c', 'synset': 'envelope.n.01'}, {'name': 'eraser', 'id': 425, 'frequency': 'c', 'synset': 'eraser.n.01'}, {'name': 'escargot', 'id': 426, 'frequency': 'r', 'synset': 'escargot.n.01'}, {'name': 'eyepatch', 'id': 427, 'frequency': 'r', 'synset': 'eyepatch.n.01'}, {'name': 'falcon', 'id': 428, 'frequency': 'r', 'synset': 'falcon.n.01'}, {'name': 'fan', 'id': 429, 'frequency': 'f', 'synset': 'fan.n.01'}, {'name': 'faucet', 'id': 430, 'frequency': 'f', 'synset': 'faucet.n.01'}, {'name': 'fedora', 'id': 431, 'frequency': 'r', 'synset': 'fedora.n.01'}, {'name': 'ferret', 'id': 432, 'frequency': 'r', 'synset': 'ferret.n.02'}, {'name': 'Ferris_wheel', 'id': 433, 'frequency': 'c', 'synset': 'ferris_wheel.n.01'}, {'name': 'ferry', 'id': 434, 'frequency': 'c', 'synset': 'ferry.n.01'}, {'name': 'fig_(fruit)', 'id': 435, 'frequency': 'r', 'synset': 'fig.n.04'}, {'name': 'fighter_jet', 'id': 436, 'frequency': 'c', 'synset': 'fighter.n.02'}, {'name': 'figurine', 'id': 437, 'frequency': 'f', 'synset': 'figurine.n.01'}, {'name': 'file_cabinet', 'id': 438, 'frequency': 'c', 'synset': 'file.n.03'}, {'name': 'file_(tool)', 'id': 439, 'frequency': 'r', 'synset': 'file.n.04'}, {'name': 'fire_alarm', 'id': 440, 'frequency': 'f', 'synset': 'fire_alarm.n.02'}, {'name': 'fire_engine', 'id': 441, 'frequency': 'f', 'synset': 'fire_engine.n.01'}, {'name': 'fire_extinguisher', 'id': 442, 'frequency': 'f', 'synset': 'fire_extinguisher.n.01'}, {'name': 'fire_hose', 'id': 443, 'frequency': 'c', 'synset': 'fire_hose.n.01'}, {'name': 'fireplace', 'id': 444, 'frequency': 'f', 'synset': 'fireplace.n.01'}, {'name': 'fireplug', 'id': 445, 'frequency': 'f', 'synset': 'fireplug.n.01'}, {'name': 'first-aid_kit', 'id': 446, 'frequency': 'r', 'synset': 'first-aid_kit.n.01'}, {'name': 'fish', 'id': 447, 'frequency': 'f', 'synset': 'fish.n.01'}, {'name': 'fish_(food)', 'id': 448, 'frequency': 'c', 'synset': 'fish.n.02'}, {'name': 'fishbowl', 'id': 449, 'frequency': 'r', 'synset': 'fishbowl.n.02'}, {'name': 'fishing_rod', 'id': 450, 'frequency': 'c', 'synset': 'fishing_rod.n.01'}, {'name': 'flag', 'id': 451, 'frequency': 'f', 'synset': 'flag.n.01'}, {'name': 'flagpole', 'id': 452, 'frequency': 'f', 'synset': 'flagpole.n.02'}, {'name': 'flamingo', 'id': 453, 'frequency': 'c', 'synset': 'flamingo.n.01'}, {'name': 'flannel', 'id': 454, 'frequency': 'c', 'synset': 'flannel.n.01'}, {'name': 'flap', 'id': 455, 'frequency': 'c', 'synset': 'flap.n.01'}, {'name': 'flash', 'id': 456, 'frequency': 'r', 'synset': 'flash.n.10'}, {'name': 'flashlight', 'id': 457, 'frequency': 'c', 'synset': 'flashlight.n.01'}, {'name': 'fleece', 'id': 458, 'frequency': 'r', 'synset': 'fleece.n.03'}, {'name': 'flip-flop_(sandal)', 'id': 459, 'frequency': 'f', 'synset': 'flip-flop.n.02'}, {'name': 'flipper_(footwear)', 'id': 460, 'frequency': 'c', 'synset': 'flipper.n.01'}, {'name': 'flower_arrangement', 'id': 461, 'frequency': 'f', 'synset': 'flower_arrangement.n.01'}, {'name': 'flute_glass', 'id': 462, 'frequency': 'c', 'synset': 'flute.n.02'}, {'name': 'foal', 'id': 463, 'frequency': 'c', 'synset': 'foal.n.01'}, {'name': 'folding_chair', 'id': 464, 'frequency': 'c', 'synset': 'folding_chair.n.01'}, {'name': 'food_processor', 'id': 465, 'frequency': 'c', 'synset': 'food_processor.n.01'}, {'name': 'football_(American)', 'id': 466, 'frequency': 'c', 'synset': 'football.n.02'}, {'name': 'football_helmet', 'id': 467, 'frequency': 'r', 'synset': 'football_helmet.n.01'}, {'name': 'footstool', 'id': 468, 'frequency': 'c', 'synset': 'footstool.n.01'}, {'name': 'fork', 'id': 469, 'frequency': 'f', 'synset': 'fork.n.01'}, {'name': 'forklift', 'id': 470, 'frequency': 'c', 'synset': 'forklift.n.01'}, {'name': 'freight_car', 'id': 471, 'frequency': 'c', 'synset': 'freight_car.n.01'}, {'name': 'French_toast', 'id': 472, 'frequency': 'c', 'synset': 'french_toast.n.01'}, {'name': 'freshener', 'id': 473, 'frequency': 'c', 'synset': 'freshener.n.01'}, {'name': 'frisbee', 'id': 474, 'frequency': 'f', 'synset': 'frisbee.n.01'}, {'name': 'frog', 'id': 475, 'frequency': 'c', 'synset': 'frog.n.01'}, {'name': 'fruit_juice', 'id': 476, 'frequency': 'c', 'synset': 'fruit_juice.n.01'}, {'name': 'frying_pan', 'id': 477, 'frequency': 'f', 'synset': 'frying_pan.n.01'}, {'name': 'fudge', 'id': 478, 'frequency': 'r', 'synset': 'fudge.n.01'}, {'name': 'funnel', 'id': 479, 'frequency': 'r', 'synset': 'funnel.n.02'}, {'name': 'futon', 'id': 480, 'frequency': 'r', 'synset': 'futon.n.01'}, {'name': 'gag', 'id': 481, 'frequency': 'r', 'synset': 'gag.n.02'}, {'name': 'garbage', 'id': 482, 'frequency': 'r', 'synset': 'garbage.n.03'}, {'name': 'garbage_truck', 'id': 483, 'frequency': 'c', 'synset': 'garbage_truck.n.01'}, {'name': 'garden_hose', 'id': 484, 'frequency': 'c', 'synset': 'garden_hose.n.01'}, {'name': 'gargle', 'id': 485, 'frequency': 'c', 'synset': 'gargle.n.01'}, {'name': 'gargoyle', 'id': 486, 'frequency': 'r', 'synset': 'gargoyle.n.02'}, {'name': 'garlic', 'id': 487, 'frequency': 'c', 'synset': 'garlic.n.02'}, {'name': 'gasmask', 'id': 488, 'frequency': 'r', 'synset': 'gasmask.n.01'}, {'name': 'gazelle', 'id': 489, 'frequency': 'c', 'synset': 'gazelle.n.01'}, {'name': 'gelatin', 'id': 490, 'frequency': 'c', 'synset': 'gelatin.n.02'}, {'name': 'gemstone', 'id': 491, 'frequency': 'r', 'synset': 'gem.n.02'}, {'name': 'generator', 'id': 492, 'frequency': 'r', 'synset': 'generator.n.02'}, {'name': 'giant_panda', 'id': 493, 'frequency': 'c', 'synset': 'giant_panda.n.01'}, {'name': 'gift_wrap', 'id': 494, 'frequency': 'c', 'synset': 'gift_wrap.n.01'}, {'name': 'ginger', 'id': 495, 'frequency': 'c', 'synset': 'ginger.n.03'}, {'name': 'giraffe', 'id': 496, 'frequency': 'f', 'synset': 'giraffe.n.01'}, {'name': 'cincture', 'id': 497, 'frequency': 'c', 'synset': 'girdle.n.02'}, {'name': 'glass_(drink_container)', 'id': 498, 'frequency': 'f', 'synset': 'glass.n.02'}, {'name': 'globe', 'id': 499, 'frequency': 'c', 'synset': 'globe.n.03'}, {'name': 'glove', 'id': 500, 'frequency': 'f', 'synset': 'glove.n.02'}, {'name': 'goat', 'id': 501, 'frequency': 'c', 'synset': 'goat.n.01'}, {'name': 'goggles', 'id': 502, 'frequency': 'f', 'synset': 'goggles.n.01'}, {'name': 'goldfish', 'id': 503, 'frequency': 'r', 'synset': 'goldfish.n.01'}, {'name': 'golf_club', 'id': 504, 'frequency': 'c', 'synset': 'golf_club.n.02'}, {'name': 'golfcart', 'id': 505, 'frequency': 'c', 'synset': 'golfcart.n.01'}, {'name': 'gondola_(boat)', 'id': 506, 'frequency': 'r', 'synset': 'gondola.n.02'}, {'name': 'goose', 'id': 507, 'frequency': 'c', 'synset': 'goose.n.01'}, {'name': 'gorilla', 'id': 508, 'frequency': 'r', 'synset': 'gorilla.n.01'}, {'name': 'gourd', 'id': 509, 'frequency': 'r', 'synset': 'gourd.n.02'}, {'name': 'grape', 'id': 510, 'frequency': 'f', 'synset': 'grape.n.01'}, {'name': 'grater', 'id': 511, 'frequency': 'c', 'synset': 'grater.n.01'}, {'name': 'gravestone', 'id': 512, 'frequency': 'c', 'synset': 'gravestone.n.01'}, {'name': 'gravy_boat', 'id': 513, 'frequency': 'r', 'synset': 'gravy_boat.n.01'}, {'name': 'green_bean', 'id': 514, 'frequency': 'f', 'synset': 'green_bean.n.02'}, {'name': 'green_onion', 'id': 515, 'frequency': 'f', 'synset': 'green_onion.n.01'}, {'name': 'griddle', 'id': 516, 'frequency': 'r', 'synset': 'griddle.n.01'}, {'name': 'grill', 'id': 517, 'frequency': 'f', 'synset': 'grill.n.02'}, {'name': 'grits', 'id': 518, 'frequency': 'r', 'synset': 'grits.n.01'}, {'name': 'grizzly', 'id': 519, 'frequency': 'c', 'synset': 'grizzly.n.01'}, {'name': 'grocery_bag', 'id': 520, 'frequency': 'c', 'synset': 'grocery_bag.n.01'}, {'name': 'guitar', 'id': 521, 'frequency': 'f', 'synset': 'guitar.n.01'}, {'name': 'gull', 'id': 522, 'frequency': 'c', 'synset': 'gull.n.02'}, {'name': 'gun', 'id': 523, 'frequency': 'c', 'synset': 'gun.n.01'}, {'name': 'hairbrush', 'id': 524, 'frequency': 'f', 'synset': 'hairbrush.n.01'}, {'name': 'hairnet', 'id': 525, 'frequency': 'c', 'synset': 'hairnet.n.01'}, {'name': 'hairpin', 'id': 526, 'frequency': 'c', 'synset': 'hairpin.n.01'}, {'name': 'halter_top', 'id': 527, 'frequency': 'r', 'synset': 'halter.n.03'}, {'name': 'ham', 'id': 528, 'frequency': 'f', 'synset': 'ham.n.01'}, {'name': 'hamburger', 'id': 529, 'frequency': 'c', 'synset': 'hamburger.n.01'}, {'name': 'hammer', 'id': 530, 'frequency': 'c', 'synset': 'hammer.n.02'}, {'name': 'hammock', 'id': 531, 'frequency': 'c', 'synset': 'hammock.n.02'}, {'name': 'hamper', 'id': 532, 'frequency': 'r', 'synset': 'hamper.n.02'}, {'name': 'hamster', 'id': 533, 'frequency': 'c', 'synset': 'hamster.n.01'}, {'name': 'hair_dryer', 'id': 534, 'frequency': 'f', 'synset': 'hand_blower.n.01'}, {'name': 'hand_glass', 'id': 535, 'frequency': 'r', 'synset': 'hand_glass.n.01'}, {'name': 'hand_towel', 'id': 536, 'frequency': 'f', 'synset': 'hand_towel.n.01'}, {'name': 'handcart', 'id': 537, 'frequency': 'c', 'synset': 'handcart.n.01'}, {'name': 'handcuff', 'id': 538, 'frequency': 'r', 'synset': 'handcuff.n.01'}, {'name': 'handkerchief', 'id': 539, 'frequency': 'c', 'synset': 'handkerchief.n.01'}, {'name': 'handle', 'id': 540, 'frequency': 'f', 'synset': 'handle.n.01'}, {'name': 'handsaw', 'id': 541, 'frequency': 'r', 'synset': 'handsaw.n.01'}, {'name': 'hardback_book', 'id': 542, 'frequency': 'r', 'synset': 'hardback.n.01'}, {'name': 'harmonium', 'id': 543, 'frequency': 'r', 'synset': 'harmonium.n.01'}, {'name': 'hat', 'id': 544, 'frequency': 'f', 'synset': 'hat.n.01'}, {'name': 'hatbox', 'id': 545, 'frequency': 'r', 'synset': 'hatbox.n.01'}, {'name': 'veil', 'id': 546, 'frequency': 'c', 'synset': 'head_covering.n.01'}, {'name': 'headband', 'id': 547, 'frequency': 'f', 'synset': 'headband.n.01'}, {'name': 'headboard', 'id': 548, 'frequency': 'f', 'synset': 'headboard.n.01'}, {'name': 'headlight', 'id': 549, 'frequency': 'f', 'synset': 'headlight.n.01'}, {'name': 'headscarf', 'id': 550, 'frequency': 'c', 'synset': 'headscarf.n.01'}, {'name': 'headset', 'id': 551, 'frequency': 'r', 'synset': 'headset.n.01'}, {'name': 'headstall_(for_horses)', 'id': 552, 'frequency': 'c', 'synset': 'headstall.n.01'}, {'name': 'heart', 'id': 553, 'frequency': 'c', 'synset': 'heart.n.02'}, {'name': 'heater', 'id': 554, 'frequency': 'c', 'synset': 'heater.n.01'}, {'name': 'helicopter', 'id': 555, 'frequency': 'c', 'synset': 'helicopter.n.01'}, {'name': 'helmet', 'id': 556, 'frequency': 'f', 'synset': 'helmet.n.02'}, {'name': 'heron', 'id': 557, 'frequency': 'r', 'synset': 'heron.n.02'}, {'name': 'highchair', 'id': 558, 'frequency': 'c', 'synset': 'highchair.n.01'}, {'name': 'hinge', 'id': 559, 'frequency': 'f', 'synset': 'hinge.n.01'}, {'name': 'hippopotamus', 'id': 560, 'frequency': 'r', 'synset': 'hippopotamus.n.01'}, {'name': 'hockey_stick', 'id': 561, 'frequency': 'r', 'synset': 'hockey_stick.n.01'}, {'name': 'hog', 'id': 562, 'frequency': 'c', 'synset': 'hog.n.03'}, {'name': 'home_plate_(baseball)', 'id': 563, 'frequency': 'f', 'synset': 'home_plate.n.01'}, {'name': 'honey', 'id': 564, 'frequency': 'c', 'synset': 'honey.n.01'}, {'name': 'fume_hood', 'id': 565, 'frequency': 'f', 'synset': 'hood.n.06'}, {'name': 'hook', 'id': 566, 'frequency': 'f', 'synset': 'hook.n.05'}, {'name': 'hookah', 'id': 567, 'frequency': 'r', 'synset': 'hookah.n.01'}, {'name': 'hornet', 'id': 568, 'frequency': 'r', 'synset': 'hornet.n.01'}, {'name': 'horse', 'id': 569, 'frequency': 'f', 'synset': 'horse.n.01'}, {'name': 'hose', 'id': 570, 'frequency': 'f', 'synset': 'hose.n.03'}, {'name': 'hot-air_balloon', 'id': 571, 'frequency': 'r', 'synset': 'hot-air_balloon.n.01'}, {'name': 'hotplate', 'id': 572, 'frequency': 'r', 'synset': 'hot_plate.n.01'}, {'name': 'hot_sauce', 'id': 573, 'frequency': 'c', 'synset': 'hot_sauce.n.01'}, {'name': 'hourglass', 'id': 574, 'frequency': 'r', 'synset': 'hourglass.n.01'}, {'name': 'houseboat', 'id': 575, 'frequency': 'r', 'synset': 'houseboat.n.01'}, {'name': 'hummingbird', 'id': 576, 'frequency': 'c', 'synset': 'hummingbird.n.01'}, {'name': 'hummus', 'id': 577, 'frequency': 'r', 'synset': 'hummus.n.01'}, {'name': 'polar_bear', 'id': 578, 'frequency': 'f', 'synset': 'ice_bear.n.01'}, {'name': 'icecream', 'id': 579, 'frequency': 'c', 'synset': 'ice_cream.n.01'}, {'name': 'popsicle', 'id': 580, 'frequency': 'r', 'synset': 'ice_lolly.n.01'}, {'name': 'ice_maker', 'id': 581, 'frequency': 'c', 'synset': 'ice_maker.n.01'}, {'name': 'ice_pack', 'id': 582, 'frequency': 'r', 'synset': 'ice_pack.n.01'}, {'name': 'ice_skate', 'id': 583, 'frequency': 'r', 'synset': 'ice_skate.n.01'}, {'name': 'igniter', 'id': 584, 'frequency': 'c', 'synset': 'igniter.n.01'}, {'name': 'inhaler', 'id': 585, 'frequency': 'r', 'synset': 'inhaler.n.01'}, {'name': 'iPod', 'id': 586, 'frequency': 'f', 'synset': 'ipod.n.01'}, {'name': 'iron_(for_clothing)', 'id': 587, 'frequency': 'c', 'synset': 'iron.n.04'}, {'name': 'ironing_board', 'id': 588, 'frequency': 'c', 'synset': 'ironing_board.n.01'}, {'name': 'jacket', 'id': 589, 'frequency': 'f', 'synset': 'jacket.n.01'}, {'name': 'jam', 'id': 590, 'frequency': 'c', 'synset': 'jam.n.01'}, {'name': 'jar', 'id': 591, 'frequency': 'f', 'synset': 'jar.n.01'}, {'name': 'jean', 'id': 592, 'frequency': 'f', 'synset': 'jean.n.01'}, {'name': 'jeep', 'id': 593, 'frequency': 'c', 'synset': 'jeep.n.01'}, {'name': 'jelly_bean', 'id': 594, 'frequency': 'r', 'synset': 'jelly_bean.n.01'}, {'name': 'jersey', 'id': 595, 'frequency': 'f', 'synset': 'jersey.n.03'}, {'name': 'jet_plane', 'id': 596, 'frequency': 'c', 'synset': 'jet.n.01'}, {'name': 'jewel', 'id': 597, 'frequency': 'r', 'synset': 'jewel.n.01'}, {'name': 'jewelry', 'id': 598, 'frequency': 'c', 'synset': 'jewelry.n.01'}, {'name': 'joystick', 'id': 599, 'frequency': 'r', 'synset': 'joystick.n.02'}, {'name': 'jumpsuit', 'id': 600, 'frequency': 'c', 'synset': 'jump_suit.n.01'}, {'name': 'kayak', 'id': 601, 'frequency': 'c', 'synset': 'kayak.n.01'}, {'name': 'keg', 'id': 602, 'frequency': 'r', 'synset': 'keg.n.02'}, {'name': 'kennel', 'id': 603, 'frequency': 'r', 'synset': 'kennel.n.01'}, {'name': 'kettle', 'id': 604, 'frequency': 'c', 'synset': 'kettle.n.01'}, {'name': 'key', 'id': 605, 'frequency': 'f', 'synset': 'key.n.01'}, {'name': 'keycard', 'id': 606, 'frequency': 'r', 'synset': 'keycard.n.01'}, {'name': 'kilt', 'id': 607, 'frequency': 'c', 'synset': 'kilt.n.01'}, {'name': 'kimono', 'id': 608, 'frequency': 'c', 'synset': 'kimono.n.01'}, {'name': 'kitchen_sink', 'id': 609, 'frequency': 'f', 'synset': 'kitchen_sink.n.01'}, {'name': 'kitchen_table', 'id': 610, 'frequency': 'r', 'synset': 'kitchen_table.n.01'}, {'name': 'kite', 'id': 611, 'frequency': 'f', 'synset': 'kite.n.03'}, {'name': 'kitten', 'id': 612, 'frequency': 'c', 'synset': 'kitten.n.01'}, {'name': 'kiwi_fruit', 'id': 613, 'frequency': 'c', 'synset': 'kiwi.n.03'}, {'name': 'knee_pad', 'id': 614, 'frequency': 'f', 'synset': 'knee_pad.n.01'}, {'name': 'knife', 'id': 615, 'frequency': 'f', 'synset': 'knife.n.01'}, {'name': 'knitting_needle', 'id': 616, 'frequency': 'r', 'synset': 'knitting_needle.n.01'}, {'name': 'knob', 'id': 617, 'frequency': 'f', 'synset': 'knob.n.02'}, {'name': 'knocker_(on_a_door)', 'id': 618, 'frequency': 'r', 'synset': 'knocker.n.05'}, {'name': 'koala', 'id': 619, 'frequency': 'r', 'synset': 'koala.n.01'}, {'name': 'lab_coat', 'id': 620, 'frequency': 'r', 'synset': 'lab_coat.n.01'}, {'name': 'ladder', 'id': 621, 'frequency': 'f', 'synset': 'ladder.n.01'}, {'name': 'ladle', 'id': 622, 'frequency': 'c', 'synset': 'ladle.n.01'}, {'name': 'ladybug', 'id': 623, 'frequency': 'c', 'synset': 'ladybug.n.01'}, {'name': 'lamb_(animal)', 'id': 624, 'frequency': 'f', 'synset': 'lamb.n.01'}, {'name': 'lamb-chop', 'id': 625, 'frequency': 'r', 'synset': 'lamb_chop.n.01'}, {'name': 'lamp', 'id': 626, 'frequency': 'f', 'synset': 'lamp.n.02'}, {'name': 'lamppost', 'id': 627, 'frequency': 'f', 'synset': 'lamppost.n.01'}, {'name': 'lampshade', 'id': 628, 'frequency': 'f', 'synset': 'lampshade.n.01'}, {'name': 'lantern', 'id': 629, 'frequency': 'c', 'synset': 'lantern.n.01'}, {'name': 'lanyard', 'id': 630, 'frequency': 'f', 'synset': 'lanyard.n.02'}, {'name': 'laptop_computer', 'id': 631, 'frequency': 'f', 'synset': 'laptop.n.01'}, {'name': 'lasagna', 'id': 632, 'frequency': 'r', 'synset': 'lasagna.n.01'}, {'name': 'latch', 'id': 633, 'frequency': 'f', 'synset': 'latch.n.02'}, {'name': 'lawn_mower', 'id': 634, 'frequency': 'r', 'synset': 'lawn_mower.n.01'}, {'name': 'leather', 'id': 635, 'frequency': 'r', 'synset': 'leather.n.01'}, {'name': 'legging_(clothing)', 'id': 636, 'frequency': 'c', 'synset': 'legging.n.01'}, {'name': 'Lego', 'id': 637, 'frequency': 'c', 'synset': 'lego.n.01'}, {'name': 'legume', 'id': 638, 'frequency': 'r', 'synset': 'legume.n.02'}, {'name': 'lemon', 'id': 639, 'frequency': 'f', 'synset': 'lemon.n.01'}, {'name': 'lemonade', 'id': 640, 'frequency': 'r', 'synset': 'lemonade.n.01'}, {'name': 'lettuce', 'id': 641, 'frequency': 'f', 'synset': 'lettuce.n.02'}, {'name': 'license_plate', 'id': 642, 'frequency': 'f', 'synset': 'license_plate.n.01'}, {'name': 'life_buoy', 'id': 643, 'frequency': 'f', 'synset': 'life_buoy.n.01'}, {'name': 'life_jacket', 'id': 644, 'frequency': 'f', 'synset': 'life_jacket.n.01'}, {'name': 'lightbulb', 'id': 645, 'frequency': 'f', 'synset': 'light_bulb.n.01'}, {'name': 'lightning_rod', 'id': 646, 'frequency': 'r', 'synset': 'lightning_rod.n.02'}, {'name': 'lime', 'id': 647, 'frequency': 'f', 'synset': 'lime.n.06'}, {'name': 'limousine', 'id': 648, 'frequency': 'r', 'synset': 'limousine.n.01'}, {'name': 'lion', 'id': 649, 'frequency': 'c', 'synset': 'lion.n.01'}, {'name': 'lip_balm', 'id': 650, 'frequency': 'c', 'synset': 'lip_balm.n.01'}, {'name': 'liquor', 'id': 651, 'frequency': 'r', 'synset': 'liquor.n.01'}, {'name': 'lizard', 'id': 652, 'frequency': 'c', 'synset': 'lizard.n.01'}, {'name': 'log', 'id': 653, 'frequency': 'f', 'synset': 'log.n.01'}, {'name': 'lollipop', 'id': 654, 'frequency': 'c', 'synset': 'lollipop.n.02'}, {'name': 'speaker_(stero_equipment)', 'id': 655, 'frequency': 'f', 'synset': 'loudspeaker.n.01'}, {'name': 'loveseat', 'id': 656, 'frequency': 'c', 'synset': 'love_seat.n.01'}, {'name': 'machine_gun', 'id': 657, 'frequency': 'r', 'synset': 'machine_gun.n.01'}, {'name': 'magazine', 'id': 658, 'frequency': 'f', 'synset': 'magazine.n.02'}, {'name': 'magnet', 'id': 659, 'frequency': 'f', 'synset': 'magnet.n.01'}, {'name': 'mail_slot', 'id': 660, 'frequency': 'c', 'synset': 'mail_slot.n.01'}, {'name': 'mailbox_(at_home)', 'id': 661, 'frequency': 'f', 'synset': 'mailbox.n.01'}, {'name': 'mallard', 'id': 662, 'frequency': 'r', 'synset': 'mallard.n.01'}, {'name': 'mallet', 'id': 663, 'frequency': 'r', 'synset': 'mallet.n.01'}, {'name': 'mammoth', 'id': 664, 'frequency': 'r', 'synset': 'mammoth.n.01'}, {'name': 'manatee', 'id': 665, 'frequency': 'r', 'synset': 'manatee.n.01'}, {'name': 'mandarin_orange', 'id': 666, 'frequency': 'c', 'synset': 'mandarin.n.05'}, {'name': 'manger', 'id': 667, 'frequency': 'c', 'synset': 'manger.n.01'}, {'name': 'manhole', 'id': 668, 'frequency': 'f', 'synset': 'manhole.n.01'}, {'name': 'map', 'id': 669, 'frequency': 'f', 'synset': 'map.n.01'}, {'name': 'marker', 'id': 670, 'frequency': 'f', 'synset': 'marker.n.03'}, {'name': 'martini', 'id': 671, 'frequency': 'r', 'synset': 'martini.n.01'}, {'name': 'mascot', 'id': 672, 'frequency': 'r', 'synset': 'mascot.n.01'}, {'name': 'mashed_potato', 'id': 673, 'frequency': 'c', 'synset': 'mashed_potato.n.01'}, {'name': 'masher', 'id': 674, 'frequency': 'r', 'synset': 'masher.n.02'}, {'name': 'mask', 'id': 675, 'frequency': 'f', 'synset': 'mask.n.04'}, {'name': 'mast', 'id': 676, 'frequency': 'f', 'synset': 'mast.n.01'}, {'name': 'mat_(gym_equipment)', 'id': 677, 'frequency': 'c', 'synset': 'mat.n.03'}, {'name': 'matchbox', 'id': 678, 'frequency': 'r', 'synset': 'matchbox.n.01'}, {'name': 'mattress', 'id': 679, 'frequency': 'f', 'synset': 'mattress.n.01'}, {'name': 'measuring_cup', 'id': 680, 'frequency': 'c', 'synset': 'measuring_cup.n.01'}, {'name': 'measuring_stick', 'id': 681, 'frequency': 'c', 'synset': 'measuring_stick.n.01'}, {'name': 'meatball', 'id': 682, 'frequency': 'c', 'synset': 'meatball.n.01'}, {'name': 'medicine', 'id': 683, 'frequency': 'c', 'synset': 'medicine.n.02'}, {'name': 'melon', 'id': 684, 'frequency': 'c', 'synset': 'melon.n.01'}, {'name': 'microphone', 'id': 685, 'frequency': 'f', 'synset': 'microphone.n.01'}, {'name': 'microscope', 'id': 686, 'frequency': 'r', 'synset': 'microscope.n.01'}, {'name': 'microwave_oven', 'id': 687, 'frequency': 'f', 'synset': 'microwave.n.02'}, {'name': 'milestone', 'id': 688, 'frequency': 'r', 'synset': 'milestone.n.01'}, {'name': 'milk', 'id': 689, 'frequency': 'f', 'synset': 'milk.n.01'}, {'name': 'milk_can', 'id': 690, 'frequency': 'r', 'synset': 'milk_can.n.01'}, {'name': 'milkshake', 'id': 691, 'frequency': 'r', 'synset': 'milkshake.n.01'}, {'name': 'minivan', 'id': 692, 'frequency': 'f', 'synset': 'minivan.n.01'}, {'name': 'mint_candy', 'id': 693, 'frequency': 'r', 'synset': 'mint.n.05'}, {'name': 'mirror', 'id': 694, 'frequency': 'f', 'synset': 'mirror.n.01'}, {'name': 'mitten', 'id': 695, 'frequency': 'c', 'synset': 'mitten.n.01'}, {'name': 'mixer_(kitchen_tool)', 'id': 696, 'frequency': 'c', 'synset': 'mixer.n.04'}, {'name': 'money', 'id': 697, 'frequency': 'c', 'synset': 'money.n.03'}, {'name': 'monitor_(computer_equipment) computer_monitor', 'id': 698, 'frequency': 'f', 'synset': 'monitor.n.04'}, {'name': 'monkey', 'id': 699, 'frequency': 'c', 'synset': 'monkey.n.01'}, {'name': 'motor', 'id': 700, 'frequency': 'f', 'synset': 'motor.n.01'}, {'name': 'motor_scooter', 'id': 701, 'frequency': 'f', 'synset': 'motor_scooter.n.01'}, {'name': 'motor_vehicle', 'id': 702, 'frequency': 'r', 'synset': 'motor_vehicle.n.01'}, {'name': 'motorcycle', 'id': 703, 'frequency': 'f', 'synset': 'motorcycle.n.01'}, {'name': 'mound_(baseball)', 'id': 704, 'frequency': 'f', 'synset': 'mound.n.01'}, {'name': 'mouse_(computer_equipment)', 'id': 705, 'frequency': 'f', 'synset': 'mouse.n.04'}, {'name': 'mousepad', 'id': 706, 'frequency': 'f', 'synset': 'mousepad.n.01'}, {'name': 'muffin', 'id': 707, 'frequency': 'c', 'synset': 'muffin.n.01'}, {'name': 'mug', 'id': 708, 'frequency': 'f', 'synset': 'mug.n.04'}, {'name': 'mushroom', 'id': 709, 'frequency': 'f', 'synset': 'mushroom.n.02'}, {'name': 'music_stool', 'id': 710, 'frequency': 'r', 'synset': 'music_stool.n.01'}, {'name': 'musical_instrument', 'id': 711, 'frequency': 'c', 'synset': 'musical_instrument.n.01'}, {'name': 'nailfile', 'id': 712, 'frequency': 'r', 'synset': 'nailfile.n.01'}, {'name': 'napkin', 'id': 713, 'frequency': 'f', 'synset': 'napkin.n.01'}, {'name': 'neckerchief', 'id': 714, 'frequency': 'r', 'synset': 'neckerchief.n.01'}, {'name': 'necklace', 'id': 715, 'frequency': 'f', 'synset': 'necklace.n.01'}, {'name': 'necktie', 'id': 716, 'frequency': 'f', 'synset': 'necktie.n.01'}, {'name': 'needle', 'id': 717, 'frequency': 'c', 'synset': 'needle.n.03'}, {'name': 'nest', 'id': 718, 'frequency': 'c', 'synset': 'nest.n.01'}, {'name': 'newspaper', 'id': 719, 'frequency': 'f', 'synset': 'newspaper.n.01'}, {'name': 'newsstand', 'id': 720, 'frequency': 'c', 'synset': 'newsstand.n.01'}, {'name': 'nightshirt', 'id': 721, 'frequency': 'c', 'synset': 'nightwear.n.01'}, {'name': 'nosebag_(for_animals)', 'id': 722, 'frequency': 'r', 'synset': 'nosebag.n.01'}, {'name': 'noseband_(for_animals)', 'id': 723, 'frequency': 'c', 'synset': 'noseband.n.01'}, {'name': 'notebook', 'id': 724, 'frequency': 'f', 'synset': 'notebook.n.01'}, {'name': 'notepad', 'id': 725, 'frequency': 'c', 'synset': 'notepad.n.01'}, {'name': 'nut', 'id': 726, 'frequency': 'f', 'synset': 'nut.n.03'}, {'name': 'nutcracker', 'id': 727, 'frequency': 'r', 'synset': 'nutcracker.n.01'}, {'name': 'oar', 'id': 728, 'frequency': 'f', 'synset': 'oar.n.01'}, {'name': 'octopus_(food)', 'id': 729, 'frequency': 'r', 'synset': 'octopus.n.01'}, {'name': 'octopus_(animal)', 'id': 730, 'frequency': 'r', 'synset': 'octopus.n.02'}, {'name': 'oil_lamp', 'id': 731, 'frequency': 'c', 'synset': 'oil_lamp.n.01'}, {'name': 'olive_oil', 'id': 732, 'frequency': 'c', 'synset': 'olive_oil.n.01'}, {'name': 'omelet', 'id': 733, 'frequency': 'r', 'synset': 'omelet.n.01'}, {'name': 'onion', 'id': 734, 'frequency': 'f', 'synset': 'onion.n.01'}, {'name': 'orange_(fruit)', 'id': 735, 'frequency': 'f', 'synset': 'orange.n.01'}, {'name': 'orange_juice', 'id': 736, 'frequency': 'c', 'synset': 'orange_juice.n.01'}, {'name': 'ostrich', 'id': 737, 'frequency': 'c', 'synset': 'ostrich.n.02'}, {'name': 'ottoman', 'id': 738, 'frequency': 'f', 'synset': 'ottoman.n.03'}, {'name': 'oven', 'id': 739, 'frequency': 'f', 'synset': 'oven.n.01'}, {'name': 'overalls_(clothing)', 'id': 740, 'frequency': 'c', 'synset': 'overall.n.01'}, {'name': 'owl', 'id': 741, 'frequency': 'c', 'synset': 'owl.n.01'}, {'name': 'packet', 'id': 742, 'frequency': 'c', 'synset': 'packet.n.03'}, {'name': 'inkpad', 'id': 743, 'frequency': 'r', 'synset': 'pad.n.03'}, {'name': 'pad', 'id': 744, 'frequency': 'c', 'synset': 'pad.n.04'}, {'name': 'paddle', 'id': 745, 'frequency': 'f', 'synset': 'paddle.n.04'}, {'name': 'padlock', 'id': 746, 'frequency': 'c', 'synset': 'padlock.n.01'}, {'name': 'paintbrush', 'id': 747, 'frequency': 'c', 'synset': 'paintbrush.n.01'}, {'name': 'painting', 'id': 748, 'frequency': 'f', 'synset': 'painting.n.01'}, {'name': 'pajamas', 'id': 749, 'frequency': 'f', 'synset': 'pajama.n.02'}, {'name': 'palette', 'id': 750, 'frequency': 'c', 'synset': 'palette.n.02'}, {'name': 'pan_(for_cooking)', 'id': 751, 'frequency': 'f', 'synset': 'pan.n.01'}, {'name': 'pan_(metal_container)', 'id': 752, 'frequency': 'r', 'synset': 'pan.n.03'}, {'name': 'pancake', 'id': 753, 'frequency': 'c', 'synset': 'pancake.n.01'}, {'name': 'pantyhose', 'id': 754, 'frequency': 'r', 'synset': 'pantyhose.n.01'}, {'name': 'papaya', 'id': 755, 'frequency': 'r', 'synset': 'papaya.n.02'}, {'name': 'paper_plate', 'id': 756, 'frequency': 'f', 'synset': 'paper_plate.n.01'}, {'name': 'paper_towel', 'id': 757, 'frequency': 'f', 'synset': 'paper_towel.n.01'}, {'name': 'paperback_book', 'id': 758, 'frequency': 'r', 'synset': 'paperback_book.n.01'}, {'name': 'paperweight', 'id': 759, 'frequency': 'r', 'synset': 'paperweight.n.01'}, {'name': 'parachute', 'id': 760, 'frequency': 'c', 'synset': 'parachute.n.01'}, {'name': 'parakeet', 'id': 761, 'frequency': 'c', 'synset': 'parakeet.n.01'}, {'name': 'parasail_(sports)', 'id': 762, 'frequency': 'c', 'synset': 'parasail.n.01'}, {'name': 'parasol', 'id': 763, 'frequency': 'c', 'synset': 'parasol.n.01'}, {'name': 'parchment', 'id': 764, 'frequency': 'r', 'synset': 'parchment.n.01'}, {'name': 'parka', 'id': 765, 'frequency': 'c', 'synset': 'parka.n.01'}, {'name': 'parking_meter', 'id': 766, 'frequency': 'f', 'synset': 'parking_meter.n.01'}, {'name': 'parrot', 'id': 767, 'frequency': 'c', 'synset': 'parrot.n.01'}, {'name': 'passenger_car_(part_of_a_train)', 'id': 768, 'frequency': 'c', 'synset': 'passenger_car.n.01'}, {'name': 'passenger_ship', 'id': 769, 'frequency': 'r', 'synset': 'passenger_ship.n.01'}, {'name': 'passport', 'id': 770, 'frequency': 'c', 'synset': 'passport.n.02'}, {'name': 'pastry', 'id': 771, 'frequency': 'f', 'synset': 'pastry.n.02'}, {'name': 'patty_(food)', 'id': 772, 'frequency': 'r', 'synset': 'patty.n.01'}, {'name': 'pea_(food)', 'id': 773, 'frequency': 'c', 'synset': 'pea.n.01'}, {'name': 'peach', 'id': 774, 'frequency': 'c', 'synset': 'peach.n.03'}, {'name': 'peanut_butter', 'id': 775, 'frequency': 'c', 'synset': 'peanut_butter.n.01'}, {'name': 'pear', 'id': 776, 'frequency': 'f', 'synset': 'pear.n.01'}, {'name': 'peeler_(tool_for_fruit_and_vegetables)', 'id': 777, 'frequency': 'c', 'synset': 'peeler.n.03'}, {'name': 'wooden_leg', 'id': 778, 'frequency': 'r', 'synset': 'peg.n.04'}, {'name': 'pegboard', 'id': 779, 'frequency': 'r', 'synset': 'pegboard.n.01'}, {'name': 'pelican', 'id': 780, 'frequency': 'c', 'synset': 'pelican.n.01'}, {'name': 'pen', 'id': 781, 'frequency': 'f', 'synset': 'pen.n.01'}, {'name': 'pencil', 'id': 782, 'frequency': 'f', 'synset': 'pencil.n.01'}, {'name': 'pencil_box', 'id': 783, 'frequency': 'r', 'synset': 'pencil_box.n.01'}, {'name': 'pencil_sharpener', 'id': 784, 'frequency': 'r', 'synset': 'pencil_sharpener.n.01'}, {'name': 'pendulum', 'id': 785, 'frequency': 'r', 'synset': 'pendulum.n.01'}, {'name': 'penguin', 'id': 786, 'frequency': 'c', 'synset': 'penguin.n.01'}, {'name': 'pennant', 'id': 787, 'frequency': 'r', 'synset': 'pennant.n.02'}, {'name': 'penny_(coin)', 'id': 788, 'frequency': 'r', 'synset': 'penny.n.02'}, {'name': 'pepper', 'id': 789, 'frequency': 'f', 'synset': 'pepper.n.03'}, {'name': 'pepper_mill', 'id': 790, 'frequency': 'c', 'synset': 'pepper_mill.n.01'}, {'name': 'perfume', 'id': 791, 'frequency': 'c', 'synset': 'perfume.n.02'}, {'name': 'persimmon', 'id': 792, 'frequency': 'r', 'synset': 'persimmon.n.02'}, {'name': 'person', 'id': 793, 'frequency': 'f', 'synset': 'person.n.01'}, {'name': 'pet', 'id': 794, 'frequency': 'c', 'synset': 'pet.n.01'}, {'name': 'pew_(church_bench)', 'id': 795, 'frequency': 'c', 'synset': 'pew.n.01'}, {'name': 'phonebook', 'id': 796, 'frequency': 'r', 'synset': 'phonebook.n.01'}, {'name': 'phonograph_record', 'id': 797, 'frequency': 'c', 'synset': 'phonograph_record.n.01'}, {'name': 'piano', 'id': 798, 'frequency': 'f', 'synset': 'piano.n.01'}, {'name': 'pickle', 'id': 799, 'frequency': 'f', 'synset': 'pickle.n.01'}, {'name': 'pickup_truck', 'id': 800, 'frequency': 'f', 'synset': 'pickup.n.01'}, {'name': 'pie', 'id': 801, 'frequency': 'c', 'synset': 'pie.n.01'}, {'name': 'pigeon', 'id': 802, 'frequency': 'c', 'synset': 'pigeon.n.01'}, {'name': 'piggy_bank', 'id': 803, 'frequency': 'r', 'synset': 'piggy_bank.n.01'}, {'name': 'pillow', 'id': 804, 'frequency': 'f', 'synset': 'pillow.n.01'}, {'name': 'pin_(non_jewelry)', 'id': 805, 'frequency': 'r', 'synset': 'pin.n.09'}, {'name': 'pineapple', 'id': 806, 'frequency': 'f', 'synset': 'pineapple.n.02'}, {'name': 'pinecone', 'id': 807, 'frequency': 'c', 'synset': 'pinecone.n.01'}, {'name': 'ping-pong_ball', 'id': 808, 'frequency': 'r', 'synset': 'ping-pong_ball.n.01'}, {'name': 'pinwheel', 'id': 809, 'frequency': 'r', 'synset': 'pinwheel.n.03'}, {'name': 'tobacco_pipe', 'id': 810, 'frequency': 'r', 'synset': 'pipe.n.01'}, {'name': 'pipe', 'id': 811, 'frequency': 'f', 'synset': 'pipe.n.02'}, {'name': 'pistol', 'id': 812, 'frequency': 'r', 'synset': 'pistol.n.01'}, {'name': 'pita_(bread)', 'id': 813, 'frequency': 'c', 'synset': 'pita.n.01'}, {'name': 'pitcher_(vessel_for_liquid)', 'id': 814, 'frequency': 'f', 'synset': 'pitcher.n.02'}, {'name': 'pitchfork', 'id': 815, 'frequency': 'r', 'synset': 'pitchfork.n.01'}, {'name': 'pizza', 'id': 816, 'frequency': 'f', 'synset': 'pizza.n.01'}, {'name': 'place_mat', 'id': 817, 'frequency': 'f', 'synset': 'place_mat.n.01'}, {'name': 'plate', 'id': 818, 'frequency': 'f', 'synset': 'plate.n.04'}, {'name': 'platter', 'id': 819, 'frequency': 'c', 'synset': 'platter.n.01'}, {'name': 'playpen', 'id': 820, 'frequency': 'r', 'synset': 'playpen.n.01'}, {'name': 'pliers', 'id': 821, 'frequency': 'c', 'synset': 'pliers.n.01'}, {'name': 'plow_(farm_equipment)', 'id': 822, 'frequency': 'r', 'synset': 'plow.n.01'}, {'name': 'plume', 'id': 823, 'frequency': 'r', 'synset': 'plume.n.02'}, {'name': 'pocket_watch', 'id': 824, 'frequency': 'r', 'synset': 'pocket_watch.n.01'}, {'name': 'pocketknife', 'id': 825, 'frequency': 'c', 'synset': 'pocketknife.n.01'}, {'name': 'poker_(fire_stirring_tool)', 'id': 826, 'frequency': 'c', 'synset': 'poker.n.01'}, {'name': 'pole', 'id': 827, 'frequency': 'f', 'synset': 'pole.n.01'}, {'name': 'polo_shirt', 'id': 828, 'frequency': 'f', 'synset': 'polo_shirt.n.01'}, {'name': 'poncho', 'id': 829, 'frequency': 'r', 'synset': 'poncho.n.01'}, {'name': 'pony', 'id': 830, 'frequency': 'c', 'synset': 'pony.n.05'}, {'name': 'pool_table', 'id': 831, 'frequency': 'r', 'synset': 'pool_table.n.01'}, {'name': 'pop_(soda)', 'id': 832, 'frequency': 'f', 'synset': 'pop.n.02'}, {'name': 'postbox_(public)', 'id': 833, 'frequency': 'c', 'synset': 'postbox.n.01'}, {'name': 'postcard', 'id': 834, 'frequency': 'c', 'synset': 'postcard.n.01'}, {'name': 'poster', 'id': 835, 'frequency': 'f', 'synset': 'poster.n.01'}, {'name': 'pot', 'id': 836, 'frequency': 'f', 'synset': 'pot.n.01'}, {'name': 'flowerpot', 'id': 837, 'frequency': 'f', 'synset': 'pot.n.04'}, {'name': 'potato', 'id': 838, 'frequency': 'f', 'synset': 'potato.n.01'}, {'name': 'potholder', 'id': 839, 'frequency': 'c', 'synset': 'potholder.n.01'}, {'name': 'pottery', 'id': 840, 'frequency': 'c', 'synset': 'pottery.n.01'}, {'name': 'pouch', 'id': 841, 'frequency': 'c', 'synset': 'pouch.n.01'}, {'name': 'power_shovel', 'id': 842, 'frequency': 'c', 'synset': 'power_shovel.n.01'}, {'name': 'prawn', 'id': 843, 'frequency': 'c', 'synset': 'prawn.n.01'}, {'name': 'pretzel', 'id': 844, 'frequency': 'c', 'synset': 'pretzel.n.01'}, {'name': 'printer', 'id': 845, 'frequency': 'f', 'synset': 'printer.n.03'}, {'name': 'projectile_(weapon)', 'id': 846, 'frequency': 'c', 'synset': 'projectile.n.01'}, {'name': 'projector', 'id': 847, 'frequency': 'c', 'synset': 'projector.n.02'}, {'name': 'propeller', 'id': 848, 'frequency': 'f', 'synset': 'propeller.n.01'}, {'name': 'prune', 'id': 849, 'frequency': 'r', 'synset': 'prune.n.01'}, {'name': 'pudding', 'id': 850, 'frequency': 'r', 'synset': 'pudding.n.01'}, {'name': 'puffer_(fish)', 'id': 851, 'frequency': 'r', 'synset': 'puffer.n.02'}, {'name': 'puffin', 'id': 852, 'frequency': 'r', 'synset': 'puffin.n.01'}, {'name': 'pug-dog', 'id': 853, 'frequency': 'r', 'synset': 'pug.n.01'}, {'name': 'pumpkin', 'id': 854, 'frequency': 'c', 'synset': 'pumpkin.n.02'}, {'name': 'puncher', 'id': 855, 'frequency': 'r', 'synset': 'punch.n.03'}, {'name': 'puppet', 'id': 856, 'frequency': 'r', 'synset': 'puppet.n.01'}, {'name': 'puppy', 'id': 857, 'frequency': 'c', 'synset': 'puppy.n.01'}, {'name': 'quesadilla', 'id': 858, 'frequency': 'r', 'synset': 'quesadilla.n.01'}, {'name': 'quiche', 'id': 859, 'frequency': 'r', 'synset': 'quiche.n.02'}, {'name': 'quilt', 'id': 860, 'frequency': 'f', 'synset': 'quilt.n.01'}, {'name': 'rabbit', 'id': 861, 'frequency': 'c', 'synset': 'rabbit.n.01'}, {'name': 'race_car', 'id': 862, 'frequency': 'r', 'synset': 'racer.n.02'}, {'name': 'racket', 'id': 863, 'frequency': 'c', 'synset': 'racket.n.04'}, {'name': 'radar', 'id': 864, 'frequency': 'r', 'synset': 'radar.n.01'}, {'name': 'radiator', 'id': 865, 'frequency': 'f', 'synset': 'radiator.n.03'}, {'name': 'radio_receiver', 'id': 866, 'frequency': 'c', 'synset': 'radio_receiver.n.01'}, {'name': 'radish', 'id': 867, 'frequency': 'c', 'synset': 'radish.n.03'}, {'name': 'raft', 'id': 868, 'frequency': 'c', 'synset': 'raft.n.01'}, {'name': 'rag_doll', 'id': 869, 'frequency': 'r', 'synset': 'rag_doll.n.01'}, {'name': 'raincoat', 'id': 870, 'frequency': 'c', 'synset': 'raincoat.n.01'}, {'name': 'ram_(animal)', 'id': 871, 'frequency': 'c', 'synset': 'ram.n.05'}, {'name': 'raspberry', 'id': 872, 'frequency': 'c', 'synset': 'raspberry.n.02'}, {'name': 'rat', 'id': 873, 'frequency': 'r', 'synset': 'rat.n.01'}, {'name': 'razorblade', 'id': 874, 'frequency': 'c', 'synset': 'razorblade.n.01'}, {'name': 'reamer_(juicer)', 'id': 875, 'frequency': 'c', 'synset': 'reamer.n.01'}, {'name': 'rearview_mirror', 'id': 876, 'frequency': 'f', 'synset': 'rearview_mirror.n.01'}, {'name': 'receipt', 'id': 877, 'frequency': 'c', 'synset': 'receipt.n.02'}, {'name': 'recliner', 'id': 878, 'frequency': 'c', 'synset': 'recliner.n.01'}, {'name': 'record_player', 'id': 879, 'frequency': 'c', 'synset': 'record_player.n.01'}, {'name': 'reflector', 'id': 880, 'frequency': 'f', 'synset': 'reflector.n.01'}, {'name': 'remote_control', 'id': 881, 'frequency': 'f', 'synset': 'remote_control.n.01'}, {'name': 'rhinoceros', 'id': 882, 'frequency': 'c', 'synset': 'rhinoceros.n.01'}, {'name': 'rib_(food)', 'id': 883, 'frequency': 'r', 'synset': 'rib.n.03'}, {'name': 'rifle', 'id': 884, 'frequency': 'c', 'synset': 'rifle.n.01'}, {'name': 'ring', 'id': 885, 'frequency': 'f', 'synset': 'ring.n.08'}, {'name': 'river_boat', 'id': 886, 'frequency': 'r', 'synset': 'river_boat.n.01'}, {'name': 'road_map', 'id': 887, 'frequency': 'r', 'synset': 'road_map.n.02'}, {'name': 'robe', 'id': 888, 'frequency': 'c', 'synset': 'robe.n.01'}, {'name': 'rocking_chair', 'id': 889, 'frequency': 'c', 'synset': 'rocking_chair.n.01'}, {'name': 'rodent', 'id': 890, 'frequency': 'r', 'synset': 'rodent.n.01'}, {'name': 'roller_skate', 'id': 891, 'frequency': 'r', 'synset': 'roller_skate.n.01'}, {'name': 'Rollerblade', 'id': 892, 'frequency': 'r', 'synset': 'rollerblade.n.01'}, {'name': 'rolling_pin', 'id': 893, 'frequency': 'c', 'synset': 'rolling_pin.n.01'}, {'name': 'root_beer', 'id': 894, 'frequency': 'r', 'synset': 'root_beer.n.01'}, {'name': 'router_(computer_equipment)', 'id': 895, 'frequency': 'c', 'synset': 'router.n.02'}, {'name': 'rubber_band', 'id': 896, 'frequency': 'f', 'synset': 'rubber_band.n.01'}, {'name': 'runner_(carpet)', 'id': 897, 'frequency': 'c', 'synset': 'runner.n.08'}, {'name': 'plastic_bag', 'id': 898, 'frequency': 'f', 'synset': 'sack.n.01'}, {'name': 'saddle_(on_an_animal)', 'id': 899, 'frequency': 'f', 'synset': 'saddle.n.01'}, {'name': 'saddle_blanket', 'id': 900, 'frequency': 'f', 'synset': 'saddle_blanket.n.01'}, {'name': 'saddlebag', 'id': 901, 'frequency': 'c', 'synset': 'saddlebag.n.01'}, {'name': 'safety_pin', 'id': 902, 'frequency': 'r', 'synset': 'safety_pin.n.01'}, {'name': 'sail', 'id': 903, 'frequency': 'f', 'synset': 'sail.n.01'}, {'name': 'salad', 'id': 904, 'frequency': 'f', 'synset': 'salad.n.01'}, {'name': 'salad_plate', 'id': 905, 'frequency': 'r', 'synset': 'salad_plate.n.01'}, {'name': 'salami', 'id': 906, 'frequency': 'c', 'synset': 'salami.n.01'}, {'name': 'salmon_(fish)', 'id': 907, 'frequency': 'c', 'synset': 'salmon.n.01'}, {'name': 'salmon_(food)', 'id': 908, 'frequency': 'r', 'synset': 'salmon.n.03'}, {'name': 'salsa', 'id': 909, 'frequency': 'c', 'synset': 'salsa.n.01'}, {'name': 'saltshaker', 'id': 910, 'frequency': 'f', 'synset': 'saltshaker.n.01'}, {'name': 'sandal_(type_of_shoe)', 'id': 911, 'frequency': 'f', 'synset': 'sandal.n.01'}, {'name': 'sandwich', 'id': 912, 'frequency': 'f', 'synset': 'sandwich.n.01'}, {'name': 'satchel', 'id': 913, 'frequency': 'r', 'synset': 'satchel.n.01'}, {'name': 'saucepan', 'id': 914, 'frequency': 'r', 'synset': 'saucepan.n.01'}, {'name': 'saucer', 'id': 915, 'frequency': 'f', 'synset': 'saucer.n.02'}, {'name': 'sausage', 'id': 916, 'frequency': 'f', 'synset': 'sausage.n.01'}, {'name': 'sawhorse', 'id': 917, 'frequency': 'r', 'synset': 'sawhorse.n.01'}, {'name': 'saxophone', 'id': 918, 'frequency': 'r', 'synset': 'sax.n.02'}, {'name': 'scale_(measuring_instrument)', 'id': 919, 'frequency': 'f', 'synset': 'scale.n.07'}, {'name': 'scarecrow', 'id': 920, 'frequency': 'r', 'synset': 'scarecrow.n.01'}, {'name': 'scarf', 'id': 921, 'frequency': 'f', 'synset': 'scarf.n.01'}, {'name': 'school_bus', 'id': 922, 'frequency': 'c', 'synset': 'school_bus.n.01'}, {'name': 'scissors', 'id': 923, 'frequency': 'f', 'synset': 'scissors.n.01'}, {'name': 'scoreboard', 'id': 924, 'frequency': 'f', 'synset': 'scoreboard.n.01'}, {'name': 'scraper', 'id': 925, 'frequency': 'r', 'synset': 'scraper.n.01'}, {'name': 'screwdriver', 'id': 926, 'frequency': 'c', 'synset': 'screwdriver.n.01'}, {'name': 'scrubbing_brush', 'id': 927, 'frequency': 'f', 'synset': 'scrub_brush.n.01'}, {'name': 'sculpture', 'id': 928, 'frequency': 'c', 'synset': 'sculpture.n.01'}, {'name': 'seabird', 'id': 929, 'frequency': 'c', 'synset': 'seabird.n.01'}, {'name': 'seahorse', 'id': 930, 'frequency': 'c', 'synset': 'seahorse.n.02'}, {'name': 'seaplane', 'id': 931, 'frequency': 'r', 'synset': 'seaplane.n.01'}, {'name': 'seashell', 'id': 932, 'frequency': 'c', 'synset': 'seashell.n.01'}, {'name': 'sewing_machine', 'id': 933, 'frequency': 'c', 'synset': 'sewing_machine.n.01'}, {'name': 'shaker', 'id': 934, 'frequency': 'c', 'synset': 'shaker.n.03'}, {'name': 'shampoo', 'id': 935, 'frequency': 'c', 'synset': 'shampoo.n.01'}, {'name': 'shark', 'id': 936, 'frequency': 'c', 'synset': 'shark.n.01'}, {'name': 'sharpener', 'id': 937, 'frequency': 'r', 'synset': 'sharpener.n.01'}, {'name': 'Sharpie', 'id': 938, 'frequency': 'r', 'synset': 'sharpie.n.03'}, {'name': 'shaver_(electric)', 'id': 939, 'frequency': 'r', 'synset': 'shaver.n.03'}, {'name': 'shaving_cream', 'id': 940, 'frequency': 'c', 'synset': 'shaving_cream.n.01'}, {'name': 'shawl', 'id': 941, 'frequency': 'r', 'synset': 'shawl.n.01'}, {'name': 'shears', 'id': 942, 'frequency': 'r', 'synset': 'shears.n.01'}, {'name': 'sheep', 'id': 943, 'frequency': 'f', 'synset': 'sheep.n.01'}, {'name': 'shepherd_dog', 'id': 944, 'frequency': 'r', 'synset': 'shepherd_dog.n.01'}, {'name': 'sherbert', 'id': 945, 'frequency': 'r', 'synset': 'sherbert.n.01'}, {'name': 'shield', 'id': 946, 'frequency': 'c', 'synset': 'shield.n.02'}, {'name': 'shirt', 'id': 947, 'frequency': 'f', 'synset': 'shirt.n.01'}, {'name': 'shoe', 'id': 948, 'frequency': 'f', 'synset': 'shoe.n.01'}, {'name': 'shopping_bag', 'id': 949, 'frequency': 'f', 'synset': 'shopping_bag.n.01'}, {'name': 'shopping_cart', 'id': 950, 'frequency': 'c', 'synset': 'shopping_cart.n.01'}, {'name': 'short_pants', 'id': 951, 'frequency': 'f', 'synset': 'short_pants.n.01'}, {'name': 'shot_glass', 'id': 952, 'frequency': 'r', 'synset': 'shot_glass.n.01'}, {'name': 'shoulder_bag', 'id': 953, 'frequency': 'f', 'synset': 'shoulder_bag.n.01'}, {'name': 'shovel', 'id': 954, 'frequency': 'c', 'synset': 'shovel.n.01'}, {'name': 'shower_head', 'id': 955, 'frequency': 'f', 'synset': 'shower.n.01'}, {'name': 'shower_cap', 'id': 956, 'frequency': 'r', 'synset': 'shower_cap.n.01'}, {'name': 'shower_curtain', 'id': 957, 'frequency': 'f', 'synset': 'shower_curtain.n.01'}, {'name': 'shredder_(for_paper)', 'id': 958, 'frequency': 'r', 'synset': 'shredder.n.01'}, {'name': 'signboard', 'id': 959, 'frequency': 'f', 'synset': 'signboard.n.01'}, {'name': 'silo', 'id': 960, 'frequency': 'c', 'synset': 'silo.n.01'}, {'name': 'sink', 'id': 961, 'frequency': 'f', 'synset': 'sink.n.01'}, {'name': 'skateboard', 'id': 962, 'frequency': 'f', 'synset': 'skateboard.n.01'}, {'name': 'skewer', 'id': 963, 'frequency': 'c', 'synset': 'skewer.n.01'}, {'name': 'ski', 'id': 964, 'frequency': 'f', 'synset': 'ski.n.01'}, {'name': 'ski_boot', 'id': 965, 'frequency': 'f', 'synset': 'ski_boot.n.01'}, {'name': 'ski_parka', 'id': 966, 'frequency': 'f', 'synset': 'ski_parka.n.01'}, {'name': 'ski_pole', 'id': 967, 'frequency': 'f', 'synset': 'ski_pole.n.01'}, {'name': 'skirt', 'id': 968, 'frequency': 'f', 'synset': 'skirt.n.02'}, {'name': 'skullcap', 'id': 969, 'frequency': 'r', 'synset': 'skullcap.n.01'}, {'name': 'sled', 'id': 970, 'frequency': 'c', 'synset': 'sled.n.01'}, {'name': 'sleeping_bag', 'id': 971, 'frequency': 'c', 'synset': 'sleeping_bag.n.01'}, {'name': 'sling_(bandage)', 'id': 972, 'frequency': 'r', 'synset': 'sling.n.05'}, {'name': 'slipper_(footwear)', 'id': 973, 'frequency': 'c', 'synset': 'slipper.n.01'}, {'name': 'smoothie', 'id': 974, 'frequency': 'r', 'synset': 'smoothie.n.02'}, {'name': 'snake', 'id': 975, 'frequency': 'r', 'synset': 'snake.n.01'}, {'name': 'snowboard', 'id': 976, 'frequency': 'f', 'synset': 'snowboard.n.01'}, {'name': 'snowman', 'id': 977, 'frequency': 'c', 'synset': 'snowman.n.01'}, {'name': 'snowmobile', 'id': 978, 'frequency': 'c', 'synset': 'snowmobile.n.01'}, {'name': 'soap', 'id': 979, 'frequency': 'f', 'synset': 'soap.n.01'}, {'name': 'soccer_ball', 'id': 980, 'frequency': 'f', 'synset': 'soccer_ball.n.01'}, {'name': 'sock', 'id': 981, 'frequency': 'f', 'synset': 'sock.n.01'}, {'name': 'sofa', 'id': 982, 'frequency': 'f', 'synset': 'sofa.n.01'}, {'name': 'softball', 'id': 983, 'frequency': 'r', 'synset': 'softball.n.01'}, {'name': 'solar_array', 'id': 984, 'frequency': 'c', 'synset': 'solar_array.n.01'}, {'name': 'sombrero', 'id': 985, 'frequency': 'r', 'synset': 'sombrero.n.02'}, {'name': 'soup', 'id': 986, 'frequency': 'f', 'synset': 'soup.n.01'}, {'name': 'soup_bowl', 'id': 987, 'frequency': 'r', 'synset': 'soup_bowl.n.01'}, {'name': 'soupspoon', 'id': 988, 'frequency': 'c', 'synset': 'soupspoon.n.01'}, {'name': 'sour_cream', 'id': 989, 'frequency': 'c', 'synset': 'sour_cream.n.01'}, {'name': 'soya_milk', 'id': 990, 'frequency': 'r', 'synset': 'soya_milk.n.01'}, {'name': 'space_shuttle', 'id': 991, 'frequency': 'r', 'synset': 'space_shuttle.n.01'}, {'name': 'sparkler_(fireworks)', 'id': 992, 'frequency': 'r', 'synset': 'sparkler.n.02'}, {'name': 'spatula', 'id': 993, 'frequency': 'f', 'synset': 'spatula.n.02'}, {'name': 'spear', 'id': 994, 'frequency': 'r', 'synset': 'spear.n.01'}, {'name': 'spectacles', 'id': 995, 'frequency': 'f', 'synset': 'spectacles.n.01'}, {'name': 'spice_rack', 'id': 996, 'frequency': 'c', 'synset': 'spice_rack.n.01'}, {'name': 'spider', 'id': 997, 'frequency': 'c', 'synset': 'spider.n.01'}, {'name': 'crawfish', 'id': 998, 'frequency': 'r', 'synset': 'spiny_lobster.n.02'}, {'name': 'sponge', 'id': 999, 'frequency': 'c', 'synset': 'sponge.n.01'}, {'name': 'spoon', 'id': 1000, 'frequency': 'f', 'synset': 'spoon.n.01'}, {'name': 'sportswear', 'id': 1001, 'frequency': 'c', 'synset': 'sportswear.n.01'}, {'name': 'spotlight', 'id': 1002, 'frequency': 'c', 'synset': 'spotlight.n.02'}, {'name': 'squid_(food)', 'id': 1003, 'frequency': 'r', 'synset': 'squid.n.01'}, {'name': 'squirrel', 'id': 1004, 'frequency': 'c', 'synset': 'squirrel.n.01'}, {'name': 'stagecoach', 'id': 1005, 'frequency': 'r', 'synset': 'stagecoach.n.01'}, {'name': 'stapler_(stapling_machine)', 'id': 1006, 'frequency': 'c', 'synset': 'stapler.n.01'}, {'name': 'starfish', 'id': 1007, 'frequency': 'c', 'synset': 'starfish.n.01'}, {'name': 'statue_(sculpture)', 'id': 1008, 'frequency': 'f', 'synset': 'statue.n.01'}, {'name': 'steak_(food)', 'id': 1009, 'frequency': 'c', 'synset': 'steak.n.01'}, {'name': 'steak_knife', 'id': 1010, 'frequency': 'r', 'synset': 'steak_knife.n.01'}, {'name': 'steering_wheel', 'id': 1011, 'frequency': 'f', 'synset': 'steering_wheel.n.01'}, {'name': 'stepladder', 'id': 1012, 'frequency': 'r', 'synset': 'step_ladder.n.01'}, {'name': 'step_stool', 'id': 1013, 'frequency': 'c', 'synset': 'step_stool.n.01'}, {'name': 'stereo_(sound_system)', 'id': 1014, 'frequency': 'c', 'synset': 'stereo.n.01'}, {'name': 'stew', 'id': 1015, 'frequency': 'r', 'synset': 'stew.n.02'}, {'name': 'stirrer', 'id': 1016, 'frequency': 'r', 'synset': 'stirrer.n.02'}, {'name': 'stirrup', 'id': 1017, 'frequency': 'f', 'synset': 'stirrup.n.01'}, {'name': 'stool', 'id': 1018, 'frequency': 'f', 'synset': 'stool.n.01'}, {'name': 'stop_sign', 'id': 1019, 'frequency': 'f', 'synset': 'stop_sign.n.01'}, {'name': 'brake_light', 'id': 1020, 'frequency': 'f', 'synset': 'stoplight.n.01'}, {'name': 'stove', 'id': 1021, 'frequency': 'f', 'synset': 'stove.n.01'}, {'name': 'strainer', 'id': 1022, 'frequency': 'c', 'synset': 'strainer.n.01'}, {'name': 'strap', 'id': 1023, 'frequency': 'f', 'synset': 'strap.n.01'}, {'name': 'straw_(for_drinking)', 'id': 1024, 'frequency': 'f', 'synset': 'straw.n.04'}, {'name': 'strawberry', 'id': 1025, 'frequency': 'f', 'synset': 'strawberry.n.01'}, {'name': 'street_sign', 'id': 1026, 'frequency': 'f', 'synset': 'street_sign.n.01'}, {'name': 'streetlight', 'id': 1027, 'frequency': 'f', 'synset': 'streetlight.n.01'}, {'name': 'string_cheese', 'id': 1028, 'frequency': 'r', 'synset': 'string_cheese.n.01'}, {'name': 'stylus', 'id': 1029, 'frequency': 'r', 'synset': 'stylus.n.02'}, {'name': 'subwoofer', 'id': 1030, 'frequency': 'r', 'synset': 'subwoofer.n.01'}, {'name': 'sugar_bowl', 'id': 1031, 'frequency': 'r', 'synset': 'sugar_bowl.n.01'}, {'name': 'sugarcane_(plant)', 'id': 1032, 'frequency': 'r', 'synset': 'sugarcane.n.01'}, {'name': 'suit_(clothing)', 'id': 1033, 'frequency': 'f', 'synset': 'suit.n.01'}, {'name': 'sunflower', 'id': 1034, 'frequency': 'c', 'synset': 'sunflower.n.01'}, {'name': 'sunglasses', 'id': 1035, 'frequency': 'f', 'synset': 'sunglasses.n.01'}, {'name': 'sunhat', 'id': 1036, 'frequency': 'c', 'synset': 'sunhat.n.01'}, {'name': 'surfboard', 'id': 1037, 'frequency': 'f', 'synset': 'surfboard.n.01'}, {'name': 'sushi', 'id': 1038, 'frequency': 'c', 'synset': 'sushi.n.01'}, {'name': 'mop', 'id': 1039, 'frequency': 'c', 'synset': 'swab.n.02'}, {'name': 'sweat_pants', 'id': 1040, 'frequency': 'c', 'synset': 'sweat_pants.n.01'}, {'name': 'sweatband', 'id': 1041, 'frequency': 'c', 'synset': 'sweatband.n.02'}, {'name': 'sweater', 'id': 1042, 'frequency': 'f', 'synset': 'sweater.n.01'}, {'name': 'sweatshirt', 'id': 1043, 'frequency': 'f', 'synset': 'sweatshirt.n.01'}, {'name': 'sweet_potato', 'id': 1044, 'frequency': 'c', 'synset': 'sweet_potato.n.02'}, {'name': 'swimsuit', 'id': 1045, 'frequency': 'f', 'synset': 'swimsuit.n.01'}, {'name': 'sword', 'id': 1046, 'frequency': 'c', 'synset': 'sword.n.01'}, {'name': 'syringe', 'id': 1047, 'frequency': 'r', 'synset': 'syringe.n.01'}, {'name': 'Tabasco_sauce', 'id': 1048, 'frequency': 'r', 'synset': 'tabasco.n.02'}, {'name': 'table-tennis_table', 'id': 1049, 'frequency': 'r', 'synset': 'table-tennis_table.n.01'}, {'name': 'table', 'id': 1050, 'frequency': 'f', 'synset': 'table.n.02'}, {'name': 'table_lamp', 'id': 1051, 'frequency': 'c', 'synset': 'table_lamp.n.01'}, {'name': 'tablecloth', 'id': 1052, 'frequency': 'f', 'synset': 'tablecloth.n.01'}, {'name': 'tachometer', 'id': 1053, 'frequency': 'r', 'synset': 'tachometer.n.01'}, {'name': 'taco', 'id': 1054, 'frequency': 'r', 'synset': 'taco.n.02'}, {'name': 'tag', 'id': 1055, 'frequency': 'f', 'synset': 'tag.n.02'}, {'name': 'taillight', 'id': 1056, 'frequency': 'f', 'synset': 'taillight.n.01'}, {'name': 'tambourine', 'id': 1057, 'frequency': 'r', 'synset': 'tambourine.n.01'}, {'name': 'army_tank', 'id': 1058, 'frequency': 'r', 'synset': 'tank.n.01'}, {'name': 'tank_(storage_vessel)', 'id': 1059, 'frequency': 'f', 'synset': 'tank.n.02'}, {'name': 'tank_top_(clothing)', 'id': 1060, 'frequency': 'f', 'synset': 'tank_top.n.01'}, {'name': 'tape_(sticky_cloth_or_paper)', 'id': 1061, 'frequency': 'f', 'synset': 'tape.n.01'}, {'name': 'tape_measure', 'id': 1062, 'frequency': 'c', 'synset': 'tape.n.04'}, {'name': 'tapestry', 'id': 1063, 'frequency': 'c', 'synset': 'tapestry.n.02'}, {'name': 'tarp', 'id': 1064, 'frequency': 'f', 'synset': 'tarpaulin.n.01'}, {'name': 'tartan', 'id': 1065, 'frequency': 'c', 'synset': 'tartan.n.01'}, {'name': 'tassel', 'id': 1066, 'frequency': 'c', 'synset': 'tassel.n.01'}, {'name': 'tea_bag', 'id': 1067, 'frequency': 'c', 'synset': 'tea_bag.n.01'}, {'name': 'teacup', 'id': 1068, 'frequency': 'c', 'synset': 'teacup.n.02'}, {'name': 'teakettle', 'id': 1069, 'frequency': 'c', 'synset': 'teakettle.n.01'}, {'name': 'teapot', 'id': 1070, 'frequency': 'f', 'synset': 'teapot.n.01'}, {'name': 'teddy_bear', 'id': 1071, 'frequency': 'f', 'synset': 'teddy.n.01'}, {'name': 'telephone', 'id': 1072, 'frequency': 'f', 'synset': 'telephone.n.01'}, {'name': 'telephone_booth', 'id': 1073, 'frequency': 'c', 'synset': 'telephone_booth.n.01'}, {'name': 'telephone_pole', 'id': 1074, 'frequency': 'f', 'synset': 'telephone_pole.n.01'}, {'name': 'telephoto_lens', 'id': 1075, 'frequency': 'r', 'synset': 'telephoto_lens.n.01'}, {'name': 'television_camera', 'id': 1076, 'frequency': 'c', 'synset': 'television_camera.n.01'}, {'name': 'television_set', 'id': 1077, 'frequency': 'f', 'synset': 'television_receiver.n.01'}, {'name': 'tennis_ball', 'id': 1078, 'frequency': 'f', 'synset': 'tennis_ball.n.01'}, {'name': 'tennis_racket', 'id': 1079, 'frequency': 'f', 'synset': 'tennis_racket.n.01'}, {'name': 'tequila', 'id': 1080, 'frequency': 'r', 'synset': 'tequila.n.01'}, {'name': 'thermometer', 'id': 1081, 'frequency': 'c', 'synset': 'thermometer.n.01'}, {'name': 'thermos_bottle', 'id': 1082, 'frequency': 'c', 'synset': 'thermos.n.01'}, {'name': 'thermostat', 'id': 1083, 'frequency': 'f', 'synset': 'thermostat.n.01'}, {'name': 'thimble', 'id': 1084, 'frequency': 'r', 'synset': 'thimble.n.02'}, {'name': 'thread', 'id': 1085, 'frequency': 'c', 'synset': 'thread.n.01'}, {'name': 'thumbtack', 'id': 1086, 'frequency': 'c', 'synset': 'thumbtack.n.01'}, {'name': 'tiara', 'id': 1087, 'frequency': 'c', 'synset': 'tiara.n.01'}, {'name': 'tiger', 'id': 1088, 'frequency': 'c', 'synset': 'tiger.n.02'}, {'name': 'tights_(clothing)', 'id': 1089, 'frequency': 'c', 'synset': 'tights.n.01'}, {'name': 'timer', 'id': 1090, 'frequency': 'c', 'synset': 'timer.n.01'}, {'name': 'tinfoil', 'id': 1091, 'frequency': 'f', 'synset': 'tinfoil.n.01'}, {'name': 'tinsel', 'id': 1092, 'frequency': 'c', 'synset': 'tinsel.n.01'}, {'name': 'tissue_paper', 'id': 1093, 'frequency': 'f', 'synset': 'tissue.n.02'}, {'name': 'toast_(food)', 'id': 1094, 'frequency': 'c', 'synset': 'toast.n.01'}, {'name': 'toaster', 'id': 1095, 'frequency': 'f', 'synset': 'toaster.n.02'}, {'name': 'toaster_oven', 'id': 1096, 'frequency': 'f', 'synset': 'toaster_oven.n.01'}, {'name': 'toilet', 'id': 1097, 'frequency': 'f', 'synset': 'toilet.n.02'}, {'name': 'toilet_tissue', 'id': 1098, 'frequency': 'f', 'synset': 'toilet_tissue.n.01'}, {'name': 'tomato', 'id': 1099, 'frequency': 'f', 'synset': 'tomato.n.01'}, {'name': 'tongs', 'id': 1100, 'frequency': 'f', 'synset': 'tongs.n.01'}, {'name': 'toolbox', 'id': 1101, 'frequency': 'c', 'synset': 'toolbox.n.01'}, {'name': 'toothbrush', 'id': 1102, 'frequency': 'f', 'synset': 'toothbrush.n.01'}, {'name': 'toothpaste', 'id': 1103, 'frequency': 'f', 'synset': 'toothpaste.n.01'}, {'name': 'toothpick', 'id': 1104, 'frequency': 'f', 'synset': 'toothpick.n.01'}, {'name': 'cover', 'id': 1105, 'frequency': 'f', 'synset': 'top.n.09'}, {'name': 'tortilla', 'id': 1106, 'frequency': 'c', 'synset': 'tortilla.n.01'}, {'name': 'tow_truck', 'id': 1107, 'frequency': 'c', 'synset': 'tow_truck.n.01'}, {'name': 'towel', 'id': 1108, 'frequency': 'f', 'synset': 'towel.n.01'}, {'name': 'towel_rack', 'id': 1109, 'frequency': 'f', 'synset': 'towel_rack.n.01'}, {'name': 'toy', 'id': 1110, 'frequency': 'f', 'synset': 'toy.n.03'}, {'name': 'tractor_(farm_equipment)', 'id': 1111, 'frequency': 'c', 'synset': 'tractor.n.01'}, {'name': 'traffic_light', 'id': 1112, 'frequency': 'f', 'synset': 'traffic_light.n.01'}, {'name': 'dirt_bike', 'id': 1113, 'frequency': 'c', 'synset': 'trail_bike.n.01'}, {'name': 'trailer_truck', 'id': 1114, 'frequency': 'f', 'synset': 'trailer_truck.n.01'}, {'name': 'train_(railroad_vehicle)', 'id': 1115, 'frequency': 'f', 'synset': 'train.n.01'}, {'name': 'trampoline', 'id': 1116, 'frequency': 'r', 'synset': 'trampoline.n.01'}, {'name': 'tray', 'id': 1117, 'frequency': 'f', 'synset': 'tray.n.01'}, {'name': 'trench_coat', 'id': 1118, 'frequency': 'r', 'synset': 'trench_coat.n.01'}, {'name': 'triangle_(musical_instrument)', 'id': 1119, 'frequency': 'r', 'synset': 'triangle.n.05'}, {'name': 'tricycle', 'id': 1120, 'frequency': 'c', 'synset': 'tricycle.n.01'}, {'name': 'tripod', 'id': 1121, 'frequency': 'f', 'synset': 'tripod.n.01'}, {'name': 'trousers', 'id': 1122, 'frequency': 'f', 'synset': 'trouser.n.01'}, {'name': 'truck', 'id': 1123, 'frequency': 'f', 'synset': 'truck.n.01'}, {'name': 'truffle_(chocolate)', 'id': 1124, 'frequency': 'r', 'synset': 'truffle.n.03'}, {'name': 'trunk', 'id': 1125, 'frequency': 'c', 'synset': 'trunk.n.02'}, {'name': 'vat', 'id': 1126, 'frequency': 'r', 'synset': 'tub.n.02'}, {'name': 'turban', 'id': 1127, 'frequency': 'c', 'synset': 'turban.n.01'}, {'name': 'turkey_(food)', 'id': 1128, 'frequency': 'c', 'synset': 'turkey.n.04'}, {'name': 'turnip', 'id': 1129, 'frequency': 'r', 'synset': 'turnip.n.01'}, {'name': 'turtle', 'id': 1130, 'frequency': 'c', 'synset': 'turtle.n.02'}, {'name': 'turtleneck_(clothing)', 'id': 1131, 'frequency': 'c', 'synset': 'turtleneck.n.01'}, {'name': 'typewriter', 'id': 1132, 'frequency': 'c', 'synset': 'typewriter.n.01'}, {'name': 'umbrella', 'id': 1133, 'frequency': 'f', 'synset': 'umbrella.n.01'}, {'name': 'underwear', 'id': 1134, 'frequency': 'f', 'synset': 'underwear.n.01'}, {'name': 'unicycle', 'id': 1135, 'frequency': 'r', 'synset': 'unicycle.n.01'}, {'name': 'urinal', 'id': 1136, 'frequency': 'f', 'synset': 'urinal.n.01'}, {'name': 'urn', 'id': 1137, 'frequency': 'c', 'synset': 'urn.n.01'}, {'name': 'vacuum_cleaner', 'id': 1138, 'frequency': 'c', 'synset': 'vacuum.n.04'}, {'name': 'vase', 'id': 1139, 'frequency': 'f', 'synset': 'vase.n.01'}, {'name': 'vending_machine', 'id': 1140, 'frequency': 'c', 'synset': 'vending_machine.n.01'}, {'name': 'vent', 'id': 1141, 'frequency': 'f', 'synset': 'vent.n.01'}, {'name': 'vest', 'id': 1142, 'frequency': 'f', 'synset': 'vest.n.01'}, {'name': 'videotape', 'id': 1143, 'frequency': 'c', 'synset': 'videotape.n.01'}, {'name': 'vinegar', 'id': 1144, 'frequency': 'r', 'synset': 'vinegar.n.01'}, {'name': 'violin', 'id': 1145, 'frequency': 'r', 'synset': 'violin.n.01'}, {'name': 'vodka', 'id': 1146, 'frequency': 'r', 'synset': 'vodka.n.01'}, {'name': 'volleyball', 'id': 1147, 'frequency': 'c', 'synset': 'volleyball.n.02'}, {'name': 'vulture', 'id': 1148, 'frequency': 'r', 'synset': 'vulture.n.01'}, {'name': 'waffle', 'id': 1149, 'frequency': 'c', 'synset': 'waffle.n.01'}, {'name': 'waffle_iron', 'id': 1150, 'frequency': 'r', 'synset': 'waffle_iron.n.01'}, {'name': 'wagon', 'id': 1151, 'frequency': 'c', 'synset': 'wagon.n.01'}, {'name': 'wagon_wheel', 'id': 1152, 'frequency': 'c', 'synset': 'wagon_wheel.n.01'}, {'name': 'walking_stick', 'id': 1153, 'frequency': 'c', 'synset': 'walking_stick.n.01'}, {'name': 'wall_clock', 'id': 1154, 'frequency': 'c', 'synset': 'wall_clock.n.01'}, {'name': 'wall_socket', 'id': 1155, 'frequency': 'f', 'synset': 'wall_socket.n.01'}, {'name': 'wallet', 'id': 1156, 'frequency': 'f', 'synset': 'wallet.n.01'}, {'name': 'walrus', 'id': 1157, 'frequency': 'r', 'synset': 'walrus.n.01'}, {'name': 'wardrobe', 'id': 1158, 'frequency': 'r', 'synset': 'wardrobe.n.01'}, {'name': 'washbasin', 'id': 1159, 'frequency': 'r', 'synset': 'washbasin.n.01'}, {'name': 'automatic_washer', 'id': 1160, 'frequency': 'c', 'synset': 'washer.n.03'}, {'name': 'watch', 'id': 1161, 'frequency': 'f', 'synset': 'watch.n.01'}, {'name': 'water_bottle', 'id': 1162, 'frequency': 'f', 'synset': 'water_bottle.n.01'}, {'name': 'water_cooler', 'id': 1163, 'frequency': 'c', 'synset': 'water_cooler.n.01'}, {'name': 'water_faucet', 'id': 1164, 'frequency': 'c', 'synset': 'water_faucet.n.01'}, {'name': 'water_heater', 'id': 1165, 'frequency': 'r', 'synset': 'water_heater.n.01'}, {'name': 'water_jug', 'id': 1166, 'frequency': 'c', 'synset': 'water_jug.n.01'}, {'name': 'water_gun', 'id': 1167, 'frequency': 'r', 'synset': 'water_pistol.n.01'}, {'name': 'water_scooter', 'id': 1168, 'frequency': 'c', 'synset': 'water_scooter.n.01'}, {'name': 'water_ski', 'id': 1169, 'frequency': 'c', 'synset': 'water_ski.n.01'}, {'name': 'water_tower', 'id': 1170, 'frequency': 'c', 'synset': 'water_tower.n.01'}, {'name': 'watering_can', 'id': 1171, 'frequency': 'c', 'synset': 'watering_can.n.01'}, {'name': 'watermelon', 'id': 1172, 'frequency': 'f', 'synset': 'watermelon.n.02'}, {'name': 'weathervane', 'id': 1173, 'frequency': 'f', 'synset': 'weathervane.n.01'}, {'name': 'webcam', 'id': 1174, 'frequency': 'c', 'synset': 'webcam.n.01'}, {'name': 'wedding_cake', 'id': 1175, 'frequency': 'c', 'synset': 'wedding_cake.n.01'}, {'name': 'wedding_ring', 'id': 1176, 'frequency': 'c', 'synset': 'wedding_ring.n.01'}, {'name': 'wet_suit', 'id': 1177, 'frequency': 'f', 'synset': 'wet_suit.n.01'}, {'name': 'wheel', 'id': 1178, 'frequency': 'f', 'synset': 'wheel.n.01'}, {'name': 'wheelchair', 'id': 1179, 'frequency': 'c', 'synset': 'wheelchair.n.01'}, {'name': 'whipped_cream', 'id': 1180, 'frequency': 'c', 'synset': 'whipped_cream.n.01'}, {'name': 'whistle', 'id': 1181, 'frequency': 'c', 'synset': 'whistle.n.03'}, {'name': 'wig', 'id': 1182, 'frequency': 'c', 'synset': 'wig.n.01'}, {'name': 'wind_chime', 'id': 1183, 'frequency': 'c', 'synset': 'wind_chime.n.01'}, {'name': 'windmill', 'id': 1184, 'frequency': 'c', 'synset': 'windmill.n.01'}, {'name': 'window_box_(for_plants)', 'id': 1185, 'frequency': 'c', 'synset': 'window_box.n.01'}, {'name': 'windshield_wiper', 'id': 1186, 'frequency': 'f', 'synset': 'windshield_wiper.n.01'}, {'name': 'windsock', 'id': 1187, 'frequency': 'c', 'synset': 'windsock.n.01'}, {'name': 'wine_bottle', 'id': 1188, 'frequency': 'f', 'synset': 'wine_bottle.n.01'}, {'name': 'wine_bucket', 'id': 1189, 'frequency': 'c', 'synset': 'wine_bucket.n.01'}, {'name': 'wineglass', 'id': 1190, 'frequency': 'f', 'synset': 'wineglass.n.01'}, {'name': 'blinder_(for_horses)', 'id': 1191, 'frequency': 'f', 'synset': 'winker.n.02'}, {'name': 'wok', 'id': 1192, 'frequency': 'c', 'synset': 'wok.n.01'}, {'name': 'wolf', 'id': 1193, 'frequency': 'r', 'synset': 'wolf.n.01'}, {'name': 'wooden_spoon', 'id': 1194, 'frequency': 'c', 'synset': 'wooden_spoon.n.02'}, {'name': 'wreath', 'id': 1195, 'frequency': 'c', 'synset': 'wreath.n.01'}, {'name': 'wrench', 'id': 1196, 'frequency': 'c', 'synset': 'wrench.n.03'}, {'name': 'wristband', 'id': 1197, 'frequency': 'f', 'synset': 'wristband.n.01'}, {'name': 'wristlet', 'id': 1198, 'frequency': 'f', 'synset': 'wristlet.n.01'}, {'name': 'yacht', 'id': 1199, 'frequency': 'c', 'synset': 'yacht.n.01'}, {'name': 'yogurt', 'id': 1200, 'frequency': 'c', 'synset': 'yogurt.n.01'}, {'name': 'yoke_(animal_equipment)', 'id': 1201, 'frequency': 'c', 'synset': 'yoke.n.07'}, {'name': 'zebra', 'id': 1202, 'frequency': 'f', 'synset': 'zebra.n.01'}, {'name': 'zucchini', 'id': 1203, 'frequency': 'c', 'synset': 'zucchini.n.02'}, {'id': 1204, 'synset': 'organism.n.01', 'name': 'organism'}, {'id': 1205, 'synset': 'benthos.n.02', 'name': 'benthos'}, {'id': 1206, 'synset': 'heterotroph.n.01', 'name': 'heterotroph'}, {'id': 1207, 'synset': 'cell.n.02', 'name': 'cell'}, {'id': 1208, 'synset': 'animal.n.01', 'name': 'animal'}, {'id': 1209, 'synset': 'plant.n.02', 'name': 'plant'}, {'id': 1210, 'synset': 'food.n.01', 'name': 'food'}, {'id': 1211, 'synset': 'artifact.n.01', 'name': 'artifact'}, {'id': 1212, 'synset': 'hop.n.01', 'name': 'hop'}, {'id': 1213, 'synset': 'check-in.n.01', 'name': 'check-in'}, {'id': 1214, 'synset': 'dressage.n.01', 'name': 'dressage'}, {'id': 1215, 'synset': 'curvet.n.01', 'name': 'curvet'}, {'id': 1216, 'synset': 'piaffe.n.01', 'name': 'piaffe'}, {'id': 1217, 'synset': 'funambulism.n.01', 'name': 'funambulism'}, {'id': 1218, 'synset': 'rock_climbing.n.01', 'name': 'rock_climbing'}, {'id': 1219, 'synset': 'contact_sport.n.01', 'name': 'contact_sport'}, {'id': 1220, 'synset': 'outdoor_sport.n.01', 'name': 'outdoor_sport'}, {'id': 1221, 'synset': 'gymnastics.n.01', 'name': 'gymnastics'}, {'id': 1222, 'synset': 'acrobatics.n.01', 'name': 'acrobatics'}, {'id': 1223, 'synset': 'track_and_field.n.01', 'name': 'track_and_field'}, {'id': 1224, 'synset': 'track.n.11', 'name': 'track'}, {'id': 1225, 'synset': 'jumping.n.01', 'name': 'jumping'}, {'id': 1226, 'synset': 'broad_jump.n.02', 'name': 'broad_jump'}, {'id': 1227, 'synset': 'high_jump.n.02', 'name': 'high_jump'}, {'id': 1228, 'synset': 'fosbury_flop.n.01', 'name': 'Fosbury_flop'}, {'id': 1229, 'synset': 'skiing.n.01', 'name': 'skiing'}, {'id': 1230, 'synset': 'cross-country_skiing.n.01', 'name': 'cross-country_skiing'}, {'id': 1231, 'synset': 'ski_jumping.n.01', 'name': 'ski_jumping'}, {'id': 1232, 'synset': 'water_sport.n.01', 'name': 'water_sport'}, {'id': 1233, 'synset': 'swimming.n.01', 'name': 'swimming'}, {'id': 1234, 'synset': 'bathe.n.01', 'name': 'bathe'}, {'id': 1235, 'synset': 'dip.n.08', 'name': 'dip'}, {'id': 1236, 'synset': 'dive.n.02', 'name': 'dive'}, {'id': 1237, 'synset': 'floating.n.01', 'name': 'floating'}, {'id': 1238, 'synset': "dead-man's_float.n.01", 'name': "dead-man's_float"}, {'id': 1239, 'synset': 'belly_flop.n.01', 'name': 'belly_flop'}, {'id': 1240, 'synset': 'cliff_diving.n.01', 'name': 'cliff_diving'}, {'id': 1241, 'synset': 'flip.n.05', 'name': 'flip'}, {'id': 1242, 'synset': 'gainer.n.03', 'name': 'gainer'}, {'id': 1243, 'synset': 'half_gainer.n.01', 'name': 'half_gainer'}, {'id': 1244, 'synset': 'jackknife.n.02', 'name': 'jackknife'}, {'id': 1245, 'synset': 'swan_dive.n.01', 'name': 'swan_dive'}, {'id': 1246, 'synset': 'skin_diving.n.01', 'name': 'skin_diving'}, {'id': 1247, 'synset': 'scuba_diving.n.01', 'name': 'scuba_diving'}, {'id': 1248, 'synset': 'snorkeling.n.01', 'name': 'snorkeling'}, {'id': 1249, 'synset': 'surfing.n.01', 'name': 'surfing'}, {'id': 1250, 'synset': 'water-skiing.n.01', 'name': 'water-skiing'}, {'id': 1251, 'synset': 'rowing.n.01', 'name': 'rowing'}, {'id': 1252, 'synset': 'sculling.n.01', 'name': 'sculling'}, {'id': 1253, 'synset': 'boxing.n.01', 'name': 'boxing'}, {'id': 1254, 'synset': 'professional_boxing.n.01', 'name': 'professional_boxing'}, {'id': 1255, 'synset': 'in-fighting.n.02', 'name': 'in-fighting'}, {'id': 1256, 'synset': 'fight.n.05', 'name': 'fight'}, {'id': 1257, 'synset': 'rope-a-dope.n.01', 'name': 'rope-a-dope'}, {'id': 1258, 'synset': 'spar.n.03', 'name': 'spar'}, {'id': 1259, 'synset': 'archery.n.01', 'name': 'archery'}, {'id': 1260, 'synset': 'sledding.n.01', 'name': 'sledding'}, {'id': 1261, 'synset': 'tobogganing.n.01', 'name': 'tobogganing'}, {'id': 1262, 'synset': 'luging.n.01', 'name': 'luging'}, {'id': 1263, 'synset': 'bobsledding.n.01', 'name': 'bobsledding'}, {'id': 1264, 'synset': 'wrestling.n.02', 'name': 'wrestling'}, {'id': 1265, 'synset': 'greco-roman_wrestling.n.01', 'name': 'Greco-Roman_wrestling'}, {'id': 1266, 'synset': 'professional_wrestling.n.01', 'name': 'professional_wrestling'}, {'id': 1267, 'synset': 'sumo.n.01', 'name': 'sumo'}, {'id': 1268, 'synset': 'skating.n.01', 'name': 'skating'}, {'id': 1269, 'synset': 'ice_skating.n.01', 'name': 'ice_skating'}, {'id': 1270, 'synset': 'figure_skating.n.01', 'name': 'figure_skating'}, {'id': 1271, 'synset': 'rollerblading.n.01', 'name': 'rollerblading'}, {'id': 1272, 'synset': 'roller_skating.n.01', 'name': 'roller_skating'}, {'id': 1273, 'synset': 'skateboarding.n.01', 'name': 'skateboarding'}, {'id': 1274, 'synset': 'speed_skating.n.01', 'name': 'speed_skating'}, {'id': 1275, 'synset': 'racing.n.01', 'name': 'racing'}, {'id': 1276, 'synset': 'auto_racing.n.01', 'name': 'auto_racing'}, {'id': 1277, 'synset': 'boat_racing.n.01', 'name': 'boat_racing'}, {'id': 1278, 'synset': 'hydroplane_racing.n.01', 'name': 'hydroplane_racing'}, {'id': 1279, 'synset': 'camel_racing.n.01', 'name': 'camel_racing'}, {'id': 1280, 'synset': 'greyhound_racing.n.01', 'name': 'greyhound_racing'}, {'id': 1281, 'synset': 'horse_racing.n.01', 'name': 'horse_racing'}, {'id': 1282, 'synset': 'riding.n.01', 'name': 'riding'}, {'id': 1283, 'synset': 'equestrian_sport.n.01', 'name': 'equestrian_sport'}, {'id': 1284, 'synset': 'pony-trekking.n.01', 'name': 'pony-trekking'}, {'id': 1285, 'synset': 'showjumping.n.01', 'name': 'showjumping'}, {'id': 1286, 'synset': 'cross-country_riding.n.01', 'name': 'cross-country_riding'}, {'id': 1287, 'synset': 'cycling.n.01', 'name': 'cycling'}, {'id': 1288, 'synset': 'bicycling.n.01', 'name': 'bicycling'}, {'id': 1289, 'synset': 'motorcycling.n.01', 'name': 'motorcycling'}, {'id': 1290, 'synset': 'dune_cycling.n.01', 'name': 'dune_cycling'}, {'id': 1291, 'synset': 'blood_sport.n.01', 'name': 'blood_sport'}, {'id': 1292, 'synset': 'bullfighting.n.01', 'name': 'bullfighting'}, {'id': 1293, 'synset': 'cockfighting.n.01', 'name': 'cockfighting'}, {'id': 1294, 'synset': 'hunt.n.08', 'name': 'hunt'}, {'id': 1295, 'synset': 'battue.n.01', 'name': 'battue'}, {'id': 1296, 'synset': 'beagling.n.01', 'name': 'beagling'}, {'id': 1297, 'synset': 'coursing.n.01', 'name': 'coursing'}, {'id': 1298, 'synset': 'deer_hunting.n.01', 'name': 'deer_hunting'}, {'id': 1299, 'synset': 'ducking.n.01', 'name': 'ducking'}, {'id': 1300, 'synset': 'fox_hunting.n.01', 'name': 'fox_hunting'}, {'id': 1301, 'synset': 'pigsticking.n.01', 'name': 'pigsticking'}, {'id': 1302, 'synset': 'fishing.n.01', 'name': 'fishing'}, {'id': 1303, 'synset': 'angling.n.01', 'name': 'angling'}, {'id': 1304, 'synset': 'fly-fishing.n.01', 'name': 'fly-fishing'}, {'id': 1305, 'synset': 'troll.n.04', 'name': 'troll'}, {'id': 1306, 'synset': 'casting.n.03', 'name': 'casting'}, {'id': 1307, 'synset': 'bait_casting.n.01', 'name': 'bait_casting'}, {'id': 1308, 'synset': 'fly_casting.n.01', 'name': 'fly_casting'}, {'id': 1309, 'synset': 'overcast.n.04', 'name': 'overcast'}, {'id': 1310, 'synset': 'surf_casting.n.01', 'name': 'surf_casting'}, {'id': 1311, 'synset': 'day_game.n.01', 'name': 'day_game'}, {'id': 1312, 'synset': 'athletic_game.n.01', 'name': 'athletic_game'}, {'id': 1313, 'synset': 'ice_hockey.n.01', 'name': 'ice_hockey'}, {'id': 1314, 'synset': 'tetherball.n.01', 'name': 'tetherball'}, {'id': 1315, 'synset': 'water_polo.n.01', 'name': 'water_polo'}, {'id': 1316, 'synset': 'outdoor_game.n.01', 'name': 'outdoor_game'}, {'id': 1317, 'synset': 'golf.n.01', 'name': 'golf'}, {'id': 1318, 'synset': 'professional_golf.n.01', 'name': 'professional_golf'}, {'id': 1319, 'synset': 'round_of_golf.n.01', 'name': 'round_of_golf'}, {'id': 1320, 'synset': 'medal_play.n.01', 'name': 'medal_play'}, {'id': 1321, 'synset': 'match_play.n.01', 'name': 'match_play'}, {'id': 1322, 'synset': 'miniature_golf.n.01', 'name': 'miniature_golf'}, {'id': 1323, 'synset': 'croquet.n.01', 'name': 'croquet'}, {'id': 1324, 'synset': 'quoits.n.01', 'name': 'quoits'}, {'id': 1325, 'synset': 'shuffleboard.n.01', 'name': 'shuffleboard'}, {'id': 1326, 'synset': 'field_game.n.01', 'name': 'field_game'}, {'id': 1327, 'synset': 'field_hockey.n.01', 'name': 'field_hockey'}, {'id': 1328, 'synset': 'shinny.n.01', 'name': 'shinny'}, {'id': 1329, 'synset': 'football.n.01', 'name': 'football'}, {'id': 1330, 'synset': 'american_football.n.01', 'name': 'American_football'}, {'id': 1331, 'synset': 'professional_football.n.01', 'name': 'professional_football'}, {'id': 1332, 'synset': 'touch_football.n.01', 'name': 'touch_football'}, {'id': 1333, 'synset': 'hurling.n.01', 'name': 'hurling'}, {'id': 1334, 'synset': 'rugby.n.01', 'name': 'rugby'}, {'id': 1335, 'synset': 'ball_game.n.01', 'name': 'ball_game'}, {'id': 1336, 'synset': 'baseball.n.01', 'name': 'baseball'}, {'id': 1337, 'synset': 'ball.n.11', 'name': 'ball'}, {'id': 1338, 'synset': 'professional_baseball.n.01', 'name': 'professional_baseball'}, {'id': 1339, 'synset': 'hardball.n.02', 'name': 'hardball'}, {'id': 1340, 'synset': 'perfect_game.n.01', 'name': 'perfect_game'}, {'id': 1341, 'synset': 'no-hit_game.n.01', 'name': 'no-hit_game'}, {'id': 1342, 'synset': 'one-hitter.n.01', 'name': 'one-hitter'}, {'id': 1343, 'synset': 'two-hitter.n.01', 'name': 'two-hitter'}, {'id': 1344, 'synset': 'three-hitter.n.01', 'name': 'three-hitter'}, {'id': 1345, 'synset': 'four-hitter.n.01', 'name': 'four-hitter'}, {'id': 1346, 'synset': 'five-hitter.n.01', 'name': 'five-hitter'}, {'id': 1347, 'synset': 'softball.n.02', 'name': 'softball'}, {'id': 1348, 'synset': 'rounders.n.01', 'name': 'rounders'}, {'id': 1349, 'synset': 'stickball.n.01', 'name': 'stickball'}, {'id': 1350, 'synset': 'cricket.n.02', 'name': 'cricket'}, {'id': 1351, 'synset': 'lacrosse.n.01', 'name': 'lacrosse'}, {'id': 1352, 'synset': 'polo.n.02', 'name': 'polo'}, {'id': 1353, 'synset': 'pushball.n.01', 'name': 'pushball'}, {'id': 1354, 'synset': 'soccer.n.01', 'name': 'soccer'}, {'id': 1355, 'synset': 'court_game.n.01', 'name': 'court_game'}, {'id': 1356, 'synset': 'handball.n.02', 'name': 'handball'}, {'id': 1357, 'synset': 'racquetball.n.02', 'name': 'racquetball'}, {'id': 1358, 'synset': 'fives.n.01', 'name': 'fives'}, {'id': 1359, 'synset': 'squash.n.03', 'name': 'squash'}, {'id': 1360, 'synset': 'volleyball.n.01', 'name': 'volleyball'}, {'id': 1361, 'synset': 'jai_alai.n.01', 'name': 'jai_alai'}, {'id': 1362, 'synset': 'badminton.n.01', 'name': 'badminton'}, {'id': 1363, 'synset': 'battledore.n.02', 'name': 'battledore'}, {'id': 1364, 'synset': 'basketball.n.01', 'name': 'basketball'}, {'id': 1365, 'synset': 'professional_basketball.n.01', 'name': 'professional_basketball'}, {'id': 1366, 'synset': 'deck_tennis.n.01', 'name': 'deck_tennis'}, {'id': 1367, 'synset': 'netball.n.01', 'name': 'netball'}, {'id': 1368, 'synset': 'tennis.n.01', 'name': 'tennis'}, {'id': 1369, 'synset': 'professional_tennis.n.01', 'name': 'professional_tennis'}, {'id': 1370, 'synset': 'singles.n.02', 'name': 'singles'}, {'id': 1371, 'synset': 'singles.n.01', 'name': 'singles'}, {'id': 1372, 'synset': 'doubles.n.02', 'name': 'doubles'}, {'id': 1373, 'synset': 'doubles.n.01', 'name': 'doubles'}, {'id': 1374, 'synset': 'royal_tennis.n.01', 'name': 'royal_tennis'}, {'id': 1375, 'synset': 'pallone.n.01', 'name': 'pallone'}, {'id': 1376, 'synset': 'sport.n.01', 'name': 'sport'}, {'id': 1377, 'synset': 'clasp.n.02', 'name': 'clasp'}, {'id': 1378, 'synset': 'judo.n.01', 'name': 'judo'}, {'id': 1379, 'synset': 'team_sport.n.01', 'name': 'team_sport'}, {'id': 1380, 'synset': 'last_supper.n.01', 'name': 'Last_Supper'}, {'id': 1381, 'synset': 'seder.n.01', 'name': 'Seder'}, {'id': 1382, 'synset': 'camping.n.01', 'name': 'camping'}, {'id': 1383, 'synset': 'pest.n.04', 'name': 'pest'}, {'id': 1384, 'synset': 'critter.n.01', 'name': 'critter'}, {'id': 1385, 'synset': 'creepy-crawly.n.01', 'name': 'creepy-crawly'}, {'id': 1386, 'synset': 'darter.n.02', 'name': 'darter'}, {'id': 1387, 'synset': 'peeper.n.03', 'name': 'peeper'}, {'id': 1388, 'synset': 'homeotherm.n.01', 'name': 'homeotherm'}, {'id': 1389, 'synset': 'poikilotherm.n.01', 'name': 'poikilotherm'}, {'id': 1390, 'synset': 'range_animal.n.01', 'name': 'range_animal'}, {'id': 1391, 'synset': 'scavenger.n.03', 'name': 'scavenger'}, {'id': 1392, 'synset': 'bottom-feeder.n.02', 'name': 'bottom-feeder'}, {'id': 1393, 'synset': 'bottom-feeder.n.01', 'name': 'bottom-feeder'}, {'id': 1394, 'synset': 'work_animal.n.01', 'name': 'work_animal'}, {'id': 1395, 'synset': 'beast_of_burden.n.01', 'name': 'beast_of_burden'}, {'id': 1396, 'synset': 'draft_animal.n.01', 'name': 'draft_animal'}, {'id': 1397, 'synset': 'pack_animal.n.01', 'name': 'pack_animal'}, {'id': 1398, 'synset': 'domestic_animal.n.01', 'name': 'domestic_animal'}, {'id': 1399, 'synset': 'feeder.n.01', 'name': 'feeder'}, {'id': 1400, 'synset': 'feeder.n.06', 'name': 'feeder'}, {'id': 1401, 'synset': 'stocker.n.01', 'name': 'stocker'}, {'id': 1402, 'synset': 'hatchling.n.01', 'name': 'hatchling'}, {'id': 1403, 'synset': 'head.n.02', 'name': 'head'}, {'id': 1404, 'synset': 'migrator.n.02', 'name': 'migrator'}, {'id': 1405, 'synset': 'molter.n.01', 'name': 'molter'}, {'id': 1406, 'synset': 'stayer.n.01', 'name': 'stayer'}, {'id': 1407, 'synset': 'stunt.n.02', 'name': 'stunt'}, {'id': 1408, 'synset': 'marine_animal.n.01', 'name': 'marine_animal'}, {'id': 1409, 'synset': 'by-catch.n.01', 'name': 'by-catch'}, {'id': 1410, 'synset': 'female.n.01', 'name': 'female'}, {'id': 1411, 'synset': 'hen.n.04', 'name': 'hen'}, {'id': 1412, 'synset': 'male.n.01', 'name': 'male'}, {'id': 1413, 'synset': 'adult.n.02', 'name': 'adult'}, {'id': 1414, 'synset': 'young.n.01', 'name': 'young'}, {'id': 1415, 'synset': 'orphan.n.04', 'name': 'orphan'}, {'id': 1416, 'synset': 'young_mammal.n.01', 'name': 'young_mammal'}, {'id': 1417, 'synset': 'baby.n.06', 'name': 'baby'}, {'id': 1418, 'synset': 'pup.n.01', 'name': 'pup'}, {'id': 1419, 'synset': 'wolf_pup.n.01', 'name': 'wolf_pup'}, {'id': 1420, 'synset': 'lion_cub.n.01', 'name': 'lion_cub'}, {'id': 1421, 'synset': 'bear_cub.n.01', 'name': 'bear_cub'}, {'id': 1422, 'synset': 'tiger_cub.n.01', 'name': 'tiger_cub'}, {'id': 1423, 'synset': 'kit.n.03', 'name': 'kit'}, {'id': 1424, 'synset': 'suckling.n.03', 'name': 'suckling'}, {'id': 1425, 'synset': 'sire.n.03', 'name': 'sire'}, {'id': 1426, 'synset': 'dam.n.03', 'name': 'dam'}, {'id': 1427, 'synset': 'thoroughbred.n.03', 'name': 'thoroughbred'}, {'id': 1428, 'synset': 'giant.n.01', 'name': 'giant'}, {'id': 1429, 'synset': 'mutant.n.02', 'name': 'mutant'}, {'id': 1430, 'synset': 'carnivore.n.02', 'name': 'carnivore'}, {'id': 1431, 'synset': 'herbivore.n.01', 'name': 'herbivore'}, {'id': 1432, 'synset': 'insectivore.n.02', 'name': 'insectivore'}, {'id': 1433, 'synset': 'acrodont.n.01', 'name': 'acrodont'}, {'id': 1434, 'synset': 'pleurodont.n.01', 'name': 'pleurodont'}, {'id': 1435, 'synset': 'microorganism.n.01', 'name': 'microorganism'}, {'id': 1436, 'synset': 'monohybrid.n.01', 'name': 'monohybrid'}, {'id': 1437, 'synset': 'arbovirus.n.01', 'name': 'arbovirus'}, {'id': 1438, 'synset': 'adenovirus.n.01', 'name': 'adenovirus'}, {'id': 1439, 'synset': 'arenavirus.n.01', 'name': 'arenavirus'}, {'id': 1440, 'synset': 'marburg_virus.n.01', 'name': 'Marburg_virus'}, {'id': 1441, 'synset': 'arenaviridae.n.01', 'name': 'Arenaviridae'}, {'id': 1442, 'synset': 'vesiculovirus.n.01', 'name': 'vesiculovirus'}, {'id': 1443, 'synset': 'reoviridae.n.01', 'name': 'Reoviridae'}, {'id': 1444, 'synset': 'variola_major.n.02', 'name': 'variola_major'}, {'id': 1445, 'synset': 'viroid.n.01', 'name': 'viroid'}, {'id': 1446, 'synset': 'coliphage.n.01', 'name': 'coliphage'}, {'id': 1447, 'synset': 'paramyxovirus.n.01', 'name': 'paramyxovirus'}, {'id': 1448, 'synset': 'poliovirus.n.01', 'name': 'poliovirus'}, {'id': 1449, 'synset': 'herpes.n.02', 'name': 'herpes'}, {'id': 1450, 'synset': 'herpes_simplex_1.n.01', 'name': 'herpes_simplex_1'}, {'id': 1451, 'synset': 'herpes_zoster.n.02', 'name': 'herpes_zoster'}, {'id': 1452, 'synset': 'herpes_varicella_zoster.n.01', 'name': 'herpes_varicella_zoster'}, {'id': 1453, 'synset': 'cytomegalovirus.n.01', 'name': 'cytomegalovirus'}, {'id': 1454, 'synset': 'varicella_zoster_virus.n.01', 'name': 'varicella_zoster_virus'}, {'id': 1455, 'synset': 'polyoma.n.01', 'name': 'polyoma'}, {'id': 1456, 'synset': 'lyssavirus.n.01', 'name': 'lyssavirus'}, {'id': 1457, 'synset': 'reovirus.n.01', 'name': 'reovirus'}, {'id': 1458, 'synset': 'rotavirus.n.01', 'name': 'rotavirus'}, {'id': 1459, 'synset': 'moneran.n.01', 'name': 'moneran'}, {'id': 1460, 'synset': 'archaebacteria.n.01', 'name': 'archaebacteria'}, {'id': 1461, 'synset': 'bacteroid.n.01', 'name': 'bacteroid'}, {'id': 1462, 'synset': 'bacillus_anthracis.n.01', 'name': 'Bacillus_anthracis'}, {'id': 1463, 'synset': 'yersinia_pestis.n.01', 'name': 'Yersinia_pestis'}, {'id': 1464, 'synset': 'brucella.n.01', 'name': 'Brucella'}, {'id': 1465, 'synset': 'spirillum.n.02', 'name': 'spirillum'}, {'id': 1466, 'synset': 'botulinus.n.01', 'name': 'botulinus'}, {'id': 1467, 'synset': 'clostridium_perfringens.n.01', 'name': 'clostridium_perfringens'}, {'id': 1468, 'synset': 'cyanobacteria.n.01', 'name': 'cyanobacteria'}, {'id': 1469, 'synset': 'trichodesmium.n.01', 'name': 'trichodesmium'}, {'id': 1470, 'synset': 'nitric_bacteria.n.01', 'name': 'nitric_bacteria'}, {'id': 1471, 'synset': 'spirillum.n.01', 'name': 'spirillum'}, {'id': 1472, 'synset': 'francisella.n.01', 'name': 'Francisella'}, {'id': 1473, 'synset': 'gonococcus.n.01', 'name': 'gonococcus'}, {'id': 1474, 'synset': 'corynebacterium_diphtheriae.n.01', 'name': 'Corynebacterium_diphtheriae'}, {'id': 1475, 'synset': 'enteric_bacteria.n.01', 'name': 'enteric_bacteria'}, {'id': 1476, 'synset': 'klebsiella.n.01', 'name': 'klebsiella'}, {'id': 1477, 'synset': 'salmonella_typhimurium.n.01', 'name': 'Salmonella_typhimurium'}, {'id': 1478, 'synset': 'typhoid_bacillus.n.01', 'name': 'typhoid_bacillus'}, {'id': 1479, 'synset': 'nitrate_bacterium.n.01', 'name': 'nitrate_bacterium'}, {'id': 1480, 'synset': 'nitrite_bacterium.n.01', 'name': 'nitrite_bacterium'}, {'id': 1481, 'synset': 'actinomycete.n.01', 'name': 'actinomycete'}, {'id': 1482, 'synset': 'streptomyces.n.01', 'name': 'streptomyces'}, {'id': 1483, 'synset': 'streptomyces_erythreus.n.01', 'name': 'Streptomyces_erythreus'}, {'id': 1484, 'synset': 'streptomyces_griseus.n.01', 'name': 'Streptomyces_griseus'}, {'id': 1485, 'synset': 'tubercle_bacillus.n.01', 'name': 'tubercle_bacillus'}, {'id': 1486, 'synset': 'pus-forming_bacteria.n.01', 'name': 'pus-forming_bacteria'}, {'id': 1487, 'synset': 'streptobacillus.n.01', 'name': 'streptobacillus'}, {'id': 1488, 'synset': 'myxobacteria.n.01', 'name': 'myxobacteria'}, {'id': 1489, 'synset': 'staphylococcus.n.01', 'name': 'staphylococcus'}, {'id': 1490, 'synset': 'diplococcus.n.01', 'name': 'diplococcus'}, {'id': 1491, 'synset': 'pneumococcus.n.01', 'name': 'pneumococcus'}, {'id': 1492, 'synset': 'streptococcus.n.01', 'name': 'streptococcus'}, {'id': 1493, 'synset': 'spirochete.n.01', 'name': 'spirochete'}, {'id': 1494, 'synset': 'planktonic_algae.n.01', 'name': 'planktonic_algae'}, {'id': 1495, 'synset': 'zooplankton.n.01', 'name': 'zooplankton'}, {'id': 1496, 'synset': 'parasite.n.01', 'name': 'parasite'}, {'id': 1497, 'synset': 'endoparasite.n.01', 'name': 'endoparasite'}, {'id': 1498, 'synset': 'ectoparasite.n.01', 'name': 'ectoparasite'}, {'id': 1499, 'synset': 'pathogen.n.01', 'name': 'pathogen'}, {'id': 1500, 'synset': 'commensal.n.01', 'name': 'commensal'}, {'id': 1501, 'synset': 'myrmecophile.n.01', 'name': 'myrmecophile'}, {'id': 1502, 'synset': 'protoctist.n.01', 'name': 'protoctist'}, {'id': 1503, 'synset': 'protozoan.n.01', 'name': 'protozoan'}, {'id': 1504, 'synset': 'sarcodinian.n.01', 'name': 'sarcodinian'}, {'id': 1505, 'synset': 'heliozoan.n.01', 'name': 'heliozoan'}, {'id': 1506, 'synset': 'endameba.n.01', 'name': 'endameba'}, {'id': 1507, 'synset': 'ameba.n.01', 'name': 'ameba'}, {'id': 1508, 'synset': 'globigerina.n.01', 'name': 'globigerina'}, {'id': 1509, 'synset': 'testacean.n.01', 'name': 'testacean'}, {'id': 1510, 'synset': 'arcella.n.01', 'name': 'arcella'}, {'id': 1511, 'synset': 'difflugia.n.01', 'name': 'difflugia'}, {'id': 1512, 'synset': 'ciliate.n.01', 'name': 'ciliate'}, {'id': 1513, 'synset': 'paramecium.n.01', 'name': 'paramecium'}, {'id': 1514, 'synset': 'stentor.n.03', 'name': 'stentor'}, {'id': 1515, 'synset': 'alga.n.01', 'name': 'alga'}, {'id': 1516, 'synset': 'arame.n.01', 'name': 'arame'}, {'id': 1517, 'synset': 'seagrass.n.01', 'name': 'seagrass'}, {'id': 1518, 'synset': 'golden_algae.n.01', 'name': 'golden_algae'}, {'id': 1519, 'synset': 'yellow-green_algae.n.01', 'name': 'yellow-green_algae'}, {'id': 1520, 'synset': 'brown_algae.n.01', 'name': 'brown_algae'}, {'id': 1521, 'synset': 'kelp.n.01', 'name': 'kelp'}, {'id': 1522, 'synset': 'fucoid.n.02', 'name': 'fucoid'}, {'id': 1523, 'synset': 'fucoid.n.01', 'name': 'fucoid'}, {'id': 1524, 'synset': 'fucus.n.01', 'name': 'fucus'}, {'id': 1525, 'synset': 'bladderwrack.n.01', 'name': 'bladderwrack'}, {'id': 1526, 'synset': 'green_algae.n.01', 'name': 'green_algae'}, {'id': 1527, 'synset': 'pond_scum.n.01', 'name': 'pond_scum'}, {'id': 1528, 'synset': 'chlorella.n.01', 'name': 'chlorella'}, {'id': 1529, 'synset': 'stonewort.n.01', 'name': 'stonewort'}, {'id': 1530, 'synset': 'desmid.n.01', 'name': 'desmid'}, {'id': 1531, 'synset': 'sea_moss.n.02', 'name': 'sea_moss'}, {'id': 1532, 'synset': 'eukaryote.n.01', 'name': 'eukaryote'}, {'id': 1533, 'synset': 'prokaryote.n.01', 'name': 'prokaryote'}, {'id': 1534, 'synset': 'zooid.n.01', 'name': 'zooid'}, {'id': 1535, 'synset': 'leishmania.n.01', 'name': 'Leishmania'}, {'id': 1536, 'synset': 'zoomastigote.n.01', 'name': 'zoomastigote'}, {'id': 1537, 'synset': 'polymastigote.n.01', 'name': 'polymastigote'}, {'id': 1538, 'synset': 'costia.n.01', 'name': 'costia'}, {'id': 1539, 'synset': 'giardia.n.01', 'name': 'giardia'}, {'id': 1540, 'synset': 'cryptomonad.n.01', 'name': 'cryptomonad'}, {'id': 1541, 'synset': 'sporozoan.n.01', 'name': 'sporozoan'}, {'id': 1542, 'synset': 'sporozoite.n.01', 'name': 'sporozoite'}, {'id': 1543, 'synset': 'trophozoite.n.01', 'name': 'trophozoite'}, {'id': 1544, 'synset': 'merozoite.n.01', 'name': 'merozoite'}, {'id': 1545, 'synset': 'coccidium.n.01', 'name': 'coccidium'}, {'id': 1546, 'synset': 'gregarine.n.01', 'name': 'gregarine'}, {'id': 1547, 'synset': 'plasmodium.n.02', 'name': 'plasmodium'}, {'id': 1548, 'synset': 'leucocytozoan.n.01', 'name': 'leucocytozoan'}, {'id': 1549, 'synset': 'microsporidian.n.01', 'name': 'microsporidian'}, {'id': 1550, 'synset': 'ostariophysi.n.01', 'name': 'Ostariophysi'}, {'id': 1551, 'synset': 'cypriniform_fish.n.01', 'name': 'cypriniform_fish'}, {'id': 1552, 'synset': 'loach.n.01', 'name': 'loach'}, {'id': 1553, 'synset': 'cyprinid.n.01', 'name': 'cyprinid'}, {'id': 1554, 'synset': 'carp.n.02', 'name': 'carp'}, {'id': 1555, 'synset': 'domestic_carp.n.01', 'name': 'domestic_carp'}, {'id': 1556, 'synset': 'leather_carp.n.01', 'name': 'leather_carp'}, {'id': 1557, 'synset': 'mirror_carp.n.01', 'name': 'mirror_carp'}, {'id': 1558, 'synset': 'european_bream.n.01', 'name': 'European_bream'}, {'id': 1559, 'synset': 'tench.n.01', 'name': 'tench'}, {'id': 1560, 'synset': 'dace.n.01', 'name': 'dace'}, {'id': 1561, 'synset': 'chub.n.01', 'name': 'chub'}, {'id': 1562, 'synset': 'shiner.n.04', 'name': 'shiner'}, {'id': 1563, 'synset': 'common_shiner.n.01', 'name': 'common_shiner'}, {'id': 1564, 'synset': 'roach.n.05', 'name': 'roach'}, {'id': 1565, 'synset': 'rudd.n.01', 'name': 'rudd'}, {'id': 1566, 'synset': 'minnow.n.01', 'name': 'minnow'}, {'id': 1567, 'synset': 'gudgeon.n.02', 'name': 'gudgeon'}, {'id': 1568, 'synset': 'crucian_carp.n.01', 'name': 'crucian_carp'}, {'id': 1569, 'synset': 'electric_eel.n.01', 'name': 'electric_eel'}, {'id': 1570, 'synset': 'catostomid.n.01', 'name': 'catostomid'}, {'id': 1571, 'synset': 'buffalo_fish.n.01', 'name': 'buffalo_fish'}, {'id': 1572, 'synset': 'black_buffalo.n.01', 'name': 'black_buffalo'}, {'id': 1573, 'synset': 'hog_sucker.n.01', 'name': 'hog_sucker'}, {'id': 1574, 'synset': 'redhorse.n.01', 'name': 'redhorse'}, {'id': 1575, 'synset': 'cyprinodont.n.01', 'name': 'cyprinodont'}, {'id': 1576, 'synset': 'killifish.n.01', 'name': 'killifish'}, {'id': 1577, 'synset': 'mummichog.n.01', 'name': 'mummichog'}, {'id': 1578, 'synset': 'striped_killifish.n.01', 'name': 'striped_killifish'}, {'id': 1579, 'synset': 'rivulus.n.01', 'name': 'rivulus'}, {'id': 1580, 'synset': 'flagfish.n.01', 'name': 'flagfish'}, {'id': 1581, 'synset': 'swordtail.n.01', 'name': 'swordtail'}, {'id': 1582, 'synset': 'guppy.n.01', 'name': 'guppy'}, {'id': 1583, 'synset': 'topminnow.n.01', 'name': 'topminnow'}, {'id': 1584, 'synset': 'mosquitofish.n.01', 'name': 'mosquitofish'}, {'id': 1585, 'synset': 'platy.n.01', 'name': 'platy'}, {'id': 1586, 'synset': 'mollie.n.01', 'name': 'mollie'}, {'id': 1587, 'synset': 'squirrelfish.n.02', 'name': 'squirrelfish'}, {'id': 1588, 'synset': 'reef_squirrelfish.n.01', 'name': 'reef_squirrelfish'}, {'id': 1589, 'synset': 'deepwater_squirrelfish.n.01', 'name': 'deepwater_squirrelfish'}, {'id': 1590, 'synset': 'holocentrus_ascensionis.n.01', 'name': 'Holocentrus_ascensionis'}, {'id': 1591, 'synset': 'soldierfish.n.01', 'name': 'soldierfish'}, {'id': 1592, 'synset': 'anomalops.n.01', 'name': 'anomalops'}, {'id': 1593, 'synset': 'flashlight_fish.n.01', 'name': 'flashlight_fish'}, {'id': 1594, 'synset': 'john_dory.n.01', 'name': 'John_Dory'}, {'id': 1595, 'synset': 'boarfish.n.02', 'name': 'boarfish'}, {'id': 1596, 'synset': 'boarfish.n.01', 'name': 'boarfish'}, {'id': 1597, 'synset': 'cornetfish.n.01', 'name': 'cornetfish'}, {'id': 1598, 'synset': 'stickleback.n.01', 'name': 'stickleback'}, {'id': 1599, 'synset': 'three-spined_stickleback.n.01', 'name': 'three-spined_stickleback'}, {'id': 1600, 'synset': 'ten-spined_stickleback.n.01', 'name': 'ten-spined_stickleback'}, {'id': 1601, 'synset': 'pipefish.n.01', 'name': 'pipefish'}, {'id': 1602, 'synset': 'dwarf_pipefish.n.01', 'name': 'dwarf_pipefish'}, {'id': 1603, 'synset': 'deepwater_pipefish.n.01', 'name': 'deepwater_pipefish'}, {'id': 1604, 'synset': 'snipefish.n.01', 'name': 'snipefish'}, {'id': 1605, 'synset': 'shrimpfish.n.01', 'name': 'shrimpfish'}, {'id': 1606, 'synset': 'trumpetfish.n.01', 'name': 'trumpetfish'}, {'id': 1607, 'synset': 'pellicle.n.01', 'name': 'pellicle'}, {'id': 1608, 'synset': 'embryo.n.02', 'name': 'embryo'}, {'id': 1609, 'synset': 'fetus.n.01', 'name': 'fetus'}, {'id': 1610, 'synset': 'abortus.n.01', 'name': 'abortus'}, {'id': 1611, 'synset': 'spawn.n.01', 'name': 'spawn'}, {'id': 1612, 'synset': 'blastula.n.01', 'name': 'blastula'}, {'id': 1613, 'synset': 'blastocyst.n.01', 'name': 'blastocyst'}, {'id': 1614, 'synset': 'gastrula.n.01', 'name': 'gastrula'}, {'id': 1615, 'synset': 'morula.n.01', 'name': 'morula'}, {'id': 1616, 'synset': 'yolk.n.02', 'name': 'yolk'}, {'id': 1617, 'synset': 'chordate.n.01', 'name': 'chordate'}, {'id': 1618, 'synset': 'cephalochordate.n.01', 'name': 'cephalochordate'}, {'id': 1619, 'synset': 'lancelet.n.01', 'name': 'lancelet'}, {'id': 1620, 'synset': 'tunicate.n.01', 'name': 'tunicate'}, {'id': 1621, 'synset': 'ascidian.n.01', 'name': 'ascidian'}, {'id': 1622, 'synset': 'sea_squirt.n.01', 'name': 'sea_squirt'}, {'id': 1623, 'synset': 'salp.n.01', 'name': 'salp'}, {'id': 1624, 'synset': 'doliolum.n.01', 'name': 'doliolum'}, {'id': 1625, 'synset': 'larvacean.n.01', 'name': 'larvacean'}, {'id': 1626, 'synset': 'appendicularia.n.01', 'name': 'appendicularia'}, {'id': 1627, 'synset': 'ascidian_tadpole.n.01', 'name': 'ascidian_tadpole'}, {'id': 1628, 'synset': 'vertebrate.n.01', 'name': 'vertebrate'}, {'id': 1629, 'synset': 'amniota.n.01', 'name': 'Amniota'}, {'id': 1630, 'synset': 'amniote.n.01', 'name': 'amniote'}, {'id': 1631, 'synset': 'aquatic_vertebrate.n.01', 'name': 'aquatic_vertebrate'}, {'id': 1632, 'synset': 'jawless_vertebrate.n.01', 'name': 'jawless_vertebrate'}, {'id': 1633, 'synset': 'ostracoderm.n.01', 'name': 'ostracoderm'}, {'id': 1634, 'synset': 'heterostracan.n.01', 'name': 'heterostracan'}, {'id': 1635, 'synset': 'anaspid.n.01', 'name': 'anaspid'}, {'id': 1636, 'synset': 'conodont.n.02', 'name': 'conodont'}, {'id': 1637, 'synset': 'cyclostome.n.01', 'name': 'cyclostome'}, {'id': 1638, 'synset': 'lamprey.n.01', 'name': 'lamprey'}, {'id': 1639, 'synset': 'sea_lamprey.n.01', 'name': 'sea_lamprey'}, {'id': 1640, 'synset': 'hagfish.n.01', 'name': 'hagfish'}, {'id': 1641, 'synset': 'myxine_glutinosa.n.01', 'name': 'Myxine_glutinosa'}, {'id': 1642, 'synset': 'eptatretus.n.01', 'name': 'eptatretus'}, {'id': 1643, 'synset': 'gnathostome.n.01', 'name': 'gnathostome'}, {'id': 1644, 'synset': 'placoderm.n.01', 'name': 'placoderm'}, {'id': 1645, 'synset': 'cartilaginous_fish.n.01', 'name': 'cartilaginous_fish'}, {'id': 1646, 'synset': 'holocephalan.n.01', 'name': 'holocephalan'}, {'id': 1647, 'synset': 'chimaera.n.03', 'name': 'chimaera'}, {'id': 1648, 'synset': 'rabbitfish.n.01', 'name': 'rabbitfish'}, {'id': 1649, 'synset': 'elasmobranch.n.01', 'name': 'elasmobranch'}, {'id': 1650, 'synset': 'cow_shark.n.01', 'name': 'cow_shark'}, {'id': 1651, 'synset': 'mackerel_shark.n.01', 'name': 'mackerel_shark'}, {'id': 1652, 'synset': 'porbeagle.n.01', 'name': 'porbeagle'}, {'id': 1653, 'synset': 'mako.n.01', 'name': 'mako'}, {'id': 1654, 'synset': 'shortfin_mako.n.01', 'name': 'shortfin_mako'}, {'id': 1655, 'synset': 'longfin_mako.n.01', 'name': 'longfin_mako'}, {'id': 1656, 'synset': 'bonito_shark.n.01', 'name': 'bonito_shark'}, {'id': 1657, 'synset': 'great_white_shark.n.01', 'name': 'great_white_shark'}, {'id': 1658, 'synset': 'basking_shark.n.01', 'name': 'basking_shark'}, {'id': 1659, 'synset': 'thresher.n.02', 'name': 'thresher'}, {'id': 1660, 'synset': 'carpet_shark.n.01', 'name': 'carpet_shark'}, {'id': 1661, 'synset': 'nurse_shark.n.01', 'name': 'nurse_shark'}, {'id': 1662, 'synset': 'sand_tiger.n.01', 'name': 'sand_tiger'}, {'id': 1663, 'synset': 'whale_shark.n.01', 'name': 'whale_shark'}, {'id': 1664, 'synset': 'requiem_shark.n.01', 'name': 'requiem_shark'}, {'id': 1665, 'synset': 'bull_shark.n.01', 'name': 'bull_shark'}, {'id': 1666, 'synset': 'sandbar_shark.n.02', 'name': 'sandbar_shark'}, {'id': 1667, 'synset': 'blacktip_shark.n.01', 'name': 'blacktip_shark'}, {'id': 1668, 'synset': 'whitetip_shark.n.02', 'name': 'whitetip_shark'}, {'id': 1669, 'synset': 'dusky_shark.n.01', 'name': 'dusky_shark'}, {'id': 1670, 'synset': 'lemon_shark.n.01', 'name': 'lemon_shark'}, {'id': 1671, 'synset': 'blue_shark.n.01', 'name': 'blue_shark'}, {'id': 1672, 'synset': 'tiger_shark.n.01', 'name': 'tiger_shark'}, {'id': 1673, 'synset': 'soupfin_shark.n.01', 'name': 'soupfin_shark'}, {'id': 1674, 'synset': 'dogfish.n.02', 'name': 'dogfish'}, {'id': 1675, 'synset': 'smooth_dogfish.n.01', 'name': 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1689, 'synset': 'electric_ray.n.01', 'name': 'electric_ray'}, {'id': 1690, 'synset': 'sawfish.n.01', 'name': 'sawfish'}, {'id': 1691, 'synset': 'smalltooth_sawfish.n.01', 'name': 'smalltooth_sawfish'}, {'id': 1692, 'synset': 'guitarfish.n.01', 'name': 'guitarfish'}, {'id': 1693, 'synset': 'stingray.n.01', 'name': 'stingray'}, {'id': 1694, 'synset': 'roughtail_stingray.n.01', 'name': 'roughtail_stingray'}, {'id': 1695, 'synset': 'butterfly_ray.n.01', 'name': 'butterfly_ray'}, {'id': 1696, 'synset': 'eagle_ray.n.01', 'name': 'eagle_ray'}, {'id': 1697, 'synset': 'spotted_eagle_ray.n.01', 'name': 'spotted_eagle_ray'}, {'id': 1698, 'synset': 'cownose_ray.n.01', 'name': 'cownose_ray'}, {'id': 1699, 'synset': 'manta.n.02', 'name': 'manta'}, {'id': 1700, 'synset': 'atlantic_manta.n.01', 'name': 'Atlantic_manta'}, {'id': 1701, 'synset': 'devil_ray.n.01', 'name': 'devil_ray'}, {'id': 1702, 'synset': 'skate.n.02', 'name': 'skate'}, {'id': 1703, 'synset': 'grey_skate.n.01', 'name': 'grey_skate'}, {'id': 1704, 'synset': 'little_skate.n.01', 'name': 'little_skate'}, {'id': 1705, 'synset': 'thorny_skate.n.01', 'name': 'thorny_skate'}, {'id': 1706, 'synset': 'barndoor_skate.n.01', 'name': 'barndoor_skate'}, {'id': 1707, 'synset': 'dickeybird.n.01', 'name': 'dickeybird'}, {'id': 1708, 'synset': 'fledgling.n.02', 'name': 'fledgling'}, {'id': 1709, 'synset': 'nestling.n.01', 'name': 'nestling'}, {'id': 1710, 'synset': 'cock.n.05', 'name': 'cock'}, {'id': 1711, 'synset': 'gamecock.n.01', 'name': 'gamecock'}, {'id': 1712, 'synset': 'hen.n.02', 'name': 'hen'}, {'id': 1713, 'synset': 'nester.n.02', 'name': 'nester'}, {'id': 1714, 'synset': 'night_bird.n.01', 'name': 'night_bird'}, {'id': 1715, 'synset': 'night_raven.n.02', 'name': 'night_raven'}, {'id': 1716, 'synset': 'bird_of_passage.n.02', 'name': 'bird_of_passage'}, {'id': 1717, 'synset': 'archaeopteryx.n.01', 'name': 'archaeopteryx'}, {'id': 1718, 'synset': 'archaeornis.n.01', 'name': 'archaeornis'}, {'id': 1719, 'synset': 'ratite.n.01', 'name': 'ratite'}, {'id': 1720, 'synset': 'carinate.n.01', 'name': 'carinate'}, {'id': 1721, 'synset': 'cassowary.n.01', 'name': 'cassowary'}, {'id': 1722, 'synset': 'emu.n.02', 'name': 'emu'}, {'id': 1723, 'synset': 'kiwi.n.04', 'name': 'kiwi'}, {'id': 1724, 'synset': 'rhea.n.03', 'name': 'rhea'}, {'id': 1725, 'synset': 'rhea.n.02', 'name': 'rhea'}, {'id': 1726, 'synset': 'elephant_bird.n.01', 'name': 'elephant_bird'}, {'id': 1727, 'synset': 'moa.n.01', 'name': 'moa'}, {'id': 1728, 'synset': 'passerine.n.01', 'name': 'passerine'}, {'id': 1729, 'synset': 'nonpasserine_bird.n.01', 'name': 'nonpasserine_bird'}, {'id': 1730, 'synset': 'oscine.n.01', 'name': 'oscine'}, {'id': 1731, 'synset': 'songbird.n.01', 'name': 'songbird'}, {'id': 1732, 'synset': 'honey_eater.n.01', 'name': 'honey_eater'}, {'id': 1733, 'synset': 'accentor.n.01', 'name': 'accentor'}, {'id': 1734, 'synset': 'hedge_sparrow.n.01', 'name': 'hedge_sparrow'}, {'id': 1735, 'synset': 'lark.n.03', 'name': 'lark'}, {'id': 1736, 'synset': 'skylark.n.01', 'name': 'skylark'}, {'id': 1737, 'synset': 'wagtail.n.01', 'name': 'wagtail'}, {'id': 1738, 'synset': 'pipit.n.01', 'name': 'pipit'}, {'id': 1739, 'synset': 'meadow_pipit.n.01', 'name': 'meadow_pipit'}, {'id': 1740, 'synset': 'finch.n.01', 'name': 'finch'}, {'id': 1741, 'synset': 'chaffinch.n.01', 'name': 'chaffinch'}, {'id': 1742, 'synset': 'brambling.n.01', 'name': 'brambling'}, {'id': 1743, 'synset': 'goldfinch.n.02', 'name': 'goldfinch'}, {'id': 1744, 'synset': 'linnet.n.02', 'name': 'linnet'}, {'id': 1745, 'synset': 'siskin.n.01', 'name': 'siskin'}, {'id': 1746, 'synset': 'red_siskin.n.01', 'name': 'red_siskin'}, {'id': 1747, 'synset': 'redpoll.n.02', 'name': 'redpoll'}, {'id': 1748, 'synset': 'redpoll.n.01', 'name': 'redpoll'}, {'id': 1749, 'synset': 'new_world_goldfinch.n.01', 'name': 'New_World_goldfinch'}, {'id': 1750, 'synset': 'pine_siskin.n.01', 'name': 'pine_siskin'}, {'id': 1751, 'synset': 'house_finch.n.01', 'name': 'house_finch'}, {'id': 1752, 'synset': 'purple_finch.n.01', 'name': 'purple_finch'}, {'id': 1753, 'synset': 'canary.n.04', 'name': 'canary'}, {'id': 1754, 'synset': 'common_canary.n.01', 'name': 'common_canary'}, {'id': 1755, 'synset': 'serin.n.01', 'name': 'serin'}, {'id': 1756, 'synset': 'crossbill.n.01', 'name': 'crossbill'}, {'id': 1757, 'synset': 'bullfinch.n.02', 'name': 'bullfinch'}, {'id': 1758, 'synset': 'junco.n.01', 'name': 'junco'}, {'id': 1759, 'synset': 'dark-eyed_junco.n.01', 'name': 'dark-eyed_junco'}, {'id': 1760, 'synset': 'new_world_sparrow.n.01', 'name': 'New_World_sparrow'}, {'id': 1761, 'synset': 'vesper_sparrow.n.01', 'name': 'vesper_sparrow'}, {'id': 1762, 'synset': 'white-throated_sparrow.n.01', 'name': 'white-throated_sparrow'}, {'id': 1763, 'synset': 'white-crowned_sparrow.n.01', 'name': 'white-crowned_sparrow'}, {'id': 1764, 'synset': 'chipping_sparrow.n.01', 'name': 'chipping_sparrow'}, {'id': 1765, 'synset': 'field_sparrow.n.01', 'name': 'field_sparrow'}, {'id': 1766, 'synset': 'tree_sparrow.n.02', 'name': 'tree_sparrow'}, {'id': 1767, 'synset': 'song_sparrow.n.01', 'name': 'song_sparrow'}, {'id': 1768, 'synset': 'swamp_sparrow.n.01', 'name': 'swamp_sparrow'}, {'id': 1769, 'synset': 'bunting.n.02', 'name': 'bunting'}, {'id': 1770, 'synset': 'indigo_bunting.n.01', 'name': 'indigo_bunting'}, {'id': 1771, 'synset': 'ortolan.n.01', 'name': 'ortolan'}, {'id': 1772, 'synset': 'reed_bunting.n.01', 'name': 'reed_bunting'}, {'id': 1773, 'synset': 'yellowhammer.n.02', 'name': 'yellowhammer'}, {'id': 1774, 'synset': 'yellow-breasted_bunting.n.01', 'name': 'yellow-breasted_bunting'}, {'id': 1775, 'synset': 'snow_bunting.n.01', 'name': 'snow_bunting'}, {'id': 1776, 'synset': 'honeycreeper.n.02', 'name': 'honeycreeper'}, {'id': 1777, 'synset': 'banana_quit.n.01', 'name': 'banana_quit'}, {'id': 1778, 'synset': 'sparrow.n.01', 'name': 'sparrow'}, {'id': 1779, 'synset': 'english_sparrow.n.01', 'name': 'English_sparrow'}, {'id': 1780, 'synset': 'tree_sparrow.n.01', 'name': 'tree_sparrow'}, {'id': 1781, 'synset': 'grosbeak.n.01', 'name': 'grosbeak'}, {'id': 1782, 'synset': 'evening_grosbeak.n.01', 'name': 'evening_grosbeak'}, {'id': 1783, 'synset': 'hawfinch.n.01', 'name': 'hawfinch'}, {'id': 1784, 'synset': 'pine_grosbeak.n.01', 'name': 'pine_grosbeak'}, {'id': 1785, 'synset': 'cardinal.n.04', 'name': 'cardinal'}, {'id': 1786, 'synset': 'pyrrhuloxia.n.01', 'name': 'pyrrhuloxia'}, {'id': 1787, 'synset': 'towhee.n.01', 'name': 'towhee'}, {'id': 1788, 'synset': 'chewink.n.01', 'name': 'chewink'}, {'id': 1789, 'synset': 'green-tailed_towhee.n.01', 'name': 'green-tailed_towhee'}, {'id': 1790, 'synset': 'weaver.n.02', 'name': 'weaver'}, {'id': 1791, 'synset': 'baya.n.01', 'name': 'baya'}, {'id': 1792, 'synset': 'whydah.n.01', 'name': 'whydah'}, {'id': 1793, 'synset': 'java_sparrow.n.01', 'name': 'Java_sparrow'}, {'id': 1794, 'synset': 'avadavat.n.01', 'name': 'avadavat'}, {'id': 1795, 'synset': 'grassfinch.n.01', 'name': 'grassfinch'}, {'id': 1796, 'synset': 'zebra_finch.n.01', 'name': 'zebra_finch'}, {'id': 1797, 'synset': 'honeycreeper.n.01', 'name': 'honeycreeper'}, {'id': 1798, 'synset': 'lyrebird.n.01', 'name': 'lyrebird'}, {'id': 1799, 'synset': 'scrubbird.n.01', 'name': 'scrubbird'}, {'id': 1800, 'synset': 'broadbill.n.04', 'name': 'broadbill'}, {'id': 1801, 'synset': 'tyrannid.n.01', 'name': 'tyrannid'}, {'id': 1802, 'synset': 'new_world_flycatcher.n.01', 'name': 'New_World_flycatcher'}, {'id': 1803, 'synset': 'kingbird.n.01', 'name': 'kingbird'}, {'id': 1804, 'synset': 'arkansas_kingbird.n.01', 'name': 'Arkansas_kingbird'}, {'id': 1805, 'synset': "cassin's_kingbird.n.01", 'name': "Cassin's_kingbird"}, {'id': 1806, 'synset': 'eastern_kingbird.n.01', 'name': 'eastern_kingbird'}, {'id': 1807, 'synset': 'grey_kingbird.n.01', 'name': 'grey_kingbird'}, {'id': 1808, 'synset': 'pewee.n.01', 'name': 'pewee'}, {'id': 1809, 'synset': 'western_wood_pewee.n.01', 'name': 'western_wood_pewee'}, {'id': 1810, 'synset': 'phoebe.n.03', 'name': 'phoebe'}, {'id': 1811, 'synset': 'vermillion_flycatcher.n.01', 'name': 'vermillion_flycatcher'}, {'id': 1812, 'synset': 'cotinga.n.01', 'name': 'cotinga'}, {'id': 1813, 'synset': 'cock_of_the_rock.n.02', 'name': 'cock_of_the_rock'}, {'id': 1814, 'synset': 'cock_of_the_rock.n.01', 'name': 'cock_of_the_rock'}, {'id': 1815, 'synset': 'manakin.n.03', 'name': 'manakin'}, {'id': 1816, 'synset': 'bellbird.n.01', 'name': 'bellbird'}, {'id': 1817, 'synset': 'umbrella_bird.n.01', 'name': 'umbrella_bird'}, {'id': 1818, 'synset': 'ovenbird.n.02', 'name': 'ovenbird'}, {'id': 1819, 'synset': 'antbird.n.01', 'name': 'antbird'}, {'id': 1820, 'synset': 'ant_thrush.n.01', 'name': 'ant_thrush'}, {'id': 1821, 'synset': 'ant_shrike.n.01', 'name': 'ant_shrike'}, {'id': 1822, 'synset': 'spotted_antbird.n.01', 'name': 'spotted_antbird'}, {'id': 1823, 'synset': 'woodhewer.n.01', 'name': 'woodhewer'}, {'id': 1824, 'synset': 'pitta.n.01', 'name': 'pitta'}, {'id': 1825, 'synset': 'scissortail.n.01', 'name': 'scissortail'}, {'id': 1826, 'synset': 'old_world_flycatcher.n.01', 'name': 'Old_World_flycatcher'}, {'id': 1827, 'synset': 'spotted_flycatcher.n.01', 'name': 'spotted_flycatcher'}, {'id': 1828, 'synset': 'thickhead.n.01', 'name': 'thickhead'}, {'id': 1829, 'synset': 'thrush.n.03', 'name': 'thrush'}, {'id': 1830, 'synset': 'missel_thrush.n.01', 'name': 'missel_thrush'}, {'id': 1831, 'synset': 'song_thrush.n.01', 'name': 'song_thrush'}, {'id': 1832, 'synset': 'fieldfare.n.01', 'name': 'fieldfare'}, {'id': 1833, 'synset': 'redwing.n.02', 'name': 'redwing'}, {'id': 1834, 'synset': 'blackbird.n.02', 'name': 'blackbird'}, {'id': 1835, 'synset': 'ring_ouzel.n.01', 'name': 'ring_ouzel'}, {'id': 1836, 'synset': 'robin.n.02', 'name': 'robin'}, {'id': 1837, 'synset': 'clay-colored_robin.n.01', 'name': 'clay-colored_robin'}, {'id': 1838, 'synset': 'hermit_thrush.n.01', 'name': 'hermit_thrush'}, {'id': 1839, 'synset': 'veery.n.01', 'name': 'veery'}, {'id': 1840, 'synset': 'wood_thrush.n.01', 'name': 'wood_thrush'}, {'id': 1841, 'synset': 'nightingale.n.01', 'name': 'nightingale'}, {'id': 1842, 'synset': 'thrush_nightingale.n.01', 'name': 'thrush_nightingale'}, {'id': 1843, 'synset': 'bulbul.n.01', 'name': 'bulbul'}, {'id': 1844, 'synset': 'old_world_chat.n.01', 'name': 'Old_World_chat'}, {'id': 1845, 'synset': 'stonechat.n.01', 'name': 'stonechat'}, {'id': 1846, 'synset': 'whinchat.n.01', 'name': 'whinchat'}, {'id': 1847, 'synset': 'solitaire.n.03', 'name': 'solitaire'}, {'id': 1848, 'synset': 'redstart.n.02', 'name': 'redstart'}, {'id': 1849, 'synset': 'wheatear.n.01', 'name': 'wheatear'}, {'id': 1850, 'synset': 'bluebird.n.02', 'name': 'bluebird'}, {'id': 1851, 'synset': 'robin.n.01', 'name': 'robin'}, {'id': 1852, 'synset': 'bluethroat.n.01', 'name': 'bluethroat'}, {'id': 1853, 'synset': 'warbler.n.02', 'name': 'warbler'}, {'id': 1854, 'synset': 'gnatcatcher.n.01', 'name': 'gnatcatcher'}, {'id': 1855, 'synset': 'kinglet.n.01', 'name': 'kinglet'}, {'id': 1856, 'synset': 'goldcrest.n.01', 'name': 'goldcrest'}, {'id': 1857, 'synset': 'gold-crowned_kinglet.n.01', 'name': 'gold-crowned_kinglet'}, {'id': 1858, 'synset': 'ruby-crowned_kinglet.n.01', 'name': 'ruby-crowned_kinglet'}, {'id': 1859, 'synset': 'old_world_warbler.n.01', 'name': 'Old_World_warbler'}, {'id': 1860, 'synset': 'blackcap.n.04', 'name': 'blackcap'}, {'id': 1861, 'synset': 'greater_whitethroat.n.01', 'name': 'greater_whitethroat'}, {'id': 1862, 'synset': 'lesser_whitethroat.n.01', 'name': 'lesser_whitethroat'}, {'id': 1863, 'synset': 'wood_warbler.n.02', 'name': 'wood_warbler'}, {'id': 1864, 'synset': 'sedge_warbler.n.01', 'name': 'sedge_warbler'}, {'id': 1865, 'synset': 'wren_warbler.n.01', 'name': 'wren_warbler'}, {'id': 1866, 'synset': 'tailorbird.n.01', 'name': 'tailorbird'}, {'id': 1867, 'synset': 'babbler.n.02', 'name': 'babbler'}, {'id': 1868, 'synset': 'new_world_warbler.n.01', 'name': 'New_World_warbler'}, {'id': 1869, 'synset': 'parula_warbler.n.01', 'name': 'parula_warbler'}, {'id': 1870, 'synset': "wilson's_warbler.n.01", 'name': "Wilson's_warbler"}, {'id': 1871, 'synset': 'flycatching_warbler.n.01', 'name': 'flycatching_warbler'}, {'id': 1872, 'synset': 'american_redstart.n.01', 'name': 'American_redstart'}, {'id': 1873, 'synset': 'cape_may_warbler.n.01', 'name': 'Cape_May_warbler'}, {'id': 1874, 'synset': 'yellow_warbler.n.01', 'name': 'yellow_warbler'}, {'id': 1875, 'synset': 'blackburn.n.01', 'name': 'Blackburn'}, {'id': 1876, 'synset': "audubon's_warbler.n.01", 'name': "Audubon's_warbler"}, {'id': 1877, 'synset': 'myrtle_warbler.n.01', 'name': 'myrtle_warbler'}, {'id': 1878, 'synset': 'blackpoll.n.01', 'name': 'blackpoll'}, {'id': 1879, 'synset': 'new_world_chat.n.01', 'name': 'New_World_chat'}, {'id': 1880, 'synset': 'yellow-breasted_chat.n.01', 'name': 'yellow-breasted_chat'}, {'id': 1881, 'synset': 'ovenbird.n.01', 'name': 'ovenbird'}, {'id': 1882, 'synset': 'water_thrush.n.01', 'name': 'water_thrush'}, {'id': 1883, 'synset': 'yellowthroat.n.01', 'name': 'yellowthroat'}, {'id': 1884, 'synset': 'common_yellowthroat.n.01', 'name': 'common_yellowthroat'}, {'id': 1885, 'synset': 'riflebird.n.01', 'name': 'riflebird'}, {'id': 1886, 'synset': 'new_world_oriole.n.01', 'name': 'New_World_oriole'}, {'id': 1887, 'synset': 'northern_oriole.n.01', 'name': 'northern_oriole'}, {'id': 1888, 'synset': 'baltimore_oriole.n.01', 'name': 'Baltimore_oriole'}, {'id': 1889, 'synset': "bullock's_oriole.n.01", 'name': "Bullock's_oriole"}, {'id': 1890, 'synset': 'orchard_oriole.n.01', 'name': 'orchard_oriole'}, {'id': 1891, 'synset': 'meadowlark.n.01', 'name': 'meadowlark'}, {'id': 1892, 'synset': 'eastern_meadowlark.n.01', 'name': 'eastern_meadowlark'}, {'id': 1893, 'synset': 'western_meadowlark.n.01', 'name': 'western_meadowlark'}, {'id': 1894, 'synset': 'cacique.n.01', 'name': 'cacique'}, {'id': 1895, 'synset': 'bobolink.n.01', 'name': 'bobolink'}, {'id': 1896, 'synset': 'new_world_blackbird.n.01', 'name': 'New_World_blackbird'}, {'id': 1897, 'synset': 'grackle.n.02', 'name': 'grackle'}, {'id': 1898, 'synset': 'purple_grackle.n.01', 'name': 'purple_grackle'}, {'id': 1899, 'synset': 'rusty_blackbird.n.01', 'name': 'rusty_blackbird'}, {'id': 1900, 'synset': 'cowbird.n.01', 'name': 'cowbird'}, {'id': 1901, 'synset': 'red-winged_blackbird.n.01', 'name': 'red-winged_blackbird'}, {'id': 1902, 'synset': 'old_world_oriole.n.01', 'name': 'Old_World_oriole'}, {'id': 1903, 'synset': 'golden_oriole.n.01', 'name': 'golden_oriole'}, {'id': 1904, 'synset': 'fig-bird.n.01', 'name': 'fig-bird'}, {'id': 1905, 'synset': 'starling.n.01', 'name': 'starling'}, {'id': 1906, 'synset': 'common_starling.n.01', 'name': 'common_starling'}, {'id': 1907, 'synset': 'rose-colored_starling.n.01', 'name': 'rose-colored_starling'}, {'id': 1908, 'synset': 'myna.n.01', 'name': 'myna'}, {'id': 1909, 'synset': 'crested_myna.n.01', 'name': 'crested_myna'}, {'id': 1910, 'synset': 'hill_myna.n.01', 'name': 'hill_myna'}, {'id': 1911, 'synset': 'corvine_bird.n.01', 'name': 'corvine_bird'}, {'id': 1912, 'synset': 'american_crow.n.01', 'name': 'American_crow'}, {'id': 1913, 'synset': 'raven.n.01', 'name': 'raven'}, {'id': 1914, 'synset': 'rook.n.02', 'name': 'rook'}, {'id': 1915, 'synset': 'jackdaw.n.01', 'name': 'jackdaw'}, {'id': 1916, 'synset': 'chough.n.01', 'name': 'chough'}, {'id': 1917, 'synset': 'jay.n.02', 'name': 'jay'}, {'id': 1918, 'synset': 'old_world_jay.n.01', 'name': 'Old_World_jay'}, {'id': 1919, 'synset': 'common_european_jay.n.01', 'name': 'common_European_jay'}, {'id': 1920, 'synset': 'new_world_jay.n.01', 'name': 'New_World_jay'}, {'id': 1921, 'synset': 'blue_jay.n.01', 'name': 'blue_jay'}, {'id': 1922, 'synset': 'canada_jay.n.01', 'name': 'Canada_jay'}, {'id': 1923, 'synset': 'rocky_mountain_jay.n.01', 'name': 'Rocky_Mountain_jay'}, {'id': 1924, 'synset': 'nutcracker.n.03', 'name': 'nutcracker'}, {'id': 1925, 'synset': 'common_nutcracker.n.01', 'name': 'common_nutcracker'}, {'id': 1926, 'synset': "clark's_nutcracker.n.01", 'name': "Clark's_nutcracker"}, {'id': 1927, 'synset': 'magpie.n.01', 'name': 'magpie'}, {'id': 1928, 'synset': 'european_magpie.n.01', 'name': 'European_magpie'}, {'id': 1929, 'synset': 'american_magpie.n.01', 'name': 'American_magpie'}, {'id': 1930, 'synset': 'australian_magpie.n.01', 'name': 'Australian_magpie'}, {'id': 1931, 'synset': 'butcherbird.n.02', 'name': 'butcherbird'}, {'id': 1932, 'synset': 'currawong.n.01', 'name': 'currawong'}, {'id': 1933, 'synset': 'piping_crow.n.01', 'name': 'piping_crow'}, {'id': 1934, 'synset': 'wren.n.02', 'name': 'wren'}, {'id': 1935, 'synset': 'winter_wren.n.01', 'name': 'winter_wren'}, {'id': 1936, 'synset': 'house_wren.n.01', 'name': 'house_wren'}, {'id': 1937, 'synset': 'marsh_wren.n.01', 'name': 'marsh_wren'}, {'id': 1938, 'synset': 'long-billed_marsh_wren.n.01', 'name': 'long-billed_marsh_wren'}, {'id': 1939, 'synset': 'sedge_wren.n.01', 'name': 'sedge_wren'}, {'id': 1940, 'synset': 'rock_wren.n.02', 'name': 'rock_wren'}, {'id': 1941, 'synset': 'carolina_wren.n.01', 'name': 'Carolina_wren'}, {'id': 1942, 'synset': 'cactus_wren.n.01', 'name': 'cactus_wren'}, {'id': 1943, 'synset': 'mockingbird.n.01', 'name': 'mockingbird'}, {'id': 1944, 'synset': 'blue_mockingbird.n.01', 'name': 'blue_mockingbird'}, {'id': 1945, 'synset': 'catbird.n.02', 'name': 'catbird'}, {'id': 1946, 'synset': 'thrasher.n.02', 'name': 'thrasher'}, {'id': 1947, 'synset': 'brown_thrasher.n.01', 'name': 'brown_thrasher'}, {'id': 1948, 'synset': 'new_zealand_wren.n.01', 'name': 'New_Zealand_wren'}, {'id': 1949, 'synset': 'rock_wren.n.01', 'name': 'rock_wren'}, {'id': 1950, 'synset': 'rifleman_bird.n.01', 'name': 'rifleman_bird'}, {'id': 1951, 'synset': 'creeper.n.03', 'name': 'creeper'}, {'id': 1952, 'synset': 'brown_creeper.n.01', 'name': 'brown_creeper'}, {'id': 1953, 'synset': 'european_creeper.n.01', 'name': 'European_creeper'}, {'id': 1954, 'synset': 'wall_creeper.n.01', 'name': 'wall_creeper'}, {'id': 1955, 'synset': 'european_nuthatch.n.01', 'name': 'European_nuthatch'}, {'id': 1956, 'synset': 'red-breasted_nuthatch.n.01', 'name': 'red-breasted_nuthatch'}, {'id': 1957, 'synset': 'white-breasted_nuthatch.n.01', 'name': 'white-breasted_nuthatch'}, {'id': 1958, 'synset': 'titmouse.n.01', 'name': 'titmouse'}, {'id': 1959, 'synset': 'chickadee.n.01', 'name': 'chickadee'}, {'id': 1960, 'synset': 'black-capped_chickadee.n.01', 'name': 'black-capped_chickadee'}, {'id': 1961, 'synset': 'tufted_titmouse.n.01', 'name': 'tufted_titmouse'}, {'id': 1962, 'synset': 'carolina_chickadee.n.01', 'name': 'Carolina_chickadee'}, {'id': 1963, 'synset': 'blue_tit.n.01', 'name': 'blue_tit'}, {'id': 1964, 'synset': 'bushtit.n.01', 'name': 'bushtit'}, {'id': 1965, 'synset': 'wren-tit.n.01', 'name': 'wren-tit'}, {'id': 1966, 'synset': 'verdin.n.01', 'name': 'verdin'}, {'id': 1967, 'synset': 'fairy_bluebird.n.01', 'name': 'fairy_bluebird'}, {'id': 1968, 'synset': 'swallow.n.03', 'name': 'swallow'}, {'id': 1969, 'synset': 'barn_swallow.n.01', 'name': 'barn_swallow'}, {'id': 1970, 'synset': 'cliff_swallow.n.01', 'name': 'cliff_swallow'}, {'id': 1971, 'synset': 'tree_swallow.n.02', 'name': 'tree_swallow'}, {'id': 1972, 'synset': 'white-bellied_swallow.n.01', 'name': 'white-bellied_swallow'}, {'id': 1973, 'synset': 'martin.n.05', 'name': 'martin'}, {'id': 1974, 'synset': 'house_martin.n.01', 'name': 'house_martin'}, {'id': 1975, 'synset': 'bank_martin.n.01', 'name': 'bank_martin'}, {'id': 1976, 'synset': 'purple_martin.n.01', 'name': 'purple_martin'}, {'id': 1977, 'synset': 'wood_swallow.n.01', 'name': 'wood_swallow'}, {'id': 1978, 'synset': 'tanager.n.01', 'name': 'tanager'}, {'id': 1979, 'synset': 'scarlet_tanager.n.01', 'name': 'scarlet_tanager'}, {'id': 1980, 'synset': 'western_tanager.n.01', 'name': 'western_tanager'}, {'id': 1981, 'synset': 'summer_tanager.n.01', 'name': 'summer_tanager'}, {'id': 1982, 'synset': 'hepatic_tanager.n.01', 'name': 'hepatic_tanager'}, {'id': 1983, 'synset': 'shrike.n.01', 'name': 'shrike'}, {'id': 1984, 'synset': 'butcherbird.n.01', 'name': 'butcherbird'}, {'id': 1985, 'synset': 'european_shrike.n.01', 'name': 'European_shrike'}, {'id': 1986, 'synset': 'northern_shrike.n.01', 'name': 'northern_shrike'}, {'id': 1987, 'synset': 'white-rumped_shrike.n.01', 'name': 'white-rumped_shrike'}, {'id': 1988, 'synset': 'loggerhead_shrike.n.01', 'name': 'loggerhead_shrike'}, {'id': 1989, 'synset': 'migrant_shrike.n.01', 'name': 'migrant_shrike'}, {'id': 1990, 'synset': 'bush_shrike.n.01', 'name': 'bush_shrike'}, {'id': 1991, 'synset': 'black-fronted_bush_shrike.n.01', 'name': 'black-fronted_bush_shrike'}, {'id': 1992, 'synset': 'bowerbird.n.01', 'name': 'bowerbird'}, {'id': 1993, 'synset': 'satin_bowerbird.n.01', 'name': 'satin_bowerbird'}, {'id': 1994, 'synset': 'great_bowerbird.n.01', 'name': 'great_bowerbird'}, {'id': 1995, 'synset': 'water_ouzel.n.01', 'name': 'water_ouzel'}, {'id': 1996, 'synset': 'european_water_ouzel.n.01', 'name': 'European_water_ouzel'}, {'id': 1997, 'synset': 'american_water_ouzel.n.01', 'name': 'American_water_ouzel'}, {'id': 1998, 'synset': 'vireo.n.01', 'name': 'vireo'}, {'id': 1999, 'synset': 'red-eyed_vireo.n.01', 'name': 'red-eyed_vireo'}, {'id': 2000, 'synset': 'solitary_vireo.n.01', 'name': 'solitary_vireo'}, {'id': 2001, 'synset': 'blue-headed_vireo.n.01', 'name': 'blue-headed_vireo'}, {'id': 2002, 'synset': 'waxwing.n.01', 'name': 'waxwing'}, {'id': 2003, 'synset': 'cedar_waxwing.n.01', 'name': 'cedar_waxwing'}, {'id': 2004, 'synset': 'bohemian_waxwing.n.01', 'name': 'Bohemian_waxwing'}, {'id': 2005, 'synset': 'bird_of_prey.n.01', 'name': 'bird_of_prey'}, {'id': 2006, 'synset': 'accipitriformes.n.01', 'name': 'Accipitriformes'}, {'id': 2007, 'synset': 'hawk.n.01', 'name': 'hawk'}, {'id': 2008, 'synset': 'eyas.n.01', 'name': 'eyas'}, {'id': 2009, 'synset': 'tiercel.n.01', 'name': 'tiercel'}, {'id': 2010, 'synset': 'goshawk.n.01', 'name': 'goshawk'}, {'id': 2011, 'synset': 'sparrow_hawk.n.02', 'name': 'sparrow_hawk'}, {'id': 2012, 'synset': "cooper's_hawk.n.01", 'name': "Cooper's_hawk"}, {'id': 2013, 'synset': 'chicken_hawk.n.01', 'name': 'chicken_hawk'}, {'id': 2014, 'synset': 'buteonine.n.01', 'name': 'buteonine'}, {'id': 2015, 'synset': 'redtail.n.01', 'name': 'redtail'}, {'id': 2016, 'synset': 'rough-legged_hawk.n.01', 'name': 'rough-legged_hawk'}, {'id': 2017, 'synset': 'red-shouldered_hawk.n.01', 'name': 'red-shouldered_hawk'}, {'id': 2018, 'synset': 'buzzard.n.02', 'name': 'buzzard'}, {'id': 2019, 'synset': 'honey_buzzard.n.01', 'name': 'honey_buzzard'}, {'id': 2020, 'synset': 'kite.n.04', 'name': 'kite'}, {'id': 2021, 'synset': 'black_kite.n.01', 'name': 'black_kite'}, {'id': 2022, 'synset': 'swallow-tailed_kite.n.01', 'name': 'swallow-tailed_kite'}, {'id': 2023, 'synset': 'white-tailed_kite.n.01', 'name': 'white-tailed_kite'}, {'id': 2024, 'synset': 'harrier.n.03', 'name': 'harrier'}, {'id': 2025, 'synset': 'marsh_harrier.n.01', 'name': 'marsh_harrier'}, {'id': 2026, 'synset': "montagu's_harrier.n.01", 'name': "Montagu's_harrier"}, {'id': 2027, 'synset': 'marsh_hawk.n.01', 'name': 'marsh_hawk'}, {'id': 2028, 'synset': 'harrier_eagle.n.01', 'name': 'harrier_eagle'}, {'id': 2029, 'synset': 'peregrine.n.01', 'name': 'peregrine'}, {'id': 2030, 'synset': 'falcon-gentle.n.01', 'name': 'falcon-gentle'}, {'id': 2031, 'synset': 'gyrfalcon.n.01', 'name': 'gyrfalcon'}, {'id': 2032, 'synset': 'kestrel.n.02', 'name': 'kestrel'}, {'id': 2033, 'synset': 'sparrow_hawk.n.01', 'name': 'sparrow_hawk'}, {'id': 2034, 'synset': 'pigeon_hawk.n.01', 'name': 'pigeon_hawk'}, {'id': 2035, 'synset': 'hobby.n.03', 'name': 'hobby'}, {'id': 2036, 'synset': 'caracara.n.01', 'name': 'caracara'}, {'id': 2037, 'synset': "audubon's_caracara.n.01", 'name': "Audubon's_caracara"}, {'id': 2038, 'synset': 'carancha.n.01', 'name': 'carancha'}, {'id': 2039, 'synset': 'young_bird.n.01', 'name': 'young_bird'}, {'id': 2040, 'synset': 'eaglet.n.01', 'name': 'eaglet'}, {'id': 2041, 'synset': 'harpy.n.04', 'name': 'harpy'}, {'id': 2042, 'synset': 'golden_eagle.n.01', 'name': 'golden_eagle'}, {'id': 2043, 'synset': 'tawny_eagle.n.01', 'name': 'tawny_eagle'}, {'id': 2044, 'synset': 'bald_eagle.n.01', 'name': 'bald_eagle'}, {'id': 2045, 'synset': 'sea_eagle.n.02', 'name': 'sea_eagle'}, {'id': 2046, 'synset': 'kamchatkan_sea_eagle.n.01', 'name': 'Kamchatkan_sea_eagle'}, {'id': 2047, 'synset': 'ern.n.01', 'name': 'ern'}, {'id': 2048, 'synset': 'fishing_eagle.n.01', 'name': 'fishing_eagle'}, {'id': 2049, 'synset': 'osprey.n.01', 'name': 'osprey'}, {'id': 2050, 'synset': 'aegypiidae.n.01', 'name': 'Aegypiidae'}, {'id': 2051, 'synset': 'old_world_vulture.n.01', 'name': 'Old_World_vulture'}, {'id': 2052, 'synset': 'griffon_vulture.n.01', 'name': 'griffon_vulture'}, {'id': 2053, 'synset': 'bearded_vulture.n.01', 'name': 'bearded_vulture'}, {'id': 2054, 'synset': 'egyptian_vulture.n.01', 'name': 'Egyptian_vulture'}, {'id': 2055, 'synset': 'black_vulture.n.02', 'name': 'black_vulture'}, {'id': 2056, 'synset': 'secretary_bird.n.01', 'name': 'secretary_bird'}, {'id': 2057, 'synset': 'new_world_vulture.n.01', 'name': 'New_World_vulture'}, {'id': 2058, 'synset': 'buzzard.n.01', 'name': 'buzzard'}, {'id': 2059, 'synset': 'condor.n.01', 'name': 'condor'}, {'id': 2060, 'synset': 'andean_condor.n.01', 'name': 'Andean_condor'}, {'id': 2061, 'synset': 'california_condor.n.01', 'name': 'California_condor'}, {'id': 2062, 'synset': 'black_vulture.n.01', 'name': 'black_vulture'}, {'id': 2063, 'synset': 'king_vulture.n.01', 'name': 'king_vulture'}, {'id': 2064, 'synset': 'owlet.n.01', 'name': 'owlet'}, {'id': 2065, 'synset': 'little_owl.n.01', 'name': 'little_owl'}, {'id': 2066, 'synset': 'horned_owl.n.01', 'name': 'horned_owl'}, {'id': 2067, 'synset': 'great_horned_owl.n.01', 'name': 'great_horned_owl'}, {'id': 2068, 'synset': 'great_grey_owl.n.01', 'name': 'great_grey_owl'}, {'id': 2069, 'synset': 'tawny_owl.n.01', 'name': 'tawny_owl'}, {'id': 2070, 'synset': 'barred_owl.n.01', 'name': 'barred_owl'}, {'id': 2071, 'synset': 'screech_owl.n.02', 'name': 'screech_owl'}, {'id': 2072, 'synset': 'screech_owl.n.01', 'name': 'screech_owl'}, {'id': 2073, 'synset': 'scops_owl.n.01', 'name': 'scops_owl'}, {'id': 2074, 'synset': 'spotted_owl.n.01', 'name': 'spotted_owl'}, {'id': 2075, 'synset': 'old_world_scops_owl.n.01', 'name': 'Old_World_scops_owl'}, {'id': 2076, 'synset': 'oriental_scops_owl.n.01', 'name': 'Oriental_scops_owl'}, {'id': 2077, 'synset': 'hoot_owl.n.01', 'name': 'hoot_owl'}, {'id': 2078, 'synset': 'hawk_owl.n.01', 'name': 'hawk_owl'}, {'id': 2079, 'synset': 'long-eared_owl.n.01', 'name': 'long-eared_owl'}, {'id': 2080, 'synset': 'laughing_owl.n.01', 'name': 'laughing_owl'}, {'id': 2081, 'synset': 'barn_owl.n.01', 'name': 'barn_owl'}, {'id': 2082, 'synset': 'amphibian.n.03', 'name': 'amphibian'}, {'id': 2083, 'synset': 'ichyostega.n.01', 'name': 'Ichyostega'}, {'id': 2084, 'synset': 'urodele.n.01', 'name': 'urodele'}, {'id': 2085, 'synset': 'salamander.n.01', 'name': 'salamander'}, {'id': 2086, 'synset': 'european_fire_salamander.n.01', 'name': 'European_fire_salamander'}, {'id': 2087, 'synset': 'spotted_salamander.n.02', 'name': 'spotted_salamander'}, {'id': 2088, 'synset': 'alpine_salamander.n.01', 'name': 'alpine_salamander'}, {'id': 2089, 'synset': 'newt.n.01', 'name': 'newt'}, {'id': 2090, 'synset': 'common_newt.n.01', 'name': 'common_newt'}, {'id': 2091, 'synset': 'red_eft.n.01', 'name': 'red_eft'}, {'id': 2092, 'synset': 'pacific_newt.n.01', 'name': 'Pacific_newt'}, {'id': 2093, 'synset': 'rough-skinned_newt.n.01', 'name': 'rough-skinned_newt'}, {'id': 2094, 'synset': 'california_newt.n.01', 'name': 'California_newt'}, {'id': 2095, 'synset': 'eft.n.01', 'name': 'eft'}, {'id': 2096, 'synset': 'ambystomid.n.01', 'name': 'ambystomid'}, {'id': 2097, 'synset': 'mole_salamander.n.01', 'name': 'mole_salamander'}, {'id': 2098, 'synset': 'spotted_salamander.n.01', 'name': 'spotted_salamander'}, {'id': 2099, 'synset': 'tiger_salamander.n.01', 'name': 'tiger_salamander'}, {'id': 2100, 'synset': 'axolotl.n.01', 'name': 'axolotl'}, {'id': 2101, 'synset': 'waterdog.n.01', 'name': 'waterdog'}, {'id': 2102, 'synset': 'hellbender.n.01', 'name': 'hellbender'}, {'id': 2103, 'synset': 'giant_salamander.n.01', 'name': 'giant_salamander'}, {'id': 2104, 'synset': 'olm.n.01', 'name': 'olm'}, {'id': 2105, 'synset': 'mud_puppy.n.01', 'name': 'mud_puppy'}, {'id': 2106, 'synset': 'dicamptodon.n.01', 'name': 'dicamptodon'}, {'id': 2107, 'synset': 'pacific_giant_salamander.n.01', 'name': 'Pacific_giant_salamander'}, {'id': 2108, 'synset': 'olympic_salamander.n.01', 'name': 'olympic_salamander'}, {'id': 2109, 'synset': 'lungless_salamander.n.01', 'name': 'lungless_salamander'}, {'id': 2110, 'synset': 'eastern_red-backed_salamander.n.01', 'name': 'eastern_red-backed_salamander'}, {'id': 2111, 'synset': 'western_red-backed_salamander.n.01', 'name': 'western_red-backed_salamander'}, {'id': 2112, 'synset': 'dusky_salamander.n.01', 'name': 'dusky_salamander'}, {'id': 2113, 'synset': 'climbing_salamander.n.01', 'name': 'climbing_salamander'}, {'id': 2114, 'synset': 'arboreal_salamander.n.01', 'name': 'arboreal_salamander'}, {'id': 2115, 'synset': 'slender_salamander.n.01', 'name': 'slender_salamander'}, {'id': 2116, 'synset': 'web-toed_salamander.n.01', 'name': 'web-toed_salamander'}, {'id': 2117, 'synset': 'shasta_salamander.n.01', 'name': 'Shasta_salamander'}, {'id': 2118, 'synset': 'limestone_salamander.n.01', 'name': 'limestone_salamander'}, {'id': 2119, 'synset': 'amphiuma.n.01', 'name': 'amphiuma'}, {'id': 2120, 'synset': 'siren.n.05', 'name': 'siren'}, {'id': 2121, 'synset': 'true_frog.n.01', 'name': 'true_frog'}, {'id': 2122, 'synset': 'wood-frog.n.01', 'name': 'wood-frog'}, {'id': 2123, 'synset': 'leopard_frog.n.01', 'name': 'leopard_frog'}, {'id': 2124, 'synset': 'bullfrog.n.01', 'name': 'bullfrog'}, {'id': 2125, 'synset': 'green_frog.n.01', 'name': 'green_frog'}, {'id': 2126, 'synset': 'cascades_frog.n.01', 'name': 'cascades_frog'}, {'id': 2127, 'synset': 'goliath_frog.n.01', 'name': 'goliath_frog'}, {'id': 2128, 'synset': 'pickerel_frog.n.01', 'name': 'pickerel_frog'}, {'id': 2129, 'synset': 'tarahumara_frog.n.01', 'name': 'tarahumara_frog'}, {'id': 2130, 'synset': 'grass_frog.n.01', 'name': 'grass_frog'}, {'id': 2131, 'synset': 'leptodactylid_frog.n.01', 'name': 'leptodactylid_frog'}, {'id': 2132, 'synset': 'robber_frog.n.02', 'name': 'robber_frog'}, {'id': 2133, 'synset': 'barking_frog.n.01', 'name': 'barking_frog'}, {'id': 2134, 'synset': 'crapaud.n.01', 'name': 'crapaud'}, {'id': 2135, 'synset': 'tree_frog.n.02', 'name': 'tree_frog'}, {'id': 2136, 'synset': 'tailed_frog.n.01', 'name': 'tailed_frog'}, {'id': 2137, 'synset': 'liopelma_hamiltoni.n.01', 'name': 'Liopelma_hamiltoni'}, {'id': 2138, 'synset': 'true_toad.n.01', 'name': 'true_toad'}, {'id': 2139, 'synset': 'bufo.n.01', 'name': 'bufo'}, {'id': 2140, 'synset': 'agua.n.01', 'name': 'agua'}, {'id': 2141, 'synset': 'european_toad.n.01', 'name': 'European_toad'}, {'id': 2142, 'synset': 'natterjack.n.01', 'name': 'natterjack'}, {'id': 2143, 'synset': 'american_toad.n.01', 'name': 'American_toad'}, {'id': 2144, 'synset': 'eurasian_green_toad.n.01', 'name': 'Eurasian_green_toad'}, {'id': 2145, 'synset': 'american_green_toad.n.01', 'name': 'American_green_toad'}, {'id': 2146, 'synset': 'yosemite_toad.n.01', 'name': 'Yosemite_toad'}, {'id': 2147, 'synset': 'texas_toad.n.01', 'name': 'Texas_toad'}, {'id': 2148, 'synset': 'southwestern_toad.n.01', 'name': 'southwestern_toad'}, {'id': 2149, 'synset': 'western_toad.n.01', 'name': 'western_toad'}, {'id': 2150, 'synset': 'obstetrical_toad.n.01', 'name': 'obstetrical_toad'}, {'id': 2151, 'synset': 'midwife_toad.n.01', 'name': 'midwife_toad'}, {'id': 2152, 'synset': 'fire-bellied_toad.n.01', 'name': 'fire-bellied_toad'}, {'id': 2153, 'synset': 'spadefoot.n.01', 'name': 'spadefoot'}, {'id': 2154, 'synset': 'western_spadefoot.n.01', 'name': 'western_spadefoot'}, {'id': 2155, 'synset': 'southern_spadefoot.n.01', 'name': 'southern_spadefoot'}, {'id': 2156, 'synset': 'plains_spadefoot.n.01', 'name': 'plains_spadefoot'}, {'id': 2157, 'synset': 'tree_toad.n.01', 'name': 'tree_toad'}, {'id': 2158, 'synset': 'spring_peeper.n.01', 'name': 'spring_peeper'}, {'id': 2159, 'synset': 'pacific_tree_toad.n.01', 'name': 'Pacific_tree_toad'}, {'id': 2160, 'synset': 'canyon_treefrog.n.01', 'name': 'canyon_treefrog'}, {'id': 2161, 'synset': 'chameleon_tree_frog.n.01', 'name': 'chameleon_tree_frog'}, {'id': 2162, 'synset': 'cricket_frog.n.01', 'name': 'cricket_frog'}, {'id': 2163, 'synset': 'northern_cricket_frog.n.01', 'name': 'northern_cricket_frog'}, {'id': 2164, 'synset': 'eastern_cricket_frog.n.01', 'name': 'eastern_cricket_frog'}, {'id': 2165, 'synset': 'chorus_frog.n.01', 'name': 'chorus_frog'}, {'id': 2166, 'synset': 'lowland_burrowing_treefrog.n.01', 'name': 'lowland_burrowing_treefrog'}, {'id': 2167, 'synset': 'western_narrow-mouthed_toad.n.01', 'name': 'western_narrow-mouthed_toad'}, {'id': 2168, 'synset': 'eastern_narrow-mouthed_toad.n.01', 'name': 'eastern_narrow-mouthed_toad'}, {'id': 2169, 'synset': 'sheep_frog.n.01', 'name': 'sheep_frog'}, {'id': 2170, 'synset': 'tongueless_frog.n.01', 'name': 'tongueless_frog'}, {'id': 2171, 'synset': 'surinam_toad.n.01', 'name': 'Surinam_toad'}, {'id': 2172, 'synset': 'african_clawed_frog.n.01', 'name': 'African_clawed_frog'}, {'id': 2173, 'synset': 'south_american_poison_toad.n.01', 'name': 'South_American_poison_toad'}, {'id': 2174, 'synset': 'caecilian.n.01', 'name': 'caecilian'}, {'id': 2175, 'synset': 'reptile.n.01', 'name': 'reptile'}, {'id': 2176, 'synset': 'anapsid.n.01', 'name': 'anapsid'}, {'id': 2177, 'synset': 'diapsid.n.01', 'name': 'diapsid'}, {'id': 2178, 'synset': 'diapsida.n.01', 'name': 'Diapsida'}, {'id': 2179, 'synset': 'chelonian.n.01', 'name': 'chelonian'}, {'id': 2180, 'synset': 'sea_turtle.n.01', 'name': 'sea_turtle'}, {'id': 2181, 'synset': 'green_turtle.n.01', 'name': 'green_turtle'}, {'id': 2182, 'synset': 'loggerhead.n.02', 'name': 'loggerhead'}, {'id': 2183, 'synset': 'ridley.n.01', 'name': 'ridley'}, {'id': 2184, 'synset': 'atlantic_ridley.n.01', 'name': 'Atlantic_ridley'}, {'id': 2185, 'synset': 'pacific_ridley.n.01', 'name': 'Pacific_ridley'}, {'id': 2186, 'synset': 'hawksbill_turtle.n.01', 'name': 'hawksbill_turtle'}, {'id': 2187, 'synset': 'leatherback_turtle.n.01', 'name': 'leatherback_turtle'}, {'id': 2188, 'synset': 'snapping_turtle.n.01', 'name': 'snapping_turtle'}, {'id': 2189, 'synset': 'common_snapping_turtle.n.01', 'name': 'common_snapping_turtle'}, {'id': 2190, 'synset': 'alligator_snapping_turtle.n.01', 'name': 'alligator_snapping_turtle'}, {'id': 2191, 'synset': 'mud_turtle.n.01', 'name': 'mud_turtle'}, {'id': 2192, 'synset': 'musk_turtle.n.01', 'name': 'musk_turtle'}, {'id': 2193, 'synset': 'terrapin.n.01', 'name': 'terrapin'}, {'id': 2194, 'synset': 'diamondback_terrapin.n.01', 'name': 'diamondback_terrapin'}, {'id': 2195, 'synset': 'red-bellied_terrapin.n.01', 'name': 'red-bellied_terrapin'}, {'id': 2196, 'synset': 'slider.n.03', 'name': 'slider'}, {'id': 2197, 'synset': 'cooter.n.01', 'name': 'cooter'}, {'id': 2198, 'synset': 'box_turtle.n.01', 'name': 'box_turtle'}, {'id': 2199, 'synset': 'western_box_turtle.n.01', 'name': 'Western_box_turtle'}, {'id': 2200, 'synset': 'painted_turtle.n.01', 'name': 'painted_turtle'}, {'id': 2201, 'synset': 'tortoise.n.01', 'name': 'tortoise'}, {'id': 2202, 'synset': 'european_tortoise.n.01', 'name': 'European_tortoise'}, {'id': 2203, 'synset': 'giant_tortoise.n.01', 'name': 'giant_tortoise'}, {'id': 2204, 'synset': 'gopher_tortoise.n.01', 'name': 'gopher_tortoise'}, {'id': 2205, 'synset': 'desert_tortoise.n.01', 'name': 'desert_tortoise'}, {'id': 2206, 'synset': 'texas_tortoise.n.01', 'name': 'Texas_tortoise'}, {'id': 2207, 'synset': 'soft-shelled_turtle.n.01', 'name': 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'synset': 'earless_lizard.n.01', 'name': 'earless_lizard'}, {'id': 2223, 'synset': 'collared_lizard.n.01', 'name': 'collared_lizard'}, {'id': 2224, 'synset': 'leopard_lizard.n.01', 'name': 'leopard_lizard'}, {'id': 2225, 'synset': 'spiny_lizard.n.02', 'name': 'spiny_lizard'}, {'id': 2226, 'synset': 'fence_lizard.n.01', 'name': 'fence_lizard'}, {'id': 2227, 'synset': 'western_fence_lizard.n.01', 'name': 'western_fence_lizard'}, {'id': 2228, 'synset': 'eastern_fence_lizard.n.01', 'name': 'eastern_fence_lizard'}, {'id': 2229, 'synset': 'sagebrush_lizard.n.01', 'name': 'sagebrush_lizard'}, {'id': 2230, 'synset': 'side-blotched_lizard.n.01', 'name': 'side-blotched_lizard'}, {'id': 2231, 'synset': 'tree_lizard.n.01', 'name': 'tree_lizard'}, {'id': 2232, 'synset': 'horned_lizard.n.01', 'name': 'horned_lizard'}, {'id': 2233, 'synset': 'texas_horned_lizard.n.01', 'name': 'Texas_horned_lizard'}, {'id': 2234, 'synset': 'basilisk.n.03', 'name': 'basilisk'}, {'id': 2235, 'synset': 'american_chameleon.n.01', 'name': 'American_chameleon'}, {'id': 2236, 'synset': 'worm_lizard.n.01', 'name': 'worm_lizard'}, {'id': 2237, 'synset': 'night_lizard.n.01', 'name': 'night_lizard'}, {'id': 2238, 'synset': 'skink.n.01', 'name': 'skink'}, {'id': 2239, 'synset': 'western_skink.n.01', 'name': 'western_skink'}, {'id': 2240, 'synset': 'mountain_skink.n.01', 'name': 'mountain_skink'}, {'id': 2241, 'synset': 'teiid_lizard.n.01', 'name': 'teiid_lizard'}, {'id': 2242, 'synset': 'whiptail.n.01', 'name': 'whiptail'}, {'id': 2243, 'synset': 'racerunner.n.01', 'name': 'racerunner'}, {'id': 2244, 'synset': 'plateau_striped_whiptail.n.01', 'name': 'plateau_striped_whiptail'}, {'id': 2245, 'synset': 'chihuahuan_spotted_whiptail.n.01', 'name': 'Chihuahuan_spotted_whiptail'}, {'id': 2246, 'synset': 'western_whiptail.n.01', 'name': 'western_whiptail'}, {'id': 2247, 'synset': 'checkered_whiptail.n.01', 'name': 'checkered_whiptail'}, {'id': 2248, 'synset': 'teju.n.01', 'name': 'teju'}, {'id': 2249, 'synset': 'caiman_lizard.n.01', 'name': 'caiman_lizard'}, {'id': 2250, 'synset': 'agamid.n.01', 'name': 'agamid'}, {'id': 2251, 'synset': 'agama.n.01', 'name': 'agama'}, {'id': 2252, 'synset': 'frilled_lizard.n.01', 'name': 'frilled_lizard'}, {'id': 2253, 'synset': 'moloch.n.03', 'name': 'moloch'}, {'id': 2254, 'synset': 'mountain_devil.n.02', 'name': 'mountain_devil'}, {'id': 2255, 'synset': 'anguid_lizard.n.01', 'name': 'anguid_lizard'}, {'id': 2256, 'synset': 'alligator_lizard.n.01', 'name': 'alligator_lizard'}, {'id': 2257, 'synset': 'blindworm.n.01', 'name': 'blindworm'}, {'id': 2258, 'synset': 'glass_lizard.n.01', 'name': 'glass_lizard'}, {'id': 2259, 'synset': 'legless_lizard.n.01', 'name': 'legless_lizard'}, {'id': 2260, 'synset': 'lanthanotus_borneensis.n.01', 'name': 'Lanthanotus_borneensis'}, {'id': 2261, 'synset': 'venomous_lizard.n.01', 'name': 'venomous_lizard'}, {'id': 2262, 'synset': 'gila_monster.n.01', 'name': 'Gila_monster'}, {'id': 2263, 'synset': 'beaded_lizard.n.01', 'name': 'beaded_lizard'}, {'id': 2264, 'synset': 'lacertid_lizard.n.01', 'name': 'lacertid_lizard'}, {'id': 2265, 'synset': 'sand_lizard.n.01', 'name': 'sand_lizard'}, {'id': 2266, 'synset': 'green_lizard.n.01', 'name': 'green_lizard'}, {'id': 2267, 'synset': 'chameleon.n.03', 'name': 'chameleon'}, {'id': 2268, 'synset': 'african_chameleon.n.01', 'name': 'African_chameleon'}, {'id': 2269, 'synset': 'horned_chameleon.n.01', 'name': 'horned_chameleon'}, {'id': 2270, 'synset': 'monitor.n.07', 'name': 'monitor'}, {'id': 2271, 'synset': 'african_monitor.n.01', 'name': 'African_monitor'}, {'id': 2272, 'synset': 'komodo_dragon.n.01', 'name': 'Komodo_dragon'}, {'id': 2273, 'synset': 'crocodilian_reptile.n.01', 'name': 'crocodilian_reptile'}, {'id': 2274, 'synset': 'crocodile.n.01', 'name': 'crocodile'}, {'id': 2275, 'synset': 'african_crocodile.n.01', 'name': 'African_crocodile'}, {'id': 2276, 'synset': 'asian_crocodile.n.01', 'name': 'Asian_crocodile'}, {'id': 2277, 'synset': "morlett's_crocodile.n.01", 'name': "Morlett's_crocodile"}, {'id': 2278, 'synset': 'false_gavial.n.01', 'name': 'false_gavial'}, {'id': 2279, 'synset': 'american_alligator.n.01', 'name': 'American_alligator'}, {'id': 2280, 'synset': 'chinese_alligator.n.01', 'name': 'Chinese_alligator'}, {'id': 2281, 'synset': 'caiman.n.01', 'name': 'caiman'}, {'id': 2282, 'synset': 'spectacled_caiman.n.01', 'name': 'spectacled_caiman'}, {'id': 2283, 'synset': 'gavial.n.01', 'name': 'gavial'}, {'id': 2284, 'synset': 'armored_dinosaur.n.01', 'name': 'armored_dinosaur'}, {'id': 2285, 'synset': 'stegosaur.n.01', 'name': 'stegosaur'}, {'id': 2286, 'synset': 'ankylosaur.n.01', 'name': 'ankylosaur'}, {'id': 2287, 'synset': 'edmontonia.n.01', 'name': 'Edmontonia'}, {'id': 2288, 'synset': 'bone-headed_dinosaur.n.01', 'name': 'bone-headed_dinosaur'}, {'id': 2289, 'synset': 'pachycephalosaur.n.01', 'name': 'pachycephalosaur'}, {'id': 2290, 'synset': 'ceratopsian.n.01', 'name': 'ceratopsian'}, {'id': 2291, 'synset': 'protoceratops.n.01', 'name': 'protoceratops'}, {'id': 2292, 'synset': 'triceratops.n.01', 'name': 'triceratops'}, {'id': 2293, 'synset': 'styracosaur.n.01', 'name': 'styracosaur'}, {'id': 2294, 'synset': 'psittacosaur.n.01', 'name': 'psittacosaur'}, {'id': 2295, 'synset': 'ornithopod.n.01', 'name': 'ornithopod'}, {'id': 2296, 'synset': 'hadrosaur.n.01', 'name': 'hadrosaur'}, {'id': 2297, 'synset': 'trachodon.n.01', 'name': 'trachodon'}, {'id': 2298, 'synset': 'saurischian.n.01', 'name': 'saurischian'}, {'id': 2299, 'synset': 'sauropod.n.01', 'name': 'sauropod'}, {'id': 2300, 'synset': 'apatosaur.n.01', 'name': 'apatosaur'}, {'id': 2301, 'synset': 'barosaur.n.01', 'name': 'barosaur'}, {'id': 2302, 'synset': 'diplodocus.n.01', 'name': 'diplodocus'}, {'id': 2303, 'synset': 'argentinosaur.n.01', 'name': 'argentinosaur'}, {'id': 2304, 'synset': 'theropod.n.01', 'name': 'theropod'}, {'id': 2305, 'synset': 'ceratosaur.n.01', 'name': 'ceratosaur'}, {'id': 2306, 'synset': 'coelophysis.n.01', 'name': 'coelophysis'}, {'id': 2307, 'synset': 'tyrannosaur.n.01', 'name': 'tyrannosaur'}, {'id': 2308, 'synset': 'allosaur.n.01', 'name': 'allosaur'}, {'id': 2309, 'synset': 'ornithomimid.n.01', 'name': 'ornithomimid'}, {'id': 2310, 'synset': 'maniraptor.n.01', 'name': 'maniraptor'}, {'id': 2311, 'synset': 'oviraptorid.n.01', 'name': 'oviraptorid'}, {'id': 2312, 'synset': 'velociraptor.n.01', 'name': 'velociraptor'}, {'id': 2313, 'synset': 'deinonychus.n.01', 'name': 'deinonychus'}, {'id': 2314, 'synset': 'utahraptor.n.01', 'name': 'utahraptor'}, {'id': 2315, 'synset': 'synapsid.n.01', 'name': 'synapsid'}, {'id': 2316, 'synset': 'dicynodont.n.01', 'name': 'dicynodont'}, {'id': 2317, 'synset': 'pelycosaur.n.01', 'name': 'pelycosaur'}, {'id': 2318, 'synset': 'dimetrodon.n.01', 'name': 'dimetrodon'}, {'id': 2319, 'synset': 'pterosaur.n.01', 'name': 'pterosaur'}, {'id': 2320, 'synset': 'pterodactyl.n.01', 'name': 'pterodactyl'}, {'id': 2321, 'synset': 'ichthyosaur.n.01', 'name': 'ichthyosaur'}, {'id': 2322, 'synset': 'ichthyosaurus.n.01', 'name': 'ichthyosaurus'}, {'id': 2323, 'synset': 'stenopterygius.n.01', 'name': 'stenopterygius'}, {'id': 2324, 'synset': 'plesiosaur.n.01', 'name': 'plesiosaur'}, {'id': 2325, 'synset': 'nothosaur.n.01', 'name': 'nothosaur'}, {'id': 2326, 'synset': 'colubrid_snake.n.01', 'name': 'colubrid_snake'}, {'id': 2327, 'synset': 'hoop_snake.n.01', 'name': 'hoop_snake'}, {'id': 2328, 'synset': 'thunder_snake.n.01', 'name': 'thunder_snake'}, {'id': 2329, 'synset': 'ringneck_snake.n.01', 'name': 'ringneck_snake'}, {'id': 2330, 'synset': 'hognose_snake.n.01', 'name': 'hognose_snake'}, {'id': 2331, 'synset': 'leaf-nosed_snake.n.01', 'name': 'leaf-nosed_snake'}, {'id': 2332, 'synset': 'green_snake.n.02', 'name': 'green_snake'}, {'id': 2333, 'synset': 'smooth_green_snake.n.01', 'name': 'smooth_green_snake'}, {'id': 2334, 'synset': 'rough_green_snake.n.01', 'name': 'rough_green_snake'}, {'id': 2335, 'synset': 'green_snake.n.01', 'name': 'green_snake'}, {'id': 2336, 'synset': 'racer.n.04', 'name': 'racer'}, {'id': 2337, 'synset': 'blacksnake.n.02', 'name': 'blacksnake'}, {'id': 2338, 'synset': 'blue_racer.n.01', 'name': 'blue_racer'}, {'id': 2339, 'synset': 'horseshoe_whipsnake.n.01', 'name': 'horseshoe_whipsnake'}, {'id': 2340, 'synset': 'whip-snake.n.01', 'name': 'whip-snake'}, {'id': 2341, 'synset': 'coachwhip.n.02', 'name': 'coachwhip'}, {'id': 2342, 'synset': 'california_whipsnake.n.01', 'name': 'California_whipsnake'}, {'id': 2343, 'synset': 'sonoran_whipsnake.n.01', 'name': 'Sonoran_whipsnake'}, {'id': 2344, 'synset': 'rat_snake.n.01', 'name': 'rat_snake'}, {'id': 2345, 'synset': 'corn_snake.n.01', 'name': 'corn_snake'}, {'id': 2346, 'synset': 'black_rat_snake.n.01', 'name': 'black_rat_snake'}, {'id': 2347, 'synset': 'chicken_snake.n.01', 'name': 'chicken_snake'}, {'id': 2348, 'synset': 'indian_rat_snake.n.01', 'name': 'Indian_rat_snake'}, {'id': 2349, 'synset': 'glossy_snake.n.01', 'name': 'glossy_snake'}, {'id': 2350, 'synset': 'bull_snake.n.01', 'name': 'bull_snake'}, {'id': 2351, 'synset': 'gopher_snake.n.02', 'name': 'gopher_snake'}, {'id': 2352, 'synset': 'pine_snake.n.01', 'name': 'pine_snake'}, {'id': 2353, 'synset': 'king_snake.n.01', 'name': 'king_snake'}, {'id': 2354, 'synset': 'common_kingsnake.n.01', 'name': 'common_kingsnake'}, {'id': 2355, 'synset': 'milk_snake.n.01', 'name': 'milk_snake'}, {'id': 2356, 'synset': 'garter_snake.n.01', 'name': 'garter_snake'}, {'id': 2357, 'synset': 'common_garter_snake.n.01', 'name': 'common_garter_snake'}, {'id': 2358, 'synset': 'ribbon_snake.n.01', 'name': 'ribbon_snake'}, {'id': 2359, 'synset': 'western_ribbon_snake.n.01', 'name': 'Western_ribbon_snake'}, {'id': 2360, 'synset': 'lined_snake.n.01', 'name': 'lined_snake'}, {'id': 2361, 'synset': 'ground_snake.n.01', 'name': 'ground_snake'}, {'id': 2362, 'synset': 'eastern_ground_snake.n.01', 'name': 'eastern_ground_snake'}, {'id': 2363, 'synset': 'water_snake.n.01', 'name': 'water_snake'}, {'id': 2364, 'synset': 'common_water_snake.n.01', 'name': 'common_water_snake'}, {'id': 2365, 'synset': 'water_moccasin.n.02', 'name': 'water_moccasin'}, {'id': 2366, 'synset': 'grass_snake.n.01', 'name': 'grass_snake'}, {'id': 2367, 'synset': 'viperine_grass_snake.n.01', 'name': 'viperine_grass_snake'}, {'id': 2368, 'synset': 'red-bellied_snake.n.01', 'name': 'red-bellied_snake'}, {'id': 2369, 'synset': 'sand_snake.n.01', 'name': 'sand_snake'}, {'id': 2370, 'synset': 'banded_sand_snake.n.01', 'name': 'banded_sand_snake'}, {'id': 2371, 'synset': 'black-headed_snake.n.01', 'name': 'black-headed_snake'}, {'id': 2372, 'synset': 'vine_snake.n.01', 'name': 'vine_snake'}, {'id': 2373, 'synset': 'lyre_snake.n.01', 'name': 'lyre_snake'}, {'id': 2374, 'synset': 'sonoran_lyre_snake.n.01', 'name': 'Sonoran_lyre_snake'}, {'id': 2375, 'synset': 'night_snake.n.01', 'name': 'night_snake'}, {'id': 2376, 'synset': 'blind_snake.n.01', 'name': 'blind_snake'}, {'id': 2377, 'synset': 'western_blind_snake.n.01', 'name': 'western_blind_snake'}, {'id': 2378, 'synset': 'indigo_snake.n.01', 'name': 'indigo_snake'}, {'id': 2379, 'synset': 'eastern_indigo_snake.n.01', 'name': 'eastern_indigo_snake'}, {'id': 2380, 'synset': 'constrictor.n.01', 'name': 'constrictor'}, {'id': 2381, 'synset': 'boa.n.02', 'name': 'boa'}, {'id': 2382, 'synset': 'boa_constrictor.n.01', 'name': 'boa_constrictor'}, {'id': 2383, 'synset': 'rubber_boa.n.01', 'name': 'rubber_boa'}, {'id': 2384, 'synset': 'rosy_boa.n.01', 'name': 'rosy_boa'}, {'id': 2385, 'synset': 'anaconda.n.01', 'name': 'anaconda'}, {'id': 2386, 'synset': 'python.n.01', 'name': 'python'}, {'id': 2387, 'synset': 'carpet_snake.n.01', 'name': 'carpet_snake'}, {'id': 2388, 'synset': 'reticulated_python.n.01', 'name': 'reticulated_python'}, {'id': 2389, 'synset': 'indian_python.n.01', 'name': 'Indian_python'}, {'id': 2390, 'synset': 'rock_python.n.01', 'name': 'rock_python'}, {'id': 2391, 'synset': 'amethystine_python.n.01', 'name': 'amethystine_python'}, {'id': 2392, 'synset': 'elapid.n.01', 'name': 'elapid'}, {'id': 2393, 'synset': 'coral_snake.n.02', 'name': 'coral_snake'}, {'id': 2394, 'synset': 'eastern_coral_snake.n.01', 'name': 'eastern_coral_snake'}, {'id': 2395, 'synset': 'western_coral_snake.n.01', 'name': 'western_coral_snake'}, {'id': 2396, 'synset': 'coral_snake.n.01', 'name': 'coral_snake'}, {'id': 2397, 'synset': 'african_coral_snake.n.01', 'name': 'African_coral_snake'}, {'id': 2398, 'synset': 'australian_coral_snake.n.01', 'name': 'Australian_coral_snake'}, {'id': 2399, 'synset': 'copperhead.n.02', 'name': 'copperhead'}, {'id': 2400, 'synset': 'cobra.n.01', 'name': 'cobra'}, {'id': 2401, 'synset': 'indian_cobra.n.01', 'name': 'Indian_cobra'}, {'id': 2402, 'synset': 'asp.n.02', 'name': 'asp'}, {'id': 2403, 'synset': 'black-necked_cobra.n.01', 'name': 'black-necked_cobra'}, {'id': 2404, 'synset': 'hamadryad.n.02', 'name': 'hamadryad'}, {'id': 2405, 'synset': 'ringhals.n.01', 'name': 'ringhals'}, {'id': 2406, 'synset': 'mamba.n.01', 'name': 'mamba'}, {'id': 2407, 'synset': 'black_mamba.n.01', 'name': 'black_mamba'}, {'id': 2408, 'synset': 'green_mamba.n.01', 'name': 'green_mamba'}, {'id': 2409, 'synset': 'death_adder.n.01', 'name': 'death_adder'}, {'id': 2410, 'synset': 'tiger_snake.n.01', 'name': 'tiger_snake'}, {'id': 2411, 'synset': 'australian_blacksnake.n.01', 'name': 'Australian_blacksnake'}, {'id': 2412, 'synset': 'krait.n.01', 'name': 'krait'}, {'id': 2413, 'synset': 'banded_krait.n.01', 'name': 'banded_krait'}, {'id': 2414, 'synset': 'taipan.n.01', 'name': 'taipan'}, {'id': 2415, 'synset': 'sea_snake.n.01', 'name': 'sea_snake'}, {'id': 2416, 'synset': 'viper.n.01', 'name': 'viper'}, {'id': 2417, 'synset': 'adder.n.03', 'name': 'adder'}, {'id': 2418, 'synset': 'asp.n.01', 'name': 'asp'}, {'id': 2419, 'synset': 'puff_adder.n.01', 'name': 'puff_adder'}, {'id': 2420, 'synset': 'gaboon_viper.n.01', 'name': 'gaboon_viper'}, {'id': 2421, 'synset': 'horned_viper.n.01', 'name': 'horned_viper'}, {'id': 2422, 'synset': 'pit_viper.n.01', 'name': 'pit_viper'}, {'id': 2423, 'synset': 'copperhead.n.01', 'name': 'copperhead'}, {'id': 2424, 'synset': 'water_moccasin.n.01', 'name': 'water_moccasin'}, {'id': 2425, 'synset': 'rattlesnake.n.01', 'name': 'rattlesnake'}, {'id': 2426, 'synset': 'diamondback.n.01', 'name': 'diamondback'}, {'id': 2427, 'synset': 'timber_rattlesnake.n.01', 'name': 'timber_rattlesnake'}, {'id': 2428, 'synset': 'canebrake_rattlesnake.n.01', 'name': 'canebrake_rattlesnake'}, {'id': 2429, 'synset': 'prairie_rattlesnake.n.01', 'name': 'prairie_rattlesnake'}, {'id': 2430, 'synset': 'sidewinder.n.01', 'name': 'sidewinder'}, {'id': 2431, 'synset': 'western_diamondback.n.01', 'name': 'Western_diamondback'}, {'id': 2432, 'synset': 'rock_rattlesnake.n.01', 'name': 'rock_rattlesnake'}, {'id': 2433, 'synset': 'tiger_rattlesnake.n.01', 'name': 'tiger_rattlesnake'}, {'id': 2434, 'synset': 'mojave_rattlesnake.n.01', 'name': 'Mojave_rattlesnake'}, {'id': 2435, 'synset': 'speckled_rattlesnake.n.01', 'name': 'speckled_rattlesnake'}, {'id': 2436, 'synset': 'massasauga.n.02', 'name': 'massasauga'}, {'id': 2437, 'synset': 'ground_rattler.n.01', 'name': 'ground_rattler'}, {'id': 2438, 'synset': 'fer-de-lance.n.01', 'name': 'fer-de-lance'}, {'id': 2439, 'synset': 'carcase.n.01', 'name': 'carcase'}, {'id': 2440, 'synset': 'carrion.n.01', 'name': 'carrion'}, {'id': 2441, 'synset': 'arthropod.n.01', 'name': 'arthropod'}, {'id': 2442, 'synset': 'trilobite.n.01', 'name': 'trilobite'}, {'id': 2443, 'synset': 'arachnid.n.01', 'name': 'arachnid'}, {'id': 2444, 'synset': 'harvestman.n.01', 'name': 'harvestman'}, {'id': 2445, 'synset': 'scorpion.n.03', 'name': 'scorpion'}, {'id': 2446, 'synset': 'false_scorpion.n.01', 'name': 'false_scorpion'}, {'id': 2447, 'synset': 'book_scorpion.n.01', 'name': 'book_scorpion'}, {'id': 2448, 'synset': 'whip-scorpion.n.01', 'name': 'whip-scorpion'}, {'id': 2449, 'synset': 'vinegarroon.n.01', 'name': 'vinegarroon'}, {'id': 2450, 'synset': 'orb-weaving_spider.n.01', 'name': 'orb-weaving_spider'}, {'id': 2451, 'synset': 'black_and_gold_garden_spider.n.01', 'name': 'black_and_gold_garden_spider'}, {'id': 2452, 'synset': 'barn_spider.n.01', 'name': 'barn_spider'}, {'id': 2453, 'synset': 'garden_spider.n.01', 'name': 'garden_spider'}, {'id': 2454, 'synset': 'comb-footed_spider.n.01', 'name': 'comb-footed_spider'}, {'id': 2455, 'synset': 'black_widow.n.01', 'name': 'black_widow'}, {'id': 2456, 'synset': 'tarantula.n.02', 'name': 'tarantula'}, {'id': 2457, 'synset': 'wolf_spider.n.01', 'name': 'wolf_spider'}, {'id': 2458, 'synset': 'european_wolf_spider.n.01', 'name': 'European_wolf_spider'}, {'id': 2459, 'synset': 'trap-door_spider.n.01', 'name': 'trap-door_spider'}, {'id': 2460, 'synset': 'acarine.n.01', 'name': 'acarine'}, {'id': 2461, 'synset': 'tick.n.02', 'name': 'tick'}, {'id': 2462, 'synset': 'hard_tick.n.01', 'name': 'hard_tick'}, {'id': 2463, 'synset': 'ixodes_dammini.n.01', 'name': 'Ixodes_dammini'}, {'id': 2464, 'synset': 'ixodes_neotomae.n.01', 'name': 'Ixodes_neotomae'}, {'id': 2465, 'synset': 'ixodes_pacificus.n.01', 'name': 'Ixodes_pacificus'}, {'id': 2466, 'synset': 'ixodes_scapularis.n.01', 'name': 'Ixodes_scapularis'}, {'id': 2467, 'synset': 'sheep-tick.n.02', 'name': 'sheep-tick'}, {'id': 2468, 'synset': 'ixodes_persulcatus.n.01', 'name': 'Ixodes_persulcatus'}, {'id': 2469, 'synset': 'ixodes_dentatus.n.01', 'name': 'Ixodes_dentatus'}, {'id': 2470, 'synset': 'ixodes_spinipalpis.n.01', 'name': 'Ixodes_spinipalpis'}, {'id': 2471, 'synset': 'wood_tick.n.01', 'name': 'wood_tick'}, {'id': 2472, 'synset': 'soft_tick.n.01', 'name': 'soft_tick'}, {'id': 2473, 'synset': 'mite.n.02', 'name': 'mite'}, {'id': 2474, 'synset': 'web-spinning_mite.n.01', 'name': 'web-spinning_mite'}, {'id': 2475, 'synset': 'acarid.n.01', 'name': 'acarid'}, {'id': 2476, 'synset': 'trombidiid.n.01', 'name': 'trombidiid'}, {'id': 2477, 'synset': 'trombiculid.n.01', 'name': 'trombiculid'}, {'id': 2478, 'synset': 'harvest_mite.n.01', 'name': 'harvest_mite'}, {'id': 2479, 'synset': 'acarus.n.01', 'name': 'acarus'}, {'id': 2480, 'synset': 'itch_mite.n.01', 'name': 'itch_mite'}, {'id': 2481, 'synset': 'rust_mite.n.01', 'name': 'rust_mite'}, {'id': 2482, 'synset': 'spider_mite.n.01', 'name': 'spider_mite'}, {'id': 2483, 'synset': 'red_spider.n.01', 'name': 'red_spider'}, {'id': 2484, 'synset': 'myriapod.n.01', 'name': 'myriapod'}, {'id': 2485, 'synset': 'garden_centipede.n.01', 'name': 'garden_centipede'}, {'id': 2486, 'synset': 'tardigrade.n.01', 'name': 'tardigrade'}, {'id': 2487, 'synset': 'centipede.n.01', 'name': 'centipede'}, {'id': 2488, 'synset': 'house_centipede.n.01', 'name': 'house_centipede'}, {'id': 2489, 'synset': 'millipede.n.01', 'name': 'millipede'}, {'id': 2490, 'synset': 'sea_spider.n.01', 'name': 'sea_spider'}, {'id': 2491, 'synset': 'merostomata.n.01', 'name': 'Merostomata'}, {'id': 2492, 'synset': 'horseshoe_crab.n.01', 'name': 'horseshoe_crab'}, {'id': 2493, 'synset': 'asian_horseshoe_crab.n.01', 'name': 'Asian_horseshoe_crab'}, {'id': 2494, 'synset': 'eurypterid.n.01', 'name': 'eurypterid'}, {'id': 2495, 'synset': 'tongue_worm.n.01', 'name': 'tongue_worm'}, {'id': 2496, 'synset': 'gallinaceous_bird.n.01', 'name': 'gallinaceous_bird'}, {'id': 2497, 'synset': 'domestic_fowl.n.01', 'name': 'domestic_fowl'}, {'id': 2498, 'synset': 'dorking.n.01', 'name': 'Dorking'}, {'id': 2499, 'synset': 'plymouth_rock.n.02', 'name': 'Plymouth_Rock'}, {'id': 2500, 'synset': 'cornish.n.02', 'name': 'Cornish'}, {'id': 2501, 'synset': 'rock_cornish.n.01', 'name': 'Rock_Cornish'}, {'id': 2502, 'synset': 'game_fowl.n.01', 'name': 'game_fowl'}, {'id': 2503, 'synset': 'cochin.n.01', 'name': 'cochin'}, {'id': 2504, 'synset': 'jungle_fowl.n.01', 'name': 'jungle_fowl'}, {'id': 2505, 'synset': 'jungle_cock.n.01', 'name': 'jungle_cock'}, {'id': 2506, 'synset': 'jungle_hen.n.01', 'name': 'jungle_hen'}, {'id': 2507, 'synset': 'red_jungle_fowl.n.01', 'name': 'red_jungle_fowl'}, {'id': 2508, 'synset': 'bantam.n.01', 'name': 'bantam'}, {'id': 2509, 'synset': 'chick.n.01', 'name': 'chick'}, {'id': 2510, 'synset': 'cockerel.n.01', 'name': 'cockerel'}, {'id': 2511, 'synset': 'capon.n.02', 'name': 'capon'}, {'id': 2512, 'synset': 'hen.n.01', 'name': 'hen'}, {'id': 2513, 'synset': 'cackler.n.01', 'name': 'cackler'}, {'id': 2514, 'synset': 'brood_hen.n.01', 'name': 'brood_hen'}, {'id': 2515, 'synset': 'mother_hen.n.02', 'name': 'mother_hen'}, {'id': 2516, 'synset': 'layer.n.04', 'name': 'layer'}, {'id': 2517, 'synset': 'pullet.n.02', 'name': 'pullet'}, {'id': 2518, 'synset': 'spring_chicken.n.02', 'name': 'spring_chicken'}, {'id': 2519, 'synset': 'rhode_island_red.n.01', 'name': 'Rhode_Island_red'}, {'id': 2520, 'synset': 'dominique.n.01', 'name': 'Dominique'}, {'id': 2521, 'synset': 'orpington.n.01', 'name': 'Orpington'}, {'id': 2522, 'synset': 'turkey.n.01', 'name': 'turkey'}, {'id': 2523, 'synset': 'turkey_cock.n.01', 'name': 'turkey_cock'}, {'id': 2524, 'synset': 'ocellated_turkey.n.01', 'name': 'ocellated_turkey'}, {'id': 2525, 'synset': 'grouse.n.02', 'name': 'grouse'}, {'id': 2526, 'synset': 'black_grouse.n.01', 'name': 'black_grouse'}, {'id': 2527, 'synset': 'european_black_grouse.n.01', 'name': 'European_black_grouse'}, {'id': 2528, 'synset': 'asian_black_grouse.n.01', 'name': 'Asian_black_grouse'}, {'id': 2529, 'synset': 'blackcock.n.01', 'name': 'blackcock'}, {'id': 2530, 'synset': 'greyhen.n.01', 'name': 'greyhen'}, {'id': 2531, 'synset': 'ptarmigan.n.01', 'name': 'ptarmigan'}, {'id': 2532, 'synset': 'red_grouse.n.01', 'name': 'red_grouse'}, {'id': 2533, 'synset': 'moorhen.n.02', 'name': 'moorhen'}, {'id': 2534, 'synset': 'capercaillie.n.01', 'name': 'capercaillie'}, {'id': 2535, 'synset': 'spruce_grouse.n.01', 'name': 'spruce_grouse'}, {'id': 2536, 'synset': 'sage_grouse.n.01', 'name': 'sage_grouse'}, {'id': 2537, 'synset': 'ruffed_grouse.n.01', 'name': 'ruffed_grouse'}, {'id': 2538, 'synset': 'sharp-tailed_grouse.n.01', 'name': 'sharp-tailed_grouse'}, {'id': 2539, 'synset': 'prairie_chicken.n.01', 'name': 'prairie_chicken'}, {'id': 2540, 'synset': 'greater_prairie_chicken.n.01', 'name': 'greater_prairie_chicken'}, {'id': 2541, 'synset': 'lesser_prairie_chicken.n.01', 'name': 'lesser_prairie_chicken'}, {'id': 2542, 'synset': 'heath_hen.n.01', 'name': 'heath_hen'}, {'id': 2543, 'synset': 'guan.n.01', 'name': 'guan'}, {'id': 2544, 'synset': 'curassow.n.01', 'name': 'curassow'}, {'id': 2545, 'synset': 'piping_guan.n.01', 'name': 'piping_guan'}, {'id': 2546, 'synset': 'chachalaca.n.01', 'name': 'chachalaca'}, {'id': 2547, 'synset': 'texas_chachalaca.n.01', 'name': 'Texas_chachalaca'}, {'id': 2548, 'synset': 'megapode.n.01', 'name': 'megapode'}, {'id': 2549, 'synset': 'mallee_fowl.n.01', 'name': 'mallee_fowl'}, {'id': 2550, 'synset': 'mallee_hen.n.01', 'name': 'mallee_hen'}, {'id': 2551, 'synset': 'brush_turkey.n.01', 'name': 'brush_turkey'}, {'id': 2552, 'synset': 'maleo.n.01', 'name': 'maleo'}, {'id': 2553, 'synset': 'phasianid.n.01', 'name': 'phasianid'}, {'id': 2554, 'synset': 'pheasant.n.01', 'name': 'pheasant'}, {'id': 2555, 'synset': 'ring-necked_pheasant.n.01', 'name': 'ring-necked_pheasant'}, {'id': 2556, 'synset': 'afropavo.n.01', 'name': 'afropavo'}, {'id': 2557, 'synset': 'argus.n.02', 'name': 'argus'}, {'id': 2558, 'synset': 'golden_pheasant.n.01', 'name': 'golden_pheasant'}, {'id': 2559, 'synset': 'bobwhite.n.01', 'name': 'bobwhite'}, {'id': 2560, 'synset': 'northern_bobwhite.n.01', 'name': 'northern_bobwhite'}, {'id': 2561, 'synset': 'old_world_quail.n.01', 'name': 'Old_World_quail'}, {'id': 2562, 'synset': 'migratory_quail.n.01', 'name': 'migratory_quail'}, {'id': 2563, 'synset': 'monal.n.01', 'name': 'monal'}, {'id': 2564, 'synset': 'peafowl.n.01', 'name': 'peafowl'}, {'id': 2565, 'synset': 'peachick.n.01', 'name': 'peachick'}, {'id': 2566, 'synset': 'peacock.n.02', 'name': 'peacock'}, {'id': 2567, 'synset': 'peahen.n.01', 'name': 'peahen'}, {'id': 2568, 'synset': 'blue_peafowl.n.01', 'name': 'blue_peafowl'}, {'id': 2569, 'synset': 'green_peafowl.n.01', 'name': 'green_peafowl'}, {'id': 2570, 'synset': 'quail.n.02', 'name': 'quail'}, {'id': 2571, 'synset': 'california_quail.n.01', 'name': 'California_quail'}, {'id': 2572, 'synset': 'tragopan.n.01', 'name': 'tragopan'}, {'id': 2573, 'synset': 'partridge.n.03', 'name': 'partridge'}, {'id': 2574, 'synset': 'hungarian_partridge.n.01', 'name': 'Hungarian_partridge'}, {'id': 2575, 'synset': 'red-legged_partridge.n.01', 'name': 'red-legged_partridge'}, {'id': 2576, 'synset': 'greek_partridge.n.01', 'name': 'Greek_partridge'}, {'id': 2577, 'synset': 'mountain_quail.n.01', 'name': 'mountain_quail'}, {'id': 2578, 'synset': 'guinea_fowl.n.01', 'name': 'guinea_fowl'}, {'id': 2579, 'synset': 'guinea_hen.n.02', 'name': 'guinea_hen'}, {'id': 2580, 'synset': 'hoatzin.n.01', 'name': 'hoatzin'}, {'id': 2581, 'synset': 'tinamou.n.01', 'name': 'tinamou'}, {'id': 2582, 'synset': 'columbiform_bird.n.01', 'name': 'columbiform_bird'}, {'id': 2583, 'synset': 'dodo.n.02', 'name': 'dodo'}, {'id': 2584, 'synset': 'pouter_pigeon.n.01', 'name': 'pouter_pigeon'}, {'id': 2585, 'synset': 'rock_dove.n.01', 'name': 'rock_dove'}, {'id': 2586, 'synset': 'band-tailed_pigeon.n.01', 'name': 'band-tailed_pigeon'}, {'id': 2587, 'synset': 'wood_pigeon.n.01', 'name': 'wood_pigeon'}, {'id': 2588, 'synset': 'turtledove.n.02', 'name': 'turtledove'}, {'id': 2589, 'synset': 'streptopelia_turtur.n.01', 'name': 'Streptopelia_turtur'}, {'id': 2590, 'synset': 'ringdove.n.01', 'name': 'ringdove'}, {'id': 2591, 'synset': 'australian_turtledove.n.01', 'name': 'Australian_turtledove'}, {'id': 2592, 'synset': 'mourning_dove.n.01', 'name': 'mourning_dove'}, {'id': 2593, 'synset': 'domestic_pigeon.n.01', 'name': 'domestic_pigeon'}, {'id': 2594, 'synset': 'squab.n.03', 'name': 'squab'}, {'id': 2595, 'synset': 'fairy_swallow.n.01', 'name': 'fairy_swallow'}, {'id': 2596, 'synset': 'roller.n.07', 'name': 'roller'}, {'id': 2597, 'synset': 'homing_pigeon.n.01', 'name': 'homing_pigeon'}, {'id': 2598, 'synset': 'carrier_pigeon.n.01', 'name': 'carrier_pigeon'}, {'id': 2599, 'synset': 'passenger_pigeon.n.01', 'name': 'passenger_pigeon'}, {'id': 2600, 'synset': 'sandgrouse.n.01', 'name': 'sandgrouse'}, {'id': 2601, 'synset': 'painted_sandgrouse.n.01', 'name': 'painted_sandgrouse'}, {'id': 2602, 'synset': 'pin-tailed_sandgrouse.n.01', 'name': 'pin-tailed_sandgrouse'}, {'id': 2603, 'synset': "pallas's_sandgrouse.n.01", 'name': "pallas's_sandgrouse"}, {'id': 2604, 'synset': 'popinjay.n.02', 'name': 'popinjay'}, {'id': 2605, 'synset': 'poll.n.04', 'name': 'poll'}, {'id': 2606, 'synset': 'african_grey.n.01', 'name': 'African_grey'}, {'id': 2607, 'synset': 'amazon.n.04', 'name': 'amazon'}, {'id': 2608, 'synset': 'macaw.n.01', 'name': 'macaw'}, {'id': 2609, 'synset': 'kea.n.01', 'name': 'kea'}, {'id': 2610, 'synset': 'cockatoo.n.01', 'name': 'cockatoo'}, {'id': 2611, 'synset': 'sulphur-crested_cockatoo.n.01', 'name': 'sulphur-crested_cockatoo'}, {'id': 2612, 'synset': 'pink_cockatoo.n.01', 'name': 'pink_cockatoo'}, {'id': 2613, 'synset': 'cockateel.n.01', 'name': 'cockateel'}, {'id': 2614, 'synset': 'lovebird.n.02', 'name': 'lovebird'}, {'id': 2615, 'synset': 'lory.n.01', 'name': 'lory'}, {'id': 2616, 'synset': 'lorikeet.n.01', 'name': 'lorikeet'}, {'id': 2617, 'synset': 'varied_lorikeet.n.01', 'name': 'varied_Lorikeet'}, {'id': 2618, 'synset': 'rainbow_lorikeet.n.01', 'name': 'rainbow_lorikeet'}, {'id': 2619, 'synset': 'carolina_parakeet.n.01', 'name': 'Carolina_parakeet'}, {'id': 2620, 'synset': 'budgerigar.n.01', 'name': 'budgerigar'}, {'id': 2621, 'synset': 'ring-necked_parakeet.n.01', 'name': 'ring-necked_parakeet'}, {'id': 2622, 'synset': 'cuculiform_bird.n.01', 'name': 'cuculiform_bird'}, {'id': 2623, 'synset': 'cuckoo.n.02', 'name': 'cuckoo'}, {'id': 2624, 'synset': 'european_cuckoo.n.01', 'name': 'European_cuckoo'}, {'id': 2625, 'synset': 'black-billed_cuckoo.n.01', 'name': 'black-billed_cuckoo'}, {'id': 2626, 'synset': 'roadrunner.n.01', 'name': 'roadrunner'}, {'id': 2627, 'synset': 'ani.n.01', 'name': 'ani'}, {'id': 2628, 'synset': 'coucal.n.01', 'name': 'coucal'}, {'id': 2629, 'synset': 'crow_pheasant.n.01', 'name': 'crow_pheasant'}, {'id': 2630, 'synset': 'touraco.n.01', 'name': 'touraco'}, {'id': 2631, 'synset': 'coraciiform_bird.n.01', 'name': 'coraciiform_bird'}, {'id': 2632, 'synset': 'roller.n.06', 'name': 'roller'}, {'id': 2633, 'synset': 'european_roller.n.01', 'name': 'European_roller'}, {'id': 2634, 'synset': 'ground_roller.n.01', 'name': 'ground_roller'}, {'id': 2635, 'synset': 'kingfisher.n.01', 'name': 'kingfisher'}, {'id': 2636, 'synset': 'eurasian_kingfisher.n.01', 'name': 'Eurasian_kingfisher'}, {'id': 2637, 'synset': 'belted_kingfisher.n.01', 'name': 'belted_kingfisher'}, {'id': 2638, 'synset': 'kookaburra.n.01', 'name': 'kookaburra'}, {'id': 2639, 'synset': 'bee_eater.n.01', 'name': 'bee_eater'}, {'id': 2640, 'synset': 'hornbill.n.01', 'name': 'hornbill'}, {'id': 2641, 'synset': 'hoopoe.n.01', 'name': 'hoopoe'}, {'id': 2642, 'synset': 'euopean_hoopoe.n.01', 'name': 'Euopean_hoopoe'}, {'id': 2643, 'synset': 'wood_hoopoe.n.01', 'name': 'wood_hoopoe'}, {'id': 2644, 'synset': 'motmot.n.01', 'name': 'motmot'}, {'id': 2645, 'synset': 'tody.n.01', 'name': 'tody'}, {'id': 2646, 'synset': 'apodiform_bird.n.01', 'name': 'apodiform_bird'}, {'id': 2647, 'synset': 'swift.n.03', 'name': 'swift'}, {'id': 2648, 'synset': 'european_swift.n.01', 'name': 'European_swift'}, {'id': 2649, 'synset': 'chimney_swift.n.01', 'name': 'chimney_swift'}, {'id': 2650, 'synset': 'swiftlet.n.01', 'name': 'swiftlet'}, {'id': 2651, 'synset': 'tree_swift.n.01', 'name': 'tree_swift'}, {'id': 2652, 'synset': 'archilochus_colubris.n.01', 'name': 'Archilochus_colubris'}, {'id': 2653, 'synset': 'thornbill.n.01', 'name': 'thornbill'}, {'id': 2654, 'synset': 'goatsucker.n.01', 'name': 'goatsucker'}, {'id': 2655, 'synset': 'european_goatsucker.n.01', 'name': 'European_goatsucker'}, {'id': 2656, 'synset': "chuck-will's-widow.n.01", 'name': "chuck-will's-widow"}, {'id': 2657, 'synset': 'whippoorwill.n.01', 'name': 'whippoorwill'}, {'id': 2658, 'synset': 'poorwill.n.01', 'name': 'poorwill'}, {'id': 2659, 'synset': 'frogmouth.n.01', 'name': 'frogmouth'}, {'id': 2660, 'synset': 'oilbird.n.01', 'name': 'oilbird'}, {'id': 2661, 'synset': 'piciform_bird.n.01', 'name': 'piciform_bird'}, {'id': 2662, 'synset': 'woodpecker.n.01', 'name': 'woodpecker'}, {'id': 2663, 'synset': 'green_woodpecker.n.01', 'name': 'green_woodpecker'}, {'id': 2664, 'synset': 'downy_woodpecker.n.01', 'name': 'downy_woodpecker'}, {'id': 2665, 'synset': 'flicker.n.02', 'name': 'flicker'}, {'id': 2666, 'synset': 'yellow-shafted_flicker.n.01', 'name': 'yellow-shafted_flicker'}, {'id': 2667, 'synset': 'gilded_flicker.n.01', 'name': 'gilded_flicker'}, {'id': 2668, 'synset': 'red-shafted_flicker.n.01', 'name': 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'quetzal'}, {'id': 2684, 'synset': 'resplendent_quetzel.n.01', 'name': 'resplendent_quetzel'}, {'id': 2685, 'synset': 'aquatic_bird.n.01', 'name': 'aquatic_bird'}, {'id': 2686, 'synset': 'waterfowl.n.01', 'name': 'waterfowl'}, {'id': 2687, 'synset': 'anseriform_bird.n.01', 'name': 'anseriform_bird'}, {'id': 2688, 'synset': 'drake.n.02', 'name': 'drake'}, {'id': 2689, 'synset': 'quack-quack.n.01', 'name': 'quack-quack'}, {'id': 2690, 'synset': 'diving_duck.n.01', 'name': 'diving_duck'}, {'id': 2691, 'synset': 'dabbling_duck.n.01', 'name': 'dabbling_duck'}, {'id': 2692, 'synset': 'black_duck.n.01', 'name': 'black_duck'}, {'id': 2693, 'synset': 'teal.n.02', 'name': 'teal'}, {'id': 2694, 'synset': 'greenwing.n.01', 'name': 'greenwing'}, {'id': 2695, 'synset': 'bluewing.n.01', 'name': 'bluewing'}, {'id': 2696, 'synset': 'garganey.n.01', 'name': 'garganey'}, {'id': 2697, 'synset': 'widgeon.n.01', 'name': 'widgeon'}, {'id': 2698, 'synset': 'american_widgeon.n.01', 'name': 'American_widgeon'}, {'id': 2699, 'synset': 'shoveler.n.02', 'name': 'shoveler'}, {'id': 2700, 'synset': 'pintail.n.01', 'name': 'pintail'}, {'id': 2701, 'synset': 'sheldrake.n.02', 'name': 'sheldrake'}, {'id': 2702, 'synset': 'shelduck.n.01', 'name': 'shelduck'}, {'id': 2703, 'synset': 'ruddy_duck.n.01', 'name': 'ruddy_duck'}, {'id': 2704, 'synset': 'bufflehead.n.01', 'name': 'bufflehead'}, {'id': 2705, 'synset': 'goldeneye.n.02', 'name': 'goldeneye'}, {'id': 2706, 'synset': "barrow's_goldeneye.n.01", 'name': "Barrow's_goldeneye"}, {'id': 2707, 'synset': 'canvasback.n.01', 'name': 'canvasback'}, {'id': 2708, 'synset': 'pochard.n.01', 'name': 'pochard'}, {'id': 2709, 'synset': 'redhead.n.02', 'name': 'redhead'}, {'id': 2710, 'synset': 'scaup.n.01', 'name': 'scaup'}, {'id': 2711, 'synset': 'greater_scaup.n.01', 'name': 'greater_scaup'}, {'id': 2712, 'synset': 'lesser_scaup.n.01', 'name': 'lesser_scaup'}, {'id': 2713, 'synset': 'wild_duck.n.01', 'name': 'wild_duck'}, {'id': 2714, 'synset': 'wood_duck.n.01', 'name': 'wood_duck'}, {'id': 2715, 'synset': 'wood_drake.n.01', 'name': 'wood_drake'}, {'id': 2716, 'synset': 'mandarin_duck.n.01', 'name': 'mandarin_duck'}, {'id': 2717, 'synset': 'muscovy_duck.n.01', 'name': 'muscovy_duck'}, {'id': 2718, 'synset': 'sea_duck.n.01', 'name': 'sea_duck'}, {'id': 2719, 'synset': 'eider.n.01', 'name': 'eider'}, {'id': 2720, 'synset': 'scoter.n.01', 'name': 'scoter'}, {'id': 2721, 'synset': 'common_scoter.n.01', 'name': 'common_scoter'}, {'id': 2722, 'synset': 'old_squaw.n.01', 'name': 'old_squaw'}, {'id': 2723, 'synset': 'merganser.n.01', 'name': 'merganser'}, {'id': 2724, 'synset': 'goosander.n.01', 'name': 'goosander'}, {'id': 2725, 'synset': 'american_merganser.n.01', 'name': 'American_merganser'}, {'id': 2726, 'synset': 'red-breasted_merganser.n.01', 'name': 'red-breasted_merganser'}, {'id': 2727, 'synset': 'smew.n.01', 'name': 'smew'}, {'id': 2728, 'synset': 'hooded_merganser.n.01', 'name': 'hooded_merganser'}, {'id': 2729, 'synset': 'gosling.n.01', 'name': 'gosling'}, {'id': 2730, 'synset': 'gander.n.01', 'name': 'gander'}, {'id': 2731, 'synset': 'chinese_goose.n.01', 'name': 'Chinese_goose'}, {'id': 2732, 'synset': 'greylag.n.01', 'name': 'greylag'}, {'id': 2733, 'synset': 'blue_goose.n.01', 'name': 'blue_goose'}, {'id': 2734, 'synset': 'snow_goose.n.01', 'name': 'snow_goose'}, {'id': 2735, 'synset': 'brant.n.01', 'name': 'brant'}, {'id': 2736, 'synset': 'common_brant_goose.n.01', 'name': 'common_brant_goose'}, {'id': 2737, 'synset': 'honker.n.03', 'name': 'honker'}, {'id': 2738, 'synset': 'barnacle_goose.n.01', 'name': 'barnacle_goose'}, {'id': 2739, 'synset': 'coscoroba.n.01', 'name': 'coscoroba'}, {'id': 2740, 'synset': 'swan.n.01', 'name': 'swan'}, {'id': 2741, 'synset': 'cob.n.04', 'name': 'cob'}, {'id': 2742, 'synset': 'pen.n.05', 'name': 'pen'}, {'id': 2743, 'synset': 'cygnet.n.01', 'name': 'cygnet'}, {'id': 2744, 'synset': 'mute_swan.n.01', 'name': 'mute_swan'}, {'id': 2745, 'synset': 'whooper.n.02', 'name': 'whooper'}, {'id': 2746, 'synset': 'tundra_swan.n.01', 'name': 'tundra_swan'}, {'id': 2747, 'synset': 'whistling_swan.n.01', 'name': 'whistling_swan'}, {'id': 2748, 'synset': "bewick's_swan.n.01", 'name': "Bewick's_swan"}, {'id': 2749, 'synset': 'trumpeter.n.04', 'name': 'trumpeter'}, {'id': 2750, 'synset': 'black_swan.n.01', 'name': 'black_swan'}, {'id': 2751, 'synset': 'screamer.n.03', 'name': 'screamer'}, {'id': 2752, 'synset': 'horned_screamer.n.01', 'name': 'horned_screamer'}, {'id': 2753, 'synset': 'crested_screamer.n.01', 'name': 'crested_screamer'}, {'id': 2754, 'synset': 'chaja.n.01', 'name': 'chaja'}, {'id': 2755, 'synset': 'mammal.n.01', 'name': 'mammal'}, {'id': 2756, 'synset': 'female_mammal.n.01', 'name': 'female_mammal'}, {'id': 2757, 'synset': 'tusker.n.01', 'name': 'tusker'}, {'id': 2758, 'synset': 'prototherian.n.01', 'name': 'prototherian'}, {'id': 2759, 'synset': 'monotreme.n.01', 'name': 'monotreme'}, {'id': 2760, 'synset': 'echidna.n.02', 'name': 'echidna'}, {'id': 2761, 'synset': 'echidna.n.01', 'name': 'echidna'}, {'id': 2762, 'synset': 'platypus.n.01', 'name': 'platypus'}, {'id': 2763, 'synset': 'marsupial.n.01', 'name': 'marsupial'}, {'id': 2764, 'synset': 'opossum.n.02', 'name': 'opossum'}, {'id': 2765, 'synset': 'common_opossum.n.01', 'name': 'common_opossum'}, {'id': 2766, 'synset': 'crab-eating_opossum.n.01', 'name': 'crab-eating_opossum'}, {'id': 2767, 'synset': 'opossum_rat.n.01', 'name': 'opossum_rat'}, {'id': 2768, 'synset': 'bandicoot.n.01', 'name': 'bandicoot'}, {'id': 2769, 'synset': 'rabbit-eared_bandicoot.n.01', 'name': 'rabbit-eared_bandicoot'}, {'id': 2770, 'synset': 'kangaroo.n.01', 'name': 'kangaroo'}, {'id': 2771, 'synset': 'giant_kangaroo.n.01', 'name': 'giant_kangaroo'}, {'id': 2772, 'synset': 'wallaby.n.01', 'name': 'wallaby'}, {'id': 2773, 'synset': 'common_wallaby.n.01', 'name': 'common_wallaby'}, {'id': 2774, 'synset': 'hare_wallaby.n.01', 'name': 'hare_wallaby'}, {'id': 2775, 'synset': 'nail-tailed_wallaby.n.01', 'name': 'nail-tailed_wallaby'}, {'id': 2776, 'synset': 'rock_wallaby.n.01', 'name': 'rock_wallaby'}, {'id': 2777, 'synset': 'pademelon.n.01', 'name': 'pademelon'}, {'id': 2778, 'synset': 'tree_wallaby.n.01', 'name': 'tree_wallaby'}, {'id': 2779, 'synset': 'musk_kangaroo.n.01', 'name': 'musk_kangaroo'}, {'id': 2780, 'synset': 'rat_kangaroo.n.01', 'name': 'rat_kangaroo'}, {'id': 2781, 'synset': 'potoroo.n.01', 'name': 'potoroo'}, {'id': 2782, 'synset': 'bettong.n.01', 'name': 'bettong'}, {'id': 2783, 'synset': 'jerboa_kangaroo.n.01', 'name': 'jerboa_kangaroo'}, {'id': 2784, 'synset': 'phalanger.n.01', 'name': 'phalanger'}, {'id': 2785, 'synset': 'cuscus.n.01', 'name': 'cuscus'}, {'id': 2786, 'synset': 'brush-tailed_phalanger.n.01', 'name': 'brush-tailed_phalanger'}, {'id': 2787, 'synset': 'flying_phalanger.n.01', 'name': 'flying_phalanger'}, {'id': 2788, 'synset': 'wombat.n.01', 'name': 'wombat'}, {'id': 2789, 'synset': 'dasyurid_marsupial.n.01', 'name': 'dasyurid_marsupial'}, {'id': 2790, 'synset': 'dasyure.n.01', 'name': 'dasyure'}, {'id': 2791, 'synset': 'eastern_dasyure.n.01', 'name': 'eastern_dasyure'}, {'id': 2792, 'synset': 'native_cat.n.01', 'name': 'native_cat'}, {'id': 2793, 'synset': 'thylacine.n.01', 'name': 'thylacine'}, {'id': 2794, 'synset': 'tasmanian_devil.n.01', 'name': 'Tasmanian_devil'}, {'id': 2795, 'synset': 'pouched_mouse.n.01', 'name': 'pouched_mouse'}, {'id': 2796, 'synset': 'numbat.n.01', 'name': 'numbat'}, {'id': 2797, 'synset': 'pouched_mole.n.01', 'name': 'pouched_mole'}, {'id': 2798, 'synset': 'placental.n.01', 'name': 'placental'}, {'id': 2799, 'synset': 'livestock.n.01', 'name': 'livestock'}, {'id': 2800, 'synset': 'cow.n.02', 'name': 'cow'}, {'id': 2801, 'synset': 'calf.n.04', 'name': 'calf'}, {'id': 2802, 'synset': 'yearling.n.03', 'name': 'yearling'}, {'id': 2803, 'synset': 'buck.n.05', 'name': 'buck'}, {'id': 2804, 'synset': 'doe.n.02', 'name': 'doe'}, {'id': 2805, 'synset': 'insectivore.n.01', 'name': 'insectivore'}, {'id': 2806, 'synset': 'mole.n.06', 'name': 'mole'}, {'id': 2807, 'synset': 'starnose_mole.n.01', 'name': 'starnose_mole'}, {'id': 2808, 'synset': "brewer's_mole.n.01", 'name': "brewer's_mole"}, {'id': 2809, 'synset': 'golden_mole.n.01', 'name': 'golden_mole'}, {'id': 2810, 'synset': 'shrew_mole.n.01', 'name': 'shrew_mole'}, {'id': 2811, 'synset': 'asiatic_shrew_mole.n.01', 'name': 'Asiatic_shrew_mole'}, {'id': 2812, 'synset': 'american_shrew_mole.n.01', 'name': 'American_shrew_mole'}, {'id': 2813, 'synset': 'shrew.n.02', 'name': 'shrew'}, {'id': 2814, 'synset': 'common_shrew.n.01', 'name': 'common_shrew'}, {'id': 2815, 'synset': 'masked_shrew.n.01', 'name': 'masked_shrew'}, {'id': 2816, 'synset': 'short-tailed_shrew.n.01', 'name': 'short-tailed_shrew'}, {'id': 2817, 'synset': 'water_shrew.n.01', 'name': 'water_shrew'}, {'id': 2818, 'synset': 'american_water_shrew.n.01', 'name': 'American_water_shrew'}, {'id': 2819, 'synset': 'european_water_shrew.n.01', 'name': 'European_water_shrew'}, {'id': 2820, 'synset': 'mediterranean_water_shrew.n.01', 'name': 'Mediterranean_water_shrew'}, {'id': 2821, 'synset': 'least_shrew.n.01', 'name': 'least_shrew'}, {'id': 2822, 'synset': 'hedgehog.n.02', 'name': 'hedgehog'}, {'id': 2823, 'synset': 'tenrec.n.01', 'name': 'tenrec'}, {'id': 2824, 'synset': 'tailless_tenrec.n.01', 'name': 'tailless_tenrec'}, {'id': 2825, 'synset': 'otter_shrew.n.01', 'name': 'otter_shrew'}, {'id': 2826, 'synset': 'eiderdown.n.02', 'name': 'eiderdown'}, {'id': 2827, 'synset': 'aftershaft.n.01', 'name': 'aftershaft'}, {'id': 2828, 'synset': 'sickle_feather.n.01', 'name': 'sickle_feather'}, {'id': 2829, 'synset': 'contour_feather.n.01', 'name': 'contour_feather'}, {'id': 2830, 'synset': 'bastard_wing.n.01', 'name': 'bastard_wing'}, {'id': 2831, 'synset': 'saddle_hackle.n.01', 'name': 'saddle_hackle'}, {'id': 2832, 'synset': 'encolure.n.01', 'name': 'encolure'}, {'id': 2833, 'synset': 'hair.n.06', 'name': 'hair'}, {'id': 2834, 'synset': 'squama.n.01', 'name': 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'coral'}, {'id': 2866, 'synset': 'gorgonian.n.01', 'name': 'gorgonian'}, {'id': 2867, 'synset': 'sea_feather.n.01', 'name': 'sea_feather'}, {'id': 2868, 'synset': 'sea_fan.n.01', 'name': 'sea_fan'}, {'id': 2869, 'synset': 'red_coral.n.02', 'name': 'red_coral'}, {'id': 2870, 'synset': 'stony_coral.n.01', 'name': 'stony_coral'}, {'id': 2871, 'synset': 'brain_coral.n.01', 'name': 'brain_coral'}, {'id': 2872, 'synset': 'staghorn_coral.n.01', 'name': 'staghorn_coral'}, {'id': 2873, 'synset': 'mushroom_coral.n.01', 'name': 'mushroom_coral'}, {'id': 2874, 'synset': 'ctenophore.n.01', 'name': 'ctenophore'}, {'id': 2875, 'synset': 'beroe.n.01', 'name': 'beroe'}, {'id': 2876, 'synset': 'platyctenean.n.01', 'name': 'platyctenean'}, {'id': 2877, 'synset': 'sea_gooseberry.n.01', 'name': 'sea_gooseberry'}, {'id': 2878, 'synset': "venus's_girdle.n.01", 'name': "Venus's_girdle"}, {'id': 2879, 'synset': 'worm.n.01', 'name': 'worm'}, {'id': 2880, 'synset': 'helminth.n.01', 'name': 'helminth'}, {'id': 2881, 'synset': 'woodworm.n.01', 'name': 'woodworm'}, {'id': 2882, 'synset': 'woodborer.n.01', 'name': 'woodborer'}, {'id': 2883, 'synset': 'acanthocephalan.n.01', 'name': 'acanthocephalan'}, {'id': 2884, 'synset': 'arrowworm.n.01', 'name': 'arrowworm'}, {'id': 2885, 'synset': 'bladder_worm.n.01', 'name': 'bladder_worm'}, {'id': 2886, 'synset': 'flatworm.n.01', 'name': 'flatworm'}, {'id': 2887, 'synset': 'planarian.n.01', 'name': 'planarian'}, {'id': 2888, 'synset': 'fluke.n.05', 'name': 'fluke'}, {'id': 2889, 'synset': 'cercaria.n.01', 'name': 'cercaria'}, {'id': 2890, 'synset': 'liver_fluke.n.01', 'name': 'liver_fluke'}, {'id': 2891, 'synset': 'fasciolopsis_buski.n.01', 'name': 'Fasciolopsis_buski'}, {'id': 2892, 'synset': 'schistosome.n.01', 'name': 'schistosome'}, {'id': 2893, 'synset': 'tapeworm.n.01', 'name': 'tapeworm'}, {'id': 2894, 'synset': 'echinococcus.n.01', 'name': 'echinococcus'}, {'id': 2895, 'synset': 'taenia.n.02', 'name': 'taenia'}, {'id': 2896, 'synset': 'ribbon_worm.n.01', 'name': 'ribbon_worm'}, {'id': 2897, 'synset': 'beard_worm.n.01', 'name': 'beard_worm'}, {'id': 2898, 'synset': 'rotifer.n.01', 'name': 'rotifer'}, {'id': 2899, 'synset': 'nematode.n.01', 'name': 'nematode'}, {'id': 2900, 'synset': 'common_roundworm.n.01', 'name': 'common_roundworm'}, {'id': 2901, 'synset': 'chicken_roundworm.n.01', 'name': 'chicken_roundworm'}, {'id': 2902, 'synset': 'pinworm.n.01', 'name': 'pinworm'}, {'id': 2903, 'synset': 'eelworm.n.01', 'name': 'eelworm'}, {'id': 2904, 'synset': 'vinegar_eel.n.01', 'name': 'vinegar_eel'}, {'id': 2905, 'synset': 'trichina.n.01', 'name': 'trichina'}, {'id': 2906, 'synset': 'hookworm.n.01', 'name': 'hookworm'}, {'id': 2907, 'synset': 'filaria.n.02', 'name': 'filaria'}, {'id': 2908, 'synset': 'guinea_worm.n.02', 'name': 'Guinea_worm'}, {'id': 2909, 'synset': 'annelid.n.01', 'name': 'annelid'}, {'id': 2910, 'synset': 'archiannelid.n.01', 'name': 'archiannelid'}, {'id': 2911, 'synset': 'oligochaete.n.01', 'name': 'oligochaete'}, {'id': 2912, 'synset': 'earthworm.n.01', 'name': 'earthworm'}, {'id': 2913, 'synset': 'polychaete.n.01', 'name': 'polychaete'}, {'id': 2914, 'synset': 'lugworm.n.01', 'name': 'lugworm'}, {'id': 2915, 'synset': 'sea_mouse.n.01', 'name': 'sea_mouse'}, {'id': 2916, 'synset': 'bloodworm.n.01', 'name': 'bloodworm'}, {'id': 2917, 'synset': 'leech.n.01', 'name': 'leech'}, {'id': 2918, 'synset': 'medicinal_leech.n.01', 'name': 'medicinal_leech'}, {'id': 2919, 'synset': 'horseleech.n.01', 'name': 'horseleech'}, {'id': 2920, 'synset': 'mollusk.n.01', 'name': 'mollusk'}, {'id': 2921, 'synset': 'scaphopod.n.01', 'name': 'scaphopod'}, {'id': 2922, 'synset': 'tooth_shell.n.01', 'name': 'tooth_shell'}, {'id': 2923, 'synset': 'gastropod.n.01', 'name': 'gastropod'}, {'id': 2924, 'synset': 'abalone.n.01', 'name': 'abalone'}, {'id': 2925, 'synset': 'ormer.n.01', 'name': 'ormer'}, {'id': 2926, 'synset': 'scorpion_shell.n.01', 'name': 'scorpion_shell'}, {'id': 2927, 'synset': 'conch.n.01', 'name': 'conch'}, {'id': 2928, 'synset': 'giant_conch.n.01', 'name': 'giant_conch'}, {'id': 2929, 'synset': 'snail.n.01', 'name': 'snail'}, {'id': 2930, 'synset': 'edible_snail.n.01', 'name': 'edible_snail'}, {'id': 2931, 'synset': 'garden_snail.n.01', 'name': 'garden_snail'}, {'id': 2932, 'synset': 'brown_snail.n.01', 'name': 'brown_snail'}, {'id': 2933, 'synset': 'helix_hortensis.n.01', 'name': 'Helix_hortensis'}, {'id': 2934, 'synset': 'slug.n.07', 'name': 'slug'}, {'id': 2935, 'synset': 'seasnail.n.02', 'name': 'seasnail'}, {'id': 2936, 'synset': 'neritid.n.01', 'name': 'neritid'}, {'id': 2937, 'synset': 'nerita.n.01', 'name': 'nerita'}, {'id': 2938, 'synset': 'bleeding_tooth.n.01', 'name': 'bleeding_tooth'}, {'id': 2939, 'synset': 'neritina.n.01', 'name': 'neritina'}, {'id': 2940, 'synset': 'whelk.n.02', 'name': 'whelk'}, {'id': 2941, 'synset': 'moon_shell.n.01', 'name': 'moon_shell'}, {'id': 2942, 'synset': 'periwinkle.n.04', 'name': 'periwinkle'}, {'id': 2943, 'synset': 'limpet.n.02', 'name': 'limpet'}, {'id': 2944, 'synset': 'common_limpet.n.01', 'name': 'common_limpet'}, {'id': 2945, 'synset': 'keyhole_limpet.n.01', 'name': 'keyhole_limpet'}, {'id': 2946, 'synset': 'river_limpet.n.01', 'name': 'river_limpet'}, {'id': 2947, 'synset': 'sea_slug.n.01', 'name': 'sea_slug'}, {'id': 2948, 'synset': 'sea_hare.n.01', 'name': 'sea_hare'}, {'id': 2949, 'synset': 'hermissenda_crassicornis.n.01', 'name': 'Hermissenda_crassicornis'}, {'id': 2950, 'synset': 'bubble_shell.n.01', 'name': 'bubble_shell'}, {'id': 2951, 'synset': 'physa.n.01', 'name': 'physa'}, {'id': 2952, 'synset': 'cowrie.n.01', 'name': 'cowrie'}, {'id': 2953, 'synset': 'money_cowrie.n.01', 'name': 'money_cowrie'}, {'id': 2954, 'synset': 'tiger_cowrie.n.01', 'name': 'tiger_cowrie'}, {'id': 2955, 'synset': 'solenogaster.n.01', 'name': 'solenogaster'}, {'id': 2956, 'synset': 'chiton.n.02', 'name': 'chiton'}, {'id': 2957, 'synset': 'bivalve.n.01', 'name': 'bivalve'}, {'id': 2958, 'synset': 'spat.n.03', 'name': 'spat'}, {'id': 2959, 'synset': 'clam.n.01', 'name': 'clam'}, {'id': 2960, 'synset': 'soft-shell_clam.n.02', 'name': 'soft-shell_clam'}, {'id': 2961, 'synset': 'quahog.n.02', 'name': 'quahog'}, {'id': 2962, 'synset': 'littleneck.n.02', 'name': 'littleneck'}, {'id': 2963, 'synset': 'cherrystone.n.02', 'name': 'cherrystone'}, {'id': 2964, 'synset': 'geoduck.n.01', 'name': 'geoduck'}, {'id': 2965, 'synset': 'razor_clam.n.01', 'name': 'razor_clam'}, {'id': 2966, 'synset': 'giant_clam.n.01', 'name': 'giant_clam'}, {'id': 2967, 'synset': 'cockle.n.02', 'name': 'cockle'}, {'id': 2968, 'synset': 'edible_cockle.n.01', 'name': 'edible_cockle'}, {'id': 2969, 'synset': 'oyster.n.01', 'name': 'oyster'}, {'id': 2970, 'synset': 'japanese_oyster.n.01', 'name': 'Japanese_oyster'}, {'id': 2971, 'synset': 'virginia_oyster.n.01', 'name': 'Virginia_oyster'}, {'id': 2972, 'synset': 'pearl_oyster.n.01', 'name': 'pearl_oyster'}, {'id': 2973, 'synset': 'saddle_oyster.n.01', 'name': 'saddle_oyster'}, {'id': 2974, 'synset': 'window_oyster.n.01', 'name': 'window_oyster'}, {'id': 2975, 'synset': 'ark_shell.n.01', 'name': 'ark_shell'}, {'id': 2976, 'synset': 'blood_clam.n.01', 'name': 'blood_clam'}, {'id': 2977, 'synset': 'mussel.n.02', 'name': 'mussel'}, {'id': 2978, 'synset': 'marine_mussel.n.01', 'name': 'marine_mussel'}, {'id': 2979, 'synset': 'edible_mussel.n.01', 'name': 'edible_mussel'}, {'id': 2980, 'synset': 'freshwater_mussel.n.01', 'name': 'freshwater_mussel'}, {'id': 2981, 'synset': 'pearly-shelled_mussel.n.01', 'name': 'pearly-shelled_mussel'}, {'id': 2982, 'synset': 'thin-shelled_mussel.n.01', 'name': 'thin-shelled_mussel'}, {'id': 2983, 'synset': 'zebra_mussel.n.01', 'name': 'zebra_mussel'}, {'id': 2984, 'synset': 'scallop.n.04', 'name': 'scallop'}, {'id': 2985, 'synset': 'bay_scallop.n.02', 'name': 'bay_scallop'}, {'id': 2986, 'synset': 'sea_scallop.n.02', 'name': 'sea_scallop'}, {'id': 2987, 'synset': 'shipworm.n.01', 'name': 'shipworm'}, {'id': 2988, 'synset': 'teredo.n.01', 'name': 'teredo'}, {'id': 2989, 'synset': 'piddock.n.01', 'name': 'piddock'}, {'id': 2990, 'synset': 'cephalopod.n.01', 'name': 'cephalopod'}, {'id': 2991, 'synset': 'chambered_nautilus.n.01', 'name': 'chambered_nautilus'}, {'id': 2992, 'synset': 'octopod.n.01', 'name': 'octopod'}, {'id': 2993, 'synset': 'paper_nautilus.n.01', 'name': 'paper_nautilus'}, {'id': 2994, 'synset': 'decapod.n.02', 'name': 'decapod'}, {'id': 2995, 'synset': 'squid.n.02', 'name': 'squid'}, {'id': 2996, 'synset': 'loligo.n.01', 'name': 'loligo'}, {'id': 2997, 'synset': 'ommastrephes.n.01', 'name': 'ommastrephes'}, {'id': 2998, 'synset': 'architeuthis.n.01', 'name': 'architeuthis'}, {'id': 2999, 'synset': 'cuttlefish.n.01', 'name': 'cuttlefish'}, {'id': 3000, 'synset': 'spirula.n.01', 'name': 'spirula'}, {'id': 3001, 'synset': 'crustacean.n.01', 'name': 'crustacean'}, {'id': 3002, 'synset': 'malacostracan_crustacean.n.01', 'name': 'malacostracan_crustacean'}, {'id': 3003, 'synset': 'decapod_crustacean.n.01', 'name': 'decapod_crustacean'}, {'id': 3004, 'synset': 'brachyuran.n.01', 'name': 'brachyuran'}, {'id': 3005, 'synset': 'stone_crab.n.02', 'name': 'stone_crab'}, {'id': 3006, 'synset': 'hard-shell_crab.n.01', 'name': 'hard-shell_crab'}, {'id': 3007, 'synset': 'soft-shell_crab.n.02', 'name': 'soft-shell_crab'}, {'id': 3008, 'synset': 'dungeness_crab.n.02', 'name': 'Dungeness_crab'}, {'id': 3009, 'synset': 'rock_crab.n.01', 'name': 'rock_crab'}, {'id': 3010, 'synset': 'jonah_crab.n.01', 'name': 'Jonah_crab'}, {'id': 3011, 'synset': 'swimming_crab.n.01', 'name': 'swimming_crab'}, {'id': 3012, 'synset': 'english_lady_crab.n.01', 'name': 'English_lady_crab'}, {'id': 3013, 'synset': 'american_lady_crab.n.01', 'name': 'American_lady_crab'}, {'id': 3014, 'synset': 'blue_crab.n.02', 'name': 'blue_crab'}, {'id': 3015, 'synset': 'fiddler_crab.n.01', 'name': 'fiddler_crab'}, {'id': 3016, 'synset': 'pea_crab.n.01', 'name': 'pea_crab'}, {'id': 3017, 'synset': 'king_crab.n.03', 'name': 'king_crab'}, {'id': 3018, 'synset': 'spider_crab.n.01', 'name': 'spider_crab'}, {'id': 3019, 'synset': 'european_spider_crab.n.01', 'name': 'European_spider_crab'}, {'id': 3020, 'synset': 'giant_crab.n.01', 'name': 'giant_crab'}, {'id': 3021, 'synset': 'lobster.n.02', 'name': 'lobster'}, {'id': 3022, 'synset': 'true_lobster.n.01', 'name': 'true_lobster'}, {'id': 3023, 'synset': 'american_lobster.n.02', 'name': 'American_lobster'}, {'id': 3024, 'synset': 'european_lobster.n.02', 'name': 'European_lobster'}, {'id': 3025, 'synset': 'cape_lobster.n.01', 'name': 'Cape_lobster'}, {'id': 3026, 'synset': 'norway_lobster.n.01', 'name': 'Norway_lobster'}, {'id': 3027, 'synset': 'crayfish.n.03', 'name': 'crayfish'}, {'id': 3028, 'synset': 'old_world_crayfish.n.01', 'name': 'Old_World_crayfish'}, {'id': 3029, 'synset': 'american_crayfish.n.01', 'name': 'American_crayfish'}, {'id': 3030, 'synset': 'hermit_crab.n.01', 'name': 'hermit_crab'}, {'id': 3031, 'synset': 'shrimp.n.03', 'name': 'shrimp'}, {'id': 3032, 'synset': 'snapping_shrimp.n.01', 'name': 'snapping_shrimp'}, {'id': 3033, 'synset': 'prawn.n.02', 'name': 'prawn'}, {'id': 3034, 'synset': 'long-clawed_prawn.n.01', 'name': 'long-clawed_prawn'}, {'id': 3035, 'synset': 'tropical_prawn.n.01', 'name': 'tropical_prawn'}, {'id': 3036, 'synset': 'krill.n.01', 'name': 'krill'}, {'id': 3037, 'synset': 'euphausia_pacifica.n.01', 'name': 'Euphausia_pacifica'}, {'id': 3038, 'synset': 'opossum_shrimp.n.01', 'name': 'opossum_shrimp'}, {'id': 3039, 'synset': 'stomatopod.n.01', 'name': 'stomatopod'}, {'id': 3040, 'synset': 'mantis_shrimp.n.01', 'name': 'mantis_shrimp'}, {'id': 3041, 'synset': 'squilla.n.01', 'name': 'squilla'}, {'id': 3042, 'synset': 'isopod.n.01', 'name': 'isopod'}, {'id': 3043, 'synset': 'woodlouse.n.01', 'name': 'woodlouse'}, {'id': 3044, 'synset': 'pill_bug.n.01', 'name': 'pill_bug'}, {'id': 3045, 'synset': 'sow_bug.n.01', 'name': 'sow_bug'}, {'id': 3046, 'synset': 'sea_louse.n.01', 'name': 'sea_louse'}, {'id': 3047, 'synset': 'amphipod.n.01', 'name': 'amphipod'}, {'id': 3048, 'synset': 'skeleton_shrimp.n.01', 'name': 'skeleton_shrimp'}, {'id': 3049, 'synset': 'whale_louse.n.01', 'name': 'whale_louse'}, {'id': 3050, 'synset': 'daphnia.n.01', 'name': 'daphnia'}, {'id': 3051, 'synset': 'fairy_shrimp.n.01', 'name': 'fairy_shrimp'}, {'id': 3052, 'synset': 'brine_shrimp.n.01', 'name': 'brine_shrimp'}, {'id': 3053, 'synset': 'tadpole_shrimp.n.01', 'name': 'tadpole_shrimp'}, {'id': 3054, 'synset': 'copepod.n.01', 'name': 'copepod'}, {'id': 3055, 'synset': 'cyclops.n.02', 'name': 'cyclops'}, {'id': 3056, 'synset': 'seed_shrimp.n.01', 'name': 'seed_shrimp'}, {'id': 3057, 'synset': 'barnacle.n.01', 'name': 'barnacle'}, {'id': 3058, 'synset': 'acorn_barnacle.n.01', 'name': 'acorn_barnacle'}, {'id': 3059, 'synset': 'goose_barnacle.n.01', 'name': 'goose_barnacle'}, {'id': 3060, 'synset': 'onychophoran.n.01', 'name': 'onychophoran'}, {'id': 3061, 'synset': 'wading_bird.n.01', 'name': 'wading_bird'}, {'id': 3062, 'synset': 'stork.n.01', 'name': 'stork'}, {'id': 3063, 'synset': 'white_stork.n.01', 'name': 'white_stork'}, {'id': 3064, 'synset': 'black_stork.n.01', 'name': 'black_stork'}, {'id': 3065, 'synset': 'adjutant_bird.n.01', 'name': 'adjutant_bird'}, {'id': 3066, 'synset': 'marabou.n.01', 'name': 'marabou'}, {'id': 3067, 'synset': 'openbill.n.01', 'name': 'openbill'}, {'id': 3068, 'synset': 'jabiru.n.03', 'name': 'jabiru'}, {'id': 3069, 'synset': 'saddlebill.n.01', 'name': 'saddlebill'}, {'id': 3070, 'synset': 'policeman_bird.n.01', 'name': 'policeman_bird'}, {'id': 3071, 'synset': 'wood_ibis.n.02', 'name': 'wood_ibis'}, {'id': 3072, 'synset': 'shoebill.n.01', 'name': 'shoebill'}, {'id': 3073, 'synset': 'ibis.n.01', 'name': 'ibis'}, {'id': 3074, 'synset': 'wood_ibis.n.01', 'name': 'wood_ibis'}, {'id': 3075, 'synset': 'sacred_ibis.n.01', 'name': 'sacred_ibis'}, {'id': 3076, 'synset': 'spoonbill.n.01', 'name': 'spoonbill'}, {'id': 3077, 'synset': 'common_spoonbill.n.01', 'name': 'common_spoonbill'}, {'id': 3078, 'synset': 'roseate_spoonbill.n.01', 'name': 'roseate_spoonbill'}, {'id': 3079, 'synset': 'great_blue_heron.n.01', 'name': 'great_blue_heron'}, {'id': 3080, 'synset': 'great_white_heron.n.03', 'name': 'great_white_heron'}, {'id': 3081, 'synset': 'egret.n.01', 'name': 'egret'}, {'id': 3082, 'synset': 'little_blue_heron.n.01', 'name': 'little_blue_heron'}, {'id': 3083, 'synset': 'snowy_egret.n.01', 'name': 'snowy_egret'}, {'id': 3084, 'synset': 'little_egret.n.01', 'name': 'little_egret'}, {'id': 3085, 'synset': 'great_white_heron.n.02', 'name': 'great_white_heron'}, {'id': 3086, 'synset': 'american_egret.n.01', 'name': 'American_egret'}, {'id': 3087, 'synset': 'cattle_egret.n.01', 'name': 'cattle_egret'}, {'id': 3088, 'synset': 'night_heron.n.01', 'name': 'night_heron'}, {'id': 3089, 'synset': 'black-crowned_night_heron.n.01', 'name': 'black-crowned_night_heron'}, {'id': 3090, 'synset': 'yellow-crowned_night_heron.n.01', 'name': 'yellow-crowned_night_heron'}, {'id': 3091, 'synset': 'boatbill.n.01', 'name': 'boatbill'}, {'id': 3092, 'synset': 'bittern.n.01', 'name': 'bittern'}, {'id': 3093, 'synset': 'american_bittern.n.01', 'name': 'American_bittern'}, {'id': 3094, 'synset': 'european_bittern.n.01', 'name': 'European_bittern'}, {'id': 3095, 'synset': 'least_bittern.n.01', 'name': 'least_bittern'}, {'id': 3096, 'synset': 'crane.n.05', 'name': 'crane'}, {'id': 3097, 'synset': 'whooping_crane.n.01', 'name': 'whooping_crane'}, {'id': 3098, 'synset': 'courlan.n.01', 'name': 'courlan'}, {'id': 3099, 'synset': 'limpkin.n.01', 'name': 'limpkin'}, {'id': 3100, 'synset': 'crested_cariama.n.01', 'name': 'crested_cariama'}, {'id': 3101, 'synset': 'chunga.n.01', 'name': 'chunga'}, {'id': 3102, 'synset': 'rail.n.05', 'name': 'rail'}, {'id': 3103, 'synset': 'weka.n.01', 'name': 'weka'}, {'id': 3104, 'synset': 'crake.n.01', 'name': 'crake'}, {'id': 3105, 'synset': 'corncrake.n.01', 'name': 'corncrake'}, {'id': 3106, 'synset': 'spotted_crake.n.01', 'name': 'spotted_crake'}, {'id': 3107, 'synset': 'gallinule.n.01', 'name': 'gallinule'}, {'id': 3108, 'synset': 'florida_gallinule.n.01', 'name': 'Florida_gallinule'}, {'id': 3109, 'synset': 'moorhen.n.01', 'name': 'moorhen'}, {'id': 3110, 'synset': 'purple_gallinule.n.01', 'name': 'purple_gallinule'}, {'id': 3111, 'synset': 'european_gallinule.n.01', 'name': 'European_gallinule'}, {'id': 3112, 'synset': 'american_gallinule.n.01', 'name': 'American_gallinule'}, {'id': 3113, 'synset': 'notornis.n.01', 'name': 'notornis'}, {'id': 3114, 'synset': 'coot.n.01', 'name': 'coot'}, {'id': 3115, 'synset': 'american_coot.n.01', 'name': 'American_coot'}, {'id': 3116, 'synset': 'old_world_coot.n.01', 'name': 'Old_World_coot'}, {'id': 3117, 'synset': 'bustard.n.01', 'name': 'bustard'}, {'id': 3118, 'synset': 'great_bustard.n.01', 'name': 'great_bustard'}, {'id': 3119, 'synset': 'plain_turkey.n.01', 'name': 'plain_turkey'}, {'id': 3120, 'synset': 'button_quail.n.01', 'name': 'button_quail'}, {'id': 3121, 'synset': 'striped_button_quail.n.01', 'name': 'striped_button_quail'}, {'id': 3122, 'synset': 'plain_wanderer.n.01', 'name': 'plain_wanderer'}, {'id': 3123, 'synset': 'trumpeter.n.03', 'name': 'trumpeter'}, {'id': 3124, 'synset': 'brazilian_trumpeter.n.01', 'name': 'Brazilian_trumpeter'}, {'id': 3125, 'synset': 'shorebird.n.01', 'name': 'shorebird'}, {'id': 3126, 'synset': 'plover.n.01', 'name': 'plover'}, {'id': 3127, 'synset': 'piping_plover.n.01', 'name': 'piping_plover'}, {'id': 3128, 'synset': 'killdeer.n.01', 'name': 'killdeer'}, {'id': 3129, 'synset': 'dotterel.n.01', 'name': 'dotterel'}, {'id': 3130, 'synset': 'golden_plover.n.01', 'name': 'golden_plover'}, {'id': 3131, 'synset': 'lapwing.n.01', 'name': 'lapwing'}, {'id': 3132, 'synset': 'turnstone.n.01', 'name': 'turnstone'}, {'id': 3133, 'synset': 'ruddy_turnstone.n.01', 'name': 'ruddy_turnstone'}, {'id': 3134, 'synset': 'black_turnstone.n.01', 'name': 'black_turnstone'}, {'id': 3135, 'synset': 'sandpiper.n.01', 'name': 'sandpiper'}, {'id': 3136, 'synset': 'surfbird.n.01', 'name': 'surfbird'}, {'id': 3137, 'synset': 'european_sandpiper.n.01', 'name': 'European_sandpiper'}, {'id': 3138, 'synset': 'spotted_sandpiper.n.01', 'name': 'spotted_sandpiper'}, {'id': 3139, 'synset': 'least_sandpiper.n.01', 'name': 'least_sandpiper'}, {'id': 3140, 'synset': 'red-backed_sandpiper.n.01', 'name': 'red-backed_sandpiper'}, {'id': 3141, 'synset': 'greenshank.n.01', 'name': 'greenshank'}, {'id': 3142, 'synset': 'redshank.n.01', 'name': 'redshank'}, {'id': 3143, 'synset': 'yellowlegs.n.01', 'name': 'yellowlegs'}, {'id': 3144, 'synset': 'greater_yellowlegs.n.01', 'name': 'greater_yellowlegs'}, {'id': 3145, 'synset': 'lesser_yellowlegs.n.01', 'name': 'lesser_yellowlegs'}, {'id': 3146, 'synset': 'pectoral_sandpiper.n.01', 'name': 'pectoral_sandpiper'}, {'id': 3147, 'synset': 'knot.n.07', 'name': 'knot'}, {'id': 3148, 'synset': 'curlew_sandpiper.n.01', 'name': 'curlew_sandpiper'}, {'id': 3149, 'synset': 'sanderling.n.01', 'name': 'sanderling'}, {'id': 3150, 'synset': 'upland_sandpiper.n.01', 'name': 'upland_sandpiper'}, {'id': 3151, 'synset': 'ruff.n.03', 'name': 'ruff'}, {'id': 3152, 'synset': 'reeve.n.01', 'name': 'reeve'}, {'id': 3153, 'synset': 'tattler.n.02', 'name': 'tattler'}, {'id': 3154, 'synset': 'polynesian_tattler.n.01', 'name': 'Polynesian_tattler'}, {'id': 3155, 'synset': 'willet.n.01', 'name': 'willet'}, {'id': 3156, 'synset': 'woodcock.n.01', 'name': 'woodcock'}, {'id': 3157, 'synset': 'eurasian_woodcock.n.01', 'name': 'Eurasian_woodcock'}, {'id': 3158, 'synset': 'american_woodcock.n.01', 'name': 'American_woodcock'}, {'id': 3159, 'synset': 'snipe.n.01', 'name': 'snipe'}, {'id': 3160, 'synset': 'whole_snipe.n.01', 'name': 'whole_snipe'}, {'id': 3161, 'synset': "wilson's_snipe.n.01", 'name': "Wilson's_snipe"}, {'id': 3162, 'synset': 'great_snipe.n.01', 'name': 'great_snipe'}, {'id': 3163, 'synset': 'jacksnipe.n.01', 'name': 'jacksnipe'}, {'id': 3164, 'synset': 'dowitcher.n.01', 'name': 'dowitcher'}, {'id': 3165, 'synset': 'greyback.n.02', 'name': 'greyback'}, {'id': 3166, 'synset': 'red-breasted_snipe.n.01', 'name': 'red-breasted_snipe'}, {'id': 3167, 'synset': 'curlew.n.01', 'name': 'curlew'}, {'id': 3168, 'synset': 'european_curlew.n.01', 'name': 'European_curlew'}, {'id': 3169, 'synset': 'eskimo_curlew.n.01', 'name': 'Eskimo_curlew'}, {'id': 3170, 'synset': 'godwit.n.01', 'name': 'godwit'}, {'id': 3171, 'synset': 'hudsonian_godwit.n.01', 'name': 'Hudsonian_godwit'}, {'id': 3172, 'synset': 'stilt.n.04', 'name': 'stilt'}, {'id': 3173, 'synset': 'black-necked_stilt.n.01', 'name': 'black-necked_stilt'}, {'id': 3174, 'synset': 'black-winged_stilt.n.01', 'name': 'black-winged_stilt'}, {'id': 3175, 'synset': 'white-headed_stilt.n.01', 'name': 'white-headed_stilt'}, {'id': 3176, 'synset': 'kaki.n.02', 'name': 'kaki'}, {'id': 3177, 'synset': 'stilt.n.03', 'name': 'stilt'}, {'id': 3178, 'synset': 'banded_stilt.n.01', 'name': 'banded_stilt'}, {'id': 3179, 'synset': 'avocet.n.01', 'name': 'avocet'}, {'id': 3180, 'synset': 'oystercatcher.n.01', 'name': 'oystercatcher'}, {'id': 3181, 'synset': 'phalarope.n.01', 'name': 'phalarope'}, {'id': 3182, 'synset': 'red_phalarope.n.01', 'name': 'red_phalarope'}, {'id': 3183, 'synset': 'northern_phalarope.n.01', 'name': 'northern_phalarope'}, {'id': 3184, 'synset': "wilson's_phalarope.n.01", 'name': "Wilson's_phalarope"}, {'id': 3185, 'synset': 'pratincole.n.01', 'name': 'pratincole'}, {'id': 3186, 'synset': 'courser.n.04', 'name': 'courser'}, {'id': 3187, 'synset': 'cream-colored_courser.n.01', 'name': 'cream-colored_courser'}, {'id': 3188, 'synset': 'crocodile_bird.n.01', 'name': 'crocodile_bird'}, {'id': 3189, 'synset': 'stone_curlew.n.01', 'name': 'stone_curlew'}, {'id': 3190, 'synset': 'coastal_diving_bird.n.01', 'name': 'coastal_diving_bird'}, {'id': 3191, 'synset': 'larid.n.01', 'name': 'larid'}, {'id': 3192, 'synset': 'mew.n.02', 'name': 'mew'}, {'id': 3193, 'synset': 'black-backed_gull.n.01', 'name': 'black-backed_gull'}, {'id': 3194, 'synset': 'herring_gull.n.01', 'name': 'herring_gull'}, {'id': 3195, 'synset': 'laughing_gull.n.01', 'name': 'laughing_gull'}, {'id': 3196, 'synset': 'ivory_gull.n.01', 'name': 'ivory_gull'}, {'id': 3197, 'synset': 'kittiwake.n.01', 'name': 'kittiwake'}, {'id': 3198, 'synset': 'tern.n.01', 'name': 'tern'}, {'id': 3199, 'synset': 'sea_swallow.n.01', 'name': 'sea_swallow'}, {'id': 3200, 'synset': 'skimmer.n.04', 'name': 'skimmer'}, {'id': 3201, 'synset': 'jaeger.n.01', 'name': 'jaeger'}, {'id': 3202, 'synset': 'parasitic_jaeger.n.01', 'name': 'parasitic_jaeger'}, {'id': 3203, 'synset': 'skua.n.01', 'name': 'skua'}, {'id': 3204, 'synset': 'great_skua.n.01', 'name': 'great_skua'}, {'id': 3205, 'synset': 'auk.n.01', 'name': 'auk'}, {'id': 3206, 'synset': 'auklet.n.01', 'name': 'auklet'}, {'id': 3207, 'synset': 'razorbill.n.01', 'name': 'razorbill'}, {'id': 3208, 'synset': 'little_auk.n.01', 'name': 'little_auk'}, {'id': 3209, 'synset': 'guillemot.n.01', 'name': 'guillemot'}, {'id': 3210, 'synset': 'black_guillemot.n.01', 'name': 'black_guillemot'}, {'id': 3211, 'synset': 'pigeon_guillemot.n.01', 'name': 'pigeon_guillemot'}, {'id': 3212, 'synset': 'murre.n.01', 'name': 'murre'}, {'id': 3213, 'synset': 'common_murre.n.01', 'name': 'common_murre'}, {'id': 3214, 'synset': 'thick-billed_murre.n.01', 'name': 'thick-billed_murre'}, {'id': 3215, 'synset': 'atlantic_puffin.n.01', 'name': 'Atlantic_puffin'}, {'id': 3216, 'synset': 'horned_puffin.n.01', 'name': 'horned_puffin'}, {'id': 3217, 'synset': 'tufted_puffin.n.01', 'name': 'tufted_puffin'}, {'id': 3218, 'synset': 'gaviiform_seabird.n.01', 'name': 'gaviiform_seabird'}, {'id': 3219, 'synset': 'loon.n.02', 'name': 'loon'}, {'id': 3220, 'synset': 'podicipitiform_seabird.n.01', 'name': 'podicipitiform_seabird'}, {'id': 3221, 'synset': 'grebe.n.01', 'name': 'grebe'}, {'id': 3222, 'synset': 'great_crested_grebe.n.01', 'name': 'great_crested_grebe'}, {'id': 3223, 'synset': 'red-necked_grebe.n.01', 'name': 'red-necked_grebe'}, {'id': 3224, 'synset': 'black-necked_grebe.n.01', 'name': 'black-necked_grebe'}, {'id': 3225, 'synset': 'dabchick.n.01', 'name': 'dabchick'}, {'id': 3226, 'synset': 'pied-billed_grebe.n.01', 'name': 'pied-billed_grebe'}, {'id': 3227, 'synset': 'pelecaniform_seabird.n.01', 'name': 'pelecaniform_seabird'}, {'id': 3228, 'synset': 'white_pelican.n.01', 'name': 'white_pelican'}, {'id': 3229, 'synset': 'old_world_white_pelican.n.01', 'name': 'Old_world_white_pelican'}, {'id': 3230, 'synset': 'frigate_bird.n.01', 'name': 'frigate_bird'}, {'id': 3231, 'synset': 'gannet.n.01', 'name': 'gannet'}, {'id': 3232, 'synset': 'solan.n.01', 'name': 'solan'}, {'id': 3233, 'synset': 'booby.n.02', 'name': 'booby'}, {'id': 3234, 'synset': 'cormorant.n.01', 'name': 'cormorant'}, {'id': 3235, 'synset': 'snakebird.n.01', 'name': 'snakebird'}, {'id': 3236, 'synset': 'water_turkey.n.01', 'name': 'water_turkey'}, {'id': 3237, 'synset': 'tropic_bird.n.01', 'name': 'tropic_bird'}, {'id': 3238, 'synset': 'sphenisciform_seabird.n.01', 'name': 'sphenisciform_seabird'}, {'id': 3239, 'synset': 'adelie.n.01', 'name': 'Adelie'}, {'id': 3240, 'synset': 'king_penguin.n.01', 'name': 'king_penguin'}, {'id': 3241, 'synset': 'emperor_penguin.n.01', 'name': 'emperor_penguin'}, {'id': 3242, 'synset': 'jackass_penguin.n.01', 'name': 'jackass_penguin'}, {'id': 3243, 'synset': 'rock_hopper.n.01', 'name': 'rock_hopper'}, {'id': 3244, 'synset': 'pelagic_bird.n.01', 'name': 'pelagic_bird'}, {'id': 3245, 'synset': 'procellariiform_seabird.n.01', 'name': 'procellariiform_seabird'}, {'id': 3246, 'synset': 'albatross.n.02', 'name': 'albatross'}, {'id': 3247, 'synset': 'wandering_albatross.n.01', 'name': 'wandering_albatross'}, {'id': 3248, 'synset': 'black-footed_albatross.n.01', 'name': 'black-footed_albatross'}, {'id': 3249, 'synset': 'petrel.n.01', 'name': 'petrel'}, {'id': 3250, 'synset': 'white-chinned_petrel.n.01', 'name': 'white-chinned_petrel'}, {'id': 3251, 'synset': 'giant_petrel.n.01', 'name': 'giant_petrel'}, {'id': 3252, 'synset': 'fulmar.n.01', 'name': 'fulmar'}, {'id': 3253, 'synset': 'shearwater.n.01', 'name': 'shearwater'}, {'id': 3254, 'synset': 'manx_shearwater.n.01', 'name': 'Manx_shearwater'}, {'id': 3255, 'synset': 'storm_petrel.n.01', 'name': 'storm_petrel'}, {'id': 3256, 'synset': 'stormy_petrel.n.01', 'name': 'stormy_petrel'}, {'id': 3257, 'synset': "mother_carey's_chicken.n.01", 'name': "Mother_Carey's_chicken"}, {'id': 3258, 'synset': 'diving_petrel.n.01', 'name': 'diving_petrel'}, {'id': 3259, 'synset': 'aquatic_mammal.n.01', 'name': 'aquatic_mammal'}, {'id': 3260, 'synset': 'cetacean.n.01', 'name': 'cetacean'}, {'id': 3261, 'synset': 'whale.n.02', 'name': 'whale'}, {'id': 3262, 'synset': 'baleen_whale.n.01', 'name': 'baleen_whale'}, {'id': 3263, 'synset': 'right_whale.n.01', 'name': 'right_whale'}, {'id': 3264, 'synset': 'bowhead.n.01', 'name': 'bowhead'}, {'id': 3265, 'synset': 'rorqual.n.01', 'name': 'rorqual'}, {'id': 3266, 'synset': 'blue_whale.n.01', 'name': 'blue_whale'}, {'id': 3267, 'synset': 'finback.n.01', 'name': 'finback'}, {'id': 3268, 'synset': 'sei_whale.n.01', 'name': 'sei_whale'}, {'id': 3269, 'synset': 'lesser_rorqual.n.01', 'name': 'lesser_rorqual'}, {'id': 3270, 'synset': 'humpback.n.03', 'name': 'humpback'}, {'id': 3271, 'synset': 'grey_whale.n.01', 'name': 'grey_whale'}, {'id': 3272, 'synset': 'toothed_whale.n.01', 'name': 'toothed_whale'}, {'id': 3273, 'synset': 'sperm_whale.n.01', 'name': 'sperm_whale'}, {'id': 3274, 'synset': 'pygmy_sperm_whale.n.01', 'name': 'pygmy_sperm_whale'}, {'id': 3275, 'synset': 'dwarf_sperm_whale.n.01', 'name': 'dwarf_sperm_whale'}, {'id': 3276, 'synset': 'beaked_whale.n.01', 'name': 'beaked_whale'}, {'id': 3277, 'synset': 'bottle-nosed_whale.n.01', 'name': 'bottle-nosed_whale'}, {'id': 3278, 'synset': 'common_dolphin.n.01', 'name': 'common_dolphin'}, {'id': 3279, 'synset': 'bottlenose_dolphin.n.01', 'name': 'bottlenose_dolphin'}, {'id': 3280, 'synset': 'atlantic_bottlenose_dolphin.n.01', 'name': 'Atlantic_bottlenose_dolphin'}, {'id': 3281, 'synset': 'pacific_bottlenose_dolphin.n.01', 'name': 'Pacific_bottlenose_dolphin'}, {'id': 3282, 'synset': 'porpoise.n.01', 'name': 'porpoise'}, {'id': 3283, 'synset': 'harbor_porpoise.n.01', 'name': 'harbor_porpoise'}, {'id': 3284, 'synset': 'vaquita.n.01', 'name': 'vaquita'}, {'id': 3285, 'synset': 'grampus.n.02', 'name': 'grampus'}, {'id': 3286, 'synset': 'killer_whale.n.01', 'name': 'killer_whale'}, {'id': 3287, 'synset': 'pilot_whale.n.01', 'name': 'pilot_whale'}, {'id': 3288, 'synset': 'river_dolphin.n.01', 'name': 'river_dolphin'}, {'id': 3289, 'synset': 'narwhal.n.01', 'name': 'narwhal'}, {'id': 3290, 'synset': 'white_whale.n.01', 'name': 'white_whale'}, {'id': 3291, 'synset': 'sea_cow.n.01', 'name': 'sea_cow'}, {'id': 3292, 'synset': 'dugong.n.01', 'name': 'dugong'}, {'id': 3293, 'synset': "steller's_sea_cow.n.01", 'name': "Steller's_sea_cow"}, {'id': 3294, 'synset': 'carnivore.n.01', 'name': 'carnivore'}, {'id': 3295, 'synset': 'omnivore.n.02', 'name': 'omnivore'}, {'id': 3296, 'synset': 'pinniped_mammal.n.01', 'name': 'pinniped_mammal'}, {'id': 3297, 'synset': 'seal.n.09', 'name': 'seal'}, {'id': 3298, 'synset': 'crabeater_seal.n.01', 'name': 'crabeater_seal'}, {'id': 3299, 'synset': 'eared_seal.n.01', 'name': 'eared_seal'}, {'id': 3300, 'synset': 'fur_seal.n.02', 'name': 'fur_seal'}, {'id': 3301, 'synset': 'guadalupe_fur_seal.n.01', 'name': 'guadalupe_fur_seal'}, {'id': 3302, 'synset': 'fur_seal.n.01', 'name': 'fur_seal'}, {'id': 3303, 'synset': 'alaska_fur_seal.n.01', 'name': 'Alaska_fur_seal'}, {'id': 3304, 'synset': 'sea_lion.n.01', 'name': 'sea_lion'}, {'id': 3305, 'synset': 'south_american_sea_lion.n.01', 'name': 'South_American_sea_lion'}, {'id': 3306, 'synset': 'california_sea_lion.n.01', 'name': 'California_sea_lion'}, {'id': 3307, 'synset': 'australian_sea_lion.n.01', 'name': 'Australian_sea_lion'}, {'id': 3308, 'synset': 'steller_sea_lion.n.01', 'name': 'Steller_sea_lion'}, {'id': 3309, 'synset': 'earless_seal.n.01', 'name': 'earless_seal'}, {'id': 3310, 'synset': 'harbor_seal.n.01', 'name': 'harbor_seal'}, {'id': 3311, 'synset': 'harp_seal.n.01', 'name': 'harp_seal'}, {'id': 3312, 'synset': 'elephant_seal.n.01', 'name': 'elephant_seal'}, {'id': 3313, 'synset': 'bearded_seal.n.01', 'name': 'bearded_seal'}, {'id': 3314, 'synset': 'hooded_seal.n.01', 'name': 'hooded_seal'}, {'id': 3315, 'synset': 'atlantic_walrus.n.01', 'name': 'Atlantic_walrus'}, {'id': 3316, 'synset': 'pacific_walrus.n.01', 'name': 'Pacific_walrus'}, {'id': 3317, 'synset': 'fissipedia.n.01', 'name': 'Fissipedia'}, {'id': 3318, 'synset': 'fissiped_mammal.n.01', 'name': 'fissiped_mammal'}, {'id': 3319, 'synset': 'aardvark.n.01', 'name': 'aardvark'}, {'id': 3320, 'synset': 'canine.n.02', 'name': 'canine'}, {'id': 3321, 'synset': 'bitch.n.04', 'name': 'bitch'}, {'id': 3322, 'synset': 'brood_bitch.n.01', 'name': 'brood_bitch'}, {'id': 3323, 'synset': 'pooch.n.01', 'name': 'pooch'}, {'id': 3324, 'synset': 'cur.n.01', 'name': 'cur'}, {'id': 3325, 'synset': 'feist.n.01', 'name': 'feist'}, {'id': 3326, 'synset': 'pariah_dog.n.01', 'name': 'pariah_dog'}, {'id': 3327, 'synset': 'lapdog.n.01', 'name': 'lapdog'}, {'id': 3328, 'synset': 'toy_dog.n.01', 'name': 'toy_dog'}, {'id': 3329, 'synset': 'chihuahua.n.03', 'name': 'Chihuahua'}, {'id': 3330, 'synset': 'japanese_spaniel.n.01', 'name': 'Japanese_spaniel'}, {'id': 3331, 'synset': 'maltese_dog.n.01', 'name': 'Maltese_dog'}, {'id': 3332, 'synset': 'pekinese.n.01', 'name': 'Pekinese'}, {'id': 3333, 'synset': 'shih-tzu.n.01', 'name': 'Shih-Tzu'}, {'id': 3334, 'synset': 'toy_spaniel.n.01', 'name': 'toy_spaniel'}, {'id': 3335, 'synset': 'english_toy_spaniel.n.01', 'name': 'English_toy_spaniel'}, {'id': 3336, 'synset': 'blenheim_spaniel.n.01', 'name': 'Blenheim_spaniel'}, {'id': 3337, 'synset': 'king_charles_spaniel.n.01', 'name': 'King_Charles_spaniel'}, {'id': 3338, 'synset': 'papillon.n.01', 'name': 'papillon'}, {'id': 3339, 'synset': 'toy_terrier.n.01', 'name': 'toy_terrier'}, {'id': 3340, 'synset': 'hunting_dog.n.01', 'name': 'hunting_dog'}, {'id': 3341, 'synset': 'courser.n.03', 'name': 'courser'}, {'id': 3342, 'synset': 'rhodesian_ridgeback.n.01', 'name': 'Rhodesian_ridgeback'}, {'id': 3343, 'synset': 'hound.n.01', 'name': 'hound'}, {'id': 3344, 'synset': 'afghan_hound.n.01', 'name': 'Afghan_hound'}, {'id': 3345, 'synset': 'basset.n.01', 'name': 'basset'}, {'id': 3346, 'synset': 'beagle.n.01', 'name': 'beagle'}, {'id': 3347, 'synset': 'bloodhound.n.01', 'name': 'bloodhound'}, {'id': 3348, 'synset': 'bluetick.n.01', 'name': 'bluetick'}, {'id': 3349, 'synset': 'boarhound.n.01', 'name': 'boarhound'}, {'id': 3350, 'synset': 'coonhound.n.01', 'name': 'coonhound'}, {'id': 3351, 'synset': 'coondog.n.01', 'name': 'coondog'}, {'id': 3352, 'synset': 'black-and-tan_coonhound.n.01', 'name': 'black-and-tan_coonhound'}, {'id': 3353, 'synset': 'dachshund.n.01', 'name': 'dachshund'}, {'id': 3354, 'synset': 'sausage_dog.n.01', 'name': 'sausage_dog'}, {'id': 3355, 'synset': 'foxhound.n.01', 'name': 'foxhound'}, {'id': 3356, 'synset': 'american_foxhound.n.01', 'name': 'American_foxhound'}, {'id': 3357, 'synset': 'walker_hound.n.01', 'name': 'Walker_hound'}, {'id': 3358, 'synset': 'english_foxhound.n.01', 'name': 'English_foxhound'}, {'id': 3359, 'synset': 'harrier.n.02', 'name': 'harrier'}, {'id': 3360, 'synset': 'plott_hound.n.01', 'name': 'Plott_hound'}, {'id': 3361, 'synset': 'redbone.n.01', 'name': 'redbone'}, {'id': 3362, 'synset': 'wolfhound.n.01', 'name': 'wolfhound'}, {'id': 3363, 'synset': 'borzoi.n.01', 'name': 'borzoi'}, {'id': 3364, 'synset': 'irish_wolfhound.n.01', 'name': 'Irish_wolfhound'}, {'id': 3365, 'synset': 'greyhound.n.01', 'name': 'greyhound'}, {'id': 3366, 'synset': 'italian_greyhound.n.01', 'name': 'Italian_greyhound'}, {'id': 3367, 'synset': 'whippet.n.01', 'name': 'whippet'}, {'id': 3368, 'synset': 'ibizan_hound.n.01', 'name': 'Ibizan_hound'}, {'id': 3369, 'synset': 'norwegian_elkhound.n.01', 'name': 'Norwegian_elkhound'}, {'id': 3370, 'synset': 'otterhound.n.01', 'name': 'otterhound'}, {'id': 3371, 'synset': 'saluki.n.01', 'name': 'Saluki'}, {'id': 3372, 'synset': 'scottish_deerhound.n.01', 'name': 'Scottish_deerhound'}, {'id': 3373, 'synset': 'staghound.n.01', 'name': 'staghound'}, {'id': 3374, 'synset': 'weimaraner.n.01', 'name': 'Weimaraner'}, {'id': 3375, 'synset': 'terrier.n.01', 'name': 'terrier'}, {'id': 3376, 'synset': 'bullterrier.n.01', 'name': 'bullterrier'}, {'id': 3377, 'synset': 'staffordshire_bullterrier.n.01', 'name': 'Staffordshire_bullterrier'}, {'id': 3378, 'synset': 'american_staffordshire_terrier.n.01', 'name': 'American_Staffordshire_terrier'}, {'id': 3379, 'synset': 'bedlington_terrier.n.01', 'name': 'Bedlington_terrier'}, {'id': 3380, 'synset': 'border_terrier.n.01', 'name': 'Border_terrier'}, {'id': 3381, 'synset': 'kerry_blue_terrier.n.01', 'name': 'Kerry_blue_terrier'}, {'id': 3382, 'synset': 'irish_terrier.n.01', 'name': 'Irish_terrier'}, {'id': 3383, 'synset': 'norfolk_terrier.n.01', 'name': 'Norfolk_terrier'}, {'id': 3384, 'synset': 'norwich_terrier.n.01', 'name': 'Norwich_terrier'}, {'id': 3385, 'synset': 'yorkshire_terrier.n.01', 'name': 'Yorkshire_terrier'}, {'id': 3386, 'synset': 'rat_terrier.n.01', 'name': 'rat_terrier'}, {'id': 3387, 'synset': 'manchester_terrier.n.01', 'name': 'Manchester_terrier'}, {'id': 3388, 'synset': 'toy_manchester.n.01', 'name': 'toy_Manchester'}, {'id': 3389, 'synset': 'fox_terrier.n.01', 'name': 'fox_terrier'}, {'id': 3390, 'synset': 'smooth-haired_fox_terrier.n.01', 'name': 'smooth-haired_fox_terrier'}, {'id': 3391, 'synset': 'wire-haired_fox_terrier.n.01', 'name': 'wire-haired_fox_terrier'}, {'id': 3392, 'synset': 'wirehair.n.01', 'name': 'wirehair'}, {'id': 3393, 'synset': 'lakeland_terrier.n.01', 'name': 'Lakeland_terrier'}, {'id': 3394, 'synset': 'welsh_terrier.n.01', 'name': 'Welsh_terrier'}, {'id': 3395, 'synset': 'sealyham_terrier.n.01', 'name': 'Sealyham_terrier'}, {'id': 3396, 'synset': 'airedale.n.01', 'name': 'Airedale'}, {'id': 3397, 'synset': 'cairn.n.02', 'name': 'cairn'}, {'id': 3398, 'synset': 'australian_terrier.n.01', 'name': 'Australian_terrier'}, {'id': 3399, 'synset': 'dandie_dinmont.n.01', 'name': 'Dandie_Dinmont'}, {'id': 3400, 'synset': 'boston_bull.n.01', 'name': 'Boston_bull'}, {'id': 3401, 'synset': 'schnauzer.n.01', 'name': 'schnauzer'}, {'id': 3402, 'synset': 'miniature_schnauzer.n.01', 'name': 'miniature_schnauzer'}, {'id': 3403, 'synset': 'giant_schnauzer.n.01', 'name': 'giant_schnauzer'}, {'id': 3404, 'synset': 'standard_schnauzer.n.01', 'name': 'standard_schnauzer'}, {'id': 3405, 'synset': 'scotch_terrier.n.01', 'name': 'Scotch_terrier'}, {'id': 3406, 'synset': 'tibetan_terrier.n.01', 'name': 'Tibetan_terrier'}, {'id': 3407, 'synset': 'silky_terrier.n.01', 'name': 'silky_terrier'}, {'id': 3408, 'synset': 'skye_terrier.n.01', 'name': 'Skye_terrier'}, {'id': 3409, 'synset': 'clydesdale_terrier.n.01', 'name': 'Clydesdale_terrier'}, {'id': 3410, 'synset': 'soft-coated_wheaten_terrier.n.01', 'name': 'soft-coated_wheaten_terrier'}, {'id': 3411, 'synset': 'west_highland_white_terrier.n.01', 'name': 'West_Highland_white_terrier'}, {'id': 3412, 'synset': 'lhasa.n.02', 'name': 'Lhasa'}, {'id': 3413, 'synset': 'sporting_dog.n.01', 'name': 'sporting_dog'}, {'id': 3414, 'synset': 'bird_dog.n.01', 'name': 'bird_dog'}, {'id': 3415, 'synset': 'water_dog.n.02', 'name': 'water_dog'}, {'id': 3416, 'synset': 'retriever.n.01', 'name': 'retriever'}, {'id': 3417, 'synset': 'flat-coated_retriever.n.01', 'name': 'flat-coated_retriever'}, {'id': 3418, 'synset': 'curly-coated_retriever.n.01', 'name': 'curly-coated_retriever'}, {'id': 3419, 'synset': 'golden_retriever.n.01', 'name': 'golden_retriever'}, {'id': 3420, 'synset': 'labrador_retriever.n.01', 'name': 'Labrador_retriever'}, {'id': 3421, 'synset': 'chesapeake_bay_retriever.n.01', 'name': 'Chesapeake_Bay_retriever'}, {'id': 3422, 'synset': 'pointer.n.04', 'name': 'pointer'}, {'id': 3423, 'synset': 'german_short-haired_pointer.n.01', 'name': 'German_short-haired_pointer'}, {'id': 3424, 'synset': 'setter.n.02', 'name': 'setter'}, {'id': 3425, 'synset': 'vizsla.n.01', 'name': 'vizsla'}, {'id': 3426, 'synset': 'english_setter.n.01', 'name': 'English_setter'}, {'id': 3427, 'synset': 'irish_setter.n.01', 'name': 'Irish_setter'}, {'id': 3428, 'synset': 'gordon_setter.n.01', 'name': 'Gordon_setter'}, {'id': 3429, 'synset': 'spaniel.n.01', 'name': 'spaniel'}, {'id': 3430, 'synset': 'brittany_spaniel.n.01', 'name': 'Brittany_spaniel'}, {'id': 3431, 'synset': 'clumber.n.01', 'name': 'clumber'}, {'id': 3432, 'synset': 'field_spaniel.n.01', 'name': 'field_spaniel'}, {'id': 3433, 'synset': 'springer_spaniel.n.01', 'name': 'springer_spaniel'}, {'id': 3434, 'synset': 'english_springer.n.01', 'name': 'English_springer'}, {'id': 3435, 'synset': 'welsh_springer_spaniel.n.01', 'name': 'Welsh_springer_spaniel'}, {'id': 3436, 'synset': 'cocker_spaniel.n.01', 'name': 'cocker_spaniel'}, {'id': 3437, 'synset': 'sussex_spaniel.n.01', 'name': 'Sussex_spaniel'}, {'id': 3438, 'synset': 'water_spaniel.n.01', 'name': 'water_spaniel'}, {'id': 3439, 'synset': 'american_water_spaniel.n.01', 'name': 'American_water_spaniel'}, {'id': 3440, 'synset': 'irish_water_spaniel.n.01', 'name': 'Irish_water_spaniel'}, {'id': 3441, 'synset': 'griffon.n.03', 'name': 'griffon'}, {'id': 3442, 'synset': 'working_dog.n.01', 'name': 'working_dog'}, {'id': 3443, 'synset': 'watchdog.n.02', 'name': 'watchdog'}, {'id': 3444, 'synset': 'kuvasz.n.01', 'name': 'kuvasz'}, {'id': 3445, 'synset': 'attack_dog.n.01', 'name': 'attack_dog'}, {'id': 3446, 'synset': 'housedog.n.01', 'name': 'housedog'}, {'id': 3447, 'synset': 'schipperke.n.01', 'name': 'schipperke'}, {'id': 3448, 'synset': 'belgian_sheepdog.n.01', 'name': 'Belgian_sheepdog'}, {'id': 3449, 'synset': 'groenendael.n.01', 'name': 'groenendael'}, {'id': 3450, 'synset': 'malinois.n.01', 'name': 'malinois'}, {'id': 3451, 'synset': 'briard.n.01', 'name': 'briard'}, {'id': 3452, 'synset': 'kelpie.n.02', 'name': 'kelpie'}, {'id': 3453, 'synset': 'komondor.n.01', 'name': 'komondor'}, {'id': 3454, 'synset': 'old_english_sheepdog.n.01', 'name': 'Old_English_sheepdog'}, {'id': 3455, 'synset': 'shetland_sheepdog.n.01', 'name': 'Shetland_sheepdog'}, {'id': 3456, 'synset': 'collie.n.01', 'name': 'collie'}, {'id': 3457, 'synset': 'border_collie.n.01', 'name': 'Border_collie'}, {'id': 3458, 'synset': 'bouvier_des_flandres.n.01', 'name': 'Bouvier_des_Flandres'}, {'id': 3459, 'synset': 'rottweiler.n.01', 'name': 'Rottweiler'}, {'id': 3460, 'synset': 'german_shepherd.n.01', 'name': 'German_shepherd'}, {'id': 3461, 'synset': 'police_dog.n.01', 'name': 'police_dog'}, {'id': 3462, 'synset': 'pinscher.n.01', 'name': 'pinscher'}, {'id': 3463, 'synset': 'doberman.n.01', 'name': 'Doberman'}, {'id': 3464, 'synset': 'miniature_pinscher.n.01', 'name': 'miniature_pinscher'}, {'id': 3465, 'synset': 'sennenhunde.n.01', 'name': 'Sennenhunde'}, {'id': 3466, 'synset': 'greater_swiss_mountain_dog.n.01', 'name': 'Greater_Swiss_Mountain_dog'}, {'id': 3467, 'synset': 'bernese_mountain_dog.n.01', 'name': 'Bernese_mountain_dog'}, {'id': 3468, 'synset': 'appenzeller.n.01', 'name': 'Appenzeller'}, {'id': 3469, 'synset': 'entlebucher.n.01', 'name': 'EntleBucher'}, {'id': 3470, 'synset': 'boxer.n.04', 'name': 'boxer'}, {'id': 3471, 'synset': 'mastiff.n.01', 'name': 'mastiff'}, {'id': 3472, 'synset': 'bull_mastiff.n.01', 'name': 'bull_mastiff'}, {'id': 3473, 'synset': 'tibetan_mastiff.n.01', 'name': 'Tibetan_mastiff'}, {'id': 3474, 'synset': 'french_bulldog.n.01', 'name': 'French_bulldog'}, {'id': 3475, 'synset': 'great_dane.n.01', 'name': 'Great_Dane'}, {'id': 3476, 'synset': 'guide_dog.n.01', 'name': 'guide_dog'}, {'id': 3477, 'synset': 'seeing_eye_dog.n.01', 'name': 'Seeing_Eye_dog'}, {'id': 3478, 'synset': 'hearing_dog.n.01', 'name': 'hearing_dog'}, {'id': 3479, 'synset': 'saint_bernard.n.01', 'name': 'Saint_Bernard'}, {'id': 3480, 'synset': 'seizure-alert_dog.n.01', 'name': 'seizure-alert_dog'}, {'id': 3481, 'synset': 'sled_dog.n.01', 'name': 'sled_dog'}, {'id': 3482, 'synset': 'eskimo_dog.n.01', 'name': 'Eskimo_dog'}, {'id': 3483, 'synset': 'malamute.n.01', 'name': 'malamute'}, {'id': 3484, 'synset': 'siberian_husky.n.01', 'name': 'Siberian_husky'}, {'id': 3485, 'synset': 'liver-spotted_dalmatian.n.01', 'name': 'liver-spotted_dalmatian'}, {'id': 3486, 'synset': 'affenpinscher.n.01', 'name': 'affenpinscher'}, {'id': 3487, 'synset': 'basenji.n.01', 'name': 'basenji'}, {'id': 3488, 'synset': 'leonberg.n.01', 'name': 'Leonberg'}, {'id': 3489, 'synset': 'newfoundland.n.01', 'name': 'Newfoundland'}, {'id': 3490, 'synset': 'great_pyrenees.n.01', 'name': 'Great_Pyrenees'}, {'id': 3491, 'synset': 'spitz.n.01', 'name': 'spitz'}, {'id': 3492, 'synset': 'samoyed.n.03', 'name': 'Samoyed'}, {'id': 3493, 'synset': 'pomeranian.n.01', 'name': 'Pomeranian'}, {'id': 3494, 'synset': 'chow.n.03', 'name': 'chow'}, {'id': 3495, 'synset': 'keeshond.n.01', 'name': 'keeshond'}, {'id': 3496, 'synset': 'griffon.n.02', 'name': 'griffon'}, {'id': 3497, 'synset': 'brabancon_griffon.n.01', 'name': 'Brabancon_griffon'}, {'id': 3498, 'synset': 'corgi.n.01', 'name': 'corgi'}, {'id': 3499, 'synset': 'pembroke.n.01', 'name': 'Pembroke'}, {'id': 3500, 'synset': 'cardigan.n.02', 'name': 'Cardigan'}, {'id': 3501, 'synset': 'poodle.n.01', 'name': 'poodle'}, {'id': 3502, 'synset': 'toy_poodle.n.01', 'name': 'toy_poodle'}, {'id': 3503, 'synset': 'miniature_poodle.n.01', 'name': 'miniature_poodle'}, {'id': 3504, 'synset': 'standard_poodle.n.01', 'name': 'standard_poodle'}, {'id': 3505, 'synset': 'large_poodle.n.01', 'name': 'large_poodle'}, {'id': 3506, 'synset': 'mexican_hairless.n.01', 'name': 'Mexican_hairless'}, {'id': 3507, 'synset': 'timber_wolf.n.01', 'name': 'timber_wolf'}, {'id': 3508, 'synset': 'white_wolf.n.01', 'name': 'white_wolf'}, {'id': 3509, 'synset': 'red_wolf.n.01', 'name': 'red_wolf'}, {'id': 3510, 'synset': 'coyote.n.01', 'name': 'coyote'}, {'id': 3511, 'synset': 'coydog.n.01', 'name': 'coydog'}, {'id': 3512, 'synset': 'jackal.n.01', 'name': 'jackal'}, {'id': 3513, 'synset': 'wild_dog.n.01', 'name': 'wild_dog'}, {'id': 3514, 'synset': 'dingo.n.01', 'name': 'dingo'}, {'id': 3515, 'synset': 'dhole.n.01', 'name': 'dhole'}, {'id': 3516, 'synset': 'crab-eating_dog.n.01', 'name': 'crab-eating_dog'}, {'id': 3517, 'synset': 'raccoon_dog.n.01', 'name': 'raccoon_dog'}, {'id': 3518, 'synset': 'african_hunting_dog.n.01', 'name': 'African_hunting_dog'}, {'id': 3519, 'synset': 'hyena.n.01', 'name': 'hyena'}, {'id': 3520, 'synset': 'striped_hyena.n.01', 'name': 'striped_hyena'}, {'id': 3521, 'synset': 'brown_hyena.n.01', 'name': 'brown_hyena'}, {'id': 3522, 'synset': 'spotted_hyena.n.01', 'name': 'spotted_hyena'}, {'id': 3523, 'synset': 'aardwolf.n.01', 'name': 'aardwolf'}, {'id': 3524, 'synset': 'fox.n.01', 'name': 'fox'}, {'id': 3525, 'synset': 'vixen.n.02', 'name': 'vixen'}, {'id': 3526, 'synset': 'reynard.n.01', 'name': 'Reynard'}, {'id': 3527, 'synset': 'red_fox.n.03', 'name': 'red_fox'}, {'id': 3528, 'synset': 'black_fox.n.01', 'name': 'black_fox'}, {'id': 3529, 'synset': 'silver_fox.n.01', 'name': 'silver_fox'}, {'id': 3530, 'synset': 'red_fox.n.02', 'name': 'red_fox'}, {'id': 3531, 'synset': 'kit_fox.n.02', 'name': 'kit_fox'}, {'id': 3532, 'synset': 'kit_fox.n.01', 'name': 'kit_fox'}, {'id': 3533, 'synset': 'arctic_fox.n.01', 'name': 'Arctic_fox'}, {'id': 3534, 'synset': 'blue_fox.n.01', 'name': 'blue_fox'}, {'id': 3535, 'synset': 'grey_fox.n.01', 'name': 'grey_fox'}, {'id': 3536, 'synset': 'feline.n.01', 'name': 'feline'}, {'id': 3537, 'synset': 'domestic_cat.n.01', 'name': 'domestic_cat'}, {'id': 3538, 'synset': 'kitty.n.04', 'name': 'kitty'}, {'id': 3539, 'synset': 'mouser.n.01', 'name': 'mouser'}, {'id': 3540, 'synset': 'alley_cat.n.01', 'name': 'alley_cat'}, {'id': 3541, 'synset': 'stray.n.01', 'name': 'stray'}, {'id': 3542, 'synset': 'tom.n.02', 'name': 'tom'}, {'id': 3543, 'synset': 'gib.n.02', 'name': 'gib'}, {'id': 3544, 'synset': 'tabby.n.02', 'name': 'tabby'}, {'id': 3545, 'synset': 'tabby.n.01', 'name': 'tabby'}, {'id': 3546, 'synset': 'tiger_cat.n.02', 'name': 'tiger_cat'}, {'id': 3547, 'synset': 'tortoiseshell.n.03', 'name': 'tortoiseshell'}, {'id': 3548, 'synset': 'persian_cat.n.01', 'name': 'Persian_cat'}, {'id': 3549, 'synset': 'angora.n.04', 'name': 'Angora'}, {'id': 3550, 'synset': 'siamese_cat.n.01', 'name': 'Siamese_cat'}, {'id': 3551, 'synset': 'blue_point_siamese.n.01', 'name': 'blue_point_Siamese'}, {'id': 3552, 'synset': 'burmese_cat.n.01', 'name': 'Burmese_cat'}, {'id': 3553, 'synset': 'egyptian_cat.n.01', 'name': 'Egyptian_cat'}, {'id': 3554, 'synset': 'maltese.n.03', 'name': 'Maltese'}, {'id': 3555, 'synset': 'abyssinian.n.01', 'name': 'Abyssinian'}, {'id': 3556, 'synset': 'manx.n.02', 'name': 'Manx'}, {'id': 3557, 'synset': 'wildcat.n.03', 'name': 'wildcat'}, {'id': 3558, 'synset': 'sand_cat.n.01', 'name': 'sand_cat'}, {'id': 3559, 'synset': 'european_wildcat.n.01', 'name': 'European_wildcat'}, {'id': 3560, 'synset': 'ocelot.n.01', 'name': 'ocelot'}, {'id': 3561, 'synset': 'jaguarundi.n.01', 'name': 'jaguarundi'}, {'id': 3562, 'synset': 'kaffir_cat.n.01', 'name': 'kaffir_cat'}, {'id': 3563, 'synset': 'jungle_cat.n.01', 'name': 'jungle_cat'}, {'id': 3564, 'synset': 'serval.n.01', 'name': 'serval'}, {'id': 3565, 'synset': 'leopard_cat.n.01', 'name': 'leopard_cat'}, {'id': 3566, 'synset': 'margay.n.01', 'name': 'margay'}, {'id': 3567, 'synset': 'manul.n.01', 'name': 'manul'}, {'id': 3568, 'synset': 'lynx.n.02', 'name': 'lynx'}, {'id': 3569, 'synset': 'common_lynx.n.01', 'name': 'common_lynx'}, {'id': 3570, 'synset': 'canada_lynx.n.01', 'name': 'Canada_lynx'}, {'id': 3571, 'synset': 'bobcat.n.01', 'name': 'bobcat'}, {'id': 3572, 'synset': 'spotted_lynx.n.01', 'name': 'spotted_lynx'}, {'id': 3573, 'synset': 'caracal.n.01', 'name': 'caracal'}, {'id': 3574, 'synset': 'big_cat.n.01', 'name': 'big_cat'}, {'id': 3575, 'synset': 'leopard.n.02', 'name': 'leopard'}, {'id': 3576, 'synset': 'leopardess.n.01', 'name': 'leopardess'}, {'id': 3577, 'synset': 'panther.n.02', 'name': 'panther'}, {'id': 3578, 'synset': 'snow_leopard.n.01', 'name': 'snow_leopard'}, {'id': 3579, 'synset': 'jaguar.n.01', 'name': 'jaguar'}, {'id': 3580, 'synset': 'lioness.n.01', 'name': 'lioness'}, {'id': 3581, 'synset': 'lionet.n.01', 'name': 'lionet'}, {'id': 3582, 'synset': 'bengal_tiger.n.01', 'name': 'Bengal_tiger'}, {'id': 3583, 'synset': 'tigress.n.01', 'name': 'tigress'}, {'id': 3584, 'synset': 'liger.n.01', 'name': 'liger'}, {'id': 3585, 'synset': 'tiglon.n.01', 'name': 'tiglon'}, {'id': 3586, 'synset': 'cheetah.n.01', 'name': 'cheetah'}, {'id': 3587, 'synset': 'saber-toothed_tiger.n.01', 'name': 'saber-toothed_tiger'}, {'id': 3588, 'synset': 'smiledon_californicus.n.01', 'name': 'Smiledon_californicus'}, {'id': 3589, 'synset': 'brown_bear.n.01', 'name': 'brown_bear'}, {'id': 3590, 'synset': 'bruin.n.01', 'name': 'bruin'}, {'id': 3591, 'synset': 'syrian_bear.n.01', 'name': 'Syrian_bear'}, {'id': 3592, 'synset': 'alaskan_brown_bear.n.01', 'name': 'Alaskan_brown_bear'}, {'id': 3593, 'synset': 'american_black_bear.n.01', 'name': 'American_black_bear'}, {'id': 3594, 'synset': 'cinnamon_bear.n.01', 'name': 'cinnamon_bear'}, {'id': 3595, 'synset': 'asiatic_black_bear.n.01', 'name': 'Asiatic_black_bear'}, {'id': 3596, 'synset': 'sloth_bear.n.01', 'name': 'sloth_bear'}, {'id': 3597, 'synset': 'viverrine.n.01', 'name': 'viverrine'}, {'id': 3598, 'synset': 'civet.n.01', 'name': 'civet'}, {'id': 3599, 'synset': 'large_civet.n.01', 'name': 'large_civet'}, {'id': 3600, 'synset': 'small_civet.n.01', 'name': 'small_civet'}, {'id': 3601, 'synset': 'binturong.n.01', 'name': 'binturong'}, {'id': 3602, 'synset': 'cryptoprocta.n.01', 'name': 'Cryptoprocta'}, {'id': 3603, 'synset': 'fossa.n.03', 'name': 'fossa'}, {'id': 3604, 'synset': 'fanaloka.n.01', 'name': 'fanaloka'}, {'id': 3605, 'synset': 'genet.n.03', 'name': 'genet'}, {'id': 3606, 'synset': 'banded_palm_civet.n.01', 'name': 'banded_palm_civet'}, {'id': 3607, 'synset': 'mongoose.n.01', 'name': 'mongoose'}, {'id': 3608, 'synset': 'indian_mongoose.n.01', 'name': 'Indian_mongoose'}, {'id': 3609, 'synset': 'ichneumon.n.01', 'name': 'ichneumon'}, {'id': 3610, 'synset': 'palm_cat.n.01', 'name': 'palm_cat'}, {'id': 3611, 'synset': 'meerkat.n.01', 'name': 'meerkat'}, {'id': 3612, 'synset': 'slender-tailed_meerkat.n.01', 'name': 'slender-tailed_meerkat'}, {'id': 3613, 'synset': 'suricate.n.01', 'name': 'suricate'}, {'id': 3614, 'synset': 'fruit_bat.n.01', 'name': 'fruit_bat'}, {'id': 3615, 'synset': 'flying_fox.n.01', 'name': 'flying_fox'}, {'id': 3616, 'synset': 'pteropus_capestratus.n.01', 'name': 'Pteropus_capestratus'}, {'id': 3617, 'synset': 'pteropus_hypomelanus.n.01', 'name': 'Pteropus_hypomelanus'}, {'id': 3618, 'synset': 'harpy.n.03', 'name': 'harpy'}, {'id': 3619, 'synset': 'cynopterus_sphinx.n.01', 'name': 'Cynopterus_sphinx'}, {'id': 3620, 'synset': 'carnivorous_bat.n.01', 'name': 'carnivorous_bat'}, {'id': 3621, 'synset': 'mouse-eared_bat.n.01', 'name': 'mouse-eared_bat'}, {'id': 3622, 'synset': 'leafnose_bat.n.01', 'name': 'leafnose_bat'}, {'id': 3623, 'synset': 'macrotus.n.01', 'name': 'macrotus'}, {'id': 3624, 'synset': 'spearnose_bat.n.01', 'name': 'spearnose_bat'}, {'id': 3625, 'synset': 'phyllostomus_hastatus.n.01', 'name': 'Phyllostomus_hastatus'}, {'id': 3626, 'synset': 'hognose_bat.n.01', 'name': 'hognose_bat'}, {'id': 3627, 'synset': 'horseshoe_bat.n.02', 'name': 'horseshoe_bat'}, {'id': 3628, 'synset': 'horseshoe_bat.n.01', 'name': 'horseshoe_bat'}, {'id': 3629, 'synset': 'orange_bat.n.01', 'name': 'orange_bat'}, {'id': 3630, 'synset': 'false_vampire.n.01', 'name': 'false_vampire'}, {'id': 3631, 'synset': 'big-eared_bat.n.01', 'name': 'big-eared_bat'}, {'id': 3632, 'synset': 'vespertilian_bat.n.01', 'name': 'vespertilian_bat'}, {'id': 3633, 'synset': 'frosted_bat.n.01', 'name': 'frosted_bat'}, {'id': 3634, 'synset': 'red_bat.n.01', 'name': 'red_bat'}, {'id': 3635, 'synset': 'brown_bat.n.01', 'name': 'brown_bat'}, {'id': 3636, 'synset': 'little_brown_bat.n.01', 'name': 'little_brown_bat'}, {'id': 3637, 'synset': 'cave_myotis.n.01', 'name': 'cave_myotis'}, {'id': 3638, 'synset': 'big_brown_bat.n.01', 'name': 'big_brown_bat'}, {'id': 3639, 'synset': 'serotine.n.01', 'name': 'serotine'}, {'id': 3640, 'synset': 'pallid_bat.n.01', 'name': 'pallid_bat'}, {'id': 3641, 'synset': 'pipistrelle.n.01', 'name': 'pipistrelle'}, {'id': 3642, 'synset': 'eastern_pipistrel.n.01', 'name': 'eastern_pipistrel'}, {'id': 3643, 'synset': 'jackass_bat.n.01', 'name': 'jackass_bat'}, {'id': 3644, 'synset': 'long-eared_bat.n.01', 'name': 'long-eared_bat'}, {'id': 3645, 'synset': 'western_big-eared_bat.n.01', 'name': 'western_big-eared_bat'}, {'id': 3646, 'synset': 'freetail.n.01', 'name': 'freetail'}, {'id': 3647, 'synset': 'guano_bat.n.01', 'name': 'guano_bat'}, {'id': 3648, 'synset': 'pocketed_bat.n.01', 'name': 'pocketed_bat'}, {'id': 3649, 'synset': 'mastiff_bat.n.01', 'name': 'mastiff_bat'}, {'id': 3650, 'synset': 'vampire_bat.n.01', 'name': 'vampire_bat'}, {'id': 3651, 'synset': 'desmodus_rotundus.n.01', 'name': 'Desmodus_rotundus'}, {'id': 3652, 'synset': 'hairy-legged_vampire_bat.n.01', 'name': 'hairy-legged_vampire_bat'}, {'id': 3653, 'synset': 'predator.n.02', 'name': 'predator'}, {'id': 3654, 'synset': 'prey.n.02', 'name': 'prey'}, {'id': 3655, 'synset': 'game.n.04', 'name': 'game'}, {'id': 3656, 'synset': 'big_game.n.01', 'name': 'big_game'}, {'id': 3657, 'synset': 'game_bird.n.01', 'name': 'game_bird'}, {'id': 3658, 'synset': 'fossorial_mammal.n.01', 'name': 'fossorial_mammal'}, {'id': 3659, 'synset': 'tetrapod.n.01', 'name': 'tetrapod'}, {'id': 3660, 'synset': 'quadruped.n.01', 'name': 'quadruped'}, {'id': 3661, 'synset': 'hexapod.n.01', 'name': 'hexapod'}, {'id': 3662, 'synset': 'biped.n.01', 'name': 'biped'}, {'id': 3663, 'synset': 'insect.n.01', 'name': 'insect'}, {'id': 3664, 'synset': 'social_insect.n.01', 'name': 'social_insect'}, {'id': 3665, 'synset': 'holometabola.n.01', 'name': 'holometabola'}, {'id': 3666, 'synset': 'defoliator.n.01', 'name': 'defoliator'}, {'id': 3667, 'synset': 'pollinator.n.01', 'name': 'pollinator'}, {'id': 3668, 'synset': 'gallfly.n.03', 'name': 'gallfly'}, {'id': 3669, 'synset': 'scorpion_fly.n.01', 'name': 'scorpion_fly'}, {'id': 3670, 'synset': 'hanging_fly.n.01', 'name': 'hanging_fly'}, {'id': 3671, 'synset': 'collembolan.n.01', 'name': 'collembolan'}, {'id': 3672, 'synset': 'tiger_beetle.n.01', 'name': 'tiger_beetle'}, {'id': 3673, 'synset': 'two-spotted_ladybug.n.01', 'name': 'two-spotted_ladybug'}, {'id': 3674, 'synset': 'mexican_bean_beetle.n.01', 'name': 'Mexican_bean_beetle'}, {'id': 3675, 'synset': 'hippodamia_convergens.n.01', 'name': 'Hippodamia_convergens'}, {'id': 3676, 'synset': 'vedalia.n.01', 'name': 'vedalia'}, {'id': 3677, 'synset': 'ground_beetle.n.01', 'name': 'ground_beetle'}, {'id': 3678, 'synset': 'bombardier_beetle.n.01', 'name': 'bombardier_beetle'}, {'id': 3679, 'synset': 'calosoma.n.01', 'name': 'calosoma'}, {'id': 3680, 'synset': 'searcher.n.03', 'name': 'searcher'}, {'id': 3681, 'synset': 'firefly.n.02', 'name': 'firefly'}, {'id': 3682, 'synset': 'glowworm.n.01', 'name': 'glowworm'}, {'id': 3683, 'synset': 'long-horned_beetle.n.01', 'name': 'long-horned_beetle'}, {'id': 3684, 'synset': 'sawyer.n.02', 'name': 'sawyer'}, {'id': 3685, 'synset': 'pine_sawyer.n.01', 'name': 'pine_sawyer'}, {'id': 3686, 'synset': 'leaf_beetle.n.01', 'name': 'leaf_beetle'}, {'id': 3687, 'synset': 'flea_beetle.n.01', 'name': 'flea_beetle'}, {'id': 3688, 'synset': 'colorado_potato_beetle.n.01', 'name': 'Colorado_potato_beetle'}, {'id': 3689, 'synset': 'carpet_beetle.n.01', 'name': 'carpet_beetle'}, {'id': 3690, 'synset': 'buffalo_carpet_beetle.n.01', 'name': 'buffalo_carpet_beetle'}, {'id': 3691, 'synset': 'black_carpet_beetle.n.01', 'name': 'black_carpet_beetle'}, {'id': 3692, 'synset': 'clerid_beetle.n.01', 'name': 'clerid_beetle'}, {'id': 3693, 'synset': 'bee_beetle.n.01', 'name': 'bee_beetle'}, {'id': 3694, 'synset': 'lamellicorn_beetle.n.01', 'name': 'lamellicorn_beetle'}, {'id': 3695, 'synset': 'scarabaeid_beetle.n.01', 'name': 'scarabaeid_beetle'}, {'id': 3696, 'synset': 'dung_beetle.n.01', 'name': 'dung_beetle'}, {'id': 3697, 'synset': 'scarab.n.01', 'name': 'scarab'}, {'id': 3698, 'synset': 'tumblebug.n.01', 'name': 'tumblebug'}, {'id': 3699, 'synset': 'dorbeetle.n.01', 'name': 'dorbeetle'}, {'id': 3700, 'synset': 'june_beetle.n.01', 'name': 'June_beetle'}, {'id': 3701, 'synset': 'green_june_beetle.n.01', 'name': 'green_June_beetle'}, {'id': 3702, 'synset': 'japanese_beetle.n.01', 'name': 'Japanese_beetle'}, {'id': 3703, 'synset': 'oriental_beetle.n.01', 'name': 'Oriental_beetle'}, {'id': 3704, 'synset': 'rhinoceros_beetle.n.01', 'name': 'rhinoceros_beetle'}, {'id': 3705, 'synset': 'melolonthid_beetle.n.01', 'name': 'melolonthid_beetle'}, {'id': 3706, 'synset': 'cockchafer.n.01', 'name': 'cockchafer'}, {'id': 3707, 'synset': 'rose_chafer.n.02', 'name': 'rose_chafer'}, {'id': 3708, 'synset': 'rose_chafer.n.01', 'name': 'rose_chafer'}, {'id': 3709, 'synset': 'stag_beetle.n.01', 'name': 'stag_beetle'}, {'id': 3710, 'synset': 'elaterid_beetle.n.01', 'name': 'elaterid_beetle'}, {'id': 3711, 'synset': 'click_beetle.n.01', 'name': 'click_beetle'}, {'id': 3712, 'synset': 'firefly.n.01', 'name': 'firefly'}, {'id': 3713, 'synset': 'wireworm.n.01', 'name': 'wireworm'}, {'id': 3714, 'synset': 'water_beetle.n.01', 'name': 'water_beetle'}, {'id': 3715, 'synset': 'whirligig_beetle.n.01', 'name': 'whirligig_beetle'}, {'id': 3716, 'synset': 'deathwatch_beetle.n.01', 'name': 'deathwatch_beetle'}, {'id': 3717, 'synset': 'weevil.n.01', 'name': 'weevil'}, {'id': 3718, 'synset': 'snout_beetle.n.01', 'name': 'snout_beetle'}, {'id': 3719, 'synset': 'boll_weevil.n.01', 'name': 'boll_weevil'}, {'id': 3720, 'synset': 'blister_beetle.n.01', 'name': 'blister_beetle'}, {'id': 3721, 'synset': 'oil_beetle.n.01', 'name': 'oil_beetle'}, {'id': 3722, 'synset': 'spanish_fly.n.01', 'name': 'Spanish_fly'}, {'id': 3723, 'synset': 'dutch-elm_beetle.n.01', 'name': 'Dutch-elm_beetle'}, {'id': 3724, 'synset': 'bark_beetle.n.01', 'name': 'bark_beetle'}, {'id': 3725, 'synset': 'spruce_bark_beetle.n.01', 'name': 'spruce_bark_beetle'}, {'id': 3726, 'synset': 'rove_beetle.n.01', 'name': 'rove_beetle'}, {'id': 3727, 'synset': 'darkling_beetle.n.01', 'name': 'darkling_beetle'}, {'id': 3728, 'synset': 'mealworm.n.01', 'name': 'mealworm'}, {'id': 3729, 'synset': 'flour_beetle.n.01', 'name': 'flour_beetle'}, {'id': 3730, 'synset': 'seed_beetle.n.01', 'name': 'seed_beetle'}, {'id': 3731, 'synset': 'pea_weevil.n.01', 'name': 'pea_weevil'}, {'id': 3732, 'synset': 'bean_weevil.n.01', 'name': 'bean_weevil'}, {'id': 3733, 'synset': 'rice_weevil.n.01', 'name': 'rice_weevil'}, {'id': 3734, 'synset': 'asian_longhorned_beetle.n.01', 'name': 'Asian_longhorned_beetle'}, {'id': 3735, 'synset': 'web_spinner.n.01', 'name': 'web_spinner'}, {'id': 3736, 'synset': 'louse.n.01', 'name': 'louse'}, {'id': 3737, 'synset': 'common_louse.n.01', 'name': 'common_louse'}, {'id': 3738, 'synset': 'head_louse.n.01', 'name': 'head_louse'}, {'id': 3739, 'synset': 'body_louse.n.01', 'name': 'body_louse'}, {'id': 3740, 'synset': 'crab_louse.n.01', 'name': 'crab_louse'}, {'id': 3741, 'synset': 'bird_louse.n.01', 'name': 'bird_louse'}, {'id': 3742, 'synset': 'flea.n.01', 'name': 'flea'}, {'id': 3743, 'synset': 'pulex_irritans.n.01', 'name': 'Pulex_irritans'}, {'id': 3744, 'synset': 'dog_flea.n.01', 'name': 'dog_flea'}, {'id': 3745, 'synset': 'cat_flea.n.01', 'name': 'cat_flea'}, {'id': 3746, 'synset': 'chigoe.n.01', 'name': 'chigoe'}, {'id': 3747, 'synset': 'sticktight.n.02', 'name': 'sticktight'}, {'id': 3748, 'synset': 'dipterous_insect.n.01', 'name': 'dipterous_insect'}, {'id': 3749, 'synset': 'gall_midge.n.01', 'name': 'gall_midge'}, {'id': 3750, 'synset': 'hessian_fly.n.01', 'name': 'Hessian_fly'}, {'id': 3751, 'synset': 'fly.n.01', 'name': 'fly'}, {'id': 3752, 'synset': 'housefly.n.01', 'name': 'housefly'}, {'id': 3753, 'synset': 'tsetse_fly.n.01', 'name': 'tsetse_fly'}, {'id': 3754, 'synset': 'blowfly.n.01', 'name': 'blowfly'}, {'id': 3755, 'synset': 'bluebottle.n.02', 'name': 'bluebottle'}, {'id': 3756, 'synset': 'greenbottle.n.01', 'name': 'greenbottle'}, {'id': 3757, 'synset': 'flesh_fly.n.01', 'name': 'flesh_fly'}, {'id': 3758, 'synset': 'tachina_fly.n.01', 'name': 'tachina_fly'}, {'id': 3759, 'synset': 'gadfly.n.02', 'name': 'gadfly'}, {'id': 3760, 'synset': 'botfly.n.01', 'name': 'botfly'}, {'id': 3761, 'synset': 'human_botfly.n.01', 'name': 'human_botfly'}, {'id': 3762, 'synset': 'sheep_botfly.n.01', 'name': 'sheep_botfly'}, {'id': 3763, 'synset': 'warble_fly.n.01', 'name': 'warble_fly'}, {'id': 3764, 'synset': 'horsefly.n.02', 'name': 'horsefly'}, {'id': 3765, 'synset': 'bee_fly.n.01', 'name': 'bee_fly'}, {'id': 3766, 'synset': 'robber_fly.n.01', 'name': 'robber_fly'}, {'id': 3767, 'synset': 'fruit_fly.n.01', 'name': 'fruit_fly'}, {'id': 3768, 'synset': 'apple_maggot.n.01', 'name': 'apple_maggot'}, {'id': 3769, 'synset': 'mediterranean_fruit_fly.n.01', 'name': 'Mediterranean_fruit_fly'}, {'id': 3770, 'synset': 'drosophila.n.01', 'name': 'drosophila'}, {'id': 3771, 'synset': 'vinegar_fly.n.01', 'name': 'vinegar_fly'}, {'id': 3772, 'synset': 'leaf_miner.n.01', 'name': 'leaf_miner'}, {'id': 3773, 'synset': 'louse_fly.n.01', 'name': 'louse_fly'}, {'id': 3774, 'synset': 'horse_tick.n.01', 'name': 'horse_tick'}, {'id': 3775, 'synset': 'sheep_ked.n.01', 'name': 'sheep_ked'}, {'id': 3776, 'synset': 'horn_fly.n.01', 'name': 'horn_fly'}, {'id': 3777, 'synset': 'mosquito.n.01', 'name': 'mosquito'}, {'id': 3778, 'synset': 'wiggler.n.02', 'name': 'wiggler'}, {'id': 3779, 'synset': 'gnat.n.02', 'name': 'gnat'}, {'id': 3780, 'synset': 'yellow-fever_mosquito.n.01', 'name': 'yellow-fever_mosquito'}, {'id': 3781, 'synset': 'asian_tiger_mosquito.n.01', 'name': 'Asian_tiger_mosquito'}, {'id': 3782, 'synset': 'anopheline.n.01', 'name': 'anopheline'}, {'id': 3783, 'synset': 'malarial_mosquito.n.01', 'name': 'malarial_mosquito'}, {'id': 3784, 'synset': 'common_mosquito.n.01', 'name': 'common_mosquito'}, {'id': 3785, 'synset': 'culex_quinquefasciatus.n.01', 'name': 'Culex_quinquefasciatus'}, {'id': 3786, 'synset': 'gnat.n.01', 'name': 'gnat'}, {'id': 3787, 'synset': 'punkie.n.01', 'name': 'punkie'}, {'id': 3788, 'synset': 'midge.n.01', 'name': 'midge'}, {'id': 3789, 'synset': 'fungus_gnat.n.02', 'name': 'fungus_gnat'}, {'id': 3790, 'synset': 'psychodid.n.01', 'name': 'psychodid'}, {'id': 3791, 'synset': 'sand_fly.n.01', 'name': 'sand_fly'}, {'id': 3792, 'synset': 'fungus_gnat.n.01', 'name': 'fungus_gnat'}, {'id': 3793, 'synset': 'armyworm.n.03', 'name': 'armyworm'}, {'id': 3794, 'synset': 'crane_fly.n.01', 'name': 'crane_fly'}, {'id': 3795, 'synset': 'blackfly.n.02', 'name': 'blackfly'}, {'id': 3796, 'synset': 'hymenopterous_insect.n.01', 'name': 'hymenopterous_insect'}, {'id': 3797, 'synset': 'bee.n.01', 'name': 'bee'}, {'id': 3798, 'synset': 'drone.n.01', 'name': 'drone'}, {'id': 3799, 'synset': 'queen_bee.n.01', 'name': 'queen_bee'}, {'id': 3800, 'synset': 'worker.n.03', 'name': 'worker'}, {'id': 3801, 'synset': 'soldier.n.02', 'name': 'soldier'}, {'id': 3802, 'synset': 'worker_bee.n.01', 'name': 'worker_bee'}, {'id': 3803, 'synset': 'honeybee.n.01', 'name': 'honeybee'}, {'id': 3804, 'synset': 'africanized_bee.n.01', 'name': 'Africanized_bee'}, {'id': 3805, 'synset': 'black_bee.n.01', 'name': 'black_bee'}, {'id': 3806, 'synset': 'carniolan_bee.n.01', 'name': 'Carniolan_bee'}, {'id': 3807, 'synset': 'italian_bee.n.01', 'name': 'Italian_bee'}, {'id': 3808, 'synset': 'carpenter_bee.n.01', 'name': 'carpenter_bee'}, {'id': 3809, 'synset': 'bumblebee.n.01', 'name': 'bumblebee'}, {'id': 3810, 'synset': 'cuckoo-bumblebee.n.01', 'name': 'cuckoo-bumblebee'}, {'id': 3811, 'synset': 'andrena.n.01', 'name': 'andrena'}, {'id': 3812, 'synset': 'nomia_melanderi.n.01', 'name': 'Nomia_melanderi'}, {'id': 3813, 'synset': 'leaf-cutting_bee.n.01', 'name': 'leaf-cutting_bee'}, {'id': 3814, 'synset': 'mason_bee.n.01', 'name': 'mason_bee'}, {'id': 3815, 'synset': 'potter_bee.n.01', 'name': 'potter_bee'}, {'id': 3816, 'synset': 'wasp.n.02', 'name': 'wasp'}, {'id': 3817, 'synset': 'vespid.n.01', 'name': 'vespid'}, {'id': 3818, 'synset': 'paper_wasp.n.01', 'name': 'paper_wasp'}, {'id': 3819, 'synset': 'giant_hornet.n.01', 'name': 'giant_hornet'}, {'id': 3820, 'synset': 'common_wasp.n.01', 'name': 'common_wasp'}, {'id': 3821, 'synset': 'bald-faced_hornet.n.01', 'name': 'bald-faced_hornet'}, {'id': 3822, 'synset': 'yellow_jacket.n.02', 'name': 'yellow_jacket'}, {'id': 3823, 'synset': 'polistes_annularis.n.01', 'name': 'Polistes_annularis'}, {'id': 3824, 'synset': 'mason_wasp.n.02', 'name': 'mason_wasp'}, {'id': 3825, 'synset': 'potter_wasp.n.01', 'name': 'potter_wasp'}, {'id': 3826, 'synset': 'mutillidae.n.01', 'name': 'Mutillidae'}, {'id': 3827, 'synset': 'velvet_ant.n.01', 'name': 'velvet_ant'}, {'id': 3828, 'synset': 'sphecoid_wasp.n.01', 'name': 'sphecoid_wasp'}, {'id': 3829, 'synset': 'mason_wasp.n.01', 'name': 'mason_wasp'}, {'id': 3830, 'synset': 'digger_wasp.n.01', 'name': 'digger_wasp'}, {'id': 3831, 'synset': 'cicada_killer.n.01', 'name': 'cicada_killer'}, {'id': 3832, 'synset': 'mud_dauber.n.01', 'name': 'mud_dauber'}, {'id': 3833, 'synset': 'gall_wasp.n.01', 'name': 'gall_wasp'}, {'id': 3834, 'synset': 'chalcid_fly.n.01', 'name': 'chalcid_fly'}, {'id': 3835, 'synset': 'strawworm.n.02', 'name': 'strawworm'}, {'id': 3836, 'synset': 'chalcis_fly.n.01', 'name': 'chalcis_fly'}, {'id': 3837, 'synset': 'ichneumon_fly.n.01', 'name': 'ichneumon_fly'}, {'id': 3838, 'synset': 'sawfly.n.01', 'name': 'sawfly'}, {'id': 3839, 'synset': 'birch_leaf_miner.n.01', 'name': 'birch_leaf_miner'}, {'id': 3840, 'synset': 'ant.n.01', 'name': 'ant'}, {'id': 3841, 'synset': 'pharaoh_ant.n.01', 'name': 'pharaoh_ant'}, {'id': 3842, 'synset': 'little_black_ant.n.01', 'name': 'little_black_ant'}, {'id': 3843, 'synset': 'army_ant.n.01', 'name': 'army_ant'}, {'id': 3844, 'synset': 'carpenter_ant.n.01', 'name': 'carpenter_ant'}, {'id': 3845, 'synset': 'fire_ant.n.01', 'name': 'fire_ant'}, {'id': 3846, 'synset': 'wood_ant.n.01', 'name': 'wood_ant'}, {'id': 3847, 'synset': 'slave_ant.n.01', 'name': 'slave_ant'}, {'id': 3848, 'synset': 'formica_fusca.n.01', 'name': 'Formica_fusca'}, {'id': 3849, 'synset': 'slave-making_ant.n.01', 'name': 'slave-making_ant'}, {'id': 3850, 'synset': 'sanguinary_ant.n.01', 'name': 'sanguinary_ant'}, {'id': 3851, 'synset': 'bulldog_ant.n.01', 'name': 'bulldog_ant'}, {'id': 3852, 'synset': 'amazon_ant.n.01', 'name': 'Amazon_ant'}, {'id': 3853, 'synset': 'termite.n.01', 'name': 'termite'}, {'id': 3854, 'synset': 'dry-wood_termite.n.01', 'name': 'dry-wood_termite'}, {'id': 3855, 'synset': 'reticulitermes_lucifugus.n.01', 'name': 'Reticulitermes_lucifugus'}, {'id': 3856, 'synset': 'mastotermes_darwiniensis.n.01', 'name': 'Mastotermes_darwiniensis'}, {'id': 3857, 'synset': 'mastotermes_electrodominicus.n.01', 'name': 'Mastotermes_electrodominicus'}, {'id': 3858, 'synset': 'powder-post_termite.n.01', 'name': 'powder-post_termite'}, {'id': 3859, 'synset': 'orthopterous_insect.n.01', 'name': 'orthopterous_insect'}, {'id': 3860, 'synset': 'grasshopper.n.01', 'name': 'grasshopper'}, {'id': 3861, 'synset': 'short-horned_grasshopper.n.01', 'name': 'short-horned_grasshopper'}, {'id': 3862, 'synset': 'locust.n.01', 'name': 'locust'}, {'id': 3863, 'synset': 'migratory_locust.n.01', 'name': 'migratory_locust'}, {'id': 3864, 'synset': 'migratory_grasshopper.n.01', 'name': 'migratory_grasshopper'}, {'id': 3865, 'synset': 'long-horned_grasshopper.n.01', 'name': 'long-horned_grasshopper'}, {'id': 3866, 'synset': 'katydid.n.01', 'name': 'katydid'}, {'id': 3867, 'synset': 'mormon_cricket.n.01', 'name': 'mormon_cricket'}, {'id': 3868, 'synset': 'sand_cricket.n.01', 'name': 'sand_cricket'}, {'id': 3869, 'synset': 'cricket.n.01', 'name': 'cricket'}, {'id': 3870, 'synset': 'mole_cricket.n.01', 'name': 'mole_cricket'}, {'id': 3871, 'synset': 'european_house_cricket.n.01', 'name': 'European_house_cricket'}, {'id': 3872, 'synset': 'field_cricket.n.01', 'name': 'field_cricket'}, {'id': 3873, 'synset': 'tree_cricket.n.01', 'name': 'tree_cricket'}, {'id': 3874, 'synset': 'snowy_tree_cricket.n.01', 'name': 'snowy_tree_cricket'}, {'id': 3875, 'synset': 'phasmid.n.01', 'name': 'phasmid'}, {'id': 3876, 'synset': 'walking_stick.n.02', 'name': 'walking_stick'}, {'id': 3877, 'synset': 'diapheromera.n.01', 'name': 'diapheromera'}, {'id': 3878, 'synset': 'walking_leaf.n.02', 'name': 'walking_leaf'}, {'id': 3879, 'synset': 'oriental_cockroach.n.01', 'name': 'oriental_cockroach'}, {'id': 3880, 'synset': 'american_cockroach.n.01', 'name': 'American_cockroach'}, {'id': 3881, 'synset': 'australian_cockroach.n.01', 'name': 'Australian_cockroach'}, {'id': 3882, 'synset': 'german_cockroach.n.01', 'name': 'German_cockroach'}, {'id': 3883, 'synset': 'giant_cockroach.n.01', 'name': 'giant_cockroach'}, {'id': 3884, 'synset': 'mantis.n.01', 'name': 'mantis'}, {'id': 3885, 'synset': 'praying_mantis.n.01', 'name': 'praying_mantis'}, {'id': 3886, 'synset': 'bug.n.01', 'name': 'bug'}, {'id': 3887, 'synset': 'hemipterous_insect.n.01', 'name': 'hemipterous_insect'}, {'id': 3888, 'synset': 'leaf_bug.n.01', 'name': 'leaf_bug'}, {'id': 3889, 'synset': 'mirid_bug.n.01', 'name': 'mirid_bug'}, {'id': 3890, 'synset': 'four-lined_plant_bug.n.01', 'name': 'four-lined_plant_bug'}, {'id': 3891, 'synset': 'lygus_bug.n.01', 'name': 'lygus_bug'}, {'id': 3892, 'synset': 'tarnished_plant_bug.n.01', 'name': 'tarnished_plant_bug'}, {'id': 3893, 'synset': 'lace_bug.n.01', 'name': 'lace_bug'}, {'id': 3894, 'synset': 'lygaeid.n.01', 'name': 'lygaeid'}, {'id': 3895, 'synset': 'chinch_bug.n.01', 'name': 'chinch_bug'}, {'id': 3896, 'synset': 'coreid_bug.n.01', 'name': 'coreid_bug'}, {'id': 3897, 'synset': 'squash_bug.n.01', 'name': 'squash_bug'}, {'id': 3898, 'synset': 'leaf-footed_bug.n.01', 'name': 'leaf-footed_bug'}, {'id': 3899, 'synset': 'bedbug.n.01', 'name': 'bedbug'}, {'id': 3900, 'synset': 'backswimmer.n.01', 'name': 'backswimmer'}, {'id': 3901, 'synset': 'true_bug.n.01', 'name': 'true_bug'}, {'id': 3902, 'synset': 'heteropterous_insect.n.01', 'name': 'heteropterous_insect'}, {'id': 3903, 'synset': 'water_bug.n.01', 'name': 'water_bug'}, {'id': 3904, 'synset': 'giant_water_bug.n.01', 'name': 'giant_water_bug'}, {'id': 3905, 'synset': 'water_scorpion.n.01', 'name': 'water_scorpion'}, {'id': 3906, 'synset': 'water_boatman.n.01', 'name': 'water_boatman'}, {'id': 3907, 'synset': 'water_strider.n.01', 'name': 'water_strider'}, {'id': 3908, 'synset': 'common_pond-skater.n.01', 'name': 'common_pond-skater'}, {'id': 3909, 'synset': 'assassin_bug.n.01', 'name': 'assassin_bug'}, {'id': 3910, 'synset': 'conenose.n.01', 'name': 'conenose'}, {'id': 3911, 'synset': 'wheel_bug.n.01', 'name': 'wheel_bug'}, {'id': 3912, 'synset': 'firebug.n.02', 'name': 'firebug'}, {'id': 3913, 'synset': 'cotton_stainer.n.01', 'name': 'cotton_stainer'}, {'id': 3914, 'synset': 'homopterous_insect.n.01', 'name': 'homopterous_insect'}, {'id': 3915, 'synset': 'whitefly.n.01', 'name': 'whitefly'}, {'id': 3916, 'synset': 'citrus_whitefly.n.01', 'name': 'citrus_whitefly'}, {'id': 3917, 'synset': 'greenhouse_whitefly.n.01', 'name': 'greenhouse_whitefly'}, {'id': 3918, 'synset': 'sweet-potato_whitefly.n.01', 'name': 'sweet-potato_whitefly'}, {'id': 3919, 'synset': 'superbug.n.02', 'name': 'superbug'}, {'id': 3920, 'synset': 'cotton_strain.n.01', 'name': 'cotton_strain'}, {'id': 3921, 'synset': 'coccid_insect.n.01', 'name': 'coccid_insect'}, {'id': 3922, 'synset': 'scale_insect.n.01', 'name': 'scale_insect'}, {'id': 3923, 'synset': 'soft_scale.n.01', 'name': 'soft_scale'}, {'id': 3924, 'synset': 'brown_soft_scale.n.01', 'name': 'brown_soft_scale'}, {'id': 3925, 'synset': 'armored_scale.n.01', 'name': 'armored_scale'}, {'id': 3926, 'synset': 'san_jose_scale.n.01', 'name': 'San_Jose_scale'}, {'id': 3927, 'synset': 'cochineal_insect.n.01', 'name': 'cochineal_insect'}, {'id': 3928, 'synset': 'mealybug.n.01', 'name': 'mealybug'}, {'id': 3929, 'synset': 'citrophilous_mealybug.n.01', 'name': 'citrophilous_mealybug'}, {'id': 3930, 'synset': 'comstock_mealybug.n.01', 'name': 'Comstock_mealybug'}, {'id': 3931, 'synset': 'citrus_mealybug.n.01', 'name': 'citrus_mealybug'}, {'id': 3932, 'synset': 'plant_louse.n.01', 'name': 'plant_louse'}, {'id': 3933, 'synset': 'aphid.n.01', 'name': 'aphid'}, {'id': 3934, 'synset': 'apple_aphid.n.01', 'name': 'apple_aphid'}, {'id': 3935, 'synset': 'blackfly.n.01', 'name': 'blackfly'}, {'id': 3936, 'synset': 'greenfly.n.01', 'name': 'greenfly'}, {'id': 3937, 'synset': 'green_peach_aphid.n.01', 'name': 'green_peach_aphid'}, {'id': 3938, 'synset': 'ant_cow.n.01', 'name': 'ant_cow'}, {'id': 3939, 'synset': 'woolly_aphid.n.01', 'name': 'woolly_aphid'}, {'id': 3940, 'synset': 'woolly_apple_aphid.n.01', 'name': 'woolly_apple_aphid'}, {'id': 3941, 'synset': 'woolly_alder_aphid.n.01', 'name': 'woolly_alder_aphid'}, {'id': 3942, 'synset': 'adelgid.n.01', 'name': 'adelgid'}, {'id': 3943, 'synset': 'balsam_woolly_aphid.n.01', 'name': 'balsam_woolly_aphid'}, {'id': 3944, 'synset': 'spruce_gall_aphid.n.01', 'name': 'spruce_gall_aphid'}, {'id': 3945, 'synset': 'woolly_adelgid.n.01', 'name': 'woolly_adelgid'}, {'id': 3946, 'synset': 'jumping_plant_louse.n.01', 'name': 'jumping_plant_louse'}, {'id': 3947, 'synset': 'cicada.n.01', 'name': 'cicada'}, {'id': 3948, 'synset': 'dog-day_cicada.n.01', 'name': 'dog-day_cicada'}, {'id': 3949, 'synset': 'seventeen-year_locust.n.01', 'name': 'seventeen-year_locust'}, {'id': 3950, 'synset': 'spittle_insect.n.01', 'name': 'spittle_insect'}, {'id': 3951, 'synset': 'froghopper.n.01', 'name': 'froghopper'}, {'id': 3952, 'synset': 'meadow_spittlebug.n.01', 'name': 'meadow_spittlebug'}, {'id': 3953, 'synset': 'pine_spittlebug.n.01', 'name': 'pine_spittlebug'}, {'id': 3954, 'synset': 'saratoga_spittlebug.n.01', 'name': 'Saratoga_spittlebug'}, {'id': 3955, 'synset': 'leafhopper.n.01', 'name': 'leafhopper'}, {'id': 3956, 'synset': 'plant_hopper.n.01', 'name': 'plant_hopper'}, {'id': 3957, 'synset': 'treehopper.n.01', 'name': 'treehopper'}, {'id': 3958, 'synset': 'lantern_fly.n.01', 'name': 'lantern_fly'}, {'id': 3959, 'synset': 'psocopterous_insect.n.01', 'name': 'psocopterous_insect'}, {'id': 3960, 'synset': 'psocid.n.01', 'name': 'psocid'}, {'id': 3961, 'synset': 'bark-louse.n.01', 'name': 'bark-louse'}, {'id': 3962, 'synset': 'booklouse.n.01', 'name': 'booklouse'}, {'id': 3963, 'synset': 'common_booklouse.n.01', 'name': 'common_booklouse'}, {'id': 3964, 'synset': 'ephemerid.n.01', 'name': 'ephemerid'}, {'id': 3965, 'synset': 'mayfly.n.01', 'name': 'mayfly'}, {'id': 3966, 'synset': 'stonefly.n.01', 'name': 'stonefly'}, {'id': 3967, 'synset': 'neuropteron.n.01', 'name': 'neuropteron'}, {'id': 3968, 'synset': 'ant_lion.n.02', 'name': 'ant_lion'}, {'id': 3969, 'synset': 'doodlebug.n.03', 'name': 'doodlebug'}, {'id': 3970, 'synset': 'lacewing.n.01', 'name': 'lacewing'}, {'id': 3971, 'synset': 'aphid_lion.n.01', 'name': 'aphid_lion'}, {'id': 3972, 'synset': 'green_lacewing.n.01', 'name': 'green_lacewing'}, {'id': 3973, 'synset': 'brown_lacewing.n.01', 'name': 'brown_lacewing'}, {'id': 3974, 'synset': 'dobson.n.02', 'name': 'dobson'}, {'id': 3975, 'synset': 'hellgrammiate.n.01', 'name': 'hellgrammiate'}, {'id': 3976, 'synset': 'fish_fly.n.01', 'name': 'fish_fly'}, {'id': 3977, 'synset': 'alderfly.n.01', 'name': 'alderfly'}, {'id': 3978, 'synset': 'snakefly.n.01', 'name': 'snakefly'}, {'id': 3979, 'synset': 'mantispid.n.01', 'name': 'mantispid'}, {'id': 3980, 'synset': 'odonate.n.01', 'name': 'odonate'}, {'id': 3981, 'synset': 'damselfly.n.01', 'name': 'damselfly'}, {'id': 3982, 'synset': 'trichopterous_insect.n.01', 'name': 'trichopterous_insect'}, {'id': 3983, 'synset': 'caddis_fly.n.01', 'name': 'caddis_fly'}, {'id': 3984, 'synset': 'caseworm.n.01', 'name': 'caseworm'}, {'id': 3985, 'synset': 'caddisworm.n.01', 'name': 'caddisworm'}, {'id': 3986, 'synset': 'thysanuran_insect.n.01', 'name': 'thysanuran_insect'}, {'id': 3987, 'synset': 'bristletail.n.01', 'name': 'bristletail'}, {'id': 3988, 'synset': 'silverfish.n.01', 'name': 'silverfish'}, {'id': 3989, 'synset': 'firebrat.n.01', 'name': 'firebrat'}, {'id': 3990, 'synset': 'jumping_bristletail.n.01', 'name': 'jumping_bristletail'}, {'id': 3991, 'synset': 'thysanopter.n.01', 'name': 'thysanopter'}, {'id': 3992, 'synset': 'thrips.n.01', 'name': 'thrips'}, {'id': 3993, 'synset': 'tobacco_thrips.n.01', 'name': 'tobacco_thrips'}, {'id': 3994, 'synset': 'onion_thrips.n.01', 'name': 'onion_thrips'}, {'id': 3995, 'synset': 'earwig.n.01', 'name': 'earwig'}, {'id': 3996, 'synset': 'common_european_earwig.n.01', 'name': 'common_European_earwig'}, {'id': 3997, 'synset': 'lepidopterous_insect.n.01', 'name': 'lepidopterous_insect'}, {'id': 3998, 'synset': 'nymphalid.n.01', 'name': 'nymphalid'}, {'id': 3999, 'synset': 'mourning_cloak.n.01', 'name': 'mourning_cloak'}, {'id': 4000, 'synset': 'tortoiseshell.n.02', 'name': 'tortoiseshell'}, {'id': 4001, 'synset': 'painted_beauty.n.01', 'name': 'painted_beauty'}, {'id': 4002, 'synset': 'admiral.n.02', 'name': 'admiral'}, {'id': 4003, 'synset': 'red_admiral.n.01', 'name': 'red_admiral'}, {'id': 4004, 'synset': 'white_admiral.n.02', 'name': 'white_admiral'}, {'id': 4005, 'synset': 'banded_purple.n.01', 'name': 'banded_purple'}, {'id': 4006, 'synset': 'red-spotted_purple.n.01', 'name': 'red-spotted_purple'}, {'id': 4007, 'synset': 'viceroy.n.02', 'name': 'viceroy'}, {'id': 4008, 'synset': 'anglewing.n.01', 'name': 'anglewing'}, {'id': 4009, 'synset': 'ringlet.n.04', 'name': 'ringlet'}, {'id': 4010, 'synset': 'comma.n.02', 'name': 'comma'}, {'id': 4011, 'synset': 'fritillary.n.02', 'name': 'fritillary'}, {'id': 4012, 'synset': 'silverspot.n.01', 'name': 'silverspot'}, {'id': 4013, 'synset': 'emperor_butterfly.n.01', 'name': 'emperor_butterfly'}, {'id': 4014, 'synset': 'purple_emperor.n.01', 'name': 'purple_emperor'}, {'id': 4015, 'synset': 'peacock.n.01', 'name': 'peacock'}, {'id': 4016, 'synset': 'danaid.n.01', 'name': 'danaid'}, {'id': 4017, 'synset': 'monarch.n.02', 'name': 'monarch'}, {'id': 4018, 'synset': 'pierid.n.01', 'name': 'pierid'}, {'id': 4019, 'synset': 'cabbage_butterfly.n.01', 'name': 'cabbage_butterfly'}, {'id': 4020, 'synset': 'small_white.n.01', 'name': 'small_white'}, {'id': 4021, 'synset': 'large_white.n.01', 'name': 'large_white'}, {'id': 4022, 'synset': 'southern_cabbage_butterfly.n.01', 'name': 'southern_cabbage_butterfly'}, {'id': 4023, 'synset': 'sulphur_butterfly.n.01', 'name': 'sulphur_butterfly'}, {'id': 4024, 'synset': 'lycaenid.n.01', 'name': 'lycaenid'}, {'id': 4025, 'synset': 'blue.n.07', 'name': 'blue'}, {'id': 4026, 'synset': 'copper.n.05', 'name': 'copper'}, {'id': 4027, 'synset': 'american_copper.n.01', 'name': 'American_copper'}, {'id': 4028, 'synset': 'hairstreak.n.01', 'name': 'hairstreak'}, {'id': 4029, 'synset': 'strymon_melinus.n.01', 'name': 'Strymon_melinus'}, {'id': 4030, 'synset': 'moth.n.01', 'name': 'moth'}, {'id': 4031, 'synset': 'moth_miller.n.01', 'name': 'moth_miller'}, {'id': 4032, 'synset': 'tortricid.n.01', 'name': 'tortricid'}, {'id': 4033, 'synset': 'leaf_roller.n.01', 'name': 'leaf_roller'}, {'id': 4034, 'synset': 'tea_tortrix.n.01', 'name': 'tea_tortrix'}, {'id': 4035, 'synset': 'orange_tortrix.n.01', 'name': 'orange_tortrix'}, {'id': 4036, 'synset': 'codling_moth.n.01', 'name': 'codling_moth'}, {'id': 4037, 'synset': 'lymantriid.n.01', 'name': 'lymantriid'}, {'id': 4038, 'synset': 'tussock_caterpillar.n.01', 'name': 'tussock_caterpillar'}, {'id': 4039, 'synset': 'gypsy_moth.n.01', 'name': 'gypsy_moth'}, {'id': 4040, 'synset': 'browntail.n.01', 'name': 'browntail'}, {'id': 4041, 'synset': 'gold-tail_moth.n.01', 'name': 'gold-tail_moth'}, {'id': 4042, 'synset': 'geometrid.n.01', 'name': 'geometrid'}, {'id': 4043, 'synset': 'paleacrita_vernata.n.01', 'name': 'Paleacrita_vernata'}, {'id': 4044, 'synset': 'alsophila_pometaria.n.01', 'name': 'Alsophila_pometaria'}, {'id': 4045, 'synset': 'cankerworm.n.01', 'name': 'cankerworm'}, {'id': 4046, 'synset': 'spring_cankerworm.n.01', 'name': 'spring_cankerworm'}, {'id': 4047, 'synset': 'fall_cankerworm.n.01', 'name': 'fall_cankerworm'}, {'id': 4048, 'synset': 'measuring_worm.n.01', 'name': 'measuring_worm'}, {'id': 4049, 'synset': 'pyralid.n.01', 'name': 'pyralid'}, {'id': 4050, 'synset': 'bee_moth.n.01', 'name': 'bee_moth'}, {'id': 4051, 'synset': 'corn_borer.n.02', 'name': 'corn_borer'}, {'id': 4052, 'synset': 'mediterranean_flour_moth.n.01', 'name': 'Mediterranean_flour_moth'}, {'id': 4053, 'synset': 'tobacco_moth.n.01', 'name': 'tobacco_moth'}, {'id': 4054, 'synset': 'almond_moth.n.01', 'name': 'almond_moth'}, {'id': 4055, 'synset': 'raisin_moth.n.01', 'name': 'raisin_moth'}, {'id': 4056, 'synset': 'tineoid.n.01', 'name': 'tineoid'}, {'id': 4057, 'synset': 'tineid.n.01', 'name': 'tineid'}, {'id': 4058, 'synset': 'clothes_moth.n.01', 'name': 'clothes_moth'}, {'id': 4059, 'synset': 'casemaking_clothes_moth.n.01', 'name': 'casemaking_clothes_moth'}, {'id': 4060, 'synset': 'webbing_clothes_moth.n.01', 'name': 'webbing_clothes_moth'}, {'id': 4061, 'synset': 'carpet_moth.n.01', 'name': 'carpet_moth'}, {'id': 4062, 'synset': 'gelechiid.n.01', 'name': 'gelechiid'}, {'id': 4063, 'synset': 'grain_moth.n.01', 'name': 'grain_moth'}, {'id': 4064, 'synset': 'angoumois_moth.n.01', 'name': 'angoumois_moth'}, {'id': 4065, 'synset': 'potato_moth.n.01', 'name': 'potato_moth'}, {'id': 4066, 'synset': 'potato_tuberworm.n.01', 'name': 'potato_tuberworm'}, {'id': 4067, 'synset': 'noctuid_moth.n.01', 'name': 'noctuid_moth'}, {'id': 4068, 'synset': 'cutworm.n.01', 'name': 'cutworm'}, {'id': 4069, 'synset': 'underwing.n.01', 'name': 'underwing'}, {'id': 4070, 'synset': 'red_underwing.n.01', 'name': 'red_underwing'}, {'id': 4071, 'synset': 'antler_moth.n.01', 'name': 'antler_moth'}, {'id': 4072, 'synset': 'heliothis_moth.n.01', 'name': 'heliothis_moth'}, {'id': 4073, 'synset': 'army_cutworm.n.01', 'name': 'army_cutworm'}, {'id': 4074, 'synset': 'armyworm.n.02', 'name': 'armyworm'}, {'id': 4075, 'synset': 'armyworm.n.01', 'name': 'armyworm'}, {'id': 4076, 'synset': 'spodoptera_exigua.n.02', 'name': 'Spodoptera_exigua'}, {'id': 4077, 'synset': 'beet_armyworm.n.01', 'name': 'beet_armyworm'}, {'id': 4078, 'synset': 'spodoptera_frugiperda.n.02', 'name': 'Spodoptera_frugiperda'}, {'id': 4079, 'synset': 'fall_armyworm.n.01', 'name': 'fall_armyworm'}, {'id': 4080, 'synset': 'hawkmoth.n.01', 'name': 'hawkmoth'}, {'id': 4081, 'synset': 'manduca_sexta.n.02', 'name': 'Manduca_sexta'}, {'id': 4082, 'synset': 'tobacco_hornworm.n.01', 'name': 'tobacco_hornworm'}, {'id': 4083, 'synset': 'manduca_quinquemaculata.n.02', 'name': 'Manduca_quinquemaculata'}, {'id': 4084, 'synset': 'tomato_hornworm.n.01', 'name': 'tomato_hornworm'}, {'id': 4085, 'synset': "death's-head_moth.n.01", 'name': "death's-head_moth"}, {'id': 4086, 'synset': 'bombycid.n.01', 'name': 'bombycid'}, {'id': 4087, 'synset': 'domestic_silkworm_moth.n.01', 'name': 'domestic_silkworm_moth'}, {'id': 4088, 'synset': 'silkworm.n.01', 'name': 'silkworm'}, {'id': 4089, 'synset': 'saturniid.n.01', 'name': 'saturniid'}, {'id': 4090, 'synset': 'emperor.n.03', 'name': 'emperor'}, {'id': 4091, 'synset': 'imperial_moth.n.01', 'name': 'imperial_moth'}, {'id': 4092, 'synset': 'giant_silkworm_moth.n.01', 'name': 'giant_silkworm_moth'}, {'id': 4093, 'synset': 'silkworm.n.02', 'name': 'silkworm'}, {'id': 4094, 'synset': 'luna_moth.n.01', 'name': 'luna_moth'}, {'id': 4095, 'synset': 'cecropia.n.02', 'name': 'cecropia'}, {'id': 4096, 'synset': 'cynthia_moth.n.01', 'name': 'cynthia_moth'}, {'id': 4097, 'synset': 'ailanthus_silkworm.n.01', 'name': 'ailanthus_silkworm'}, {'id': 4098, 'synset': 'io_moth.n.01', 'name': 'io_moth'}, {'id': 4099, 'synset': 'polyphemus_moth.n.01', 'name': 'polyphemus_moth'}, {'id': 4100, 'synset': 'pernyi_moth.n.01', 'name': 'pernyi_moth'}, {'id': 4101, 'synset': 'tussah.n.01', 'name': 'tussah'}, {'id': 4102, 'synset': 'atlas_moth.n.01', 'name': 'atlas_moth'}, {'id': 4103, 'synset': 'arctiid.n.01', 'name': 'arctiid'}, {'id': 4104, 'synset': 'tiger_moth.n.01', 'name': 'tiger_moth'}, {'id': 4105, 'synset': 'cinnabar.n.02', 'name': 'cinnabar'}, {'id': 4106, 'synset': 'lasiocampid.n.01', 'name': 'lasiocampid'}, {'id': 4107, 'synset': 'eggar.n.01', 'name': 'eggar'}, {'id': 4108, 'synset': 'tent-caterpillar_moth.n.02', 'name': 'tent-caterpillar_moth'}, {'id': 4109, 'synset': 'tent_caterpillar.n.01', 'name': 'tent_caterpillar'}, {'id': 4110, 'synset': 'tent-caterpillar_moth.n.01', 'name': 'tent-caterpillar_moth'}, {'id': 4111, 'synset': 'forest_tent_caterpillar.n.01', 'name': 'forest_tent_caterpillar'}, {'id': 4112, 'synset': 'lappet.n.03', 'name': 'lappet'}, {'id': 4113, 'synset': 'lappet_caterpillar.n.01', 'name': 'lappet_caterpillar'}, {'id': 4114, 'synset': 'webworm.n.01', 'name': 'webworm'}, {'id': 4115, 'synset': 'webworm_moth.n.01', 'name': 'webworm_moth'}, {'id': 4116, 'synset': 'hyphantria_cunea.n.02', 'name': 'Hyphantria_cunea'}, {'id': 4117, 'synset': 'fall_webworm.n.01', 'name': 'fall_webworm'}, {'id': 4118, 'synset': 'garden_webworm.n.01', 'name': 'garden_webworm'}, {'id': 4119, 'synset': 'instar.n.01', 'name': 'instar'}, {'id': 4120, 'synset': 'caterpillar.n.01', 'name': 'caterpillar'}, {'id': 4121, 'synset': 'corn_borer.n.01', 'name': 'corn_borer'}, {'id': 4122, 'synset': 'bollworm.n.01', 'name': 'bollworm'}, {'id': 4123, 'synset': 'pink_bollworm.n.01', 'name': 'pink_bollworm'}, {'id': 4124, 'synset': 'corn_earworm.n.01', 'name': 'corn_earworm'}, {'id': 4125, 'synset': 'cabbageworm.n.01', 'name': 'cabbageworm'}, {'id': 4126, 'synset': 'woolly_bear.n.01', 'name': 'woolly_bear'}, {'id': 4127, 'synset': 'woolly_bear_moth.n.01', 'name': 'woolly_bear_moth'}, {'id': 4128, 'synset': 'larva.n.01', 'name': 'larva'}, {'id': 4129, 'synset': 'nymph.n.02', 'name': 'nymph'}, {'id': 4130, 'synset': 'leptocephalus.n.01', 'name': 'leptocephalus'}, {'id': 4131, 'synset': 'grub.n.02', 'name': 'grub'}, {'id': 4132, 'synset': 'maggot.n.01', 'name': 'maggot'}, {'id': 4133, 'synset': 'leatherjacket.n.03', 'name': 'leatherjacket'}, {'id': 4134, 'synset': 'pupa.n.01', 'name': 'pupa'}, {'id': 4135, 'synset': 'chrysalis.n.01', 'name': 'chrysalis'}, {'id': 4136, 'synset': 'imago.n.02', 'name': 'imago'}, {'id': 4137, 'synset': 'queen.n.01', 'name': 'queen'}, {'id': 4138, 'synset': 'phoronid.n.01', 'name': 'phoronid'}, {'id': 4139, 'synset': 'bryozoan.n.01', 'name': 'bryozoan'}, {'id': 4140, 'synset': 'brachiopod.n.01', 'name': 'brachiopod'}, {'id': 4141, 'synset': 'peanut_worm.n.01', 'name': 'peanut_worm'}, {'id': 4142, 'synset': 'echinoderm.n.01', 'name': 'echinoderm'}, {'id': 4143, 'synset': 'brittle_star.n.01', 'name': 'brittle_star'}, {'id': 4144, 'synset': 'basket_star.n.01', 'name': 'basket_star'}, {'id': 4145, 'synset': 'astrophyton_muricatum.n.01', 'name': 'Astrophyton_muricatum'}, {'id': 4146, 'synset': 'sea_urchin.n.01', 'name': 'sea_urchin'}, {'id': 4147, 'synset': 'edible_sea_urchin.n.01', 'name': 'edible_sea_urchin'}, {'id': 4148, 'synset': 'sand_dollar.n.01', 'name': 'sand_dollar'}, {'id': 4149, 'synset': 'heart_urchin.n.01', 'name': 'heart_urchin'}, {'id': 4150, 'synset': 'crinoid.n.01', 'name': 'crinoid'}, {'id': 4151, 'synset': 'sea_lily.n.01', 'name': 'sea_lily'}, {'id': 4152, 'synset': 'feather_star.n.01', 'name': 'feather_star'}, {'id': 4153, 'synset': 'sea_cucumber.n.01', 'name': 'sea_cucumber'}, {'id': 4154, 'synset': 'trepang.n.01', 'name': 'trepang'}, {'id': 4155, 'synset': 'duplicidentata.n.01', 'name': 'Duplicidentata'}, {'id': 4156, 'synset': 'lagomorph.n.01', 'name': 'lagomorph'}, {'id': 4157, 'synset': 'leporid.n.01', 'name': 'leporid'}, {'id': 4158, 'synset': 'rabbit_ears.n.02', 'name': 'rabbit_ears'}, {'id': 4159, 'synset': 'lapin.n.02', 'name': 'lapin'}, {'id': 4160, 'synset': 'bunny.n.02', 'name': 'bunny'}, {'id': 4161, 'synset': 'european_rabbit.n.01', 'name': 'European_rabbit'}, {'id': 4162, 'synset': 'wood_rabbit.n.01', 'name': 'wood_rabbit'}, {'id': 4163, 'synset': 'eastern_cottontail.n.01', 'name': 'eastern_cottontail'}, {'id': 4164, 'synset': 'swamp_rabbit.n.02', 'name': 'swamp_rabbit'}, {'id': 4165, 'synset': 'marsh_hare.n.01', 'name': 'marsh_hare'}, {'id': 4166, 'synset': 'hare.n.01', 'name': 'hare'}, {'id': 4167, 'synset': 'leveret.n.01', 'name': 'leveret'}, {'id': 4168, 'synset': 'european_hare.n.01', 'name': 'European_hare'}, {'id': 4169, 'synset': 'jackrabbit.n.01', 'name': 'jackrabbit'}, {'id': 4170, 'synset': 'white-tailed_jackrabbit.n.01', 'name': 'white-tailed_jackrabbit'}, {'id': 4171, 'synset': 'blacktail_jackrabbit.n.01', 'name': 'blacktail_jackrabbit'}, {'id': 4172, 'synset': 'polar_hare.n.01', 'name': 'polar_hare'}, {'id': 4173, 'synset': 'snowshoe_hare.n.01', 'name': 'snowshoe_hare'}, {'id': 4174, 'synset': 'belgian_hare.n.01', 'name': 'Belgian_hare'}, {'id': 4175, 'synset': 'angora.n.03', 'name': 'Angora'}, {'id': 4176, 'synset': 'pika.n.01', 'name': 'pika'}, {'id': 4177, 'synset': 'little_chief_hare.n.01', 'name': 'little_chief_hare'}, {'id': 4178, 'synset': 'collared_pika.n.01', 'name': 'collared_pika'}, {'id': 4179, 'synset': 'mouse.n.01', 'name': 'mouse'}, {'id': 4180, 'synset': 'pocket_rat.n.01', 'name': 'pocket_rat'}, {'id': 4181, 'synset': 'murine.n.01', 'name': 'murine'}, {'id': 4182, 'synset': 'house_mouse.n.01', 'name': 'house_mouse'}, {'id': 4183, 'synset': 'harvest_mouse.n.02', 'name': 'harvest_mouse'}, {'id': 4184, 'synset': 'field_mouse.n.02', 'name': 'field_mouse'}, {'id': 4185, 'synset': 'nude_mouse.n.01', 'name': 'nude_mouse'}, {'id': 4186, 'synset': 'european_wood_mouse.n.01', 'name': 'European_wood_mouse'}, {'id': 4187, 'synset': 'brown_rat.n.01', 'name': 'brown_rat'}, {'id': 4188, 'synset': 'wharf_rat.n.02', 'name': 'wharf_rat'}, {'id': 4189, 'synset': 'sewer_rat.n.01', 'name': 'sewer_rat'}, {'id': 4190, 'synset': 'black_rat.n.01', 'name': 'black_rat'}, {'id': 4191, 'synset': 'bandicoot_rat.n.01', 'name': 'bandicoot_rat'}, {'id': 4192, 'synset': 'jerboa_rat.n.01', 'name': 'jerboa_rat'}, {'id': 4193, 'synset': 'kangaroo_mouse.n.02', 'name': 'kangaroo_mouse'}, {'id': 4194, 'synset': 'water_rat.n.03', 'name': 'water_rat'}, {'id': 4195, 'synset': 'beaver_rat.n.01', 'name': 'beaver_rat'}, {'id': 4196, 'synset': 'new_world_mouse.n.01', 'name': 'New_World_mouse'}, {'id': 4197, 'synset': 'american_harvest_mouse.n.01', 'name': 'American_harvest_mouse'}, {'id': 4198, 'synset': 'wood_mouse.n.01', 'name': 'wood_mouse'}, {'id': 4199, 'synset': 'white-footed_mouse.n.01', 'name': 'white-footed_mouse'}, {'id': 4200, 'synset': 'deer_mouse.n.01', 'name': 'deer_mouse'}, {'id': 4201, 'synset': 'cactus_mouse.n.01', 'name': 'cactus_mouse'}, {'id': 4202, 'synset': 'cotton_mouse.n.01', 'name': 'cotton_mouse'}, {'id': 4203, 'synset': 'pygmy_mouse.n.01', 'name': 'pygmy_mouse'}, {'id': 4204, 'synset': 'grasshopper_mouse.n.01', 'name': 'grasshopper_mouse'}, {'id': 4205, 'synset': 'muskrat.n.02', 'name': 'muskrat'}, {'id': 4206, 'synset': 'round-tailed_muskrat.n.01', 'name': 'round-tailed_muskrat'}, {'id': 4207, 'synset': 'cotton_rat.n.01', 'name': 'cotton_rat'}, {'id': 4208, 'synset': 'wood_rat.n.01', 'name': 'wood_rat'}, {'id': 4209, 'synset': 'dusky-footed_wood_rat.n.01', 'name': 'dusky-footed_wood_rat'}, {'id': 4210, 'synset': 'vole.n.01', 'name': 'vole'}, {'id': 4211, 'synset': 'packrat.n.02', 'name': 'packrat'}, {'id': 4212, 'synset': 'dusky-footed_woodrat.n.01', 'name': 'dusky-footed_woodrat'}, {'id': 4213, 'synset': 'eastern_woodrat.n.01', 'name': 'eastern_woodrat'}, {'id': 4214, 'synset': 'rice_rat.n.01', 'name': 'rice_rat'}, {'id': 4215, 'synset': 'pine_vole.n.01', 'name': 'pine_vole'}, {'id': 4216, 'synset': 'meadow_vole.n.01', 'name': 'meadow_vole'}, {'id': 4217, 'synset': 'water_vole.n.02', 'name': 'water_vole'}, {'id': 4218, 'synset': 'prairie_vole.n.01', 'name': 'prairie_vole'}, {'id': 4219, 'synset': 'water_vole.n.01', 'name': 'water_vole'}, {'id': 4220, 'synset': 'red-backed_mouse.n.01', 'name': 'red-backed_mouse'}, {'id': 4221, 'synset': 'phenacomys.n.01', 'name': 'phenacomys'}, {'id': 4222, 'synset': 'eurasian_hamster.n.01', 'name': 'Eurasian_hamster'}, {'id': 4223, 'synset': 'golden_hamster.n.01', 'name': 'golden_hamster'}, {'id': 4224, 'synset': 'gerbil.n.01', 'name': 'gerbil'}, {'id': 4225, 'synset': 'jird.n.01', 'name': 'jird'}, {'id': 4226, 'synset': 'tamarisk_gerbil.n.01', 'name': 'tamarisk_gerbil'}, {'id': 4227, 'synset': 'sand_rat.n.02', 'name': 'sand_rat'}, {'id': 4228, 'synset': 'lemming.n.01', 'name': 'lemming'}, {'id': 4229, 'synset': 'european_lemming.n.01', 'name': 'European_lemming'}, {'id': 4230, 'synset': 'brown_lemming.n.01', 'name': 'brown_lemming'}, {'id': 4231, 'synset': 'grey_lemming.n.01', 'name': 'grey_lemming'}, {'id': 4232, 'synset': 'pied_lemming.n.01', 'name': 'pied_lemming'}, {'id': 4233, 'synset': 'hudson_bay_collared_lemming.n.01', 'name': 'Hudson_bay_collared_lemming'}, {'id': 4234, 'synset': 'southern_bog_lemming.n.01', 'name': 'southern_bog_lemming'}, {'id': 4235, 'synset': 'northern_bog_lemming.n.01', 'name': 'northern_bog_lemming'}, {'id': 4236, 'synset': 'porcupine.n.01', 'name': 'porcupine'}, {'id': 4237, 'synset': 'old_world_porcupine.n.01', 'name': 'Old_World_porcupine'}, {'id': 4238, 'synset': 'brush-tailed_porcupine.n.01', 'name': 'brush-tailed_porcupine'}, {'id': 4239, 'synset': 'long-tailed_porcupine.n.01', 'name': 'long-tailed_porcupine'}, {'id': 4240, 'synset': 'new_world_porcupine.n.01', 'name': 'New_World_porcupine'}, {'id': 4241, 'synset': 'canada_porcupine.n.01', 'name': 'Canada_porcupine'}, {'id': 4242, 'synset': 'pocket_mouse.n.01', 'name': 'pocket_mouse'}, {'id': 4243, 'synset': 'silky_pocket_mouse.n.01', 'name': 'silky_pocket_mouse'}, {'id': 4244, 'synset': 'plains_pocket_mouse.n.01', 'name': 'plains_pocket_mouse'}, {'id': 4245, 'synset': 'hispid_pocket_mouse.n.01', 'name': 'hispid_pocket_mouse'}, {'id': 4246, 'synset': 'mexican_pocket_mouse.n.01', 'name': 'Mexican_pocket_mouse'}, {'id': 4247, 'synset': 'kangaroo_rat.n.01', 'name': 'kangaroo_rat'}, {'id': 4248, 'synset': 'ord_kangaroo_rat.n.01', 'name': 'Ord_kangaroo_rat'}, {'id': 4249, 'synset': 'kangaroo_mouse.n.01', 'name': 'kangaroo_mouse'}, {'id': 4250, 'synset': 'jumping_mouse.n.01', 'name': 'jumping_mouse'}, {'id': 4251, 'synset': 'meadow_jumping_mouse.n.01', 'name': 'meadow_jumping_mouse'}, {'id': 4252, 'synset': 'jerboa.n.01', 'name': 'jerboa'}, {'id': 4253, 'synset': 'typical_jerboa.n.01', 'name': 'typical_jerboa'}, {'id': 4254, 'synset': 'jaculus_jaculus.n.01', 'name': 'Jaculus_jaculus'}, {'id': 4255, 'synset': 'dormouse.n.01', 'name': 'dormouse'}, {'id': 4256, 'synset': 'loir.n.01', 'name': 'loir'}, {'id': 4257, 'synset': 'hazel_mouse.n.01', 'name': 'hazel_mouse'}, {'id': 4258, 'synset': 'lerot.n.01', 'name': 'lerot'}, {'id': 4259, 'synset': 'gopher.n.04', 'name': 'gopher'}, {'id': 4260, 'synset': 'plains_pocket_gopher.n.01', 'name': 'plains_pocket_gopher'}, {'id': 4261, 'synset': 'southeastern_pocket_gopher.n.01', 'name': 'southeastern_pocket_gopher'}, {'id': 4262, 'synset': 'valley_pocket_gopher.n.01', 'name': 'valley_pocket_gopher'}, {'id': 4263, 'synset': 'northern_pocket_gopher.n.01', 'name': 'northern_pocket_gopher'}, {'id': 4264, 'synset': 'tree_squirrel.n.01', 'name': 'tree_squirrel'}, {'id': 4265, 'synset': 'eastern_grey_squirrel.n.01', 'name': 'eastern_grey_squirrel'}, {'id': 4266, 'synset': 'western_grey_squirrel.n.01', 'name': 'western_grey_squirrel'}, {'id': 4267, 'synset': 'fox_squirrel.n.01', 'name': 'fox_squirrel'}, {'id': 4268, 'synset': 'black_squirrel.n.01', 'name': 'black_squirrel'}, {'id': 4269, 'synset': 'red_squirrel.n.02', 'name': 'red_squirrel'}, {'id': 4270, 'synset': 'american_red_squirrel.n.01', 'name': 'American_red_squirrel'}, {'id': 4271, 'synset': 'chickeree.n.01', 'name': 'chickeree'}, {'id': 4272, 'synset': 'antelope_squirrel.n.01', 'name': 'antelope_squirrel'}, {'id': 4273, 'synset': 'ground_squirrel.n.02', 'name': 'ground_squirrel'}, {'id': 4274, 'synset': 'mantled_ground_squirrel.n.01', 'name': 'mantled_ground_squirrel'}, {'id': 4275, 'synset': 'suslik.n.01', 'name': 'suslik'}, {'id': 4276, 'synset': 'flickertail.n.01', 'name': 'flickertail'}, {'id': 4277, 'synset': 'rock_squirrel.n.01', 'name': 'rock_squirrel'}, {'id': 4278, 'synset': 'arctic_ground_squirrel.n.01', 'name': 'Arctic_ground_squirrel'}, {'id': 4279, 'synset': 'prairie_dog.n.01', 'name': 'prairie_dog'}, {'id': 4280, 'synset': 'blacktail_prairie_dog.n.01', 'name': 'blacktail_prairie_dog'}, {'id': 4281, 'synset': 'whitetail_prairie_dog.n.01', 'name': 'whitetail_prairie_dog'}, {'id': 4282, 'synset': 'eastern_chipmunk.n.01', 'name': 'eastern_chipmunk'}, {'id': 4283, 'synset': 'chipmunk.n.01', 'name': 'chipmunk'}, {'id': 4284, 'synset': 'baronduki.n.01', 'name': 'baronduki'}, {'id': 4285, 'synset': 'american_flying_squirrel.n.01', 'name': 'American_flying_squirrel'}, {'id': 4286, 'synset': 'southern_flying_squirrel.n.01', 'name': 'southern_flying_squirrel'}, {'id': 4287, 'synset': 'northern_flying_squirrel.n.01', 'name': 'northern_flying_squirrel'}, {'id': 4288, 'synset': 'marmot.n.01', 'name': 'marmot'}, {'id': 4289, 'synset': 'groundhog.n.01', 'name': 'groundhog'}, {'id': 4290, 'synset': 'hoary_marmot.n.01', 'name': 'hoary_marmot'}, {'id': 4291, 'synset': 'yellowbelly_marmot.n.01', 'name': 'yellowbelly_marmot'}, {'id': 4292, 'synset': 'asiatic_flying_squirrel.n.01', 'name': 'Asiatic_flying_squirrel'}, {'id': 4293, 'synset': 'beaver.n.07', 'name': 'beaver'}, {'id': 4294, 'synset': 'old_world_beaver.n.01', 'name': 'Old_World_beaver'}, {'id': 4295, 'synset': 'new_world_beaver.n.01', 'name': 'New_World_beaver'}, {'id': 4296, 'synset': 'mountain_beaver.n.01', 'name': 'mountain_beaver'}, {'id': 4297, 'synset': 'cavy.n.01', 'name': 'cavy'}, {'id': 4298, 'synset': 'guinea_pig.n.02', 'name': 'guinea_pig'}, {'id': 4299, 'synset': 'aperea.n.01', 'name': 'aperea'}, {'id': 4300, 'synset': 'mara.n.02', 'name': 'mara'}, {'id': 4301, 'synset': 'capybara.n.01', 'name': 'capybara'}, {'id': 4302, 'synset': 'agouti.n.01', 'name': 'agouti'}, {'id': 4303, 'synset': 'paca.n.01', 'name': 'paca'}, {'id': 4304, 'synset': 'mountain_paca.n.01', 'name': 'mountain_paca'}, {'id': 4305, 'synset': 'coypu.n.01', 'name': 'coypu'}, {'id': 4306, 'synset': 'chinchilla.n.03', 'name': 'chinchilla'}, {'id': 4307, 'synset': 'mountain_chinchilla.n.01', 'name': 'mountain_chinchilla'}, {'id': 4308, 'synset': 'viscacha.n.01', 'name': 'viscacha'}, {'id': 4309, 'synset': 'abrocome.n.01', 'name': 'abrocome'}, {'id': 4310, 'synset': 'mole_rat.n.02', 'name': 'mole_rat'}, {'id': 4311, 'synset': 'mole_rat.n.01', 'name': 'mole_rat'}, {'id': 4312, 'synset': 'sand_rat.n.01', 'name': 'sand_rat'}, {'id': 4313, 'synset': 'naked_mole_rat.n.01', 'name': 'naked_mole_rat'}, {'id': 4314, 'synset': 'queen.n.09', 'name': 'queen'}, {'id': 4315, 'synset': 'damaraland_mole_rat.n.01', 'name': 'Damaraland_mole_rat'}, {'id': 4316, 'synset': 'ungulata.n.01', 'name': 'Ungulata'}, {'id': 4317, 'synset': 'ungulate.n.01', 'name': 'ungulate'}, {'id': 4318, 'synset': 'unguiculate.n.01', 'name': 'unguiculate'}, {'id': 4319, 'synset': 'dinoceras.n.01', 'name': 'dinoceras'}, {'id': 4320, 'synset': 'hyrax.n.01', 'name': 'hyrax'}, {'id': 4321, 'synset': 'rock_hyrax.n.01', 'name': 'rock_hyrax'}, {'id': 4322, 'synset': 'odd-toed_ungulate.n.01', 'name': 'odd-toed_ungulate'}, {'id': 4323, 'synset': 'equine.n.01', 'name': 'equine'}, {'id': 4324, 'synset': 'roan.n.02', 'name': 'roan'}, {'id': 4325, 'synset': 'stablemate.n.01', 'name': 'stablemate'}, {'id': 4326, 'synset': 'gee-gee.n.01', 'name': 'gee-gee'}, {'id': 4327, 'synset': 'eohippus.n.01', 'name': 'eohippus'}, {'id': 4328, 'synset': 'filly.n.01', 'name': 'filly'}, {'id': 4329, 'synset': 'colt.n.01', 'name': 'colt'}, {'id': 4330, 'synset': 'male_horse.n.01', 'name': 'male_horse'}, {'id': 4331, 'synset': 'ridgeling.n.01', 'name': 'ridgeling'}, {'id': 4332, 'synset': 'stallion.n.01', 'name': 'stallion'}, {'id': 4333, 'synset': 'stud.n.04', 'name': 'stud'}, {'id': 4334, 'synset': 'gelding.n.01', 'name': 'gelding'}, {'id': 4335, 'synset': 'mare.n.01', 'name': 'mare'}, {'id': 4336, 'synset': 'broodmare.n.01', 'name': 'broodmare'}, {'id': 4337, 'synset': 'saddle_horse.n.01', 'name': 'saddle_horse'}, {'id': 4338, 'synset': 'remount.n.01', 'name': 'remount'}, {'id': 4339, 'synset': 'palfrey.n.01', 'name': 'palfrey'}, {'id': 4340, 'synset': 'warhorse.n.03', 'name': 'warhorse'}, {'id': 4341, 'synset': 'cavalry_horse.n.01', 'name': 'cavalry_horse'}, {'id': 4342, 'synset': 'charger.n.01', 'name': 'charger'}, {'id': 4343, 'synset': 'steed.n.01', 'name': 'steed'}, {'id': 4344, 'synset': 'prancer.n.01', 'name': 'prancer'}, {'id': 4345, 'synset': 'hack.n.08', 'name': 'hack'}, {'id': 4346, 'synset': 'cow_pony.n.01', 'name': 'cow_pony'}, {'id': 4347, 'synset': 'quarter_horse.n.01', 'name': 'quarter_horse'}, {'id': 4348, 'synset': 'morgan.n.06', 'name': 'Morgan'}, {'id': 4349, 'synset': 'tennessee_walker.n.01', 'name': 'Tennessee_walker'}, {'id': 4350, 'synset': 'american_saddle_horse.n.01', 'name': 'American_saddle_horse'}, {'id': 4351, 'synset': 'appaloosa.n.01', 'name': 'Appaloosa'}, {'id': 4352, 'synset': 'arabian.n.02', 'name': 'Arabian'}, {'id': 4353, 'synset': 'lippizan.n.01', 'name': 'Lippizan'}, {'id': 4354, 'synset': 'pony.n.01', 'name': 'pony'}, {'id': 4355, 'synset': 'polo_pony.n.01', 'name': 'polo_pony'}, {'id': 4356, 'synset': 'mustang.n.01', 'name': 'mustang'}, {'id': 4357, 'synset': 'bronco.n.01', 'name': 'bronco'}, {'id': 4358, 'synset': 'bucking_bronco.n.01', 'name': 'bucking_bronco'}, {'id': 4359, 'synset': 'buckskin.n.01', 'name': 'buckskin'}, {'id': 4360, 'synset': 'crowbait.n.01', 'name': 'crowbait'}, {'id': 4361, 'synset': 'dun.n.01', 'name': 'dun'}, {'id': 4362, 'synset': 'grey.n.07', 'name': 'grey'}, {'id': 4363, 'synset': 'wild_horse.n.01', 'name': 'wild_horse'}, {'id': 4364, 'synset': 'tarpan.n.01', 'name': 'tarpan'}, {'id': 4365, 'synset': "przewalski's_horse.n.01", 'name': "Przewalski's_horse"}, {'id': 4366, 'synset': 'cayuse.n.01', 'name': 'cayuse'}, {'id': 4367, 'synset': 'hack.n.07', 'name': 'hack'}, {'id': 4368, 'synset': 'hack.n.06', 'name': 'hack'}, {'id': 4369, 'synset': 'plow_horse.n.01', 'name': 'plow_horse'}, {'id': 4370, 'synset': 'shetland_pony.n.01', 'name': 'Shetland_pony'}, {'id': 4371, 'synset': 'welsh_pony.n.01', 'name': 'Welsh_pony'}, {'id': 4372, 'synset': 'exmoor.n.02', 'name': 'Exmoor'}, {'id': 4373, 'synset': 'racehorse.n.01', 'name': 'racehorse'}, {'id': 4374, 'synset': 'thoroughbred.n.02', 'name': 'thoroughbred'}, {'id': 4375, 'synset': 'steeplechaser.n.01', 'name': 'steeplechaser'}, {'id': 4376, 'synset': 'racer.n.03', 'name': 'racer'}, {'id': 4377, 'synset': 'finisher.n.06', 'name': 'finisher'}, {'id': 4378, 'synset': 'pony.n.02', 'name': 'pony'}, {'id': 4379, 'synset': 'yearling.n.02', 'name': 'yearling'}, {'id': 4380, 'synset': 'dark_horse.n.02', 'name': 'dark_horse'}, {'id': 4381, 'synset': 'mudder.n.01', 'name': 'mudder'}, {'id': 4382, 'synset': 'nonstarter.n.02', 'name': 'nonstarter'}, {'id': 4383, 'synset': 'stalking-horse.n.04', 'name': 'stalking-horse'}, {'id': 4384, 'synset': 'harness_horse.n.01', 'name': 'harness_horse'}, {'id': 4385, 'synset': 'cob.n.02', 'name': 'cob'}, {'id': 4386, 'synset': 'hackney.n.02', 'name': 'hackney'}, {'id': 4387, 'synset': 'workhorse.n.02', 'name': 'workhorse'}, {'id': 4388, 'synset': 'draft_horse.n.01', 'name': 'draft_horse'}, {'id': 4389, 'synset': 'packhorse.n.01', 'name': 'packhorse'}, {'id': 4390, 'synset': 'carthorse.n.01', 'name': 'carthorse'}, {'id': 4391, 'synset': 'clydesdale.n.01', 'name': 'Clydesdale'}, {'id': 4392, 'synset': 'percheron.n.01', 'name': 'Percheron'}, {'id': 4393, 'synset': 'farm_horse.n.01', 'name': 'farm_horse'}, {'id': 4394, 'synset': 'shire.n.02', 'name': 'shire'}, {'id': 4395, 'synset': 'pole_horse.n.02', 'name': 'pole_horse'}, {'id': 4396, 'synset': 'post_horse.n.01', 'name': 'post_horse'}, {'id': 4397, 'synset': 'coach_horse.n.01', 'name': 'coach_horse'}, {'id': 4398, 'synset': 'pacer.n.02', 'name': 'pacer'}, {'id': 4399, 'synset': 'pacer.n.01', 'name': 'pacer'}, {'id': 4400, 'synset': 'trotting_horse.n.01', 'name': 'trotting_horse'}, {'id': 4401, 'synset': 'pole_horse.n.01', 'name': 'pole_horse'}, {'id': 4402, 'synset': 'stepper.n.03', 'name': 'stepper'}, {'id': 4403, 'synset': 'chestnut.n.06', 'name': 'chestnut'}, {'id': 4404, 'synset': 'liver_chestnut.n.01', 'name': 'liver_chestnut'}, {'id': 4405, 'synset': 'bay.n.07', 'name': 'bay'}, {'id': 4406, 'synset': 'sorrel.n.05', 'name': 'sorrel'}, {'id': 4407, 'synset': 'palomino.n.01', 'name': 'palomino'}, {'id': 4408, 'synset': 'pinto.n.01', 'name': 'pinto'}, {'id': 4409, 'synset': 'ass.n.03', 'name': 'ass'}, {'id': 4410, 'synset': 'burro.n.01', 'name': 'burro'}, {'id': 4411, 'synset': 'moke.n.01', 'name': 'moke'}, {'id': 4412, 'synset': 'jack.n.12', 'name': 'jack'}, {'id': 4413, 'synset': 'jennet.n.01', 'name': 'jennet'}, {'id': 4414, 'synset': 'mule.n.01', 'name': 'mule'}, {'id': 4415, 'synset': 'hinny.n.01', 'name': 'hinny'}, {'id': 4416, 'synset': 'wild_ass.n.01', 'name': 'wild_ass'}, {'id': 4417, 'synset': 'african_wild_ass.n.01', 'name': 'African_wild_ass'}, {'id': 4418, 'synset': 'kiang.n.01', 'name': 'kiang'}, {'id': 4419, 'synset': 'onager.n.02', 'name': 'onager'}, {'id': 4420, 'synset': 'chigetai.n.01', 'name': 'chigetai'}, {'id': 4421, 'synset': 'common_zebra.n.01', 'name': 'common_zebra'}, {'id': 4422, 'synset': 'mountain_zebra.n.01', 'name': 'mountain_zebra'}, {'id': 4423, 'synset': "grevy's_zebra.n.01", 'name': "grevy's_zebra"}, {'id': 4424, 'synset': 'quagga.n.01', 'name': 'quagga'}, {'id': 4425, 'synset': 'indian_rhinoceros.n.01', 'name': 'Indian_rhinoceros'}, {'id': 4426, 'synset': 'woolly_rhinoceros.n.01', 'name': 'woolly_rhinoceros'}, {'id': 4427, 'synset': 'white_rhinoceros.n.01', 'name': 'white_rhinoceros'}, {'id': 4428, 'synset': 'black_rhinoceros.n.01', 'name': 'black_rhinoceros'}, {'id': 4429, 'synset': 'tapir.n.01', 'name': 'tapir'}, {'id': 4430, 'synset': 'new_world_tapir.n.01', 'name': 'New_World_tapir'}, {'id': 4431, 'synset': 'malayan_tapir.n.01', 'name': 'Malayan_tapir'}, {'id': 4432, 'synset': 'even-toed_ungulate.n.01', 'name': 'even-toed_ungulate'}, {'id': 4433, 'synset': 'swine.n.01', 'name': 'swine'}, {'id': 4434, 'synset': 'piglet.n.01', 'name': 'piglet'}, {'id': 4435, 'synset': 'sucking_pig.n.01', 'name': 'sucking_pig'}, {'id': 4436, 'synset': 'porker.n.01', 'name': 'porker'}, {'id': 4437, 'synset': 'boar.n.02', 'name': 'boar'}, {'id': 4438, 'synset': 'sow.n.01', 'name': 'sow'}, {'id': 4439, 'synset': 'razorback.n.01', 'name': 'razorback'}, {'id': 4440, 'synset': 'wild_boar.n.01', 'name': 'wild_boar'}, {'id': 4441, 'synset': 'babirusa.n.01', 'name': 'babirusa'}, {'id': 4442, 'synset': 'warthog.n.01', 'name': 'warthog'}, {'id': 4443, 'synset': 'peccary.n.01', 'name': 'peccary'}, {'id': 4444, 'synset': 'collared_peccary.n.01', 'name': 'collared_peccary'}, {'id': 4445, 'synset': 'white-lipped_peccary.n.01', 'name': 'white-lipped_peccary'}, {'id': 4446, 'synset': 'ruminant.n.01', 'name': 'ruminant'}, {'id': 4447, 'synset': 'bovid.n.01', 'name': 'bovid'}, {'id': 4448, 'synset': 'bovine.n.01', 'name': 'bovine'}, {'id': 4449, 'synset': 'ox.n.02', 'name': 'ox'}, {'id': 4450, 'synset': 'cattle.n.01', 'name': 'cattle'}, {'id': 4451, 'synset': 'ox.n.01', 'name': 'ox'}, {'id': 4452, 'synset': 'stirk.n.01', 'name': 'stirk'}, {'id': 4453, 'synset': 'bullock.n.02', 'name': 'bullock'}, {'id': 4454, 'synset': 'bull.n.01', 'name': 'bull'}, {'id': 4455, 'synset': 'cow.n.01', 'name': 'cow'}, {'id': 4456, 'synset': 'heifer.n.01', 'name': 'heifer'}, {'id': 4457, 'synset': 'bullock.n.01', 'name': 'bullock'}, {'id': 4458, 'synset': 'dogie.n.01', 'name': 'dogie'}, {'id': 4459, 'synset': 'maverick.n.02', 'name': 'maverick'}, {'id': 4460, 'synset': 'longhorn.n.01', 'name': 'longhorn'}, {'id': 4461, 'synset': 'brahman.n.04', 'name': 'Brahman'}, {'id': 4462, 'synset': 'zebu.n.01', 'name': 'zebu'}, {'id': 4463, 'synset': 'aurochs.n.02', 'name': 'aurochs'}, {'id': 4464, 'synset': 'yak.n.02', 'name': 'yak'}, {'id': 4465, 'synset': 'banteng.n.01', 'name': 'banteng'}, {'id': 4466, 'synset': 'welsh.n.03', 'name': 'Welsh'}, {'id': 4467, 'synset': 'red_poll.n.01', 'name': 'red_poll'}, {'id': 4468, 'synset': 'santa_gertrudis.n.01', 'name': 'Santa_Gertrudis'}, {'id': 4469, 'synset': 'aberdeen_angus.n.01', 'name': 'Aberdeen_Angus'}, {'id': 4470, 'synset': 'africander.n.01', 'name': 'Africander'}, {'id': 4471, 'synset': 'dairy_cattle.n.01', 'name': 'dairy_cattle'}, {'id': 4472, 'synset': 'ayrshire.n.01', 'name': 'Ayrshire'}, {'id': 4473, 'synset': 'brown_swiss.n.01', 'name': 'Brown_Swiss'}, {'id': 4474, 'synset': 'charolais.n.01', 'name': 'Charolais'}, {'id': 4475, 'synset': 'jersey.n.05', 'name': 'Jersey'}, {'id': 4476, 'synset': 'devon.n.02', 'name': 'Devon'}, {'id': 4477, 'synset': 'grade.n.09', 'name': 'grade'}, {'id': 4478, 'synset': 'durham.n.02', 'name': 'Durham'}, {'id': 4479, 'synset': 'milking_shorthorn.n.01', 'name': 'milking_shorthorn'}, {'id': 4480, 'synset': 'galloway.n.02', 'name': 'Galloway'}, {'id': 4481, 'synset': 'friesian.n.01', 'name': 'Friesian'}, {'id': 4482, 'synset': 'guernsey.n.02', 'name': 'Guernsey'}, {'id': 4483, 'synset': 'hereford.n.01', 'name': 'Hereford'}, {'id': 4484, 'synset': 'cattalo.n.01', 'name': 'cattalo'}, {'id': 4485, 'synset': 'old_world_buffalo.n.01', 'name': 'Old_World_buffalo'}, {'id': 4486, 'synset': 'water_buffalo.n.01', 'name': 'water_buffalo'}, {'id': 4487, 'synset': 'indian_buffalo.n.01', 'name': 'Indian_buffalo'}, {'id': 4488, 'synset': 'carabao.n.01', 'name': 'carabao'}, {'id': 4489, 'synset': 'anoa.n.01', 'name': 'anoa'}, {'id': 4490, 'synset': 'tamarau.n.01', 'name': 'tamarau'}, {'id': 4491, 'synset': 'cape_buffalo.n.01', 'name': 'Cape_buffalo'}, {'id': 4492, 'synset': 'asian_wild_ox.n.01', 'name': 'Asian_wild_ox'}, {'id': 4493, 'synset': 'gaur.n.01', 'name': 'gaur'}, {'id': 4494, 'synset': 'gayal.n.01', 'name': 'gayal'}, {'id': 4495, 'synset': 'bison.n.01', 'name': 'bison'}, {'id': 4496, 'synset': 'american_bison.n.01', 'name': 'American_bison'}, {'id': 4497, 'synset': 'wisent.n.01', 'name': 'wisent'}, {'id': 4498, 'synset': 'musk_ox.n.01', 'name': 'musk_ox'}, {'id': 4499, 'synset': 'ewe.n.03', 'name': 'ewe'}, {'id': 4500, 'synset': 'wether.n.01', 'name': 'wether'}, {'id': 4501, 'synset': 'lambkin.n.01', 'name': 'lambkin'}, {'id': 4502, 'synset': 'baa-lamb.n.01', 'name': 'baa-lamb'}, {'id': 4503, 'synset': 'hog.n.02', 'name': 'hog'}, {'id': 4504, 'synset': 'teg.n.01', 'name': 'teg'}, {'id': 4505, 'synset': 'persian_lamb.n.02', 'name': 'Persian_lamb'}, {'id': 4506, 'synset': 'domestic_sheep.n.01', 'name': 'domestic_sheep'}, {'id': 4507, 'synset': 'cotswold.n.01', 'name': 'Cotswold'}, {'id': 4508, 'synset': 'hampshire.n.02', 'name': 'Hampshire'}, {'id': 4509, 'synset': 'lincoln.n.03', 'name': 'Lincoln'}, {'id': 4510, 'synset': 'exmoor.n.01', 'name': 'Exmoor'}, {'id': 4511, 'synset': 'cheviot.n.01', 'name': 'Cheviot'}, {'id': 4512, 'synset': 'broadtail.n.02', 'name': 'broadtail'}, {'id': 4513, 'synset': 'longwool.n.01', 'name': 'longwool'}, {'id': 4514, 'synset': 'merino.n.01', 'name': 'merino'}, {'id': 4515, 'synset': 'rambouillet.n.01', 'name': 'Rambouillet'}, {'id': 4516, 'synset': 'wild_sheep.n.01', 'name': 'wild_sheep'}, {'id': 4517, 'synset': 'argali.n.01', 'name': 'argali'}, {'id': 4518, 'synset': 'marco_polo_sheep.n.01', 'name': 'Marco_Polo_sheep'}, {'id': 4519, 'synset': 'urial.n.01', 'name': 'urial'}, {'id': 4520, 'synset': 'dall_sheep.n.01', 'name': 'Dall_sheep'}, {'id': 4521, 'synset': 'mountain_sheep.n.01', 'name': 'mountain_sheep'}, {'id': 4522, 'synset': 'bighorn.n.02', 'name': 'bighorn'}, {'id': 4523, 'synset': 'mouflon.n.01', 'name': 'mouflon'}, {'id': 4524, 'synset': 'aoudad.n.01', 'name': 'aoudad'}, {'id': 4525, 'synset': 'kid.n.05', 'name': 'kid'}, {'id': 4526, 'synset': 'billy.n.02', 'name': 'billy'}, {'id': 4527, 'synset': 'nanny.n.02', 'name': 'nanny'}, {'id': 4528, 'synset': 'domestic_goat.n.01', 'name': 'domestic_goat'}, {'id': 4529, 'synset': 'cashmere_goat.n.01', 'name': 'Cashmere_goat'}, {'id': 4530, 'synset': 'angora.n.02', 'name': 'Angora'}, {'id': 4531, 'synset': 'wild_goat.n.01', 'name': 'wild_goat'}, {'id': 4532, 'synset': 'bezoar_goat.n.01', 'name': 'bezoar_goat'}, {'id': 4533, 'synset': 'markhor.n.01', 'name': 'markhor'}, {'id': 4534, 'synset': 'ibex.n.01', 'name': 'ibex'}, {'id': 4535, 'synset': 'goat_antelope.n.01', 'name': 'goat_antelope'}, {'id': 4536, 'synset': 'mountain_goat.n.01', 'name': 'mountain_goat'}, {'id': 4537, 'synset': 'goral.n.01', 'name': 'goral'}, {'id': 4538, 'synset': 'serow.n.01', 'name': 'serow'}, {'id': 4539, 'synset': 'chamois.n.02', 'name': 'chamois'}, {'id': 4540, 'synset': 'takin.n.01', 'name': 'takin'}, {'id': 4541, 'synset': 'antelope.n.01', 'name': 'antelope'}, {'id': 4542, 'synset': 'blackbuck.n.01', 'name': 'blackbuck'}, {'id': 4543, 'synset': 'gerenuk.n.01', 'name': 'gerenuk'}, {'id': 4544, 'synset': 'addax.n.01', 'name': 'addax'}, {'id': 4545, 'synset': 'gnu.n.01', 'name': 'gnu'}, {'id': 4546, 'synset': 'dik-dik.n.01', 'name': 'dik-dik'}, {'id': 4547, 'synset': 'hartebeest.n.01', 'name': 'hartebeest'}, {'id': 4548, 'synset': 'sassaby.n.01', 'name': 'sassaby'}, {'id': 4549, 'synset': 'impala.n.01', 'name': 'impala'}, {'id': 4550, 'synset': "thomson's_gazelle.n.01", 'name': "Thomson's_gazelle"}, {'id': 4551, 'synset': 'gazella_subgutturosa.n.01', 'name': 'Gazella_subgutturosa'}, {'id': 4552, 'synset': 'springbok.n.01', 'name': 'springbok'}, {'id': 4553, 'synset': 'bongo.n.02', 'name': 'bongo'}, {'id': 4554, 'synset': 'kudu.n.01', 'name': 'kudu'}, {'id': 4555, 'synset': 'greater_kudu.n.01', 'name': 'greater_kudu'}, {'id': 4556, 'synset': 'lesser_kudu.n.01', 'name': 'lesser_kudu'}, {'id': 4557, 'synset': 'harnessed_antelope.n.01', 'name': 'harnessed_antelope'}, {'id': 4558, 'synset': 'nyala.n.02', 'name': 'nyala'}, {'id': 4559, 'synset': 'mountain_nyala.n.01', 'name': 'mountain_nyala'}, {'id': 4560, 'synset': 'bushbuck.n.01', 'name': 'bushbuck'}, {'id': 4561, 'synset': 'nilgai.n.01', 'name': 'nilgai'}, {'id': 4562, 'synset': 'sable_antelope.n.01', 'name': 'sable_antelope'}, {'id': 4563, 'synset': 'saiga.n.01', 'name': 'saiga'}, {'id': 4564, 'synset': 'steenbok.n.01', 'name': 'steenbok'}, {'id': 4565, 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'name': 'common_eel'}, {'id': 4838, 'synset': 'tuna.n.04', 'name': 'tuna'}, {'id': 4839, 'synset': 'moray.n.01', 'name': 'moray'}, {'id': 4840, 'synset': 'conger.n.01', 'name': 'conger'}, {'id': 4841, 'synset': 'teleost_fish.n.01', 'name': 'teleost_fish'}, {'id': 4842, 'synset': 'beaked_salmon.n.01', 'name': 'beaked_salmon'}, {'id': 4843, 'synset': 'clupeid_fish.n.01', 'name': 'clupeid_fish'}, {'id': 4844, 'synset': 'whitebait.n.02', 'name': 'whitebait'}, {'id': 4845, 'synset': 'brit.n.02', 'name': 'brit'}, {'id': 4846, 'synset': 'shad.n.02', 'name': 'shad'}, {'id': 4847, 'synset': 'common_american_shad.n.01', 'name': 'common_American_shad'}, {'id': 4848, 'synset': 'river_shad.n.01', 'name': 'river_shad'}, {'id': 4849, 'synset': 'allice_shad.n.01', 'name': 'allice_shad'}, {'id': 4850, 'synset': 'alewife.n.02', 'name': 'alewife'}, {'id': 4851, 'synset': 'menhaden.n.01', 'name': 'menhaden'}, {'id': 4852, 'synset': 'herring.n.02', 'name': 'herring'}, {'id': 4853, 'synset': 'atlantic_herring.n.01', 'name': 'Atlantic_herring'}, {'id': 4854, 'synset': 'pacific_herring.n.01', 'name': 'Pacific_herring'}, {'id': 4855, 'synset': 'sardine.n.02', 'name': 'sardine'}, {'id': 4856, 'synset': 'sild.n.01', 'name': 'sild'}, {'id': 4857, 'synset': 'brisling.n.02', 'name': 'brisling'}, {'id': 4858, 'synset': 'pilchard.n.02', 'name': 'pilchard'}, {'id': 4859, 'synset': 'pacific_sardine.n.01', 'name': 'Pacific_sardine'}, {'id': 4860, 'synset': 'anchovy.n.02', 'name': 'anchovy'}, {'id': 4861, 'synset': 'mediterranean_anchovy.n.01', 'name': 'mediterranean_anchovy'}, {'id': 4862, 'synset': 'salmonid.n.01', 'name': 'salmonid'}, {'id': 4863, 'synset': 'parr.n.02', 'name': 'parr'}, {'id': 4864, 'synset': 'blackfish.n.02', 'name': 'blackfish'}, {'id': 4865, 'synset': 'redfish.n.03', 'name': 'redfish'}, {'id': 4866, 'synset': 'atlantic_salmon.n.02', 'name': 'Atlantic_salmon'}, {'id': 4867, 'synset': 'landlocked_salmon.n.01', 'name': 'landlocked_salmon'}, {'id': 4868, 'synset': 'sockeye.n.02', 'name': 'sockeye'}, {'id': 4869, 'synset': 'chinook.n.05', 'name': 'chinook'}, {'id': 4870, 'synset': 'coho.n.02', 'name': 'coho'}, {'id': 4871, 'synset': 'trout.n.02', 'name': 'trout'}, {'id': 4872, 'synset': 'brown_trout.n.01', 'name': 'brown_trout'}, {'id': 4873, 'synset': 'rainbow_trout.n.02', 'name': 'rainbow_trout'}, {'id': 4874, 'synset': 'sea_trout.n.03', 'name': 'sea_trout'}, {'id': 4875, 'synset': 'lake_trout.n.02', 'name': 'lake_trout'}, {'id': 4876, 'synset': 'brook_trout.n.02', 'name': 'brook_trout'}, {'id': 4877, 'synset': 'char.n.03', 'name': 'char'}, {'id': 4878, 'synset': 'arctic_char.n.01', 'name': 'Arctic_char'}, {'id': 4879, 'synset': 'whitefish.n.03', 'name': 'whitefish'}, {'id': 4880, 'synset': 'lake_whitefish.n.01', 'name': 'lake_whitefish'}, {'id': 4881, 'synset': 'cisco.n.02', 'name': 'cisco'}, {'id': 4882, 'synset': 'round_whitefish.n.01', 'name': 'round_whitefish'}, {'id': 4883, 'synset': 'smelt.n.02', 'name': 'smelt'}, {'id': 4884, 'synset': 'sparling.n.02', 'name': 'sparling'}, {'id': 4885, 'synset': 'capelin.n.01', 'name': 'capelin'}, {'id': 4886, 'synset': 'tarpon.n.01', 'name': 'tarpon'}, {'id': 4887, 'synset': 'ladyfish.n.01', 'name': 'ladyfish'}, {'id': 4888, 'synset': 'bonefish.n.01', 'name': 'bonefish'}, {'id': 4889, 'synset': 'argentine.n.01', 'name': 'argentine'}, {'id': 4890, 'synset': 'lanternfish.n.01', 'name': 'lanternfish'}, {'id': 4891, 'synset': 'lizardfish.n.01', 'name': 'lizardfish'}, {'id': 4892, 'synset': 'lancetfish.n.01', 'name': 'lancetfish'}, {'id': 4893, 'synset': 'opah.n.01', 'name': 'opah'}, {'id': 4894, 'synset': 'new_world_opah.n.01', 'name': 'New_World_opah'}, {'id': 4895, 'synset': 'ribbonfish.n.02', 'name': 'ribbonfish'}, {'id': 4896, 'synset': 'dealfish.n.01', 'name': 'dealfish'}, {'id': 4897, 'synset': 'oarfish.n.01', 'name': 'oarfish'}, {'id': 4898, 'synset': 'batfish.n.01', 'name': 'batfish'}, {'id': 4899, 'synset': 'goosefish.n.01', 'name': 'goosefish'}, {'id': 4900, 'synset': 'toadfish.n.01', 'name': 'toadfish'}, {'id': 4901, 'synset': 'oyster_fish.n.01', 'name': 'oyster_fish'}, {'id': 4902, 'synset': 'frogfish.n.01', 'name': 'frogfish'}, {'id': 4903, 'synset': 'sargassum_fish.n.01', 'name': 'sargassum_fish'}, {'id': 4904, 'synset': 'needlefish.n.01', 'name': 'needlefish'}, {'id': 4905, 'synset': 'timucu.n.01', 'name': 'timucu'}, {'id': 4906, 'synset': 'flying_fish.n.01', 'name': 'flying_fish'}, {'id': 4907, 'synset': 'monoplane_flying_fish.n.01', 'name': 'monoplane_flying_fish'}, {'id': 4908, 'synset': 'halfbeak.n.01', 'name': 'halfbeak'}, {'id': 4909, 'synset': 'saury.n.01', 'name': 'saury'}, {'id': 4910, 'synset': 'spiny-finned_fish.n.01', 'name': 'spiny-finned_fish'}, {'id': 4911, 'synset': 'lingcod.n.02', 'name': 'lingcod'}, {'id': 4912, 'synset': 'percoid_fish.n.01', 'name': 'percoid_fish'}, {'id': 4913, 'synset': 'perch.n.07', 'name': 'perch'}, {'id': 4914, 'synset': 'climbing_perch.n.01', 'name': 'climbing_perch'}, {'id': 4915, 'synset': 'perch.n.06', 'name': 'perch'}, {'id': 4916, 'synset': 'yellow_perch.n.01', 'name': 'yellow_perch'}, {'id': 4917, 'synset': 'european_perch.n.01', 'name': 'European_perch'}, {'id': 4918, 'synset': 'pike-perch.n.01', 'name': 'pike-perch'}, {'id': 4919, 'synset': 'walleye.n.02', 'name': 'walleye'}, {'id': 4920, 'synset': 'blue_pike.n.01', 'name': 'blue_pike'}, {'id': 4921, 'synset': 'snail_darter.n.01', 'name': 'snail_darter'}, {'id': 4922, 'synset': 'cusk-eel.n.01', 'name': 'cusk-eel'}, {'id': 4923, 'synset': 'brotula.n.01', 'name': 'brotula'}, {'id': 4924, 'synset': 'pearlfish.n.01', 'name': 'pearlfish'}, {'id': 4925, 'synset': 'robalo.n.01', 'name': 'robalo'}, {'id': 4926, 'synset': 'snook.n.01', 'name': 'snook'}, {'id': 4927, 'synset': 'pike.n.05', 'name': 'pike'}, {'id': 4928, 'synset': 'northern_pike.n.01', 'name': 'northern_pike'}, {'id': 4929, 'synset': 'muskellunge.n.02', 'name': 'muskellunge'}, {'id': 4930, 'synset': 'pickerel.n.02', 'name': 'pickerel'}, {'id': 4931, 'synset': 'chain_pickerel.n.01', 'name': 'chain_pickerel'}, {'id': 4932, 'synset': 'redfin_pickerel.n.01', 'name': 'redfin_pickerel'}, {'id': 4933, 'synset': 'sunfish.n.03', 'name': 'sunfish'}, {'id': 4934, 'synset': 'crappie.n.02', 'name': 'crappie'}, {'id': 4935, 'synset': 'black_crappie.n.01', 'name': 'black_crappie'}, {'id': 4936, 'synset': 'white_crappie.n.01', 'name': 'white_crappie'}, {'id': 4937, 'synset': 'freshwater_bream.n.02', 'name': 'freshwater_bream'}, {'id': 4938, 'synset': 'pumpkinseed.n.01', 'name': 'pumpkinseed'}, {'id': 4939, 'synset': 'bluegill.n.01', 'name': 'bluegill'}, {'id': 4940, 'synset': 'spotted_sunfish.n.01', 'name': 'spotted_sunfish'}, {'id': 4941, 'synset': 'freshwater_bass.n.02', 'name': 'freshwater_bass'}, {'id': 4942, 'synset': 'rock_bass.n.02', 'name': 'rock_bass'}, {'id': 4943, 'synset': 'black_bass.n.02', 'name': 'black_bass'}, {'id': 4944, 'synset': 'kentucky_black_bass.n.01', 'name': 'Kentucky_black_bass'}, {'id': 4945, 'synset': 'smallmouth.n.01', 'name': 'smallmouth'}, {'id': 4946, 'synset': 'largemouth.n.01', 'name': 'largemouth'}, {'id': 4947, 'synset': 'bass.n.08', 'name': 'bass'}, {'id': 4948, 'synset': 'serranid_fish.n.01', 'name': 'serranid_fish'}, {'id': 4949, 'synset': 'white_perch.n.01', 'name': 'white_perch'}, {'id': 4950, 'synset': 'yellow_bass.n.01', 'name': 'yellow_bass'}, {'id': 4951, 'synset': 'blackmouth_bass.n.01', 'name': 'blackmouth_bass'}, {'id': 4952, 'synset': 'rock_sea_bass.n.01', 'name': 'rock_sea_bass'}, {'id': 4953, 'synset': 'striped_bass.n.02', 'name': 'striped_bass'}, {'id': 4954, 'synset': 'stone_bass.n.01', 'name': 'stone_bass'}, {'id': 4955, 'synset': 'grouper.n.02', 'name': 'grouper'}, {'id': 4956, 'synset': 'hind.n.01', 'name': 'hind'}, {'id': 4957, 'synset': 'rock_hind.n.01', 'name': 'rock_hind'}, {'id': 4958, 'synset': 'creole-fish.n.01', 'name': 'creole-fish'}, {'id': 4959, 'synset': 'jewfish.n.02', 'name': 'jewfish'}, {'id': 4960, 'synset': 'soapfish.n.01', 'name': 'soapfish'}, {'id': 4961, 'synset': 'surfperch.n.01', 'name': 'surfperch'}, {'id': 4962, 'synset': 'rainbow_seaperch.n.01', 'name': 'rainbow_seaperch'}, {'id': 4963, 'synset': 'bigeye.n.01', 'name': 'bigeye'}, {'id': 4964, 'synset': 'catalufa.n.01', 'name': 'catalufa'}, {'id': 4965, 'synset': 'cardinalfish.n.01', 'name': 'cardinalfish'}, {'id': 4966, 'synset': 'flame_fish.n.01', 'name': 'flame_fish'}, {'id': 4967, 'synset': 'tilefish.n.02', 'name': 'tilefish'}, {'id': 4968, 'synset': 'bluefish.n.01', 'name': 'bluefish'}, {'id': 4969, 'synset': 'cobia.n.01', 'name': 'cobia'}, {'id': 4970, 'synset': 'remora.n.01', 'name': 'remora'}, {'id': 4971, 'synset': 'sharksucker.n.01', 'name': 'sharksucker'}, {'id': 4972, 'synset': 'whale_sucker.n.01', 'name': 'whale_sucker'}, {'id': 4973, 'synset': 'carangid_fish.n.01', 'name': 'carangid_fish'}, {'id': 4974, 'synset': 'jack.n.11', 'name': 'jack'}, {'id': 4975, 'synset': 'crevalle_jack.n.01', 'name': 'crevalle_jack'}, {'id': 4976, 'synset': 'yellow_jack.n.03', 'name': 'yellow_jack'}, {'id': 4977, 'synset': 'runner.n.10', 'name': 'runner'}, {'id': 4978, 'synset': 'rainbow_runner.n.01', 'name': 'rainbow_runner'}, {'id': 4979, 'synset': 'leatherjacket.n.02', 'name': 'leatherjacket'}, {'id': 4980, 'synset': 'threadfish.n.01', 'name': 'threadfish'}, {'id': 4981, 'synset': 'moonfish.n.01', 'name': 'moonfish'}, {'id': 4982, 'synset': 'lookdown.n.01', 'name': 'lookdown'}, {'id': 4983, 'synset': 'amberjack.n.01', 'name': 'amberjack'}, {'id': 4984, 'synset': 'yellowtail.n.02', 'name': 'yellowtail'}, {'id': 4985, 'synset': 'kingfish.n.05', 'name': 'kingfish'}, {'id': 4986, 'synset': 'pompano.n.02', 'name': 'pompano'}, {'id': 4987, 'synset': 'florida_pompano.n.01', 'name': 'Florida_pompano'}, {'id': 4988, 'synset': 'permit.n.03', 'name': 'permit'}, {'id': 4989, 'synset': 'scad.n.01', 'name': 'scad'}, {'id': 4990, 'synset': 'horse_mackerel.n.03', 'name': 'horse_mackerel'}, {'id': 4991, 'synset': 'horse_mackerel.n.02', 'name': 'horse_mackerel'}, {'id': 4992, 'synset': 'bigeye_scad.n.01', 'name': 'bigeye_scad'}, {'id': 4993, 'synset': 'mackerel_scad.n.01', 'name': 'mackerel_scad'}, {'id': 4994, 'synset': 'round_scad.n.01', 'name': 'round_scad'}, {'id': 4995, 'synset': 'dolphinfish.n.02', 'name': 'dolphinfish'}, {'id': 4996, 'synset': 'coryphaena_hippurus.n.01', 'name': 'Coryphaena_hippurus'}, {'id': 4997, 'synset': 'coryphaena_equisetis.n.01', 'name': 'Coryphaena_equisetis'}, {'id': 4998, 'synset': 'pomfret.n.01', 'name': 'pomfret'}, {'id': 4999, 'synset': 'characin.n.01', 'name': 'characin'}, {'id': 5000, 'synset': 'tetra.n.01', 'name': 'tetra'}, {'id': 5001, 'synset': 'cardinal_tetra.n.01', 'name': 'cardinal_tetra'}, {'id': 5002, 'synset': 'piranha.n.02', 'name': 'piranha'}, {'id': 5003, 'synset': 'cichlid.n.01', 'name': 'cichlid'}, {'id': 5004, 'synset': 'bolti.n.01', 'name': 'bolti'}, {'id': 5005, 'synset': 'snapper.n.05', 'name': 'snapper'}, {'id': 5006, 'synset': 'red_snapper.n.02', 'name': 'red_snapper'}, {'id': 5007, 'synset': 'grey_snapper.n.01', 'name': 'grey_snapper'}, {'id': 5008, 'synset': 'mutton_snapper.n.01', 'name': 'mutton_snapper'}, {'id': 5009, 'synset': 'schoolmaster.n.03', 'name': 'schoolmaster'}, {'id': 5010, 'synset': 'yellowtail.n.01', 'name': 'yellowtail'}, {'id': 5011, 'synset': 'grunt.n.03', 'name': 'grunt'}, {'id': 5012, 'synset': 'margate.n.01', 'name': 'margate'}, {'id': 5013, 'synset': 'spanish_grunt.n.01', 'name': 'Spanish_grunt'}, {'id': 5014, 'synset': 'tomtate.n.01', 'name': 'tomtate'}, {'id': 5015, 'synset': 'cottonwick.n.01', 'name': 'cottonwick'}, {'id': 5016, 'synset': "sailor's-choice.n.02", 'name': "sailor's-choice"}, {'id': 5017, 'synset': 'porkfish.n.01', 'name': 'porkfish'}, {'id': 5018, 'synset': 'pompon.n.02', 'name': 'pompon'}, {'id': 5019, 'synset': 'pigfish.n.02', 'name': 'pigfish'}, {'id': 5020, 'synset': 'sparid.n.01', 'name': 'sparid'}, {'id': 5021, 'synset': 'sea_bream.n.02', 'name': 'sea_bream'}, {'id': 5022, 'synset': 'porgy.n.02', 'name': 'porgy'}, {'id': 5023, 'synset': 'red_porgy.n.01', 'name': 'red_porgy'}, {'id': 5024, 'synset': 'european_sea_bream.n.01', 'name': 'European_sea_bream'}, {'id': 5025, 'synset': 'atlantic_sea_bream.n.01', 'name': 'Atlantic_sea_bream'}, {'id': 5026, 'synset': 'sheepshead.n.01', 'name': 'sheepshead'}, {'id': 5027, 'synset': 'pinfish.n.01', 'name': 'pinfish'}, {'id': 5028, 'synset': 'sheepshead_porgy.n.01', 'name': 'sheepshead_porgy'}, {'id': 5029, 'synset': 'snapper.n.04', 'name': 'snapper'}, {'id': 5030, 'synset': 'black_bream.n.01', 'name': 'black_bream'}, {'id': 5031, 'synset': 'scup.n.04', 'name': 'scup'}, {'id': 5032, 'synset': 'scup.n.03', 'name': 'scup'}, {'id': 5033, 'synset': 'sciaenid_fish.n.01', 'name': 'sciaenid_fish'}, {'id': 5034, 'synset': 'striped_drum.n.01', 'name': 'striped_drum'}, {'id': 5035, 'synset': 'jackknife-fish.n.01', 'name': 'jackknife-fish'}, {'id': 5036, 'synset': 'silver_perch.n.01', 'name': 'silver_perch'}, {'id': 5037, 'synset': 'red_drum.n.01', 'name': 'red_drum'}, {'id': 5038, 'synset': 'mulloway.n.01', 'name': 'mulloway'}, {'id': 5039, 'synset': 'maigre.n.01', 'name': 'maigre'}, {'id': 5040, 'synset': 'croaker.n.02', 'name': 'croaker'}, {'id': 5041, 'synset': 'atlantic_croaker.n.01', 'name': 'Atlantic_croaker'}, {'id': 5042, 'synset': 'yellowfin_croaker.n.01', 'name': 'yellowfin_croaker'}, {'id': 5043, 'synset': 'whiting.n.04', 'name': 'whiting'}, {'id': 5044, 'synset': 'kingfish.n.04', 'name': 'kingfish'}, {'id': 5045, 'synset': 'king_whiting.n.01', 'name': 'king_whiting'}, {'id': 5046, 'synset': 'northern_whiting.n.01', 'name': 'northern_whiting'}, {'id': 5047, 'synset': 'corbina.n.01', 'name': 'corbina'}, {'id': 5048, 'synset': 'white_croaker.n.02', 'name': 'white_croaker'}, {'id': 5049, 'synset': 'white_croaker.n.01', 'name': 'white_croaker'}, {'id': 5050, 'synset': 'sea_trout.n.02', 'name': 'sea_trout'}, {'id': 5051, 'synset': 'weakfish.n.02', 'name': 'weakfish'}, {'id': 5052, 'synset': 'spotted_weakfish.n.01', 'name': 'spotted_weakfish'}, {'id': 5053, 'synset': 'mullet.n.03', 'name': 'mullet'}, {'id': 5054, 'synset': 'goatfish.n.01', 'name': 'goatfish'}, {'id': 5055, 'synset': 'red_goatfish.n.01', 'name': 'red_goatfish'}, {'id': 5056, 'synset': 'yellow_goatfish.n.01', 'name': 'yellow_goatfish'}, {'id': 5057, 'synset': 'mullet.n.02', 'name': 'mullet'}, {'id': 5058, 'synset': 'striped_mullet.n.01', 'name': 'striped_mullet'}, {'id': 5059, 'synset': 'white_mullet.n.01', 'name': 'white_mullet'}, {'id': 5060, 'synset': 'liza.n.01', 'name': 'liza'}, {'id': 5061, 'synset': 'silversides.n.01', 'name': 'silversides'}, {'id': 5062, 'synset': 'jacksmelt.n.01', 'name': 'jacksmelt'}, {'id': 5063, 'synset': 'barracuda.n.01', 'name': 'barracuda'}, {'id': 5064, 'synset': 'great_barracuda.n.01', 'name': 'great_barracuda'}, {'id': 5065, 'synset': 'sweeper.n.03', 'name': 'sweeper'}, {'id': 5066, 'synset': 'sea_chub.n.01', 'name': 'sea_chub'}, {'id': 5067, 'synset': 'bermuda_chub.n.01', 'name': 'Bermuda_chub'}, {'id': 5068, 'synset': 'spadefish.n.01', 'name': 'spadefish'}, {'id': 5069, 'synset': 'butterfly_fish.n.01', 'name': 'butterfly_fish'}, {'id': 5070, 'synset': 'chaetodon.n.01', 'name': 'chaetodon'}, {'id': 5071, 'synset': 'angelfish.n.01', 'name': 'angelfish'}, {'id': 5072, 'synset': 'rock_beauty.n.01', 'name': 'rock_beauty'}, {'id': 5073, 'synset': 'damselfish.n.01', 'name': 'damselfish'}, {'id': 5074, 'synset': 'beaugregory.n.01', 'name': 'beaugregory'}, {'id': 5075, 'synset': 'anemone_fish.n.01', 'name': 'anemone_fish'}, {'id': 5076, 'synset': 'clown_anemone_fish.n.01', 'name': 'clown_anemone_fish'}, {'id': 5077, 'synset': 'sergeant_major.n.02', 'name': 'sergeant_major'}, {'id': 5078, 'synset': 'wrasse.n.01', 'name': 'wrasse'}, {'id': 5079, 'synset': 'pigfish.n.01', 'name': 'pigfish'}, {'id': 5080, 'synset': 'hogfish.n.01', 'name': 'hogfish'}, {'id': 5081, 'synset': 'slippery_dick.n.01', 'name': 'slippery_dick'}, {'id': 5082, 'synset': 'puddingwife.n.01', 'name': 'puddingwife'}, {'id': 5083, 'synset': 'bluehead.n.01', 'name': 'bluehead'}, {'id': 5084, 'synset': 'pearly_razorfish.n.01', 'name': 'pearly_razorfish'}, {'id': 5085, 'synset': 'tautog.n.01', 'name': 'tautog'}, {'id': 5086, 'synset': 'cunner.n.01', 'name': 'cunner'}, {'id': 5087, 'synset': 'parrotfish.n.01', 'name': 'parrotfish'}, {'id': 5088, 'synset': 'threadfin.n.01', 'name': 'threadfin'}, {'id': 5089, 'synset': 'jawfish.n.01', 'name': 'jawfish'}, {'id': 5090, 'synset': 'stargazer.n.03', 'name': 'stargazer'}, {'id': 5091, 'synset': 'sand_stargazer.n.01', 'name': 'sand_stargazer'}, {'id': 5092, 'synset': 'blenny.n.01', 'name': 'blenny'}, {'id': 5093, 'synset': 'shanny.n.01', 'name': 'shanny'}, {'id': 5094, 'synset': 'molly_miller.n.01', 'name': 'Molly_Miller'}, {'id': 5095, 'synset': 'clinid.n.01', 'name': 'clinid'}, {'id': 5096, 'synset': 'pikeblenny.n.01', 'name': 'pikeblenny'}, {'id': 5097, 'synset': 'bluethroat_pikeblenny.n.01', 'name': 'bluethroat_pikeblenny'}, {'id': 5098, 'synset': 'gunnel.n.02', 'name': 'gunnel'}, {'id': 5099, 'synset': 'rock_gunnel.n.01', 'name': 'rock_gunnel'}, {'id': 5100, 'synset': 'eelblenny.n.01', 'name': 'eelblenny'}, {'id': 5101, 'synset': 'wrymouth.n.01', 'name': 'wrymouth'}, {'id': 5102, 'synset': 'wolffish.n.01', 'name': 'wolffish'}, {'id': 5103, 'synset': 'viviparous_eelpout.n.01', 'name': 'viviparous_eelpout'}, {'id': 5104, 'synset': 'ocean_pout.n.01', 'name': 'ocean_pout'}, {'id': 5105, 'synset': 'sand_lance.n.01', 'name': 'sand_lance'}, {'id': 5106, 'synset': 'dragonet.n.01', 'name': 'dragonet'}, {'id': 5107, 'synset': 'goby.n.01', 'name': 'goby'}, {'id': 5108, 'synset': 'mudskipper.n.01', 'name': 'mudskipper'}, {'id': 5109, 'synset': 'sleeper.n.08', 'name': 'sleeper'}, {'id': 5110, 'synset': 'flathead.n.02', 'name': 'flathead'}, {'id': 5111, 'synset': 'archerfish.n.01', 'name': 'archerfish'}, {'id': 5112, 'synset': 'surgeonfish.n.01', 'name': 'surgeonfish'}, {'id': 5113, 'synset': 'gempylid.n.01', 'name': 'gempylid'}, {'id': 5114, 'synset': 'snake_mackerel.n.01', 'name': 'snake_mackerel'}, {'id': 5115, 'synset': 'escolar.n.01', 'name': 'escolar'}, {'id': 5116, 'synset': 'oilfish.n.01', 'name': 'oilfish'}, {'id': 5117, 'synset': 'cutlassfish.n.01', 'name': 'cutlassfish'}, {'id': 5118, 'synset': 'scombroid.n.01', 'name': 'scombroid'}, {'id': 5119, 'synset': 'mackerel.n.02', 'name': 'mackerel'}, {'id': 5120, 'synset': 'common_mackerel.n.01', 'name': 'common_mackerel'}, {'id': 5121, 'synset': 'spanish_mackerel.n.03', 'name': 'Spanish_mackerel'}, {'id': 5122, 'synset': 'chub_mackerel.n.01', 'name': 'chub_mackerel'}, {'id': 5123, 'synset': 'wahoo.n.03', 'name': 'wahoo'}, {'id': 5124, 'synset': 'spanish_mackerel.n.02', 'name': 'Spanish_mackerel'}, {'id': 5125, 'synset': 'king_mackerel.n.01', 'name': 'king_mackerel'}, {'id': 5126, 'synset': 'scomberomorus_maculatus.n.01', 'name': 'Scomberomorus_maculatus'}, {'id': 5127, 'synset': 'cero.n.01', 'name': 'cero'}, {'id': 5128, 'synset': 'sierra.n.02', 'name': 'sierra'}, {'id': 5129, 'synset': 'tuna.n.03', 'name': 'tuna'}, {'id': 5130, 'synset': 'albacore.n.02', 'name': 'albacore'}, {'id': 5131, 'synset': 'bluefin.n.02', 'name': 'bluefin'}, {'id': 5132, 'synset': 'yellowfin.n.01', 'name': 'yellowfin'}, {'id': 5133, 'synset': 'bonito.n.03', 'name': 'bonito'}, {'id': 5134, 'synset': 'skipjack.n.02', 'name': 'skipjack'}, {'id': 5135, 'synset': 'chile_bonito.n.01', 'name': 'Chile_bonito'}, {'id': 5136, 'synset': 'skipjack.n.01', 'name': 'skipjack'}, {'id': 5137, 'synset': 'bonito.n.02', 'name': 'bonito'}, {'id': 5138, 'synset': 'swordfish.n.02', 'name': 'swordfish'}, {'id': 5139, 'synset': 'sailfish.n.02', 'name': 'sailfish'}, {'id': 5140, 'synset': 'atlantic_sailfish.n.01', 'name': 'Atlantic_sailfish'}, {'id': 5141, 'synset': 'billfish.n.02', 'name': 'billfish'}, {'id': 5142, 'synset': 'marlin.n.01', 'name': 'marlin'}, {'id': 5143, 'synset': 'blue_marlin.n.01', 'name': 'blue_marlin'}, {'id': 5144, 'synset': 'black_marlin.n.01', 'name': 'black_marlin'}, {'id': 5145, 'synset': 'striped_marlin.n.01', 'name': 'striped_marlin'}, {'id': 5146, 'synset': 'white_marlin.n.01', 'name': 'white_marlin'}, {'id': 5147, 'synset': 'spearfish.n.01', 'name': 'spearfish'}, {'id': 5148, 'synset': 'louvar.n.01', 'name': 'louvar'}, {'id': 5149, 'synset': 'dollarfish.n.01', 'name': 'dollarfish'}, {'id': 5150, 'synset': 'palometa.n.01', 'name': 'palometa'}, {'id': 5151, 'synset': 'harvestfish.n.01', 'name': 'harvestfish'}, {'id': 5152, 'synset': 'driftfish.n.01', 'name': 'driftfish'}, {'id': 5153, 'synset': 'barrelfish.n.01', 'name': 'barrelfish'}, {'id': 5154, 'synset': 'clingfish.n.01', 'name': 'clingfish'}, {'id': 5155, 'synset': 'tripletail.n.01', 'name': 'tripletail'}, {'id': 5156, 'synset': 'atlantic_tripletail.n.01', 'name': 'Atlantic_tripletail'}, {'id': 5157, 'synset': 'pacific_tripletail.n.01', 'name': 'Pacific_tripletail'}, {'id': 5158, 'synset': 'mojarra.n.01', 'name': 'mojarra'}, {'id': 5159, 'synset': 'yellowfin_mojarra.n.01', 'name': 'yellowfin_mojarra'}, {'id': 5160, 'synset': 'silver_jenny.n.01', 'name': 'silver_jenny'}, {'id': 5161, 'synset': 'whiting.n.03', 'name': 'whiting'}, {'id': 5162, 'synset': 'ganoid.n.01', 'name': 'ganoid'}, {'id': 5163, 'synset': 'bowfin.n.01', 'name': 'bowfin'}, {'id': 5164, 'synset': 'paddlefish.n.01', 'name': 'paddlefish'}, {'id': 5165, 'synset': 'chinese_paddlefish.n.01', 'name': 'Chinese_paddlefish'}, {'id': 5166, 'synset': 'sturgeon.n.01', 'name': 'sturgeon'}, {'id': 5167, 'synset': 'pacific_sturgeon.n.01', 'name': 'Pacific_sturgeon'}, {'id': 5168, 'synset': 'beluga.n.01', 'name': 'beluga'}, {'id': 5169, 'synset': 'gar.n.01', 'name': 'gar'}, {'id': 5170, 'synset': 'scorpaenoid.n.01', 'name': 'scorpaenoid'}, {'id': 5171, 'synset': 'scorpaenid.n.01', 'name': 'scorpaenid'}, {'id': 5172, 'synset': 'scorpionfish.n.01', 'name': 'scorpionfish'}, {'id': 5173, 'synset': 'plumed_scorpionfish.n.01', 'name': 'plumed_scorpionfish'}, {'id': 5174, 'synset': 'lionfish.n.01', 'name': 'lionfish'}, {'id': 5175, 'synset': 'stonefish.n.01', 'name': 'stonefish'}, {'id': 5176, 'synset': 'rockfish.n.02', 'name': 'rockfish'}, {'id': 5177, 'synset': 'copper_rockfish.n.01', 'name': 'copper_rockfish'}, {'id': 5178, 'synset': 'vermillion_rockfish.n.01', 'name': 'vermillion_rockfish'}, {'id': 5179, 'synset': 'red_rockfish.n.02', 'name': 'red_rockfish'}, {'id': 5180, 'synset': 'rosefish.n.02', 'name': 'rosefish'}, {'id': 5181, 'synset': 'bullhead.n.01', 'name': 'bullhead'}, {'id': 5182, 'synset': "miller's-thumb.n.01", 'name': "miller's-thumb"}, {'id': 5183, 'synset': 'sea_raven.n.01', 'name': 'sea_raven'}, {'id': 5184, 'synset': 'lumpfish.n.01', 'name': 'lumpfish'}, {'id': 5185, 'synset': 'lumpsucker.n.01', 'name': 'lumpsucker'}, {'id': 5186, 'synset': 'pogge.n.01', 'name': 'pogge'}, {'id': 5187, 'synset': 'greenling.n.01', 'name': 'greenling'}, {'id': 5188, 'synset': 'kelp_greenling.n.01', 'name': 'kelp_greenling'}, {'id': 5189, 'synset': 'painted_greenling.n.01', 'name': 'painted_greenling'}, {'id': 5190, 'synset': 'flathead.n.01', 'name': 'flathead'}, {'id': 5191, 'synset': 'gurnard.n.01', 'name': 'gurnard'}, {'id': 5192, 'synset': 'tub_gurnard.n.01', 'name': 'tub_gurnard'}, {'id': 5193, 'synset': 'sea_robin.n.01', 'name': 'sea_robin'}, {'id': 5194, 'synset': 'northern_sea_robin.n.01', 'name': 'northern_sea_robin'}, {'id': 5195, 'synset': 'flying_gurnard.n.01', 'name': 'flying_gurnard'}, {'id': 5196, 'synset': 'plectognath.n.01', 'name': 'plectognath'}, {'id': 5197, 'synset': 'triggerfish.n.01', 'name': 'triggerfish'}, {'id': 5198, 'synset': 'queen_triggerfish.n.01', 'name': 'queen_triggerfish'}, {'id': 5199, 'synset': 'filefish.n.01', 'name': 'filefish'}, {'id': 5200, 'synset': 'leatherjacket.n.01', 'name': 'leatherjacket'}, {'id': 5201, 'synset': 'boxfish.n.01', 'name': 'boxfish'}, {'id': 5202, 'synset': 'cowfish.n.01', 'name': 'cowfish'}, {'id': 5203, 'synset': 'spiny_puffer.n.01', 'name': 'spiny_puffer'}, {'id': 5204, 'synset': 'porcupinefish.n.01', 'name': 'porcupinefish'}, {'id': 5205, 'synset': 'balloonfish.n.01', 'name': 'balloonfish'}, {'id': 5206, 'synset': 'burrfish.n.01', 'name': 'burrfish'}, {'id': 5207, 'synset': 'ocean_sunfish.n.01', 'name': 'ocean_sunfish'}, {'id': 5208, 'synset': 'sharptail_mola.n.01', 'name': 'sharptail_mola'}, {'id': 5209, 'synset': 'flatfish.n.02', 'name': 'flatfish'}, {'id': 5210, 'synset': 'flounder.n.02', 'name': 'flounder'}, {'id': 5211, 'synset': 'righteye_flounder.n.01', 'name': 'righteye_flounder'}, {'id': 5212, 'synset': 'plaice.n.02', 'name': 'plaice'}, {'id': 5213, 'synset': 'european_flatfish.n.01', 'name': 'European_flatfish'}, {'id': 5214, 'synset': 'yellowtail_flounder.n.02', 'name': 'yellowtail_flounder'}, {'id': 5215, 'synset': 'winter_flounder.n.02', 'name': 'winter_flounder'}, {'id': 5216, 'synset': 'lemon_sole.n.05', 'name': 'lemon_sole'}, {'id': 5217, 'synset': 'american_plaice.n.01', 'name': 'American_plaice'}, {'id': 5218, 'synset': 'halibut.n.02', 'name': 'halibut'}, {'id': 5219, 'synset': 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'synset': 'hogchoker.n.01', 'name': 'hogchoker'}, {'id': 5235, 'synset': 'aba.n.02', 'name': 'aba'}, {'id': 5236, 'synset': 'abacus.n.02', 'name': 'abacus'}, {'id': 5237, 'synset': 'abandoned_ship.n.01', 'name': 'abandoned_ship'}, {'id': 5238, 'synset': 'a_battery.n.01', 'name': 'A_battery'}, {'id': 5239, 'synset': 'abattoir.n.01', 'name': 'abattoir'}, {'id': 5240, 'synset': 'abaya.n.01', 'name': 'abaya'}, {'id': 5241, 'synset': 'abbe_condenser.n.01', 'name': 'Abbe_condenser'}, {'id': 5242, 'synset': 'abbey.n.03', 'name': 'abbey'}, {'id': 5243, 'synset': 'abbey.n.02', 'name': 'abbey'}, {'id': 5244, 'synset': 'abbey.n.01', 'name': 'abbey'}, {'id': 5245, 'synset': 'abney_level.n.01', 'name': 'Abney_level'}, {'id': 5246, 'synset': 'abrader.n.01', 'name': 'abrader'}, {'id': 5247, 'synset': 'abrading_stone.n.01', 'name': 'abrading_stone'}, {'id': 5248, 'synset': 'abutment.n.02', 'name': 'abutment'}, {'id': 5249, 'synset': 'abutment_arch.n.01', 'name': 'abutment_arch'}, {'id': 5250, 'synset': 'academic_costume.n.01', 'name': 'academic_costume'}, {'id': 5251, 'synset': 'academic_gown.n.01', 'name': 'academic_gown'}, {'id': 5252, 'synset': 'accelerator.n.02', 'name': 'accelerator'}, {'id': 5253, 'synset': 'accelerator.n.04', 'name': 'accelerator'}, {'id': 5254, 'synset': 'accelerator.n.01', 'name': 'accelerator'}, {'id': 5255, 'synset': 'accelerometer.n.01', 'name': 'accelerometer'}, {'id': 5256, 'synset': 'accessory.n.01', 'name': 'accessory'}, {'id': 5257, 'synset': 'accommodating_lens_implant.n.01', 'name': 'accommodating_lens_implant'}, {'id': 5258, 'synset': 'accommodation.n.04', 'name': 'accommodation'}, {'id': 5259, 'synset': 'accordion.n.01', 'name': 'accordion'}, {'id': 5260, 'synset': 'acetate_disk.n.01', 'name': 'acetate_disk'}, {'id': 5261, 'synset': 'acetate_rayon.n.01', 'name': 'acetate_rayon'}, {'id': 5262, 'synset': 'achromatic_lens.n.01', 'name': 'achromatic_lens'}, {'id': 5263, 'synset': 'acoustic_delay_line.n.01', 'name': 'acoustic_delay_line'}, {'id': 5264, 'synset': 'acoustic_device.n.01', 'name': 'acoustic_device'}, {'id': 5265, 'synset': 'acoustic_guitar.n.01', 'name': 'acoustic_guitar'}, {'id': 5266, 'synset': 'acoustic_modem.n.01', 'name': 'acoustic_modem'}, {'id': 5267, 'synset': 'acropolis.n.01', 'name': 'acropolis'}, {'id': 5268, 'synset': 'acrylic.n.04', 'name': 'acrylic'}, {'id': 5269, 'synset': 'acrylic.n.03', 'name': 'acrylic'}, {'id': 5270, 'synset': 'actinometer.n.01', 'name': 'actinometer'}, {'id': 5271, 'synset': 'action.n.07', 'name': 'action'}, {'id': 5272, 'synset': 'active_matrix_screen.n.01', 'name': 'active_matrix_screen'}, {'id': 5273, 'synset': 'actuator.n.01', 'name': 'actuator'}, {'id': 5274, 'synset': 'adapter.n.02', 'name': 'adapter'}, {'id': 5275, 'synset': 'adder.n.02', 'name': 'adder'}, {'id': 5276, 'synset': 'adding_machine.n.01', 'name': 'adding_machine'}, {'id': 5277, 'synset': 'addressing_machine.n.01', 'name': 'addressing_machine'}, {'id': 5278, 'synset': 'adhesive_bandage.n.01', 'name': 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'synset': 'ballpoint.n.01', 'name': 'ballpoint'}, {'id': 5614, 'synset': 'ballroom.n.01', 'name': 'ballroom'}, {'id': 5615, 'synset': 'ball_valve.n.01', 'name': 'ball_valve'}, {'id': 5616, 'synset': 'balsa_raft.n.01', 'name': 'balsa_raft'}, {'id': 5617, 'synset': 'baluster.n.01', 'name': 'baluster'}, {'id': 5618, 'synset': 'banana_boat.n.01', 'name': 'banana_boat'}, {'id': 5619, 'synset': 'band.n.13', 'name': 'band'}, {'id': 5620, 'synset': 'bandbox.n.01', 'name': 'bandbox'}, {'id': 5621, 'synset': 'banderilla.n.01', 'name': 'banderilla'}, {'id': 5622, 'synset': 'bandoleer.n.01', 'name': 'bandoleer'}, {'id': 5623, 'synset': 'bandoneon.n.01', 'name': 'bandoneon'}, {'id': 5624, 'synset': 'bandsaw.n.01', 'name': 'bandsaw'}, {'id': 5625, 'synset': 'bandwagon.n.02', 'name': 'bandwagon'}, {'id': 5626, 'synset': 'bangalore_torpedo.n.01', 'name': 'bangalore_torpedo'}, {'id': 5627, 'synset': 'bangle.n.02', 'name': 'bangle'}, {'id': 5628, 'synset': 'bannister.n.02', 'name': 'bannister'}, {'id': 5629, 'synset': 'banquette.n.01', 'name': 'banquette'}, {'id': 5630, 'synset': 'banyan.n.02', 'name': 'banyan'}, {'id': 5631, 'synset': 'baptismal_font.n.01', 'name': 'baptismal_font'}, {'id': 5632, 'synset': 'bar.n.03', 'name': 'bar'}, {'id': 5633, 'synset': 'bar.n.02', 'name': 'bar'}, {'id': 5634, 'synset': 'barbecue.n.03', 'name': 'barbecue'}, {'id': 5635, 'synset': 'barbed_wire.n.01', 'name': 'barbed_wire'}, {'id': 5636, 'synset': 'barber_chair.n.01', 'name': 'barber_chair'}, {'id': 5637, 'synset': 'barbershop.n.01', 'name': 'barbershop'}, {'id': 5638, 'synset': 'barbette_carriage.n.01', 'name': 'barbette_carriage'}, {'id': 5639, 'synset': 'barbican.n.01', 'name': 'barbican'}, {'id': 5640, 'synset': 'bar_bit.n.01', 'name': 'bar_bit'}, {'id': 5641, 'synset': 'bareboat.n.01', 'name': 'bareboat'}, {'id': 5642, 'synset': 'barge_pole.n.01', 'name': 'barge_pole'}, {'id': 5643, 'synset': 'baritone.n.03', 'name': 'baritone'}, {'id': 5644, 'synset': 'bark.n.03', 'name': 'bark'}, {'id': 5645, 'synset': 'bar_magnet.n.01', 'name': 'bar_magnet'}, {'id': 5646, 'synset': 'bar_mask.n.01', 'name': 'bar_mask'}, {'id': 5647, 'synset': 'barn.n.01', 'name': 'barn'}, {'id': 5648, 'synset': 'barndoor.n.01', 'name': 'barndoor'}, {'id': 5649, 'synset': 'barn_door.n.01', 'name': 'barn_door'}, {'id': 5650, 'synset': 'barnyard.n.01', 'name': 'barnyard'}, {'id': 5651, 'synset': 'barograph.n.01', 'name': 'barograph'}, {'id': 5652, 'synset': 'barometer.n.01', 'name': 'barometer'}, {'id': 5653, 'synset': 'barong.n.01', 'name': 'barong'}, {'id': 5654, 'synset': 'barouche.n.01', 'name': 'barouche'}, {'id': 5655, 'synset': 'bar_printer.n.01', 'name': 'bar_printer'}, {'id': 5656, 'synset': 'barrack.n.01', 'name': 'barrack'}, {'id': 5657, 'synset': 'barrage_balloon.n.01', 'name': 'barrage_balloon'}, {'id': 5658, 'synset': 'barrel.n.01', 'name': 'barrel'}, {'id': 5659, 'synset': 'barrelhouse.n.01', 'name': 'barrelhouse'}, {'id': 5660, 'synset': 'barrel_knot.n.01', 'name': 'barrel_knot'}, {'id': 5661, 'synset': 'barrel_organ.n.01', 'name': 'barrel_organ'}, {'id': 5662, 'synset': 'barrel_vault.n.01', 'name': 'barrel_vault'}, {'id': 5663, 'synset': 'barricade.n.02', 'name': 'barricade'}, {'id': 5664, 'synset': 'barrier.n.01', 'name': 'barrier'}, {'id': 5665, 'synset': 'barroom.n.01', 'name': 'barroom'}, {'id': 5666, 'synset': 'bascule.n.01', 'name': 'bascule'}, {'id': 5667, 'synset': 'base.n.08', 'name': 'base'}, {'id': 5668, 'synset': 'baseball_equipment.n.01', 'name': 'baseball_equipment'}, {'id': 5669, 'synset': 'basement.n.01', 'name': 'basement'}, {'id': 5670, 'synset': 'basement.n.02', 'name': 'basement'}, {'id': 5671, 'synset': 'basic_point_defense_missile_system.n.01', 'name': 'basic_point_defense_missile_system'}, {'id': 5672, 'synset': 'basilica.n.02', 'name': 'basilica'}, {'id': 5673, 'synset': 'basilica.n.01', 'name': 'basilica'}, {'id': 5674, 'synset': 'basilisk.n.02', 'name': 'basilisk'}, {'id': 5675, 'synset': 'basin.n.01', 'name': 'basin'}, {'id': 5676, 'synset': 'basinet.n.01', 'name': 'basinet'}, {'id': 5677, 'synset': 'basket.n.03', 'name': 'basket'}, {'id': 5678, 'synset': 'basketball_court.n.01', 'name': 'basketball_court'}, {'id': 5679, 'synset': 'basketball_equipment.n.01', 'name': 'basketball_equipment'}, {'id': 5680, 'synset': 'basket_weave.n.01', 'name': 'basket_weave'}, {'id': 5681, 'synset': 'bass.n.07', 'name': 'bass'}, {'id': 5682, 'synset': 'bass_clarinet.n.01', 'name': 'bass_clarinet'}, {'id': 5683, 'synset': 'bass_drum.n.01', 'name': 'bass_drum'}, {'id': 5684, 'synset': 'basset_horn.n.01', 'name': 'basset_horn'}, {'id': 5685, 'synset': 'bass_fiddle.n.01', 'name': 'bass_fiddle'}, {'id': 5686, 'synset': 'bass_guitar.n.01', 'name': 'bass_guitar'}, {'id': 5687, 'synset': 'bassinet.n.01', 'name': 'bassinet'}, {'id': 5688, 'synset': 'bassinet.n.02', 'name': 'bassinet'}, {'id': 5689, 'synset': 'bassoon.n.01', 'name': 'bassoon'}, {'id': 5690, 'synset': 'baster.n.03', 'name': 'baster'}, {'id': 5691, 'synset': 'bastinado.n.01', 'name': 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'name': 'beer_mat'}, {'id': 5771, 'synset': 'beer_mug.n.01', 'name': 'beer_mug'}, {'id': 5772, 'synset': 'belaying_pin.n.01', 'name': 'belaying_pin'}, {'id': 5773, 'synset': 'belfry.n.02', 'name': 'belfry'}, {'id': 5774, 'synset': 'bell_arch.n.01', 'name': 'bell_arch'}, {'id': 5775, 'synset': 'bellarmine.n.02', 'name': 'bellarmine'}, {'id': 5776, 'synset': 'bellbottom_trousers.n.01', 'name': 'bellbottom_trousers'}, {'id': 5777, 'synset': 'bell_cote.n.01', 'name': 'bell_cote'}, {'id': 5778, 'synset': 'bell_foundry.n.01', 'name': 'bell_foundry'}, {'id': 5779, 'synset': 'bell_gable.n.01', 'name': 'bell_gable'}, {'id': 5780, 'synset': 'bell_jar.n.01', 'name': 'bell_jar'}, {'id': 5781, 'synset': 'bellows.n.01', 'name': 'bellows'}, {'id': 5782, 'synset': 'bellpull.n.01', 'name': 'bellpull'}, {'id': 5783, 'synset': 'bell_push.n.01', 'name': 'bell_push'}, {'id': 5784, 'synset': 'bell_seat.n.01', 'name': 'bell_seat'}, {'id': 5785, 'synset': 'bell_tent.n.01', 'name': 'bell_tent'}, {'id': 5786, 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{'id': 5802, 'synset': 'bevatron.n.01', 'name': 'bevatron'}, {'id': 5803, 'synset': 'bevel.n.02', 'name': 'bevel'}, {'id': 5804, 'synset': 'bevel_gear.n.01', 'name': 'bevel_gear'}, {'id': 5805, 'synset': 'b-flat_clarinet.n.01', 'name': 'B-flat_clarinet'}, {'id': 5806, 'synset': 'bib.n.01', 'name': 'bib'}, {'id': 5807, 'synset': 'bib-and-tucker.n.01', 'name': 'bib-and-tucker'}, {'id': 5808, 'synset': 'bicorn.n.01', 'name': 'bicorn'}, {'id': 5809, 'synset': 'bicycle-built-for-two.n.01', 'name': 'bicycle-built-for-two'}, {'id': 5810, 'synset': 'bicycle_chain.n.01', 'name': 'bicycle_chain'}, {'id': 5811, 'synset': 'bicycle_clip.n.01', 'name': 'bicycle_clip'}, {'id': 5812, 'synset': 'bicycle_pump.n.01', 'name': 'bicycle_pump'}, {'id': 5813, 'synset': 'bicycle_rack.n.01', 'name': 'bicycle_rack'}, {'id': 5814, 'synset': 'bicycle_seat.n.01', 'name': 'bicycle_seat'}, {'id': 5815, 'synset': 'bicycle_wheel.n.01', 'name': 'bicycle_wheel'}, {'id': 5816, 'synset': 'bidet.n.01', 'name': 'bidet'}, {'id': 5817, 'synset': 'bier.n.02', 'name': 'bier'}, {'id': 5818, 'synset': 'bier.n.01', 'name': 'bier'}, {'id': 5819, 'synset': 'bi-fold_door.n.01', 'name': 'bi-fold_door'}, {'id': 5820, 'synset': 'bifocals.n.01', 'name': 'bifocals'}, {'id': 5821, 'synset': 'big_blue.n.01', 'name': 'Big_Blue'}, {'id': 5822, 'synset': 'big_board.n.02', 'name': 'big_board'}, {'id': 5823, 'synset': 'bight.n.04', 'name': 'bight'}, {'id': 5824, 'synset': 'bikini.n.02', 'name': 'bikini'}, {'id': 5825, 'synset': 'bikini_pants.n.01', 'name': 'bikini_pants'}, {'id': 5826, 'synset': 'bilge.n.02', 'name': 'bilge'}, {'id': 5827, 'synset': 'bilge_keel.n.01', 'name': 'bilge_keel'}, {'id': 5828, 'synset': 'bilge_pump.n.01', 'name': 'bilge_pump'}, {'id': 5829, 'synset': 'bilge_well.n.01', 'name': 'bilge_well'}, {'id': 5830, 'synset': 'bill.n.08', 'name': 'bill'}, {'id': 5831, 'synset': 'billiard_ball.n.01', 'name': 'billiard_ball'}, {'id': 5832, 'synset': 'billiard_room.n.01', 'name': 'billiard_room'}, {'id': 5833, 'synset': 'bin.n.01', 'name': 'bin'}, {'id': 5834, 'synset': 'binder.n.04', 'name': 'binder'}, {'id': 5835, 'synset': 'bindery.n.01', 'name': 'bindery'}, {'id': 5836, 'synset': 'binding.n.05', 'name': 'binding'}, {'id': 5837, 'synset': 'bin_liner.n.01', 'name': 'bin_liner'}, {'id': 5838, 'synset': 'binnacle.n.01', 'name': 'binnacle'}, {'id': 5839, 'synset': 'binocular_microscope.n.01', 'name': 'binocular_microscope'}, {'id': 5840, 'synset': 'biochip.n.01', 'name': 'biochip'}, {'id': 5841, 'synset': 'biohazard_suit.n.01', 'name': 'biohazard_suit'}, {'id': 5842, 'synset': 'bioscope.n.02', 'name': 'bioscope'}, {'id': 5843, 'synset': 'biplane.n.01', 'name': 'biplane'}, {'id': 5844, 'synset': 'birch.n.03', 'name': 'birch'}, {'id': 5845, 'synset': 'birchbark_canoe.n.01', 'name': 'birchbark_canoe'}, {'id': 5846, 'synset': 'birdcall.n.02', 'name': 'birdcall'}, {'id': 5847, 'synset': 'bird_shot.n.01', 'name': 'bird_shot'}, {'id': 5848, 'synset': 'biretta.n.01', 'name': 'biretta'}, {'id': 5849, 'synset': 'bishop.n.03', 'name': 'bishop'}, {'id': 5850, 'synset': 'bistro.n.01', 'name': 'bistro'}, {'id': 5851, 'synset': 'bit.n.11', 'name': 'bit'}, {'id': 5852, 'synset': 'bit.n.05', 'name': 'bit'}, {'id': 5853, 'synset': 'bite_plate.n.01', 'name': 'bite_plate'}, {'id': 5854, 'synset': 'bitewing.n.01', 'name': 'bitewing'}, {'id': 5855, 'synset': 'bitumastic.n.01', 'name': 'bitumastic'}, {'id': 5856, 'synset': 'black.n.07', 'name': 'black'}, {'id': 5857, 'synset': 'black.n.06', 'name': 'black'}, {'id': 5858, 'synset': 'blackboard_eraser.n.01', 'name': 'blackboard_eraser'}, {'id': 5859, 'synset': 'black_box.n.01', 'name': 'black_box'}, {'id': 5860, 'synset': 'blackface.n.01', 'name': 'blackface'}, {'id': 5861, 'synset': 'blackjack.n.02', 'name': 'blackjack'}, {'id': 5862, 'synset': 'black_tie.n.02', 'name': 'black_tie'}, {'id': 5863, 'synset': 'blackwash.n.03', 'name': 'blackwash'}, {'id': 5864, 'synset': 'bladder.n.02', 'name': 'bladder'}, {'id': 5865, 'synset': 'blade.n.09', 'name': 'blade'}, {'id': 5866, 'synset': 'blade.n.08', 'name': 'blade'}, {'id': 5867, 'synset': 'blade.n.07', 'name': 'blade'}, {'id': 5868, 'synset': 'blank.n.04', 'name': 'blank'}, {'id': 5869, 'synset': 'blast_furnace.n.01', 'name': 'blast_furnace'}, {'id': 5870, 'synset': 'blasting_cap.n.01', 'name': 'blasting_cap'}, {'id': 5871, 'synset': 'blind.n.03', 'name': 'blind'}, {'id': 5872, 'synset': 'blind_curve.n.01', 'name': 'blind_curve'}, {'id': 5873, 'synset': 'blindfold.n.01', 'name': 'blindfold'}, {'id': 5874, 'synset': 'bling.n.01', 'name': 'bling'}, {'id': 5875, 'synset': 'blister_pack.n.01', 'name': 'blister_pack'}, {'id': 5876, 'synset': 'block.n.05', 'name': 'block'}, {'id': 5877, 'synset': 'blockade.n.02', 'name': 'blockade'}, {'id': 5878, 'synset': 'blockade-runner.n.01', 'name': 'blockade-runner'}, {'id': 5879, 'synset': 'block_and_tackle.n.01', 'name': 'block_and_tackle'}, {'id': 5880, 'synset': 'blockbuster.n.01', 'name': 'blockbuster'}, {'id': 5881, 'synset': 'blockhouse.n.01', 'name': 'blockhouse'}, {'id': 5882, 'synset': 'block_plane.n.01', 'name': 'block_plane'}, {'id': 5883, 'synset': 'bloodmobile.n.01', 'name': 'bloodmobile'}, {'id': 5884, 'synset': 'bloomers.n.01', 'name': 'bloomers'}, {'id': 5885, 'synset': 'blower.n.01', 'name': 'blower'}, {'id': 5886, 'synset': 'blowtorch.n.01', 'name': 'blowtorch'}, {'id': 5887, 'synset': 'blucher.n.02', 'name': 'blucher'}, {'id': 5888, 'synset': 'bludgeon.n.01', 'name': 'bludgeon'}, {'id': 5889, 'synset': 'blue.n.02', 'name': 'blue'}, {'id': 5890, 'synset': 'blue_chip.n.02', 'name': 'blue_chip'}, {'id': 5891, 'synset': 'blunderbuss.n.01', 'name': 'blunderbuss'}, {'id': 5892, 'synset': 'blunt_file.n.01', 'name': 'blunt_file'}, {'id': 5893, 'synset': 'boarding.n.02', 'name': 'boarding'}, {'id': 5894, 'synset': 'boarding_house.n.01', 'name': 'boarding_house'}, {'id': 5895, 'synset': 'boardroom.n.01', 'name': 'boardroom'}, {'id': 5896, 'synset': 'boards.n.02', 'name': 'boards'}, {'id': 5897, 'synset': 'boater.n.01', 'name': 'boater'}, {'id': 5898, 'synset': 'boat_hook.n.01', 'name': 'boat_hook'}, {'id': 5899, 'synset': 'boathouse.n.01', 'name': 'boathouse'}, {'id': 5900, 'synset': "boatswain's_chair.n.01", 'name': "boatswain's_chair"}, {'id': 5901, 'synset': 'boat_train.n.01', 'name': 'boat_train'}, {'id': 5902, 'synset': 'boatyard.n.01', 'name': 'boatyard'}, {'id': 5903, 'synset': 'bobsled.n.02', 'name': 'bobsled'}, {'id': 5904, 'synset': 'bobsled.n.01', 'name': 'bobsled'}, {'id': 5905, 'synset': 'bocce_ball.n.01', 'name': 'bocce_ball'}, {'id': 5906, 'synset': 'bodega.n.01', 'name': 'bodega'}, {'id': 5907, 'synset': 'bodice.n.01', 'name': 'bodice'}, {'id': 5908, 'synset': 'bodkin.n.04', 'name': 'bodkin'}, {'id': 5909, 'synset': 'bodkin.n.03', 'name': 'bodkin'}, {'id': 5910, 'synset': 'bodkin.n.02', 'name': 'bodkin'}, {'id': 5911, 'synset': 'body.n.11', 'name': 'body'}, {'id': 5912, 'synset': 'body_armor.n.01', 'name': 'body_armor'}, {'id': 5913, 'synset': 'body_lotion.n.01', 'name': 'body_lotion'}, {'id': 5914, 'synset': 'body_stocking.n.01', 'name': 'body_stocking'}, {'id': 5915, 'synset': 'body_plethysmograph.n.01', 'name': 'body_plethysmograph'}, {'id': 5916, 'synset': 'body_pad.n.01', 'name': 'body_pad'}, {'id': 5917, 'synset': 'bodywork.n.01', 'name': 'bodywork'}, {'id': 5918, 'synset': 'bofors_gun.n.01', 'name': 'Bofors_gun'}, {'id': 5919, 'synset': 'bogy.n.01', 'name': 'bogy'}, {'id': 5920, 'synset': 'boiler.n.01', 'name': 'boiler'}, {'id': 5921, 'synset': 'boiling_water_reactor.n.01', 'name': 'boiling_water_reactor'}, {'id': 5922, 'synset': 'bolero.n.02', 'name': 'bolero'}, {'id': 5923, 'synset': 'bollard.n.01', 'name': 'bollard'}, {'id': 5924, 'synset': 'bolo.n.02', 'name': 'bolo'}, {'id': 5925, 'synset': 'bolt.n.02', 'name': 'bolt'}, {'id': 5926, 'synset': 'bolt_cutter.n.01', 'name': 'bolt_cutter'}, {'id': 5927, 'synset': 'bomb.n.01', 'name': 'bomb'}, {'id': 5928, 'synset': 'bombazine.n.01', 'name': 'bombazine'}, {'id': 5929, 'synset': 'bomb_calorimeter.n.01', 'name': 'bomb_calorimeter'}, {'id': 5930, 'synset': 'bomber.n.01', 'name': 'bomber'}, {'id': 5931, 'synset': 'bomber_jacket.n.01', 'name': 'bomber_jacket'}, {'id': 5932, 'synset': 'bomblet.n.01', 'name': 'bomblet'}, {'id': 5933, 'synset': 'bomb_rack.n.01', 'name': 'bomb_rack'}, {'id': 5934, 'synset': 'bombshell.n.03', 'name': 'bombshell'}, {'id': 5935, 'synset': 'bomb_shelter.n.01', 'name': 'bomb_shelter'}, {'id': 5936, 'synset': 'bone-ash_cup.n.01', 'name': 'bone-ash_cup'}, {'id': 5937, 'synset': 'bone_china.n.01', 'name': 'bone_china'}, {'id': 5938, 'synset': 'bones.n.01', 'name': 'bones'}, {'id': 5939, 'synset': 'boneshaker.n.01', 'name': 'boneshaker'}, {'id': 5940, 'synset': 'bongo.n.01', 'name': 'bongo'}, {'id': 5941, 'synset': 'book.n.11', 'name': 'book'}, {'id': 5942, 'synset': 'book_bag.n.01', 'name': 'book_bag'}, {'id': 5943, 'synset': 'bookbindery.n.01', 'name': 'bookbindery'}, {'id': 5944, 'synset': 'bookend.n.01', 'name': 'bookend'}, {'id': 5945, 'synset': 'bookmobile.n.01', 'name': 'bookmobile'}, {'id': 5946, 'synset': 'bookshelf.n.01', 'name': 'bookshelf'}, {'id': 5947, 'synset': 'bookshop.n.01', 'name': 'bookshop'}, {'id': 5948, 'synset': 'boom.n.05', 'name': 'boom'}, {'id': 5949, 'synset': 'boomerang.n.01', 'name': 'boomerang'}, {'id': 5950, 'synset': 'booster.n.05', 'name': 'booster'}, {'id': 5951, 'synset': 'booster.n.04', 'name': 'booster'}, {'id': 5952, 'synset': 'boot.n.04', 'name': 'boot'}, {'id': 5953, 'synset': 'boot_camp.n.01', 'name': 'boot_camp'}, {'id': 5954, 'synset': 'bootee.n.01', 'name': 'bootee'}, {'id': 5955, 'synset': 'booth.n.02', 'name': 'booth'}, {'id': 5956, 'synset': 'booth.n.04', 'name': 'booth'}, {'id': 5957, 'synset': 'booth.n.01', 'name': 'booth'}, {'id': 5958, 'synset': 'boothose.n.01', 'name': 'boothose'}, {'id': 5959, 'synset': 'bootjack.n.01', 'name': 'bootjack'}, {'id': 5960, 'synset': 'bootlace.n.01', 'name': 'bootlace'}, {'id': 5961, 'synset': 'bootleg.n.02', 'name': 'bootleg'}, {'id': 5962, 'synset': 'bootstrap.n.01', 'name': 'bootstrap'}, {'id': 5963, 'synset': 'bore_bit.n.01', 'name': 'bore_bit'}, {'id': 5964, 'synset': 'boron_chamber.n.01', 'name': 'boron_chamber'}, {'id': 5965, 'synset': 'borstal.n.01', 'name': 'borstal'}, {'id': 5966, 'synset': 'bosom.n.03', 'name': 'bosom'}, {'id': 5967, 'synset': 'boston_rocker.n.01', 'name': 'Boston_rocker'}, {'id': 5968, 'synset': 'bota.n.01', 'name': 'bota'}, {'id': 5969, 'synset': 'bottle.n.03', 'name': 'bottle'}, {'id': 5970, 'synset': 'bottle_bank.n.01', 'name': 'bottle_bank'}, {'id': 5971, 'synset': 'bottlebrush.n.01', 'name': 'bottlebrush'}, {'id': 5972, 'synset': 'bottlecap.n.01', 'name': 'bottlecap'}, {'id': 5973, 'synset': 'bottling_plant.n.01', 'name': 'bottling_plant'}, {'id': 5974, 'synset': 'bottom.n.07', 'name': 'bottom'}, {'id': 5975, 'synset': 'boucle.n.01', 'name': 'boucle'}, {'id': 5976, 'synset': 'boudoir.n.01', 'name': 'boudoir'}, {'id': 5977, 'synset': 'boulle.n.01', 'name': 'boulle'}, {'id': 5978, 'synset': 'bouncing_betty.n.01', 'name': 'bouncing_betty'}, {'id': 5979, 'synset': 'boutique.n.01', 'name': 'boutique'}, {'id': 5980, 'synset': 'boutonniere.n.01', 'name': 'boutonniere'}, {'id': 5981, 'synset': 'bow.n.02', 'name': 'bow'}, {'id': 5982, 'synset': 'bow.n.01', 'name': 'bow'}, {'id': 5983, 'synset': 'bow_and_arrow.n.01', 'name': 'bow_and_arrow'}, {'id': 5984, 'synset': 'bowed_stringed_instrument.n.01', 'name': 'bowed_stringed_instrument'}, {'id': 5985, 'synset': 'bowie_knife.n.01', 'name': 'Bowie_knife'}, {'id': 5986, 'synset': 'bowl.n.01', 'name': 'bowl'}, {'id': 5987, 'synset': 'bowl.n.07', 'name': 'bowl'}, {'id': 5988, 'synset': 'bowline.n.01', 'name': 'bowline'}, {'id': 5989, 'synset': 'bowling_alley.n.01', 'name': 'bowling_alley'}, {'id': 5990, 'synset': 'bowling_equipment.n.01', 'name': 'bowling_equipment'}, {'id': 5991, 'synset': 'bowling_pin.n.01', 'name': 'bowling_pin'}, {'id': 5992, 'synset': 'bowling_shoe.n.01', 'name': 'bowling_shoe'}, {'id': 5993, 'synset': 'bowsprit.n.01', 'name': 'bowsprit'}, {'id': 5994, 'synset': 'bowstring.n.01', 'name': 'bowstring'}, {'id': 5995, 'synset': 'box.n.02', 'name': 'box'}, {'id': 5996, 'synset': 'box.n.08', 'name': 'box'}, {'id': 5997, 'synset': 'box_beam.n.01', 'name': 'box_beam'}, {'id': 5998, 'synset': 'box_camera.n.01', 'name': 'box_camera'}, {'id': 5999, 'synset': 'boxcar.n.01', 'name': 'boxcar'}, {'id': 6000, 'synset': 'box_coat.n.01', 'name': 'box_coat'}, {'id': 6001, 'synset': 'boxing_equipment.n.01', 'name': 'boxing_equipment'}, {'id': 6002, 'synset': 'box_office.n.02', 'name': 'box_office'}, {'id': 6003, 'synset': 'box_spring.n.01', 'name': 'box_spring'}, {'id': 6004, 'synset': 'box_wrench.n.01', 'name': 'box_wrench'}, {'id': 6005, 'synset': 'brace.n.09', 'name': 'brace'}, {'id': 6006, 'synset': 'brace.n.07', 'name': 'brace'}, {'id': 6007, 'synset': 'brace.n.01', 'name': 'brace'}, {'id': 6008, 'synset': 'brace_and_bit.n.01', 'name': 'brace_and_bit'}, {'id': 6009, 'synset': 'bracer.n.01', 'name': 'bracer'}, {'id': 6010, 'synset': 'brace_wrench.n.01', 'name': 'brace_wrench'}, {'id': 6011, 'synset': 'bracket.n.04', 'name': 'bracket'}, {'id': 6012, 'synset': 'bradawl.n.01', 'name': 'bradawl'}, {'id': 6013, 'synset': 'brake.n.01', 'name': 'brake'}, {'id': 6014, 'synset': 'brake.n.05', 'name': 'brake'}, {'id': 6015, 'synset': 'brake_band.n.01', 'name': 'brake_band'}, {'id': 6016, 'synset': 'brake_cylinder.n.01', 'name': 'brake_cylinder'}, {'id': 6017, 'synset': 'brake_disk.n.01', 'name': 'brake_disk'}, {'id': 6018, 'synset': 'brake_drum.n.01', 'name': 'brake_drum'}, {'id': 6019, 'synset': 'brake_lining.n.01', 'name': 'brake_lining'}, {'id': 6020, 'synset': 'brake_pad.n.01', 'name': 'brake_pad'}, {'id': 6021, 'synset': 'brake_pedal.n.01', 'name': 'brake_pedal'}, {'id': 6022, 'synset': 'brake_shoe.n.01', 'name': 'brake_shoe'}, {'id': 6023, 'synset': 'brake_system.n.01', 'name': 'brake_system'}, {'id': 6024, 'synset': 'brass.n.02', 'name': 'brass'}, {'id': 6025, 'synset': 'brass.n.05', 'name': 'brass'}, {'id': 6026, 'synset': 'brassard.n.01', 'name': 'brassard'}, {'id': 6027, 'synset': 'brasserie.n.01', 'name': 'brasserie'}, {'id': 6028, 'synset': 'brassie.n.01', 'name': 'brassie'}, {'id': 6029, 'synset': 'brass_knucks.n.01', 'name': 'brass_knucks'}, {'id': 6030, 'synset': 'brattice.n.01', 'name': 'brattice'}, {'id': 6031, 'synset': 'brazier.n.01', 'name': 'brazier'}, {'id': 6032, 'synset': 'breadbasket.n.03', 'name': 'breadbasket'}, {'id': 6033, 'synset': 'bread_knife.n.01', 'name': 'bread_knife'}, {'id': 6034, 'synset': 'breakable.n.01', 'name': 'breakable'}, {'id': 6035, 'synset': 'breakfast_area.n.01', 'name': 'breakfast_area'}, {'id': 6036, 'synset': 'breakfast_table.n.01', 'name': 'breakfast_table'}, {'id': 6037, 'synset': 'breakwater.n.01', 'name': 'breakwater'}, {'id': 6038, 'synset': 'breast_drill.n.01', 'name': 'breast_drill'}, {'id': 6039, 'synset': 'breast_implant.n.01', 'name': 'breast_implant'}, {'id': 6040, 'synset': 'breastplate.n.01', 'name': 'breastplate'}, {'id': 6041, 'synset': 'breast_pocket.n.01', 'name': 'breast_pocket'}, {'id': 6042, 'synset': 'breathalyzer.n.01', 'name': 'breathalyzer'}, {'id': 6043, 'synset': 'breechblock.n.01', 'name': 'breechblock'}, {'id': 6044, 'synset': 'breeches.n.01', 'name': 'breeches'}, {'id': 6045, 'synset': 'breeches_buoy.n.01', 'name': 'breeches_buoy'}, {'id': 6046, 'synset': 'breechloader.n.01', 'name': 'breechloader'}, {'id': 6047, 'synset': 'breeder_reactor.n.01', 'name': 'breeder_reactor'}, {'id': 6048, 'synset': 'bren.n.01', 'name': 'Bren'}, {'id': 6049, 'synset': 'brewpub.n.01', 'name': 'brewpub'}, {'id': 6050, 'synset': 'brick.n.01', 'name': 'brick'}, {'id': 6051, 'synset': 'brickkiln.n.01', 'name': 'brickkiln'}, {'id': 6052, 'synset': "bricklayer's_hammer.n.01", 'name': "bricklayer's_hammer"}, {'id': 6053, 'synset': 'brick_trowel.n.01', 'name': 'brick_trowel'}, {'id': 6054, 'synset': 'brickwork.n.01', 'name': 'brickwork'}, {'id': 6055, 'synset': 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6071, 'synset': 'britches.n.01', 'name': 'britches'}, {'id': 6072, 'synset': 'broad_arrow.n.03', 'name': 'broad_arrow'}, {'id': 6073, 'synset': 'broadax.n.01', 'name': 'broadax'}, {'id': 6074, 'synset': 'brochette.n.01', 'name': 'brochette'}, {'id': 6075, 'synset': 'broadcaster.n.02', 'name': 'broadcaster'}, {'id': 6076, 'synset': 'broadcloth.n.02', 'name': 'broadcloth'}, {'id': 6077, 'synset': 'broadcloth.n.01', 'name': 'broadcloth'}, {'id': 6078, 'synset': 'broad_hatchet.n.01', 'name': 'broad_hatchet'}, {'id': 6079, 'synset': 'broadloom.n.01', 'name': 'broadloom'}, {'id': 6080, 'synset': 'broadside.n.03', 'name': 'broadside'}, {'id': 6081, 'synset': 'broadsword.n.01', 'name': 'broadsword'}, {'id': 6082, 'synset': 'brocade.n.01', 'name': 'brocade'}, {'id': 6083, 'synset': 'brogan.n.01', 'name': 'brogan'}, {'id': 6084, 'synset': 'broiler.n.01', 'name': 'broiler'}, {'id': 6085, 'synset': 'broken_arch.n.01', 'name': 'broken_arch'}, {'id': 6086, 'synset': 'bronchoscope.n.01', 'name': 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{'id': 6101, 'synset': 'bucket_seat.n.01', 'name': 'bucket_seat'}, {'id': 6102, 'synset': 'bucket_shop.n.02', 'name': 'bucket_shop'}, {'id': 6103, 'synset': 'buckle.n.01', 'name': 'buckle'}, {'id': 6104, 'synset': 'buckram.n.01', 'name': 'buckram'}, {'id': 6105, 'synset': 'bucksaw.n.01', 'name': 'bucksaw'}, {'id': 6106, 'synset': 'buckskins.n.01', 'name': 'buckskins'}, {'id': 6107, 'synset': 'buff.n.05', 'name': 'buff'}, {'id': 6108, 'synset': 'buffer.n.05', 'name': 'buffer'}, {'id': 6109, 'synset': 'buffer.n.04', 'name': 'buffer'}, {'id': 6110, 'synset': 'buffet.n.01', 'name': 'buffet'}, {'id': 6111, 'synset': 'buffing_wheel.n.01', 'name': 'buffing_wheel'}, {'id': 6112, 'synset': 'bugle.n.01', 'name': 'bugle'}, {'id': 6113, 'synset': 'building.n.01', 'name': 'building'}, {'id': 6114, 'synset': 'building_complex.n.01', 'name': 'building_complex'}, {'id': 6115, 'synset': 'bulldog_clip.n.01', 'name': 'bulldog_clip'}, {'id': 6116, 'synset': 'bulldog_wrench.n.01', 'name': 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6215, 'synset': 'caller_id.n.01', 'name': 'caller_ID'}, {'id': 6216, 'synset': 'calliope.n.02', 'name': 'calliope'}, {'id': 6217, 'synset': 'calorimeter.n.01', 'name': 'calorimeter'}, {'id': 6218, 'synset': 'calpac.n.01', 'name': 'calpac'}, {'id': 6219, 'synset': 'camail.n.01', 'name': 'camail'}, {'id': 6220, 'synset': 'camber_arch.n.01', 'name': 'camber_arch'}, {'id': 6221, 'synset': 'cambric.n.01', 'name': 'cambric'}, {'id': 6222, 'synset': "camel's_hair.n.01", 'name': "camel's_hair"}, {'id': 6223, 'synset': 'camera_lucida.n.01', 'name': 'camera_lucida'}, {'id': 6224, 'synset': 'camera_obscura.n.01', 'name': 'camera_obscura'}, {'id': 6225, 'synset': 'camera_tripod.n.01', 'name': 'camera_tripod'}, {'id': 6226, 'synset': 'camise.n.01', 'name': 'camise'}, {'id': 6227, 'synset': 'camisole.n.02', 'name': 'camisole'}, {'id': 6228, 'synset': 'camisole.n.01', 'name': 'camisole'}, {'id': 6229, 'synset': 'camlet.n.02', 'name': 'camlet'}, {'id': 6230, 'synset': 'camouflage.n.03', 'name': 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'name': 'candlesnuffer'}, {'id': 6247, 'synset': 'candlewick.n.02', 'name': 'candlewick'}, {'id': 6248, 'synset': 'candy_thermometer.n.01', 'name': 'candy_thermometer'}, {'id': 6249, 'synset': 'cane.n.03', 'name': 'cane'}, {'id': 6250, 'synset': 'cangue.n.01', 'name': 'cangue'}, {'id': 6251, 'synset': 'cannery.n.01', 'name': 'cannery'}, {'id': 6252, 'synset': 'cannikin.n.02', 'name': 'cannikin'}, {'id': 6253, 'synset': 'cannikin.n.01', 'name': 'cannikin'}, {'id': 6254, 'synset': 'cannon.n.01', 'name': 'cannon'}, {'id': 6255, 'synset': 'cannon.n.04', 'name': 'cannon'}, {'id': 6256, 'synset': 'cannon.n.03', 'name': 'cannon'}, {'id': 6257, 'synset': 'cannon.n.02', 'name': 'cannon'}, {'id': 6258, 'synset': 'cannonball.n.01', 'name': 'cannonball'}, {'id': 6259, 'synset': 'canopic_jar.n.01', 'name': 'canopic_jar'}, {'id': 6260, 'synset': 'canopy.n.03', 'name': 'canopy'}, {'id': 6261, 'synset': 'canopy.n.02', 'name': 'canopy'}, {'id': 6262, 'synset': 'canopy.n.01', 'name': 'canopy'}, {'id': 6263, 'synset': 'canteen.n.05', 'name': 'canteen'}, {'id': 6264, 'synset': 'canteen.n.04', 'name': 'canteen'}, {'id': 6265, 'synset': 'canteen.n.03', 'name': 'canteen'}, {'id': 6266, 'synset': 'canteen.n.02', 'name': 'canteen'}, {'id': 6267, 'synset': 'cant_hook.n.01', 'name': 'cant_hook'}, {'id': 6268, 'synset': 'cantilever.n.01', 'name': 'cantilever'}, {'id': 6269, 'synset': 'cantilever_bridge.n.01', 'name': 'cantilever_bridge'}, {'id': 6270, 'synset': 'cantle.n.01', 'name': 'cantle'}, {'id': 6271, 'synset': 'canton_crepe.n.01', 'name': 'Canton_crepe'}, {'id': 6272, 'synset': 'canvas.n.01', 'name': 'canvas'}, {'id': 6273, 'synset': 'canvas.n.06', 'name': 'canvas'}, {'id': 6274, 'synset': 'canvas_tent.n.01', 'name': 'canvas_tent'}, {'id': 6275, 'synset': 'cap.n.04', 'name': 'cap'}, {'id': 6276, 'synset': 'capacitor.n.01', 'name': 'capacitor'}, {'id': 6277, 'synset': 'caparison.n.01', 'name': 'caparison'}, {'id': 6278, 'synset': 'capital_ship.n.01', 'name': 'capital_ship'}, {'id': 6279, 'synset': 'capitol.n.01', 'name': 'capitol'}, {'id': 6280, 'synset': 'cap_opener.n.01', 'name': 'cap_opener'}, {'id': 6281, 'synset': 'capote.n.02', 'name': 'capote'}, {'id': 6282, 'synset': 'capote.n.01', 'name': 'capote'}, {'id': 6283, 'synset': 'cap_screw.n.01', 'name': 'cap_screw'}, {'id': 6284, 'synset': 'capstan.n.01', 'name': 'capstan'}, {'id': 6285, 'synset': 'capstone.n.02', 'name': 'capstone'}, {'id': 6286, 'synset': 'capsule.n.01', 'name': 'capsule'}, {'id': 6287, 'synset': "captain's_chair.n.01", 'name': "captain's_chair"}, {'id': 6288, 'synset': 'carabiner.n.01', 'name': 'carabiner'}, {'id': 6289, 'synset': 'carafe.n.01', 'name': 'carafe'}, {'id': 6290, 'synset': 'caravansary.n.01', 'name': 'caravansary'}, {'id': 6291, 'synset': 'carbine.n.01', 'name': 'carbine'}, {'id': 6292, 'synset': 'car_bomb.n.01', 'name': 'car_bomb'}, {'id': 6293, 'synset': 'carbon_arc_lamp.n.01', 'name': 'carbon_arc_lamp'}, {'id': 6294, 'synset': 'carboy.n.01', 'name': 'carboy'}, {'id': 6295, 'synset': 'carburetor.n.01', 'name': 'carburetor'}, {'id': 6296, 'synset': 'car_carrier.n.01', 'name': 'car_carrier'}, {'id': 6297, 'synset': 'cardcase.n.01', 'name': 'cardcase'}, {'id': 6298, 'synset': 'cardiac_monitor.n.01', 'name': 'cardiac_monitor'}, {'id': 6299, 'synset': 'card_index.n.01', 'name': 'card_index'}, {'id': 6300, 'synset': 'cardiograph.n.01', 'name': 'cardiograph'}, {'id': 6301, 'synset': 'cardioid_microphone.n.01', 'name': 'cardioid_microphone'}, {'id': 6302, 'synset': 'car_door.n.01', 'name': 'car_door'}, {'id': 6303, 'synset': 'cardroom.n.01', 'name': 'cardroom'}, {'id': 6304, 'synset': 'card_table.n.02', 'name': 'card_table'}, {'id': 6305, 'synset': 'card_table.n.01', 'name': 'card_table'}, {'id': 6306, 'synset': 'car-ferry.n.01', 'name': 'car-ferry'}, {'id': 6307, 'synset': 'cargo_area.n.01', 'name': 'cargo_area'}, {'id': 6308, 'synset': 'cargo_container.n.01', 'name': 'cargo_container'}, {'id': 6309, 'synset': 'cargo_door.n.01', 'name': 'cargo_door'}, {'id': 6310, 'synset': 'cargo_hatch.n.01', 'name': 'cargo_hatch'}, {'id': 6311, 'synset': 'cargo_helicopter.n.01', 'name': 'cargo_helicopter'}, {'id': 6312, 'synset': 'cargo_liner.n.01', 'name': 'cargo_liner'}, {'id': 6313, 'synset': 'carillon.n.01', 'name': 'carillon'}, {'id': 6314, 'synset': 'car_mirror.n.01', 'name': 'car_mirror'}, {'id': 6315, 'synset': 'caroche.n.01', 'name': 'caroche'}, {'id': 6316, 'synset': 'carousel.n.02', 'name': 'carousel'}, {'id': 6317, 'synset': "carpenter's_hammer.n.01", 'name': "carpenter's_hammer"}, {'id': 6318, 'synset': "carpenter's_kit.n.01", 'name': "carpenter's_kit"}, {'id': 6319, 'synset': "carpenter's_level.n.01", 'name': "carpenter's_level"}, {'id': 6320, 'synset': "carpenter's_mallet.n.01", 'name': "carpenter's_mallet"}, {'id': 6321, 'synset': "carpenter's_rule.n.01", 'name': "carpenter's_rule"}, {'id': 6322, 'synset': "carpenter's_square.n.01", 'name': "carpenter's_square"}, {'id': 6323, 'synset': 'carpetbag.n.01', 'name': 'carpetbag'}, {'id': 6324, 'synset': 'carpet_beater.n.01', 'name': 'carpet_beater'}, {'id': 6325, 'synset': 'carpet_loom.n.01', 'name': 'carpet_loom'}, {'id': 6326, 'synset': 'carpet_pad.n.01', 'name': 'carpet_pad'}, {'id': 6327, 'synset': 'carpet_sweeper.n.01', 'name': 'carpet_sweeper'}, {'id': 6328, 'synset': 'carpet_tack.n.01', 'name': 'carpet_tack'}, {'id': 6329, 'synset': 'carport.n.01', 'name': 'carport'}, {'id': 6330, 'synset': 'carrack.n.01', 'name': 'carrack'}, {'id': 6331, 'synset': 'carrel.n.02', 'name': 'carrel'}, {'id': 6332, 'synset': 'carriage.n.04', 'name': 'carriage'}, {'id': 6333, 'synset': 'carriage_bolt.n.01', 'name': 'carriage_bolt'}, {'id': 6334, 'synset': 'carriageway.n.01', 'name': 'carriageway'}, {'id': 6335, 'synset': 'carriage_wrench.n.01', 'name': 'carriage_wrench'}, {'id': 6336, 'synset': 'carrick_bend.n.01', 'name': 'carrick_bend'}, {'id': 6337, 'synset': 'carrier.n.10', 'name': 'carrier'}, {'id': 6338, 'synset': 'carrycot.n.01', 'name': 'carrycot'}, {'id': 6339, 'synset': 'car_seat.n.01', 'name': 'car_seat'}, {'id': 6340, 'synset': 'car_tire.n.01', 'name': 'car_tire'}, {'id': 6341, 'synset': 'cartouche.n.01', 'name': 'cartouche'}, {'id': 6342, 'synset': 'car_train.n.01', 'name': 'car_train'}, {'id': 6343, 'synset': 'cartridge.n.01', 'name': 'cartridge'}, {'id': 6344, 'synset': 'cartridge.n.04', 'name': 'cartridge'}, {'id': 6345, 'synset': 'cartridge_belt.n.01', 'name': 'cartridge_belt'}, {'id': 6346, 'synset': 'cartridge_extractor.n.01', 'name': 'cartridge_extractor'}, {'id': 6347, 'synset': 'cartridge_fuse.n.01', 'name': 'cartridge_fuse'}, {'id': 6348, 'synset': 'cartridge_holder.n.01', 'name': 'cartridge_holder'}, {'id': 6349, 'synset': 'cartwheel.n.01', 'name': 'cartwheel'}, {'id': 6350, 'synset': 'carving_fork.n.01', 'name': 'carving_fork'}, {'id': 6351, 'synset': 'carving_knife.n.01', 'name': 'carving_knife'}, {'id': 6352, 'synset': 'car_wheel.n.01', 'name': 'car_wheel'}, {'id': 6353, 'synset': 'caryatid.n.01', 'name': 'caryatid'}, {'id': 6354, 'synset': 'cascade_liquefier.n.01', 'name': 'cascade_liquefier'}, {'id': 6355, 'synset': 'cascade_transformer.n.01', 'name': 'cascade_transformer'}, {'id': 6356, 'synset': 'case.n.05', 'name': 'case'}, {'id': 6357, 'synset': 'case.n.20', 'name': 'case'}, {'id': 6358, 'synset': 'case.n.18', 'name': 'case'}, {'id': 6359, 'synset': 'casein_paint.n.01', 'name': 'casein_paint'}, {'id': 6360, 'synset': 'case_knife.n.02', 'name': 'case_knife'}, {'id': 6361, 'synset': 'case_knife.n.01', 'name': 'case_knife'}, {'id': 6362, 'synset': 'casement.n.01', 'name': 'casement'}, {'id': 6363, 'synset': 'casement_window.n.01', 'name': 'casement_window'}, {'id': 6364, 'synset': 'casern.n.01', 'name': 'casern'}, {'id': 6365, 'synset': 'case_shot.n.01', 'name': 'case_shot'}, {'id': 6366, 'synset': 'cash_bar.n.01', 'name': 'cash_bar'}, {'id': 6367, 'synset': 'cashbox.n.01', 'name': 'cashbox'}, {'id': 6368, 'synset': 'cash_machine.n.01', 'name': 'cash_machine'}, {'id': 6369, 'synset': 'cashmere.n.01', 'name': 'cashmere'}, {'id': 6370, 'synset': 'casing.n.03', 'name': 'casing'}, {'id': 6371, 'synset': 'casino.n.01', 'name': 'casino'}, {'id': 6372, 'synset': 'casket.n.02', 'name': 'casket'}, {'id': 6373, 'synset': 'casque.n.01', 'name': 'casque'}, {'id': 6374, 'synset': 'casquet.n.01', 'name': 'casquet'}, {'id': 6375, 'synset': 'cassegrainian_telescope.n.01', 'name': 'Cassegrainian_telescope'}, {'id': 6376, 'synset': 'casserole.n.02', 'name': 'casserole'}, {'id': 6377, 'synset': 'cassette_deck.n.01', 'name': 'cassette_deck'}, {'id': 6378, 'synset': 'cassette_player.n.01', 'name': 'cassette_player'}, {'id': 6379, 'synset': 'cassette_recorder.n.01', 'name': 'cassette_recorder'}, {'id': 6380, 'synset': 'cassette_tape.n.01', 'name': 'cassette_tape'}, {'id': 6381, 'synset': 'cassock.n.01', 'name': 'cassock'}, {'id': 6382, 'synset': 'caster.n.03', 'name': 'caster'}, {'id': 6383, 'synset': 'caster.n.02', 'name': 'caster'}, {'id': 6384, 'synset': 'castle.n.02', 'name': 'castle'}, {'id': 6385, 'synset': 'castle.n.03', 'name': 'castle'}, {'id': 6386, 'synset': 'catacomb.n.01', 'name': 'catacomb'}, {'id': 6387, 'synset': 'catafalque.n.01', 'name': 'catafalque'}, {'id': 6388, 'synset': 'catalytic_converter.n.01', 'name': 'catalytic_converter'}, {'id': 6389, 'synset': 'catalytic_cracker.n.01', 'name': 'catalytic_cracker'}, {'id': 6390, 'synset': 'catamaran.n.01', 'name': 'catamaran'}, {'id': 6391, 'synset': 'catapult.n.03', 'name': 'catapult'}, {'id': 6392, 'synset': 'catapult.n.02', 'name': 'catapult'}, {'id': 6393, 'synset': 'catboat.n.01', 'name': 'catboat'}, {'id': 6394, 'synset': 'cat_box.n.01', 'name': 'cat_box'}, {'id': 6395, 'synset': 'catch.n.07', 'name': 'catch'}, {'id': 6396, 'synset': 'catchall.n.01', 'name': 'catchall'}, {'id': 6397, 'synset': "catcher's_mask.n.01", 'name': "catcher's_mask"}, {'id': 6398, 'synset': 'catchment.n.01', 'name': 'catchment'}, {'id': 6399, 'synset': 'caterpillar.n.02', 'name': 'Caterpillar'}, {'id': 6400, 'synset': 'cathedra.n.01', 'name': 'cathedra'}, {'id': 6401, 'synset': 'cathedral.n.01', 'name': 'cathedral'}, {'id': 6402, 'synset': 'cathedral.n.02', 'name': 'cathedral'}, {'id': 6403, 'synset': 'catheter.n.01', 'name': 'catheter'}, {'id': 6404, 'synset': 'cathode.n.01', 'name': 'cathode'}, {'id': 6405, 'synset': 'cathode-ray_tube.n.01', 'name': 'cathode-ray_tube'}, {'id': 6406, 'synset': "cat-o'-nine-tails.n.01", 'name': "cat-o'-nine-tails"}, {'id': 6407, 'synset': "cat's-paw.n.02", 'name': "cat's-paw"}, {'id': 6408, 'synset': 'catsup_bottle.n.01', 'name': 'catsup_bottle'}, {'id': 6409, 'synset': 'cattle_car.n.01', 'name': 'cattle_car'}, {'id': 6410, 'synset': 'cattle_guard.n.01', 'name': 'cattle_guard'}, {'id': 6411, 'synset': 'cattleship.n.01', 'name': 'cattleship'}, {'id': 6412, 'synset': 'cautery.n.01', 'name': 'cautery'}, {'id': 6413, 'synset': 'cavalier_hat.n.01', 'name': 'cavalier_hat'}, {'id': 6414, 'synset': 'cavalry_sword.n.01', 'name': 'cavalry_sword'}, {'id': 6415, 'synset': 'cavetto.n.01', 'name': 'cavetto'}, {'id': 6416, 'synset': 'cavity_wall.n.01', 'name': 'cavity_wall'}, {'id': 6417, 'synset': 'c_battery.n.01', 'name': 'C_battery'}, {'id': 6418, 'synset': 'c-clamp.n.01', 'name': 'C-clamp'}, {'id': 6419, 'synset': 'cd_drive.n.01', 'name': 'CD_drive'}, {'id': 6420, 'synset': 'cd-r.n.01', 'name': 'CD-R'}, {'id': 6421, 'synset': 'cd-rom.n.01', 'name': 'CD-ROM'}, {'id': 6422, 'synset': 'cd-rom_drive.n.01', 'name': 'CD-ROM_drive'}, {'id': 6423, 'synset': 'cedar_chest.n.01', 'name': 'cedar_chest'}, {'id': 6424, 'synset': 'ceiling.n.01', 'name': 'ceiling'}, {'id': 6425, 'synset': 'celesta.n.01', 'name': 'celesta'}, {'id': 6426, 'synset': 'cell.n.03', 'name': 'cell'}, {'id': 6427, 'synset': 'cell.n.07', 'name': 'cell'}, {'id': 6428, 'synset': 'cellar.n.03', 'name': 'cellar'}, {'id': 6429, 'synset': 'cellblock.n.01', 'name': 'cellblock'}, {'id': 6430, 'synset': 'cello.n.01', 'name': 'cello'}, {'id': 6431, 'synset': 'cellophane.n.01', 'name': 'cellophane'}, {'id': 6432, 'synset': 'cellulose_tape.n.01', 'name': 'cellulose_tape'}, {'id': 6433, 'synset': 'cenotaph.n.01', 'name': 'cenotaph'}, {'id': 6434, 'synset': 'censer.n.01', 'name': 'censer'}, {'id': 6435, 'synset': 'center.n.03', 'name': 'center'}, {'id': 6436, 'synset': 'center_punch.n.01', 'name': 'center_punch'}, {'id': 6437, 'synset': 'centigrade_thermometer.n.01', 'name': 'Centigrade_thermometer'}, {'id': 6438, 'synset': 'central_processing_unit.n.01', 'name': 'central_processing_unit'}, {'id': 6439, 'synset': 'centrifugal_pump.n.01', 'name': 'centrifugal_pump'}, {'id': 6440, 'synset': 'centrifuge.n.01', 'name': 'centrifuge'}, {'id': 6441, 'synset': 'ceramic.n.01', 'name': 'ceramic'}, {'id': 6442, 'synset': 'ceramic_ware.n.01', 'name': 'ceramic_ware'}, {'id': 6443, 'synset': 'cereal_bowl.n.01', 'name': 'cereal_bowl'}, {'id': 6444, 'synset': 'cereal_box.n.01', 'name': 'cereal_box'}, {'id': 6445, 'synset': 'cerecloth.n.01', 'name': 'cerecloth'}, {'id': 6446, 'synset': 'cesspool.n.01', 'name': 'cesspool'}, {'id': 6447, 'synset': 'chachka.n.02', 'name': 'chachka'}, {'id': 6448, 'synset': 'chador.n.01', 'name': 'chador'}, {'id': 6449, 'synset': 'chafing_dish.n.01', 'name': 'chafing_dish'}, {'id': 6450, 'synset': 'chain.n.03', 'name': 'chain'}, {'id': 6451, 'synset': 'chain.n.05', 'name': 'chain'}, {'id': 6452, 'synset': 'chainlink_fence.n.01', 'name': 'chainlink_fence'}, {'id': 6453, 'synset': 'chain_printer.n.01', 'name': 'chain_printer'}, {'id': 6454, 'synset': 'chain_saw.n.01', 'name': 'chain_saw'}, {'id': 6455, 'synset': 'chain_store.n.01', 'name': 'chain_store'}, {'id': 6456, 'synset': 'chain_tongs.n.01', 'name': 'chain_tongs'}, {'id': 6457, 'synset': 'chain_wrench.n.01', 'name': 'chain_wrench'}, {'id': 6458, 'synset': 'chair.n.05', 'name': 'chair'}, {'id': 6459, 'synset': 'chair_of_state.n.01', 'name': 'chair_of_state'}, {'id': 6460, 'synset': 'chairlift.n.01', 'name': 'chairlift'}, {'id': 6461, 'synset': 'chaise.n.02', 'name': 'chaise'}, {'id': 6462, 'synset': 'chalet.n.01', 'name': 'chalet'}, {'id': 6463, 'synset': 'chalk.n.04', 'name': 'chalk'}, {'id': 6464, 'synset': 'challis.n.01', 'name': 'challis'}, {'id': 6465, 'synset': 'chamberpot.n.01', 'name': 'chamberpot'}, {'id': 6466, 'synset': 'chambray.n.01', 'name': 'chambray'}, {'id': 6467, 'synset': 'chamfer_bit.n.01', 'name': 'chamfer_bit'}, {'id': 6468, 'synset': 'chamfer_plane.n.01', 'name': 'chamfer_plane'}, {'id': 6469, 'synset': 'chamois_cloth.n.01', 'name': 'chamois_cloth'}, {'id': 6470, 'synset': 'chancel.n.01', 'name': 'chancel'}, {'id': 6471, 'synset': 'chancellery.n.01', 'name': 'chancellery'}, {'id': 6472, 'synset': 'chancery.n.02', 'name': 'chancery'}, {'id': 6473, 'synset': 'chandlery.n.01', 'name': 'chandlery'}, {'id': 6474, 'synset': 'chanfron.n.01', 'name': 'chanfron'}, {'id': 6475, 'synset': 'chanter.n.01', 'name': 'chanter'}, {'id': 6476, 'synset': 'chantry.n.02', 'name': 'chantry'}, {'id': 6477, 'synset': 'chapel.n.01', 'name': 'chapel'}, {'id': 6478, 'synset': 'chapterhouse.n.02', 'name': 'chapterhouse'}, {'id': 6479, 'synset': 'chapterhouse.n.01', 'name': 'chapterhouse'}, {'id': 6480, 'synset': 'character_printer.n.01', 'name': 'character_printer'}, {'id': 6481, 'synset': 'charcuterie.n.01', 'name': 'charcuterie'}, {'id': 6482, 'synset': 'charge-exchange_accelerator.n.01', 'name': 'charge-exchange_accelerator'}, {'id': 6483, 'synset': 'charger.n.02', 'name': 'charger'}, {'id': 6484, 'synset': 'chariot.n.01', 'name': 'chariot'}, {'id': 6485, 'synset': 'chariot.n.02', 'name': 'chariot'}, {'id': 6486, 'synset': 'charnel_house.n.01', 'name': 'charnel_house'}, {'id': 6487, 'synset': 'chassis.n.03', 'name': 'chassis'}, {'id': 6488, 'synset': 'chassis.n.02', 'name': 'chassis'}, {'id': 6489, 'synset': 'chasuble.n.01', 'name': 'chasuble'}, {'id': 6490, 'synset': 'chateau.n.01', 'name': 'chateau'}, {'id': 6491, 'synset': 'chatelaine.n.02', 'name': 'chatelaine'}, {'id': 6492, 'synset': 'checker.n.03', 'name': 'checker'}, {'id': 6493, 'synset': 'checkout.n.03', 'name': 'checkout'}, {'id': 6494, 'synset': 'cheekpiece.n.01', 'name': 'cheekpiece'}, {'id': 6495, 'synset': 'cheeseboard.n.01', 'name': 'cheeseboard'}, {'id': 6496, 'synset': 'cheesecloth.n.01', 'name': 'cheesecloth'}, {'id': 6497, 'synset': 'cheese_cutter.n.01', 'name': 'cheese_cutter'}, {'id': 6498, 'synset': 'cheese_press.n.01', 'name': 'cheese_press'}, {'id': 6499, 'synset': 'chemical_bomb.n.01', 'name': 'chemical_bomb'}, {'id': 6500, 'synset': 'chemical_plant.n.01', 'name': 'chemical_plant'}, {'id': 6501, 'synset': 'chemical_reactor.n.01', 'name': 'chemical_reactor'}, {'id': 6502, 'synset': 'chemise.n.02', 'name': 'chemise'}, {'id': 6503, 'synset': 'chemise.n.01', 'name': 'chemise'}, {'id': 6504, 'synset': 'chenille.n.02', 'name': 'chenille'}, {'id': 6505, 'synset': 'chessman.n.01', 'name': 'chessman'}, {'id': 6506, 'synset': 'chest.n.02', 'name': 'chest'}, {'id': 6507, 'synset': 'chesterfield.n.02', 'name': 'chesterfield'}, {'id': 6508, 'synset': 'chest_of_drawers.n.01', 'name': 'chest_of_drawers'}, {'id': 6509, 'synset': 'chest_protector.n.01', 'name': 'chest_protector'}, {'id': 6510, 'synset': 'cheval-de-frise.n.01', 'name': 'cheval-de-frise'}, {'id': 6511, 'synset': 'cheval_glass.n.01', 'name': 'cheval_glass'}, {'id': 6512, 'synset': 'chicane.n.02', 'name': 'chicane'}, {'id': 6513, 'synset': 'chicken_coop.n.01', 'name': 'chicken_coop'}, {'id': 6514, 'synset': 'chicken_wire.n.01', 'name': 'chicken_wire'}, {'id': 6515, 'synset': 'chicken_yard.n.01', 'name': 'chicken_yard'}, {'id': 6516, 'synset': 'chiffon.n.01', 'name': 'chiffon'}, {'id': 6517, 'synset': 'chiffonier.n.01', 'name': 'chiffonier'}, {'id': 6518, 'synset': "child's_room.n.01", 'name': "child's_room"}, {'id': 6519, 'synset': 'chimney_breast.n.01', 'name': 'chimney_breast'}, {'id': 6520, 'synset': 'chimney_corner.n.01', 'name': 'chimney_corner'}, {'id': 6521, 'synset': 'china.n.02', 'name': 'china'}, {'id': 6522, 'synset': 'china_cabinet.n.01', 'name': 'china_cabinet'}, {'id': 6523, 'synset': 'chinchilla.n.02', 'name': 'chinchilla'}, {'id': 6524, 'synset': 'chinese_lantern.n.01', 'name': 'Chinese_lantern'}, {'id': 6525, 'synset': 'chinese_puzzle.n.01', 'name': 'Chinese_puzzle'}, {'id': 6526, 'synset': 'chinning_bar.n.01', 'name': 'chinning_bar'}, {'id': 6527, 'synset': 'chino.n.02', 'name': 'chino'}, {'id': 6528, 'synset': 'chino.n.01', 'name': 'chino'}, {'id': 6529, 'synset': 'chin_rest.n.01', 'name': 'chin_rest'}, {'id': 6530, 'synset': 'chin_strap.n.01', 'name': 'chin_strap'}, {'id': 6531, 'synset': 'chintz.n.01', 'name': 'chintz'}, {'id': 6532, 'synset': 'chip.n.07', 'name': 'chip'}, {'id': 6533, 'synset': 'chisel.n.01', 'name': 'chisel'}, {'id': 6534, 'synset': 'chlamys.n.02', 'name': 'chlamys'}, {'id': 6535, 'synset': 'choir.n.03', 'name': 'choir'}, {'id': 6536, 'synset': 'choir_loft.n.01', 'name': 'choir_loft'}, {'id': 6537, 'synset': 'choke.n.02', 'name': 'choke'}, {'id': 6538, 'synset': 'choke.n.01', 'name': 'choke'}, {'id': 6539, 'synset': 'chokey.n.01', 'name': 'chokey'}, {'id': 6540, 'synset': 'choo-choo.n.01', 'name': 'choo-choo'}, {'id': 6541, 'synset': 'chopine.n.01', 'name': 'chopine'}, {'id': 6542, 'synset': 'chordophone.n.01', 'name': 'chordophone'}, {'id': 6543, 'synset': 'christmas_stocking.n.01', 'name': 'Christmas_stocking'}, {'id': 6544, 'synset': 'chronograph.n.01', 'name': 'chronograph'}, {'id': 6545, 'synset': 'chronometer.n.01', 'name': 'chronometer'}, {'id': 6546, 'synset': 'chronoscope.n.01', 'name': 'chronoscope'}, {'id': 6547, 'synset': 'chuck.n.03', 'name': 'chuck'}, {'id': 6548, 'synset': 'chuck_wagon.n.01', 'name': 'chuck_wagon'}, {'id': 6549, 'synset': 'chukka.n.02', 'name': 'chukka'}, {'id': 6550, 'synset': 'church.n.02', 'name': 'church'}, {'id': 6551, 'synset': 'church_bell.n.01', 'name': 'church_bell'}, {'id': 6552, 'synset': 'church_hat.n.01', 'name': 'church_hat'}, {'id': 6553, 'synset': 'church_key.n.01', 'name': 'church_key'}, {'id': 6554, 'synset': 'church_tower.n.01', 'name': 'church_tower'}, {'id': 6555, 'synset': 'churidars.n.01', 'name': 'churidars'}, {'id': 6556, 'synset': 'churn.n.01', 'name': 'churn'}, {'id': 6557, 'synset': 'ciderpress.n.01', 'name': 'ciderpress'}, {'id': 6558, 'synset': 'cigar_band.n.01', 'name': 'cigar_band'}, {'id': 6559, 'synset': 'cigar_cutter.n.01', 'name': 'cigar_cutter'}, {'id': 6560, 'synset': 'cigarette_butt.n.01', 'name': 'cigarette_butt'}, {'id': 6561, 'synset': 'cigarette_holder.n.01', 'name': 'cigarette_holder'}, {'id': 6562, 'synset': 'cigar_lighter.n.01', 'name': 'cigar_lighter'}, {'id': 6563, 'synset': 'cinch.n.02', 'name': 'cinch'}, {'id': 6564, 'synset': 'cinema.n.02', 'name': 'cinema'}, {'id': 6565, 'synset': 'cinquefoil.n.02', 'name': 'cinquefoil'}, {'id': 6566, 'synset': 'circle.n.08', 'name': 'circle'}, {'id': 6567, 'synset': 'circlet.n.02', 'name': 'circlet'}, {'id': 6568, 'synset': 'circuit.n.01', 'name': 'circuit'}, {'id': 6569, 'synset': 'circuit_board.n.01', 'name': 'circuit_board'}, {'id': 6570, 'synset': 'circuit_breaker.n.01', 'name': 'circuit_breaker'}, {'id': 6571, 'synset': 'circuitry.n.01', 'name': 'circuitry'}, {'id': 6572, 'synset': 'circular_plane.n.01', 'name': 'circular_plane'}, {'id': 6573, 'synset': 'circular_saw.n.01', 'name': 'circular_saw'}, {'id': 6574, 'synset': 'circus_tent.n.01', 'name': 'circus_tent'}, {'id': 6575, 'synset': 'cistern.n.03', 'name': 'cistern'}, {'id': 6576, 'synset': 'cittern.n.01', 'name': 'cittern'}, {'id': 6577, 'synset': 'city_hall.n.01', 'name': 'city_hall'}, {'id': 6578, 'synset': 'cityscape.n.02', 'name': 'cityscape'}, {'id': 6579, 'synset': 'city_university.n.01', 'name': 'city_university'}, {'id': 6580, 'synset': 'civies.n.01', 'name': 'civies'}, {'id': 6581, 'synset': 'civilian_clothing.n.01', 'name': 'civilian_clothing'}, {'id': 6582, 'synset': 'clack_valve.n.01', 'name': 'clack_valve'}, {'id': 6583, 'synset': 'clamp.n.01', 'name': 'clamp'}, {'id': 6584, 'synset': 'clamshell.n.02', 'name': 'clamshell'}, {'id': 6585, 'synset': 'clapper.n.03', 'name': 'clapper'}, {'id': 6586, 'synset': 'clapperboard.n.01', 'name': 'clapperboard'}, {'id': 6587, 'synset': 'clarence.n.01', 'name': 'clarence'}, {'id': 6588, 'synset': 'clark_cell.n.01', 'name': 'Clark_cell'}, {'id': 6589, 'synset': 'clasp_knife.n.01', 'name': 'clasp_knife'}, {'id': 6590, 'synset': 'classroom.n.01', 'name': 'classroom'}, {'id': 6591, 'synset': 'clavichord.n.01', 'name': 'clavichord'}, {'id': 6592, 'synset': 'clavier.n.02', 'name': 'clavier'}, {'id': 6593, 'synset': 'clay_pigeon.n.01', 'name': 'clay_pigeon'}, {'id': 6594, 'synset': 'claymore_mine.n.01', 'name': 'claymore_mine'}, {'id': 6595, 'synset': 'claymore.n.01', 'name': 'claymore'}, {'id': 6596, 'synset': 'cleaners.n.01', 'name': 'cleaners'}, {'id': 6597, 'synset': 'cleaning_implement.n.01', 'name': 'cleaning_implement'}, {'id': 6598, 'synset': 'cleaning_pad.n.01', 'name': 'cleaning_pad'}, {'id': 6599, 'synset': 'clean_room.n.01', 'name': 'clean_room'}, {'id': 6600, 'synset': 'clearway.n.01', 'name': 'clearway'}, {'id': 6601, 'synset': 'cleat.n.01', 'name': 'cleat'}, {'id': 6602, 'synset': 'cleats.n.01', 'name': 'cleats'}, {'id': 6603, 'synset': 'cleaver.n.01', 'name': 'cleaver'}, {'id': 6604, 'synset': 'clerestory.n.01', 'name': 'clerestory'}, {'id': 6605, 'synset': 'clevis.n.01', 'name': 'clevis'}, {'id': 6606, 'synset': 'clews.n.01', 'name': 'clews'}, {'id': 6607, 'synset': 'cliff_dwelling.n.01', 'name': 'cliff_dwelling'}, {'id': 6608, 'synset': 'climbing_frame.n.01', 'name': 'climbing_frame'}, {'id': 6609, 'synset': 'clinch.n.03', 'name': 'clinch'}, {'id': 6610, 'synset': 'clinch.n.02', 'name': 'clinch'}, {'id': 6611, 'synset': 'clincher.n.03', 'name': 'clincher'}, {'id': 6612, 'synset': 'clinic.n.03', 'name': 'clinic'}, {'id': 6613, 'synset': 'clinical_thermometer.n.01', 'name': 'clinical_thermometer'}, {'id': 6614, 'synset': 'clinker.n.02', 'name': 'clinker'}, {'id': 6615, 'synset': 'clinometer.n.01', 'name': 'clinometer'}, {'id': 6616, 'synset': 'clip_lead.n.01', 'name': 'clip_lead'}, {'id': 6617, 'synset': 'clip-on.n.01', 'name': 'clip-on'}, {'id': 6618, 'synset': 'clipper.n.04', 'name': 'clipper'}, {'id': 6619, 'synset': 'clipper.n.02', 'name': 'clipper'}, {'id': 6620, 'synset': 'cloak.n.01', 'name': 'cloak'}, {'id': 6621, 'synset': 'cloakroom.n.02', 'name': 'cloakroom'}, {'id': 6622, 'synset': 'cloche.n.02', 'name': 'cloche'}, {'id': 6623, 'synset': 'cloche.n.01', 'name': 'cloche'}, {'id': 6624, 'synset': 'clock_pendulum.n.01', 'name': 'clock_pendulum'}, {'id': 6625, 'synset': 'clock_radio.n.01', 'name': 'clock_radio'}, {'id': 6626, 'synset': 'clockwork.n.01', 'name': 'clockwork'}, {'id': 6627, 'synset': 'clog.n.01', 'name': 'clog'}, {'id': 6628, 'synset': 'cloisonne.n.01', 'name': 'cloisonne'}, {'id': 6629, 'synset': 'cloister.n.02', 'name': 'cloister'}, {'id': 6630, 'synset': 'closed_circuit.n.01', 'name': 'closed_circuit'}, {'id': 6631, 'synset': 'closed-circuit_television.n.01', 'name': 'closed-circuit_television'}, {'id': 6632, 'synset': 'closed_loop.n.01', 'name': 'closed_loop'}, {'id': 6633, 'synset': 'closet.n.04', 'name': 'closet'}, {'id': 6634, 'synset': 'closeup_lens.n.01', 'name': 'closeup_lens'}, {'id': 6635, 'synset': 'cloth_cap.n.01', 'name': 'cloth_cap'}, {'id': 6636, 'synset': 'cloth_covering.n.01', 'name': 'cloth_covering'}, {'id': 6637, 'synset': 'clothesbrush.n.01', 'name': 'clothesbrush'}, {'id': 6638, 'synset': 'clothes_closet.n.01', 'name': 'clothes_closet'}, {'id': 6639, 'synset': 'clothes_dryer.n.01', 'name': 'clothes_dryer'}, {'id': 6640, 'synset': 'clotheshorse.n.01', 'name': 'clotheshorse'}, {'id': 6641, 'synset': 'clothes_tree.n.01', 'name': 'clothes_tree'}, {'id': 6642, 'synset': 'clothing.n.01', 'name': 'clothing'}, {'id': 6643, 'synset': 'clothing_store.n.01', 'name': 'clothing_store'}, {'id': 6644, 'synset': 'clout_nail.n.01', 'name': 'clout_nail'}, {'id': 6645, 'synset': 'clove_hitch.n.01', 'name': 'clove_hitch'}, {'id': 6646, 'synset': 'club_car.n.01', 'name': 'club_car'}, {'id': 6647, 'synset': 'clubroom.n.01', 'name': 'clubroom'}, {'id': 6648, 'synset': 'cluster_bomb.n.01', 'name': 'cluster_bomb'}, {'id': 6649, 'synset': 'clutch.n.07', 'name': 'clutch'}, {'id': 6650, 'synset': 'clutch.n.06', 'name': 'clutch'}, {'id': 6651, 'synset': 'coach.n.04', 'name': 'coach'}, {'id': 6652, 'synset': 'coach_house.n.01', 'name': 'coach_house'}, {'id': 6653, 'synset': 'coal_car.n.01', 'name': 'coal_car'}, {'id': 6654, 'synset': 'coal_chute.n.01', 'name': 'coal_chute'}, {'id': 6655, 'synset': 'coal_house.n.01', 'name': 'coal_house'}, {'id': 6656, 'synset': 'coal_shovel.n.01', 'name': 'coal_shovel'}, {'id': 6657, 'synset': 'coaming.n.01', 'name': 'coaming'}, {'id': 6658, 'synset': 'coaster_brake.n.01', 'name': 'coaster_brake'}, {'id': 6659, 'synset': 'coat_button.n.01', 'name': 'coat_button'}, {'id': 6660, 'synset': 'coat_closet.n.01', 'name': 'coat_closet'}, {'id': 6661, 'synset': 'coatdress.n.01', 'name': 'coatdress'}, {'id': 6662, 'synset': 'coatee.n.01', 'name': 'coatee'}, {'id': 6663, 'synset': 'coating.n.01', 'name': 'coating'}, {'id': 6664, 'synset': 'coating.n.03', 'name': 'coating'}, {'id': 6665, 'synset': 'coat_of_paint.n.01', 'name': 'coat_of_paint'}, {'id': 6666, 'synset': 'coattail.n.01', 'name': 'coattail'}, {'id': 6667, 'synset': 'coaxial_cable.n.01', 'name': 'coaxial_cable'}, {'id': 6668, 'synset': 'cobweb.n.03', 'name': 'cobweb'}, {'id': 6669, 'synset': 'cobweb.n.01', 'name': 'cobweb'}, {'id': 6670, 'synset': 'cockcroft_and_walton_accelerator.n.01', 'name': 'Cockcroft_and_Walton_accelerator'}, {'id': 6671, 'synset': 'cocked_hat.n.01', 'name': 'cocked_hat'}, {'id': 6672, 'synset': 'cockhorse.n.01', 'name': 'cockhorse'}, {'id': 6673, 'synset': 'cockleshell.n.01', 'name': 'cockleshell'}, {'id': 6674, 'synset': 'cockpit.n.01', 'name': 'cockpit'}, {'id': 6675, 'synset': 'cockpit.n.03', 'name': 'cockpit'}, {'id': 6676, 'synset': 'cockpit.n.02', 'name': 'cockpit'}, {'id': 6677, 'synset': 'cockscomb.n.03', 'name': 'cockscomb'}, {'id': 6678, 'synset': 'cocktail_dress.n.01', 'name': 'cocktail_dress'}, {'id': 6679, 'synset': 'cocktail_lounge.n.01', 'name': 'cocktail_lounge'}, {'id': 6680, 'synset': 'cocktail_shaker.n.01', 'name': 'cocktail_shaker'}, {'id': 6681, 'synset': 'cocotte.n.02', 'name': 'cocotte'}, {'id': 6682, 'synset': 'codpiece.n.01', 'name': 'codpiece'}, {'id': 6683, 'synset': 'coelostat.n.01', 'name': 'coelostat'}, {'id': 6684, 'synset': 'coffee_can.n.01', 'name': 'coffee_can'}, {'id': 6685, 'synset': 'coffee_cup.n.01', 'name': 'coffee_cup'}, {'id': 6686, 'synset': 'coffee_filter.n.01', 'name': 'coffee_filter'}, {'id': 6687, 'synset': 'coffee_mill.n.01', 'name': 'coffee_mill'}, {'id': 6688, 'synset': 'coffee_mug.n.01', 'name': 'coffee_mug'}, {'id': 6689, 'synset': 'coffee_stall.n.01', 'name': 'coffee_stall'}, {'id': 6690, 'synset': 'coffee_urn.n.01', 'name': 'coffee_urn'}, {'id': 6691, 'synset': 'coffer.n.02', 'name': 'coffer'}, {'id': 6692, 'synset': 'coffey_still.n.01', 'name': 'Coffey_still'}, {'id': 6693, 'synset': 'coffin.n.01', 'name': 'coffin'}, {'id': 6694, 'synset': 'cog.n.02', 'name': 'cog'}, {'id': 6695, 'synset': 'coif.n.02', 'name': 'coif'}, {'id': 6696, 'synset': 'coil.n.01', 'name': 'coil'}, {'id': 6697, 'synset': 'coil.n.06', 'name': 'coil'}, {'id': 6698, 'synset': 'coil.n.03', 'name': 'coil'}, {'id': 6699, 'synset': 'coil_spring.n.01', 'name': 'coil_spring'}, {'id': 6700, 'synset': 'coin_box.n.01', 'name': 'coin_box'}, {'id': 6701, 'synset': 'cold_cathode.n.01', 'name': 'cold_cathode'}, {'id': 6702, 'synset': 'cold_chisel.n.01', 'name': 'cold_chisel'}, {'id': 6703, 'synset': 'cold_cream.n.01', 'name': 'cold_cream'}, {'id': 6704, 'synset': 'cold_frame.n.01', 'name': 'cold_frame'}, {'id': 6705, 'synset': 'collar.n.01', 'name': 'collar'}, {'id': 6706, 'synset': 'collar.n.03', 'name': 'collar'}, {'id': 6707, 'synset': 'college.n.03', 'name': 'college'}, {'id': 6708, 'synset': 'collet.n.02', 'name': 'collet'}, {'id': 6709, 'synset': 'collider.n.01', 'name': 'collider'}, {'id': 6710, 'synset': 'colliery.n.01', 'name': 'colliery'}, {'id': 6711, 'synset': 'collimator.n.02', 'name': 'collimator'}, {'id': 6712, 'synset': 'collimator.n.01', 'name': 'collimator'}, {'id': 6713, 'synset': 'cologne.n.02', 'name': 'cologne'}, {'id': 6714, 'synset': 'colonnade.n.01', 'name': 'colonnade'}, {'id': 6715, 'synset': 'colonoscope.n.01', 'name': 'colonoscope'}, {'id': 6716, 'synset': 'colorimeter.n.01', 'name': 'colorimeter'}, {'id': 6717, 'synset': 'colors.n.02', 'name': 'colors'}, {'id': 6718, 'synset': 'color_television.n.01', 'name': 'color_television'}, {'id': 6719, 'synset': 'color_tube.n.01', 'name': 'color_tube'}, {'id': 6720, 'synset': 'color_wash.n.01', 'name': 'color_wash'}, {'id': 6721, 'synset': 'colt.n.02', 'name': 'Colt'}, {'id': 6722, 'synset': 'colter.n.01', 'name': 'colter'}, {'id': 6723, 'synset': 'columbarium.n.03', 'name': 'columbarium'}, {'id': 6724, 'synset': 'columbarium.n.02', 'name': 'columbarium'}, {'id': 6725, 'synset': 'column.n.07', 'name': 'column'}, {'id': 6726, 'synset': 'column.n.06', 'name': 'column'}, {'id': 6727, 'synset': 'comb.n.01', 'name': 'comb'}, {'id': 6728, 'synset': 'comb.n.03', 'name': 'comb'}, {'id': 6729, 'synset': 'comber.n.03', 'name': 'comber'}, {'id': 6730, 'synset': 'combination_plane.n.01', 'name': 'combination_plane'}, {'id': 6731, 'synset': 'combine.n.01', 'name': 'combine'}, {'id': 6732, 'synset': 'command_module.n.01', 'name': 'command_module'}, {'id': 6733, 'synset': 'commissary.n.01', 'name': 'commissary'}, {'id': 6734, 'synset': 'commissary.n.02', 'name': 'commissary'}, {'id': 6735, 'synset': 'commodity.n.01', 'name': 'commodity'}, {'id': 6736, 'synset': 'common_ax.n.01', 'name': 'common_ax'}, {'id': 6737, 'synset': 'common_room.n.01', 'name': 'common_room'}, {'id': 6738, 'synset': 'communications_satellite.n.01', 'name': 'communications_satellite'}, {'id': 6739, 'synset': 'communication_system.n.01', 'name': 'communication_system'}, {'id': 6740, 'synset': 'community_center.n.01', 'name': 'community_center'}, {'id': 6741, 'synset': 'commutator.n.01', 'name': 'commutator'}, {'id': 6742, 'synset': 'commuter.n.01', 'name': 'commuter'}, {'id': 6743, 'synset': 'compact.n.01', 'name': 'compact'}, {'id': 6744, 'synset': 'compact.n.03', 'name': 'compact'}, {'id': 6745, 'synset': 'compact_disk.n.01', 'name': 'compact_disk'}, {'id': 6746, 'synset': 'compact-disk_burner.n.01', 'name': 'compact-disk_burner'}, {'id': 6747, 'synset': 'companionway.n.01', 'name': 'companionway'}, {'id': 6748, 'synset': 'compartment.n.02', 'name': 'compartment'}, {'id': 6749, 'synset': 'compartment.n.01', 'name': 'compartment'}, {'id': 6750, 'synset': 'compass.n.04', 'name': 'compass'}, {'id': 6751, 'synset': 'compass_card.n.01', 'name': 'compass_card'}, {'id': 6752, 'synset': 'compass_saw.n.01', 'name': 'compass_saw'}, {'id': 6753, 'synset': 'compound.n.03', 'name': 'compound'}, {'id': 6754, 'synset': 'compound_lens.n.01', 'name': 'compound_lens'}, {'id': 6755, 'synset': 'compound_lever.n.01', 'name': 'compound_lever'}, {'id': 6756, 'synset': 'compound_microscope.n.01', 'name': 'compound_microscope'}, {'id': 6757, 'synset': 'compress.n.01', 'name': 'compress'}, {'id': 6758, 'synset': 'compression_bandage.n.01', 'name': 'compression_bandage'}, {'id': 6759, 'synset': 'compressor.n.01', 'name': 'compressor'}, {'id': 6760, 'synset': 'computer.n.01', 'name': 'computer'}, {'id': 6761, 'synset': 'computer_circuit.n.01', 'name': 'computer_circuit'}, {'id': 6762, 'synset': 'computerized_axial_tomography_scanner.n.01', 'name': 'computerized_axial_tomography_scanner'}, {'id': 6763, 'synset': 'computer_monitor.n.01', 'name': 'computer_monitor'}, {'id': 6764, 'synset': 'computer_network.n.01', 'name': 'computer_network'}, {'id': 6765, 'synset': 'computer_screen.n.01', 'name': 'computer_screen'}, {'id': 6766, 'synset': 'computer_store.n.01', 'name': 'computer_store'}, {'id': 6767, 'synset': 'computer_system.n.01', 'name': 'computer_system'}, {'id': 6768, 'synset': 'concentration_camp.n.01', 'name': 'concentration_camp'}, {'id': 6769, 'synset': 'concert_grand.n.01', 'name': 'concert_grand'}, {'id': 6770, 'synset': 'concert_hall.n.01', 'name': 'concert_hall'}, {'id': 6771, 'synset': 'concertina.n.02', 'name': 'concertina'}, {'id': 6772, 'synset': 'concertina.n.01', 'name': 'concertina'}, {'id': 6773, 'synset': 'concrete_mixer.n.01', 'name': 'concrete_mixer'}, {'id': 6774, 'synset': 'condensation_pump.n.01', 'name': 'condensation_pump'}, {'id': 6775, 'synset': 'condenser.n.04', 'name': 'condenser'}, {'id': 6776, 'synset': 'condenser.n.03', 'name': 'condenser'}, {'id': 6777, 'synset': 'condenser.n.02', 'name': 'condenser'}, {'id': 6778, 'synset': 'condenser_microphone.n.01', 'name': 'condenser_microphone'}, {'id': 6779, 'synset': 'condominium.n.02', 'name': 'condominium'}, {'id': 6780, 'synset': 'condominium.n.01', 'name': 'condominium'}, {'id': 6781, 'synset': 'conductor.n.04', 'name': 'conductor'}, {'id': 6782, 'synset': 'cone_clutch.n.01', 'name': 'cone_clutch'}, {'id': 6783, 'synset': 'confectionery.n.02', 'name': 'confectionery'}, {'id': 6784, 'synset': 'conference_center.n.01', 'name': 'conference_center'}, {'id': 6785, 'synset': 'conference_room.n.01', 'name': 'conference_room'}, {'id': 6786, 'synset': 'conference_table.n.01', 'name': 'conference_table'}, {'id': 6787, 'synset': 'confessional.n.01', 'name': 'confessional'}, {'id': 6788, 'synset': 'conformal_projection.n.01', 'name': 'conformal_projection'}, {'id': 6789, 'synset': 'congress_boot.n.01', 'name': 'congress_boot'}, {'id': 6790, 'synset': 'conic_projection.n.01', 'name': 'conic_projection'}, {'id': 6791, 'synset': 'connecting_rod.n.01', 'name': 'connecting_rod'}, {'id': 6792, 'synset': 'connecting_room.n.01', 'name': 'connecting_room'}, {'id': 6793, 'synset': 'connection.n.03', 'name': 'connection'}, {'id': 6794, 'synset': 'conning_tower.n.02', 'name': 'conning_tower'}, {'id': 6795, 'synset': 'conning_tower.n.01', 'name': 'conning_tower'}, {'id': 6796, 'synset': 'conservatory.n.03', 'name': 'conservatory'}, {'id': 6797, 'synset': 'conservatory.n.02', 'name': 'conservatory'}, {'id': 6798, 'synset': 'console.n.03', 'name': 'console'}, {'id': 6799, 'synset': 'console.n.02', 'name': 'console'}, {'id': 6800, 'synset': 'console_table.n.01', 'name': 'console_table'}, {'id': 6801, 'synset': 'consulate.n.01', 'name': 'consulate'}, {'id': 6802, 'synset': 'contact.n.07', 'name': 'contact'}, {'id': 6803, 'synset': 'contact.n.09', 'name': 'contact'}, {'id': 6804, 'synset': 'container.n.01', 'name': 'container'}, {'id': 6805, 'synset': 'container_ship.n.01', 'name': 'container_ship'}, {'id': 6806, 'synset': 'containment.n.02', 'name': 'containment'}, {'id': 6807, 'synset': 'contrabassoon.n.01', 'name': 'contrabassoon'}, {'id': 6808, 'synset': 'control_center.n.01', 'name': 'control_center'}, {'id': 6809, 'synset': 'control_circuit.n.01', 'name': 'control_circuit'}, {'id': 6810, 'synset': 'control_key.n.01', 'name': 'control_key'}, {'id': 6811, 'synset': 'control_panel.n.01', 'name': 'control_panel'}, {'id': 6812, 'synset': 'control_rod.n.01', 'name': 'control_rod'}, {'id': 6813, 'synset': 'control_room.n.01', 'name': 'control_room'}, {'id': 6814, 'synset': 'control_system.n.01', 'name': 'control_system'}, {'id': 6815, 'synset': 'control_tower.n.01', 'name': 'control_tower'}, {'id': 6816, 'synset': 'convector.n.01', 'name': 'convector'}, {'id': 6817, 'synset': 'convenience_store.n.01', 'name': 'convenience_store'}, {'id': 6818, 'synset': 'convent.n.01', 'name': 'convent'}, {'id': 6819, 'synset': 'conventicle.n.02', 'name': 'conventicle'}, {'id': 6820, 'synset': 'converging_lens.n.01', 'name': 'converging_lens'}, {'id': 6821, 'synset': 'converter.n.01', 'name': 'converter'}, {'id': 6822, 'synset': 'conveyance.n.03', 'name': 'conveyance'}, {'id': 6823, 'synset': 'conveyer_belt.n.01', 'name': 'conveyer_belt'}, {'id': 6824, 'synset': 'cookfire.n.01', 'name': 'cookfire'}, {'id': 6825, 'synset': 'cookhouse.n.02', 'name': 'cookhouse'}, {'id': 6826, 'synset': 'cookie_cutter.n.01', 'name': 'cookie_cutter'}, {'id': 6827, 'synset': 'cookie_jar.n.01', 'name': 'cookie_jar'}, {'id': 6828, 'synset': 'cookie_sheet.n.01', 'name': 'cookie_sheet'}, {'id': 6829, 'synset': 'cookstove.n.01', 'name': 'cookstove'}, {'id': 6830, 'synset': 'coolant_system.n.01', 'name': 'coolant_system'}, {'id': 6831, 'synset': 'cooling_system.n.02', 'name': 'cooling_system'}, {'id': 6832, 'synset': 'cooling_system.n.01', 'name': 'cooling_system'}, {'id': 6833, 'synset': 'cooling_tower.n.01', 'name': 'cooling_tower'}, {'id': 6834, 'synset': 'coonskin_cap.n.01', 'name': 'coonskin_cap'}, {'id': 6835, 'synset': 'cope.n.02', 'name': 'cope'}, {'id': 6836, 'synset': 'coping_saw.n.01', 'name': 'coping_saw'}, {'id': 6837, 'synset': 'copperware.n.01', 'name': 'copperware'}, {'id': 6838, 'synset': 'copyholder.n.01', 'name': 'copyholder'}, {'id': 6839, 'synset': 'coquille.n.02', 'name': 'coquille'}, {'id': 6840, 'synset': 'coracle.n.01', 'name': 'coracle'}, {'id': 6841, 'synset': 'corbel.n.01', 'name': 'corbel'}, {'id': 6842, 'synset': 'corbel_arch.n.01', 'name': 'corbel_arch'}, {'id': 6843, 'synset': 'corbel_step.n.01', 'name': 'corbel_step'}, {'id': 6844, 'synset': 'corbie_gable.n.01', 'name': 'corbie_gable'}, {'id': 6845, 'synset': 'cord.n.04', 'name': 'cord'}, {'id': 6846, 'synset': 'cord.n.03', 'name': 'cord'}, {'id': 6847, 'synset': 'cordage.n.02', 'name': 'cordage'}, {'id': 6848, 'synset': 'cords.n.01', 'name': 'cords'}, {'id': 6849, 'synset': 'core.n.10', 'name': 'core'}, {'id': 6850, 'synset': 'core_bit.n.01', 'name': 'core_bit'}, {'id': 6851, 'synset': 'core_drill.n.01', 'name': 'core_drill'}, {'id': 6852, 'synset': 'corer.n.01', 'name': 'corer'}, {'id': 6853, 'synset': 'corker.n.02', 'name': 'corker'}, {'id': 6854, 'synset': 'corncrib.n.01', 'name': 'corncrib'}, {'id': 6855, 'synset': 'corner.n.11', 'name': 'corner'}, {'id': 6856, 'synset': 'corner.n.03', 'name': 'corner'}, {'id': 6857, 'synset': 'corner_post.n.01', 'name': 'corner_post'}, {'id': 6858, 'synset': 'cornice.n.03', 'name': 'cornice'}, {'id': 6859, 'synset': 'cornice.n.02', 'name': 'cornice'}, {'id': 6860, 'synset': 'correctional_institution.n.01', 'name': 'correctional_institution'}, {'id': 6861, 'synset': 'corrugated_fastener.n.01', 'name': 'corrugated_fastener'}, {'id': 6862, 'synset': 'corselet.n.01', 'name': 'corselet'}, {'id': 6863, 'synset': 'cosmetic.n.01', 'name': 'cosmetic'}, {'id': 6864, 'synset': 'cosmotron.n.01', 'name': 'cosmotron'}, {'id': 6865, 'synset': 'costume.n.01', 'name': 'costume'}, {'id': 6866, 'synset': 'costume.n.02', 'name': 'costume'}, {'id': 6867, 'synset': 'costume.n.03', 'name': 'costume'}, {'id': 6868, 'synset': 'cosy.n.01', 'name': 'cosy'}, {'id': 6869, 'synset': 'cot.n.03', 'name': 'cot'}, {'id': 6870, 'synset': 'cottage_tent.n.01', 'name': 'cottage_tent'}, {'id': 6871, 'synset': 'cotter.n.03', 'name': 'cotter'}, {'id': 6872, 'synset': 'cotter_pin.n.01', 'name': 'cotter_pin'}, {'id': 6873, 'synset': 'cotton.n.02', 'name': 'cotton'}, {'id': 6874, 'synset': 'cotton_flannel.n.01', 'name': 'cotton_flannel'}, {'id': 6875, 'synset': 'cotton_mill.n.01', 'name': 'cotton_mill'}, {'id': 6876, 'synset': 'couch.n.03', 'name': 'couch'}, {'id': 6877, 'synset': 'couch.n.02', 'name': 'couch'}, {'id': 6878, 'synset': 'couchette.n.01', 'name': 'couchette'}, {'id': 6879, 'synset': 'coude_telescope.n.01', 'name': 'coude_telescope'}, {'id': 6880, 'synset': 'counter.n.01', 'name': 'counter'}, {'id': 6881, 'synset': 'counter.n.03', 'name': 'counter'}, {'id': 6882, 'synset': 'counter.n.02', 'name': 'counter'}, {'id': 6883, 'synset': 'counterbore.n.01', 'name': 'counterbore'}, {'id': 6884, 'synset': 'counter_tube.n.01', 'name': 'counter_tube'}, {'id': 6885, 'synset': 'country_house.n.01', 'name': 'country_house'}, {'id': 6886, 'synset': 'country_store.n.01', 'name': 'country_store'}, {'id': 6887, 'synset': 'coupe.n.01', 'name': 'coupe'}, {'id': 6888, 'synset': 'coupling.n.02', 'name': 'coupling'}, {'id': 6889, 'synset': 'court.n.10', 'name': 'court'}, {'id': 6890, 'synset': 'court.n.04', 'name': 'court'}, {'id': 6891, 'synset': 'court.n.02', 'name': 'court'}, {'id': 6892, 'synset': 'court.n.09', 'name': 'court'}, {'id': 6893, 'synset': 'courtelle.n.01', 'name': 'Courtelle'}, {'id': 6894, 'synset': 'courthouse.n.02', 'name': 'courthouse'}, {'id': 6895, 'synset': 'courthouse.n.01', 'name': 'courthouse'}, {'id': 6896, 'synset': 'covered_bridge.n.01', 'name': 'covered_bridge'}, {'id': 6897, 'synset': 'covered_couch.n.01', 'name': 'covered_couch'}, {'id': 6898, 'synset': 'covered_wagon.n.01', 'name': 'covered_wagon'}, {'id': 6899, 'synset': 'covering.n.02', 'name': 'covering'}, {'id': 6900, 'synset': 'coverlet.n.01', 'name': 'coverlet'}, {'id': 6901, 'synset': 'cover_plate.n.01', 'name': 'cover_plate'}, {'id': 6902, 'synset': 'cowbarn.n.01', 'name': 'cowbarn'}, {'id': 6903, 'synset': 'cowboy_boot.n.01', 'name': 'cowboy_boot'}, {'id': 6904, 'synset': 'cowhide.n.03', 'name': 'cowhide'}, {'id': 6905, 'synset': 'cowl.n.02', 'name': 'cowl'}, {'id': 6906, 'synset': 'cow_pen.n.01', 'name': 'cow_pen'}, {'id': 6907, 'synset': 'cpu_board.n.01', 'name': 'CPU_board'}, {'id': 6908, 'synset': 'crackle.n.02', 'name': 'crackle'}, {'id': 6909, 'synset': 'cradle.n.01', 'name': 'cradle'}, {'id': 6910, 'synset': 'craft.n.02', 'name': 'craft'}, {'id': 6911, 'synset': 'cramp.n.03', 'name': 'cramp'}, {'id': 6912, 'synset': 'crampon.n.02', 'name': 'crampon'}, {'id': 6913, 'synset': 'crampon.n.01', 'name': 'crampon'}, {'id': 6914, 'synset': 'crane.n.04', 'name': 'crane'}, {'id': 6915, 'synset': 'craniometer.n.01', 'name': 'craniometer'}, {'id': 6916, 'synset': 'crank.n.04', 'name': 'crank'}, {'id': 6917, 'synset': 'crankcase.n.01', 'name': 'crankcase'}, {'id': 6918, 'synset': 'crankshaft.n.01', 'name': 'crankshaft'}, {'id': 6919, 'synset': 'crash_barrier.n.01', 'name': 'crash_barrier'}, {'id': 6920, 'synset': 'crash_helmet.n.01', 'name': 'crash_helmet'}, {'id': 6921, 'synset': 'cravat.n.01', 'name': 'cravat'}, {'id': 6922, 'synset': 'crazy_quilt.n.01', 'name': 'crazy_quilt'}, {'id': 6923, 'synset': 'cream.n.03', 'name': 'cream'}, {'id': 6924, 'synset': 'creche.n.01', 'name': 'creche'}, {'id': 6925, 'synset': 'creche.n.02', 'name': 'creche'}, {'id': 6926, 'synset': 'credenza.n.01', 'name': 'credenza'}, {'id': 6927, 'synset': 'creel.n.01', 'name': 'creel'}, {'id': 6928, 'synset': 'crematory.n.02', 'name': 'crematory'}, {'id': 6929, 'synset': 'crematory.n.01', 'name': 'crematory'}, {'id': 6930, 'synset': 'crepe.n.03', 'name': 'crepe'}, {'id': 6931, 'synset': 'crepe_de_chine.n.01', 'name': 'crepe_de_Chine'}, {'id': 6932, 'synset': 'crescent_wrench.n.01', 'name': 'crescent_wrench'}, {'id': 6933, 'synset': 'cretonne.n.01', 'name': 'cretonne'}, {'id': 6934, 'synset': 'crib.n.03', 'name': 'crib'}, {'id': 6935, 'synset': 'cricket_ball.n.01', 'name': 'cricket_ball'}, {'id': 6936, 'synset': 'cricket_bat.n.01', 'name': 'cricket_bat'}, {'id': 6937, 'synset': 'cricket_equipment.n.01', 'name': 'cricket_equipment'}, {'id': 6938, 'synset': 'cringle.n.01', 'name': 'cringle'}, {'id': 6939, 'synset': 'crinoline.n.03', 'name': 'crinoline'}, {'id': 6940, 'synset': 'crinoline.n.02', 'name': 'crinoline'}, {'id': 6941, 'synset': 'crochet_needle.n.01', 'name': 'crochet_needle'}, {'id': 6942, 'synset': 'crock_pot.n.01', 'name': 'Crock_Pot'}, {'id': 6943, 'synset': 'crook.n.03', 'name': 'crook'}, {'id': 6944, 'synset': 'crookes_radiometer.n.01', 'name': 'Crookes_radiometer'}, {'id': 6945, 'synset': 'crookes_tube.n.01', 'name': 'Crookes_tube'}, {'id': 6946, 'synset': 'croquet_ball.n.01', 'name': 'croquet_ball'}, {'id': 6947, 'synset': 'croquet_equipment.n.01', 'name': 'croquet_equipment'}, {'id': 6948, 'synset': 'croquet_mallet.n.01', 'name': 'croquet_mallet'}, {'id': 6949, 'synset': 'cross.n.01', 'name': 'cross'}, {'id': 6950, 'synset': 'crossbar.n.03', 'name': 'crossbar'}, {'id': 6951, 'synset': 'crossbar.n.02', 'name': 'crossbar'}, {'id': 6952, 'synset': 'crossbench.n.01', 'name': 'crossbench'}, {'id': 6953, 'synset': 'cross_bit.n.01', 'name': 'cross_bit'}, {'id': 6954, 'synset': 'crossbow.n.01', 'name': 'crossbow'}, {'id': 6955, 'synset': 'crosscut_saw.n.01', 'name': 'crosscut_saw'}, {'id': 6956, 'synset': 'crossjack.n.01', 'name': 'crossjack'}, {'id': 6957, 'synset': 'crosspiece.n.02', 'name': 'crosspiece'}, {'id': 6958, 'synset': 'crotchet.n.04', 'name': 'crotchet'}, {'id': 6959, 'synset': "croupier's_rake.n.01", 'name': "croupier's_rake"}, {'id': 6960, 'synset': 'crown.n.11', 'name': 'crown'}, {'id': 6961, 'synset': 'crown_jewels.n.01', 'name': 'crown_jewels'}, {'id': 6962, 'synset': 'crown_lens.n.01', 'name': 'crown_lens'}, {'id': 6963, 'synset': "crow's_nest.n.01", 'name': "crow's_nest"}, {'id': 6964, 'synset': 'crucible.n.01', 'name': 'crucible'}, {'id': 6965, 'synset': 'cruet.n.01', 'name': 'cruet'}, {'id': 6966, 'synset': 'cruet-stand.n.01', 'name': 'cruet-stand'}, {'id': 6967, 'synset': 'cruise_control.n.01', 'name': 'cruise_control'}, {'id': 6968, 'synset': 'cruise_missile.n.01', 'name': 'cruise_missile'}, {'id': 6969, 'synset': 'cruiser.n.02', 'name': 'cruiser'}, {'id': 6970, 'synset': 'crupper.n.01', 'name': 'crupper'}, {'id': 6971, 'synset': 'cruse.n.01', 'name': 'cruse'}, {'id': 6972, 'synset': 'crusher.n.01', 'name': 'crusher'}, {'id': 6973, 'synset': 'cryometer.n.01', 'name': 'cryometer'}, {'id': 6974, 'synset': 'cryoscope.n.01', 'name': 'cryoscope'}, {'id': 6975, 'synset': 'cryostat.n.01', 'name': 'cryostat'}, {'id': 6976, 'synset': 'crypt.n.01', 'name': 'crypt'}, {'id': 6977, 'synset': 'crystal.n.06', 'name': 'crystal'}, {'id': 6978, 'synset': 'crystal_detector.n.01', 'name': 'crystal_detector'}, {'id': 6979, 'synset': 'crystal_microphone.n.01', 'name': 'crystal_microphone'}, {'id': 6980, 'synset': 'crystal_oscillator.n.01', 'name': 'crystal_oscillator'}, {'id': 6981, 'synset': 'crystal_set.n.01', 'name': 'crystal_set'}, {'id': 6982, 'synset': 'cubitiere.n.01', 'name': 'cubitiere'}, {'id': 6983, 'synset': 'cucking_stool.n.01', 'name': 'cucking_stool'}, {'id': 6984, 'synset': 'cuckoo_clock.n.01', 'name': 'cuckoo_clock'}, {'id': 6985, 'synset': 'cuddy.n.01', 'name': 'cuddy'}, {'id': 6986, 'synset': 'cudgel.n.01', 'name': 'cudgel'}, {'id': 6987, 'synset': 'cue.n.04', 'name': 'cue'}, {'id': 6988, 'synset': 'cue_ball.n.01', 'name': 'cue_ball'}, {'id': 6989, 'synset': 'cuff.n.01', 'name': 'cuff'}, {'id': 6990, 'synset': 'cuirass.n.01', 'name': 'cuirass'}, {'id': 6991, 'synset': 'cuisse.n.01', 'name': 'cuisse'}, {'id': 6992, 'synset': 'cul.n.01', 'name': 'cul'}, {'id': 6993, 'synset': 'culdoscope.n.01', 'name': 'culdoscope'}, {'id': 6994, 'synset': 'cullis.n.01', 'name': 'cullis'}, {'id': 6995, 'synset': 'culotte.n.01', 'name': 'culotte'}, {'id': 6996, 'synset': 'cultivator.n.02', 'name': 'cultivator'}, {'id': 6997, 'synset': 'culverin.n.02', 'name': 'culverin'}, {'id': 6998, 'synset': 'culverin.n.01', 'name': 'culverin'}, {'id': 6999, 'synset': 'culvert.n.01', 'name': 'culvert'}, {'id': 7000, 'synset': 'cup_hook.n.01', 'name': 'cup_hook'}, {'id': 7001, 'synset': 'cupola.n.02', 'name': 'cupola'}, {'id': 7002, 'synset': 'cupola.n.01', 'name': 'cupola'}, {'id': 7003, 'synset': 'curb.n.02', 'name': 'curb'}, {'id': 7004, 'synset': 'curb_roof.n.01', 'name': 'curb_roof'}, {'id': 7005, 'synset': 'curbstone.n.01', 'name': 'curbstone'}, {'id': 7006, 'synset': 'curette.n.01', 'name': 'curette'}, {'id': 7007, 'synset': 'currycomb.n.01', 'name': 'currycomb'}, {'id': 7008, 'synset': 'cursor.n.01', 'name': 'cursor'}, {'id': 7009, 'synset': 'customhouse.n.01', 'name': 'customhouse'}, {'id': 7010, 'synset': 'cutaway.n.01', 'name': 'cutaway'}, {'id': 7011, 'synset': 'cutlas.n.01', 'name': 'cutlas'}, {'id': 7012, 'synset': 'cutoff.n.03', 'name': 'cutoff'}, {'id': 7013, 'synset': 'cutout.n.01', 'name': 'cutout'}, {'id': 7014, 'synset': 'cutter.n.06', 'name': 'cutter'}, {'id': 7015, 'synset': 'cutter.n.05', 'name': 'cutter'}, {'id': 7016, 'synset': 'cutting_implement.n.01', 'name': 'cutting_implement'}, {'id': 7017, 'synset': 'cutting_room.n.01', 'name': 'cutting_room'}, {'id': 7018, 'synset': 'cutty_stool.n.01', 'name': 'cutty_stool'}, {'id': 7019, 'synset': 'cutwork.n.01', 'name': 'cutwork'}, {'id': 7020, 'synset': 'cybercafe.n.01', 'name': 'cybercafe'}, {'id': 7021, 'synset': 'cyclopean_masonry.n.01', 'name': 'cyclopean_masonry'}, {'id': 7022, 'synset': 'cyclostyle.n.01', 'name': 'cyclostyle'}, {'id': 7023, 'synset': 'cyclotron.n.01', 'name': 'cyclotron'}, {'id': 7024, 'synset': 'cylinder.n.03', 'name': 'cylinder'}, {'id': 7025, 'synset': 'cylinder_lock.n.01', 'name': 'cylinder_lock'}, {'id': 7026, 'synset': 'dacha.n.01', 'name': 'dacha'}, {'id': 7027, 'synset': 'dacron.n.01', 'name': 'Dacron'}, {'id': 7028, 'synset': 'dado.n.02', 'name': 'dado'}, {'id': 7029, 'synset': 'dado_plane.n.01', 'name': 'dado_plane'}, {'id': 7030, 'synset': 'dairy.n.01', 'name': 'dairy'}, {'id': 7031, 'synset': 'dais.n.01', 'name': 'dais'}, {'id': 7032, 'synset': 'daisy_print_wheel.n.01', 'name': 'daisy_print_wheel'}, {'id': 7033, 'synset': 'daisywheel_printer.n.01', 'name': 'daisywheel_printer'}, {'id': 7034, 'synset': 'dam.n.01', 'name': 'dam'}, {'id': 7035, 'synset': 'damask.n.02', 'name': 'damask'}, {'id': 7036, 'synset': 'dampener.n.01', 'name': 'dampener'}, {'id': 7037, 'synset': 'damper.n.02', 'name': 'damper'}, {'id': 7038, 'synset': 'damper_block.n.01', 'name': 'damper_block'}, {'id': 7039, 'synset': 'dark_lantern.n.01', 'name': 'dark_lantern'}, {'id': 7040, 'synset': 'darkroom.n.01', 'name': 'darkroom'}, {'id': 7041, 'synset': 'darning_needle.n.01', 'name': 'darning_needle'}, {'id': 7042, 'synset': 'dart.n.02', 'name': 'dart'}, {'id': 7043, 'synset': 'dart.n.01', 'name': 'dart'}, {'id': 7044, 'synset': 'dashboard.n.02', 'name': 'dashboard'}, {'id': 7045, 'synset': 'dashiki.n.01', 'name': 'dashiki'}, {'id': 7046, 'synset': 'dash-pot.n.01', 'name': 'dash-pot'}, {'id': 7047, 'synset': 'data_converter.n.01', 'name': 'data_converter'}, {'id': 7048, 'synset': 'data_input_device.n.01', 'name': 'data_input_device'}, {'id': 7049, 'synset': 'data_multiplexer.n.01', 'name': 'data_multiplexer'}, {'id': 7050, 'synset': 'data_system.n.01', 'name': 'data_system'}, {'id': 7051, 'synset': 'davenport.n.03', 'name': 'davenport'}, {'id': 7052, 'synset': 'davenport.n.02', 'name': 'davenport'}, {'id': 7053, 'synset': 'davit.n.01', 'name': 'davit'}, {'id': 7054, 'synset': 'daybed.n.01', 'name': 'daybed'}, {'id': 7055, 'synset': 'daybook.n.02', 'name': 'daybook'}, {'id': 7056, 'synset': 'day_nursery.n.01', 'name': 'day_nursery'}, {'id': 7057, 'synset': 'day_school.n.03', 'name': 'day_school'}, {'id': 7058, 'synset': 'dead_axle.n.01', 'name': 'dead_axle'}, {'id': 7059, 'synset': 'deadeye.n.02', 'name': 'deadeye'}, {'id': 7060, 'synset': 'deadhead.n.02', 'name': 'deadhead'}, {'id': 7061, 'synset': 'deanery.n.01', 'name': 'deanery'}, {'id': 7062, 'synset': 'deathbed.n.02', 'name': 'deathbed'}, {'id': 7063, 'synset': 'death_camp.n.01', 'name': 'death_camp'}, {'id': 7064, 'synset': 'death_house.n.01', 'name': 'death_house'}, {'id': 7065, 'synset': 'death_knell.n.02', 'name': 'death_knell'}, {'id': 7066, 'synset': 'death_seat.n.01', 'name': 'death_seat'}, {'id': 7067, 'synset': 'deck.n.02', 'name': 'deck'}, {'id': 7068, 'synset': 'deck.n.04', 'name': 'deck'}, {'id': 7069, 'synset': 'deck-house.n.01', 'name': 'deck-house'}, {'id': 7070, 'synset': 'deckle.n.02', 'name': 'deckle'}, {'id': 7071, 'synset': 'deckle_edge.n.01', 'name': 'deckle_edge'}, {'id': 7072, 'synset': 'declinometer.n.01', 'name': 'declinometer'}, {'id': 7073, 'synset': 'decoder.n.02', 'name': 'decoder'}, {'id': 7074, 'synset': 'decolletage.n.01', 'name': 'decolletage'}, {'id': 7075, 'synset': 'decoupage.n.01', 'name': 'decoupage'}, {'id': 7076, 'synset': 'dedicated_file_server.n.01', 'name': 'dedicated_file_server'}, {'id': 7077, 'synset': 'deep-freeze.n.01', 'name': 'deep-freeze'}, {'id': 7078, 'synset': 'deerstalker.n.01', 'name': 'deerstalker'}, {'id': 7079, 'synset': 'defense_system.n.01', 'name': 'defense_system'}, {'id': 7080, 'synset': 'defensive_structure.n.01', 'name': 'defensive_structure'}, {'id': 7081, 'synset': 'defibrillator.n.01', 'name': 'defibrillator'}, {'id': 7082, 'synset': 'defilade.n.01', 'name': 'defilade'}, {'id': 7083, 'synset': 'deflector.n.01', 'name': 'deflector'}, {'id': 7084, 'synset': 'delayed_action.n.01', 'name': 'delayed_action'}, {'id': 7085, 'synset': 'delay_line.n.01', 'name': 'delay_line'}, {'id': 7086, 'synset': 'delft.n.01', 'name': 'delft'}, {'id': 7087, 'synset': 'delicatessen.n.02', 'name': 'delicatessen'}, {'id': 7088, 'synset': 'delivery_truck.n.01', 'name': 'delivery_truck'}, {'id': 7089, 'synset': 'delta_wing.n.01', 'name': 'delta_wing'}, {'id': 7090, 'synset': 'demijohn.n.01', 'name': 'demijohn'}, {'id': 7091, 'synset': 'demitasse.n.02', 'name': 'demitasse'}, {'id': 7092, 'synset': 'den.n.04', 'name': 'den'}, {'id': 7093, 'synset': 'denim.n.02', 'name': 'denim'}, {'id': 7094, 'synset': 'densimeter.n.01', 'name': 'densimeter'}, {'id': 7095, 'synset': 'densitometer.n.01', 'name': 'densitometer'}, {'id': 7096, 'synset': 'dental_appliance.n.01', 'name': 'dental_appliance'}, {'id': 7097, 'synset': 'dental_implant.n.01', 'name': 'dental_implant'}, {'id': 7098, 'synset': "dentist's_drill.n.01", 'name': "dentist's_drill"}, {'id': 7099, 'synset': 'denture.n.01', 'name': 'denture'}, {'id': 7100, 'synset': 'deodorant.n.01', 'name': 'deodorant'}, {'id': 7101, 'synset': 'department_store.n.01', 'name': 'department_store'}, {'id': 7102, 'synset': 'departure_lounge.n.01', 'name': 'departure_lounge'}, {'id': 7103, 'synset': 'depilatory.n.02', 'name': 'depilatory'}, {'id': 7104, 'synset': 'depressor.n.03', 'name': 'depressor'}, {'id': 7105, 'synset': 'depth_finder.n.01', 'name': 'depth_finder'}, {'id': 7106, 'synset': 'depth_gauge.n.01', 'name': 'depth_gauge'}, {'id': 7107, 'synset': 'derrick.n.02', 'name': 'derrick'}, {'id': 7108, 'synset': 'derrick.n.01', 'name': 'derrick'}, {'id': 7109, 'synset': 'derringer.n.01', 'name': 'derringer'}, {'id': 7110, 'synset': 'desk_phone.n.01', 'name': 'desk_phone'}, {'id': 7111, 'synset': 'desktop_computer.n.01', 'name': 'desktop_computer'}, {'id': 7112, 'synset': 'dessert_spoon.n.01', 'name': 'dessert_spoon'}, {'id': 7113, 'synset': 'destroyer.n.01', 'name': 'destroyer'}, {'id': 7114, 'synset': 'destroyer_escort.n.01', 'name': 'destroyer_escort'}, {'id': 7115, 'synset': 'detached_house.n.01', 'name': 'detached_house'}, {'id': 7116, 'synset': 'detector.n.01', 'name': 'detector'}, {'id': 7117, 'synset': 'detector.n.03', 'name': 'detector'}, {'id': 7118, 'synset': 'detention_home.n.01', 'name': 'detention_home'}, {'id': 7119, 'synset': 'detonating_fuse.n.01', 'name': 'detonating_fuse'}, {'id': 7120, 'synset': 'detonator.n.01', 'name': 'detonator'}, {'id': 7121, 'synset': 'developer.n.02', 'name': 'developer'}, {'id': 7122, 'synset': 'device.n.01', 'name': 'device'}, {'id': 7123, 'synset': 'dewar_flask.n.01', 'name': 'Dewar_flask'}, {'id': 7124, 'synset': 'dhoti.n.01', 'name': 'dhoti'}, {'id': 7125, 'synset': 'dhow.n.01', 'name': 'dhow'}, {'id': 7126, 'synset': 'dial.n.04', 'name': 'dial'}, {'id': 7127, 'synset': 'dial.n.03', 'name': 'dial'}, {'id': 7128, 'synset': 'dial.n.02', 'name': 'dial'}, {'id': 7129, 'synset': 'dialog_box.n.01', 'name': 'dialog_box'}, {'id': 7130, 'synset': 'dial_telephone.n.01', 'name': 'dial_telephone'}, {'id': 7131, 'synset': 'dialyzer.n.01', 'name': 'dialyzer'}, {'id': 7132, 'synset': 'diamante.n.02', 'name': 'diamante'}, {'id': 7133, 'synset': 'diaper.n.02', 'name': 'diaper'}, {'id': 7134, 'synset': 'diaphone.n.01', 'name': 'diaphone'}, {'id': 7135, 'synset': 'diaphragm.n.01', 'name': 'diaphragm'}, {'id': 7136, 'synset': 'diaphragm.n.04', 'name': 'diaphragm'}, {'id': 7137, 'synset': 'diathermy_machine.n.01', 'name': 'diathermy_machine'}, {'id': 7138, 'synset': 'dibble.n.01', 'name': 'dibble'}, {'id': 7139, 'synset': 'dice_cup.n.01', 'name': 'dice_cup'}, {'id': 7140, 'synset': 'dicer.n.01', 'name': 'dicer'}, {'id': 7141, 'synset': 'dickey.n.02', 'name': 'dickey'}, {'id': 7142, 'synset': 'dickey.n.01', 'name': 'dickey'}, {'id': 7143, 'synset': 'dictaphone.n.01', 'name': 'Dictaphone'}, {'id': 7144, 'synset': 'die.n.03', 'name': 'die'}, {'id': 7145, 'synset': 'diesel.n.02', 'name': 'diesel'}, {'id': 7146, 'synset': 'diesel-electric_locomotive.n.01', 'name': 'diesel-electric_locomotive'}, {'id': 7147, 'synset': 'diesel-hydraulic_locomotive.n.01', 'name': 'diesel-hydraulic_locomotive'}, {'id': 7148, 'synset': 'diesel_locomotive.n.01', 'name': 'diesel_locomotive'}, {'id': 7149, 'synset': 'diestock.n.01', 'name': 'diestock'}, {'id': 7150, 'synset': 'differential_analyzer.n.01', 'name': 'differential_analyzer'}, {'id': 7151, 'synset': 'differential_gear.n.01', 'name': 'differential_gear'}, {'id': 7152, 'synset': 'diffuser.n.02', 'name': 'diffuser'}, {'id': 7153, 'synset': 'diffuser.n.01', 'name': 'diffuser'}, {'id': 7154, 'synset': 'digester.n.01', 'name': 'digester'}, {'id': 7155, 'synset': 'diggings.n.02', 'name': 'diggings'}, {'id': 7156, 'synset': 'digital-analog_converter.n.01', 'name': 'digital-analog_converter'}, {'id': 7157, 'synset': 'digital_audiotape.n.01', 'name': 'digital_audiotape'}, {'id': 7158, 'synset': 'digital_camera.n.01', 'name': 'digital_camera'}, {'id': 7159, 'synset': 'digital_clock.n.01', 'name': 'digital_clock'}, {'id': 7160, 'synset': 'digital_computer.n.01', 'name': 'digital_computer'}, {'id': 7161, 'synset': 'digital_display.n.01', 'name': 'digital_display'}, {'id': 7162, 'synset': 'digital_subscriber_line.n.01', 'name': 'digital_subscriber_line'}, {'id': 7163, 'synset': 'digital_voltmeter.n.01', 'name': 'digital_voltmeter'}, {'id': 7164, 'synset': 'digital_watch.n.01', 'name': 'digital_watch'}, {'id': 7165, 'synset': 'digitizer.n.01', 'name': 'digitizer'}, {'id': 7166, 'synset': 'dilator.n.03', 'name': 'dilator'}, {'id': 7167, 'synset': 'dildo.n.01', 'name': 'dildo'}, {'id': 7168, 'synset': 'dimity.n.01', 'name': 'dimity'}, {'id': 7169, 'synset': 'dimmer.n.01', 'name': 'dimmer'}, {'id': 7170, 'synset': 'diner.n.03', 'name': 'diner'}, {'id': 7171, 'synset': 'dinette.n.01', 'name': 'dinette'}, {'id': 7172, 'synset': 'dining_area.n.01', 'name': 'dining_area'}, {'id': 7173, 'synset': 'dining_car.n.01', 'name': 'dining_car'}, {'id': 7174, 'synset': 'dining-hall.n.01', 'name': 'dining-hall'}, {'id': 7175, 'synset': 'dining_room.n.01', 'name': 'dining_room'}, {'id': 7176, 'synset': 'dining-room_furniture.n.01', 'name': 'dining-room_furniture'}, {'id': 7177, 'synset': 'dining-room_table.n.01', 'name': 'dining-room_table'}, {'id': 7178, 'synset': 'dinner_bell.n.01', 'name': 'dinner_bell'}, {'id': 7179, 'synset': 'dinner_dress.n.01', 'name': 'dinner_dress'}, {'id': 7180, 'synset': 'dinner_napkin.n.01', 'name': 'dinner_napkin'}, {'id': 7181, 'synset': 'dinner_pail.n.01', 'name': 'dinner_pail'}, {'id': 7182, 'synset': 'dinner_table.n.01', 'name': 'dinner_table'}, {'id': 7183, 'synset': 'dinner_theater.n.01', 'name': 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'synset': 'discharge_lamp.n.01', 'name': 'discharge_lamp'}, {'id': 7200, 'synset': 'discharge_pipe.n.01', 'name': 'discharge_pipe'}, {'id': 7201, 'synset': 'disco.n.02', 'name': 'disco'}, {'id': 7202, 'synset': 'discount_house.n.01', 'name': 'discount_house'}, {'id': 7203, 'synset': 'discus.n.02', 'name': 'discus'}, {'id': 7204, 'synset': 'disguise.n.02', 'name': 'disguise'}, {'id': 7205, 'synset': 'dishpan.n.01', 'name': 'dishpan'}, {'id': 7206, 'synset': 'dish_rack.n.01', 'name': 'dish_rack'}, {'id': 7207, 'synset': 'disk.n.02', 'name': 'disk'}, {'id': 7208, 'synset': 'disk_brake.n.01', 'name': 'disk_brake'}, {'id': 7209, 'synset': 'disk_clutch.n.01', 'name': 'disk_clutch'}, {'id': 7210, 'synset': 'disk_controller.n.01', 'name': 'disk_controller'}, {'id': 7211, 'synset': 'disk_drive.n.01', 'name': 'disk_drive'}, {'id': 7212, 'synset': 'diskette.n.01', 'name': 'diskette'}, {'id': 7213, 'synset': 'disk_harrow.n.01', 'name': 'disk_harrow'}, {'id': 7214, 'synset': 'dispatch_case.n.01', 'name': 'dispatch_case'}, {'id': 7215, 'synset': 'dispensary.n.01', 'name': 'dispensary'}, {'id': 7216, 'synset': 'display.n.06', 'name': 'display'}, {'id': 7217, 'synset': 'display_adapter.n.01', 'name': 'display_adapter'}, {'id': 7218, 'synset': 'display_panel.n.01', 'name': 'display_panel'}, {'id': 7219, 'synset': 'display_window.n.01', 'name': 'display_window'}, {'id': 7220, 'synset': 'disposal.n.04', 'name': 'disposal'}, {'id': 7221, 'synset': 'disrupting_explosive.n.01', 'name': 'disrupting_explosive'}, {'id': 7222, 'synset': 'distaff.n.02', 'name': 'distaff'}, {'id': 7223, 'synset': 'distillery.n.01', 'name': 'distillery'}, {'id': 7224, 'synset': 'distributor.n.04', 'name': 'distributor'}, {'id': 7225, 'synset': 'distributor_cam.n.01', 'name': 'distributor_cam'}, {'id': 7226, 'synset': 'distributor_cap.n.01', 'name': 'distributor_cap'}, {'id': 7227, 'synset': 'distributor_housing.n.01', 'name': 'distributor_housing'}, {'id': 7228, 'synset': 'distributor_point.n.01', 'name': 'distributor_point'}, {'id': 7229, 'synset': 'ditch.n.01', 'name': 'ditch'}, {'id': 7230, 'synset': 'ditch_spade.n.01', 'name': 'ditch_spade'}, {'id': 7231, 'synset': 'ditty_bag.n.01', 'name': 'ditty_bag'}, {'id': 7232, 'synset': 'divan.n.01', 'name': 'divan'}, {'id': 7233, 'synset': 'divan.n.04', 'name': 'divan'}, {'id': 7234, 'synset': 'dive_bomber.n.01', 'name': 'dive_bomber'}, {'id': 7235, 'synset': 'diverging_lens.n.01', 'name': 'diverging_lens'}, {'id': 7236, 'synset': 'divided_highway.n.01', 'name': 'divided_highway'}, {'id': 7237, 'synset': 'divider.n.04', 'name': 'divider'}, {'id': 7238, 'synset': 'diving_bell.n.01', 'name': 'diving_bell'}, {'id': 7239, 'synset': 'divining_rod.n.01', 'name': 'divining_rod'}, {'id': 7240, 'synset': 'diving_suit.n.01', 'name': 'diving_suit'}, {'id': 7241, 'synset': 'dixie.n.02', 'name': 'dixie'}, {'id': 7242, 'synset': 'dock.n.05', 'name': 'dock'}, {'id': 7243, 'synset': 'doeskin.n.02', 'name': 'doeskin'}, {'id': 7244, 'synset': 'dogcart.n.01', 'name': 'dogcart'}, {'id': 7245, 'synset': 'doggie_bag.n.01', 'name': 'doggie_bag'}, {'id': 7246, 'synset': 'dogsled.n.01', 'name': 'dogsled'}, {'id': 7247, 'synset': 'dog_wrench.n.01', 'name': 'dog_wrench'}, {'id': 7248, 'synset': 'doily.n.01', 'name': 'doily'}, {'id': 7249, 'synset': 'dolly.n.02', 'name': 'dolly'}, {'id': 7250, 'synset': 'dolman.n.02', 'name': 'dolman'}, {'id': 7251, 'synset': 'dolman.n.01', 'name': 'dolman'}, {'id': 7252, 'synset': 'dolman_sleeve.n.01', 'name': 'dolman_sleeve'}, {'id': 7253, 'synset': 'dolmen.n.01', 'name': 'dolmen'}, {'id': 7254, 'synset': 'dome.n.04', 'name': 'dome'}, {'id': 7255, 'synset': 'dome.n.03', 'name': 'dome'}, {'id': 7256, 'synset': 'domino.n.03', 'name': 'domino'}, {'id': 7257, 'synset': 'dongle.n.01', 'name': 'dongle'}, {'id': 7258, 'synset': 'donkey_jacket.n.01', 'name': 'donkey_jacket'}, {'id': 7259, 'synset': 'door.n.01', 'name': 'door'}, {'id': 7260, 'synset': 'door.n.05', 'name': 'door'}, {'id': 7261, 'synset': 'door.n.04', 'name': 'door'}, {'id': 7262, 'synset': 'doorbell.n.01', 'name': 'doorbell'}, {'id': 7263, 'synset': 'doorframe.n.01', 'name': 'doorframe'}, {'id': 7264, 'synset': 'doorjamb.n.01', 'name': 'doorjamb'}, {'id': 7265, 'synset': 'doorlock.n.01', 'name': 'doorlock'}, {'id': 7266, 'synset': 'doornail.n.01', 'name': 'doornail'}, {'id': 7267, 'synset': 'doorplate.n.01', 'name': 'doorplate'}, {'id': 7268, 'synset': 'doorsill.n.01', 'name': 'doorsill'}, {'id': 7269, 'synset': 'doorstop.n.01', 'name': 'doorstop'}, {'id': 7270, 'synset': 'doppler_radar.n.01', 'name': 'Doppler_radar'}, {'id': 7271, 'synset': 'dormer.n.01', 'name': 'dormer'}, {'id': 7272, 'synset': 'dormer_window.n.01', 'name': 'dormer_window'}, {'id': 7273, 'synset': 'dormitory.n.01', 'name': 'dormitory'}, {'id': 7274, 'synset': 'dormitory.n.02', 'name': 'dormitory'}, {'id': 7275, 'synset': 'dosemeter.n.01', 'name': 'dosemeter'}, {'id': 7276, 'synset': 'dossal.n.01', 'name': 'dossal'}, {'id': 7277, 'synset': 'dot_matrix_printer.n.01', 'name': 'dot_matrix_printer'}, {'id': 7278, 'synset': 'double_bed.n.01', 'name': 'double_bed'}, {'id': 7279, 'synset': 'double-bitted_ax.n.01', 'name': 'double-bitted_ax'}, {'id': 7280, 'synset': 'double_boiler.n.01', 'name': 'double_boiler'}, {'id': 7281, 'synset': 'double-breasted_jacket.n.01', 'name': 'double-breasted_jacket'}, {'id': 7282, 'synset': 'double-breasted_suit.n.01', 'name': 'double-breasted_suit'}, {'id': 7283, 'synset': 'double_door.n.01', 'name': 'double_door'}, {'id': 7284, 'synset': 'double_glazing.n.01', 'name': 'double_glazing'}, {'id': 7285, 'synset': 'double-hung_window.n.01', 'name': 'double-hung_window'}, {'id': 7286, 'synset': 'double_knit.n.01', 'name': 'double_knit'}, {'id': 7287, 'synset': 'doubler.n.01', 'name': 'doubler'}, {'id': 7288, 'synset': 'double_reed.n.02', 'name': 'double_reed'}, {'id': 7289, 'synset': 'double-reed_instrument.n.01', 'name': 'double-reed_instrument'}, {'id': 7290, 'synset': 'doublet.n.01', 'name': 'doublet'}, {'id': 7291, 'synset': 'doubletree.n.01', 'name': 'doubletree'}, {'id': 7292, 'synset': 'douche.n.01', 'name': 'douche'}, {'id': 7293, 'synset': 'dovecote.n.01', 'name': 'dovecote'}, {'id': 7294, 'synset': "dover's_powder.n.01", 'name': "Dover's_powder"}, {'id': 7295, 'synset': 'dovetail.n.01', 'name': 'dovetail'}, {'id': 7296, 'synset': 'dovetail_plane.n.01', 'name': 'dovetail_plane'}, {'id': 7297, 'synset': 'dowel.n.01', 'name': 'dowel'}, {'id': 7298, 'synset': 'downstage.n.01', 'name': 'downstage'}, {'id': 7299, 'synset': 'drafting_instrument.n.01', 'name': 'drafting_instrument'}, {'id': 7300, 'synset': 'drafting_table.n.01', 'name': 'drafting_table'}, {'id': 7301, 'synset': 'dragunov.n.01', 'name': 'Dragunov'}, {'id': 7302, 'synset': 'drainage_ditch.n.01', 'name': 'drainage_ditch'}, {'id': 7303, 'synset': 'drainage_system.n.01', 'name': 'drainage_system'}, {'id': 7304, 'synset': 'drain_basket.n.01', 'name': 'drain_basket'}, {'id': 7305, 'synset': 'drainplug.n.01', 'name': 'drainplug'}, {'id': 7306, 'synset': 'drape.n.03', 'name': 'drape'}, {'id': 7307, 'synset': 'drapery.n.02', 'name': 'drapery'}, {'id': 7308, 'synset': 'drawbar.n.01', 'name': 'drawbar'}, {'id': 7309, 'synset': 'drawbridge.n.01', 'name': 'drawbridge'}, {'id': 7310, 'synset': 'drawing_chalk.n.01', 'name': 'drawing_chalk'}, {'id': 7311, 'synset': 'drawing_room.n.01', 'name': 'drawing_room'}, {'id': 7312, 'synset': 'drawing_room.n.02', 'name': 'drawing_room'}, {'id': 7313, 'synset': 'drawknife.n.01', 'name': 'drawknife'}, {'id': 7314, 'synset': 'drawstring_bag.n.01', 'name': 'drawstring_bag'}, {'id': 7315, 'synset': 'dray.n.01', 'name': 'dray'}, {'id': 7316, 'synset': 'dreadnought.n.01', 'name': 'dreadnought'}, {'id': 7317, 'synset': 'dredge.n.01', 'name': 'dredge'}, {'id': 7318, 'synset': 'dredger.n.01', 'name': 'dredger'}, {'id': 7319, 'synset': 'dredging_bucket.n.01', 'name': 'dredging_bucket'}, {'id': 7320, 'synset': 'dress_blues.n.01', 'name': 'dress_blues'}, {'id': 7321, 'synset': 'dressing.n.04', 'name': 'dressing'}, {'id': 7322, 'synset': 'dressing_case.n.01', 'name': 'dressing_case'}, {'id': 7323, 'synset': 'dressing_gown.n.01', 'name': 'dressing_gown'}, {'id': 7324, 'synset': 'dressing_room.n.01', 'name': 'dressing_room'}, {'id': 7325, 'synset': 'dressing_sack.n.01', 'name': 'dressing_sack'}, {'id': 7326, 'synset': 'dressing_table.n.01', 'name': 'dressing_table'}, {'id': 7327, 'synset': 'dress_rack.n.01', 'name': 'dress_rack'}, {'id': 7328, 'synset': 'dress_shirt.n.01', 'name': 'dress_shirt'}, {'id': 7329, 'synset': 'dress_uniform.n.01', 'name': 'dress_uniform'}, {'id': 7330, 'synset': 'drift_net.n.01', 'name': 'drift_net'}, {'id': 7331, 'synset': 'electric_drill.n.01', 'name': 'electric_drill'}, {'id': 7332, 'synset': 'drilling_platform.n.01', 'name': 'drilling_platform'}, {'id': 7333, 'synset': 'drill_press.n.01', 'name': 'drill_press'}, {'id': 7334, 'synset': 'drill_rig.n.01', 'name': 'drill_rig'}, {'id': 7335, 'synset': 'drinking_fountain.n.01', 'name': 'drinking_fountain'}, {'id': 7336, 'synset': 'drinking_vessel.n.01', 'name': 'drinking_vessel'}, {'id': 7337, 'synset': 'drip_loop.n.01', 'name': 'drip_loop'}, {'id': 7338, 'synset': 'drip_mat.n.01', 'name': 'drip_mat'}, {'id': 7339, 'synset': 'drip_pan.n.02', 'name': 'drip_pan'}, {'id': 7340, 'synset': 'dripping_pan.n.01', 'name': 'dripping_pan'}, {'id': 7341, 'synset': 'drip_pot.n.01', 'name': 'drip_pot'}, {'id': 7342, 'synset': 'drive.n.02', 'name': 'drive'}, {'id': 7343, 'synset': 'drive.n.10', 'name': 'drive'}, {'id': 7344, 'synset': 'drive_line.n.01', 'name': 'drive_line'}, {'id': 7345, 'synset': 'driver.n.05', 'name': 'driver'}, {'id': 7346, 'synset': 'driveshaft.n.01', 'name': 'driveshaft'}, {'id': 7347, 'synset': 'driveway.n.01', 'name': 'driveway'}, {'id': 7348, 'synset': 'driving_iron.n.01', 'name': 'driving_iron'}, {'id': 7349, 'synset': 'driving_wheel.n.01', 'name': 'driving_wheel'}, {'id': 7350, 'synset': 'drogue.n.04', 'name': 'drogue'}, {'id': 7351, 'synset': 'drogue_parachute.n.01', 'name': 'drogue_parachute'}, {'id': 7352, 'synset': 'drone.n.05', 'name': 'drone'}, {'id': 7353, 'synset': 'drop_arch.n.01', 'name': 'drop_arch'}, {'id': 7354, 'synset': 'drop_cloth.n.02', 'name': 'drop_cloth'}, {'id': 7355, 'synset': 'drop_curtain.n.01', 'name': 'drop_curtain'}, {'id': 7356, 'synset': 'drop_forge.n.01', 'name': 'drop_forge'}, {'id': 7357, 'synset': 'drop-leaf_table.n.01', 'name': 'drop-leaf_table'}, {'id': 7358, 'synset': 'droshky.n.01', 'name': 'droshky'}, {'id': 7359, 'synset': 'drove.n.03', 'name': 'drove'}, {'id': 7360, 'synset': 'drugget.n.01', 'name': 'drugget'}, {'id': 7361, 'synset': 'drugstore.n.01', 'name': 'drugstore'}, {'id': 7362, 'synset': 'drum.n.04', 'name': 'drum'}, {'id': 7363, 'synset': 'drum_brake.n.01', 'name': 'drum_brake'}, {'id': 7364, 'synset': 'drumhead.n.01', 'name': 'drumhead'}, {'id': 7365, 'synset': 'drum_printer.n.01', 'name': 'drum_printer'}, {'id': 7366, 'synset': 'drum_sander.n.01', 'name': 'drum_sander'}, {'id': 7367, 'synset': 'dry_battery.n.01', 'name': 'dry_battery'}, {'id': 7368, 'synset': 'dry-bulb_thermometer.n.01', 'name': 'dry-bulb_thermometer'}, {'id': 7369, 'synset': 'dry_cell.n.01', 'name': 'dry_cell'}, {'id': 7370, 'synset': 'dry_dock.n.01', 'name': 'dry_dock'}, {'id': 7371, 'synset': 'dryer.n.01', 'name': 'dryer'}, {'id': 7372, 'synset': 'dry_fly.n.01', 'name': 'dry_fly'}, {'id': 7373, 'synset': 'dry_kiln.n.01', 'name': 'dry_kiln'}, {'id': 7374, 'synset': 'dry_masonry.n.01', 'name': 'dry_masonry'}, {'id': 7375, 'synset': 'dry_point.n.02', 'name': 'dry_point'}, {'id': 7376, 'synset': 'dry_wall.n.02', 'name': 'dry_wall'}, {'id': 7377, 'synset': 'dual_scan_display.n.01', 'name': 'dual_scan_display'}, {'id': 7378, 'synset': 'duck.n.04', 'name': 'duck'}, {'id': 7379, 'synset': 'duckboard.n.01', 'name': 'duckboard'}, {'id': 7380, 'synset': 'duckpin.n.01', 'name': 'duckpin'}, {'id': 7381, 'synset': 'dudeen.n.01', 'name': 'dudeen'}, {'id': 7382, 'synset': 'duffel.n.02', 'name': 'duffel'}, {'id': 7383, 'synset': 'duffel_coat.n.01', 'name': 'duffel_coat'}, {'id': 7384, 'synset': 'dugout.n.01', 'name': 'dugout'}, {'id': 7385, 'synset': 'dugout_canoe.n.01', 'name': 'dugout_canoe'}, {'id': 7386, 'synset': 'dulciana.n.01', 'name': 'dulciana'}, {'id': 7387, 'synset': 'dulcimer.n.02', 'name': 'dulcimer'}, {'id': 7388, 'synset': 'dulcimer.n.01', 'name': 'dulcimer'}, {'id': 7389, 'synset': 'dumb_bomb.n.01', 'name': 'dumb_bomb'}, {'id': 7390, 'synset': 'dumbwaiter.n.01', 'name': 'dumbwaiter'}, {'id': 7391, 'synset': 'dumdum.n.01', 'name': 'dumdum'}, {'id': 7392, 'synset': 'dumpcart.n.01', 'name': 'dumpcart'}, {'id': 7393, 'synset': 'dump_truck.n.01', 'name': 'dump_truck'}, {'id': 7394, 'synset': 'dumpy_level.n.01', 'name': 'Dumpy_level'}, {'id': 7395, 'synset': 'dunce_cap.n.01', 'name': 'dunce_cap'}, {'id': 7396, 'synset': 'dune_buggy.n.01', 'name': 'dune_buggy'}, {'id': 7397, 'synset': 'dungeon.n.02', 'name': 'dungeon'}, {'id': 7398, 'synset': 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'earflap'}, {'id': 7414, 'synset': 'early_warning_radar.n.01', 'name': 'early_warning_radar'}, {'id': 7415, 'synset': 'early_warning_system.n.01', 'name': 'early_warning_system'}, {'id': 7416, 'synset': 'earmuff.n.01', 'name': 'earmuff'}, {'id': 7417, 'synset': 'earplug.n.02', 'name': 'earplug'}, {'id': 7418, 'synset': 'earthenware.n.01', 'name': 'earthenware'}, {'id': 7419, 'synset': 'earthwork.n.01', 'name': 'earthwork'}, {'id': 7420, 'synset': 'easy_chair.n.01', 'name': 'easy_chair'}, {'id': 7421, 'synset': 'eaves.n.01', 'name': 'eaves'}, {'id': 7422, 'synset': 'ecclesiastical_attire.n.01', 'name': 'ecclesiastical_attire'}, {'id': 7423, 'synset': 'echinus.n.01', 'name': 'echinus'}, {'id': 7424, 'synset': 'echocardiograph.n.01', 'name': 'echocardiograph'}, {'id': 7425, 'synset': 'edger.n.02', 'name': 'edger'}, {'id': 7426, 'synset': 'edge_tool.n.01', 'name': 'edge_tool'}, {'id': 7427, 'synset': 'efficiency_apartment.n.01', 'name': 'efficiency_apartment'}, {'id': 7428, 'synset': 'egg-and-dart.n.01', 'name': 'egg-and-dart'}, {'id': 7429, 'synset': 'egg_timer.n.01', 'name': 'egg_timer'}, {'id': 7430, 'synset': 'eiderdown.n.01', 'name': 'eiderdown'}, {'id': 7431, 'synset': 'eight_ball.n.01', 'name': 'eight_ball'}, {'id': 7432, 'synset': 'ejection_seat.n.01', 'name': 'ejection_seat'}, {'id': 7433, 'synset': 'elastic.n.02', 'name': 'elastic'}, {'id': 7434, 'synset': 'elastic_bandage.n.01', 'name': 'elastic_bandage'}, {'id': 7435, 'synset': 'elastoplast.n.01', 'name': 'Elastoplast'}, {'id': 7436, 'synset': 'elbow.n.04', 'name': 'elbow'}, {'id': 7437, 'synset': 'elbow_pad.n.01', 'name': 'elbow_pad'}, {'id': 7438, 'synset': 'electric.n.01', 'name': 'electric'}, {'id': 7439, 'synset': 'electrical_cable.n.01', 'name': 'electrical_cable'}, {'id': 7440, 'synset': 'electrical_contact.n.01', 'name': 'electrical_contact'}, {'id': 7441, 'synset': 'electrical_converter.n.01', 'name': 'electrical_converter'}, {'id': 7442, 'synset': 'electrical_device.n.01', 'name': 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'name': 'electrolytic'}, {'id': 7469, 'synset': 'electrolytic_cell.n.01', 'name': 'electrolytic_cell'}, {'id': 7470, 'synset': 'electromagnet.n.01', 'name': 'electromagnet'}, {'id': 7471, 'synset': 'electrometer.n.01', 'name': 'electrometer'}, {'id': 7472, 'synset': 'electromyograph.n.01', 'name': 'electromyograph'}, {'id': 7473, 'synset': 'electron_accelerator.n.01', 'name': 'electron_accelerator'}, {'id': 7474, 'synset': 'electron_gun.n.01', 'name': 'electron_gun'}, {'id': 7475, 'synset': 'electronic_balance.n.01', 'name': 'electronic_balance'}, {'id': 7476, 'synset': 'electronic_converter.n.01', 'name': 'electronic_converter'}, {'id': 7477, 'synset': 'electronic_device.n.01', 'name': 'electronic_device'}, {'id': 7478, 'synset': 'electronic_equipment.n.01', 'name': 'electronic_equipment'}, {'id': 7479, 'synset': 'electronic_fetal_monitor.n.01', 'name': 'electronic_fetal_monitor'}, {'id': 7480, 'synset': 'electronic_instrument.n.01', 'name': 'electronic_instrument'}, {'id': 7481, 'synset': 'electronic_voltmeter.n.01', 'name': 'electronic_voltmeter'}, {'id': 7482, 'synset': 'electron_microscope.n.01', 'name': 'electron_microscope'}, {'id': 7483, 'synset': 'electron_multiplier.n.01', 'name': 'electron_multiplier'}, {'id': 7484, 'synset': 'electrophorus.n.01', 'name': 'electrophorus'}, {'id': 7485, 'synset': 'electroscope.n.01', 'name': 'electroscope'}, {'id': 7486, 'synset': 'electrostatic_generator.n.01', 'name': 'electrostatic_generator'}, {'id': 7487, 'synset': 'electrostatic_printer.n.01', 'name': 'electrostatic_printer'}, {'id': 7488, 'synset': 'elevator.n.01', 'name': 'elevator'}, {'id': 7489, 'synset': 'elevator.n.02', 'name': 'elevator'}, {'id': 7490, 'synset': 'elevator_shaft.n.01', 'name': 'elevator_shaft'}, {'id': 7491, 'synset': 'embankment.n.01', 'name': 'embankment'}, {'id': 7492, 'synset': 'embassy.n.01', 'name': 'embassy'}, {'id': 7493, 'synset': 'embellishment.n.02', 'name': 'embellishment'}, {'id': 7494, 'synset': 'emergency_room.n.01', 'name': 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'enginery'}, {'id': 7511, 'synset': 'english_horn.n.01', 'name': 'English_horn'}, {'id': 7512, 'synset': 'english_saddle.n.01', 'name': 'English_saddle'}, {'id': 7513, 'synset': 'enlarger.n.01', 'name': 'enlarger'}, {'id': 7514, 'synset': 'ensemble.n.05', 'name': 'ensemble'}, {'id': 7515, 'synset': 'ensign.n.03', 'name': 'ensign'}, {'id': 7516, 'synset': 'entablature.n.01', 'name': 'entablature'}, {'id': 7517, 'synset': 'entertainment_center.n.01', 'name': 'entertainment_center'}, {'id': 7518, 'synset': 'entrenching_tool.n.01', 'name': 'entrenching_tool'}, {'id': 7519, 'synset': 'entrenchment.n.01', 'name': 'entrenchment'}, {'id': 7520, 'synset': 'envelope.n.02', 'name': 'envelope'}, {'id': 7521, 'synset': 'envelope.n.06', 'name': 'envelope'}, {'id': 7522, 'synset': 'eolith.n.01', 'name': 'eolith'}, {'id': 7523, 'synset': 'epauliere.n.01', 'name': 'epauliere'}, {'id': 7524, 'synset': 'epee.n.01', 'name': 'epee'}, {'id': 7525, 'synset': 'epergne.n.01', 'name': 'epergne'}, {'id': 7526, 'synset': 'epicyclic_train.n.01', 'name': 'epicyclic_train'}, {'id': 7527, 'synset': 'epidiascope.n.01', 'name': 'epidiascope'}, {'id': 7528, 'synset': 'epilating_wax.n.01', 'name': 'epilating_wax'}, {'id': 7529, 'synset': 'equalizer.n.01', 'name': 'equalizer'}, {'id': 7530, 'synset': 'equatorial.n.01', 'name': 'equatorial'}, {'id': 7531, 'synset': 'equipment.n.01', 'name': 'equipment'}, {'id': 7532, 'synset': 'erasable_programmable_read-only_memory.n.01', 'name': 'erasable_programmable_read-only_memory'}, {'id': 7533, 'synset': 'erecting_prism.n.01', 'name': 'erecting_prism'}, {'id': 7534, 'synset': 'erection.n.02', 'name': 'erection'}, {'id': 7535, 'synset': 'erlenmeyer_flask.n.01', 'name': 'Erlenmeyer_flask'}, {'id': 7536, 'synset': 'escape_hatch.n.01', 'name': 'escape_hatch'}, {'id': 7537, 'synset': 'escapement.n.01', 'name': 'escapement'}, {'id': 7538, 'synset': 'escape_wheel.n.01', 'name': 'escape_wheel'}, {'id': 7539, 'synset': 'escarpment.n.02', 'name': 'escarpment'}, {'id': 7540, 'synset': 'escutcheon.n.03', 'name': 'escutcheon'}, {'id': 7541, 'synset': 'esophagoscope.n.01', 'name': 'esophagoscope'}, {'id': 7542, 'synset': 'espadrille.n.01', 'name': 'espadrille'}, {'id': 7543, 'synset': 'espalier.n.01', 'name': 'espalier'}, {'id': 7544, 'synset': 'espresso_maker.n.01', 'name': 'espresso_maker'}, {'id': 7545, 'synset': 'espresso_shop.n.01', 'name': 'espresso_shop'}, {'id': 7546, 'synset': 'establishment.n.04', 'name': 'establishment'}, {'id': 7547, 'synset': 'estaminet.n.01', 'name': 'estaminet'}, {'id': 7548, 'synset': 'estradiol_patch.n.01', 'name': 'estradiol_patch'}, {'id': 7549, 'synset': 'etagere.n.01', 'name': 'etagere'}, {'id': 7550, 'synset': 'etamine.n.01', 'name': 'etamine'}, {'id': 7551, 'synset': 'etching.n.02', 'name': 'etching'}, {'id': 7552, 'synset': 'ethernet.n.01', 'name': 'ethernet'}, {'id': 7553, 'synset': 'ethernet_cable.n.01', 'name': 'ethernet_cable'}, {'id': 7554, 'synset': 'eton_jacket.n.01', 'name': 'Eton_jacket'}, {'id': 7555, 'synset': 'etui.n.01', 'name': 'etui'}, {'id': 7556, 'synset': 'eudiometer.n.01', 'name': 'eudiometer'}, {'id': 7557, 'synset': 'euphonium.n.01', 'name': 'euphonium'}, {'id': 7558, 'synset': 'evaporative_cooler.n.01', 'name': 'evaporative_cooler'}, {'id': 7559, 'synset': 'evening_bag.n.01', 'name': 'evening_bag'}, {'id': 7560, 'synset': 'exercise_bike.n.01', 'name': 'exercise_bike'}, {'id': 7561, 'synset': 'exercise_device.n.01', 'name': 'exercise_device'}, {'id': 7562, 'synset': 'exhaust.n.02', 'name': 'exhaust'}, {'id': 7563, 'synset': 'exhaust_fan.n.01', 'name': 'exhaust_fan'}, {'id': 7564, 'synset': 'exhaust_valve.n.01', 'name': 'exhaust_valve'}, {'id': 7565, 'synset': 'exhibition_hall.n.01', 'name': 'exhibition_hall'}, {'id': 7566, 'synset': 'exocet.n.01', 'name': 'Exocet'}, {'id': 7567, 'synset': 'expansion_bit.n.01', 'name': 'expansion_bit'}, {'id': 7568, 'synset': 'expansion_bolt.n.01', 'name': 'expansion_bolt'}, {'id': 7569, 'synset': 'explosive_detection_system.n.01', 'name': 'explosive_detection_system'}, {'id': 7570, 'synset': 'explosive_device.n.01', 'name': 'explosive_device'}, {'id': 7571, 'synset': 'explosive_trace_detection.n.01', 'name': 'explosive_trace_detection'}, {'id': 7572, 'synset': 'express.n.02', 'name': 'express'}, {'id': 7573, 'synset': 'extension.n.10', 'name': 'extension'}, {'id': 7574, 'synset': 'extension_cord.n.01', 'name': 'extension_cord'}, {'id': 7575, 'synset': 'external-combustion_engine.n.01', 'name': 'external-combustion_engine'}, {'id': 7576, 'synset': 'external_drive.n.01', 'name': 'external_drive'}, {'id': 7577, 'synset': 'extractor.n.01', 'name': 'extractor'}, {'id': 7578, 'synset': 'eyebrow_pencil.n.01', 'name': 'eyebrow_pencil'}, {'id': 7579, 'synset': 'eyecup.n.01', 'name': 'eyecup'}, {'id': 7580, 'synset': 'eyeliner.n.01', 'name': 'eyeliner'}, {'id': 7581, 'synset': 'eyepiece.n.01', 'name': 'eyepiece'}, {'id': 7582, 'synset': 'eyeshadow.n.01', 'name': 'eyeshadow'}, {'id': 7583, 'synset': 'fabric.n.01', 'name': 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'faience.n.01', 'name': 'faience'}, {'id': 7600, 'synset': 'faille.n.01', 'name': 'faille'}, {'id': 7601, 'synset': 'fairlead.n.01', 'name': 'fairlead'}, {'id': 7602, 'synset': 'fairy_light.n.01', 'name': 'fairy_light'}, {'id': 7603, 'synset': 'falchion.n.01', 'name': 'falchion'}, {'id': 7604, 'synset': 'fallboard.n.01', 'name': 'fallboard'}, {'id': 7605, 'synset': 'fallout_shelter.n.01', 'name': 'fallout_shelter'}, {'id': 7606, 'synset': 'false_face.n.01', 'name': 'false_face'}, {'id': 7607, 'synset': 'false_teeth.n.01', 'name': 'false_teeth'}, {'id': 7608, 'synset': 'family_room.n.01', 'name': 'family_room'}, {'id': 7609, 'synset': 'fan_belt.n.01', 'name': 'fan_belt'}, {'id': 7610, 'synset': 'fan_blade.n.01', 'name': 'fan_blade'}, {'id': 7611, 'synset': 'fancy_dress.n.01', 'name': 'fancy_dress'}, {'id': 7612, 'synset': 'fanion.n.01', 'name': 'fanion'}, {'id': 7613, 'synset': 'fanlight.n.03', 'name': 'fanlight'}, {'id': 7614, 'synset': 'fanjet.n.02', 'name': 'fanjet'}, {'id': 7615, 'synset': 'fanjet.n.01', 'name': 'fanjet'}, {'id': 7616, 'synset': 'fanny_pack.n.01', 'name': 'fanny_pack'}, {'id': 7617, 'synset': 'fan_tracery.n.01', 'name': 'fan_tracery'}, {'id': 7618, 'synset': 'fan_vaulting.n.01', 'name': 'fan_vaulting'}, {'id': 7619, 'synset': 'farm_building.n.01', 'name': 'farm_building'}, {'id': 7620, 'synset': "farmer's_market.n.01", 'name': "farmer's_market"}, {'id': 7621, 'synset': 'farmhouse.n.01', 'name': 'farmhouse'}, {'id': 7622, 'synset': 'farm_machine.n.01', 'name': 'farm_machine'}, {'id': 7623, 'synset': 'farmplace.n.01', 'name': 'farmplace'}, {'id': 7624, 'synset': 'farmyard.n.01', 'name': 'farmyard'}, {'id': 7625, 'synset': 'farthingale.n.01', 'name': 'farthingale'}, {'id': 7626, 'synset': 'fastener.n.02', 'name': 'fastener'}, {'id': 7627, 'synset': 'fast_reactor.n.01', 'name': 'fast_reactor'}, {'id': 7628, 'synset': 'fat_farm.n.01', 'name': 'fat_farm'}, {'id': 7629, 'synset': 'fatigues.n.01', 'name': 'fatigues'}, {'id': 7630, 'synset': 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'field_glass.n.01', 'name': 'field_glass'}, {'id': 7662, 'synset': 'field_hockey_ball.n.01', 'name': 'field_hockey_ball'}, {'id': 7663, 'synset': 'field_hospital.n.01', 'name': 'field_hospital'}, {'id': 7664, 'synset': 'field_house.n.01', 'name': 'field_house'}, {'id': 7665, 'synset': 'field_lens.n.01', 'name': 'field_lens'}, {'id': 7666, 'synset': 'field_magnet.n.01', 'name': 'field_magnet'}, {'id': 7667, 'synset': 'field-sequential_color_television.n.01', 'name': 'field-sequential_color_television'}, {'id': 7668, 'synset': 'field_tent.n.01', 'name': 'field_tent'}, {'id': 7669, 'synset': 'fieldwork.n.01', 'name': 'fieldwork'}, {'id': 7670, 'synset': 'fife.n.01', 'name': 'fife'}, {'id': 7671, 'synset': 'fifth_wheel.n.02', 'name': 'fifth_wheel'}, {'id': 7672, 'synset': 'fighting_chair.n.01', 'name': 'fighting_chair'}, {'id': 7673, 'synset': 'fig_leaf.n.02', 'name': 'fig_leaf'}, {'id': 7674, 'synset': 'figure_eight.n.01', 'name': 'figure_eight'}, {'id': 7675, 'synset': 'figure_loom.n.01', 'name': 'figure_loom'}, {'id': 7676, 'synset': 'figure_skate.n.01', 'name': 'figure_skate'}, {'id': 7677, 'synset': 'filament.n.04', 'name': 'filament'}, {'id': 7678, 'synset': 'filature.n.01', 'name': 'filature'}, {'id': 7679, 'synset': 'file_folder.n.01', 'name': 'file_folder'}, {'id': 7680, 'synset': 'file_server.n.01', 'name': 'file_server'}, {'id': 7681, 'synset': 'filigree.n.01', 'name': 'filigree'}, {'id': 7682, 'synset': 'filling.n.05', 'name': 'filling'}, {'id': 7683, 'synset': 'film.n.03', 'name': 'film'}, {'id': 7684, 'synset': 'film.n.05', 'name': 'film'}, {'id': 7685, 'synset': 'film_advance.n.01', 'name': 'film_advance'}, {'id': 7686, 'synset': 'filter.n.01', 'name': 'filter'}, {'id': 7687, 'synset': 'filter.n.02', 'name': 'filter'}, {'id': 7688, 'synset': 'finder.n.03', 'name': 'finder'}, {'id': 7689, 'synset': 'finery.n.01', 'name': 'finery'}, {'id': 7690, 'synset': 'fine-tooth_comb.n.01', 'name': 'fine-tooth_comb'}, {'id': 7691, 'synset': 'finger.n.03', 'name': 'finger'}, {'id': 7692, 'synset': 'fingerboard.n.03', 'name': 'fingerboard'}, {'id': 7693, 'synset': 'finger_bowl.n.01', 'name': 'finger_bowl'}, {'id': 7694, 'synset': 'finger_paint.n.01', 'name': 'finger_paint'}, {'id': 7695, 'synset': 'finger-painting.n.01', 'name': 'finger-painting'}, {'id': 7696, 'synset': 'finger_plate.n.01', 'name': 'finger_plate'}, {'id': 7697, 'synset': 'fingerstall.n.01', 'name': 'fingerstall'}, {'id': 7698, 'synset': 'finish_coat.n.02', 'name': 'finish_coat'}, {'id': 7699, 'synset': 'finish_coat.n.01', 'name': 'finish_coat'}, {'id': 7700, 'synset': 'finisher.n.05', 'name': 'finisher'}, {'id': 7701, 'synset': 'fin_keel.n.01', 'name': 'fin_keel'}, {'id': 7702, 'synset': 'fipple.n.01', 'name': 'fipple'}, {'id': 7703, 'synset': 'fipple_flute.n.01', 'name': 'fipple_flute'}, {'id': 7704, 'synset': 'fire.n.04', 'name': 'fire'}, {'id': 7705, 'synset': 'firearm.n.01', 'name': 'firearm'}, {'id': 7706, 'synset': 'fire_bell.n.01', 'name': 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'fixative.n.02', 'name': 'fixative'}, {'id': 7737, 'synset': 'fixer-upper.n.01', 'name': 'fixer-upper'}, {'id': 7738, 'synset': 'flageolet.n.02', 'name': 'flageolet'}, {'id': 7739, 'synset': 'flagon.n.01', 'name': 'flagon'}, {'id': 7740, 'synset': 'flagship.n.02', 'name': 'flagship'}, {'id': 7741, 'synset': 'flail.n.01', 'name': 'flail'}, {'id': 7742, 'synset': 'flambeau.n.01', 'name': 'flambeau'}, {'id': 7743, 'synset': 'flamethrower.n.01', 'name': 'flamethrower'}, {'id': 7744, 'synset': 'flange.n.01', 'name': 'flange'}, {'id': 7745, 'synset': 'flannel.n.03', 'name': 'flannel'}, {'id': 7746, 'synset': 'flannelette.n.01', 'name': 'flannelette'}, {'id': 7747, 'synset': 'flap.n.05', 'name': 'flap'}, {'id': 7748, 'synset': 'flash.n.09', 'name': 'flash'}, {'id': 7749, 'synset': 'flash_camera.n.01', 'name': 'flash_camera'}, {'id': 7750, 'synset': 'flasher.n.02', 'name': 'flasher'}, {'id': 7751, 'synset': 'flashlight_battery.n.01', 'name': 'flashlight_battery'}, {'id': 7752, 'synset': 'flash_memory.n.01', 'name': 'flash_memory'}, {'id': 7753, 'synset': 'flask.n.01', 'name': 'flask'}, {'id': 7754, 'synset': 'flat_arch.n.01', 'name': 'flat_arch'}, {'id': 7755, 'synset': 'flatbed.n.02', 'name': 'flatbed'}, {'id': 7756, 'synset': 'flatbed_press.n.01', 'name': 'flatbed_press'}, {'id': 7757, 'synset': 'flat_bench.n.01', 'name': 'flat_bench'}, {'id': 7758, 'synset': 'flatcar.n.01', 'name': 'flatcar'}, {'id': 7759, 'synset': 'flat_file.n.01', 'name': 'flat_file'}, {'id': 7760, 'synset': 'flatlet.n.01', 'name': 'flatlet'}, {'id': 7761, 'synset': 'flat_panel_display.n.01', 'name': 'flat_panel_display'}, {'id': 7762, 'synset': 'flats.n.01', 'name': 'flats'}, {'id': 7763, 'synset': 'flat_tip_screwdriver.n.01', 'name': 'flat_tip_screwdriver'}, {'id': 7764, 'synset': 'fleet_ballistic_missile_submarine.n.01', 'name': 'fleet_ballistic_missile_submarine'}, {'id': 7765, 'synset': 'fleur-de-lis.n.02', 'name': 'fleur-de-lis'}, {'id': 7766, 'synset': 'flight_simulator.n.01', 'name': 'flight_simulator'}, {'id': 7767, 'synset': 'flintlock.n.02', 'name': 'flintlock'}, {'id': 7768, 'synset': 'flintlock.n.01', 'name': 'flintlock'}, {'id': 7769, 'synset': 'float.n.05', 'name': 'float'}, {'id': 7770, 'synset': 'floating_dock.n.01', 'name': 'floating_dock'}, {'id': 7771, 'synset': 'floatplane.n.01', 'name': 'floatplane'}, {'id': 7772, 'synset': 'flood.n.03', 'name': 'flood'}, {'id': 7773, 'synset': 'floor.n.01', 'name': 'floor'}, {'id': 7774, 'synset': 'floor.n.02', 'name': 'floor'}, {'id': 7775, 'synset': 'floor.n.09', 'name': 'floor'}, {'id': 7776, 'synset': 'floorboard.n.02', 'name': 'floorboard'}, {'id': 7777, 'synset': 'floor_cover.n.01', 'name': 'floor_cover'}, {'id': 7778, 'synset': 'floor_joist.n.01', 'name': 'floor_joist'}, {'id': 7779, 'synset': 'floor_lamp.n.01', 'name': 'floor_lamp'}, {'id': 7780, 'synset': 'flophouse.n.01', 'name': 'flophouse'}, {'id': 7781, 'synset': 'florist.n.02', 'name': 'florist'}, {'id': 7782, 'synset': 'floss.n.01', 'name': 'floss'}, {'id': 7783, 'synset': 'flotsam.n.01', 'name': 'flotsam'}, {'id': 7784, 'synset': 'flour_bin.n.01', 'name': 'flour_bin'}, {'id': 7785, 'synset': 'flour_mill.n.01', 'name': 'flour_mill'}, {'id': 7786, 'synset': 'flowerbed.n.01', 'name': 'flowerbed'}, {'id': 7787, 'synset': 'flugelhorn.n.01', 'name': 'flugelhorn'}, {'id': 7788, 'synset': 'fluid_drive.n.01', 'name': 'fluid_drive'}, {'id': 7789, 'synset': 'fluid_flywheel.n.01', 'name': 'fluid_flywheel'}, {'id': 7790, 'synset': 'flume.n.02', 'name': 'flume'}, {'id': 7791, 'synset': 'fluorescent_lamp.n.01', 'name': 'fluorescent_lamp'}, {'id': 7792, 'synset': 'fluoroscope.n.01', 'name': 'fluoroscope'}, {'id': 7793, 'synset': 'flush_toilet.n.01', 'name': 'flush_toilet'}, {'id': 7794, 'synset': 'flute.n.01', 'name': 'flute'}, {'id': 7795, 'synset': 'flux_applicator.n.01', 'name': 'flux_applicator'}, {'id': 7796, 'synset': 'fluxmeter.n.01', 'name': 'fluxmeter'}, {'id': 7797, 'synset': 'fly.n.05', 'name': 'fly'}, {'id': 7798, 'synset': 'flying_boat.n.01', 'name': 'flying_boat'}, {'id': 7799, 'synset': 'flying_buttress.n.01', 'name': 'flying_buttress'}, {'id': 7800, 'synset': 'flying_carpet.n.01', 'name': 'flying_carpet'}, {'id': 7801, 'synset': 'flying_jib.n.01', 'name': 'flying_jib'}, {'id': 7802, 'synset': 'fly_rod.n.01', 'name': 'fly_rod'}, {'id': 7803, 'synset': 'fly_tent.n.01', 'name': 'fly_tent'}, {'id': 7804, 'synset': 'flytrap.n.01', 'name': 'flytrap'}, {'id': 7805, 'synset': 'flywheel.n.01', 'name': 'flywheel'}, {'id': 7806, 'synset': 'fob.n.03', 'name': 'fob'}, {'id': 7807, 'synset': 'foghorn.n.02', 'name': 'foghorn'}, {'id': 7808, 'synset': 'foglamp.n.01', 'name': 'foglamp'}, {'id': 7809, 'synset': 'foil.n.05', 'name': 'foil'}, {'id': 7810, 'synset': 'fold.n.06', 'name': 'fold'}, {'id': 7811, 'synset': 'folder.n.02', 'name': 'folder'}, {'id': 7812, 'synset': 'folding_door.n.01', 'name': 'folding_door'}, {'id': 7813, 'synset': 'folding_saw.n.01', 'name': 'folding_saw'}, {'id': 7814, 'synset': 'food_court.n.01', 'name': 'food_court'}, {'id': 7815, 'synset': 'food_hamper.n.01', 'name': 'food_hamper'}, {'id': 7816, 'synset': 'foot.n.11', 'name': 'foot'}, {'id': 7817, 'synset': 'footage.n.01', 'name': 'footage'}, {'id': 7818, 'synset': 'football_stadium.n.01', 'name': 'football_stadium'}, {'id': 7819, 'synset': 'footbath.n.01', 'name': 'footbath'}, {'id': 7820, 'synset': 'foot_brake.n.01', 'name': 'foot_brake'}, {'id': 7821, 'synset': 'footbridge.n.01', 'name': 'footbridge'}, {'id': 7822, 'synset': 'foothold.n.02', 'name': 'foothold'}, {'id': 7823, 'synset': 'footlocker.n.01', 'name': 'footlocker'}, {'id': 7824, 'synset': 'foot_rule.n.01', 'name': 'foot_rule'}, {'id': 7825, 'synset': 'footwear.n.02', 'name': 'footwear'}, {'id': 7826, 'synset': 'footwear.n.01', 'name': 'footwear'}, {'id': 7827, 'synset': 'forceps.n.01', 'name': 'forceps'}, {'id': 7828, 'synset': 'force_pump.n.01', 'name': 'force_pump'}, {'id': 7829, 'synset': 'fore-and-after.n.01', 'name': 'fore-and-after'}, {'id': 7830, 'synset': 'fore-and-aft_sail.n.01', 'name': 'fore-and-aft_sail'}, {'id': 7831, 'synset': 'forecastle.n.01', 'name': 'forecastle'}, {'id': 7832, 'synset': 'forecourt.n.01', 'name': 'forecourt'}, {'id': 7833, 'synset': 'foredeck.n.01', 'name': 'foredeck'}, {'id': 7834, 'synset': 'fore_edge.n.01', 'name': 'fore_edge'}, {'id': 7835, 'synset': 'foreground.n.02', 'name': 'foreground'}, {'id': 7836, 'synset': 'foremast.n.01', 'name': 'foremast'}, {'id': 7837, 'synset': 'fore_plane.n.01', 'name': 'fore_plane'}, {'id': 7838, 'synset': 'foresail.n.01', 'name': 'foresail'}, {'id': 7839, 'synset': 'forestay.n.01', 'name': 'forestay'}, {'id': 7840, 'synset': 'foretop.n.01', 'name': 'foretop'}, {'id': 7841, 'synset': 'fore-topmast.n.01', 'name': 'fore-topmast'}, {'id': 7842, 'synset': 'fore-topsail.n.01', 'name': 'fore-topsail'}, {'id': 7843, 'synset': 'forge.n.01', 'name': 'forge'}, {'id': 7844, 'synset': 'fork.n.04', 'name': 'fork'}, {'id': 7845, 'synset': 'formalwear.n.01', 'name': 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'four-stroke_engine.n.01', 'name': 'four-stroke_engine'}, {'id': 7861, 'synset': 'four-wheel_drive.n.02', 'name': 'four-wheel_drive'}, {'id': 7862, 'synset': 'four-wheel_drive.n.01', 'name': 'four-wheel_drive'}, {'id': 7863, 'synset': 'four-wheeler.n.01', 'name': 'four-wheeler'}, {'id': 7864, 'synset': 'fowling_piece.n.01', 'name': 'fowling_piece'}, {'id': 7865, 'synset': 'foxhole.n.01', 'name': 'foxhole'}, {'id': 7866, 'synset': 'fragmentation_bomb.n.01', 'name': 'fragmentation_bomb'}, {'id': 7867, 'synset': 'frail.n.02', 'name': 'frail'}, {'id': 7868, 'synset': 'fraise.n.02', 'name': 'fraise'}, {'id': 7869, 'synset': 'frame.n.10', 'name': 'frame'}, {'id': 7870, 'synset': 'frame.n.01', 'name': 'frame'}, {'id': 7871, 'synset': 'frame_buffer.n.01', 'name': 'frame_buffer'}, {'id': 7872, 'synset': 'framework.n.03', 'name': 'framework'}, {'id': 7873, 'synset': 'francis_turbine.n.01', 'name': 'Francis_turbine'}, {'id': 7874, 'synset': 'franking_machine.n.01', 'name': 'franking_machine'}, {'id': 7875, 'synset': 'free_house.n.01', 'name': 'free_house'}, {'id': 7876, 'synset': 'free-reed.n.01', 'name': 'free-reed'}, {'id': 7877, 'synset': 'free-reed_instrument.n.01', 'name': 'free-reed_instrument'}, {'id': 7878, 'synset': 'freewheel.n.01', 'name': 'freewheel'}, {'id': 7879, 'synset': 'freight_elevator.n.01', 'name': 'freight_elevator'}, {'id': 7880, 'synset': 'freight_liner.n.01', 'name': 'freight_liner'}, {'id': 7881, 'synset': 'freight_train.n.01', 'name': 'freight_train'}, {'id': 7882, 'synset': 'french_door.n.01', 'name': 'French_door'}, {'id': 7883, 'synset': 'french_horn.n.01', 'name': 'French_horn'}, {'id': 7884, 'synset': 'french_polish.n.02', 'name': 'French_polish'}, {'id': 7885, 'synset': 'french_roof.n.01', 'name': 'French_roof'}, {'id': 7886, 'synset': 'french_window.n.01', 'name': 'French_window'}, {'id': 7887, 'synset': 'fresnel_lens.n.01', 'name': 'Fresnel_lens'}, {'id': 7888, 'synset': 'fret.n.04', 'name': 'fret'}, {'id': 7889, 'synset': 'friary.n.01', 'name': 'friary'}, {'id': 7890, 'synset': 'friction_clutch.n.01', 'name': 'friction_clutch'}, {'id': 7891, 'synset': 'frieze.n.02', 'name': 'frieze'}, {'id': 7892, 'synset': 'frieze.n.01', 'name': 'frieze'}, {'id': 7893, 'synset': 'frigate.n.02', 'name': 'frigate'}, {'id': 7894, 'synset': 'frigate.n.01', 'name': 'frigate'}, {'id': 7895, 'synset': 'frill.n.03', 'name': 'frill'}, {'id': 7896, 'synset': 'frock.n.01', 'name': 'frock'}, {'id': 7897, 'synset': 'frock_coat.n.01', 'name': 'frock_coat'}, {'id': 7898, 'synset': 'frontlet.n.01', 'name': 'frontlet'}, {'id': 7899, 'synset': 'front_porch.n.01', 'name': 'front_porch'}, {'id': 7900, 'synset': 'front_projector.n.01', 'name': 'front_projector'}, {'id': 7901, 'synset': 'fruit_machine.n.01', 'name': 'fruit_machine'}, {'id': 7902, 'synset': 'fuel_filter.n.01', 'name': 'fuel_filter'}, {'id': 7903, 'synset': 'fuel_gauge.n.01', 'name': 'fuel_gauge'}, {'id': 7904, 'synset': 'fuel_injection.n.01', 'name': 'fuel_injection'}, {'id': 7905, 'synset': 'fuel_system.n.01', 'name': 'fuel_system'}, {'id': 7906, 'synset': 'full-dress_uniform.n.01', 'name': 'full-dress_uniform'}, {'id': 7907, 'synset': 'full_metal_jacket.n.01', 'name': 'full_metal_jacket'}, {'id': 7908, 'synset': 'full_skirt.n.01', 'name': 'full_skirt'}, {'id': 7909, 'synset': 'fumigator.n.02', 'name': 'fumigator'}, {'id': 7910, 'synset': 'funeral_home.n.01', 'name': 'funeral_home'}, {'id': 7911, 'synset': 'funny_wagon.n.01', 'name': 'funny_wagon'}, {'id': 7912, 'synset': 'fur.n.03', 'name': 'fur'}, {'id': 7913, 'synset': 'fur_coat.n.01', 'name': 'fur_coat'}, {'id': 7914, 'synset': 'fur_hat.n.01', 'name': 'fur_hat'}, {'id': 7915, 'synset': 'furnace.n.01', 'name': 'furnace'}, {'id': 7916, 'synset': 'furnace_lining.n.01', 'name': 'furnace_lining'}, {'id': 7917, 'synset': 'furnace_room.n.01', 'name': 'furnace_room'}, {'id': 7918, 'synset': 'furnishing.n.02', 'name': 'furnishing'}, {'id': 7919, 'synset': 'furnishing.n.01', 'name': 'furnishing'}, {'id': 7920, 'synset': 'furniture.n.01', 'name': 'furniture'}, {'id': 7921, 'synset': 'fur-piece.n.01', 'name': 'fur-piece'}, {'id': 7922, 'synset': 'furrow.n.01', 'name': 'furrow'}, {'id': 7923, 'synset': 'fuse.n.01', 'name': 'fuse'}, {'id': 7924, 'synset': 'fusee_drive.n.01', 'name': 'fusee_drive'}, {'id': 7925, 'synset': 'fuselage.n.01', 'name': 'fuselage'}, {'id': 7926, 'synset': 'fusil.n.01', 'name': 'fusil'}, {'id': 7927, 'synset': 'fustian.n.02', 'name': 'fustian'}, {'id': 7928, 'synset': 'gabardine.n.01', 'name': 'gabardine'}, {'id': 7929, 'synset': 'gable.n.01', 'name': 'gable'}, {'id': 7930, 'synset': 'gable_roof.n.01', 'name': 'gable_roof'}, {'id': 7931, 'synset': 'gadgetry.n.01', 'name': 'gadgetry'}, {'id': 7932, 'synset': 'gaff.n.03', 'name': 'gaff'}, {'id': 7933, 'synset': 'gaff.n.02', 'name': 'gaff'}, {'id': 7934, 'synset': 'gaff.n.01', 'name': 'gaff'}, {'id': 7935, 'synset': 'gaffsail.n.01', 'name': 'gaffsail'}, {'id': 7936, 'synset': 'gaff_topsail.n.01', 'name': 'gaff_topsail'}, {'id': 7937, 'synset': 'gaiter.n.03', 'name': 'gaiter'}, {'id': 7938, 'synset': 'gaiter.n.02', 'name': 'gaiter'}, {'id': 7939, 'synset': 'galilean_telescope.n.01', 'name': 'Galilean_telescope'}, {'id': 7940, 'synset': 'galleon.n.01', 'name': 'galleon'}, {'id': 7941, 'synset': 'gallery.n.04', 'name': 'gallery'}, {'id': 7942, 'synset': 'gallery.n.03', 'name': 'gallery'}, {'id': 7943, 'synset': 'galley.n.04', 'name': 'galley'}, {'id': 7944, 'synset': 'galley.n.03', 'name': 'galley'}, {'id': 7945, 'synset': 'galley.n.02', 'name': 'galley'}, {'id': 7946, 'synset': 'gallows.n.01', 'name': 'gallows'}, {'id': 7947, 'synset': 'gallows_tree.n.01', 'name': 'gallows_tree'}, {'id': 7948, 'synset': 'galvanometer.n.01', 'name': 'galvanometer'}, {'id': 7949, 'synset': 'gambling_house.n.01', 'name': 'gambling_house'}, {'id': 7950, 'synset': 'gambrel.n.01', 'name': 'gambrel'}, {'id': 7951, 'synset': 'game.n.09', 'name': 'game'}, {'id': 7952, 'synset': 'gamebag.n.01', 'name': 'gamebag'}, {'id': 7953, 'synset': 'game_equipment.n.01', 'name': 'game_equipment'}, {'id': 7954, 'synset': 'gaming_table.n.01', 'name': 'gaming_table'}, {'id': 7955, 'synset': 'gamp.n.01', 'name': 'gamp'}, {'id': 7956, 'synset': 'gangplank.n.01', 'name': 'gangplank'}, {'id': 7957, 'synset': 'gangsaw.n.01', 'name': 'gangsaw'}, {'id': 7958, 'synset': 'gangway.n.01', 'name': 'gangway'}, {'id': 7959, 'synset': 'gantlet.n.04', 'name': 'gantlet'}, {'id': 7960, 'synset': 'gantry.n.01', 'name': 'gantry'}, {'id': 7961, 'synset': 'garage.n.01', 'name': 'garage'}, {'id': 7962, 'synset': 'garage.n.02', 'name': 'garage'}, {'id': 7963, 'synset': 'garand_rifle.n.01', 'name': 'Garand_rifle'}, {'id': 7964, 'synset': 'garboard.n.01', 'name': 'garboard'}, {'id': 7965, 'synset': 'garden.n.01', 'name': 'garden'}, {'id': 7966, 'synset': 'garden.n.03', 'name': 'garden'}, {'id': 7967, 'synset': 'garden_rake.n.01', 'name': 'garden_rake'}, {'id': 7968, 'synset': 'garden_spade.n.01', 'name': 'garden_spade'}, {'id': 7969, 'synset': 'garden_tool.n.01', 'name': 'garden_tool'}, {'id': 7970, 'synset': 'garden_trowel.n.01', 'name': 'garden_trowel'}, {'id': 7971, 'synset': 'gargoyle.n.01', 'name': 'gargoyle'}, {'id': 7972, 'synset': 'garibaldi.n.02', 'name': 'garibaldi'}, {'id': 7973, 'synset': 'garlic_press.n.01', 'name': 'garlic_press'}, {'id': 7974, 'synset': 'garment.n.01', 'name': 'garment'}, {'id': 7975, 'synset': 'garment_bag.n.01', 'name': 'garment_bag'}, {'id': 7976, 'synset': 'garrison_cap.n.01', 'name': 'garrison_cap'}, {'id': 7977, 'synset': 'garrote.n.01', 'name': 'garrote'}, {'id': 7978, 'synset': 'garter.n.01', 'name': 'garter'}, {'id': 7979, 'synset': 'garter_belt.n.01', 'name': 'garter_belt'}, {'id': 7980, 'synset': 'garter_stitch.n.01', 'name': 'garter_stitch'}, {'id': 7981, 'synset': 'gas_guzzler.n.01', 'name': 'gas_guzzler'}, {'id': 7982, 'synset': 'gas_shell.n.01', 'name': 'gas_shell'}, {'id': 7983, 'synset': 'gas_bracket.n.01', 'name': 'gas_bracket'}, {'id': 7984, 'synset': 'gas_burner.n.01', 'name': 'gas_burner'}, {'id': 7985, 'synset': 'gas-cooled_reactor.n.01', 'name': 'gas-cooled_reactor'}, {'id': 7986, 'synset': 'gas-discharge_tube.n.01', 'name': 'gas-discharge_tube'}, {'id': 7987, 'synset': 'gas_engine.n.01', 'name': 'gas_engine'}, {'id': 7988, 'synset': 'gas_fixture.n.01', 'name': 'gas_fixture'}, {'id': 7989, 'synset': 'gas_furnace.n.01', 'name': 'gas_furnace'}, {'id': 7990, 'synset': 'gas_gun.n.01', 'name': 'gas_gun'}, {'id': 7991, 'synset': 'gas_heater.n.01', 'name': 'gas_heater'}, {'id': 7992, 'synset': 'gas_holder.n.01', 'name': 'gas_holder'}, {'id': 7993, 'synset': 'gasket.n.01', 'name': 'gasket'}, {'id': 7994, 'synset': 'gas_lamp.n.01', 'name': 'gas_lamp'}, {'id': 7995, 'synset': 'gas_maser.n.01', 'name': 'gas_maser'}, {'id': 7996, 'synset': 'gas_meter.n.01', 'name': 'gas_meter'}, {'id': 7997, 'synset': 'gasoline_engine.n.01', 'name': 'gasoline_engine'}, {'id': 7998, 'synset': 'gasoline_gauge.n.01', 'name': 'gasoline_gauge'}, {'id': 7999, 'synset': 'gas_oven.n.02', 'name': 'gas_oven'}, {'id': 8000, 'synset': 'gas_oven.n.01', 'name': 'gas_oven'}, {'id': 8001, 'synset': 'gas_pump.n.01', 'name': 'gas_pump'}, {'id': 8002, 'synset': 'gas_range.n.01', 'name': 'gas_range'}, {'id': 8003, 'synset': 'gas_ring.n.01', 'name': 'gas_ring'}, {'id': 8004, 'synset': 'gas_tank.n.01', 'name': 'gas_tank'}, {'id': 8005, 'synset': 'gas_thermometer.n.01', 'name': 'gas_thermometer'}, {'id': 8006, 'synset': 'gastroscope.n.01', 'name': 'gastroscope'}, {'id': 8007, 'synset': 'gas_turbine.n.01', 'name': 'gas_turbine'}, {'id': 8008, 'synset': 'gas-turbine_ship.n.01', 'name': 'gas-turbine_ship'}, {'id': 8009, 'synset': 'gat.n.01', 'name': 'gat'}, {'id': 8010, 'synset': 'gate.n.01', 'name': 'gate'}, {'id': 8011, 'synset': 'gatehouse.n.01', 'name': 'gatehouse'}, {'id': 8012, 'synset': 'gateleg_table.n.01', 'name': 'gateleg_table'}, {'id': 8013, 'synset': 'gatepost.n.01', 'name': 'gatepost'}, {'id': 8014, 'synset': 'gathered_skirt.n.01', 'name': 'gathered_skirt'}, {'id': 8015, 'synset': 'gatling_gun.n.01', 'name': 'Gatling_gun'}, {'id': 8016, 'synset': 'gauge.n.01', 'name': 'gauge'}, {'id': 8017, 'synset': 'gauntlet.n.03', 'name': 'gauntlet'}, {'id': 8018, 'synset': 'gauntlet.n.02', 'name': 'gauntlet'}, {'id': 8019, 'synset': 'gauze.n.02', 'name': 'gauze'}, {'id': 8020, 'synset': 'gauze.n.01', 'name': 'gauze'}, {'id': 8021, 'synset': 'gavel.n.01', 'name': 'gavel'}, {'id': 8022, 'synset': 'gazebo.n.01', 'name': 'gazebo'}, {'id': 8023, 'synset': 'gear.n.01', 'name': 'gear'}, {'id': 8024, 'synset': 'gear.n.04', 'name': 'gear'}, {'id': 8025, 'synset': 'gear.n.03', 'name': 'gear'}, {'id': 8026, 'synset': 'gearbox.n.01', 'name': 'gearbox'}, {'id': 8027, 'synset': 'gearing.n.01', 'name': 'gearing'}, {'id': 8028, 'synset': 'gearset.n.01', 'name': 'gearset'}, {'id': 8029, 'synset': 'gearshift.n.01', 'name': 'gearshift'}, {'id': 8030, 'synset': 'geiger_counter.n.01', 'name': 'Geiger_counter'}, {'id': 8031, 'synset': 'geiger_tube.n.01', 'name': 'Geiger_tube'}, {'id': 8032, 'synset': 'gene_chip.n.01', 'name': 'gene_chip'}, {'id': 8033, 'synset': 'general-purpose_bomb.n.01', 'name': 'general-purpose_bomb'}, {'id': 8034, 'synset': 'generator.n.01', 'name': 'generator'}, {'id': 8035, 'synset': 'generator.n.04', 'name': 'generator'}, {'id': 8036, 'synset': 'geneva_gown.n.01', 'name': 'Geneva_gown'}, {'id': 8037, 'synset': 'geodesic_dome.n.01', 'name': 'geodesic_dome'}, {'id': 8038, 'synset': 'georgette.n.01', 'name': 'georgette'}, {'id': 8039, 'synset': 'gharry.n.01', 'name': 'gharry'}, {'id': 8040, 'synset': 'ghat.n.01', 'name': 'ghat'}, {'id': 8041, 'synset': 'ghetto_blaster.n.01', 'name': 'ghetto_blaster'}, {'id': 8042, 'synset': 'gift_shop.n.01', 'name': 'gift_shop'}, {'id': 8043, 'synset': 'gift_wrapping.n.01', 'name': 'gift_wrapping'}, {'id': 8044, 'synset': 'gig.n.05', 'name': 'gig'}, {'id': 8045, 'synset': 'gig.n.04', 'name': 'gig'}, {'id': 8046, 'synset': 'gig.n.01', 'name': 'gig'}, {'id': 8047, 'synset': 'gig.n.03', 'name': 'gig'}, {'id': 8048, 'synset': 'gildhall.n.01', 'name': 'gildhall'}, {'id': 8049, 'synset': 'gill_net.n.01', 'name': 'gill_net'}, {'id': 8050, 'synset': 'gilt.n.01', 'name': 'gilt'}, {'id': 8051, 'synset': 'gimbal.n.01', 'name': 'gimbal'}, {'id': 8052, 'synset': 'gingham.n.01', 'name': 'gingham'}, {'id': 8053, 'synset': 'girandole.n.01', 'name': 'girandole'}, {'id': 8054, 'synset': 'girder.n.01', 'name': 'girder'}, {'id': 8055, 'synset': 'glass.n.07', 'name': 'glass'}, {'id': 8056, 'synset': 'glass_cutter.n.03', 'name': 'glass_cutter'}, {'id': 8057, 'synset': 'glasses_case.n.01', 'name': 'glasses_case'}, {'id': 8058, 'synset': 'glebe_house.n.01', 'name': 'glebe_house'}, {'id': 8059, 'synset': 'glengarry.n.01', 'name': 'Glengarry'}, {'id': 8060, 'synset': 'glider.n.01', 'name': 'glider'}, {'id': 8061, 'synset': 'global_positioning_system.n.01', 'name': 'Global_Positioning_System'}, {'id': 8062, 'synset': 'glockenspiel.n.01', 'name': 'glockenspiel'}, {'id': 8063, 'synset': 'glory_hole.n.01', 'name': 'glory_hole'}, {'id': 8064, 'synset': 'glove_compartment.n.01', 'name': 'glove_compartment'}, {'id': 8065, 'synset': 'glow_lamp.n.01', 'name': 'glow_lamp'}, {'id': 8066, 'synset': 'glow_tube.n.01', 'name': 'glow_tube'}, {'id': 8067, 'synset': 'glyptic_art.n.01', 'name': 'glyptic_art'}, {'id': 8068, 'synset': 'glyptics.n.01', 'name': 'glyptics'}, {'id': 8069, 'synset': 'gnomon.n.01', 'name': 'gnomon'}, {'id': 8070, 'synset': 'goal.n.03', 'name': 'goal'}, {'id': 8071, 'synset': 'goalmouth.n.01', 'name': 'goalmouth'}, {'id': 8072, 'synset': 'goalpost.n.01', 'name': 'goalpost'}, {'id': 8073, 'synset': 'goblet.n.01', 'name': 'goblet'}, {'id': 8074, 'synset': 'godown.n.01', 'name': 'godown'}, {'id': 8075, 'synset': 'go-kart.n.01', 'name': 'go-kart'}, {'id': 8076, 'synset': 'gold_plate.n.02', 'name': 'gold_plate'}, {'id': 8077, 'synset': 'golf_bag.n.01', 'name': 'golf_bag'}, {'id': 8078, 'synset': 'golf_ball.n.01', 'name': 'golf_ball'}, {'id': 8079, 'synset': 'golf-club_head.n.01', 'name': 'golf-club_head'}, {'id': 8080, 'synset': 'golf_equipment.n.01', 'name': 'golf_equipment'}, {'id': 8081, 'synset': 'golf_glove.n.01', 'name': 'golf_glove'}, {'id': 8082, 'synset': 'golliwog.n.01', 'name': 'golliwog'}, {'id': 8083, 'synset': 'gong.n.01', 'name': 'gong'}, {'id': 8084, 'synset': 'goniometer.n.01', 'name': 'goniometer'}, {'id': 8085, 'synset': 'gordian_knot.n.02', 'name': 'Gordian_knot'}, {'id': 8086, 'synset': 'gorget.n.01', 'name': 'gorget'}, {'id': 8087, 'synset': 'gossamer.n.01', 'name': 'gossamer'}, {'id': 8088, 'synset': 'gothic_arch.n.01', 'name': 'Gothic_arch'}, {'id': 8089, 'synset': 'gouache.n.01', 'name': 'gouache'}, {'id': 8090, 'synset': 'gouge.n.02', 'name': 'gouge'}, {'id': 8091, 'synset': 'gourd.n.01', 'name': 'gourd'}, {'id': 8092, 'synset': 'government_building.n.01', 'name': 'government_building'}, {'id': 8093, 'synset': 'government_office.n.01', 'name': 'government_office'}, {'id': 8094, 'synset': 'gown.n.01', 'name': 'gown'}, {'id': 8095, 'synset': 'gown.n.05', 'name': 'gown'}, {'id': 8096, 'synset': 'gown.n.04', 'name': 'gown'}, {'id': 8097, 'synset': 'grab.n.01', 'name': 'grab'}, {'id': 8098, 'synset': 'grab_bag.n.02', 'name': 'grab_bag'}, {'id': 8099, 'synset': 'grab_bar.n.01', 'name': 'grab_bar'}, {'id': 8100, 'synset': 'grace_cup.n.01', 'name': 'grace_cup'}, {'id': 8101, 'synset': 'grade_separation.n.01', 'name': 'grade_separation'}, {'id': 8102, 'synset': 'graduated_cylinder.n.01', 'name': 'graduated_cylinder'}, {'id': 8103, 'synset': 'graffito.n.01', 'name': 'graffito'}, {'id': 8104, 'synset': 'gramophone.n.01', 'name': 'gramophone'}, {'id': 8105, 'synset': 'granary.n.01', 'name': 'granary'}, {'id': 8106, 'synset': 'grandfather_clock.n.01', 'name': 'grandfather_clock'}, {'id': 8107, 'synset': 'grand_piano.n.01', 'name': 'grand_piano'}, {'id': 8108, 'synset': 'graniteware.n.01', 'name': 'graniteware'}, {'id': 8109, 'synset': 'granny_knot.n.01', 'name': 'granny_knot'}, {'id': 8110, 'synset': 'grape_arbor.n.01', 'name': 'grape_arbor'}, {'id': 8111, 'synset': 'grapnel.n.02', 'name': 'grapnel'}, {'id': 8112, 'synset': 'grapnel.n.01', 'name': 'grapnel'}, {'id': 8113, 'synset': 'grass_skirt.n.01', 'name': 'grass_skirt'}, {'id': 8114, 'synset': 'grate.n.01', 'name': 'grate'}, {'id': 8115, 'synset': 'grate.n.03', 'name': 'grate'}, {'id': 8116, 'synset': 'graver.n.01', 'name': 'graver'}, {'id': 8117, 'synset': 'gravimeter.n.02', 'name': 'gravimeter'}, {'id': 8118, 'synset': 'gravure.n.03', 'name': 'gravure'}, {'id': 8119, 'synset': 'grey.n.06', 'name': 'grey'}, {'id': 8120, 'synset': 'grease-gun.n.01', 'name': 'grease-gun'}, {'id': 8121, 'synset': 'greasepaint.n.01', 'name': 'greasepaint'}, {'id': 8122, 'synset': 'greasy_spoon.n.01', 'name': 'greasy_spoon'}, {'id': 8123, 'synset': 'greatcoat.n.01', 'name': 'greatcoat'}, {'id': 8124, 'synset': 'great_hall.n.01', 'name': 'great_hall'}, {'id': 8125, 'synset': 'greave.n.01', 'name': 'greave'}, {'id': 8126, 'synset': 'greengrocery.n.02', 'name': 'greengrocery'}, {'id': 8127, 'synset': 'greenhouse.n.01', 'name': 'greenhouse'}, {'id': 8128, 'synset': 'grenade.n.01', 'name': 'grenade'}, {'id': 8129, 'synset': 'grid.n.05', 'name': 'grid'}, {'id': 8130, 'synset': 'grille.n.02', 'name': 'grille'}, {'id': 8131, 'synset': 'grillroom.n.01', 'name': 'grillroom'}, {'id': 8132, 'synset': 'grinder.n.04', 'name': 'grinder'}, {'id': 8133, 'synset': 'grinding_wheel.n.01', 'name': 'grinding_wheel'}, {'id': 8134, 'synset': 'grindstone.n.01', 'name': 'grindstone'}, {'id': 8135, 'synset': 'gripsack.n.01', 'name': 'gripsack'}, {'id': 8136, 'synset': 'gristmill.n.01', 'name': 'gristmill'}, {'id': 8137, 'synset': 'grocery_store.n.01', 'name': 'grocery_store'}, {'id': 8138, 'synset': 'grogram.n.01', 'name': 'grogram'}, {'id': 8139, 'synset': 'groined_vault.n.01', 'name': 'groined_vault'}, {'id': 8140, 'synset': 'groover.n.01', 'name': 'groover'}, {'id': 8141, 'synset': 'grosgrain.n.01', 'name': 'grosgrain'}, {'id': 8142, 'synset': 'gros_point.n.01', 'name': 'gros_point'}, {'id': 8143, 'synset': 'ground.n.09', 'name': 'ground'}, {'id': 8144, 'synset': 'ground_bait.n.01', 'name': 'ground_bait'}, {'id': 8145, 'synset': 'ground_control.n.01', 'name': 'ground_control'}, {'id': 8146, 'synset': 'ground_floor.n.01', 'name': 'ground_floor'}, {'id': 8147, 'synset': 'groundsheet.n.01', 'name': 'groundsheet'}, {'id': 8148, 'synset': 'g-string.n.01', 'name': 'G-string'}, {'id': 8149, 'synset': 'guard.n.03', 'name': 'guard'}, {'id': 8150, 'synset': 'guard_boat.n.01', 'name': 'guard_boat'}, {'id': 8151, 'synset': 'guardroom.n.02', 'name': 'guardroom'}, {'id': 8152, 'synset': 'guardroom.n.01', 'name': 'guardroom'}, {'id': 8153, 'synset': 'guard_ship.n.01', 'name': 'guard_ship'}, {'id': 8154, 'synset': "guard's_van.n.01", 'name': "guard's_van"}, {'id': 8155, 'synset': 'gueridon.n.01', 'name': 'gueridon'}, {'id': 8156, 'synset': 'guarnerius.n.03', 'name': 'Guarnerius'}, {'id': 8157, 'synset': 'guesthouse.n.01', 'name': 'guesthouse'}, {'id': 8158, 'synset': 'guestroom.n.01', 'name': 'guestroom'}, {'id': 8159, 'synset': 'guidance_system.n.01', 'name': 'guidance_system'}, {'id': 8160, 'synset': 'guided_missile.n.01', 'name': 'guided_missile'}, {'id': 8161, 'synset': 'guided_missile_cruiser.n.01', 'name': 'guided_missile_cruiser'}, {'id': 8162, 'synset': 'guided_missile_frigate.n.01', 'name': 'guided_missile_frigate'}, {'id': 8163, 'synset': 'guildhall.n.01', 'name': 'guildhall'}, {'id': 8164, 'synset': 'guilloche.n.01', 'name': 'guilloche'}, {'id': 8165, 'synset': 'guillotine.n.02', 'name': 'guillotine'}, {'id': 8166, 'synset': 'guimpe.n.02', 'name': 'guimpe'}, {'id': 8167, 'synset': 'guimpe.n.01', 'name': 'guimpe'}, {'id': 8168, 'synset': 'guitar_pick.n.01', 'name': 'guitar_pick'}, {'id': 8169, 'synset': 'gulag.n.01', 'name': 'gulag'}, {'id': 8170, 'synset': 'gunboat.n.01', 'name': 'gunboat'}, {'id': 8171, 'synset': 'gun_carriage.n.01', 'name': 'gun_carriage'}, {'id': 8172, 'synset': 'gun_case.n.01', 'name': 'gun_case'}, {'id': 8173, 'synset': 'gun_emplacement.n.01', 'name': 'gun_emplacement'}, {'id': 8174, 'synset': 'gun_enclosure.n.01', 'name': 'gun_enclosure'}, {'id': 8175, 'synset': 'gunlock.n.01', 'name': 'gunlock'}, {'id': 8176, 'synset': 'gunnery.n.01', 'name': 'gunnery'}, {'id': 8177, 'synset': 'gunnysack.n.01', 'name': 'gunnysack'}, {'id': 8178, 'synset': 'gun_pendulum.n.01', 'name': 'gun_pendulum'}, {'id': 8179, 'synset': 'gun_room.n.01', 'name': 'gun_room'}, {'id': 8180, 'synset': 'gunsight.n.01', 'name': 'gunsight'}, {'id': 8181, 'synset': 'gun_trigger.n.01', 'name': 'gun_trigger'}, {'id': 8182, 'synset': 'gurney.n.01', 'name': 'gurney'}, {'id': 8183, 'synset': 'gusher.n.01', 'name': 'gusher'}, {'id': 8184, 'synset': 'gusset.n.03', 'name': 'gusset'}, {'id': 8185, 'synset': 'gusset.n.02', 'name': 'gusset'}, {'id': 8186, 'synset': 'guy.n.03', 'name': 'guy'}, {'id': 8187, 'synset': 'gymnastic_apparatus.n.01', 'name': 'gymnastic_apparatus'}, {'id': 8188, 'synset': 'gym_shoe.n.01', 'name': 'gym_shoe'}, {'id': 8189, 'synset': 'gym_suit.n.01', 'name': 'gym_suit'}, {'id': 8190, 'synset': 'gymslip.n.01', 'name': 'gymslip'}, {'id': 8191, 'synset': 'gypsy_cab.n.01', 'name': 'gypsy_cab'}, {'id': 8192, 'synset': 'gyrocompass.n.01', 'name': 'gyrocompass'}, {'id': 8193, 'synset': 'gyroscope.n.01', 'name': 'gyroscope'}, {'id': 8194, 'synset': 'gyrostabilizer.n.01', 'name': 'gyrostabilizer'}, {'id': 8195, 'synset': 'habergeon.n.01', 'name': 'habergeon'}, {'id': 8196, 'synset': 'habit.n.03', 'name': 'habit'}, {'id': 8197, 'synset': 'habit.n.05', 'name': 'habit'}, {'id': 8198, 'synset': 'hacienda.n.02', 'name': 'hacienda'}, {'id': 8199, 'synset': 'hacksaw.n.01', 'name': 'hacksaw'}, {'id': 8200, 'synset': 'haft.n.01', 'name': 'haft'}, {'id': 8201, 'synset': 'haircloth.n.01', 'name': 'haircloth'}, {'id': 8202, 'synset': 'hairdressing.n.01', 'name': 'hairdressing'}, {'id': 8203, 'synset': 'hairpiece.n.01', 'name': 'hairpiece'}, {'id': 8204, 'synset': 'hair_shirt.n.01', 'name': 'hair_shirt'}, {'id': 8205, 'synset': 'hair_slide.n.01', 'name': 'hair_slide'}, {'id': 8206, 'synset': 'hair_spray.n.01', 'name': 'hair_spray'}, {'id': 8207, 'synset': 'hairspring.n.01', 'name': 'hairspring'}, {'id': 8208, 'synset': 'hair_trigger.n.01', 'name': 'hair_trigger'}, {'id': 8209, 'synset': 'halberd.n.01', 'name': 'halberd'}, {'id': 8210, 'synset': 'half_binding.n.01', 'name': 'half_binding'}, {'id': 8211, 'synset': 'half_hatchet.n.01', 'name': 'half_hatchet'}, {'id': 8212, 'synset': 'half_hitch.n.01', 'name': 'half_hitch'}, {'id': 8213, 'synset': 'half_track.n.01', 'name': 'half_track'}, {'id': 8214, 'synset': 'hall.n.13', 'name': 'hall'}, {'id': 8215, 'synset': 'hall.n.03', 'name': 'hall'}, {'id': 8216, 'synset': 'hall.n.12', 'name': 'hall'}, {'id': 8217, 'synset': 'hall_of_fame.n.01', 'name': 'Hall_of_Fame'}, {'id': 8218, 'synset': 'hall_of_residence.n.01', 'name': 'hall_of_residence'}, {'id': 8219, 'synset': 'hallstand.n.01', 'name': 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{'id': 8236, 'synset': 'hand_grenade.n.01', 'name': 'hand_grenade'}, {'id': 8237, 'synset': 'hand-held_computer.n.01', 'name': 'hand-held_computer'}, {'id': 8238, 'synset': 'handhold.n.01', 'name': 'handhold'}, {'id': 8239, 'synset': 'handlebar.n.01', 'name': 'handlebar'}, {'id': 8240, 'synset': 'handloom.n.01', 'name': 'handloom'}, {'id': 8241, 'synset': 'hand_lotion.n.01', 'name': 'hand_lotion'}, {'id': 8242, 'synset': 'hand_luggage.n.01', 'name': 'hand_luggage'}, {'id': 8243, 'synset': 'hand-me-down.n.01', 'name': 'hand-me-down'}, {'id': 8244, 'synset': 'hand_mower.n.01', 'name': 'hand_mower'}, {'id': 8245, 'synset': 'hand_pump.n.01', 'name': 'hand_pump'}, {'id': 8246, 'synset': 'handrest.n.01', 'name': 'handrest'}, {'id': 8247, 'synset': 'handset.n.01', 'name': 'handset'}, {'id': 8248, 'synset': 'hand_shovel.n.01', 'name': 'hand_shovel'}, {'id': 8249, 'synset': 'handspike.n.01', 'name': 'handspike'}, {'id': 8250, 'synset': 'handstamp.n.01', 'name': 'handstamp'}, {'id': 8251, 'synset': 'hand_throttle.n.01', 'name': 'hand_throttle'}, {'id': 8252, 'synset': 'hand_tool.n.01', 'name': 'hand_tool'}, {'id': 8253, 'synset': 'hand_truck.n.01', 'name': 'hand_truck'}, {'id': 8254, 'synset': 'handwear.n.01', 'name': 'handwear'}, {'id': 8255, 'synset': 'handwheel.n.02', 'name': 'handwheel'}, {'id': 8256, 'synset': 'handwheel.n.01', 'name': 'handwheel'}, {'id': 8257, 'synset': 'hangar_queen.n.01', 'name': 'hangar_queen'}, {'id': 8258, 'synset': 'hanger.n.02', 'name': 'hanger'}, {'id': 8259, 'synset': 'hang_glider.n.02', 'name': 'hang_glider'}, {'id': 8260, 'synset': "hangman's_rope.n.01", 'name': "hangman's_rope"}, {'id': 8261, 'synset': 'hank.n.01', 'name': 'hank'}, {'id': 8262, 'synset': 'hansom.n.01', 'name': 'hansom'}, {'id': 8263, 'synset': 'harbor.n.02', 'name': 'harbor'}, {'id': 8264, 'synset': 'hard_disc.n.01', 'name': 'hard_disc'}, {'id': 8265, 'synset': 'hard_hat.n.02', 'name': 'hard_hat'}, {'id': 8266, 'synset': 'hardtop.n.01', 'name': 'hardtop'}, {'id': 8267, 'synset': 'hardware.n.02', 'name': 'hardware'}, {'id': 8268, 'synset': 'hardware_store.n.01', 'name': 'hardware_store'}, {'id': 8269, 'synset': 'harmonica.n.01', 'name': 'harmonica'}, {'id': 8270, 'synset': 'harness.n.02', 'name': 'harness'}, {'id': 8271, 'synset': 'harness.n.01', 'name': 'harness'}, {'id': 8272, 'synset': 'harp.n.01', 'name': 'harp'}, {'id': 8273, 'synset': 'harp.n.02', 'name': 'harp'}, {'id': 8274, 'synset': 'harpoon.n.01', 'name': 'harpoon'}, {'id': 8275, 'synset': 'harpoon_gun.n.01', 'name': 'harpoon_gun'}, {'id': 8276, 'synset': 'harpoon_log.n.01', 'name': 'harpoon_log'}, {'id': 8277, 'synset': 'harpsichord.n.01', 'name': 'harpsichord'}, {'id': 8278, 'synset': 'harris_tweed.n.01', 'name': 'Harris_Tweed'}, {'id': 8279, 'synset': 'harrow.n.01', 'name': 'harrow'}, {'id': 8280, 'synset': 'harvester.n.02', 'name': 'harvester'}, {'id': 8281, 'synset': 'hash_house.n.01', 'name': 'hash_house'}, {'id': 8282, 'synset': 'hasp.n.01', 'name': 'hasp'}, {'id': 8283, 'synset': 'hatch.n.03', 'name': 'hatch'}, {'id': 8284, 'synset': 'hatchback.n.02', 'name': 'hatchback'}, {'id': 8285, 'synset': 'hatchback.n.01', 'name': 'hatchback'}, {'id': 8286, 'synset': 'hatchel.n.01', 'name': 'hatchel'}, {'id': 8287, 'synset': 'hatchet.n.02', 'name': 'hatchet'}, {'id': 8288, 'synset': 'hatpin.n.01', 'name': 'hatpin'}, {'id': 8289, 'synset': 'hauberk.n.01', 'name': 'hauberk'}, {'id': 8290, 'synset': 'hawaiian_guitar.n.01', 'name': 'Hawaiian_guitar'}, {'id': 8291, 'synset': 'hawse.n.01', 'name': 'hawse'}, {'id': 8292, 'synset': 'hawser.n.01', 'name': 'hawser'}, {'id': 8293, 'synset': 'hawser_bend.n.01', 'name': 'hawser_bend'}, {'id': 8294, 'synset': 'hay_bale.n.01', 'name': 'hay_bale'}, {'id': 8295, 'synset': 'hayfork.n.01', 'name': 'hayfork'}, {'id': 8296, 'synset': 'hayloft.n.01', 'name': 'hayloft'}, {'id': 8297, 'synset': 'haymaker.n.01', 'name': 'haymaker'}, {'id': 8298, 'synset': 'hayrack.n.02', 'name': 'hayrack'}, {'id': 8299, 'synset': 'hayrack.n.01', 'name': 'hayrack'}, {'id': 8300, 'synset': 'hazard.n.03', 'name': 'hazard'}, {'id': 8301, 'synset': 'head.n.31', 'name': 'head'}, {'id': 8302, 'synset': 'head.n.30', 'name': 'head'}, {'id': 8303, 'synset': 'head.n.29', 'name': 'head'}, {'id': 8304, 'synset': 'headdress.n.01', 'name': 'headdress'}, {'id': 8305, 'synset': 'header.n.05', 'name': 'header'}, {'id': 8306, 'synset': 'header.n.04', 'name': 'header'}, {'id': 8307, 'synset': 'header.n.03', 'name': 'header'}, {'id': 8308, 'synset': 'header.n.02', 'name': 'header'}, {'id': 8309, 'synset': 'headfast.n.01', 'name': 'headfast'}, {'id': 8310, 'synset': 'head_gasket.n.01', 'name': 'head_gasket'}, {'id': 8311, 'synset': 'head_gate.n.02', 'name': 'head_gate'}, {'id': 8312, 'synset': 'headgear.n.03', 'name': 'headgear'}, {'id': 8313, 'synset': 'headpiece.n.02', 'name': 'headpiece'}, {'id': 8314, 'synset': 'headpin.n.01', 'name': 'headpin'}, {'id': 8315, 'synset': 'headquarters.n.01', 'name': 'headquarters'}, {'id': 8316, 'synset': 'headrace.n.01', 'name': 'headrace'}, {'id': 8317, 'synset': 'headrest.n.02', 'name': 'headrest'}, {'id': 8318, 'synset': 'headsail.n.01', 'name': 'headsail'}, {'id': 8319, 'synset': 'head_shop.n.01', 'name': 'head_shop'}, {'id': 8320, 'synset': 'headstock.n.01', 'name': 'headstock'}, {'id': 8321, 'synset': 'health_spa.n.01', 'name': 'health_spa'}, {'id': 8322, 'synset': 'hearing_aid.n.02', 'name': 'hearing_aid'}, {'id': 8323, 'synset': 'hearing_aid.n.01', 'name': 'hearing_aid'}, {'id': 8324, 'synset': 'hearse.n.01', 'name': 'hearse'}, {'id': 8325, 'synset': 'hearth.n.02', 'name': 'hearth'}, {'id': 8326, 'synset': 'hearthrug.n.01', 'name': 'hearthrug'}, {'id': 8327, 'synset': 'heart-lung_machine.n.01', 'name': 'heart-lung_machine'}, {'id': 8328, 'synset': 'heat_engine.n.01', 'name': 'heat_engine'}, {'id': 8329, 'synset': 'heat_exchanger.n.01', 'name': 'heat_exchanger'}, {'id': 8330, 'synset': 'heating_pad.n.01', 'name': 'heating_pad'}, {'id': 8331, 'synset': 'heat_lamp.n.01', 'name': 'heat_lamp'}, {'id': 8332, 'synset': 'heat_pump.n.01', 'name': 'heat_pump'}, {'id': 8333, 'synset': 'heat-seeking_missile.n.01', 'name': 'heat-seeking_missile'}, {'id': 8334, 'synset': 'heat_shield.n.01', 'name': 'heat_shield'}, {'id': 8335, 'synset': 'heat_sink.n.01', 'name': 'heat_sink'}, {'id': 8336, 'synset': 'heaume.n.01', 'name': 'heaume'}, {'id': 8337, 'synset': 'heaver.n.01', 'name': 'heaver'}, {'id': 8338, 'synset': 'heavier-than-air_craft.n.01', 'name': 'heavier-than-air_craft'}, {'id': 8339, 'synset': 'heckelphone.n.01', 'name': 'heckelphone'}, {'id': 8340, 'synset': 'hectograph.n.01', 'name': 'hectograph'}, {'id': 8341, 'synset': 'hedge.n.01', 'name': 'hedge'}, {'id': 8342, 'synset': 'hedge_trimmer.n.01', 'name': 'hedge_trimmer'}, {'id': 8343, 'synset': 'helicon.n.01', 'name': 'helicon'}, {'id': 8344, 'synset': 'heliograph.n.01', 'name': 'heliograph'}, {'id': 8345, 'synset': 'heliometer.n.01', 'name': 'heliometer'}, {'id': 8346, 'synset': 'helm.n.01', 'name': 'helm'}, {'id': 8347, 'synset': 'helmet.n.01', 'name': 'helmet'}, {'id': 8348, 'synset': 'hematocrit.n.02', 'name': 'hematocrit'}, {'id': 8349, 'synset': 'hemming-stitch.n.01', 'name': 'hemming-stitch'}, {'id': 8350, 'synset': 'hemostat.n.01', 'name': 'hemostat'}, {'id': 8351, 'synset': 'hemstitch.n.01', 'name': 'hemstitch'}, {'id': 8352, 'synset': 'henroost.n.01', 'name': 'henroost'}, {'id': 8353, 'synset': 'heraldry.n.02', 'name': 'heraldry'}, {'id': 8354, 'synset': 'hermitage.n.01', 'name': 'hermitage'}, {'id': 8355, 'synset': 'herringbone.n.01', 'name': 'herringbone'}, {'id': 8356, 'synset': 'herringbone.n.02', 'name': 'herringbone'}, {'id': 8357, 'synset': 'herschelian_telescope.n.01', 'name': 'Herschelian_telescope'}, {'id': 8358, 'synset': 'hessian_boot.n.01', 'name': 'Hessian_boot'}, {'id': 8359, 'synset': 'heterodyne_receiver.n.01', 'name': 'heterodyne_receiver'}, {'id': 8360, 'synset': 'hibachi.n.01', 'name': 'hibachi'}, {'id': 8361, 'synset': 'hideaway.n.02', 'name': 'hideaway'}, {'id': 8362, 'synset': 'hi-fi.n.01', 'name': 'hi-fi'}, {'id': 8363, 'synset': 'high_altar.n.01', 'name': 'high_altar'}, {'id': 8364, 'synset': 'high-angle_gun.n.01', 'name': 'high-angle_gun'}, {'id': 8365, 'synset': 'highball_glass.n.01', 'name': 'highball_glass'}, {'id': 8366, 'synset': 'highboard.n.01', 'name': 'highboard'}, {'id': 8367, 'synset': 'highboy.n.01', 'name': 'highboy'}, {'id': 8368, 'synset': 'high_gear.n.01', 'name': 'high_gear'}, {'id': 8369, 'synset': 'high-hat_cymbal.n.01', 'name': 'high-hat_cymbal'}, {'id': 8370, 'synset': 'highlighter.n.02', 'name': 'highlighter'}, {'id': 8371, 'synset': 'highlighter.n.01', 'name': 'highlighter'}, {'id': 8372, 'synset': 'high-pass_filter.n.01', 'name': 'high-pass_filter'}, {'id': 8373, 'synset': 'high-rise.n.01', 'name': 'high-rise'}, {'id': 8374, 'synset': 'high_table.n.01', 'name': 'high_table'}, {'id': 8375, 'synset': 'high-warp_loom.n.01', 'name': 'high-warp_loom'}, {'id': 8376, 'synset': 'hijab.n.01', 'name': 'hijab'}, {'id': 8377, 'synset': 'hinging_post.n.01', 'name': 'hinging_post'}, {'id': 8378, 'synset': 'hip_boot.n.01', 'name': 'hip_boot'}, {'id': 8379, 'synset': 'hipflask.n.01', 'name': 'hipflask'}, {'id': 8380, 'synset': 'hip_pad.n.01', 'name': 'hip_pad'}, {'id': 8381, 'synset': 'hip_pocket.n.01', 'name': 'hip_pocket'}, {'id': 8382, 'synset': 'hippodrome.n.01', 'name': 'hippodrome'}, {'id': 8383, 'synset': 'hip_roof.n.01', 'name': 'hip_roof'}, {'id': 8384, 'synset': 'hitch.n.05', 'name': 'hitch'}, {'id': 8385, 'synset': 'hitch.n.04', 'name': 'hitch'}, {'id': 8386, 'synset': 'hitching_post.n.01', 'name': 'hitching_post'}, {'id': 8387, 'synset': 'hitchrack.n.01', 'name': 'hitchrack'}, {'id': 8388, 'synset': 'hob.n.03', 'name': 'hob'}, {'id': 8389, 'synset': 'hobble_skirt.n.01', 'name': 'hobble_skirt'}, {'id': 8390, 'synset': 'hockey_skate.n.01', 'name': 'hockey_skate'}, {'id': 8391, 'synset': 'hod.n.01', 'name': 'hod'}, {'id': 8392, 'synset': 'hodoscope.n.01', 'name': 'hodoscope'}, {'id': 8393, 'synset': 'hoe.n.01', 'name': 'hoe'}, {'id': 8394, 'synset': 'hoe_handle.n.01', 'name': 'hoe_handle'}, {'id': 8395, 'synset': 'hogshead.n.02', 'name': 'hogshead'}, {'id': 8396, 'synset': 'hoist.n.01', 'name': 'hoist'}, {'id': 8397, 'synset': 'hold.n.07', 'name': 'hold'}, {'id': 8398, 'synset': 'holder.n.01', 'name': 'holder'}, {'id': 8399, 'synset': 'holding_cell.n.01', 'name': 'holding_cell'}, {'id': 8400, 'synset': 'holding_device.n.01', 'name': 'holding_device'}, {'id': 8401, 'synset': 'holding_pen.n.01', 'name': 'holding_pen'}, {'id': 8402, 'synset': 'hollowware.n.01', 'name': 'hollowware'}, {'id': 8403, 'synset': 'holster.n.01', 'name': 'holster'}, {'id': 8404, 'synset': 'holster.n.02', 'name': 'holster'}, {'id': 8405, 'synset': 'holy_of_holies.n.02', 'name': 'holy_of_holies'}, {'id': 8406, 'synset': 'home.n.09', 'name': 'home'}, {'id': 8407, 'synset': 'home_appliance.n.01', 'name': 'home_appliance'}, {'id': 8408, 'synset': 'home_computer.n.01', 'name': 'home_computer'}, {'id': 8409, 'synset': 'home_room.n.01', 'name': 'home_room'}, {'id': 8410, 'synset': 'homespun.n.01', 'name': 'homespun'}, {'id': 8411, 'synset': 'homestead.n.03', 'name': 'homestead'}, {'id': 8412, 'synset': 'home_theater.n.01', 'name': 'home_theater'}, {'id': 8413, 'synset': 'homing_torpedo.n.01', 'name': 'homing_torpedo'}, {'id': 8414, 'synset': 'hone.n.01', 'name': 'hone'}, {'id': 8415, 'synset': 'honeycomb.n.02', 'name': 'honeycomb'}, {'id': 8416, 'synset': 'hood.n.09', 'name': 'hood'}, {'id': 8417, 'synset': 'hood.n.08', 'name': 'hood'}, {'id': 8418, 'synset': 'hood.n.07', 'name': 'hood'}, {'id': 8419, 'synset': 'hood.n.05', 'name': 'hood'}, {'id': 8420, 'synset': 'hood_latch.n.01', 'name': 'hood_latch'}, {'id': 8421, 'synset': 'hook.n.04', 'name': 'hook'}, {'id': 8422, 'synset': 'hook.n.01', 'name': 'hook'}, {'id': 8423, 'synset': 'hook_and_eye.n.01', 'name': 'hook_and_eye'}, {'id': 8424, 'synset': 'hookup.n.02', 'name': 'hookup'}, {'id': 8425, 'synset': 'hookup.n.01', 'name': 'hookup'}, {'id': 8426, 'synset': 'hook_wrench.n.01', 'name': 'hook_wrench'}, {'id': 8427, 'synset': 'hoopskirt.n.01', 'name': 'hoopskirt'}, {'id': 8428, 'synset': 'hoosegow.n.01', 'name': 'hoosegow'}, {'id': 8429, 'synset': 'hoover.n.04', 'name': 'Hoover'}, {'id': 8430, 'synset': 'hope_chest.n.01', 'name': 'hope_chest'}, {'id': 8431, 'synset': 'hopper.n.01', 'name': 'hopper'}, {'id': 8432, 'synset': 'hopsacking.n.01', 'name': 'hopsacking'}, {'id': 8433, 'synset': 'horizontal_bar.n.01', 'name': 'horizontal_bar'}, {'id': 8434, 'synset': 'horizontal_stabilizer.n.01', 'name': 'horizontal_stabilizer'}, {'id': 8435, 'synset': 'horizontal_tail.n.01', 'name': 'horizontal_tail'}, {'id': 8436, 'synset': 'horn.n.09', 'name': 'horn'}, {'id': 8437, 'synset': 'horn.n.01', 'name': 'horn'}, {'id': 8438, 'synset': 'horn.n.08', 'name': 'horn'}, {'id': 8439, 'synset': 'horn_button.n.01', 'name': 'horn_button'}, {'id': 8440, 'synset': 'hornpipe.n.03', 'name': 'hornpipe'}, {'id': 8441, 'synset': 'horse.n.02', 'name': 'horse'}, {'id': 8442, 'synset': 'horsebox.n.01', 'name': 'horsebox'}, {'id': 8443, 'synset': 'horsecar.n.01', 'name': 'horsecar'}, {'id': 8444, 'synset': 'horse_cart.n.01', 'name': 'horse_cart'}, {'id': 8445, 'synset': 'horsecloth.n.01', 'name': 'horsecloth'}, {'id': 8446, 'synset': 'horse-drawn_vehicle.n.01', 'name': 'horse-drawn_vehicle'}, {'id': 8447, 'synset': 'horsehair.n.02', 'name': 'horsehair'}, {'id': 8448, 'synset': 'horsehair_wig.n.01', 'name': 'horsehair_wig'}, {'id': 8449, 'synset': 'horseless_carriage.n.01', 'name': 'horseless_carriage'}, {'id': 8450, 'synset': 'horse_pistol.n.01', 'name': 'horse_pistol'}, {'id': 8451, 'synset': 'horseshoe.n.02', 'name': 'horseshoe'}, {'id': 8452, 'synset': 'horseshoe.n.01', 'name': 'horseshoe'}, {'id': 8453, 'synset': 'horse-trail.n.01', 'name': 'horse-trail'}, {'id': 8454, 'synset': 'horsewhip.n.01', 'name': 'horsewhip'}, {'id': 8455, 'synset': 'hose.n.02', 'name': 'hose'}, {'id': 8456, 'synset': 'hosiery.n.01', 'name': 'hosiery'}, {'id': 8457, 'synset': 'hospice.n.01', 'name': 'hospice'}, {'id': 8458, 'synset': 'hospital.n.01', 'name': 'hospital'}, {'id': 8459, 'synset': 'hospital_bed.n.01', 'name': 'hospital_bed'}, {'id': 8460, 'synset': 'hospital_room.n.01', 'name': 'hospital_room'}, {'id': 8461, 'synset': 'hospital_ship.n.01', 'name': 'hospital_ship'}, {'id': 8462, 'synset': 'hospital_train.n.01', 'name': 'hospital_train'}, {'id': 8463, 'synset': 'hostel.n.02', 'name': 'hostel'}, {'id': 8464, 'synset': 'hostel.n.01', 'name': 'hostel'}, {'id': 8465, 'synset': 'hotel.n.01', 'name': 'hotel'}, {'id': 8466, 'synset': 'hotel-casino.n.02', 'name': 'hotel-casino'}, {'id': 8467, 'synset': 'hotel-casino.n.01', 'name': 'hotel-casino'}, {'id': 8468, 'synset': 'hotel_room.n.01', 'name': 'hotel_room'}, {'id': 8469, 'synset': 'hot_line.n.01', 'name': 'hot_line'}, {'id': 8470, 'synset': 'hot_pants.n.02', 'name': 'hot_pants'}, {'id': 8471, 'synset': 'hot_rod.n.01', 'name': 'hot_rod'}, {'id': 8472, 'synset': 'hot_spot.n.03', 'name': 'hot_spot'}, {'id': 8473, 'synset': 'hot_tub.n.01', 'name': 'hot_tub'}, {'id': 8474, 'synset': 'hot-water_bottle.n.01', 'name': 'hot-water_bottle'}, {'id': 8475, 'synset': 'houndstooth_check.n.01', 'name': 'houndstooth_check'}, {'id': 8476, 'synset': 'hour_hand.n.01', 'name': 'hour_hand'}, {'id': 8477, 'synset': 'house.n.01', 'name': 'house'}, {'id': 8478, 'synset': 'house.n.12', 'name': 'house'}, {'id': 8479, 'synset': 'houselights.n.01', 'name': 'houselights'}, {'id': 8480, 'synset': 'house_of_cards.n.02', 'name': 'house_of_cards'}, {'id': 8481, 'synset': 'house_of_correction.n.01', 'name': 'house_of_correction'}, {'id': 8482, 'synset': 'house_paint.n.01', 'name': 'house_paint'}, {'id': 8483, 'synset': 'housetop.n.01', 'name': 'housetop'}, {'id': 8484, 'synset': 'housing.n.01', 'name': 'housing'}, {'id': 8485, 'synset': 'hovel.n.01', 'name': 'hovel'}, {'id': 8486, 'synset': 'hovercraft.n.01', 'name': 'hovercraft'}, {'id': 8487, 'synset': 'howdah.n.01', 'name': 'howdah'}, {'id': 8488, 'synset': 'huarache.n.01', 'name': 'huarache'}, {'id': 8489, 'synset': 'hub-and-spoke.n.01', 'name': 'hub-and-spoke'}, {'id': 8490, 'synset': 'hubcap.n.01', 'name': 'hubcap'}, {'id': 8491, 'synset': 'huck.n.01', 'name': 'huck'}, {'id': 8492, 'synset': 'hug-me-tight.n.01', 'name': 'hug-me-tight'}, {'id': 8493, 'synset': 'hula-hoop.n.01', 'name': 'hula-hoop'}, {'id': 8494, 'synset': 'hulk.n.02', 'name': 'hulk'}, {'id': 8495, 'synset': 'hull.n.06', 'name': 'hull'}, {'id': 8496, 'synset': 'humeral_veil.n.01', 'name': 'humeral_veil'}, {'id': 8497, 'synset': 'humvee.n.01', 'name': 'Humvee'}, {'id': 8498, 'synset': 'hunter.n.04', 'name': 'hunter'}, {'id': 8499, 'synset': 'hunting_knife.n.01', 'name': 'hunting_knife'}, {'id': 8500, 'synset': 'hurdle.n.01', 'name': 'hurdle'}, {'id': 8501, 'synset': 'hurricane_deck.n.01', 'name': 'hurricane_deck'}, {'id': 8502, 'synset': 'hurricane_lamp.n.01', 'name': 'hurricane_lamp'}, {'id': 8503, 'synset': 'hut.n.01', 'name': 'hut'}, {'id': 8504, 'synset': 'hutch.n.01', 'name': 'hutch'}, {'id': 8505, 'synset': 'hutment.n.01', 'name': 'hutment'}, {'id': 8506, 'synset': 'hydraulic_brake.n.01', 'name': 'hydraulic_brake'}, {'id': 8507, 'synset': 'hydraulic_press.n.01', 'name': 'hydraulic_press'}, {'id': 8508, 'synset': 'hydraulic_pump.n.01', 'name': 'hydraulic_pump'}, {'id': 8509, 'synset': 'hydraulic_system.n.01', 'name': 'hydraulic_system'}, {'id': 8510, 'synset': 'hydraulic_transmission.n.01', 'name': 'hydraulic_transmission'}, {'id': 8511, 'synset': 'hydroelectric_turbine.n.01', 'name': 'hydroelectric_turbine'}, {'id': 8512, 'synset': 'hydrofoil.n.02', 'name': 'hydrofoil'}, {'id': 8513, 'synset': 'hydrofoil.n.01', 'name': 'hydrofoil'}, {'id': 8514, 'synset': 'hydrogen_bomb.n.01', 'name': 'hydrogen_bomb'}, {'id': 8515, 'synset': 'hydrometer.n.01', 'name': 'hydrometer'}, {'id': 8516, 'synset': 'hygrodeik.n.01', 'name': 'hygrodeik'}, {'id': 8517, 'synset': 'hygrometer.n.01', 'name': 'hygrometer'}, {'id': 8518, 'synset': 'hygroscope.n.01', 'name': 'hygroscope'}, {'id': 8519, 'synset': 'hyperbaric_chamber.n.01', 'name': 'hyperbaric_chamber'}, {'id': 8520, 'synset': 'hypercoaster.n.01', 'name': 'hypercoaster'}, {'id': 8521, 'synset': 'hypermarket.n.01', 'name': 'hypermarket'}, {'id': 8522, 'synset': 'hypodermic_needle.n.01', 'name': 'hypodermic_needle'}, {'id': 8523, 'synset': 'hypodermic_syringe.n.01', 'name': 'hypodermic_syringe'}, {'id': 8524, 'synset': 'hypsometer.n.01', 'name': 'hypsometer'}, {'id': 8525, 'synset': 'hysterosalpingogram.n.01', 'name': 'hysterosalpingogram'}, {'id': 8526, 'synset': 'i-beam.n.01', 'name': 'I-beam'}, {'id': 8527, 'synset': 'ice_ax.n.01', 'name': 'ice_ax'}, {'id': 8528, 'synset': 'iceboat.n.02', 'name': 'iceboat'}, {'id': 8529, 'synset': 'icebreaker.n.01', 'name': 'icebreaker'}, {'id': 8530, 'synset': 'iced-tea_spoon.n.01', 'name': 'iced-tea_spoon'}, {'id': 8531, 'synset': 'ice_hockey_rink.n.01', 'name': 'ice_hockey_rink'}, {'id': 8532, 'synset': 'ice_machine.n.01', 'name': 'ice_machine'}, {'id': 8533, 'synset': 'icepick.n.01', 'name': 'icepick'}, {'id': 8534, 'synset': 'ice_rink.n.01', 'name': 'ice_rink'}, {'id': 8535, 'synset': 'ice_tongs.n.01', 'name': 'ice_tongs'}, {'id': 8536, 'synset': 'icetray.n.01', 'name': 'icetray'}, {'id': 8537, 'synset': 'iconoscope.n.01', 'name': 'iconoscope'}, {'id': 8538, 'synset': 'identikit.n.01', 'name': 'Identikit'}, {'id': 8539, 'synset': 'idle_pulley.n.01', 'name': 'idle_pulley'}, {'id': 8540, 'synset': 'igloo.n.01', 'name': 'igloo'}, {'id': 8541, 'synset': 'ignition_coil.n.01', 'name': 'ignition_coil'}, {'id': 8542, 'synset': 'ignition_key.n.01', 'name': 'ignition_key'}, {'id': 8543, 'synset': 'ignition_switch.n.01', 'name': 'ignition_switch'}, {'id': 8544, 'synset': 'imaret.n.01', 'name': 'imaret'}, {'id': 8545, 'synset': 'immovable_bandage.n.01', 'name': 'immovable_bandage'}, {'id': 8546, 'synset': 'impact_printer.n.01', 'name': 'impact_printer'}, {'id': 8547, 'synset': 'impeller.n.01', 'name': 'impeller'}, {'id': 8548, 'synset': 'implant.n.01', 'name': 'implant'}, {'id': 8549, 'synset': 'implement.n.01', 'name': 'implement'}, {'id': 8550, 'synset': 'impression.n.07', 'name': 'impression'}, {'id': 8551, 'synset': 'imprint.n.05', 'name': 'imprint'}, {'id': 8552, 'synset': 'improvised_explosive_device.n.01', 'name': 'improvised_explosive_device'}, {'id': 8553, 'synset': 'impulse_turbine.n.01', 'name': 'impulse_turbine'}, {'id': 8554, 'synset': 'in-basket.n.01', 'name': 'in-basket'}, {'id': 8555, 'synset': 'incendiary_bomb.n.01', 'name': 'incendiary_bomb'}, {'id': 8556, 'synset': 'incinerator.n.01', 'name': 'incinerator'}, {'id': 8557, 'synset': 'inclined_plane.n.01', 'name': 'inclined_plane'}, {'id': 8558, 'synset': 'inclinometer.n.02', 'name': 'inclinometer'}, {'id': 8559, 'synset': 'inclinometer.n.01', 'name': 'inclinometer'}, {'id': 8560, 'synset': 'incrustation.n.03', 'name': 'incrustation'}, {'id': 8561, 'synset': 'incubator.n.01', 'name': 'incubator'}, {'id': 8562, 'synset': 'index_register.n.01', 'name': 'index_register'}, {'id': 8563, 'synset': 'indiaman.n.01', 'name': 'Indiaman'}, {'id': 8564, 'synset': 'indian_club.n.01', 'name': 'Indian_club'}, {'id': 8565, 'synset': 'indicator.n.03', 'name': 'indicator'}, {'id': 8566, 'synset': 'induction_coil.n.01', 'name': 'induction_coil'}, {'id': 8567, 'synset': 'inductor.n.01', 'name': 'inductor'}, {'id': 8568, 'synset': 'industrial_watercourse.n.01', 'name': 'industrial_watercourse'}, {'id': 8569, 'synset': 'inertial_guidance_system.n.01', 'name': 'inertial_guidance_system'}, {'id': 8570, 'synset': 'inflater.n.01', 'name': 'inflater'}, {'id': 8571, 'synset': 'injector.n.01', 'name': 'injector'}, {'id': 8572, 'synset': 'ink_bottle.n.01', 'name': 'ink_bottle'}, {'id': 8573, 'synset': 'ink_eraser.n.01', 'name': 'ink_eraser'}, {'id': 8574, 'synset': 'ink-jet_printer.n.01', 'name': 'ink-jet_printer'}, {'id': 8575, 'synset': 'inkle.n.01', 'name': 'inkle'}, {'id': 8576, 'synset': 'inkstand.n.02', 'name': 'inkstand'}, {'id': 8577, 'synset': 'inkwell.n.01', 'name': 'inkwell'}, {'id': 8578, 'synset': 'inlay.n.01', 'name': 'inlay'}, {'id': 8579, 'synset': 'inside_caliper.n.01', 'name': 'inside_caliper'}, {'id': 8580, 'synset': 'insole.n.01', 'name': 'insole'}, {'id': 8581, 'synset': 'instep.n.02', 'name': 'instep'}, {'id': 8582, 'synset': 'instillator.n.01', 'name': 'instillator'}, {'id': 8583, 'synset': 'institution.n.02', 'name': 'institution'}, {'id': 8584, 'synset': 'instrument.n.01', 'name': 'instrument'}, {'id': 8585, 'synset': 'instrument_of_punishment.n.01', 'name': 'instrument_of_punishment'}, {'id': 8586, 'synset': 'instrument_of_torture.n.01', 'name': 'instrument_of_torture'}, {'id': 8587, 'synset': 'intaglio.n.02', 'name': 'intaglio'}, {'id': 8588, 'synset': 'intake_valve.n.01', 'name': 'intake_valve'}, {'id': 8589, 'synset': 'integrated_circuit.n.01', 'name': 'integrated_circuit'}, {'id': 8590, 'synset': 'integrator.n.01', 'name': 'integrator'}, {'id': 8591, 'synset': 'intelnet.n.01', 'name': 'Intelnet'}, {'id': 8592, 'synset': 'interceptor.n.01', 'name': 'interceptor'}, {'id': 8593, 'synset': 'interchange.n.01', 'name': 'interchange'}, {'id': 8594, 'synset': 'intercommunication_system.n.01', 'name': 'intercommunication_system'}, {'id': 8595, 'synset': 'intercontinental_ballistic_missile.n.01', 'name': 'intercontinental_ballistic_missile'}, {'id': 8596, 'synset': 'interface.n.04', 'name': 'interface'}, {'id': 8597, 'synset': 'interferometer.n.01', 'name': 'interferometer'}, {'id': 8598, 'synset': 'interior_door.n.01', 'name': 'interior_door'}, {'id': 8599, 'synset': 'internal-combustion_engine.n.01', 'name': 'internal-combustion_engine'}, {'id': 8600, 'synset': 'internal_drive.n.01', 'name': 'internal_drive'}, {'id': 8601, 'synset': 'internet.n.01', 'name': 'internet'}, {'id': 8602, 'synset': 'interphone.n.01', 'name': 'interphone'}, {'id': 8603, 'synset': 'interrupter.n.01', 'name': 'interrupter'}, {'id': 8604, 'synset': 'intersection.n.02', 'name': 'intersection'}, {'id': 8605, 'synset': 'interstice.n.02', 'name': 'interstice'}, {'id': 8606, 'synset': 'intraocular_lens.n.01', 'name': 'intraocular_lens'}, {'id': 8607, 'synset': 'intravenous_pyelogram.n.01', 'name': 'intravenous_pyelogram'}, {'id': 8608, 'synset': 'inverter.n.01', 'name': 'inverter'}, {'id': 8609, 'synset': 'ion_engine.n.01', 'name': 'ion_engine'}, {'id': 8610, 'synset': 'ionization_chamber.n.01', 'name': 'ionization_chamber'}, {'id': 8611, 'synset': 'video_ipod.n.01', 'name': 'video_iPod'}, {'id': 8612, 'synset': 'iron.n.02', 'name': 'iron'}, {'id': 8613, 'synset': 'iron.n.03', 'name': 'iron'}, {'id': 8614, 'synset': 'irons.n.01', 'name': 'irons'}, {'id': 8615, 'synset': 'ironclad.n.01', 'name': 'ironclad'}, {'id': 8616, 'synset': 'iron_foundry.n.01', 'name': 'iron_foundry'}, {'id': 8617, 'synset': 'iron_horse.n.01', 'name': 'iron_horse'}, {'id': 8618, 'synset': 'ironing.n.01', 'name': 'ironing'}, {'id': 8619, 'synset': 'iron_lung.n.01', 'name': 'iron_lung'}, {'id': 8620, 'synset': 'ironmongery.n.01', 'name': 'ironmongery'}, {'id': 8621, 'synset': 'ironworks.n.01', 'name': 'ironworks'}, {'id': 8622, 'synset': 'irrigation_ditch.n.01', 'name': 'irrigation_ditch'}, {'id': 8623, 'synset': 'izar.n.01', 'name': 'izar'}, {'id': 8624, 'synset': 'jabot.n.01', 'name': 'jabot'}, {'id': 8625, 'synset': 'jack.n.10', 'name': 'jack'}, {'id': 8626, 'synset': 'jack.n.07', 'name': 'jack'}, {'id': 8627, 'synset': 'jack.n.06', 'name': 'jack'}, {'id': 8628, 'synset': 'jack.n.05', 'name': 'jack'}, {'id': 8629, 'synset': 'jacket.n.02', 'name': 'jacket'}, {'id': 8630, 'synset': 'jacket.n.05', 'name': 'jacket'}, {'id': 8631, 'synset': 'jack-in-the-box.n.01', 'name': 'jack-in-the-box'}, {'id': 8632, 'synset': "jack-o'-lantern.n.02", 'name': "jack-o'-lantern"}, {'id': 8633, 'synset': 'jack_plane.n.01', 'name': 'jack_plane'}, {'id': 8634, 'synset': "jacob's_ladder.n.02", 'name': "Jacob's_ladder"}, {'id': 8635, 'synset': 'jaconet.n.01', 'name': 'jaconet'}, {'id': 8636, 'synset': 'jacquard_loom.n.01', 'name': 'Jacquard_loom'}, {'id': 8637, 'synset': 'jacquard.n.02', 'name': 'jacquard'}, {'id': 8638, 'synset': 'jag.n.03', 'name': 'jag'}, {'id': 8639, 'synset': 'jail.n.01', 'name': 'jail'}, {'id': 8640, 'synset': 'jalousie.n.02', 'name': 'jalousie'}, {'id': 8641, 'synset': 'jamb.n.01', 'name': 'jamb'}, {'id': 8642, 'synset': 'jammer.n.01', 'name': 'jammer'}, {'id': 8643, 'synset': 'jampot.n.01', 'name': 'jampot'}, {'id': 8644, 'synset': 'japan.n.04', 'name': 'japan'}, {'id': 8645, 'synset': 'jarvik_heart.n.01', 'name': 'Jarvik_heart'}, {'id': 8646, 'synset': 'jaunting_car.n.01', 'name': 'jaunting_car'}, {'id': 8647, 'synset': 'javelin.n.02', 'name': 'javelin'}, {'id': 8648, 'synset': 'jaw.n.03', 'name': 'jaw'}, {'id': 8649, 'synset': 'jaws_of_life.n.01', 'name': 'Jaws_of_Life'}, {'id': 8650, 'synset': 'jellaba.n.01', 'name': 'jellaba'}, {'id': 8651, 'synset': 'jerkin.n.01', 'name': 'jerkin'}, {'id': 8652, 'synset': 'jeroboam.n.02', 'name': 'jeroboam'}, {'id': 8653, 'synset': 'jersey.n.04', 'name': 'jersey'}, {'id': 8654, 'synset': 'jet_bridge.n.01', 'name': 'jet_bridge'}, {'id': 8655, 'synset': 'jet_engine.n.01', 'name': 'jet_engine'}, {'id': 8656, 'synset': 'jetliner.n.01', 'name': 'jetliner'}, {'id': 8657, 'synset': "jeweler's_glass.n.01", 'name': "jeweler's_glass"}, {'id': 8658, 'synset': 'jewelled_headdress.n.01', 'name': 'jewelled_headdress'}, {'id': 8659, 'synset': "jew's_harp.n.01", 'name': "jew's_harp"}, {'id': 8660, 'synset': 'jib.n.01', 'name': 'jib'}, {'id': 8661, 'synset': 'jibboom.n.01', 'name': 'jibboom'}, {'id': 8662, 'synset': 'jig.n.03', 'name': 'jig'}, {'id': 8663, 'synset': 'jig.n.02', 'name': 'jig'}, {'id': 8664, 'synset': 'jiggermast.n.01', 'name': 'jiggermast'}, {'id': 8665, 'synset': 'jigsaw.n.02', 'name': 'jigsaw'}, {'id': 8666, 'synset': 'jigsaw_puzzle.n.01', 'name': 'jigsaw_puzzle'}, {'id': 8667, 'synset': 'jinrikisha.n.01', 'name': 'jinrikisha'}, {'id': 8668, 'synset': 'jobcentre.n.01', 'name': 'jobcentre'}, {'id': 8669, 'synset': 'jodhpurs.n.01', 'name': 'jodhpurs'}, {'id': 8670, 'synset': 'jodhpur.n.01', 'name': 'jodhpur'}, {'id': 8671, 'synset': 'joinery.n.01', 'name': 'joinery'}, {'id': 8672, 'synset': 'joint.n.05', 'name': 'joint'}, {'id': 8673, 'synset': 'joint_direct_attack_munition.n.01', 'name': 'Joint_Direct_Attack_Munition'}, {'id': 8674, 'synset': 'jointer.n.01', 'name': 'jointer'}, {'id': 8675, 'synset': 'joist.n.01', 'name': 'joist'}, {'id': 8676, 'synset': 'jolly_boat.n.01', 'name': 'jolly_boat'}, {'id': 8677, 'synset': 'jorum.n.01', 'name': 'jorum'}, {'id': 8678, 'synset': 'joss_house.n.01', 'name': 'joss_house'}, {'id': 8679, 'synset': 'journal_bearing.n.01', 'name': 'journal_bearing'}, {'id': 8680, 'synset': 'journal_box.n.01', 'name': 'journal_box'}, {'id': 8681, 'synset': 'jungle_gym.n.01', 'name': 'jungle_gym'}, {'id': 8682, 'synset': 'junk.n.02', 'name': 'junk'}, {'id': 8683, 'synset': 'jug.n.01', 'name': 'jug'}, {'id': 8684, 'synset': 'jukebox.n.01', 'name': 'jukebox'}, {'id': 8685, 'synset': 'jumbojet.n.01', 'name': 'jumbojet'}, {'id': 8686, 'synset': 'jumper.n.07', 'name': 'jumper'}, {'id': 8687, 'synset': 'jumper.n.06', 'name': 'jumper'}, {'id': 8688, 'synset': 'jumper.n.05', 'name': 'jumper'}, {'id': 8689, 'synset': 'jumper.n.04', 'name': 'jumper'}, {'id': 8690, 'synset': 'jumper_cable.n.01', 'name': 'jumper_cable'}, {'id': 8691, 'synset': 'jump_seat.n.01', 'name': 'jump_seat'}, {'id': 8692, 'synset': 'jump_suit.n.02', 'name': 'jump_suit'}, {'id': 8693, 'synset': 'junction.n.01', 'name': 'junction'}, {'id': 8694, 'synset': 'junction.n.04', 'name': 'junction'}, {'id': 8695, 'synset': 'junction_barrier.n.01', 'name': 'junction_barrier'}, {'id': 8696, 'synset': 'junk_shop.n.01', 'name': 'junk_shop'}, {'id': 8697, 'synset': 'jury_box.n.01', 'name': 'jury_box'}, {'id': 8698, 'synset': 'jury_mast.n.01', 'name': 'jury_mast'}, {'id': 8699, 'synset': 'kachina.n.03', 'name': 'kachina'}, {'id': 8700, 'synset': 'kaffiyeh.n.01', 'name': 'kaffiyeh'}, {'id': 8701, 'synset': 'kalansuwa.n.01', 'name': 'kalansuwa'}, {'id': 8702, 'synset': 'kalashnikov.n.01', 'name': 'Kalashnikov'}, {'id': 8703, 'synset': 'kameez.n.01', 'name': 'kameez'}, {'id': 8704, 'synset': 'kanzu.n.01', 'name': 'kanzu'}, {'id': 8705, 'synset': 'katharometer.n.01', 'name': 'katharometer'}, {'id': 8706, 'synset': 'kazoo.n.01', 'name': 'kazoo'}, {'id': 8707, 'synset': 'keel.n.03', 'name': 'keel'}, {'id': 8708, 'synset': 'keelboat.n.01', 'name': 'keelboat'}, {'id': 8709, 'synset': 'keelson.n.01', 'name': 'keelson'}, {'id': 8710, 'synset': 'keep.n.02', 'name': 'keep'}, {'id': 8711, 'synset': 'kepi.n.01', 'name': 'kepi'}, {'id': 8712, 'synset': 'keratoscope.n.01', 'name': 'keratoscope'}, {'id': 8713, 'synset': 'kerchief.n.01', 'name': 'kerchief'}, {'id': 8714, 'synset': 'ketch.n.01', 'name': 'ketch'}, {'id': 8715, 'synset': 'kettle.n.04', 'name': 'kettle'}, {'id': 8716, 'synset': 'key.n.15', 'name': 'key'}, {'id': 8717, 'synset': 'keyboard.n.01', 'name': 'keyboard'}, {'id': 8718, 'synset': 'keyboard_buffer.n.01', 'name': 'keyboard_buffer'}, {'id': 8719, 'synset': 'keyboard_instrument.n.01', 'name': 'keyboard_instrument'}, {'id': 8720, 'synset': 'keyhole.n.01', 'name': 'keyhole'}, {'id': 8721, 'synset': 'keyhole_saw.n.01', 'name': 'keyhole_saw'}, {'id': 8722, 'synset': 'khadi.n.01', 'name': 'khadi'}, {'id': 8723, 'synset': 'khaki.n.01', 'name': 'khaki'}, {'id': 8724, 'synset': 'khakis.n.01', 'name': 'khakis'}, {'id': 8725, 'synset': 'khimar.n.01', 'name': 'khimar'}, {'id': 8726, 'synset': 'khukuri.n.01', 'name': 'khukuri'}, {'id': 8727, 'synset': 'kick_pleat.n.01', 'name': 'kick_pleat'}, {'id': 8728, 'synset': 'kicksorter.n.01', 'name': 'kicksorter'}, {'id': 8729, 'synset': 'kickstand.n.01', 'name': 'kickstand'}, {'id': 8730, 'synset': 'kick_starter.n.01', 'name': 'kick_starter'}, {'id': 8731, 'synset': 'kid_glove.n.01', 'name': 'kid_glove'}, {'id': 8732, 'synset': 'kiln.n.01', 'name': 'kiln'}, {'id': 8733, 'synset': 'kinescope.n.01', 'name': 'kinescope'}, {'id': 8734, 'synset': 'kinetoscope.n.01', 'name': 'Kinetoscope'}, {'id': 8735, 'synset': 'king.n.10', 'name': 'king'}, {'id': 8736, 'synset': 'king.n.08', 'name': 'king'}, {'id': 8737, 'synset': 'kingbolt.n.01', 'name': 'kingbolt'}, {'id': 8738, 'synset': 'king_post.n.01', 'name': 'king_post'}, {'id': 8739, 'synset': "kipp's_apparatus.n.01", 'name': "Kipp's_apparatus"}, {'id': 8740, 'synset': 'kirk.n.01', 'name': 'kirk'}, {'id': 8741, 'synset': 'kirpan.n.01', 'name': 'kirpan'}, {'id': 8742, 'synset': 'kirtle.n.02', 'name': 'kirtle'}, {'id': 8743, 'synset': 'kirtle.n.01', 'name': 'kirtle'}, {'id': 8744, 'synset': 'kit.n.02', 'name': 'kit'}, {'id': 8745, 'synset': 'kit.n.01', 'name': 'kit'}, {'id': 8746, 'synset': 'kitbag.n.01', 'name': 'kitbag'}, {'id': 8747, 'synset': 'kitchen.n.01', 'name': 'kitchen'}, {'id': 8748, 'synset': 'kitchen_appliance.n.01', 'name': 'kitchen_appliance'}, {'id': 8749, 'synset': 'kitchenette.n.01', 'name': 'kitchenette'}, {'id': 8750, 'synset': 'kitchen_utensil.n.01', 'name': 'kitchen_utensil'}, {'id': 8751, 'synset': 'kitchenware.n.01', 'name': 'kitchenware'}, {'id': 8752, 'synset': 'kite_balloon.n.01', 'name': 'kite_balloon'}, {'id': 8753, 'synset': 'klaxon.n.01', 'name': 'klaxon'}, {'id': 8754, 'synset': 'klieg_light.n.01', 'name': 'klieg_light'}, {'id': 8755, 'synset': 'klystron.n.01', 'name': 'klystron'}, {'id': 8756, 'synset': 'knee_brace.n.01', 'name': 'knee_brace'}, {'id': 8757, 'synset': 'knee-high.n.01', 'name': 'knee-high'}, {'id': 8758, 'synset': 'knee_piece.n.01', 'name': 'knee_piece'}, {'id': 8759, 'synset': 'knife.n.02', 'name': 'knife'}, {'id': 8760, 'synset': 'knife_blade.n.01', 'name': 'knife_blade'}, {'id': 8761, 'synset': 'knight.n.02', 'name': 'knight'}, {'id': 8762, 'synset': 'knit.n.01', 'name': 'knit'}, {'id': 8763, 'synset': 'knitting_machine.n.01', 'name': 'knitting_machine'}, {'id': 8764, 'synset': 'knitwear.n.01', 'name': 'knitwear'}, {'id': 8765, 'synset': 'knob.n.01', 'name': 'knob'}, {'id': 8766, 'synset': 'knob.n.04', 'name': 'knob'}, {'id': 8767, 'synset': 'knobble.n.01', 'name': 'knobble'}, {'id': 8768, 'synset': 'knobkerrie.n.01', 'name': 'knobkerrie'}, {'id': 8769, 'synset': 'knot.n.02', 'name': 'knot'}, {'id': 8770, 'synset': 'knuckle_joint.n.02', 'name': 'knuckle_joint'}, {'id': 8771, 'synset': 'kohl.n.01', 'name': 'kohl'}, {'id': 8772, 'synset': 'koto.n.01', 'name': 'koto'}, {'id': 8773, 'synset': 'kraal.n.02', 'name': 'kraal'}, {'id': 8774, 'synset': 'kremlin.n.02', 'name': 'kremlin'}, {'id': 8775, 'synset': 'kris.n.01', 'name': 'kris'}, {'id': 8776, 'synset': 'krummhorn.n.01', 'name': 'krummhorn'}, {'id': 8777, 'synset': "kundt's_tube.n.01", 'name': "Kundt's_tube"}, {'id': 8778, 'synset': 'kurdistan.n.02', 'name': 'Kurdistan'}, {'id': 8779, 'synset': 'kurta.n.01', 'name': 'kurta'}, {'id': 8780, 'synset': 'kylix.n.01', 'name': 'kylix'}, {'id': 8781, 'synset': 'kymograph.n.01', 'name': 'kymograph'}, {'id': 8782, 'synset': 'lab_bench.n.01', 'name': 'lab_bench'}, {'id': 8783, 'synset': 'lace.n.02', 'name': 'lace'}, {'id': 8784, 'synset': 'lacquer.n.02', 'name': 'lacquer'}, {'id': 8785, 'synset': 'lacquerware.n.01', 'name': 'lacquerware'}, {'id': 8786, 'synset': 'lacrosse_ball.n.01', 'name': 'lacrosse_ball'}, {'id': 8787, 'synset': 'ladder-back.n.02', 'name': 'ladder-back'}, {'id': 8788, 'synset': 'ladder-back.n.01', 'name': 'ladder-back'}, {'id': 8789, 'synset': 'ladder_truck.n.01', 'name': 'ladder_truck'}, {'id': 8790, 'synset': "ladies'_room.n.01", 'name': "ladies'_room"}, {'id': 8791, 'synset': 'lady_chapel.n.01', 'name': 'lady_chapel'}, {'id': 8792, 'synset': 'lagerphone.n.01', 'name': 'lagerphone'}, {'id': 8793, 'synset': 'lag_screw.n.01', 'name': 'lag_screw'}, {'id': 8794, 'synset': 'lake_dwelling.n.01', 'name': 'lake_dwelling'}, {'id': 8795, 'synset': 'lally.n.01', 'name': 'lally'}, {'id': 8796, 'synset': 'lamasery.n.01', 'name': 'lamasery'}, {'id': 8797, 'synset': 'lambrequin.n.02', 'name': 'lambrequin'}, {'id': 8798, 'synset': 'lame.n.02', 'name': 'lame'}, {'id': 8799, 'synset': 'laminar_flow_clean_room.n.01', 'name': 'laminar_flow_clean_room'}, {'id': 8800, 'synset': 'laminate.n.01', 'name': 'laminate'}, {'id': 8801, 'synset': 'lamination.n.01', 'name': 'lamination'}, {'id': 8802, 'synset': 'lamp.n.01', 'name': 'lamp'}, {'id': 8803, 'synset': 'lamp_house.n.01', 'name': 'lamp_house'}, {'id': 8804, 'synset': 'lanai.n.02', 'name': 'lanai'}, {'id': 8805, 'synset': 'lancet_arch.n.01', 'name': 'lancet_arch'}, {'id': 8806, 'synset': 'lancet_window.n.01', 'name': 'lancet_window'}, {'id': 8807, 'synset': 'landau.n.02', 'name': 'landau'}, {'id': 8808, 'synset': 'lander.n.02', 'name': 'lander'}, {'id': 8809, 'synset': 'landing_craft.n.01', 'name': 'landing_craft'}, {'id': 8810, 'synset': 'landing_flap.n.01', 'name': 'landing_flap'}, {'id': 8811, 'synset': 'landing_gear.n.01', 'name': 'landing_gear'}, {'id': 8812, 'synset': 'landing_net.n.01', 'name': 'landing_net'}, {'id': 8813, 'synset': 'landing_skid.n.01', 'name': 'landing_skid'}, {'id': 8814, 'synset': 'land_line.n.01', 'name': 'land_line'}, {'id': 8815, 'synset': 'land_mine.n.01', 'name': 'land_mine'}, {'id': 8816, 'synset': 'land_office.n.01', 'name': 'land_office'}, {'id': 8817, 'synset': 'lanolin.n.02', 'name': 'lanolin'}, {'id': 8818, 'synset': 'lanyard.n.01', 'name': 'lanyard'}, {'id': 8819, 'synset': 'lap.n.03', 'name': 'lap'}, {'id': 8820, 'synset': 'laparoscope.n.01', 'name': 'laparoscope'}, {'id': 8821, 'synset': 'lapboard.n.01', 'name': 'lapboard'}, {'id': 8822, 'synset': 'lapel.n.01', 'name': 'lapel'}, {'id': 8823, 'synset': 'lap_joint.n.01', 'name': 'lap_joint'}, {'id': 8824, 'synset': 'laryngoscope.n.01', 'name': 'laryngoscope'}, {'id': 8825, 'synset': 'laser.n.01', 'name': 'laser'}, {'id': 8826, 'synset': 'laser-guided_bomb.n.01', 'name': 'laser-guided_bomb'}, {'id': 8827, 'synset': 'laser_printer.n.01', 'name': 'laser_printer'}, {'id': 8828, 'synset': 'lash.n.02', 'name': 'lash'}, {'id': 8829, 'synset': 'lashing.n.02', 'name': 'lashing'}, {'id': 8830, 'synset': 'lasso.n.02', 'name': 'lasso'}, {'id': 8831, 'synset': 'latch.n.01', 'name': 'latch'}, {'id': 8832, 'synset': 'latchet.n.01', 'name': 'latchet'}, {'id': 8833, 'synset': 'latchkey.n.01', 'name': 'latchkey'}, {'id': 8834, 'synset': 'lateen.n.01', 'name': 'lateen'}, {'id': 8835, 'synset': 'latex_paint.n.01', 'name': 'latex_paint'}, {'id': 8836, 'synset': 'lath.n.01', 'name': 'lath'}, {'id': 8837, 'synset': 'lathe.n.01', 'name': 'lathe'}, {'id': 8838, 'synset': 'latrine.n.01', 'name': 'latrine'}, {'id': 8839, 'synset': 'lattice.n.03', 'name': 'lattice'}, {'id': 8840, 'synset': 'launch.n.01', 'name': 'launch'}, {'id': 8841, 'synset': 'launcher.n.01', 'name': 'launcher'}, {'id': 8842, 'synset': 'laundry.n.01', 'name': 'laundry'}, {'id': 8843, 'synset': 'laundry_cart.n.01', 'name': 'laundry_cart'}, {'id': 8844, 'synset': 'laundry_truck.n.01', 'name': 'laundry_truck'}, {'id': 8845, 'synset': 'lavalava.n.01', 'name': 'lavalava'}, {'id': 8846, 'synset': 'lavaliere.n.01', 'name': 'lavaliere'}, {'id': 8847, 'synset': 'laver.n.02', 'name': 'laver'}, {'id': 8848, 'synset': 'lawn_chair.n.01', 'name': 'lawn_chair'}, {'id': 8849, 'synset': 'lawn_furniture.n.01', 'name': 'lawn_furniture'}, {'id': 8850, 'synset': 'layette.n.01', 'name': 'layette'}, {'id': 8851, 'synset': 'lead-acid_battery.n.01', 'name': 'lead-acid_battery'}, {'id': 8852, 'synset': 'lead-in.n.02', 'name': 'lead-in'}, {'id': 8853, 'synset': 'leading_rein.n.01', 'name': 'leading_rein'}, {'id': 8854, 'synset': 'lead_pencil.n.01', 'name': 'lead_pencil'}, {'id': 8855, 'synset': 'leaf_spring.n.01', 'name': 'leaf_spring'}, {'id': 8856, 'synset': 'lean-to.n.01', 'name': 'lean-to'}, {'id': 8857, 'synset': 'lean-to_tent.n.01', 'name': 'lean-to_tent'}, {'id': 8858, 'synset': 'leash.n.01', 'name': 'leash'}, {'id': 8859, 'synset': 'leatherette.n.01', 'name': 'leatherette'}, {'id': 8860, 'synset': 'leather_strip.n.01', 'name': 'leather_strip'}, {'id': 8861, 'synset': 'leclanche_cell.n.01', 'name': 'Leclanche_cell'}, {'id': 8862, 'synset': 'lectern.n.01', 'name': 'lectern'}, {'id': 8863, 'synset': 'lecture_room.n.01', 'name': 'lecture_room'}, {'id': 8864, 'synset': 'lederhosen.n.01', 'name': 'lederhosen'}, {'id': 8865, 'synset': 'ledger_board.n.01', 'name': 'ledger_board'}, {'id': 8866, 'synset': 'leg.n.07', 'name': 'leg'}, {'id': 8867, 'synset': 'leg.n.03', 'name': 'leg'}, {'id': 8868, 'synset': 'leiden_jar.n.01', 'name': 'Leiden_jar'}, {'id': 8869, 'synset': 'leisure_wear.n.01', 'name': 'leisure_wear'}, {'id': 8870, 'synset': 'lens.n.01', 'name': 'lens'}, {'id': 8871, 'synset': 'lens.n.05', 'name': 'lens'}, {'id': 8872, 'synset': 'lens_cap.n.01', 'name': 'lens_cap'}, {'id': 8873, 'synset': 'lens_implant.n.01', 'name': 'lens_implant'}, {'id': 8874, 'synset': 'leotard.n.01', 'name': 'leotard'}, {'id': 8875, 'synset': 'letter_case.n.01', 'name': 'letter_case'}, {'id': 8876, 'synset': 'letter_opener.n.01', 'name': 'letter_opener'}, {'id': 8877, 'synset': 'levee.n.03', 'name': 'levee'}, {'id': 8878, 'synset': 'level.n.05', 'name': 'level'}, {'id': 8879, 'synset': 'lever.n.01', 'name': 'lever'}, {'id': 8880, 'synset': 'lever.n.03', 'name': 'lever'}, {'id': 8881, 'synset': 'lever.n.02', 'name': 'lever'}, {'id': 8882, 'synset': 'lever_lock.n.01', 'name': 'lever_lock'}, {'id': 8883, 'synset': "levi's.n.01", 'name': "Levi's"}, {'id': 8884, 'synset': 'liberty_ship.n.01', 'name': 'Liberty_ship'}, {'id': 8885, 'synset': 'library.n.01', 'name': 'library'}, {'id': 8886, 'synset': 'library.n.05', 'name': 'library'}, {'id': 8887, 'synset': 'lid.n.02', 'name': 'lid'}, {'id': 8888, 'synset': 'liebig_condenser.n.01', 'name': 'Liebig_condenser'}, {'id': 8889, 'synset': 'lie_detector.n.01', 'name': 'lie_detector'}, {'id': 8890, 'synset': 'lifeboat.n.01', 'name': 'lifeboat'}, {'id': 8891, 'synset': 'life_office.n.01', 'name': 'life_office'}, {'id': 8892, 'synset': 'life_preserver.n.01', 'name': 'life_preserver'}, {'id': 8893, 'synset': 'life-support_system.n.02', 'name': 'life-support_system'}, {'id': 8894, 'synset': 'life-support_system.n.01', 'name': 'life-support_system'}, {'id': 8895, 'synset': 'lifting_device.n.01', 'name': 'lifting_device'}, {'id': 8896, 'synset': 'lift_pump.n.01', 'name': 'lift_pump'}, {'id': 8897, 'synset': 'ligament.n.02', 'name': 'ligament'}, {'id': 8898, 'synset': 'ligature.n.03', 'name': 'ligature'}, {'id': 8899, 'synset': 'light.n.02', 'name': 'light'}, {'id': 8900, 'synset': 'light_arm.n.01', 'name': 'light_arm'}, {'id': 8901, 'synset': 'light_circuit.n.01', 'name': 'light_circuit'}, {'id': 8902, 'synset': 'light-emitting_diode.n.01', 'name': 'light-emitting_diode'}, {'id': 8903, 'synset': 'lighter.n.02', 'name': 'lighter'}, {'id': 8904, 'synset': 'lighter-than-air_craft.n.01', 'name': 'lighter-than-air_craft'}, {'id': 8905, 'synset': 'light_filter.n.01', 'name': 'light_filter'}, {'id': 8906, 'synset': 'lighting.n.02', 'name': 'lighting'}, {'id': 8907, 'synset': 'light_machine_gun.n.01', 'name': 'light_machine_gun'}, {'id': 8908, 'synset': 'light_meter.n.01', 'name': 'light_meter'}, {'id': 8909, 'synset': 'light_microscope.n.01', 'name': 'light_microscope'}, {'id': 8910, 'synset': 'light_pen.n.01', 'name': 'light_pen'}, {'id': 8911, 'synset': 'lightship.n.01', 'name': 'lightship'}, {'id': 8912, 'synset': 'lilo.n.01', 'name': 'Lilo'}, {'id': 8913, 'synset': 'limber.n.01', 'name': 'limber'}, {'id': 8914, 'synset': 'limekiln.n.01', 'name': 'limekiln'}, {'id': 8915, 'synset': 'limiter.n.01', 'name': 'limiter'}, {'id': 8916, 'synset': 'linear_accelerator.n.01', 'name': 'linear_accelerator'}, {'id': 8917, 'synset': 'linen.n.01', 'name': 'linen'}, {'id': 8918, 'synset': 'line_printer.n.01', 'name': 'line_printer'}, {'id': 8919, 'synset': 'liner.n.04', 'name': 'liner'}, {'id': 8920, 'synset': 'liner.n.03', 'name': 'liner'}, {'id': 8921, 'synset': 'lingerie.n.01', 'name': 'lingerie'}, {'id': 8922, 'synset': 'lining.n.01', 'name': 'lining'}, {'id': 8923, 'synset': 'link.n.09', 'name': 'link'}, {'id': 8924, 'synset': 'linkage.n.03', 'name': 'linkage'}, {'id': 8925, 'synset': 'link_trainer.n.01', 'name': 'Link_trainer'}, {'id': 8926, 'synset': 'linocut.n.02', 'name': 'linocut'}, {'id': 8927, 'synset': 'linoleum_knife.n.01', 'name': 'linoleum_knife'}, {'id': 8928, 'synset': 'linotype.n.01', 'name': 'Linotype'}, {'id': 8929, 'synset': 'linsey-woolsey.n.01', 'name': 'linsey-woolsey'}, {'id': 8930, 'synset': 'linstock.n.01', 'name': 'linstock'}, {'id': 8931, 'synset': 'lion-jaw_forceps.n.01', 'name': 'lion-jaw_forceps'}, {'id': 8932, 'synset': 'lip-gloss.n.01', 'name': 'lip-gloss'}, {'id': 8933, 'synset': 'lipstick.n.01', 'name': 'lipstick'}, {'id': 8934, 'synset': 'liqueur_glass.n.01', 'name': 'liqueur_glass'}, {'id': 8935, 'synset': 'liquid_crystal_display.n.01', 'name': 'liquid_crystal_display'}, {'id': 8936, 'synset': 'liquid_metal_reactor.n.01', 'name': 'liquid_metal_reactor'}, {'id': 8937, 'synset': 'lisle.n.01', 'name': 'lisle'}, {'id': 8938, 'synset': 'lister.n.03', 'name': 'lister'}, {'id': 8939, 'synset': 'litterbin.n.01', 'name': 'litterbin'}, {'id': 8940, 'synset': 'little_theater.n.01', 'name': 'little_theater'}, {'id': 8941, 'synset': 'live_axle.n.01', 'name': 'live_axle'}, {'id': 8942, 'synset': 'living_quarters.n.01', 'name': 'living_quarters'}, {'id': 8943, 'synset': 'living_room.n.01', 'name': 'living_room'}, {'id': 8944, 'synset': 'load.n.09', 'name': 'load'}, {'id': 8945, 'synset': 'loafer.n.02', 'name': 'Loafer'}, {'id': 8946, 'synset': 'loaner.n.02', 'name': 'loaner'}, {'id': 8947, 'synset': 'lobe.n.04', 'name': 'lobe'}, {'id': 8948, 'synset': 'lobster_pot.n.01', 'name': 'lobster_pot'}, {'id': 8949, 'synset': 'local.n.01', 'name': 'local'}, {'id': 8950, 'synset': 'local_area_network.n.01', 'name': 'local_area_network'}, {'id': 8951, 'synset': 'local_oscillator.n.01', 'name': 'local_oscillator'}, {'id': 8952, 'synset': 'lochaber_ax.n.01', 'name': 'Lochaber_ax'}, {'id': 8953, 'synset': 'lock.n.01', 'name': 'lock'}, {'id': 8954, 'synset': 'lock.n.05', 'name': 'lock'}, {'id': 8955, 'synset': 'lock.n.04', 'name': 'lock'}, {'id': 8956, 'synset': 'lock.n.03', 'name': 'lock'}, {'id': 8957, 'synset': 'lockage.n.02', 'name': 'lockage'}, {'id': 8958, 'synset': 'locker.n.02', 'name': 'locker'}, {'id': 8959, 'synset': 'locker_room.n.01', 'name': 'locker_room'}, {'id': 8960, 'synset': 'locket.n.01', 'name': 'locket'}, {'id': 8961, 'synset': 'lock-gate.n.01', 'name': 'lock-gate'}, {'id': 8962, 'synset': 'locking_pliers.n.01', 'name': 'locking_pliers'}, {'id': 8963, 'synset': 'lockring.n.01', 'name': 'lockring'}, {'id': 8964, 'synset': 'lockstitch.n.01', 'name': 'lockstitch'}, {'id': 8965, 'synset': 'lockup.n.01', 'name': 'lockup'}, {'id': 8966, 'synset': 'locomotive.n.01', 'name': 'locomotive'}, {'id': 8967, 'synset': 'lodge.n.05', 'name': 'lodge'}, {'id': 8968, 'synset': 'lodge.n.04', 'name': 'lodge'}, {'id': 8969, 'synset': 'lodge.n.03', 'name': 'lodge'}, {'id': 8970, 'synset': 'lodging_house.n.01', 'name': 'lodging_house'}, {'id': 8971, 'synset': 'loft.n.02', 'name': 'loft'}, {'id': 8972, 'synset': 'loft.n.04', 'name': 'loft'}, {'id': 8973, 'synset': 'loft.n.01', 'name': 'loft'}, {'id': 8974, 'synset': 'log_cabin.n.01', 'name': 'log_cabin'}, {'id': 8975, 'synset': 'loggia.n.01', 'name': 'loggia'}, {'id': 8976, 'synset': 'longbow.n.01', 'name': 'longbow'}, {'id': 8977, 'synset': 'long_iron.n.01', 'name': 'long_iron'}, {'id': 8978, 'synset': 'long_johns.n.01', 'name': 'long_johns'}, {'id': 8979, 'synset': 'long_sleeve.n.01', 'name': 'long_sleeve'}, {'id': 8980, 'synset': 'long_tom.n.01', 'name': 'long_tom'}, {'id': 8981, 'synset': 'long_trousers.n.01', 'name': 'long_trousers'}, {'id': 8982, 'synset': 'long_underwear.n.01', 'name': 'long_underwear'}, {'id': 8983, 'synset': 'looking_glass.n.01', 'name': 'looking_glass'}, {'id': 8984, 'synset': 'lookout.n.03', 'name': 'lookout'}, {'id': 8985, 'synset': 'loom.n.01', 'name': 'loom'}, {'id': 8986, 'synset': 'loop_knot.n.01', 'name': 'loop_knot'}, {'id': 8987, 'synset': 'lorgnette.n.01', 'name': 'lorgnette'}, {'id': 8988, 'synset': 'lorraine_cross.n.01', 'name': 'Lorraine_cross'}, {'id': 8989, 'synset': 'lorry.n.02', 'name': 'lorry'}, {'id': 8990, 'synset': 'lota.n.01', 'name': 'lota'}, {'id': 8991, 'synset': 'lotion.n.01', 'name': 'lotion'}, {'id': 8992, 'synset': 'lounge.n.02', 'name': 'lounge'}, {'id': 8993, 'synset': 'lounger.n.03', 'name': 'lounger'}, {'id': 8994, 'synset': 'lounging_jacket.n.01', 'name': 'lounging_jacket'}, {'id': 8995, 'synset': 'lounging_pajama.n.01', 'name': 'lounging_pajama'}, {'id': 8996, 'synset': 'loungewear.n.01', 'name': 'loungewear'}, {'id': 8997, 'synset': 'loupe.n.01', 'name': 'loupe'}, {'id': 8998, 'synset': 'louvered_window.n.01', 'name': 'louvered_window'}, {'id': 8999, 'synset': 'love_knot.n.01', 'name': 'love_knot'}, {'id': 9000, 'synset': 'loving_cup.n.01', 'name': 'loving_cup'}, {'id': 9001, 'synset': 'lowboy.n.01', 'name': 'lowboy'}, {'id': 9002, 'synset': 'low-pass_filter.n.01', 'name': 'low-pass_filter'}, {'id': 9003, 'synset': 'low-warp-loom.n.01', 'name': 'low-warp-loom'}, {'id': 9004, 'synset': 'lp.n.01', 'name': 'LP'}, {'id': 9005, 'synset': 'l-plate.n.01', 'name': 'L-plate'}, {'id': 9006, 'synset': "lubber's_hole.n.01", 'name': "lubber's_hole"}, {'id': 9007, 'synset': 'lubricating_system.n.01', 'name': 'lubricating_system'}, {'id': 9008, 'synset': 'luff.n.01', 'name': 'luff'}, {'id': 9009, 'synset': 'lug.n.03', 'name': 'lug'}, {'id': 9010, 'synset': 'luge.n.01', 'name': 'luge'}, {'id': 9011, 'synset': 'luger.n.01', 'name': 'Luger'}, {'id': 9012, 'synset': 'luggage_carrier.n.01', 'name': 'luggage_carrier'}, {'id': 9013, 'synset': 'luggage_compartment.n.01', 'name': 'luggage_compartment'}, {'id': 9014, 'synset': 'luggage_rack.n.01', 'name': 'luggage_rack'}, {'id': 9015, 'synset': 'lugger.n.01', 'name': 'lugger'}, {'id': 9016, 'synset': 'lugsail.n.01', 'name': 'lugsail'}, {'id': 9017, 'synset': 'lug_wrench.n.01', 'name': 'lug_wrench'}, {'id': 9018, 'synset': 'lumberjack.n.02', 'name': 'lumberjack'}, {'id': 9019, 'synset': 'lumbermill.n.01', 'name': 'lumbermill'}, {'id': 9020, 'synset': 'lunar_excursion_module.n.01', 'name': 'lunar_excursion_module'}, {'id': 9021, 'synset': 'lunchroom.n.01', 'name': 'lunchroom'}, {'id': 9022, 'synset': 'lunette.n.01', 'name': 'lunette'}, {'id': 9023, 'synset': 'lungi.n.01', 'name': 'lungi'}, {'id': 9024, 'synset': 'lunula.n.02', 'name': 'lunula'}, {'id': 9025, 'synset': 'lusterware.n.01', 'name': 'lusterware'}, {'id': 9026, 'synset': 'lute.n.02', 'name': 'lute'}, {'id': 9027, 'synset': 'luxury_liner.n.01', 'name': 'luxury_liner'}, {'id': 9028, 'synset': 'lyceum.n.02', 'name': 'lyceum'}, {'id': 9029, 'synset': 'lychgate.n.01', 'name': 'lychgate'}, {'id': 9030, 'synset': 'lyre.n.01', 'name': 'lyre'}, {'id': 9031, 'synset': 'machete.n.01', 'name': 'machete'}, {'id': 9032, 'synset': 'machicolation.n.01', 'name': 'machicolation'}, {'id': 9033, 'synset': 'machine.n.01', 'name': 'machine'}, {'id': 9034, 'synset': 'machine.n.04', 'name': 'machine'}, {'id': 9035, 'synset': 'machine_bolt.n.01', 'name': 'machine_bolt'}, {'id': 9036, 'synset': 'machinery.n.01', 'name': 'machinery'}, {'id': 9037, 'synset': 'machine_screw.n.01', 'name': 'machine_screw'}, {'id': 9038, 'synset': 'machine_tool.n.01', 'name': 'machine_tool'}, {'id': 9039, 'synset': "machinist's_vise.n.01", 'name': "machinist's_vise"}, {'id': 9040, 'synset': 'machmeter.n.01', 'name': 'machmeter'}, {'id': 9041, 'synset': 'mackinaw.n.04', 'name': 'mackinaw'}, {'id': 9042, 'synset': 'mackinaw.n.03', 'name': 'mackinaw'}, {'id': 9043, 'synset': 'mackinaw.n.01', 'name': 'mackinaw'}, {'id': 9044, 'synset': 'mackintosh.n.01', 'name': 'mackintosh'}, {'id': 9045, 'synset': 'macrame.n.01', 'name': 'macrame'}, {'id': 9046, 'synset': 'madras.n.03', 'name': 'madras'}, {'id': 9047, 'synset': 'mae_west.n.02', 'name': 'Mae_West'}, {'id': 9048, 'synset': 'magazine_rack.n.01', 'name': 'magazine_rack'}, {'id': 9049, 'synset': 'magic_lantern.n.01', 'name': 'magic_lantern'}, {'id': 9050, 'synset': 'magnetic_bottle.n.01', 'name': 'magnetic_bottle'}, {'id': 9051, 'synset': 'magnetic_compass.n.01', 'name': 'magnetic_compass'}, {'id': 9052, 'synset': 'magnetic_core_memory.n.01', 'name': 'magnetic_core_memory'}, {'id': 9053, 'synset': 'magnetic_disk.n.01', 'name': 'magnetic_disk'}, {'id': 9054, 'synset': 'magnetic_head.n.01', 'name': 'magnetic_head'}, {'id': 9055, 'synset': 'magnetic_mine.n.01', 'name': 'magnetic_mine'}, {'id': 9056, 'synset': 'magnetic_needle.n.01', 'name': 'magnetic_needle'}, {'id': 9057, 'synset': 'magnetic_recorder.n.01', 'name': 'magnetic_recorder'}, {'id': 9058, 'synset': 'magnetic_stripe.n.01', 'name': 'magnetic_stripe'}, {'id': 9059, 'synset': 'magnetic_tape.n.01', 'name': 'magnetic_tape'}, {'id': 9060, 'synset': 'magneto.n.01', 'name': 'magneto'}, {'id': 9061, 'synset': 'magnetometer.n.01', 'name': 'magnetometer'}, {'id': 9062, 'synset': 'magnetron.n.01', 'name': 'magnetron'}, {'id': 9063, 'synset': 'magnifier.n.01', 'name': 'magnifier'}, {'id': 9064, 'synset': 'magnum.n.01', 'name': 'magnum'}, {'id': 9065, 'synset': 'magnus_hitch.n.01', 'name': 'magnus_hitch'}, {'id': 9066, 'synset': 'mail.n.03', 'name': 'mail'}, {'id': 9067, 'synset': 'mailbag.n.02', 'name': 'mailbag'}, {'id': 9068, 'synset': 'mailbag.n.01', 'name': 'mailbag'}, {'id': 9069, 'synset': 'mailboat.n.01', 'name': 'mailboat'}, {'id': 9070, 'synset': 'mail_car.n.01', 'name': 'mail_car'}, {'id': 9071, 'synset': 'maildrop.n.01', 'name': 'maildrop'}, {'id': 9072, 'synset': 'mailer.n.04', 'name': 'mailer'}, {'id': 9073, 'synset': 'maillot.n.02', 'name': 'maillot'}, {'id': 9074, 'synset': 'maillot.n.01', 'name': 'maillot'}, {'id': 9075, 'synset': 'mailsorter.n.01', 'name': 'mailsorter'}, {'id': 9076, 'synset': 'mail_train.n.01', 'name': 'mail_train'}, {'id': 9077, 'synset': 'mainframe.n.01', 'name': 'mainframe'}, {'id': 9078, 'synset': 'mainmast.n.01', 'name': 'mainmast'}, {'id': 9079, 'synset': 'main_rotor.n.01', 'name': 'main_rotor'}, {'id': 9080, 'synset': 'mainsail.n.01', 'name': 'mainsail'}, {'id': 9081, 'synset': 'mainspring.n.01', 'name': 'mainspring'}, {'id': 9082, 'synset': 'main-topmast.n.01', 'name': 'main-topmast'}, {'id': 9083, 'synset': 'main-topsail.n.01', 'name': 'main-topsail'}, {'id': 9084, 'synset': 'main_yard.n.01', 'name': 'main_yard'}, {'id': 9085, 'synset': 'maisonette.n.02', 'name': 'maisonette'}, {'id': 9086, 'synset': 'majolica.n.01', 'name': 'majolica'}, {'id': 9087, 'synset': 'makeup.n.01', 'name': 'makeup'}, {'id': 9088, 'synset': 'maksutov_telescope.n.01', 'name': 'Maksutov_telescope'}, {'id': 9089, 'synset': 'malacca.n.02', 'name': 'malacca'}, {'id': 9090, 'synset': 'mallet.n.03', 'name': 'mallet'}, {'id': 9091, 'synset': 'mallet.n.02', 'name': 'mallet'}, {'id': 9092, 'synset': 'mammogram.n.01', 'name': 'mammogram'}, {'id': 9093, 'synset': 'mandola.n.01', 'name': 'mandola'}, {'id': 9094, 'synset': 'mandolin.n.01', 'name': 'mandolin'}, {'id': 9095, 'synset': 'mangle.n.01', 'name': 'mangle'}, {'id': 9096, 'synset': 'manhole_cover.n.01', 'name': 'manhole_cover'}, {'id': 9097, 'synset': 'man-of-war.n.01', 'name': 'man-of-war'}, {'id': 9098, 'synset': 'manometer.n.01', 'name': 'manometer'}, {'id': 9099, 'synset': 'manor.n.01', 'name': 'manor'}, {'id': 9100, 'synset': 'manor_hall.n.01', 'name': 'manor_hall'}, {'id': 9101, 'synset': 'manpad.n.01', 'name': 'MANPAD'}, {'id': 9102, 'synset': 'mansard.n.01', 'name': 'mansard'}, {'id': 9103, 'synset': 'manse.n.02', 'name': 'manse'}, {'id': 9104, 'synset': 'mansion.n.02', 'name': 'mansion'}, {'id': 9105, 'synset': 'mantel.n.01', 'name': 'mantel'}, {'id': 9106, 'synset': 'mantelet.n.02', 'name': 'mantelet'}, {'id': 9107, 'synset': 'mantilla.n.01', 'name': 'mantilla'}, {'id': 9108, 'synset': 'mao_jacket.n.01', 'name': 'Mao_jacket'}, {'id': 9109, 'synset': 'maquiladora.n.01', 'name': 'maquiladora'}, {'id': 9110, 'synset': 'maraca.n.01', 'name': 'maraca'}, {'id': 9111, 'synset': 'marble.n.02', 'name': 'marble'}, {'id': 9112, 'synset': 'marching_order.n.01', 'name': 'marching_order'}, {'id': 9113, 'synset': 'marimba.n.01', 'name': 'marimba'}, {'id': 9114, 'synset': 'marina.n.01', 'name': 'marina'}, {'id': 9115, 'synset': 'marketplace.n.02', 'name': 'marketplace'}, {'id': 9116, 'synset': 'marlinespike.n.01', 'name': 'marlinespike'}, {'id': 9117, 'synset': 'marocain.n.01', 'name': 'marocain'}, {'id': 9118, 'synset': 'marquee.n.02', 'name': 'marquee'}, {'id': 9119, 'synset': 'marquetry.n.01', 'name': 'marquetry'}, {'id': 9120, 'synset': 'marriage_bed.n.01', 'name': 'marriage_bed'}, {'id': 9121, 'synset': 'martello_tower.n.01', 'name': 'martello_tower'}, {'id': 9122, 'synset': 'martingale.n.01', 'name': 'martingale'}, {'id': 9123, 'synset': 'mascara.n.01', 'name': 'mascara'}, {'id': 9124, 'synset': 'maser.n.01', 'name': 'maser'}, {'id': 9125, 'synset': 'mashie.n.01', 'name': 'mashie'}, {'id': 9126, 'synset': 'mashie_niblick.n.01', 'name': 'mashie_niblick'}, {'id': 9127, 'synset': 'masjid.n.01', 'name': 'masjid'}, {'id': 9128, 'synset': 'mask.n.01', 'name': 'mask'}, {'id': 9129, 'synset': 'masonite.n.01', 'name': 'Masonite'}, {'id': 9130, 'synset': 'mason_jar.n.01', 'name': 'Mason_jar'}, {'id': 9131, 'synset': 'masonry.n.01', 'name': 'masonry'}, {'id': 9132, 'synset': "mason's_level.n.01", 'name': "mason's_level"}, {'id': 9133, 'synset': 'massage_parlor.n.02', 'name': 'massage_parlor'}, {'id': 9134, 'synset': 'massage_parlor.n.01', 'name': 'massage_parlor'}, {'id': 9135, 'synset': 'mass_spectrograph.n.01', 'name': 'mass_spectrograph'}, {'id': 9136, 'synset': 'mass_spectrometer.n.01', 'name': 'mass_spectrometer'}, {'id': 9137, 'synset': 'mast.n.04', 'name': 'mast'}, {'id': 9138, 'synset': 'mastaba.n.01', 'name': 'mastaba'}, {'id': 9139, 'synset': 'master_bedroom.n.01', 'name': 'master_bedroom'}, {'id': 9140, 'synset': 'masterpiece.n.01', 'name': 'masterpiece'}, {'id': 9141, 'synset': 'mat.n.01', 'name': 'mat'}, {'id': 9142, 'synset': 'match.n.01', 'name': 'match'}, {'id': 9143, 'synset': 'match.n.03', 'name': 'match'}, {'id': 9144, 'synset': 'matchboard.n.01', 'name': 'matchboard'}, {'id': 9145, 'synset': 'matchbook.n.01', 'name': 'matchbook'}, {'id': 9146, 'synset': 'matchlock.n.01', 'name': 'matchlock'}, {'id': 9147, 'synset': 'match_plane.n.01', 'name': 'match_plane'}, {'id': 9148, 'synset': 'matchstick.n.01', 'name': 'matchstick'}, {'id': 9149, 'synset': 'material.n.04', 'name': 'material'}, {'id': 9150, 'synset': 'materiel.n.01', 'name': 'materiel'}, {'id': 9151, 'synset': 'maternity_hospital.n.01', 'name': 'maternity_hospital'}, {'id': 9152, 'synset': 'maternity_ward.n.01', 'name': 'maternity_ward'}, {'id': 9153, 'synset': 'matrix.n.06', 'name': 'matrix'}, {'id': 9154, 'synset': 'matthew_walker.n.01', 'name': 'Matthew_Walker'}, {'id': 9155, 'synset': 'matting.n.01', 'name': 'matting'}, {'id': 9156, 'synset': 'mattock.n.01', 'name': 'mattock'}, {'id': 9157, 'synset': 'mattress_cover.n.01', 'name': 'mattress_cover'}, {'id': 9158, 'synset': 'maul.n.01', 'name': 'maul'}, {'id': 9159, 'synset': 'maulstick.n.01', 'name': 'maulstick'}, {'id': 9160, 'synset': 'mauser.n.02', 'name': 'Mauser'}, {'id': 9161, 'synset': 'mausoleum.n.01', 'name': 'mausoleum'}, {'id': 9162, 'synset': 'maxi.n.01', 'name': 'maxi'}, {'id': 9163, 'synset': 'maxim_gun.n.01', 'name': 'Maxim_gun'}, {'id': 9164, 'synset': 'maximum_and_minimum_thermometer.n.01', 'name': 'maximum_and_minimum_thermometer'}, {'id': 9165, 'synset': 'maypole.n.01', 'name': 'maypole'}, {'id': 9166, 'synset': 'maze.n.01', 'name': 'maze'}, {'id': 9167, 'synset': 'mazer.n.01', 'name': 'mazer'}, {'id': 9168, 'synset': 'means.n.02', 'name': 'means'}, {'id': 9169, 'synset': 'measure.n.09', 'name': 'measure'}, {'id': 9170, 'synset': 'measuring_instrument.n.01', 'name': 'measuring_instrument'}, {'id': 9171, 'synset': 'meat_counter.n.01', 'name': 'meat_counter'}, {'id': 9172, 'synset': 'meat_grinder.n.01', 'name': 'meat_grinder'}, {'id': 9173, 'synset': 'meat_hook.n.01', 'name': 'meat_hook'}, {'id': 9174, 'synset': 'meat_house.n.02', 'name': 'meat_house'}, {'id': 9175, 'synset': 'meat_safe.n.01', 'name': 'meat_safe'}, {'id': 9176, 'synset': 'meat_thermometer.n.01', 'name': 'meat_thermometer'}, {'id': 9177, 'synset': 'mechanical_device.n.01', 'name': 'mechanical_device'}, {'id': 9178, 'synset': 'mechanical_piano.n.01', 'name': 'mechanical_piano'}, {'id': 9179, 'synset': 'mechanical_system.n.01', 'name': 'mechanical_system'}, {'id': 9180, 'synset': 'mechanism.n.05', 'name': 'mechanism'}, {'id': 9181, 'synset': 'medical_building.n.01', 'name': 'medical_building'}, {'id': 9182, 'synset': 'medical_instrument.n.01', 'name': 'medical_instrument'}, {'id': 9183, 'synset': 'medicine_ball.n.01', 'name': 'medicine_ball'}, {'id': 9184, 'synset': 'medicine_chest.n.01', 'name': 'medicine_chest'}, {'id': 9185, 'synset': 'medline.n.01', 'name': 'MEDLINE'}, {'id': 9186, 'synset': 'megalith.n.01', 'name': 'megalith'}, {'id': 9187, 'synset': 'megaphone.n.01', 'name': 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'name': 'mercury_thermometer'}, {'id': 9203, 'synset': 'mercury-vapor_lamp.n.01', 'name': 'mercury-vapor_lamp'}, {'id': 9204, 'synset': 'mercy_seat.n.02', 'name': 'mercy_seat'}, {'id': 9205, 'synset': 'merlon.n.01', 'name': 'merlon'}, {'id': 9206, 'synset': 'mess.n.05', 'name': 'mess'}, {'id': 9207, 'synset': 'mess_jacket.n.01', 'name': 'mess_jacket'}, {'id': 9208, 'synset': 'mess_kit.n.01', 'name': 'mess_kit'}, {'id': 9209, 'synset': 'messuage.n.01', 'name': 'messuage'}, {'id': 9210, 'synset': 'metal_detector.n.01', 'name': 'metal_detector'}, {'id': 9211, 'synset': 'metallic.n.01', 'name': 'metallic'}, {'id': 9212, 'synset': 'metal_screw.n.01', 'name': 'metal_screw'}, {'id': 9213, 'synset': 'metal_wood.n.01', 'name': 'metal_wood'}, {'id': 9214, 'synset': 'meteorological_balloon.n.01', 'name': 'meteorological_balloon'}, {'id': 9215, 'synset': 'meter.n.02', 'name': 'meter'}, {'id': 9216, 'synset': 'meterstick.n.01', 'name': 'meterstick'}, {'id': 9217, 'synset': 'metronome.n.01', 'name': 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'synset': 'minicar.n.01', 'name': 'minicar'}, {'id': 9264, 'synset': 'minicomputer.n.01', 'name': 'minicomputer'}, {'id': 9265, 'synset': 'ministry.n.02', 'name': 'ministry'}, {'id': 9266, 'synset': 'miniskirt.n.01', 'name': 'miniskirt'}, {'id': 9267, 'synset': 'minisub.n.01', 'name': 'minisub'}, {'id': 9268, 'synset': 'miniver.n.01', 'name': 'miniver'}, {'id': 9269, 'synset': 'mink.n.02', 'name': 'mink'}, {'id': 9270, 'synset': 'minster.n.01', 'name': 'minster'}, {'id': 9271, 'synset': 'mint.n.06', 'name': 'mint'}, {'id': 9272, 'synset': 'minute_hand.n.01', 'name': 'minute_hand'}, {'id': 9273, 'synset': 'minuteman.n.02', 'name': 'Minuteman'}, {'id': 9274, 'synset': 'missile.n.01', 'name': 'missile'}, {'id': 9275, 'synset': 'missile_defense_system.n.01', 'name': 'missile_defense_system'}, {'id': 9276, 'synset': 'miter_box.n.01', 'name': 'miter_box'}, {'id': 9277, 'synset': 'miter_joint.n.01', 'name': 'miter_joint'}, {'id': 9278, 'synset': 'mixer.n.03', 'name': 'mixer'}, {'id': 9279, 'synset': 'mixing_bowl.n.01', 'name': 'mixing_bowl'}, {'id': 9280, 'synset': 'mixing_faucet.n.01', 'name': 'mixing_faucet'}, {'id': 9281, 'synset': 'mizzen.n.02', 'name': 'mizzen'}, {'id': 9282, 'synset': 'mizzenmast.n.01', 'name': 'mizzenmast'}, {'id': 9283, 'synset': 'mobcap.n.01', 'name': 'mobcap'}, {'id': 9284, 'synset': 'mobile_home.n.01', 'name': 'mobile_home'}, {'id': 9285, 'synset': 'moccasin.n.01', 'name': 'moccasin'}, {'id': 9286, 'synset': 'mock-up.n.01', 'name': 'mock-up'}, {'id': 9287, 'synset': 'mod_con.n.01', 'name': 'mod_con'}, {'id': 9288, 'synset': 'model_t.n.01', 'name': 'Model_T'}, {'id': 9289, 'synset': 'modem.n.01', 'name': 'modem'}, {'id': 9290, 'synset': 'modillion.n.01', 'name': 'modillion'}, {'id': 9291, 'synset': 'module.n.03', 'name': 'module'}, {'id': 9292, 'synset': 'module.n.02', 'name': 'module'}, {'id': 9293, 'synset': 'mohair.n.01', 'name': 'mohair'}, {'id': 9294, 'synset': 'moire.n.01', 'name': 'moire'}, {'id': 9295, 'synset': 'mold.n.02', 'name': 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'synset': 'mortar.n.03', 'name': 'mortar'}, {'id': 9327, 'synset': 'mortarboard.n.02', 'name': 'mortarboard'}, {'id': 9328, 'synset': 'mortise_joint.n.02', 'name': 'mortise_joint'}, {'id': 9329, 'synset': 'mosaic.n.05', 'name': 'mosaic'}, {'id': 9330, 'synset': 'mosque.n.01', 'name': 'mosque'}, {'id': 9331, 'synset': 'mosquito_net.n.01', 'name': 'mosquito_net'}, {'id': 9332, 'synset': 'motel.n.01', 'name': 'motel'}, {'id': 9333, 'synset': 'motel_room.n.01', 'name': 'motel_room'}, {'id': 9334, 'synset': 'mother_hubbard.n.01', 'name': 'Mother_Hubbard'}, {'id': 9335, 'synset': 'motion-picture_camera.n.01', 'name': 'motion-picture_camera'}, {'id': 9336, 'synset': 'motion-picture_film.n.01', 'name': 'motion-picture_film'}, {'id': 9337, 'synset': 'motley.n.03', 'name': 'motley'}, {'id': 9338, 'synset': 'motley.n.02', 'name': 'motley'}, {'id': 9339, 'synset': 'motorboat.n.01', 'name': 'motorboat'}, {'id': 9340, 'synset': 'motor_hotel.n.01', 'name': 'motor_hotel'}, {'id': 9341, 'synset': 'motorized_wheelchair.n.01', 'name': 'motorized_wheelchair'}, {'id': 9342, 'synset': 'mound.n.04', 'name': 'mound'}, {'id': 9343, 'synset': 'mount.n.04', 'name': 'mount'}, {'id': 9344, 'synset': 'mountain_bike.n.01', 'name': 'mountain_bike'}, {'id': 9345, 'synset': 'mountain_tent.n.01', 'name': 'mountain_tent'}, {'id': 9346, 'synset': 'mouse_button.n.01', 'name': 'mouse_button'}, {'id': 9347, 'synset': 'mousetrap.n.01', 'name': 'mousetrap'}, {'id': 9348, 'synset': 'mousse.n.03', 'name': 'mousse'}, {'id': 9349, 'synset': 'mouthpiece.n.06', 'name': 'mouthpiece'}, {'id': 9350, 'synset': 'mouthpiece.n.02', 'name': 'mouthpiece'}, {'id': 9351, 'synset': 'mouthpiece.n.04', 'name': 'mouthpiece'}, {'id': 9352, 'synset': 'movement.n.10', 'name': 'movement'}, {'id': 9353, 'synset': 'movie_projector.n.01', 'name': 'movie_projector'}, {'id': 9354, 'synset': 'moving-coil_galvanometer.n.01', 'name': 'moving-coil_galvanometer'}, {'id': 9355, 'synset': 'moving_van.n.01', 'name': 'moving_van'}, {'id': 9356, 'synset': 'mud_brick.n.01', 'name': 'mud_brick'}, {'id': 9357, 'synset': 'mudguard.n.01', 'name': 'mudguard'}, {'id': 9358, 'synset': 'mudhif.n.01', 'name': 'mudhif'}, {'id': 9359, 'synset': 'muff.n.01', 'name': 'muff'}, {'id': 9360, 'synset': 'muffle.n.01', 'name': 'muffle'}, {'id': 9361, 'synset': 'muffler.n.02', 'name': 'muffler'}, {'id': 9362, 'synset': 'mufti.n.02', 'name': 'mufti'}, {'id': 9363, 'synset': 'mulch.n.01', 'name': 'mulch'}, {'id': 9364, 'synset': 'mule.n.02', 'name': 'mule'}, {'id': 9365, 'synset': 'multichannel_recorder.n.01', 'name': 'multichannel_recorder'}, {'id': 9366, 'synset': 'multiengine_airplane.n.01', 'name': 'multiengine_airplane'}, {'id': 9367, 'synset': 'multiplex.n.02', 'name': 'multiplex'}, {'id': 9368, 'synset': 'multiplexer.n.01', 'name': 'multiplexer'}, {'id': 9369, 'synset': 'multiprocessor.n.01', 'name': 'multiprocessor'}, {'id': 9370, 'synset': 'multistage_rocket.n.01', 'name': 'multistage_rocket'}, {'id': 9371, 'synset': 'munition.n.02', 'name': 'munition'}, {'id': 9372, 'synset': 'murphy_bed.n.01', 'name': 'Murphy_bed'}, {'id': 9373, 'synset': 'musette.n.01', 'name': 'musette'}, {'id': 9374, 'synset': 'musette_pipe.n.01', 'name': 'musette_pipe'}, {'id': 9375, 'synset': 'museum.n.01', 'name': 'museum'}, {'id': 9376, 'synset': 'mushroom_anchor.n.01', 'name': 'mushroom_anchor'}, {'id': 9377, 'synset': 'music_box.n.01', 'name': 'music_box'}, {'id': 9378, 'synset': 'music_hall.n.01', 'name': 'music_hall'}, {'id': 9379, 'synset': 'music_school.n.02', 'name': 'music_school'}, {'id': 9380, 'synset': 'music_stand.n.01', 'name': 'music_stand'}, {'id': 9381, 'synset': 'musket.n.01', 'name': 'musket'}, {'id': 9382, 'synset': 'musket_ball.n.01', 'name': 'musket_ball'}, {'id': 9383, 'synset': 'muslin.n.01', 'name': 'muslin'}, {'id': 9384, 'synset': 'mustache_cup.n.01', 'name': 'mustache_cup'}, {'id': 9385, 'synset': 'mustard_plaster.n.01', 'name': 'mustard_plaster'}, {'id': 9386, 'synset': 'mute.n.02', 'name': 'mute'}, {'id': 9387, 'synset': 'muzzle_loader.n.01', 'name': 'muzzle_loader'}, {'id': 9388, 'synset': 'muzzle.n.03', 'name': 'muzzle'}, {'id': 9389, 'synset': 'myelogram.n.01', 'name': 'myelogram'}, {'id': 9390, 'synset': 'nacelle.n.01', 'name': 'nacelle'}, {'id': 9391, 'synset': 'nail.n.02', 'name': 'nail'}, {'id': 9392, 'synset': 'nailbrush.n.01', 'name': 'nailbrush'}, {'id': 9393, 'synset': 'nailhead.n.02', 'name': 'nailhead'}, {'id': 9394, 'synset': 'nailhead.n.01', 'name': 'nailhead'}, {'id': 9395, 'synset': 'nail_polish.n.01', 'name': 'nail_polish'}, {'id': 9396, 'synset': 'nainsook.n.01', 'name': 'nainsook'}, {'id': 9397, 'synset': "napier's_bones.n.01", 'name': "Napier's_bones"}, {'id': 9398, 'synset': 'nard.n.01', 'name': 'nard'}, {'id': 9399, 'synset': 'narrowbody_aircraft.n.01', 'name': 'narrowbody_aircraft'}, {'id': 9400, 'synset': 'narrow_wale.n.01', 'name': 'narrow_wale'}, {'id': 9401, 'synset': 'narthex.n.02', 'name': 'narthex'}, {'id': 9402, 'synset': 'narthex.n.01', 'name': 'narthex'}, {'id': 9403, 'synset': 'nasotracheal_tube.n.01', 'name': 'nasotracheal_tube'}, {'id': 9404, 'synset': 'national_monument.n.01', 'name': 'national_monument'}, {'id': 9405, 'synset': 'nautilus.n.01', 'name': 'nautilus'}, {'id': 9406, 'synset': 'navigational_system.n.01', 'name': 'navigational_system'}, {'id': 9407, 'synset': 'naval_equipment.n.01', 'name': 'naval_equipment'}, {'id': 9408, 'synset': 'naval_gun.n.01', 'name': 'naval_gun'}, {'id': 9409, 'synset': 'naval_missile.n.01', 'name': 'naval_missile'}, {'id': 9410, 'synset': 'naval_radar.n.01', 'name': 'naval_radar'}, {'id': 9411, 'synset': 'naval_tactical_data_system.n.01', 'name': 'naval_tactical_data_system'}, {'id': 9412, 'synset': 'naval_weaponry.n.01', 'name': 'naval_weaponry'}, {'id': 9413, 'synset': 'nave.n.01', 'name': 'nave'}, {'id': 9414, 'synset': 'navigational_instrument.n.01', 'name': 'navigational_instrument'}, {'id': 9415, 'synset': 'nebuchadnezzar.n.02', 'name': 'nebuchadnezzar'}, {'id': 9416, 'synset': 'neckband.n.01', 'name': 'neckband'}, {'id': 9417, 'synset': 'neck_brace.n.01', 'name': 'neck_brace'}, {'id': 9418, 'synset': 'neckcloth.n.01', 'name': 'neckcloth'}, {'id': 9419, 'synset': 'necklet.n.01', 'name': 'necklet'}, {'id': 9420, 'synset': 'neckline.n.01', 'name': 'neckline'}, {'id': 9421, 'synset': 'neckpiece.n.01', 'name': 'neckpiece'}, {'id': 9422, 'synset': 'neckwear.n.01', 'name': 'neckwear'}, {'id': 9423, 'synset': 'needle.n.02', 'name': 'needle'}, {'id': 9424, 'synset': 'needlenose_pliers.n.01', 'name': 'needlenose_pliers'}, {'id': 9425, 'synset': 'needlework.n.01', 'name': 'needlework'}, {'id': 9426, 'synset': 'negative.n.02', 'name': 'negative'}, {'id': 9427, 'synset': 'negative_magnetic_pole.n.01', 'name': 'negative_magnetic_pole'}, {'id': 9428, 'synset': 'negative_pole.n.01', 'name': 'negative_pole'}, {'id': 9429, 'synset': 'negligee.n.01', 'name': 'negligee'}, {'id': 9430, 'synset': 'neolith.n.01', 'name': 'neolith'}, {'id': 9431, 'synset': 'neon_lamp.n.01', 'name': 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'nib.n.01', 'name': 'nib'}, {'id': 9449, 'synset': 'niblick.n.01', 'name': 'niblick'}, {'id': 9450, 'synset': 'nicad.n.01', 'name': 'nicad'}, {'id': 9451, 'synset': 'nickel-iron_battery.n.01', 'name': 'nickel-iron_battery'}, {'id': 9452, 'synset': 'nicol_prism.n.01', 'name': 'Nicol_prism'}, {'id': 9453, 'synset': 'night_bell.n.01', 'name': 'night_bell'}, {'id': 9454, 'synset': 'nightcap.n.02', 'name': 'nightcap'}, {'id': 9455, 'synset': 'nightgown.n.01', 'name': 'nightgown'}, {'id': 9456, 'synset': 'night_latch.n.01', 'name': 'night_latch'}, {'id': 9457, 'synset': 'night-light.n.01', 'name': 'night-light'}, {'id': 9458, 'synset': 'nightshirt.n.01', 'name': 'nightshirt'}, {'id': 9459, 'synset': 'ninepin.n.01', 'name': 'ninepin'}, {'id': 9460, 'synset': 'ninepin_ball.n.01', 'name': 'ninepin_ball'}, {'id': 9461, 'synset': 'ninon.n.01', 'name': 'ninon'}, {'id': 9462, 'synset': 'nipple.n.02', 'name': 'nipple'}, {'id': 9463, 'synset': 'nipple_shield.n.01', 'name': 'nipple_shield'}, {'id': 9464, 'synset': 'niqab.n.01', 'name': 'niqab'}, {'id': 9465, 'synset': 'nissen_hut.n.01', 'name': 'Nissen_hut'}, {'id': 9466, 'synset': 'nogging.n.01', 'name': 'nogging'}, {'id': 9467, 'synset': 'noisemaker.n.01', 'name': 'noisemaker'}, {'id': 9468, 'synset': 'nonsmoker.n.02', 'name': 'nonsmoker'}, {'id': 9469, 'synset': 'non-volatile_storage.n.01', 'name': 'non-volatile_storage'}, {'id': 9470, 'synset': 'norfolk_jacket.n.01', 'name': 'Norfolk_jacket'}, {'id': 9471, 'synset': 'noria.n.01', 'name': 'noria'}, {'id': 9472, 'synset': 'nose_flute.n.01', 'name': 'nose_flute'}, {'id': 9473, 'synset': 'nosewheel.n.01', 'name': 'nosewheel'}, {'id': 9474, 'synset': 'notebook.n.02', 'name': 'notebook'}, {'id': 9475, 'synset': 'nuclear-powered_ship.n.01', 'name': 'nuclear-powered_ship'}, {'id': 9476, 'synset': 'nuclear_reactor.n.01', 'name': 'nuclear_reactor'}, {'id': 9477, 'synset': 'nuclear_rocket.n.01', 'name': 'nuclear_rocket'}, {'id': 9478, 'synset': 'nuclear_weapon.n.01', 'name': 'nuclear_weapon'}, {'id': 9479, 'synset': 'nude.n.01', 'name': 'nude'}, {'id': 9480, 'synset': 'numdah.n.01', 'name': 'numdah'}, {'id': 9481, 'synset': "nun's_habit.n.01", 'name': "nun's_habit"}, {'id': 9482, 'synset': 'nursery.n.01', 'name': 'nursery'}, {'id': 9483, 'synset': 'nut_and_bolt.n.01', 'name': 'nut_and_bolt'}, {'id': 9484, 'synset': 'nylon.n.02', 'name': 'nylon'}, {'id': 9485, 'synset': 'nylons.n.01', 'name': 'nylons'}, {'id': 9486, 'synset': 'oast.n.01', 'name': 'oast'}, {'id': 9487, 'synset': 'oast_house.n.01', 'name': 'oast_house'}, {'id': 9488, 'synset': 'obelisk.n.01', 'name': 'obelisk'}, {'id': 9489, 'synset': 'object_ball.n.01', 'name': 'object_ball'}, {'id': 9490, 'synset': 'objective.n.02', 'name': 'objective'}, {'id': 9491, 'synset': 'oblique_bandage.n.01', 'name': 'oblique_bandage'}, {'id': 9492, 'synset': 'oboe.n.01', 'name': 'oboe'}, {'id': 9493, 'synset': 'oboe_da_caccia.n.01', 'name': 'oboe_da_caccia'}, {'id': 9494, 'synset': "oboe_d'amore.n.01", 'name': "oboe_d'amore"}, {'id': 9495, 'synset': 'observation_dome.n.01', 'name': 'observation_dome'}, {'id': 9496, 'synset': 'observatory.n.01', 'name': 'observatory'}, {'id': 9497, 'synset': 'obstacle.n.02', 'name': 'obstacle'}, {'id': 9498, 'synset': 'obturator.n.01', 'name': 'obturator'}, {'id': 9499, 'synset': 'ocarina.n.01', 'name': 'ocarina'}, {'id': 9500, 'synset': 'octant.n.01', 'name': 'octant'}, {'id': 9501, 'synset': 'odd-leg_caliper.n.01', 'name': 'odd-leg_caliper'}, {'id': 9502, 'synset': 'odometer.n.01', 'name': 'odometer'}, {'id': 9503, 'synset': 'oeil_de_boeuf.n.01', 'name': 'oeil_de_boeuf'}, {'id': 9504, 'synset': 'office.n.01', 'name': 'office'}, {'id': 9505, 'synset': 'office_building.n.01', 'name': 'office_building'}, {'id': 9506, 'synset': 'office_furniture.n.01', 'name': 'office_furniture'}, {'id': 9507, 'synset': "officer's_mess.n.01", 'name': "officer's_mess"}, {'id': 9508, 'synset': 'off-line_equipment.n.01', 'name': 'off-line_equipment'}, {'id': 9509, 'synset': 'ogee.n.01', 'name': 'ogee'}, {'id': 9510, 'synset': 'ogee_arch.n.01', 'name': 'ogee_arch'}, {'id': 9511, 'synset': 'ohmmeter.n.01', 'name': 'ohmmeter'}, {'id': 9512, 'synset': 'oil.n.02', 'name': 'oil'}, {'id': 9513, 'synset': 'oilcan.n.01', 'name': 'oilcan'}, {'id': 9514, 'synset': 'oilcloth.n.01', 'name': 'oilcloth'}, {'id': 9515, 'synset': 'oil_filter.n.01', 'name': 'oil_filter'}, {'id': 9516, 'synset': 'oil_heater.n.01', 'name': 'oil_heater'}, {'id': 9517, 'synset': 'oil_paint.n.01', 'name': 'oil_paint'}, {'id': 9518, 'synset': 'oil_pump.n.01', 'name': 'oil_pump'}, {'id': 9519, 'synset': 'oil_refinery.n.01', 'name': 'oil_refinery'}, {'id': 9520, 'synset': 'oilskin.n.01', 'name': 'oilskin'}, {'id': 9521, 'synset': 'oil_slick.n.01', 'name': 'oil_slick'}, {'id': 9522, 'synset': 'oilstone.n.01', 'name': 'oilstone'}, {'id': 9523, 'synset': 'oil_tanker.n.01', 'name': 'oil_tanker'}, {'id': 9524, 'synset': 'old_school_tie.n.01', 'name': 'old_school_tie'}, {'id': 9525, 'synset': 'olive_drab.n.03', 'name': 'olive_drab'}, {'id': 9526, 'synset': 'olive_drab.n.02', 'name': 'olive_drab'}, {'id': 9527, 'synset': 'olympian_zeus.n.01', 'name': 'Olympian_Zeus'}, {'id': 9528, 'synset': 'omelet_pan.n.01', 'name': 'omelet_pan'}, {'id': 9529, 'synset': 'omnidirectional_antenna.n.01', 'name': 'omnidirectional_antenna'}, {'id': 9530, 'synset': 'omnirange.n.01', 'name': 'omnirange'}, {'id': 9531, 'synset': 'onion_dome.n.01', 'name': 'onion_dome'}, {'id': 9532, 'synset': 'open-air_market.n.01', 'name': 'open-air_market'}, {'id': 9533, 'synset': 'open_circuit.n.01', 'name': 'open_circuit'}, {'id': 9534, 'synset': 'open-end_wrench.n.01', 'name': 'open-end_wrench'}, {'id': 9535, 'synset': 'opener.n.03', 'name': 'opener'}, {'id': 9536, 'synset': 'open-hearth_furnace.n.01', 'name': 'open-hearth_furnace'}, {'id': 9537, 'synset': 'openside_plane.n.01', 'name': 'openside_plane'}, {'id': 9538, 'synset': 'open_sight.n.01', 'name': 'open_sight'}, {'id': 9539, 'synset': 'openwork.n.01', 'name': 'openwork'}, {'id': 9540, 'synset': 'opera.n.03', 'name': 'opera'}, {'id': 9541, 'synset': 'opera_cloak.n.01', 'name': 'opera_cloak'}, {'id': 9542, 'synset': 'operating_microscope.n.01', 'name': 'operating_microscope'}, {'id': 9543, 'synset': 'operating_room.n.01', 'name': 'operating_room'}, {'id': 9544, 'synset': 'operating_table.n.01', 'name': 'operating_table'}, {'id': 9545, 'synset': 'ophthalmoscope.n.01', 'name': 'ophthalmoscope'}, {'id': 9546, 'synset': 'optical_device.n.01', 'name': 'optical_device'}, {'id': 9547, 'synset': 'optical_disk.n.01', 'name': 'optical_disk'}, {'id': 9548, 'synset': 'optical_instrument.n.01', 'name': 'optical_instrument'}, {'id': 9549, 'synset': 'optical_pyrometer.n.01', 'name': 'optical_pyrometer'}, {'id': 9550, 'synset': 'optical_telescope.n.01', 'name': 'optical_telescope'}, {'id': 9551, 'synset': 'orchestra_pit.n.01', 'name': 'orchestra_pit'}, {'id': 9552, 'synset': 'ordinary.n.04', 'name': 'ordinary'}, {'id': 9553, 'synset': 'organ.n.05', 'name': 'organ'}, {'id': 9554, 'synset': 'organdy.n.01', 'name': 'organdy'}, {'id': 9555, 'synset': 'organic_light-emitting_diode.n.01', 'name': 'organic_light-emitting_diode'}, {'id': 9556, 'synset': 'organ_loft.n.01', 'name': 'organ_loft'}, {'id': 9557, 'synset': 'organ_pipe.n.01', 'name': 'organ_pipe'}, {'id': 9558, 'synset': 'organza.n.01', 'name': 'organza'}, {'id': 9559, 'synset': 'oriel.n.01', 'name': 'oriel'}, {'id': 9560, 'synset': 'oriflamme.n.02', 'name': 'oriflamme'}, {'id': 9561, 'synset': 'o_ring.n.01', 'name': 'O_ring'}, {'id': 9562, 'synset': 'orlon.n.01', 'name': 'Orlon'}, {'id': 9563, 'synset': 'orlop_deck.n.01', 'name': 'orlop_deck'}, {'id': 9564, 'synset': 'orphanage.n.02', 'name': 'orphanage'}, {'id': 9565, 'synset': 'orphrey.n.01', 'name': 'orphrey'}, {'id': 9566, 'synset': 'orrery.n.01', 'name': 'orrery'}, {'id': 9567, 'synset': 'orthicon.n.01', 'name': 'orthicon'}, {'id': 9568, 'synset': 'orthochromatic_film.n.01', 'name': 'orthochromatic_film'}, {'id': 9569, 'synset': 'orthopter.n.01', 'name': 'orthopter'}, {'id': 9570, 'synset': 'orthoscope.n.01', 'name': 'orthoscope'}, {'id': 9571, 'synset': 'oscillograph.n.01', 'name': 'oscillograph'}, {'id': 9572, 'synset': 'oscilloscope.n.01', 'name': 'oscilloscope'}, {'id': 9573, 'synset': 'ossuary.n.01', 'name': 'ossuary'}, {'id': 9574, 'synset': 'otoscope.n.01', 'name': 'otoscope'}, {'id': 9575, 'synset': 'oubliette.n.01', 'name': 'oubliette'}, {'id': 9576, 'synset': 'out-basket.n.01', 'name': 'out-basket'}, {'id': 9577, 'synset': 'outboard_motor.n.01', 'name': 'outboard_motor'}, {'id': 9578, 'synset': 'outboard_motorboat.n.01', 'name': 'outboard_motorboat'}, {'id': 9579, 'synset': 'outbuilding.n.01', 'name': 'outbuilding'}, {'id': 9580, 'synset': 'outerwear.n.01', 'name': 'outerwear'}, {'id': 9581, 'synset': 'outfall.n.01', 'name': 'outfall'}, {'id': 9582, 'synset': 'outfit.n.02', 'name': 'outfit'}, {'id': 9583, 'synset': 'outfitter.n.02', 'name': 'outfitter'}, {'id': 9584, 'synset': 'outhouse.n.01', 'name': 'outhouse'}, {'id': 9585, 'synset': 'output_device.n.01', 'name': 'output_device'}, {'id': 9586, 'synset': 'outrigger.n.01', 'name': 'outrigger'}, {'id': 9587, 'synset': 'outrigger_canoe.n.01', 'name': 'outrigger_canoe'}, {'id': 9588, 'synset': 'outside_caliper.n.01', 'name': 'outside_caliper'}, {'id': 9589, 'synset': 'outside_mirror.n.01', 'name': 'outside_mirror'}, {'id': 9590, 'synset': 'outwork.n.01', 'name': 'outwork'}, {'id': 9591, 'synset': 'oven_thermometer.n.01', 'name': 'oven_thermometer'}, {'id': 9592, 'synset': 'overall.n.02', 'name': 'overall'}, {'id': 9593, 'synset': 'overcoat.n.02', 'name': 'overcoat'}, {'id': 9594, 'synset': 'overdrive.n.02', 'name': 'overdrive'}, {'id': 9595, 'synset': 'overgarment.n.01', 'name': 'overgarment'}, {'id': 9596, 'synset': 'overhand_knot.n.01', 'name': 'overhand_knot'}, {'id': 9597, 'synset': 'overhang.n.01', 'name': 'overhang'}, {'id': 9598, 'synset': 'overhead_projector.n.01', 'name': 'overhead_projector'}, {'id': 9599, 'synset': 'overmantel.n.01', 'name': 'overmantel'}, {'id': 9600, 'synset': 'overnighter.n.02', 'name': 'overnighter'}, {'id': 9601, 'synset': 'overpass.n.01', 'name': 'overpass'}, {'id': 9602, 'synset': 'override.n.01', 'name': 'override'}, {'id': 9603, 'synset': 'overshoe.n.01', 'name': 'overshoe'}, {'id': 9604, 'synset': 'overskirt.n.01', 'name': 'overskirt'}, {'id': 9605, 'synset': 'oxbow.n.03', 'name': 'oxbow'}, {'id': 9606, 'synset': 'oxbridge.n.01', 'name': 'Oxbridge'}, {'id': 9607, 'synset': 'oxcart.n.01', 'name': 'oxcart'}, {'id': 9608, 'synset': 'oxeye.n.03', 'name': 'oxeye'}, {'id': 9609, 'synset': 'oxford.n.04', 'name': 'oxford'}, {'id': 9610, 'synset': 'oximeter.n.01', 'name': 'oximeter'}, {'id': 9611, 'synset': 'oxyacetylene_torch.n.01', 'name': 'oxyacetylene_torch'}, {'id': 9612, 'synset': 'oxygen_mask.n.01', 'name': 'oxygen_mask'}, {'id': 9613, 'synset': 'oyster_bar.n.01', 'name': 'oyster_bar'}, {'id': 9614, 'synset': 'oyster_bed.n.01', 'name': 'oyster_bed'}, {'id': 9615, 'synset': 'pace_car.n.01', 'name': 'pace_car'}, {'id': 9616, 'synset': 'pacemaker.n.03', 'name': 'pacemaker'}, {'id': 9617, 'synset': 'pack.n.03', 'name': 'pack'}, {'id': 9618, 'synset': 'pack.n.09', 'name': 'pack'}, {'id': 9619, 'synset': 'pack.n.07', 'name': 'pack'}, {'id': 9620, 'synset': 'package.n.02', 'name': 'package'}, {'id': 9621, 'synset': 'package_store.n.01', 'name': 'package_store'}, {'id': 9622, 'synset': 'packaging.n.03', 'name': 'packaging'}, {'id': 9623, 'synset': 'packing_box.n.02', 'name': 'packing_box'}, {'id': 9624, 'synset': 'packinghouse.n.02', 'name': 'packinghouse'}, {'id': 9625, 'synset': 'packinghouse.n.01', 'name': 'packinghouse'}, {'id': 9626, 'synset': 'packing_needle.n.01', 'name': 'packing_needle'}, {'id': 9627, 'synset': 'packsaddle.n.01', 'name': 'packsaddle'}, {'id': 9628, 'synset': 'paddle.n.02', 'name': 'paddle'}, {'id': 9629, 'synset': 'paddle.n.01', 'name': 'paddle'}, {'id': 9630, 'synset': 'paddle_box.n.01', 'name': 'paddle_box'}, {'id': 9631, 'synset': 'paddle_steamer.n.01', 'name': 'paddle_steamer'}, {'id': 9632, 'synset': 'paddlewheel.n.01', 'name': 'paddlewheel'}, {'id': 9633, 'synset': 'paddock.n.01', 'name': 'paddock'}, {'id': 9634, 'synset': 'page_printer.n.01', 'name': 'page_printer'}, {'id': 9635, 'synset': 'paint.n.01', 'name': 'paint'}, {'id': 9636, 'synset': 'paintball.n.01', 'name': 'paintball'}, {'id': 9637, 'synset': 'paintball_gun.n.01', 'name': 'paintball_gun'}, {'id': 9638, 'synset': 'paintbox.n.01', 'name': 'paintbox'}, {'id': 9639, 'synset': 'paisley.n.01', 'name': 'paisley'}, {'id': 9640, 'synset': 'pajama.n.01', 'name': 'pajama'}, {'id': 9641, 'synset': 'palace.n.04', 'name': 'palace'}, {'id': 9642, 'synset': 'palace.n.01', 'name': 'palace'}, {'id': 9643, 'synset': 'palace.n.03', 'name': 'palace'}, {'id': 9644, 'synset': 'palanquin.n.01', 'name': 'palanquin'}, {'id': 9645, 'synset': 'paleolith.n.01', 'name': 'paleolith'}, {'id': 9646, 'synset': 'palestra.n.01', 'name': 'palestra'}, {'id': 9647, 'synset': 'palette_knife.n.01', 'name': 'palette_knife'}, {'id': 9648, 'synset': 'palisade.n.01', 'name': 'palisade'}, {'id': 9649, 'synset': 'pallet.n.03', 'name': 'pallet'}, {'id': 9650, 'synset': 'pallette.n.01', 'name': 'pallette'}, {'id': 9651, 'synset': 'pallium.n.04', 'name': 'pallium'}, {'id': 9652, 'synset': 'pallium.n.03', 'name': 'pallium'}, {'id': 9653, 'synset': 'pancake_turner.n.01', 'name': 'pancake_turner'}, {'id': 9654, 'synset': 'panchromatic_film.n.01', 'name': 'panchromatic_film'}, {'id': 9655, 'synset': 'panda_car.n.01', 'name': 'panda_car'}, {'id': 9656, 'synset': 'paneling.n.01', 'name': 'paneling'}, {'id': 9657, 'synset': 'panhandle.n.02', 'name': 'panhandle'}, {'id': 9658, 'synset': 'panic_button.n.01', 'name': 'panic_button'}, {'id': 9659, 'synset': 'pannier.n.02', 'name': 'pannier'}, {'id': 9660, 'synset': 'pannier.n.01', 'name': 'pannier'}, {'id': 9661, 'synset': 'pannikin.n.01', 'name': 'pannikin'}, {'id': 9662, 'synset': 'panopticon.n.02', 'name': 'panopticon'}, {'id': 9663, 'synset': 'panopticon.n.01', 'name': 'panopticon'}, {'id': 9664, 'synset': 'panpipe.n.01', 'name': 'panpipe'}, {'id': 9665, 'synset': 'pantaloon.n.03', 'name': 'pantaloon'}, {'id': 9666, 'synset': 'pantechnicon.n.01', 'name': 'pantechnicon'}, {'id': 9667, 'synset': 'pantheon.n.03', 'name': 'pantheon'}, {'id': 9668, 'synset': 'pantheon.n.02', 'name': 'pantheon'}, {'id': 9669, 'synset': 'pantie.n.01', 'name': 'pantie'}, {'id': 9670, 'synset': 'panting.n.02', 'name': 'panting'}, {'id': 9671, 'synset': 'pant_leg.n.01', 'name': 'pant_leg'}, {'id': 9672, 'synset': 'pantograph.n.01', 'name': 'pantograph'}, {'id': 9673, 'synset': 'pantry.n.01', 'name': 'pantry'}, {'id': 9674, 'synset': 'pants_suit.n.01', 'name': 'pants_suit'}, {'id': 9675, 'synset': 'panty_girdle.n.01', 'name': 'panty_girdle'}, {'id': 9676, 'synset': 'panzer.n.01', 'name': 'panzer'}, {'id': 9677, 'synset': 'paper_chain.n.01', 'name': 'paper_chain'}, {'id': 9678, 'synset': 'paper_clip.n.01', 'name': 'paper_clip'}, {'id': 9679, 'synset': 'paper_cutter.n.01', 'name': 'paper_cutter'}, {'id': 9680, 'synset': 'paper_fastener.n.01', 'name': 'paper_fastener'}, {'id': 9681, 'synset': 'paper_feed.n.01', 'name': 'paper_feed'}, {'id': 9682, 'synset': 'paper_mill.n.01', 'name': 'paper_mill'}, {'id': 9683, 'synset': 'parabolic_mirror.n.01', 'name': 'parabolic_mirror'}, {'id': 9684, 'synset': 'parabolic_reflector.n.01', 'name': 'parabolic_reflector'}, {'id': 9685, 'synset': 'parallel_bars.n.01', 'name': 'parallel_bars'}, {'id': 9686, 'synset': 'parallel_circuit.n.01', 'name': 'parallel_circuit'}, {'id': 9687, 'synset': 'parallel_interface.n.01', 'name': 'parallel_interface'}, {'id': 9688, 'synset': 'parang.n.01', 'name': 'parang'}, {'id': 9689, 'synset': 'parapet.n.02', 'name': 'parapet'}, {'id': 9690, 'synset': 'parapet.n.01', 'name': 'parapet'}, {'id': 9691, 'synset': 'parer.n.02', 'name': 'parer'}, {'id': 9692, 'synset': 'parfait_glass.n.01', 'name': 'parfait_glass'}, {'id': 9693, 'synset': 'pargeting.n.02', 'name': 'pargeting'}, {'id': 9694, 'synset': 'pari-mutuel_machine.n.01', 'name': 'pari-mutuel_machine'}, {'id': 9695, 'synset': 'park_bench.n.01', 'name': 'park_bench'}, {'id': 9696, 'synset': 'parlor.n.01', 'name': 'parlor'}, {'id': 9697, 'synset': 'parquet.n.01', 'name': 'parquet'}, {'id': 9698, 'synset': 'parquetry.n.01', 'name': 'parquetry'}, {'id': 9699, 'synset': 'parsonage.n.01', 'name': 'parsonage'}, {'id': 9700, 'synset': 'parsons_table.n.01', 'name': 'Parsons_table'}, {'id': 9701, 'synset': 'partial_denture.n.01', 'name': 'partial_denture'}, {'id': 9702, 'synset': 'particle_detector.n.01', 'name': 'particle_detector'}, {'id': 9703, 'synset': 'partition.n.01', 'name': 'partition'}, {'id': 9704, 'synset': 'parts_bin.n.01', 'name': 'parts_bin'}, {'id': 9705, 'synset': 'party_line.n.02', 'name': 'party_line'}, {'id': 9706, 'synset': 'party_wall.n.01', 'name': 'party_wall'}, {'id': 9707, 'synset': 'parvis.n.01', 'name': 'parvis'}, {'id': 9708, 'synset': 'passenger_train.n.01', 'name': 'passenger_train'}, {'id': 9709, 'synset': 'passenger_van.n.01', 'name': 'passenger_van'}, {'id': 9710, 'synset': 'passe-partout.n.02', 'name': 'passe-partout'}, {'id': 9711, 'synset': 'passive_matrix_display.n.01', 'name': 'passive_matrix_display'}, {'id': 9712, 'synset': 'passkey.n.01', 'name': 'passkey'}, {'id': 9713, 'synset': 'pass-through.n.01', 'name': 'pass-through'}, {'id': 9714, 'synset': 'pastry_cart.n.01', 'name': 'pastry_cart'}, {'id': 9715, 'synset': 'patch.n.03', 'name': 'patch'}, {'id': 9716, 'synset': 'patchcord.n.01', 'name': 'patchcord'}, {'id': 9717, 'synset': 'patchouli.n.02', 'name': 'patchouli'}, {'id': 9718, 'synset': 'patch_pocket.n.01', 'name': 'patch_pocket'}, {'id': 9719, 'synset': 'patchwork.n.02', 'name': 'patchwork'}, {'id': 9720, 'synset': 'patent_log.n.01', 'name': 'patent_log'}, {'id': 9721, 'synset': 'paternoster.n.02', 'name': 'paternoster'}, {'id': 9722, 'synset': 'patina.n.01', 'name': 'patina'}, {'id': 9723, 'synset': 'patio.n.01', 'name': 'patio'}, {'id': 9724, 'synset': 'patisserie.n.01', 'name': 'patisserie'}, {'id': 9725, 'synset': 'patka.n.01', 'name': 'patka'}, {'id': 9726, 'synset': 'patrol_boat.n.01', 'name': 'patrol_boat'}, {'id': 9727, 'synset': 'patty-pan.n.01', 'name': 'patty-pan'}, {'id': 9728, 'synset': 'pave.n.01', 'name': 'pave'}, {'id': 9729, 'synset': 'pavilion.n.01', 'name': 'pavilion'}, {'id': 9730, 'synset': 'pavior.n.01', 'name': 'pavior'}, {'id': 9731, 'synset': 'pavis.n.01', 'name': 'pavis'}, {'id': 9732, 'synset': 'pawn.n.03', 'name': 'pawn'}, {'id': 9733, 'synset': "pawnbroker's_shop.n.01", 'name': "pawnbroker's_shop"}, {'id': 9734, 'synset': 'pay-phone.n.01', 'name': 'pay-phone'}, {'id': 9735, 'synset': 'pc_board.n.01', 'name': 'PC_board'}, {'id': 9736, 'synset': 'peach_orchard.n.01', 'name': 'peach_orchard'}, {'id': 9737, 'synset': 'pea_jacket.n.01', 'name': 'pea_jacket'}, {'id': 9738, 'synset': 'peavey.n.01', 'name': 'peavey'}, {'id': 9739, 'synset': 'pectoral.n.02', 'name': 'pectoral'}, {'id': 9740, 'synset': 'pedal.n.02', 'name': 'pedal'}, {'id': 9741, 'synset': 'pedal_pusher.n.01', 'name': 'pedal_pusher'}, {'id': 9742, 'synset': 'pedestal.n.03', 'name': 'pedestal'}, {'id': 9743, 'synset': 'pedestal_table.n.01', 'name': 'pedestal_table'}, {'id': 9744, 'synset': 'pedestrian_crossing.n.01', 'name': 'pedestrian_crossing'}, {'id': 9745, 'synset': 'pedicab.n.01', 'name': 'pedicab'}, {'id': 9746, 'synset': 'pediment.n.01', 'name': 'pediment'}, {'id': 9747, 'synset': 'pedometer.n.01', 'name': 'pedometer'}, {'id': 9748, 'synset': 'peep_sight.n.01', 'name': 'peep_sight'}, {'id': 9749, 'synset': 'peg.n.01', 'name': 'peg'}, {'id': 9750, 'synset': 'peg.n.06', 'name': 'peg'}, {'id': 9751, 'synset': 'peg.n.05', 'name': 'peg'}, {'id': 9752, 'synset': 'pelham.n.01', 'name': 'Pelham'}, {'id': 9753, 'synset': 'pelican_crossing.n.01', 'name': 'pelican_crossing'}, {'id': 9754, 'synset': 'pelisse.n.01', 'name': 'pelisse'}, {'id': 9755, 'synset': 'pelvimeter.n.01', 'name': 'pelvimeter'}, {'id': 9756, 'synset': 'penal_colony.n.01', 'name': 'penal_colony'}, {'id': 9757, 'synset': 'penal_institution.n.01', 'name': 'penal_institution'}, {'id': 9758, 'synset': 'penalty_box.n.01', 'name': 'penalty_box'}, {'id': 9759, 'synset': 'pen-and-ink.n.01', 'name': 'pen-and-ink'}, {'id': 9760, 'synset': 'pencil.n.04', 'name': 'pencil'}, {'id': 9761, 'synset': 'pendant_earring.n.01', 'name': 'pendant_earring'}, {'id': 9762, 'synset': 'pendulum_clock.n.01', 'name': 'pendulum_clock'}, {'id': 9763, 'synset': 'pendulum_watch.n.01', 'name': 'pendulum_watch'}, {'id': 9764, 'synset': 'penetration_bomb.n.01', 'name': 'penetration_bomb'}, {'id': 9765, 'synset': 'penile_implant.n.01', 'name': 'penile_implant'}, {'id': 9766, 'synset': 'penitentiary.n.01', 'name': 'penitentiary'}, {'id': 9767, 'synset': 'penknife.n.01', 'name': 'penknife'}, {'id': 9768, 'synset': 'penlight.n.01', 'name': 'penlight'}, {'id': 9769, 'synset': 'pennant.n.03', 'name': 'pennant'}, {'id': 9770, 'synset': 'pennywhistle.n.01', 'name': 'pennywhistle'}, {'id': 9771, 'synset': 'penthouse.n.01', 'name': 'penthouse'}, {'id': 9772, 'synset': 'pentode.n.01', 'name': 'pentode'}, {'id': 9773, 'synset': 'peplos.n.01', 'name': 'peplos'}, {'id': 9774, 'synset': 'peplum.n.01', 'name': 'peplum'}, {'id': 9775, 'synset': 'pepper_shaker.n.01', 'name': 'pepper_shaker'}, {'id': 9776, 'synset': 'pepper_spray.n.01', 'name': 'pepper_spray'}, {'id': 9777, 'synset': 'percale.n.01', 'name': 'percale'}, {'id': 9778, 'synset': 'percolator.n.01', 'name': 'percolator'}, {'id': 9779, 'synset': 'percussion_cap.n.01', 'name': 'percussion_cap'}, {'id': 9780, 'synset': 'percussion_instrument.n.01', 'name': 'percussion_instrument'}, {'id': 9781, 'synset': 'perforation.n.01', 'name': 'perforation'}, {'id': 9782, 'synset': 'perfumery.n.03', 'name': 'perfumery'}, {'id': 9783, 'synset': 'perfumery.n.02', 'name': 'perfumery'}, {'id': 9784, 'synset': 'perfumery.n.01', 'name': 'perfumery'}, {'id': 9785, 'synset': 'peripheral.n.01', 'name': 'peripheral'}, {'id': 9786, 'synset': 'periscope.n.01', 'name': 'periscope'}, {'id': 9787, 'synset': 'peristyle.n.01', 'name': 'peristyle'}, {'id': 9788, 'synset': 'periwig.n.01', 'name': 'periwig'}, {'id': 9789, 'synset': 'permanent_press.n.01', 'name': 'permanent_press'}, {'id': 9790, 'synset': 'perpetual_motion_machine.n.01', 'name': 'perpetual_motion_machine'}, {'id': 9791, 'synset': 'personal_computer.n.01', 'name': 'personal_computer'}, {'id': 9792, 'synset': 'personal_digital_assistant.n.01', 'name': 'personal_digital_assistant'}, {'id': 9793, 'synset': 'personnel_carrier.n.01', 'name': 'personnel_carrier'}, {'id': 9794, 'synset': 'pestle.n.03', 'name': 'pestle'}, {'id': 9795, 'synset': 'pestle.n.02', 'name': 'pestle'}, {'id': 9796, 'synset': 'petcock.n.01', 'name': 'petcock'}, {'id': 9797, 'synset': 'petri_dish.n.01', 'name': 'Petri_dish'}, {'id': 9798, 'synset': 'petrolatum_gauze.n.01', 'name': 'petrolatum_gauze'}, {'id': 9799, 'synset': 'pet_shop.n.01', 'name': 'pet_shop'}, {'id': 9800, 'synset': 'petticoat.n.01', 'name': 'petticoat'}, {'id': 9801, 'synset': 'phial.n.01', 'name': 'phial'}, {'id': 9802, 'synset': 'phillips_screw.n.01', 'name': 'Phillips_screw'}, {'id': 9803, 'synset': 'phillips_screwdriver.n.01', 'name': 'Phillips_screwdriver'}, {'id': 9804, 'synset': 'phonograph_needle.n.01', 'name': 'phonograph_needle'}, {'id': 9805, 'synset': 'photocathode.n.01', 'name': 'photocathode'}, {'id': 9806, 'synset': 'photocoagulator.n.01', 'name': 'photocoagulator'}, {'id': 9807, 'synset': 'photocopier.n.01', 'name': 'photocopier'}, {'id': 9808, 'synset': 'photographic_equipment.n.01', 'name': 'photographic_equipment'}, {'id': 9809, 'synset': 'photographic_paper.n.01', 'name': 'photographic_paper'}, {'id': 9810, 'synset': 'photometer.n.01', 'name': 'photometer'}, {'id': 9811, 'synset': 'photomicrograph.n.01', 'name': 'photomicrograph'}, {'id': 9812, 'synset': 'photostat.n.02', 'name': 'Photostat'}, {'id': 9813, 'synset': 'photostat.n.01', 'name': 'photostat'}, {'id': 9814, 'synset': 'physical_pendulum.n.01', 'name': 'physical_pendulum'}, {'id': 9815, 'synset': 'piano_action.n.01', 'name': 'piano_action'}, {'id': 9816, 'synset': 'piano_keyboard.n.01', 'name': 'piano_keyboard'}, {'id': 9817, 'synset': 'piano_wire.n.01', 'name': 'piano_wire'}, {'id': 9818, 'synset': 'piccolo.n.01', 'name': 'piccolo'}, {'id': 9819, 'synset': 'pick.n.07', 'name': 'pick'}, {'id': 9820, 'synset': 'pick.n.06', 'name': 'pick'}, {'id': 9821, 'synset': 'pick.n.05', 'name': 'pick'}, {'id': 9822, 'synset': 'pickelhaube.n.01', 'name': 'pickelhaube'}, {'id': 9823, 'synset': 'picket_boat.n.01', 'name': 'picket_boat'}, {'id': 9824, 'synset': 'picket_fence.n.01', 'name': 'picket_fence'}, {'id': 9825, 'synset': 'picket_ship.n.01', 'name': 'picket_ship'}, {'id': 9826, 'synset': 'pickle_barrel.n.01', 'name': 'pickle_barrel'}, {'id': 9827, 'synset': 'picture_frame.n.01', 'name': 'picture_frame'}, {'id': 9828, 'synset': 'picture_hat.n.01', 'name': 'picture_hat'}, {'id': 9829, 'synset': 'picture_rail.n.01', 'name': 'picture_rail'}, {'id': 9830, 'synset': 'picture_window.n.01', 'name': 'picture_window'}, {'id': 9831, 'synset': 'piece_of_cloth.n.01', 'name': 'piece_of_cloth'}, {'id': 9832, 'synset': 'pied-a-terre.n.01', 'name': 'pied-a-terre'}, {'id': 9833, 'synset': 'pier.n.03', 'name': 'pier'}, {'id': 9834, 'synset': 'pier.n.02', 'name': 'pier'}, {'id': 9835, 'synset': 'pier_arch.n.01', 'name': 'pier_arch'}, {'id': 9836, 'synset': 'pier_glass.n.01', 'name': 'pier_glass'}, {'id': 9837, 'synset': 'pier_table.n.01', 'name': 'pier_table'}, {'id': 9838, 'synset': 'pieta.n.01', 'name': 'pieta'}, {'id': 9839, 'synset': 'piezometer.n.01', 'name': 'piezometer'}, {'id': 9840, 'synset': 'pig_bed.n.01', 'name': 'pig_bed'}, {'id': 9841, 'synset': 'piggery.n.01', 'name': 'piggery'}, {'id': 9842, 'synset': 'pilaster.n.01', 'name': 'pilaster'}, {'id': 9843, 'synset': 'pile.n.06', 'name': 'pile'}, {'id': 9844, 'synset': 'pile_driver.n.01', 'name': 'pile_driver'}, {'id': 9845, 'synset': 'pill_bottle.n.01', 'name': 'pill_bottle'}, {'id': 9846, 'synset': 'pillbox.n.01', 'name': 'pillbox'}, {'id': 9847, 'synset': 'pillion.n.01', 'name': 'pillion'}, {'id': 9848, 'synset': 'pillory.n.01', 'name': 'pillory'}, {'id': 9849, 'synset': 'pillow_block.n.01', 'name': 'pillow_block'}, {'id': 9850, 'synset': 'pillow_lace.n.01', 'name': 'pillow_lace'}, {'id': 9851, 'synset': 'pillow_sham.n.01', 'name': 'pillow_sham'}, {'id': 9852, 'synset': 'pilot_bit.n.01', 'name': 'pilot_bit'}, {'id': 9853, 'synset': 'pilot_boat.n.01', 'name': 'pilot_boat'}, {'id': 9854, 'synset': 'pilot_burner.n.01', 'name': 'pilot_burner'}, {'id': 9855, 'synset': 'pilot_cloth.n.01', 'name': 'pilot_cloth'}, {'id': 9856, 'synset': 'pilot_engine.n.01', 'name': 'pilot_engine'}, {'id': 9857, 'synset': 'pilothouse.n.01', 'name': 'pilothouse'}, {'id': 9858, 'synset': 'pilot_light.n.02', 'name': 'pilot_light'}, {'id': 9859, 'synset': 'pin.n.08', 'name': 'pin'}, {'id': 9860, 'synset': 'pin.n.07', 'name': 'pin'}, {'id': 9861, 'synset': 'pinata.n.01', 'name': 'pinata'}, {'id': 9862, 'synset': 'pinball_machine.n.01', 'name': 'pinball_machine'}, {'id': 9863, 'synset': 'pince-nez.n.01', 'name': 'pince-nez'}, {'id': 9864, 'synset': 'pincer.n.01', 'name': 'pincer'}, {'id': 9865, 'synset': 'pinch_bar.n.01', 'name': 'pinch_bar'}, {'id': 9866, 'synset': 'pincurl_clip.n.01', 'name': 'pincurl_clip'}, {'id': 9867, 'synset': 'pinfold.n.01', 'name': 'pinfold'}, {'id': 9868, 'synset': 'pinhead.n.02', 'name': 'pinhead'}, {'id': 9869, 'synset': 'pinion.n.01', 'name': 'pinion'}, {'id': 9870, 'synset': 'pinnacle.n.01', 'name': 'pinnacle'}, {'id': 9871, 'synset': 'pinprick.n.02', 'name': 'pinprick'}, {'id': 9872, 'synset': 'pinstripe.n.03', 'name': 'pinstripe'}, {'id': 9873, 'synset': 'pinstripe.n.02', 'name': 'pinstripe'}, {'id': 9874, 'synset': 'pinstripe.n.01', 'name': 'pinstripe'}, {'id': 9875, 'synset': 'pintle.n.01', 'name': 'pintle'}, {'id': 9876, 'synset': 'pinwheel.n.02', 'name': 'pinwheel'}, {'id': 9877, 'synset': 'tabor_pipe.n.01', 'name': 'tabor_pipe'}, {'id': 9878, 'synset': 'pipe.n.04', 'name': 'pipe'}, {'id': 9879, 'synset': 'pipe_bomb.n.01', 'name': 'pipe_bomb'}, {'id': 9880, 'synset': 'pipe_cleaner.n.01', 'name': 'pipe_cleaner'}, {'id': 9881, 'synset': 'pipe_cutter.n.01', 'name': 'pipe_cutter'}, {'id': 9882, 'synset': 'pipefitting.n.01', 'name': 'pipefitting'}, {'id': 9883, 'synset': 'pipet.n.01', 'name': 'pipet'}, {'id': 9884, 'synset': 'pipe_vise.n.01', 'name': 'pipe_vise'}, {'id': 9885, 'synset': 'pipe_wrench.n.01', 'name': 'pipe_wrench'}, {'id': 9886, 'synset': 'pique.n.01', 'name': 'pique'}, {'id': 9887, 'synset': 'pirate.n.03', 'name': 'pirate'}, {'id': 9888, 'synset': 'piste.n.02', 'name': 'piste'}, {'id': 9889, 'synset': 'pistol_grip.n.01', 'name': 'pistol_grip'}, {'id': 9890, 'synset': 'piston.n.02', 'name': 'piston'}, {'id': 9891, 'synset': 'piston_ring.n.01', 'name': 'piston_ring'}, {'id': 9892, 'synset': 'piston_rod.n.01', 'name': 'piston_rod'}, {'id': 9893, 'synset': 'pit.n.07', 'name': 'pit'}, {'id': 9894, 'synset': 'pitching_wedge.n.01', 'name': 'pitching_wedge'}, {'id': 9895, 'synset': 'pitch_pipe.n.01', 'name': 'pitch_pipe'}, {'id': 9896, 'synset': 'pith_hat.n.01', 'name': 'pith_hat'}, {'id': 9897, 'synset': 'piton.n.01', 'name': 'piton'}, {'id': 9898, 'synset': 'pitot-static_tube.n.01', 'name': 'Pitot-static_tube'}, {'id': 9899, 'synset': 'pitot_tube.n.01', 'name': 'Pitot_tube'}, {'id': 9900, 'synset': 'pitsaw.n.01', 'name': 'pitsaw'}, {'id': 9901, 'synset': 'pivot.n.02', 'name': 'pivot'}, {'id': 9902, 'synset': 'pivoting_window.n.01', 'name': 'pivoting_window'}, {'id': 9903, 'synset': 'pizzeria.n.01', 'name': 'pizzeria'}, {'id': 9904, 'synset': 'place_of_business.n.01', 'name': 'place_of_business'}, {'id': 9905, 'synset': 'place_of_worship.n.01', 'name': 'place_of_worship'}, {'id': 9906, 'synset': 'placket.n.01', 'name': 'placket'}, {'id': 9907, 'synset': 'planchet.n.01', 'name': 'planchet'}, {'id': 9908, 'synset': 'plane.n.05', 'name': 'plane'}, {'id': 9909, 'synset': 'plane.n.04', 'name': 'plane'}, {'id': 9910, 'synset': 'plane_seat.n.01', 'name': 'plane_seat'}, {'id': 9911, 'synset': 'planetarium.n.03', 'name': 'planetarium'}, {'id': 9912, 'synset': 'planetarium.n.02', 'name': 'planetarium'}, {'id': 9913, 'synset': 'planetarium.n.01', 'name': 'planetarium'}, {'id': 9914, 'synset': 'planetary_gear.n.01', 'name': 'planetary_gear'}, {'id': 9915, 'synset': 'plank-bed.n.01', 'name': 'plank-bed'}, {'id': 9916, 'synset': 'planking.n.02', 'name': 'planking'}, {'id': 9917, 'synset': 'planner.n.02', 'name': 'planner'}, {'id': 9918, 'synset': 'plant.n.01', 'name': 'plant'}, {'id': 9919, 'synset': 'planter.n.03', 'name': 'planter'}, {'id': 9920, 'synset': 'plaster.n.05', 'name': 'plaster'}, {'id': 9921, 'synset': 'plasterboard.n.01', 'name': 'plasterboard'}, {'id': 9922, 'synset': 'plastering_trowel.n.01', 'name': 'plastering_trowel'}, {'id': 9923, 'synset': 'plastic_bag.n.01', 'name': 'plastic_bag'}, {'id': 9924, 'synset': 'plastic_bomb.n.01', 'name': 'plastic_bomb'}, {'id': 9925, 'synset': 'plastic_laminate.n.01', 'name': 'plastic_laminate'}, {'id': 9926, 'synset': 'plastic_wrap.n.01', 'name': 'plastic_wrap'}, {'id': 9927, 'synset': 'plastron.n.03', 'name': 'plastron'}, {'id': 9928, 'synset': 'plastron.n.02', 'name': 'plastron'}, {'id': 9929, 'synset': 'plastron.n.01', 'name': 'plastron'}, {'id': 9930, 'synset': 'plate.n.14', 'name': 'plate'}, {'id': 9931, 'synset': 'plate.n.13', 'name': 'plate'}, {'id': 9932, 'synset': 'plate.n.12', 'name': 'plate'}, {'id': 9933, 'synset': 'platen.n.03', 'name': 'platen'}, {'id': 9934, 'synset': 'platen.n.01', 'name': 'platen'}, {'id': 9935, 'synset': 'plate_rack.n.01', 'name': 'plate_rack'}, {'id': 9936, 'synset': 'plate_rail.n.01', 'name': 'plate_rail'}, {'id': 9937, 'synset': 'platform.n.01', 'name': 'platform'}, {'id': 9938, 'synset': 'platform.n.04', 'name': 'platform'}, {'id': 9939, 'synset': 'platform.n.03', 'name': 'platform'}, {'id': 9940, 'synset': 'platform_bed.n.01', 'name': 'platform_bed'}, {'id': 9941, 'synset': 'platform_rocker.n.01', 'name': 'platform_rocker'}, {'id': 9942, 'synset': 'plating.n.01', 'name': 'plating'}, {'id': 9943, 'synset': 'playback.n.02', 'name': 'playback'}, {'id': 9944, 'synset': 'playbox.n.01', 'name': 'playbox'}, {'id': 9945, 'synset': 'playground.n.02', 'name': 'playground'}, {'id': 9946, 'synset': 'playsuit.n.01', 'name': 'playsuit'}, {'id': 9947, 'synset': 'plaza.n.02', 'name': 'plaza'}, {'id': 9948, 'synset': 'pleat.n.01', 'name': 'pleat'}, {'id': 9949, 'synset': 'plenum.n.02', 'name': 'plenum'}, {'id': 9950, 'synset': 'plethysmograph.n.01', 'name': 'plethysmograph'}, {'id': 9951, 'synset': 'pleximeter.n.01', 'name': 'pleximeter'}, {'id': 9952, 'synset': 'plexor.n.01', 'name': 'plexor'}, {'id': 9953, 'synset': 'plimsoll.n.02', 'name': 'plimsoll'}, {'id': 9954, 'synset': 'plotter.n.04', 'name': 'plotter'}, {'id': 9955, 'synset': 'plug.n.01', 'name': 'plug'}, {'id': 9956, 'synset': 'plug.n.05', 'name': 'plug'}, {'id': 9957, 'synset': 'plug_fuse.n.01', 'name': 'plug_fuse'}, {'id': 9958, 'synset': 'plughole.n.01', 'name': 'plughole'}, {'id': 9959, 'synset': 'plumb_bob.n.01', 'name': 'plumb_bob'}, {'id': 9960, 'synset': 'plumb_level.n.01', 'name': 'plumb_level'}, {'id': 9961, 'synset': 'plunger.n.03', 'name': 'plunger'}, {'id': 9962, 'synset': 'plus_fours.n.01', 'name': 'plus_fours'}, {'id': 9963, 'synset': 'plush.n.01', 'name': 'plush'}, {'id': 9964, 'synset': 'plywood.n.01', 'name': 'plywood'}, {'id': 9965, 'synset': 'pneumatic_drill.n.01', 'name': 'pneumatic_drill'}, {'id': 9966, 'synset': 'p-n_junction.n.01', 'name': 'p-n_junction'}, {'id': 9967, 'synset': 'p-n-p_transistor.n.01', 'name': 'p-n-p_transistor'}, {'id': 9968, 'synset': 'poacher.n.02', 'name': 'poacher'}, {'id': 9969, 'synset': 'pocket.n.01', 'name': 'pocket'}, {'id': 9970, 'synset': 'pocket_battleship.n.01', 'name': 'pocket_battleship'}, {'id': 9971, 'synset': 'pocketcomb.n.01', 'name': 'pocketcomb'}, {'id': 9972, 'synset': 'pocket_flap.n.01', 'name': 'pocket_flap'}, {'id': 9973, 'synset': 'pocket-handkerchief.n.01', 'name': 'pocket-handkerchief'}, {'id': 9974, 'synset': 'pod.n.04', 'name': 'pod'}, {'id': 9975, 'synset': 'pogo_stick.n.01', 'name': 'pogo_stick'}, {'id': 9976, 'synset': 'point-and-shoot_camera.n.01', 'name': 'point-and-shoot_camera'}, {'id': 9977, 'synset': 'pointed_arch.n.01', 'name': 'pointed_arch'}, {'id': 9978, 'synset': 'pointing_trowel.n.01', 'name': 'pointing_trowel'}, {'id': 9979, 'synset': 'point_lace.n.01', 'name': 'point_lace'}, {'id': 9980, 'synset': 'polarimeter.n.01', 'name': 'polarimeter'}, {'id': 9981, 'synset': 'polaroid.n.01', 'name': 'Polaroid'}, {'id': 9982, 'synset': 'polaroid_camera.n.01', 'name': 'Polaroid_camera'}, {'id': 9983, 'synset': 'pole.n.09', 'name': 'pole'}, {'id': 9984, 'synset': 'poleax.n.02', 'name': 'poleax'}, {'id': 9985, 'synset': 'poleax.n.01', 'name': 'poleax'}, {'id': 9986, 'synset': 'police_boat.n.01', 'name': 'police_boat'}, {'id': 9987, 'synset': 'police_van.n.01', 'name': 'police_van'}, {'id': 9988, 'synset': 'polling_booth.n.01', 'name': 'polling_booth'}, {'id': 9989, 'synset': 'polo_ball.n.01', 'name': 'polo_ball'}, {'id': 9990, 'synset': 'polo_mallet.n.01', 'name': 'polo_mallet'}, {'id': 9991, 'synset': 'polonaise.n.01', 'name': 'polonaise'}, {'id': 9992, 'synset': 'polyester.n.03', 'name': 'polyester'}, {'id': 9993, 'synset': 'polygraph.n.01', 'name': 'polygraph'}, {'id': 9994, 'synset': 'pomade.n.01', 'name': 'pomade'}, {'id': 9995, 'synset': 'pommel_horse.n.01', 'name': 'pommel_horse'}, {'id': 9996, 'synset': 'pongee.n.01', 'name': 'pongee'}, {'id': 9997, 'synset': 'poniard.n.01', 'name': 'poniard'}, {'id': 9998, 'synset': 'pontifical.n.01', 'name': 'pontifical'}, {'id': 9999, 'synset': 'pontoon.n.01', 'name': 'pontoon'}, {'id': 10000, 'synset': 'pontoon_bridge.n.01', 'name': 'pontoon_bridge'}, {'id': 10001, 'synset': 'pony_cart.n.01', 'name': 'pony_cart'}, {'id': 10002, 'synset': 'pool_ball.n.01', 'name': 'pool_ball'}, {'id': 10003, 'synset': 'poolroom.n.01', 'name': 'poolroom'}, {'id': 10004, 'synset': 'poop_deck.n.01', 'name': 'poop_deck'}, {'id': 10005, 'synset': 'poor_box.n.01', 'name': 'poor_box'}, {'id': 10006, 'synset': 'poorhouse.n.01', 'name': 'poorhouse'}, {'id': 10007, 'synset': 'pop_bottle.n.01', 'name': 'pop_bottle'}, {'id': 10008, 'synset': 'popgun.n.01', 'name': 'popgun'}, {'id': 10009, 'synset': 'poplin.n.01', 'name': 'poplin'}, {'id': 10010, 'synset': 'popper.n.03', 'name': 'popper'}, {'id': 10011, 'synset': 'poppet.n.01', 'name': 'poppet'}, {'id': 10012, 'synset': 'pop_tent.n.01', 'name': 'pop_tent'}, {'id': 10013, 'synset': 'porcelain.n.01', 'name': 'porcelain'}, {'id': 10014, 'synset': 'porch.n.01', 'name': 'porch'}, {'id': 10015, 'synset': 'porkpie.n.01', 'name': 'porkpie'}, {'id': 10016, 'synset': 'porringer.n.01', 'name': 'porringer'}, {'id': 10017, 'synset': 'portable.n.01', 'name': 'portable'}, {'id': 10018, 'synset': 'portable_computer.n.01', 'name': 'portable_computer'}, {'id': 10019, 'synset': 'portable_circular_saw.n.01', 'name': 'portable_circular_saw'}, {'id': 10020, 'synset': 'portcullis.n.01', 'name': 'portcullis'}, {'id': 10021, 'synset': 'porte-cochere.n.02', 'name': 'porte-cochere'}, {'id': 10022, 'synset': 'porte-cochere.n.01', 'name': 'porte-cochere'}, {'id': 10023, 'synset': 'portfolio.n.01', 'name': 'portfolio'}, {'id': 10024, 'synset': 'porthole.n.01', 'name': 'porthole'}, {'id': 10025, 'synset': 'portico.n.01', 'name': 'portico'}, {'id': 10026, 'synset': 'portiere.n.01', 'name': 'portiere'}, {'id': 10027, 'synset': 'portmanteau.n.02', 'name': 'portmanteau'}, {'id': 10028, 'synset': 'portrait_camera.n.01', 'name': 'portrait_camera'}, {'id': 10029, 'synset': 'portrait_lens.n.01', 'name': 'portrait_lens'}, {'id': 10030, 'synset': 'positive_pole.n.02', 'name': 'positive_pole'}, {'id': 10031, 'synset': 'positive_pole.n.01', 'name': 'positive_pole'}, {'id': 10032, 'synset': 'positron_emission_tomography_scanner.n.01', 'name': 'positron_emission_tomography_scanner'}, {'id': 10033, 'synset': 'post.n.04', 'name': 'post'}, {'id': 10034, 'synset': 'postage_meter.n.01', 'name': 'postage_meter'}, {'id': 10035, 'synset': 'post_and_lintel.n.01', 'name': 'post_and_lintel'}, {'id': 10036, 'synset': 'post_chaise.n.01', 'name': 'post_chaise'}, {'id': 10037, 'synset': 'postern.n.01', 'name': 'postern'}, {'id': 10038, 'synset': 'post_exchange.n.01', 'name': 'post_exchange'}, {'id': 10039, 'synset': 'posthole_digger.n.01', 'name': 'posthole_digger'}, {'id': 10040, 'synset': 'post_horn.n.01', 'name': 'post_horn'}, {'id': 10041, 'synset': 'posthouse.n.01', 'name': 'posthouse'}, {'id': 10042, 'synset': 'potbelly.n.02', 'name': 'potbelly'}, {'id': 10043, 'synset': 'potemkin_village.n.01', 'name': 'Potemkin_village'}, {'id': 10044, 'synset': 'potential_divider.n.01', 'name': 'potential_divider'}, {'id': 10045, 'synset': 'potentiometer.n.02', 'name': 'potentiometer'}, {'id': 10046, 'synset': 'potentiometer.n.01', 'name': 'potentiometer'}, {'id': 10047, 'synset': 'potpourri.n.03', 'name': 'potpourri'}, {'id': 10048, 'synset': 'potsherd.n.01', 'name': 'potsherd'}, {'id': 10049, 'synset': "potter's_wheel.n.01", 'name': "potter's_wheel"}, {'id': 10050, 'synset': 'pottle.n.01', 'name': 'pottle'}, {'id': 10051, 'synset': 'potty_seat.n.01', 'name': 'potty_seat'}, {'id': 10052, 'synset': 'poultice.n.01', 'name': 'poultice'}, {'id': 10053, 'synset': 'pound.n.13', 'name': 'pound'}, {'id': 10054, 'synset': 'pound_net.n.01', 'name': 'pound_net'}, {'id': 10055, 'synset': 'powder.n.03', 'name': 'powder'}, {'id': 10056, 'synset': 'powder_and_shot.n.01', 'name': 'powder_and_shot'}, {'id': 10057, 'synset': 'powdered_mustard.n.01', 'name': 'powdered_mustard'}, {'id': 10058, 'synset': 'powder_horn.n.01', 'name': 'powder_horn'}, {'id': 10059, 'synset': 'powder_keg.n.02', 'name': 'powder_keg'}, {'id': 10060, 'synset': 'power_brake.n.01', 'name': 'power_brake'}, {'id': 10061, 'synset': 'power_cord.n.01', 'name': 'power_cord'}, {'id': 10062, 'synset': 'power_drill.n.01', 'name': 'power_drill'}, {'id': 10063, 'synset': 'power_line.n.01', 'name': 'power_line'}, {'id': 10064, 'synset': 'power_loom.n.01', 'name': 'power_loom'}, {'id': 10065, 'synset': 'power_mower.n.01', 'name': 'power_mower'}, {'id': 10066, 'synset': 'power_pack.n.01', 'name': 'power_pack'}, {'id': 10067, 'synset': 'power_saw.n.01', 'name': 'power_saw'}, {'id': 10068, 'synset': 'power_steering.n.01', 'name': 'power_steering'}, {'id': 10069, 'synset': 'power_takeoff.n.01', 'name': 'power_takeoff'}, {'id': 10070, 'synset': 'power_tool.n.01', 'name': 'power_tool'}, {'id': 10071, 'synset': 'praetorium.n.01', 'name': 'praetorium'}, {'id': 10072, 'synset': 'prayer_rug.n.01', 'name': 'prayer_rug'}, {'id': 10073, 'synset': 'prayer_shawl.n.01', 'name': 'prayer_shawl'}, {'id': 10074, 'synset': 'precipitator.n.01', 'name': 'precipitator'}, {'id': 10075, 'synset': 'prefab.n.01', 'name': 'prefab'}, {'id': 10076, 'synset': 'presbytery.n.01', 'name': 'presbytery'}, {'id': 10077, 'synset': 'presence_chamber.n.01', 'name': 'presence_chamber'}, {'id': 10078, 'synset': 'press.n.07', 'name': 'press'}, {'id': 10079, 'synset': 'press.n.03', 'name': 'press'}, {'id': 10080, 'synset': 'press.n.06', 'name': 'press'}, {'id': 10081, 'synset': 'press_box.n.01', 'name': 'press_box'}, {'id': 10082, 'synset': 'press_gallery.n.01', 'name': 'press_gallery'}, {'id': 10083, 'synset': 'press_of_sail.n.01', 'name': 'press_of_sail'}, {'id': 10084, 'synset': 'pressure_cabin.n.01', 'name': 'pressure_cabin'}, {'id': 10085, 'synset': 'pressure_cooker.n.01', 'name': 'pressure_cooker'}, {'id': 10086, 'synset': 'pressure_dome.n.01', 'name': 'pressure_dome'}, {'id': 10087, 'synset': 'pressure_gauge.n.01', 'name': 'pressure_gauge'}, {'id': 10088, 'synset': 'pressurized_water_reactor.n.01', 'name': 'pressurized_water_reactor'}, {'id': 10089, 'synset': 'pressure_suit.n.01', 'name': 'pressure_suit'}, {'id': 10090, 'synset': 'pricket.n.01', 'name': 'pricket'}, {'id': 10091, 'synset': 'prie-dieu.n.01', 'name': 'prie-dieu'}, {'id': 10092, 'synset': 'primary_coil.n.01', 'name': 'primary_coil'}, {'id': 10093, 'synset': 'primus_stove.n.01', 'name': 'Primus_stove'}, {'id': 10094, 'synset': 'prince_albert.n.02', 'name': 'Prince_Albert'}, {'id': 10095, 'synset': 'print.n.06', 'name': 'print'}, {'id': 10096, 'synset': 'print_buffer.n.01', 'name': 'print_buffer'}, {'id': 10097, 'synset': 'printed_circuit.n.01', 'name': 'printed_circuit'}, {'id': 10098, 'synset': 'printer.n.02', 'name': 'printer'}, {'id': 10099, 'synset': 'printer_cable.n.01', 'name': 'printer_cable'}, {'id': 10100, 'synset': 'priory.n.01', 'name': 'priory'}, {'id': 10101, 'synset': 'prison.n.01', 'name': 'prison'}, {'id': 10102, 'synset': 'prison_camp.n.01', 'name': 'prison_camp'}, {'id': 10103, 'synset': 'privateer.n.02', 'name': 'privateer'}, {'id': 10104, 'synset': 'private_line.n.01', 'name': 'private_line'}, {'id': 10105, 'synset': 'privet_hedge.n.01', 'name': 'privet_hedge'}, {'id': 10106, 'synset': 'probe.n.02', 'name': 'probe'}, {'id': 10107, 'synset': 'proctoscope.n.01', 'name': 'proctoscope'}, {'id': 10108, 'synset': 'prod.n.02', 'name': 'prod'}, {'id': 10109, 'synset': 'production_line.n.01', 'name': 'production_line'}, {'id': 10110, 'synset': 'projector.n.01', 'name': 'projector'}, {'id': 10111, 'synset': 'prolonge.n.01', 'name': 'prolonge'}, {'id': 10112, 'synset': 'prolonge_knot.n.01', 'name': 'prolonge_knot'}, {'id': 10113, 'synset': 'prompter.n.02', 'name': 'prompter'}, {'id': 10114, 'synset': 'prong.n.01', 'name': 'prong'}, {'id': 10115, 'synset': 'propeller_plane.n.01', 'name': 'propeller_plane'}, {'id': 10116, 'synset': 'propjet.n.01', 'name': 'propjet'}, {'id': 10117, 'synset': 'proportional_counter_tube.n.01', 'name': 'proportional_counter_tube'}, {'id': 10118, 'synset': 'propulsion_system.n.01', 'name': 'propulsion_system'}, {'id': 10119, 'synset': 'proscenium.n.02', 'name': 'proscenium'}, {'id': 10120, 'synset': 'proscenium_arch.n.01', 'name': 'proscenium_arch'}, {'id': 10121, 'synset': 'prosthesis.n.01', 'name': 'prosthesis'}, {'id': 10122, 'synset': 'protective_covering.n.01', 'name': 'protective_covering'}, {'id': 10123, 'synset': 'protective_garment.n.01', 'name': 'protective_garment'}, {'id': 10124, 'synset': 'proton_accelerator.n.01', 'name': 'proton_accelerator'}, {'id': 10125, 'synset': 'protractor.n.01', 'name': 'protractor'}, {'id': 10126, 'synset': 'pruner.n.02', 'name': 'pruner'}, {'id': 10127, 'synset': 'pruning_knife.n.01', 'name': 'pruning_knife'}, {'id': 10128, 'synset': 'pruning_saw.n.01', 'name': 'pruning_saw'}, {'id': 10129, 'synset': 'pruning_shears.n.01', 'name': 'pruning_shears'}, {'id': 10130, 'synset': 'psaltery.n.01', 'name': 'psaltery'}, {'id': 10131, 'synset': 'psychrometer.n.01', 'name': 'psychrometer'}, {'id': 10132, 'synset': 'pt_boat.n.01', 'name': 'PT_boat'}, {'id': 10133, 'synset': 'public_address_system.n.01', 'name': 'public_address_system'}, {'id': 10134, 'synset': 'public_house.n.01', 'name': 'public_house'}, {'id': 10135, 'synset': 'public_toilet.n.01', 'name': 'public_toilet'}, {'id': 10136, 'synset': 'public_transport.n.01', 'name': 'public_transport'}, {'id': 10137, 'synset': 'public_works.n.01', 'name': 'public_works'}, {'id': 10138, 'synset': 'puck.n.02', 'name': 'puck'}, {'id': 10139, 'synset': 'pull.n.04', 'name': 'pull'}, {'id': 10140, 'synset': 'pullback.n.01', 'name': 'pullback'}, {'id': 10141, 'synset': 'pull_chain.n.01', 'name': 'pull_chain'}, {'id': 10142, 'synset': 'pulley.n.01', 'name': 'pulley'}, {'id': 10143, 'synset': 'pull-off.n.01', 'name': 'pull-off'}, {'id': 10144, 'synset': 'pullman.n.01', 'name': 'Pullman'}, {'id': 10145, 'synset': 'pullover.n.01', 'name': 'pullover'}, {'id': 10146, 'synset': 'pull-through.n.01', 'name': 'pull-through'}, {'id': 10147, 'synset': 'pulse_counter.n.01', 'name': 'pulse_counter'}, {'id': 10148, 'synset': 'pulse_generator.n.01', 'name': 'pulse_generator'}, {'id': 10149, 'synset': 'pulse_timing_circuit.n.01', 'name': 'pulse_timing_circuit'}, {'id': 10150, 'synset': 'pump.n.01', 'name': 'pump'}, {'id': 10151, 'synset': 'pump.n.03', 'name': 'pump'}, {'id': 10152, 'synset': 'pump_action.n.01', 'name': 'pump_action'}, {'id': 10153, 'synset': 'pump_house.n.01', 'name': 'pump_house'}, {'id': 10154, 'synset': 'pump_room.n.01', 'name': 'pump_room'}, {'id': 10155, 'synset': 'pump-type_pliers.n.01', 'name': 'pump-type_pliers'}, {'id': 10156, 'synset': 'pump_well.n.01', 'name': 'pump_well'}, {'id': 10157, 'synset': 'punchboard.n.01', 'name': 'punchboard'}, {'id': 10158, 'synset': 'punch_bowl.n.01', 'name': 'punch_bowl'}, {'id': 10159, 'synset': 'punching_bag.n.02', 'name': 'punching_bag'}, {'id': 10160, 'synset': 'punch_pliers.n.01', 'name': 'punch_pliers'}, {'id': 10161, 'synset': 'punch_press.n.01', 'name': 'punch_press'}, {'id': 10162, 'synset': 'punnet.n.01', 'name': 'punnet'}, {'id': 10163, 'synset': 'punt.n.02', 'name': 'punt'}, {'id': 10164, 'synset': 'pup_tent.n.01', 'name': 'pup_tent'}, {'id': 10165, 'synset': 'purdah.n.03', 'name': 'purdah'}, {'id': 10166, 'synset': 'purifier.n.01', 'name': 'purifier'}, {'id': 10167, 'synset': 'purl.n.02', 'name': 'purl'}, {'id': 10168, 'synset': 'purse.n.03', 'name': 'purse'}, {'id': 10169, 'synset': 'push-bike.n.01', 'name': 'push-bike'}, {'id': 10170, 'synset': 'push_broom.n.01', 'name': 'push_broom'}, {'id': 10171, 'synset': 'push_button.n.01', 'name': 'push_button'}, {'id': 10172, 'synset': 'push-button_radio.n.01', 'name': 'push-button_radio'}, {'id': 10173, 'synset': 'pusher.n.04', 'name': 'pusher'}, {'id': 10174, 'synset': 'put-put.n.01', 'name': 'put-put'}, {'id': 10175, 'synset': 'puttee.n.01', 'name': 'puttee'}, {'id': 10176, 'synset': 'putter.n.02', 'name': 'putter'}, {'id': 10177, 'synset': 'putty_knife.n.01', 'name': 'putty_knife'}, {'id': 10178, 'synset': 'puzzle.n.02', 'name': 'puzzle'}, {'id': 10179, 'synset': 'pylon.n.02', 'name': 'pylon'}, {'id': 10180, 'synset': 'pylon.n.01', 'name': 'pylon'}, {'id': 10181, 'synset': 'pyramidal_tent.n.01', 'name': 'pyramidal_tent'}, {'id': 10182, 'synset': 'pyrograph.n.01', 'name': 'pyrograph'}, {'id': 10183, 'synset': 'pyrometer.n.01', 'name': 'pyrometer'}, {'id': 10184, 'synset': 'pyrometric_cone.n.01', 'name': 'pyrometric_cone'}, {'id': 10185, 'synset': 'pyrostat.n.01', 'name': 'pyrostat'}, {'id': 10186, 'synset': 'pyx.n.02', 'name': 'pyx'}, {'id': 10187, 'synset': 'pyx.n.01', 'name': 'pyx'}, {'id': 10188, 'synset': 'pyxis.n.03', 'name': 'pyxis'}, {'id': 10189, 'synset': 'quad.n.04', 'name': 'quad'}, {'id': 10190, 'synset': 'quadrant.n.04', 'name': 'quadrant'}, {'id': 10191, 'synset': 'quadraphony.n.01', 'name': 'quadraphony'}, {'id': 10192, 'synset': 'quartering.n.02', 'name': 'quartering'}, {'id': 10193, 'synset': 'quarterstaff.n.01', 'name': 'quarterstaff'}, {'id': 10194, 'synset': 'quartz_battery.n.01', 'name': 'quartz_battery'}, {'id': 10195, 'synset': 'quartz_lamp.n.01', 'name': 'quartz_lamp'}, {'id': 10196, 'synset': 'queen.n.08', 'name': 'queen'}, {'id': 10197, 'synset': 'queen.n.07', 'name': 'queen'}, {'id': 10198, 'synset': 'queen_post.n.01', 'name': 'queen_post'}, {'id': 10199, 'synset': 'quern.n.01', 'name': 'quern'}, {'id': 10200, 'synset': 'quill.n.01', 'name': 'quill'}, {'id': 10201, 'synset': 'quilted_bedspread.n.01', 'name': 'quilted_bedspread'}, {'id': 10202, 'synset': 'quilting.n.02', 'name': 'quilting'}, {'id': 10203, 'synset': 'quipu.n.01', 'name': 'quipu'}, {'id': 10204, 'synset': 'quirk_molding.n.01', 'name': 'quirk_molding'}, {'id': 10205, 'synset': 'quirt.n.01', 'name': 'quirt'}, {'id': 10206, 'synset': 'quiver.n.03', 'name': 'quiver'}, {'id': 10207, 'synset': 'quoin.n.02', 'name': 'quoin'}, {'id': 10208, 'synset': 'quoit.n.01', 'name': 'quoit'}, {'id': 10209, 'synset': 'qwerty_keyboard.n.01', 'name': 'QWERTY_keyboard'}, {'id': 10210, 'synset': 'rabbet.n.01', 'name': 'rabbet'}, {'id': 10211, 'synset': 'rabbet_joint.n.01', 'name': 'rabbet_joint'}, {'id': 10212, 'synset': 'rabbit_ears.n.01', 'name': 'rabbit_ears'}, {'id': 10213, 'synset': 'rabbit_hutch.n.01', 'name': 'rabbit_hutch'}, {'id': 10214, 'synset': 'raceabout.n.01', 'name': 'raceabout'}, {'id': 10215, 'synset': 'raceway.n.01', 'name': 'raceway'}, {'id': 10216, 'synset': 'racing_boat.n.01', 'name': 'racing_boat'}, {'id': 10217, 'synset': 'racing_gig.n.01', 'name': 'racing_gig'}, {'id': 10218, 'synset': 'racing_skiff.n.01', 'name': 'racing_skiff'}, {'id': 10219, 'synset': 'rack.n.05', 'name': 'rack'}, {'id': 10220, 'synset': 'rack.n.01', 'name': 'rack'}, {'id': 10221, 'synset': 'rack.n.04', 'name': 'rack'}, {'id': 10222, 'synset': 'rack_and_pinion.n.01', 'name': 'rack_and_pinion'}, {'id': 10223, 'synset': 'racquetball.n.01', 'name': 'racquetball'}, {'id': 10224, 'synset': 'radial.n.01', 'name': 'radial'}, {'id': 10225, 'synset': 'radial_engine.n.01', 'name': 'radial_engine'}, {'id': 10226, 'synset': 'radiation_pyrometer.n.01', 'name': 'radiation_pyrometer'}, {'id': 10227, 'synset': 'radiator.n.02', 'name': 'radiator'}, {'id': 10228, 'synset': 'radiator_cap.n.01', 'name': 'radiator_cap'}, {'id': 10229, 'synset': 'radiator_hose.n.01', 'name': 'radiator_hose'}, {'id': 10230, 'synset': 'radio.n.03', 'name': 'radio'}, {'id': 10231, 'synset': 'radio_antenna.n.01', 'name': 'radio_antenna'}, {'id': 10232, 'synset': 'radio_chassis.n.01', 'name': 'radio_chassis'}, {'id': 10233, 'synset': 'radio_compass.n.01', 'name': 'radio_compass'}, {'id': 10234, 'synset': 'radiogram.n.02', 'name': 'radiogram'}, {'id': 10235, 'synset': 'radio_interferometer.n.01', 'name': 'radio_interferometer'}, {'id': 10236, 'synset': 'radio_link.n.01', 'name': 'radio_link'}, {'id': 10237, 'synset': 'radiometer.n.01', 'name': 'radiometer'}, {'id': 10238, 'synset': 'radiomicrometer.n.01', 'name': 'radiomicrometer'}, {'id': 10239, 'synset': 'radio-phonograph.n.01', 'name': 'radio-phonograph'}, {'id': 10240, 'synset': 'radiotelegraph.n.02', 'name': 'radiotelegraph'}, {'id': 10241, 'synset': 'radiotelephone.n.02', 'name': 'radiotelephone'}, {'id': 10242, 'synset': 'radio_telescope.n.01', 'name': 'radio_telescope'}, {'id': 10243, 'synset': 'radiotherapy_equipment.n.01', 'name': 'radiotherapy_equipment'}, {'id': 10244, 'synset': 'radio_transmitter.n.01', 'name': 'radio_transmitter'}, {'id': 10245, 'synset': 'radome.n.01', 'name': 'radome'}, {'id': 10246, 'synset': 'rafter.n.01', 'name': 'rafter'}, {'id': 10247, 'synset': 'raft_foundation.n.01', 'name': 'raft_foundation'}, {'id': 10248, 'synset': 'rag.n.01', 'name': 'rag'}, {'id': 10249, 'synset': 'ragbag.n.02', 'name': 'ragbag'}, {'id': 10250, 'synset': 'raglan.n.01', 'name': 'raglan'}, {'id': 10251, 'synset': 'raglan_sleeve.n.01', 'name': 'raglan_sleeve'}, {'id': 10252, 'synset': 'rail.n.04', 'name': 'rail'}, {'id': 10253, 'synset': 'rail_fence.n.01', 'name': 'rail_fence'}, {'id': 10254, 'synset': 'railhead.n.01', 'name': 'railhead'}, {'id': 10255, 'synset': 'railing.n.01', 'name': 'railing'}, {'id': 10256, 'synset': 'railing.n.02', 'name': 'railing'}, {'id': 10257, 'synset': 'railroad_bed.n.01', 'name': 'railroad_bed'}, {'id': 10258, 'synset': 'railroad_tunnel.n.01', 'name': 'railroad_tunnel'}, {'id': 10259, 'synset': 'rain_barrel.n.01', 'name': 'rain_barrel'}, {'id': 10260, 'synset': 'rain_gauge.n.01', 'name': 'rain_gauge'}, {'id': 10261, 'synset': 'rain_stick.n.01', 'name': 'rain_stick'}, {'id': 10262, 'synset': 'rake.n.03', 'name': 'rake'}, {'id': 10263, 'synset': 'rake_handle.n.01', 'name': 'rake_handle'}, {'id': 10264, 'synset': 'ram_disk.n.01', 'name': 'RAM_disk'}, {'id': 10265, 'synset': 'ramekin.n.02', 'name': 'ramekin'}, {'id': 10266, 'synset': 'ramjet.n.01', 'name': 'ramjet'}, {'id': 10267, 'synset': 'rammer.n.01', 'name': 'rammer'}, {'id': 10268, 'synset': 'ramp.n.01', 'name': 'ramp'}, {'id': 10269, 'synset': 'rampant_arch.n.01', 'name': 'rampant_arch'}, {'id': 10270, 'synset': 'rampart.n.01', 'name': 'rampart'}, {'id': 10271, 'synset': 'ramrod.n.01', 'name': 'ramrod'}, {'id': 10272, 'synset': 'ramrod.n.03', 'name': 'ramrod'}, {'id': 10273, 'synset': 'ranch.n.01', 'name': 'ranch'}, {'id': 10274, 'synset': 'ranch_house.n.01', 'name': 'ranch_house'}, {'id': 10275, 'synset': 'random-access_memory.n.01', 'name': 'random-access_memory'}, {'id': 10276, 'synset': 'rangefinder.n.01', 'name': 'rangefinder'}, {'id': 10277, 'synset': 'range_hood.n.01', 'name': 'range_hood'}, {'id': 10278, 'synset': 'range_pole.n.01', 'name': 'range_pole'}, {'id': 10279, 'synset': 'rapier.n.01', 'name': 'rapier'}, {'id': 10280, 'synset': 'rariora.n.01', 'name': 'rariora'}, {'id': 10281, 'synset': 'rasp.n.02', 'name': 'rasp'}, {'id': 10282, 'synset': 'ratchet.n.01', 'name': 'ratchet'}, {'id': 10283, 'synset': 'ratchet_wheel.n.01', 'name': 'ratchet_wheel'}, {'id': 10284, 'synset': 'rathskeller.n.01', 'name': 'rathskeller'}, {'id': 10285, 'synset': 'ratline.n.01', 'name': 'ratline'}, {'id': 10286, 'synset': 'rat-tail_file.n.01', 'name': 'rat-tail_file'}, {'id': 10287, 'synset': 'rattan.n.03', 'name': 'rattan'}, {'id': 10288, 'synset': 'rattrap.n.03', 'name': 'rattrap'}, {'id': 10289, 'synset': 'rayon.n.01', 'name': 'rayon'}, {'id': 10290, 'synset': 'razor.n.01', 'name': 'razor'}, {'id': 10291, 'synset': 'reaction-propulsion_engine.n.01', 'name': 'reaction-propulsion_engine'}, {'id': 10292, 'synset': 'reaction_turbine.n.01', 'name': 'reaction_turbine'}, {'id': 10293, 'synset': 'reactor.n.01', 'name': 'reactor'}, {'id': 10294, 'synset': 'reading_lamp.n.01', 'name': 'reading_lamp'}, {'id': 10295, 'synset': 'reading_room.n.01', 'name': 'reading_room'}, {'id': 10296, 'synset': 'read-only_memory.n.01', 'name': 'read-only_memory'}, {'id': 10297, 'synset': 'read-only_memory_chip.n.01', 'name': 'read-only_memory_chip'}, {'id': 10298, 'synset': 'readout.n.03', 'name': 'readout'}, {'id': 10299, 'synset': 'read/write_head.n.01', 'name': 'read/write_head'}, {'id': 10300, 'synset': 'ready-to-wear.n.01', 'name': 'ready-to-wear'}, {'id': 10301, 'synset': 'real_storage.n.01', 'name': 'real_storage'}, {'id': 10302, 'synset': 'reamer.n.02', 'name': 'reamer'}, {'id': 10303, 'synset': 'reaumur_thermometer.n.01', 'name': 'Reaumur_thermometer'}, {'id': 10304, 'synset': 'rebozo.n.01', 'name': 'rebozo'}, {'id': 10305, 'synset': 'receiver.n.01', 'name': 'receiver'}, {'id': 10306, 'synset': 'receptacle.n.01', 'name': 'receptacle'}, {'id': 10307, 'synset': 'reception_desk.n.01', 'name': 'reception_desk'}, {'id': 10308, 'synset': 'reception_room.n.01', 'name': 'reception_room'}, {'id': 10309, 'synset': 'recess.n.04', 'name': 'recess'}, {'id': 10310, 'synset': 'reciprocating_engine.n.01', 'name': 'reciprocating_engine'}, {'id': 10311, 'synset': 'reconnaissance_plane.n.01', 'name': 'reconnaissance_plane'}, {'id': 10312, 'synset': 'reconnaissance_vehicle.n.01', 'name': 'reconnaissance_vehicle'}, {'id': 10313, 'synset': 'record_changer.n.01', 'name': 'record_changer'}, {'id': 10314, 'synset': 'recorder.n.01', 'name': 'recorder'}, {'id': 10315, 'synset': 'recording.n.03', 'name': 'recording'}, {'id': 10316, 'synset': 'recording_system.n.01', 'name': 'recording_system'}, {'id': 10317, 'synset': 'record_sleeve.n.01', 'name': 'record_sleeve'}, {'id': 10318, 'synset': 'recovery_room.n.01', 'name': 'recovery_room'}, {'id': 10319, 'synset': 'recreational_vehicle.n.01', 'name': 'recreational_vehicle'}, {'id': 10320, 'synset': 'recreation_room.n.01', 'name': 'recreation_room'}, {'id': 10321, 'synset': 'recycling_bin.n.01', 'name': 'recycling_bin'}, {'id': 10322, 'synset': 'recycling_plant.n.01', 'name': 'recycling_plant'}, {'id': 10323, 'synset': 'redbrick_university.n.01', 'name': 'redbrick_university'}, {'id': 10324, 'synset': 'red_carpet.n.01', 'name': 'red_carpet'}, {'id': 10325, 'synset': 'redoubt.n.02', 'name': 'redoubt'}, {'id': 10326, 'synset': 'redoubt.n.01', 'name': 'redoubt'}, {'id': 10327, 'synset': 'reduction_gear.n.01', 'name': 'reduction_gear'}, {'id': 10328, 'synset': 'reed_pipe.n.01', 'name': 'reed_pipe'}, {'id': 10329, 'synset': 'reed_stop.n.01', 'name': 'reed_stop'}, {'id': 10330, 'synset': 'reef_knot.n.01', 'name': 'reef_knot'}, {'id': 10331, 'synset': 'reel.n.03', 'name': 'reel'}, {'id': 10332, 'synset': 'reel.n.01', 'name': 'reel'}, {'id': 10333, 'synset': 'refectory.n.01', 'name': 'refectory'}, {'id': 10334, 'synset': 'refectory_table.n.01', 'name': 'refectory_table'}, {'id': 10335, 'synset': 'refinery.n.01', 'name': 'refinery'}, {'id': 10336, 'synset': 'reflecting_telescope.n.01', 'name': 'reflecting_telescope'}, {'id': 10337, 'synset': 'reflectometer.n.01', 'name': 'reflectometer'}, {'id': 10338, 'synset': 'reflex_camera.n.01', 'name': 'reflex_camera'}, {'id': 10339, 'synset': 'reflux_condenser.n.01', 'name': 'reflux_condenser'}, {'id': 10340, 'synset': 'reformatory.n.01', 'name': 'reformatory'}, {'id': 10341, 'synset': 'reformer.n.02', 'name': 'reformer'}, {'id': 10342, 'synset': 'refracting_telescope.n.01', 'name': 'refracting_telescope'}, {'id': 10343, 'synset': 'refractometer.n.01', 'name': 'refractometer'}, {'id': 10344, 'synset': 'refrigeration_system.n.01', 'name': 'refrigeration_system'}, {'id': 10345, 'synset': 'refrigerator.n.01', 'name': 'refrigerator'}, {'id': 10346, 'synset': 'refrigerator_car.n.01', 'name': 'refrigerator_car'}, {'id': 10347, 'synset': 'refuge.n.03', 'name': 'refuge'}, {'id': 10348, 'synset': 'regalia.n.01', 'name': 'regalia'}, {'id': 10349, 'synset': 'regimentals.n.01', 'name': 'regimentals'}, {'id': 10350, 'synset': 'regulator.n.01', 'name': 'regulator'}, {'id': 10351, 'synset': 'rein.n.01', 'name': 'rein'}, {'id': 10352, 'synset': 'relay.n.05', 'name': 'relay'}, {'id': 10353, 'synset': 'release.n.08', 'name': 'release'}, {'id': 10354, 'synset': 'religious_residence.n.01', 'name': 'religious_residence'}, {'id': 10355, 'synset': 'reliquary.n.01', 'name': 'reliquary'}, {'id': 10356, 'synset': 'remote_terminal.n.01', 'name': 'remote_terminal'}, {'id': 10357, 'synset': 'removable_disk.n.01', 'name': 'removable_disk'}, {'id': 10358, 'synset': 'rendering.n.05', 'name': 'rendering'}, {'id': 10359, 'synset': 'rep.n.02', 'name': 'rep'}, {'id': 10360, 'synset': 'repair_shop.n.01', 'name': 'repair_shop'}, {'id': 10361, 'synset': 'repeater.n.04', 'name': 'repeater'}, {'id': 10362, 'synset': 'repeating_firearm.n.01', 'name': 'repeating_firearm'}, {'id': 10363, 'synset': 'repository.n.03', 'name': 'repository'}, {'id': 10364, 'synset': 'reproducer.n.01', 'name': 'reproducer'}, {'id': 10365, 'synset': 'rerebrace.n.01', 'name': 'rerebrace'}, {'id': 10366, 'synset': 'rescue_equipment.n.01', 'name': 'rescue_equipment'}, {'id': 10367, 'synset': 'research_center.n.01', 'name': 'research_center'}, {'id': 10368, 'synset': 'reseau.n.02', 'name': 'reseau'}, {'id': 10369, 'synset': 'reservoir.n.03', 'name': 'reservoir'}, {'id': 10370, 'synset': 'reset.n.01', 'name': 'reset'}, {'id': 10371, 'synset': 'reset_button.n.01', 'name': 'reset_button'}, {'id': 10372, 'synset': 'residence.n.02', 'name': 'residence'}, {'id': 10373, 'synset': 'resistance_pyrometer.n.01', 'name': 'resistance_pyrometer'}, {'id': 10374, 'synset': 'resistor.n.01', 'name': 'resistor'}, {'id': 10375, 'synset': 'resonator.n.03', 'name': 'resonator'}, {'id': 10376, 'synset': 'resonator.n.01', 'name': 'resonator'}, {'id': 10377, 'synset': 'resort_hotel.n.02', 'name': 'resort_hotel'}, {'id': 10378, 'synset': 'respirator.n.01', 'name': 'respirator'}, {'id': 10379, 'synset': 'restaurant.n.01', 'name': 'restaurant'}, {'id': 10380, 'synset': 'rest_house.n.01', 'name': 'rest_house'}, {'id': 10381, 'synset': 'restraint.n.06', 'name': 'restraint'}, {'id': 10382, 'synset': 'resuscitator.n.01', 'name': 'resuscitator'}, {'id': 10383, 'synset': 'retainer.n.03', 'name': 'retainer'}, {'id': 10384, 'synset': 'retaining_wall.n.01', 'name': 'retaining_wall'}, {'id': 10385, 'synset': 'reticle.n.01', 'name': 'reticle'}, {'id': 10386, 'synset': 'reticulation.n.02', 'name': 'reticulation'}, {'id': 10387, 'synset': 'reticule.n.01', 'name': 'reticule'}, {'id': 10388, 'synset': 'retort.n.02', 'name': 'retort'}, {'id': 10389, 'synset': 'retractor.n.01', 'name': 'retractor'}, {'id': 10390, 'synset': 'return_key.n.01', 'name': 'return_key'}, {'id': 10391, 'synset': 'reverberatory_furnace.n.01', 'name': 'reverberatory_furnace'}, {'id': 10392, 'synset': 'revers.n.01', 'name': 'revers'}, {'id': 10393, 'synset': 'reverse.n.02', 'name': 'reverse'}, {'id': 10394, 'synset': 'reversible.n.01', 'name': 'reversible'}, {'id': 10395, 'synset': 'revetment.n.02', 'name': 'revetment'}, {'id': 10396, 'synset': 'revetment.n.01', 'name': 'revetment'}, {'id': 10397, 'synset': 'revolver.n.01', 'name': 'revolver'}, {'id': 10398, 'synset': 'revolving_door.n.02', 'name': 'revolving_door'}, {'id': 10399, 'synset': 'rheometer.n.01', 'name': 'rheometer'}, {'id': 10400, 'synset': 'rheostat.n.01', 'name': 'rheostat'}, {'id': 10401, 'synset': 'rhinoscope.n.01', 'name': 'rhinoscope'}, {'id': 10402, 'synset': 'rib.n.01', 'name': 'rib'}, {'id': 10403, 'synset': 'riband.n.01', 'name': 'riband'}, {'id': 10404, 'synset': 'ribbed_vault.n.01', 'name': 'ribbed_vault'}, {'id': 10405, 'synset': 'ribbing.n.01', 'name': 'ribbing'}, {'id': 10406, 'synset': 'ribbon_development.n.01', 'name': 'ribbon_development'}, {'id': 10407, 'synset': 'rib_joint_pliers.n.01', 'name': 'rib_joint_pliers'}, {'id': 10408, 'synset': 'ricer.n.01', 'name': 'ricer'}, {'id': 10409, 'synset': 'riddle.n.02', 'name': 'riddle'}, {'id': 10410, 'synset': 'ride.n.02', 'name': 'ride'}, {'id': 10411, 'synset': 'ridge.n.06', 'name': 'ridge'}, {'id': 10412, 'synset': 'ridge_rope.n.01', 'name': 'ridge_rope'}, {'id': 10413, 'synset': 'riding_boot.n.01', 'name': 'riding_boot'}, {'id': 10414, 'synset': 'riding_crop.n.01', 'name': 'riding_crop'}, {'id': 10415, 'synset': 'riding_mower.n.01', 'name': 'riding_mower'}, {'id': 10416, 'synset': 'rifle_ball.n.01', 'name': 'rifle_ball'}, {'id': 10417, 'synset': 'rifle_grenade.n.01', 'name': 'rifle_grenade'}, {'id': 10418, 'synset': 'rig.n.01', 'name': 'rig'}, {'id': 10419, 'synset': 'rigger.n.02', 'name': 'rigger'}, {'id': 10420, 'synset': 'rigger.n.04', 'name': 'rigger'}, {'id': 10421, 'synset': 'rigging.n.01', 'name': 'rigging'}, {'id': 10422, 'synset': 'rigout.n.01', 'name': 'rigout'}, {'id': 10423, 'synset': 'ringlet.n.03', 'name': 'ringlet'}, {'id': 10424, 'synset': 'rings.n.01', 'name': 'rings'}, {'id': 10425, 'synset': 'rink.n.01', 'name': 'rink'}, {'id': 10426, 'synset': 'riot_gun.n.01', 'name': 'riot_gun'}, {'id': 10427, 'synset': 'ripcord.n.02', 'name': 'ripcord'}, {'id': 10428, 'synset': 'ripcord.n.01', 'name': 'ripcord'}, {'id': 10429, 'synset': 'ripping_bar.n.01', 'name': 'ripping_bar'}, {'id': 10430, 'synset': 'ripping_chisel.n.01', 'name': 'ripping_chisel'}, {'id': 10431, 'synset': 'ripsaw.n.01', 'name': 'ripsaw'}, {'id': 10432, 'synset': 'riser.n.03', 'name': 'riser'}, {'id': 10433, 'synset': 'riser.n.02', 'name': 'riser'}, {'id': 10434, 'synset': 'ritz.n.03', 'name': 'Ritz'}, {'id': 10435, 'synset': 'rivet.n.02', 'name': 'rivet'}, {'id': 10436, 'synset': 'riveting_machine.n.01', 'name': 'riveting_machine'}, {'id': 10437, 'synset': 'roach_clip.n.01', 'name': 'roach_clip'}, {'id': 10438, 'synset': 'road.n.01', 'name': 'road'}, {'id': 10439, 'synset': 'roadbed.n.01', 'name': 'roadbed'}, {'id': 10440, 'synset': 'roadblock.n.02', 'name': 'roadblock'}, {'id': 10441, 'synset': 'roadhouse.n.01', 'name': 'roadhouse'}, {'id': 10442, 'synset': 'roadster.n.01', 'name': 'roadster'}, {'id': 10443, 'synset': 'roadway.n.01', 'name': 'roadway'}, {'id': 10444, 'synset': 'roaster.n.04', 'name': 'roaster'}, {'id': 10445, 'synset': 'robotics_equipment.n.01', 'name': 'robotics_equipment'}, {'id': 10446, 'synset': 'rochon_prism.n.01', 'name': 'Rochon_prism'}, {'id': 10447, 'synset': 'rock_bit.n.01', 'name': 'rock_bit'}, {'id': 10448, 'synset': 'rocker.n.07', 'name': 'rocker'}, {'id': 10449, 'synset': 'rocker.n.05', 'name': 'rocker'}, {'id': 10450, 'synset': 'rocker_arm.n.01', 'name': 'rocker_arm'}, {'id': 10451, 'synset': 'rocket.n.02', 'name': 'rocket'}, {'id': 10452, 'synset': 'rocket.n.01', 'name': 'rocket'}, {'id': 10453, 'synset': 'rod.n.01', 'name': 'rod'}, {'id': 10454, 'synset': 'rodeo.n.02', 'name': 'rodeo'}, {'id': 10455, 'synset': 'roll.n.04', 'name': 'roll'}, {'id': 10456, 'synset': 'roller.n.04', 'name': 'roller'}, {'id': 10457, 'synset': 'roller.n.03', 'name': 'roller'}, {'id': 10458, 'synset': 'roller_bandage.n.01', 'name': 'roller_bandage'}, {'id': 10459, 'synset': 'in-line_skate.n.01', 'name': 'in-line_skate'}, {'id': 10460, 'synset': 'roller_blind.n.01', 'name': 'roller_blind'}, {'id': 10461, 'synset': 'roller_coaster.n.02', 'name': 'roller_coaster'}, {'id': 10462, 'synset': 'roller_towel.n.01', 'name': 'roller_towel'}, {'id': 10463, 'synset': 'roll_film.n.01', 'name': 'roll_film'}, {'id': 10464, 'synset': 'rolling_hitch.n.01', 'name': 'rolling_hitch'}, {'id': 10465, 'synset': 'rolling_mill.n.01', 'name': 'rolling_mill'}, {'id': 10466, 'synset': 'rolling_stock.n.01', 'name': 'rolling_stock'}, {'id': 10467, 'synset': 'roll-on.n.02', 'name': 'roll-on'}, {'id': 10468, 'synset': 'roll-on.n.01', 'name': 'roll-on'}, {'id': 10469, 'synset': 'roll-on_roll-off.n.01', 'name': 'roll-on_roll-off'}, {'id': 10470, 'synset': 'rolodex.n.01', 'name': 'Rolodex'}, {'id': 10471, 'synset': 'roman_arch.n.01', 'name': 'Roman_arch'}, {'id': 10472, 'synset': 'roman_building.n.01', 'name': 'Roman_building'}, {'id': 10473, 'synset': 'romper.n.02', 'name': 'romper'}, {'id': 10474, 'synset': 'rood_screen.n.01', 'name': 'rood_screen'}, {'id': 10475, 'synset': 'roof.n.01', 'name': 'roof'}, {'id': 10476, 'synset': 'roof.n.02', 'name': 'roof'}, {'id': 10477, 'synset': 'roofing.n.01', 'name': 'roofing'}, {'id': 10478, 'synset': 'room.n.01', 'name': 'room'}, {'id': 10479, 'synset': 'roomette.n.01', 'name': 'roomette'}, {'id': 10480, 'synset': 'room_light.n.01', 'name': 'room_light'}, {'id': 10481, 'synset': 'roost.n.01', 'name': 'roost'}, {'id': 10482, 'synset': 'rope.n.01', 'name': 'rope'}, {'id': 10483, 'synset': 'rope_bridge.n.01', 'name': 'rope_bridge'}, {'id': 10484, 'synset': 'rope_tow.n.01', 'name': 'rope_tow'}, {'id': 10485, 'synset': 'rose_water.n.01', 'name': 'rose_water'}, {'id': 10486, 'synset': 'rose_window.n.01', 'name': 'rose_window'}, {'id': 10487, 'synset': 'rosin_bag.n.01', 'name': 'rosin_bag'}, {'id': 10488, 'synset': 'rotary_actuator.n.01', 'name': 'rotary_actuator'}, {'id': 10489, 'synset': 'rotary_engine.n.01', 'name': 'rotary_engine'}, {'id': 10490, 'synset': 'rotary_press.n.01', 'name': 'rotary_press'}, {'id': 10491, 'synset': 'rotating_mechanism.n.01', 'name': 'rotating_mechanism'}, {'id': 10492, 'synset': 'rotating_shaft.n.01', 'name': 'rotating_shaft'}, {'id': 10493, 'synset': 'rotisserie.n.02', 'name': 'rotisserie'}, {'id': 10494, 'synset': 'rotisserie.n.01', 'name': 'rotisserie'}, {'id': 10495, 'synset': 'rotor.n.03', 'name': 'rotor'}, {'id': 10496, 'synset': 'rotor.n.01', 'name': 'rotor'}, {'id': 10497, 'synset': 'rotor.n.02', 'name': 'rotor'}, {'id': 10498, 'synset': 'rotor_blade.n.01', 'name': 'rotor_blade'}, {'id': 10499, 'synset': 'rotor_head.n.01', 'name': 'rotor_head'}, {'id': 10500, 'synset': 'rotunda.n.02', 'name': 'rotunda'}, {'id': 10501, 'synset': 'rotunda.n.01', 'name': 'rotunda'}, {'id': 10502, 'synset': 'rouge.n.01', 'name': 'rouge'}, {'id': 10503, 'synset': 'roughcast.n.02', 'name': 'roughcast'}, {'id': 10504, 'synset': 'rouleau.n.02', 'name': 'rouleau'}, {'id': 10505, 'synset': 'roulette.n.02', 'name': 'roulette'}, {'id': 10506, 'synset': 'roulette_ball.n.01', 'name': 'roulette_ball'}, {'id': 10507, 'synset': 'roulette_wheel.n.01', 'name': 'roulette_wheel'}, {'id': 10508, 'synset': 'round.n.01', 'name': 'round'}, {'id': 10509, 'synset': 'round_arch.n.01', 'name': 'round_arch'}, {'id': 10510, 'synset': 'round-bottom_flask.n.01', 'name': 'round-bottom_flask'}, {'id': 10511, 'synset': 'roundel.n.02', 'name': 'roundel'}, {'id': 10512, 'synset': 'round_file.n.01', 'name': 'round_file'}, {'id': 10513, 'synset': 'roundhouse.n.01', 'name': 'roundhouse'}, {'id': 10514, 'synset': 'router.n.03', 'name': 'router'}, {'id': 10515, 'synset': 'router_plane.n.01', 'name': 'router_plane'}, {'id': 10516, 'synset': 'rowel.n.01', 'name': 'rowel'}, {'id': 10517, 'synset': 'row_house.n.01', 'name': 'row_house'}, {'id': 10518, 'synset': 'rowing_boat.n.01', 'name': 'rowing_boat'}, {'id': 10519, 'synset': 'rowlock_arch.n.01', 'name': 'rowlock_arch'}, {'id': 10520, 'synset': 'royal.n.01', 'name': 'royal'}, {'id': 10521, 'synset': 'royal_mast.n.01', 'name': 'royal_mast'}, {'id': 10522, 'synset': 'rubber_boot.n.01', 'name': 'rubber_boot'}, {'id': 10523, 'synset': 'rubber_bullet.n.01', 'name': 'rubber_bullet'}, {'id': 10524, 'synset': 'rubber_eraser.n.01', 'name': 'rubber_eraser'}, {'id': 10525, 'synset': 'rudder.n.02', 'name': 'rudder'}, {'id': 10526, 'synset': 'rudder.n.01', 'name': 'rudder'}, {'id': 10527, 'synset': 'rudder_blade.n.01', 'name': 'rudder_blade'}, {'id': 10528, 'synset': 'rug.n.01', 'name': 'rug'}, {'id': 10529, 'synset': 'rugby_ball.n.01', 'name': 'rugby_ball'}, {'id': 10530, 'synset': 'ruin.n.02', 'name': 'ruin'}, {'id': 10531, 'synset': 'rule.n.12', 'name': 'rule'}, {'id': 10532, 'synset': 'rumble.n.02', 'name': 'rumble'}, {'id': 10533, 'synset': 'rumble_seat.n.01', 'name': 'rumble_seat'}, {'id': 10534, 'synset': 'rummer.n.01', 'name': 'rummer'}, {'id': 10535, 'synset': 'rumpus_room.n.01', 'name': 'rumpus_room'}, {'id': 10536, 'synset': 'runcible_spoon.n.01', 'name': 'runcible_spoon'}, {'id': 10537, 'synset': 'rundle.n.01', 'name': 'rundle'}, {'id': 10538, 'synset': 'running_shoe.n.01', 'name': 'running_shoe'}, {'id': 10539, 'synset': 'running_suit.n.01', 'name': 'running_suit'}, {'id': 10540, 'synset': 'runway.n.04', 'name': 'runway'}, {'id': 10541, 'synset': 'rushlight.n.01', 'name': 'rushlight'}, {'id': 10542, 'synset': 'russet.n.01', 'name': 'russet'}, {'id': 10543, 'synset': 'rya.n.01', 'name': 'rya'}, {'id': 10544, 'synset': 'saber.n.01', 'name': 'saber'}, {'id': 10545, 'synset': 'saber_saw.n.01', 'name': 'saber_saw'}, {'id': 10546, 'synset': 'sable.n.04', 'name': 'sable'}, {'id': 10547, 'synset': 'sable.n.01', 'name': 'sable'}, {'id': 10548, 'synset': 'sable_coat.n.01', 'name': 'sable_coat'}, {'id': 10549, 'synset': 'sabot.n.01', 'name': 'sabot'}, {'id': 10550, 'synset': 'sachet.n.01', 'name': 'sachet'}, {'id': 10551, 'synset': 'sack.n.05', 'name': 'sack'}, {'id': 10552, 'synset': 'sackbut.n.01', 'name': 'sackbut'}, {'id': 10553, 'synset': 'sackcloth.n.02', 'name': 'sackcloth'}, {'id': 10554, 'synset': 'sackcloth.n.01', 'name': 'sackcloth'}, {'id': 10555, 'synset': 'sack_coat.n.01', 'name': 'sack_coat'}, {'id': 10556, 'synset': 'sacking.n.01', 'name': 'sacking'}, {'id': 10557, 'synset': 'saddle_oxford.n.01', 'name': 'saddle_oxford'}, {'id': 10558, 'synset': 'saddlery.n.02', 'name': 'saddlery'}, {'id': 10559, 'synset': 'saddle_seat.n.01', 'name': 'saddle_seat'}, {'id': 10560, 'synset': 'saddle_stitch.n.01', 'name': 'saddle_stitch'}, {'id': 10561, 'synset': 'safe.n.01', 'name': 'safe'}, {'id': 10562, 'synset': 'safe.n.02', 'name': 'safe'}, {'id': 10563, 'synset': 'safe-deposit.n.01', 'name': 'safe-deposit'}, {'id': 10564, 'synset': 'safe_house.n.01', 'name': 'safe_house'}, {'id': 10565, 'synset': 'safety_arch.n.01', 'name': 'safety_arch'}, {'id': 10566, 'synset': 'safety_belt.n.01', 'name': 'safety_belt'}, {'id': 10567, 'synset': 'safety_bicycle.n.01', 'name': 'safety_bicycle'}, {'id': 10568, 'synset': 'safety_bolt.n.01', 'name': 'safety_bolt'}, {'id': 10569, 'synset': 'safety_curtain.n.01', 'name': 'safety_curtain'}, {'id': 10570, 'synset': 'safety_fuse.n.01', 'name': 'safety_fuse'}, {'id': 10571, 'synset': 'safety_lamp.n.01', 'name': 'safety_lamp'}, {'id': 10572, 'synset': 'safety_match.n.01', 'name': 'safety_match'}, {'id': 10573, 'synset': 'safety_net.n.02', 'name': 'safety_net'}, {'id': 10574, 'synset': 'safety_rail.n.01', 'name': 'safety_rail'}, {'id': 10575, 'synset': 'safety_razor.n.01', 'name': 'safety_razor'}, {'id': 10576, 'synset': 'safety_valve.n.01', 'name': 'safety_valve'}, {'id': 10577, 'synset': 'sail.n.03', 'name': 'sail'}, {'id': 10578, 'synset': 'sailboat.n.01', 'name': 'sailboat'}, {'id': 10579, 'synset': 'sailcloth.n.01', 'name': 'sailcloth'}, {'id': 10580, 'synset': 'sailing_vessel.n.01', 'name': 'sailing_vessel'}, {'id': 10581, 'synset': 'sailing_warship.n.01', 'name': 'sailing_warship'}, {'id': 10582, 'synset': 'sailor_cap.n.01', 'name': 'sailor_cap'}, {'id': 10583, 'synset': 'sailor_suit.n.01', 'name': 'sailor_suit'}, {'id': 10584, 'synset': 'salad_bar.n.01', 'name': 'salad_bar'}, {'id': 10585, 'synset': 'salad_bowl.n.02', 'name': 'salad_bowl'}, {'id': 10586, 'synset': 'salinometer.n.01', 'name': 'salinometer'}, {'id': 10587, 'synset': 'sallet.n.01', 'name': 'sallet'}, {'id': 10588, 'synset': 'salon.n.03', 'name': 'salon'}, {'id': 10589, 'synset': 'salon.n.01', 'name': 'salon'}, {'id': 10590, 'synset': 'salon.n.02', 'name': 'salon'}, {'id': 10591, 'synset': 'saltbox.n.01', 'name': 'saltbox'}, {'id': 10592, 'synset': 'saltcellar.n.01', 'name': 'saltcellar'}, {'id': 10593, 'synset': 'saltworks.n.01', 'name': 'saltworks'}, {'id': 10594, 'synset': 'salver.n.01', 'name': 'salver'}, {'id': 10595, 'synset': 'salwar.n.01', 'name': 'salwar'}, {'id': 10596, 'synset': 'sam_browne_belt.n.01', 'name': 'Sam_Browne_belt'}, {'id': 10597, 'synset': 'samisen.n.01', 'name': 'samisen'}, {'id': 10598, 'synset': 'samite.n.01', 'name': 'samite'}, {'id': 10599, 'synset': 'samovar.n.01', 'name': 'samovar'}, {'id': 10600, 'synset': 'sampan.n.01', 'name': 'sampan'}, {'id': 10601, 'synset': 'sandbag.n.01', 'name': 'sandbag'}, {'id': 10602, 'synset': 'sandblaster.n.01', 'name': 'sandblaster'}, {'id': 10603, 'synset': 'sandbox.n.01', 'name': 'sandbox'}, {'id': 10604, 'synset': 'sandglass.n.01', 'name': 'sandglass'}, {'id': 10605, 'synset': 'sand_wedge.n.01', 'name': 'sand_wedge'}, {'id': 10606, 'synset': 'sandwich_board.n.01', 'name': 'sandwich_board'}, {'id': 10607, 'synset': 'sanitary_napkin.n.01', 'name': 'sanitary_napkin'}, {'id': 10608, 'synset': 'cling_film.n.01', 'name': 'cling_film'}, {'id': 10609, 'synset': 'sarcenet.n.01', 'name': 'sarcenet'}, {'id': 10610, 'synset': 'sarcophagus.n.01', 'name': 'sarcophagus'}, {'id': 10611, 'synset': 'sari.n.01', 'name': 'sari'}, {'id': 10612, 'synset': 'sarong.n.01', 'name': 'sarong'}, {'id': 10613, 'synset': 'sash.n.01', 'name': 'sash'}, {'id': 10614, 'synset': 'sash_fastener.n.01', 'name': 'sash_fastener'}, {'id': 10615, 'synset': 'sash_window.n.01', 'name': 'sash_window'}, {'id': 10616, 'synset': 'sateen.n.01', 'name': 'sateen'}, {'id': 10617, 'synset': 'satellite.n.01', 'name': 'satellite'}, {'id': 10618, 'synset': 'satellite_receiver.n.01', 'name': 'satellite_receiver'}, {'id': 10619, 'synset': 'satellite_television.n.01', 'name': 'satellite_television'}, {'id': 10620, 'synset': 'satellite_transmitter.n.01', 'name': 'satellite_transmitter'}, {'id': 10621, 'synset': 'satin.n.01', 'name': 'satin'}, {'id': 10622, 'synset': 'saturday_night_special.n.01', 'name': 'Saturday_night_special'}, {'id': 10623, 'synset': 'saucepot.n.01', 'name': 'saucepot'}, {'id': 10624, 'synset': 'sauna.n.01', 'name': 'sauna'}, {'id': 10625, 'synset': 'savings_bank.n.02', 'name': 'savings_bank'}, {'id': 10626, 'synset': 'saw.n.02', 'name': 'saw'}, {'id': 10627, 'synset': 'sawed-off_shotgun.n.01', 'name': 'sawed-off_shotgun'}, {'id': 10628, 'synset': 'sawmill.n.01', 'name': 'sawmill'}, {'id': 10629, 'synset': 'saw_set.n.01', 'name': 'saw_set'}, {'id': 10630, 'synset': 'saxhorn.n.01', 'name': 'saxhorn'}, {'id': 10631, 'synset': 'scabbard.n.01', 'name': 'scabbard'}, {'id': 10632, 'synset': 'scaffolding.n.01', 'name': 'scaffolding'}, {'id': 10633, 'synset': 'scale.n.08', 'name': 'scale'}, {'id': 10634, 'synset': 'scaler.n.01', 'name': 'scaler'}, {'id': 10635, 'synset': 'scaling_ladder.n.01', 'name': 'scaling_ladder'}, {'id': 10636, 'synset': 'scalpel.n.01', 'name': 'scalpel'}, {'id': 10637, 'synset': 'scanner.n.04', 'name': 'scanner'}, {'id': 10638, 'synset': 'scanner.n.03', 'name': 'scanner'}, {'id': 10639, 'synset': 'scanner.n.02', 'name': 'scanner'}, {'id': 10640, 'synset': 'scantling.n.01', 'name': 'scantling'}, {'id': 10641, 'synset': 'scarf_joint.n.01', 'name': 'scarf_joint'}, {'id': 10642, 'synset': 'scatter_rug.n.01', 'name': 'scatter_rug'}, {'id': 10643, 'synset': 'scauper.n.01', 'name': 'scauper'}, {'id': 10644, 'synset': 'schmidt_telescope.n.01', 'name': 'Schmidt_telescope'}, {'id': 10645, 'synset': 'school.n.02', 'name': 'school'}, {'id': 10646, 'synset': 'schoolbag.n.01', 'name': 'schoolbag'}, {'id': 10647, 'synset': 'school_bell.n.01', 'name': 'school_bell'}, {'id': 10648, 'synset': 'school_ship.n.01', 'name': 'school_ship'}, {'id': 10649, 'synset': 'school_system.n.01', 'name': 'school_system'}, {'id': 10650, 'synset': 'schooner.n.02', 'name': 'schooner'}, {'id': 10651, 'synset': 'schooner.n.01', 'name': 'schooner'}, {'id': 10652, 'synset': 'scientific_instrument.n.01', 'name': 'scientific_instrument'}, {'id': 10653, 'synset': 'scimitar.n.01', 'name': 'scimitar'}, {'id': 10654, 'synset': 'scintillation_counter.n.01', 'name': 'scintillation_counter'}, {'id': 10655, 'synset': 'sclerometer.n.01', 'name': 'sclerometer'}, {'id': 10656, 'synset': 'scoinson_arch.n.01', 'name': 'scoinson_arch'}, {'id': 10657, 'synset': 'sconce.n.04', 'name': 'sconce'}, {'id': 10658, 'synset': 'sconce.n.03', 'name': 'sconce'}, {'id': 10659, 'synset': 'scoop.n.06', 'name': 'scoop'}, {'id': 10660, 'synset': 'scooter.n.02', 'name': 'scooter'}, {'id': 10661, 'synset': 'scouring_pad.n.01', 'name': 'scouring_pad'}, {'id': 10662, 'synset': 'scow.n.02', 'name': 'scow'}, {'id': 10663, 'synset': 'scow.n.01', 'name': 'scow'}, {'id': 10664, 'synset': 'scratcher.n.03', 'name': 'scratcher'}, {'id': 10665, 'synset': 'screen.n.05', 'name': 'screen'}, {'id': 10666, 'synset': 'screen.n.04', 'name': 'screen'}, {'id': 10667, 'synset': 'screen.n.09', 'name': 'screen'}, {'id': 10668, 'synset': 'screen.n.03', 'name': 'screen'}, {'id': 10669, 'synset': 'screen_door.n.01', 'name': 'screen_door'}, {'id': 10670, 'synset': 'screening.n.02', 'name': 'screening'}, {'id': 10671, 'synset': 'screw.n.04', 'name': 'screw'}, {'id': 10672, 'synset': 'screw.n.03', 'name': 'screw'}, {'id': 10673, 'synset': 'screw.n.02', 'name': 'screw'}, {'id': 10674, 'synset': 'screw_eye.n.01', 'name': 'screw_eye'}, {'id': 10675, 'synset': 'screw_key.n.01', 'name': 'screw_key'}, {'id': 10676, 'synset': 'screw_thread.n.01', 'name': 'screw_thread'}, {'id': 10677, 'synset': 'screwtop.n.01', 'name': 'screwtop'}, {'id': 10678, 'synset': 'screw_wrench.n.01', 'name': 'screw_wrench'}, {'id': 10679, 'synset': 'scriber.n.01', 'name': 'scriber'}, {'id': 10680, 'synset': 'scrim.n.01', 'name': 'scrim'}, {'id': 10681, 'synset': 'scrimshaw.n.01', 'name': 'scrimshaw'}, {'id': 10682, 'synset': 'scriptorium.n.01', 'name': 'scriptorium'}, {'id': 10683, 'synset': 'scrubber.n.03', 'name': 'scrubber'}, {'id': 10684, 'synset': 'scrub_plane.n.01', 'name': 'scrub_plane'}, {'id': 10685, 'synset': 'scuffer.n.01', 'name': 'scuffer'}, {'id': 10686, 'synset': 'scuffle.n.02', 'name': 'scuffle'}, {'id': 10687, 'synset': 'scull.n.02', 'name': 'scull'}, {'id': 10688, 'synset': 'scull.n.01', 'name': 'scull'}, {'id': 10689, 'synset': 'scullery.n.01', 'name': 'scullery'}, {'id': 10690, 'synset': 'scuttle.n.01', 'name': 'scuttle'}, {'id': 10691, 'synset': 'scyphus.n.01', 'name': 'scyphus'}, {'id': 10692, 'synset': 'scythe.n.01', 'name': 'scythe'}, {'id': 10693, 'synset': 'seabag.n.01', 'name': 'seabag'}, {'id': 10694, 'synset': 'sea_boat.n.01', 'name': 'sea_boat'}, {'id': 10695, 'synset': 'sea_chest.n.01', 'name': 'sea_chest'}, {'id': 10696, 'synset': 'sealing_wax.n.01', 'name': 'sealing_wax'}, {'id': 10697, 'synset': 'sealskin.n.02', 'name': 'sealskin'}, {'id': 10698, 'synset': 'seam.n.01', 'name': 'seam'}, {'id': 10699, 'synset': 'searchlight.n.01', 'name': 'searchlight'}, {'id': 10700, 'synset': 'searing_iron.n.01', 'name': 'searing_iron'}, {'id': 10701, 'synset': 'seat.n.04', 'name': 'seat'}, {'id': 10702, 'synset': 'seat.n.03', 'name': 'seat'}, {'id': 10703, 'synset': 'seat.n.09', 'name': 'seat'}, {'id': 10704, 'synset': 'seat_belt.n.01', 'name': 'seat_belt'}, {'id': 10705, 'synset': 'secateurs.n.01', 'name': 'secateurs'}, {'id': 10706, 'synset': 'secondary_coil.n.01', 'name': 'secondary_coil'}, {'id': 10707, 'synset': 'second_balcony.n.01', 'name': 'second_balcony'}, {'id': 10708, 'synset': 'second_base.n.01', 'name': 'second_base'}, {'id': 10709, 'synset': 'second_hand.n.02', 'name': 'second_hand'}, {'id': 10710, 'synset': 'secretary.n.04', 'name': 'secretary'}, {'id': 10711, 'synset': 'sectional.n.01', 'name': 'sectional'}, {'id': 10712, 'synset': 'security_blanket.n.02', 'name': 'security_blanket'}, {'id': 10713, 'synset': 'security_system.n.02', 'name': 'security_system'}, {'id': 10714, 'synset': 'security_system.n.01', 'name': 'security_system'}, {'id': 10715, 'synset': 'sedan.n.01', 'name': 'sedan'}, {'id': 10716, 'synset': 'sedan.n.02', 'name': 'sedan'}, {'id': 10717, 'synset': 'seeder.n.02', 'name': 'seeder'}, {'id': 10718, 'synset': 'seeker.n.02', 'name': 'seeker'}, {'id': 10719, 'synset': 'seersucker.n.01', 'name': 'seersucker'}, {'id': 10720, 'synset': 'segmental_arch.n.01', 'name': 'segmental_arch'}, {'id': 10721, 'synset': 'segway.n.01', 'name': 'Segway'}, {'id': 10722, 'synset': 'seidel.n.01', 'name': 'seidel'}, {'id': 10723, 'synset': 'seine.n.02', 'name': 'seine'}, {'id': 10724, 'synset': 'seismograph.n.01', 'name': 'seismograph'}, {'id': 10725, 'synset': 'selector.n.02', 'name': 'selector'}, {'id': 10726, 'synset': 'selenium_cell.n.01', 'name': 'selenium_cell'}, {'id': 10727, 'synset': 'self-propelled_vehicle.n.01', 'name': 'self-propelled_vehicle'}, {'id': 10728, 'synset': 'self-registering_thermometer.n.01', 'name': 'self-registering_thermometer'}, {'id': 10729, 'synset': 'self-starter.n.02', 'name': 'self-starter'}, {'id': 10730, 'synset': 'selsyn.n.01', 'name': 'selsyn'}, {'id': 10731, 'synset': 'selvage.n.02', 'name': 'selvage'}, {'id': 10732, 'synset': 'semaphore.n.01', 'name': 'semaphore'}, {'id': 10733, 'synset': 'semiautomatic_firearm.n.01', 'name': 'semiautomatic_firearm'}, {'id': 10734, 'synset': 'semiautomatic_pistol.n.01', 'name': 'semiautomatic_pistol'}, {'id': 10735, 'synset': 'semiconductor_device.n.01', 'name': 'semiconductor_device'}, {'id': 10736, 'synset': 'semi-detached_house.n.01', 'name': 'semi-detached_house'}, {'id': 10737, 'synset': 'semigloss.n.01', 'name': 'semigloss'}, {'id': 10738, 'synset': 'semitrailer.n.01', 'name': 'semitrailer'}, {'id': 10739, 'synset': 'sennit.n.01', 'name': 'sennit'}, {'id': 10740, 'synset': 'sensitometer.n.01', 'name': 'sensitometer'}, {'id': 10741, 'synset': 'sentry_box.n.01', 'name': 'sentry_box'}, {'id': 10742, 'synset': 'separate.n.02', 'name': 'separate'}, {'id': 10743, 'synset': 'septic_tank.n.01', 'name': 'septic_tank'}, {'id': 10744, 'synset': 'sequence.n.03', 'name': 'sequence'}, {'id': 10745, 'synset': 'sequencer.n.01', 'name': 'sequencer'}, {'id': 10746, 'synset': 'serape.n.01', 'name': 'serape'}, {'id': 10747, 'synset': 'serge.n.01', 'name': 'serge'}, {'id': 10748, 'synset': 'serger.n.01', 'name': 'serger'}, {'id': 10749, 'synset': 'serial_port.n.01', 'name': 'serial_port'}, {'id': 10750, 'synset': 'serpent.n.03', 'name': 'serpent'}, {'id': 10751, 'synset': 'serration.n.03', 'name': 'serration'}, {'id': 10752, 'synset': 'server.n.04', 'name': 'server'}, {'id': 10753, 'synset': 'server.n.03', 'name': 'server'}, {'id': 10754, 'synset': 'service_club.n.02', 'name': 'service_club'}, {'id': 10755, 'synset': 'serving_cart.n.01', 'name': 'serving_cart'}, {'id': 10756, 'synset': 'serving_dish.n.01', 'name': 'serving_dish'}, {'id': 10757, 'synset': 'servo.n.01', 'name': 'servo'}, {'id': 10758, 'synset': 'set.n.13', 'name': 'set'}, {'id': 10759, 'synset': 'set_gun.n.01', 'name': 'set_gun'}, {'id': 10760, 'synset': 'setscrew.n.02', 'name': 'setscrew'}, {'id': 10761, 'synset': 'setscrew.n.01', 'name': 'setscrew'}, {'id': 10762, 'synset': 'set_square.n.01', 'name': 'set_square'}, {'id': 10763, 'synset': 'settee.n.02', 'name': 'settee'}, {'id': 10764, 'synset': 'settle.n.01', 'name': 'settle'}, {'id': 10765, 'synset': 'settlement_house.n.01', 'name': 'settlement_house'}, {'id': 10766, 'synset': 'seventy-eight.n.02', 'name': 'seventy-eight'}, {'id': 10767, 'synset': 'seven_wonders_of_the_ancient_world.n.01', 'name': 'Seven_Wonders_of_the_Ancient_World'}, {'id': 10768, 'synset': 'sewage_disposal_plant.n.01', 'name': 'sewage_disposal_plant'}, {'id': 10769, 'synset': 'sewer.n.01', 'name': 'sewer'}, {'id': 10770, 'synset': 'sewing_basket.n.01', 'name': 'sewing_basket'}, {'id': 10771, 'synset': 'sewing_kit.n.01', 'name': 'sewing_kit'}, {'id': 10772, 'synset': 'sewing_needle.n.01', 'name': 'sewing_needle'}, {'id': 10773, 'synset': 'sewing_room.n.01', 'name': 'sewing_room'}, {'id': 10774, 'synset': 'sextant.n.02', 'name': 'sextant'}, {'id': 10775, 'synset': 'sgraffito.n.01', 'name': 'sgraffito'}, {'id': 10776, 'synset': 'shackle.n.01', 'name': 'shackle'}, {'id': 10777, 'synset': 'shackle.n.02', 'name': 'shackle'}, {'id': 10778, 'synset': 'shade.n.03', 'name': 'shade'}, {'id': 10779, 'synset': 'shadow_box.n.01', 'name': 'shadow_box'}, {'id': 10780, 'synset': 'shaft.n.03', 'name': 'shaft'}, {'id': 10781, 'synset': 'shag_rug.n.01', 'name': 'shag_rug'}, {'id': 10782, 'synset': 'shank.n.04', 'name': 'shank'}, {'id': 10783, 'synset': 'shank.n.03', 'name': 'shank'}, {'id': 10784, 'synset': 'shantung.n.01', 'name': 'shantung'}, {'id': 10785, 'synset': 'shaper.n.02', 'name': 'shaper'}, {'id': 10786, 'synset': 'shaping_tool.n.01', 'name': 'shaping_tool'}, {'id': 10787, 'synset': 'sharkskin.n.01', 'name': 'sharkskin'}, {'id': 10788, 'synset': 'shaving_brush.n.01', 'name': 'shaving_brush'}, {'id': 10789, 'synset': 'shaving_foam.n.01', 'name': 'shaving_foam'}, {'id': 10790, 'synset': 'shawm.n.01', 'name': 'shawm'}, {'id': 10791, 'synset': 'sheath.n.01', 'name': 'sheath'}, {'id': 10792, 'synset': 'sheathing.n.01', 'name': 'sheathing'}, {'id': 10793, 'synset': 'shed.n.01', 'name': 'shed'}, {'id': 10794, 'synset': 'sheep_bell.n.01', 'name': 'sheep_bell'}, {'id': 10795, 'synset': 'sheepshank.n.01', 'name': 'sheepshank'}, {'id': 10796, 'synset': 'sheepskin_coat.n.01', 'name': 'sheepskin_coat'}, {'id': 10797, 'synset': 'sheepwalk.n.01', 'name': 'sheepwalk'}, {'id': 10798, 'synset': 'sheet.n.03', 'name': 'sheet'}, {'id': 10799, 'synset': 'sheet_bend.n.01', 'name': 'sheet_bend'}, {'id': 10800, 'synset': 'sheeting.n.01', 'name': 'sheeting'}, {'id': 10801, 'synset': 'sheet_pile.n.01', 'name': 'sheet_pile'}, {'id': 10802, 'synset': 'sheetrock.n.01', 'name': 'Sheetrock'}, {'id': 10803, 'synset': 'shelf.n.01', 'name': 'shelf'}, {'id': 10804, 'synset': 'shelf_bracket.n.01', 'name': 'shelf_bracket'}, {'id': 10805, 'synset': 'shell.n.01', 'name': 'shell'}, {'id': 10806, 'synset': 'shell.n.08', 'name': 'shell'}, {'id': 10807, 'synset': 'shell.n.07', 'name': 'shell'}, {'id': 10808, 'synset': 'shellac.n.02', 'name': 'shellac'}, {'id': 10809, 'synset': 'shelter.n.01', 'name': 'shelter'}, {'id': 10810, 'synset': 'shelter.n.02', 'name': 'shelter'}, {'id': 10811, 'synset': 'shelter.n.05', 'name': 'shelter'}, {'id': 10812, 'synset': 'sheltered_workshop.n.01', 'name': 'sheltered_workshop'}, {'id': 10813, 'synset': 'sheraton.n.01', 'name': 'Sheraton'}, {'id': 10814, 'synset': 'shield.n.01', 'name': 'shield'}, {'id': 10815, 'synset': 'shielding.n.03', 'name': 'shielding'}, {'id': 10816, 'synset': 'shift_key.n.01', 'name': 'shift_key'}, {'id': 10817, 'synset': 'shillelagh.n.01', 'name': 'shillelagh'}, {'id': 10818, 'synset': 'shim.n.01', 'name': 'shim'}, {'id': 10819, 'synset': 'shingle.n.03', 'name': 'shingle'}, {'id': 10820, 'synset': 'shin_guard.n.01', 'name': 'shin_guard'}, {'id': 10821, 'synset': 'ship.n.01', 'name': 'ship'}, {'id': 10822, 'synset': 'shipboard_system.n.01', 'name': 'shipboard_system'}, {'id': 10823, 'synset': 'shipping.n.02', 'name': 'shipping'}, {'id': 10824, 'synset': 'shipping_room.n.01', 'name': 'shipping_room'}, {'id': 10825, 'synset': 'ship-towed_long-range_acoustic_detection_system.n.01', 'name': 'ship-towed_long-range_acoustic_detection_system'}, {'id': 10826, 'synset': 'shipwreck.n.01', 'name': 'shipwreck'}, {'id': 10827, 'synset': 'shirt_button.n.01', 'name': 'shirt_button'}, {'id': 10828, 'synset': 'shirtdress.n.01', 'name': 'shirtdress'}, {'id': 10829, 'synset': 'shirtfront.n.01', 'name': 'shirtfront'}, {'id': 10830, 'synset': 'shirting.n.01', 'name': 'shirting'}, {'id': 10831, 'synset': 'shirtsleeve.n.01', 'name': 'shirtsleeve'}, {'id': 10832, 'synset': 'shirttail.n.02', 'name': 'shirttail'}, {'id': 10833, 'synset': 'shirtwaist.n.01', 'name': 'shirtwaist'}, {'id': 10834, 'synset': 'shiv.n.01', 'name': 'shiv'}, {'id': 10835, 'synset': 'shock_absorber.n.01', 'name': 'shock_absorber'}, {'id': 10836, 'synset': 'shoe.n.02', 'name': 'shoe'}, {'id': 10837, 'synset': 'shoebox.n.02', 'name': 'shoebox'}, {'id': 10838, 'synset': 'shoehorn.n.01', 'name': 'shoehorn'}, {'id': 10839, 'synset': 'shoe_shop.n.01', 'name': 'shoe_shop'}, {'id': 10840, 'synset': 'shoetree.n.01', 'name': 'shoetree'}, {'id': 10841, 'synset': 'shofar.n.01', 'name': 'shofar'}, {'id': 10842, 'synset': 'shoji.n.01', 'name': 'shoji'}, {'id': 10843, 'synset': 'shooting_brake.n.01', 'name': 'shooting_brake'}, {'id': 10844, 'synset': 'shooting_lodge.n.01', 'name': 'shooting_lodge'}, {'id': 10845, 'synset': 'shooting_stick.n.01', 'name': 'shooting_stick'}, {'id': 10846, 'synset': 'shop.n.01', 'name': 'shop'}, {'id': 10847, 'synset': 'shop_bell.n.01', 'name': 'shop_bell'}, {'id': 10848, 'synset': 'shopping_basket.n.01', 'name': 'shopping_basket'}, {'id': 10849, 'synset': 'short_circuit.n.01', 'name': 'short_circuit'}, {'id': 10850, 'synset': 'short_iron.n.01', 'name': 'short_iron'}, {'id': 10851, 'synset': 'short_sleeve.n.01', 'name': 'short_sleeve'}, {'id': 10852, 'synset': 'shortwave_diathermy_machine.n.01', 'name': 'shortwave_diathermy_machine'}, {'id': 10853, 'synset': 'shot.n.12', 'name': 'shot'}, {'id': 10854, 'synset': 'shotgun.n.01', 'name': 'shotgun'}, {'id': 10855, 'synset': 'shotgun_shell.n.01', 'name': 'shotgun_shell'}, {'id': 10856, 'synset': 'shot_tower.n.01', 'name': 'shot_tower'}, {'id': 10857, 'synset': 'shoulder.n.04', 'name': 'shoulder'}, {'id': 10858, 'synset': 'shouldered_arch.n.01', 'name': 'shouldered_arch'}, {'id': 10859, 'synset': 'shoulder_holster.n.01', 'name': 'shoulder_holster'}, {'id': 10860, 'synset': 'shoulder_pad.n.01', 'name': 'shoulder_pad'}, {'id': 10861, 'synset': 'shoulder_patch.n.01', 'name': 'shoulder_patch'}, {'id': 10862, 'synset': 'shovel.n.03', 'name': 'shovel'}, {'id': 10863, 'synset': 'shovel_hat.n.01', 'name': 'shovel_hat'}, {'id': 10864, 'synset': 'showboat.n.01', 'name': 'showboat'}, {'id': 10865, 'synset': 'shower_room.n.01', 'name': 'shower_room'}, {'id': 10866, 'synset': 'shower_stall.n.01', 'name': 'shower_stall'}, {'id': 10867, 'synset': 'showroom.n.01', 'name': 'showroom'}, {'id': 10868, 'synset': 'shrapnel.n.01', 'name': 'shrapnel'}, {'id': 10869, 'synset': 'shrimper.n.01', 'name': 'shrimper'}, {'id': 10870, 'synset': 'shrine.n.01', 'name': 'shrine'}, {'id': 10871, 'synset': 'shrink-wrap.n.01', 'name': 'shrink-wrap'}, {'id': 10872, 'synset': 'shunt.n.03', 'name': 'shunt'}, {'id': 10873, 'synset': 'shunt.n.02', 'name': 'shunt'}, {'id': 10874, 'synset': 'shunter.n.01', 'name': 'shunter'}, {'id': 10875, 'synset': 'shutter.n.02', 'name': 'shutter'}, {'id': 10876, 'synset': 'shutter.n.01', 'name': 'shutter'}, {'id': 10877, 'synset': 'shuttle.n.03', 'name': 'shuttle'}, {'id': 10878, 'synset': 'shuttle.n.02', 'name': 'shuttle'}, {'id': 10879, 'synset': 'shuttle_bus.n.01', 'name': 'shuttle_bus'}, {'id': 10880, 'synset': 'shuttlecock.n.01', 'name': 'shuttlecock'}, {'id': 10881, 'synset': 'shuttle_helicopter.n.01', 'name': 'shuttle_helicopter'}, {'id': 10882, 'synset': 'sibley_tent.n.01', 'name': 'Sibley_tent'}, {'id': 10883, 'synset': 'sickbay.n.01', 'name': 'sickbay'}, {'id': 10884, 'synset': 'sickbed.n.01', 'name': 'sickbed'}, {'id': 10885, 'synset': 'sickle.n.01', 'name': 'sickle'}, {'id': 10886, 'synset': 'sickroom.n.01', 'name': 'sickroom'}, {'id': 10887, 'synset': 'sideboard.n.02', 'name': 'sideboard'}, {'id': 10888, 'synset': 'sidecar.n.02', 'name': 'sidecar'}, {'id': 10889, 'synset': 'side_chapel.n.01', 'name': 'side_chapel'}, {'id': 10890, 'synset': 'sidelight.n.01', 'name': 'sidelight'}, {'id': 10891, 'synset': 'sidesaddle.n.01', 'name': 'sidesaddle'}, {'id': 10892, 'synset': 'sidewalk.n.01', 'name': 'sidewalk'}, {'id': 10893, 'synset': 'sidewall.n.02', 'name': 'sidewall'}, {'id': 10894, 'synset': 'side-wheeler.n.01', 'name': 'side-wheeler'}, {'id': 10895, 'synset': 'sidewinder.n.02', 'name': 'sidewinder'}, {'id': 10896, 'synset': 'sieve.n.01', 'name': 'sieve'}, {'id': 10897, 'synset': 'sifter.n.01', 'name': 'sifter'}, {'id': 10898, 'synset': 'sights.n.01', 'name': 'sights'}, {'id': 10899, 'synset': 'sigmoidoscope.n.01', 'name': 'sigmoidoscope'}, {'id': 10900, 'synset': 'signal_box.n.01', 'name': 'signal_box'}, {'id': 10901, 'synset': 'signaling_device.n.01', 'name': 'signaling_device'}, {'id': 10902, 'synset': 'silencer.n.02', 'name': 'silencer'}, {'id': 10903, 'synset': 'silent_butler.n.01', 'name': 'silent_butler'}, {'id': 10904, 'synset': 'silex.n.02', 'name': 'Silex'}, {'id': 10905, 'synset': 'silk.n.01', 'name': 'silk'}, {'id': 10906, 'synset': 'silks.n.01', 'name': 'silks'}, {'id': 10907, 'synset': 'silver_plate.n.02', 'name': 'silver_plate'}, {'id': 10908, 'synset': 'silverpoint.n.01', 'name': 'silverpoint'}, {'id': 10909, 'synset': 'simple_pendulum.n.01', 'name': 'simple_pendulum'}, {'id': 10910, 'synset': 'simulator.n.01', 'name': 'simulator'}, {'id': 10911, 'synset': 'single_bed.n.01', 'name': 'single_bed'}, {'id': 10912, 'synset': 'single-breasted_jacket.n.01', 'name': 'single-breasted_jacket'}, {'id': 10913, 'synset': 'single-breasted_suit.n.01', 'name': 'single-breasted_suit'}, {'id': 10914, 'synset': 'single_prop.n.01', 'name': 'single_prop'}, {'id': 10915, 'synset': 'single-reed_instrument.n.01', 'name': 'single-reed_instrument'}, {'id': 10916, 'synset': 'single-rotor_helicopter.n.01', 'name': 'single-rotor_helicopter'}, {'id': 10917, 'synset': 'singlestick.n.01', 'name': 'singlestick'}, {'id': 10918, 'synset': 'singlet.n.01', 'name': 'singlet'}, {'id': 10919, 'synset': 'siren.n.04', 'name': 'siren'}, {'id': 10920, 'synset': 'sister_ship.n.01', 'name': 'sister_ship'}, {'id': 10921, 'synset': 'sitar.n.01', 'name': 'sitar'}, {'id': 10922, 'synset': 'sitz_bath.n.01', 'name': 'sitz_bath'}, {'id': 10923, 'synset': 'six-pack.n.01', 'name': 'six-pack'}, {'id': 10924, 'synset': 'skate.n.01', 'name': 'skate'}, {'id': 10925, 'synset': 'skeg.n.01', 'name': 'skeg'}, {'id': 10926, 'synset': 'skein.n.01', 'name': 'skein'}, {'id': 10927, 'synset': 'skeleton.n.04', 'name': 'skeleton'}, {'id': 10928, 'synset': 'skeleton_key.n.01', 'name': 'skeleton_key'}, {'id': 10929, 'synset': 'skep.n.02', 'name': 'skep'}, {'id': 10930, 'synset': 'skep.n.01', 'name': 'skep'}, {'id': 10931, 'synset': 'sketch.n.01', 'name': 'sketch'}, {'id': 10932, 'synset': 'sketcher.n.02', 'name': 'sketcher'}, {'id': 10933, 'synset': 'skew_arch.n.01', 'name': 'skew_arch'}, {'id': 10934, 'synset': 'ski_binding.n.01', 'name': 'ski_binding'}, {'id': 10935, 'synset': 'skibob.n.01', 'name': 'skibob'}, {'id': 10936, 'synset': 'ski_cap.n.01', 'name': 'ski_cap'}, {'id': 10937, 'synset': 'skidder.n.03', 'name': 'skidder'}, {'id': 10938, 'synset': 'skid_lid.n.01', 'name': 'skid_lid'}, {'id': 10939, 'synset': 'skiff.n.01', 'name': 'skiff'}, {'id': 10940, 'synset': 'ski_jump.n.01', 'name': 'ski_jump'}, {'id': 10941, 'synset': 'ski_lodge.n.01', 'name': 'ski_lodge'}, {'id': 10942, 'synset': 'ski_mask.n.01', 'name': 'ski_mask'}, {'id': 10943, 'synset': 'skimmer.n.02', 'name': 'skimmer'}, {'id': 10944, 'synset': 'ski-plane.n.01', 'name': 'ski-plane'}, {'id': 10945, 'synset': 'ski_rack.n.01', 'name': 'ski_rack'}, {'id': 10946, 'synset': 'skirt.n.01', 'name': 'skirt'}, {'id': 10947, 'synset': 'ski_tow.n.01', 'name': 'ski_tow'}, {'id': 10948, 'synset': 'skivvies.n.01', 'name': 'Skivvies'}, {'id': 10949, 'synset': 'skybox.n.01', 'name': 'skybox'}, {'id': 10950, 'synset': 'skyhook.n.02', 'name': 'skyhook'}, {'id': 10951, 'synset': 'skylight.n.01', 'name': 'skylight'}, {'id': 10952, 'synset': 'skysail.n.01', 'name': 'skysail'}, {'id': 10953, 'synset': 'skyscraper.n.01', 'name': 'skyscraper'}, {'id': 10954, 'synset': 'skywalk.n.01', 'name': 'skywalk'}, {'id': 10955, 'synset': 'slacks.n.01', 'name': 'slacks'}, {'id': 10956, 'synset': 'slack_suit.n.01', 'name': 'slack_suit'}, {'id': 10957, 'synset': 'slasher.n.02', 'name': 'slasher'}, {'id': 10958, 'synset': 'slash_pocket.n.01', 'name': 'slash_pocket'}, {'id': 10959, 'synset': 'slat.n.01', 'name': 'slat'}, {'id': 10960, 'synset': 'slate.n.01', 'name': 'slate'}, {'id': 10961, 'synset': 'slate_pencil.n.01', 'name': 'slate_pencil'}, {'id': 10962, 'synset': 'slate_roof.n.01', 'name': 'slate_roof'}, {'id': 10963, 'synset': 'sleeper.n.07', 'name': 'sleeper'}, {'id': 10964, 'synset': 'sleeper.n.06', 'name': 'sleeper'}, {'id': 10965, 'synset': 'sleeping_car.n.01', 'name': 'sleeping_car'}, {'id': 10966, 'synset': 'sleeve.n.01', 'name': 'sleeve'}, {'id': 10967, 'synset': 'sleeve.n.02', 'name': 'sleeve'}, {'id': 10968, 'synset': 'sleigh_bed.n.01', 'name': 'sleigh_bed'}, {'id': 10969, 'synset': 'sleigh_bell.n.01', 'name': 'sleigh_bell'}, {'id': 10970, 'synset': 'slice_bar.n.01', 'name': 'slice_bar'}, {'id': 10971, 'synset': 'slicer.n.03', 'name': 'slicer'}, {'id': 10972, 'synset': 'slicer.n.02', 'name': 'slicer'}, {'id': 10973, 'synset': 'slide.n.04', 'name': 'slide'}, {'id': 10974, 'synset': 'slide_fastener.n.01', 'name': 'slide_fastener'}, {'id': 10975, 'synset': 'slide_projector.n.01', 'name': 'slide_projector'}, {'id': 10976, 'synset': 'slide_rule.n.01', 'name': 'slide_rule'}, {'id': 10977, 'synset': 'slide_valve.n.01', 'name': 'slide_valve'}, {'id': 10978, 'synset': 'sliding_door.n.01', 'name': 'sliding_door'}, {'id': 10979, 'synset': 'sliding_seat.n.01', 'name': 'sliding_seat'}, {'id': 10980, 'synset': 'sliding_window.n.01', 'name': 'sliding_window'}, {'id': 10981, 'synset': 'sling.n.04', 'name': 'sling'}, {'id': 10982, 'synset': 'slingback.n.01', 'name': 'slingback'}, {'id': 10983, 'synset': 'slinger_ring.n.01', 'name': 'slinger_ring'}, {'id': 10984, 'synset': 'slip_clutch.n.01', 'name': 'slip_clutch'}, {'id': 10985, 'synset': 'slipcover.n.01', 'name': 'slipcover'}, {'id': 10986, 'synset': 'slip-joint_pliers.n.01', 'name': 'slip-joint_pliers'}, {'id': 10987, 'synset': 'slipknot.n.01', 'name': 'slipknot'}, {'id': 10988, 'synset': 'slip-on.n.01', 'name': 'slip-on'}, {'id': 10989, 'synset': 'slip_ring.n.01', 'name': 'slip_ring'}, {'id': 10990, 'synset': 'slit_lamp.n.01', 'name': 'slit_lamp'}, {'id': 10991, 'synset': 'slit_trench.n.01', 'name': 'slit_trench'}, {'id': 10992, 'synset': 'sloop.n.01', 'name': 'sloop'}, {'id': 10993, 'synset': 'sloop_of_war.n.01', 'name': 'sloop_of_war'}, {'id': 10994, 'synset': 'slop_basin.n.01', 'name': 'slop_basin'}, {'id': 10995, 'synset': 'slop_pail.n.01', 'name': 'slop_pail'}, {'id': 10996, 'synset': 'slops.n.02', 'name': 'slops'}, {'id': 10997, 'synset': 'slopshop.n.01', 'name': 'slopshop'}, {'id': 10998, 'synset': 'slot.n.07', 'name': 'slot'}, {'id': 10999, 'synset': 'slot_machine.n.01', 'name': 'slot_machine'}, {'id': 11000, 'synset': 'sluice.n.01', 'name': 'sluice'}, {'id': 11001, 'synset': 'smack.n.03', 'name': 'smack'}, {'id': 11002, 'synset': 'small_boat.n.01', 'name': 'small_boat'}, {'id': 11003, 'synset': 'small_computer_system_interface.n.01', 'name': 'small_computer_system_interface'}, {'id': 11004, 'synset': 'small_ship.n.01', 'name': 'small_ship'}, {'id': 11005, 'synset': 'small_stores.n.01', 'name': 'small_stores'}, {'id': 11006, 'synset': 'smart_bomb.n.01', 'name': 'smart_bomb'}, {'id': 11007, 'synset': 'smelling_bottle.n.01', 'name': 'smelling_bottle'}, {'id': 11008, 'synset': 'smocking.n.01', 'name': 'smocking'}, {'id': 11009, 'synset': 'smoke_bomb.n.01', 'name': 'smoke_bomb'}, {'id': 11010, 'synset': 'smokehouse.n.01', 'name': 'smokehouse'}, {'id': 11011, 'synset': 'smoker.n.03', 'name': 'smoker'}, {'id': 11012, 'synset': 'smoke_screen.n.01', 'name': 'smoke_screen'}, {'id': 11013, 'synset': 'smoking_room.n.01', 'name': 'smoking_room'}, {'id': 11014, 'synset': 'smoothbore.n.01', 'name': 'smoothbore'}, {'id': 11015, 'synset': 'smooth_plane.n.01', 'name': 'smooth_plane'}, {'id': 11016, 'synset': 'snack_bar.n.01', 'name': 'snack_bar'}, {'id': 11017, 'synset': 'snaffle.n.01', 'name': 'snaffle'}, {'id': 11018, 'synset': 'snap.n.10', 'name': 'snap'}, {'id': 11019, 'synset': 'snap_brim.n.01', 'name': 'snap_brim'}, {'id': 11020, 'synset': 'snap-brim_hat.n.01', 'name': 'snap-brim_hat'}, {'id': 11021, 'synset': 'snare.n.05', 'name': 'snare'}, {'id': 11022, 'synset': 'snare_drum.n.01', 'name': 'snare_drum'}, {'id': 11023, 'synset': 'snatch_block.n.01', 'name': 'snatch_block'}, {'id': 11024, 'synset': 'snifter.n.01', 'name': 'snifter'}, {'id': 11025, 'synset': 'sniper_rifle.n.01', 'name': 'sniper_rifle'}, {'id': 11026, 'synset': 'snips.n.01', 'name': 'snips'}, {'id': 11027, 'synset': 'sno-cat.n.01', 'name': 'Sno-cat'}, {'id': 11028, 'synset': 'snood.n.01', 'name': 'snood'}, {'id': 11029, 'synset': 'snorkel.n.02', 'name': 'snorkel'}, {'id': 11030, 'synset': 'snorkel.n.01', 'name': 'snorkel'}, {'id': 11031, 'synset': 'snowbank.n.01', 'name': 'snowbank'}, {'id': 11032, 'synset': 'snowplow.n.01', 'name': 'snowplow'}, {'id': 11033, 'synset': 'snowshoe.n.01', 'name': 'snowshoe'}, {'id': 11034, 'synset': 'snowsuit.n.01', 'name': 'snowsuit'}, {'id': 11035, 'synset': 'snow_thrower.n.01', 'name': 'snow_thrower'}, {'id': 11036, 'synset': 'snuffbox.n.01', 'name': 'snuffbox'}, {'id': 11037, 'synset': 'snuffer.n.01', 'name': 'snuffer'}, {'id': 11038, 'synset': 'snuffers.n.01', 'name': 'snuffers'}, {'id': 11039, 'synset': 'soapbox.n.01', 'name': 'soapbox'}, {'id': 11040, 'synset': 'soap_dish.n.01', 'name': 'soap_dish'}, {'id': 11041, 'synset': 'soap_dispenser.n.01', 'name': 'soap_dispenser'}, {'id': 11042, 'synset': 'soap_pad.n.01', 'name': 'soap_pad'}, {'id': 11043, 'synset': 'socket.n.02', 'name': 'socket'}, {'id': 11044, 'synset': 'socket_wrench.n.01', 'name': 'socket_wrench'}, {'id': 11045, 'synset': 'socle.n.01', 'name': 'socle'}, {'id': 11046, 'synset': 'soda_can.n.01', 'name': 'soda_can'}, {'id': 11047, 'synset': 'soda_fountain.n.02', 'name': 'soda_fountain'}, {'id': 11048, 'synset': 'soda_fountain.n.01', 'name': 'soda_fountain'}, {'id': 11049, 'synset': 'sod_house.n.01', 'name': 'sod_house'}, {'id': 11050, 'synset': 'sodium-vapor_lamp.n.01', 'name': 'sodium-vapor_lamp'}, {'id': 11051, 'synset': 'soffit.n.01', 'name': 'soffit'}, {'id': 11052, 'synset': 'soft_pedal.n.01', 'name': 'soft_pedal'}, {'id': 11053, 'synset': 'soil_pipe.n.01', 'name': 'soil_pipe'}, {'id': 11054, 'synset': 'solar_cell.n.01', 'name': 'solar_cell'}, {'id': 11055, 'synset': 'solar_dish.n.01', 'name': 'solar_dish'}, {'id': 11056, 'synset': 'solar_heater.n.01', 'name': 'solar_heater'}, {'id': 11057, 'synset': 'solar_house.n.01', 'name': 'solar_house'}, {'id': 11058, 'synset': 'solar_telescope.n.01', 'name': 'solar_telescope'}, {'id': 11059, 'synset': 'solar_thermal_system.n.01', 'name': 'solar_thermal_system'}, {'id': 11060, 'synset': 'soldering_iron.n.01', 'name': 'soldering_iron'}, {'id': 11061, 'synset': 'solenoid.n.01', 'name': 'solenoid'}, {'id': 11062, 'synset': 'solleret.n.01', 'name': 'solleret'}, {'id': 11063, 'synset': 'sonic_depth_finder.n.01', 'name': 'sonic_depth_finder'}, {'id': 11064, 'synset': 'sonogram.n.01', 'name': 'sonogram'}, {'id': 11065, 'synset': 'sonograph.n.01', 'name': 'sonograph'}, {'id': 11066, 'synset': 'sorter.n.02', 'name': 'sorter'}, {'id': 11067, 'synset': 'souk.n.01', 'name': 'souk'}, {'id': 11068, 'synset': 'sound_bow.n.01', 'name': 'sound_bow'}, {'id': 11069, 'synset': 'soundbox.n.01', 'name': 'soundbox'}, {'id': 11070, 'synset': 'sound_camera.n.01', 'name': 'sound_camera'}, {'id': 11071, 'synset': 'sounder.n.01', 'name': 'sounder'}, {'id': 11072, 'synset': 'sound_film.n.01', 'name': 'sound_film'}, {'id': 11073, 'synset': 'sounding_board.n.02', 'name': 'sounding_board'}, {'id': 11074, 'synset': 'sounding_rocket.n.01', 'name': 'sounding_rocket'}, {'id': 11075, 'synset': 'sound_recording.n.01', 'name': 'sound_recording'}, {'id': 11076, 'synset': 'sound_spectrograph.n.01', 'name': 'sound_spectrograph'}, {'id': 11077, 'synset': 'soup_ladle.n.01', 'name': 'soup_ladle'}, {'id': 11078, 'synset': 'source_of_illumination.n.01', 'name': 'source_of_illumination'}, {'id': 11079, 'synset': 'sourdine.n.02', 'name': 'sourdine'}, {'id': 11080, 'synset': 'soutache.n.01', 'name': 'soutache'}, {'id': 11081, 'synset': 'soutane.n.01', 'name': 'soutane'}, {'id': 11082, 'synset': "sou'wester.n.02", 'name': "sou'wester"}, {'id': 11083, 'synset': 'soybean_future.n.01', 'name': 'soybean_future'}, {'id': 11084, 'synset': 'space_bar.n.01', 'name': 'space_bar'}, {'id': 11085, 'synset': 'space_capsule.n.01', 'name': 'space_capsule'}, {'id': 11086, 'synset': 'spacecraft.n.01', 'name': 'spacecraft'}, {'id': 11087, 'synset': 'space_heater.n.01', 'name': 'space_heater'}, {'id': 11088, 'synset': 'space_helmet.n.01', 'name': 'space_helmet'}, {'id': 11089, 'synset': 'space_rocket.n.01', 'name': 'space_rocket'}, {'id': 11090, 'synset': 'space_station.n.01', 'name': 'space_station'}, {'id': 11091, 'synset': 'spacesuit.n.01', 'name': 'spacesuit'}, {'id': 11092, 'synset': 'spade.n.02', 'name': 'spade'}, {'id': 11093, 'synset': 'spade_bit.n.01', 'name': 'spade_bit'}, {'id': 11094, 'synset': 'spaghetti_junction.n.01', 'name': 'spaghetti_junction'}, {'id': 11095, 'synset': 'spandau.n.01', 'name': 'Spandau'}, {'id': 11096, 'synset': 'spandex.n.01', 'name': 'spandex'}, {'id': 11097, 'synset': 'spandrel.n.01', 'name': 'spandrel'}, {'id': 11098, 'synset': 'spanker.n.02', 'name': 'spanker'}, {'id': 11099, 'synset': 'spar.n.02', 'name': 'spar'}, {'id': 11100, 'synset': 'sparge_pipe.n.01', 'name': 'sparge_pipe'}, {'id': 11101, 'synset': 'spark_arrester.n.02', 'name': 'spark_arrester'}, {'id': 11102, 'synset': 'spark_arrester.n.01', 'name': 'spark_arrester'}, {'id': 11103, 'synset': 'spark_chamber.n.01', 'name': 'spark_chamber'}, {'id': 11104, 'synset': 'spark_coil.n.01', 'name': 'spark_coil'}, {'id': 11105, 'synset': 'spark_gap.n.01', 'name': 'spark_gap'}, {'id': 11106, 'synset': 'spark_lever.n.01', 'name': 'spark_lever'}, {'id': 11107, 'synset': 'spark_plug.n.01', 'name': 'spark_plug'}, {'id': 11108, 'synset': 'sparkplug_wrench.n.01', 'name': 'sparkplug_wrench'}, {'id': 11109, 'synset': 'spark_transmitter.n.01', 'name': 'spark_transmitter'}, {'id': 11110, 'synset': 'spat.n.02', 'name': 'spat'}, {'id': 11111, 'synset': 'spatula.n.01', 'name': 'spatula'}, {'id': 11112, 'synset': 'speakerphone.n.01', 'name': 'speakerphone'}, {'id': 11113, 'synset': 'speaking_trumpet.n.01', 'name': 'speaking_trumpet'}, {'id': 11114, 'synset': 'spear.n.02', 'name': 'spear'}, {'id': 11115, 'synset': 'specialty_store.n.01', 'name': 'specialty_store'}, {'id': 11116, 'synset': 'specimen_bottle.n.01', 'name': 'specimen_bottle'}, {'id': 11117, 'synset': 'spectacle.n.02', 'name': 'spectacle'}, {'id': 11118, 'synset': 'spectator_pump.n.01', 'name': 'spectator_pump'}, {'id': 11119, 'synset': 'spectrograph.n.01', 'name': 'spectrograph'}, {'id': 11120, 'synset': 'spectrophotometer.n.01', 'name': 'spectrophotometer'}, {'id': 11121, 'synset': 'spectroscope.n.01', 'name': 'spectroscope'}, {'id': 11122, 'synset': 'speculum.n.02', 'name': 'speculum'}, {'id': 11123, 'synset': 'speedboat.n.01', 'name': 'speedboat'}, {'id': 11124, 'synset': 'speed_bump.n.01', 'name': 'speed_bump'}, {'id': 11125, 'synset': 'speedometer.n.01', 'name': 'speedometer'}, {'id': 11126, 'synset': 'speed_skate.n.01', 'name': 'speed_skate'}, {'id': 11127, 'synset': 'spherometer.n.01', 'name': 'spherometer'}, {'id': 11128, 'synset': 'sphygmomanometer.n.01', 'name': 'sphygmomanometer'}, {'id': 11129, 'synset': 'spicemill.n.01', 'name': 'spicemill'}, {'id': 11130, 'synset': 'spider.n.03', 'name': 'spider'}, {'id': 11131, 'synset': 'spider_web.n.01', 'name': 'spider_web'}, {'id': 11132, 'synset': 'spike.n.02', 'name': 'spike'}, {'id': 11133, 'synset': 'spike.n.11', 'name': 'spike'}, {'id': 11134, 'synset': 'spindle.n.04', 'name': 'spindle'}, {'id': 11135, 'synset': 'spindle.n.03', 'name': 'spindle'}, {'id': 11136, 'synset': 'spindle.n.02', 'name': 'spindle'}, {'id': 11137, 'synset': 'spin_dryer.n.01', 'name': 'spin_dryer'}, {'id': 11138, 'synset': 'spinet.n.02', 'name': 'spinet'}, {'id': 11139, 'synset': 'spinet.n.01', 'name': 'spinet'}, {'id': 11140, 'synset': 'spinnaker.n.01', 'name': 'spinnaker'}, {'id': 11141, 'synset': 'spinner.n.03', 'name': 'spinner'}, {'id': 11142, 'synset': 'spinning_frame.n.01', 'name': 'spinning_frame'}, {'id': 11143, 'synset': 'spinning_jenny.n.01', 'name': 'spinning_jenny'}, {'id': 11144, 'synset': 'spinning_machine.n.01', 'name': 'spinning_machine'}, {'id': 11145, 'synset': 'spinning_rod.n.01', 'name': 'spinning_rod'}, {'id': 11146, 'synset': 'spinning_wheel.n.01', 'name': 'spinning_wheel'}, {'id': 11147, 'synset': 'spiral_bandage.n.01', 'name': 'spiral_bandage'}, {'id': 11148, 'synset': 'spiral_ratchet_screwdriver.n.01', 'name': 'spiral_ratchet_screwdriver'}, {'id': 11149, 'synset': 'spiral_spring.n.01', 'name': 'spiral_spring'}, {'id': 11150, 'synset': 'spirit_lamp.n.01', 'name': 'spirit_lamp'}, {'id': 11151, 'synset': 'spirit_stove.n.01', 'name': 'spirit_stove'}, {'id': 11152, 'synset': 'spirometer.n.01', 'name': 'spirometer'}, {'id': 11153, 'synset': 'spit.n.03', 'name': 'spit'}, {'id': 11154, 'synset': 'spittoon.n.01', 'name': 'spittoon'}, {'id': 11155, 'synset': 'splashboard.n.02', 'name': 'splashboard'}, {'id': 11156, 'synset': 'splasher.n.01', 'name': 'splasher'}, {'id': 11157, 'synset': 'splice.n.01', 'name': 'splice'}, {'id': 11158, 'synset': 'splicer.n.03', 'name': 'splicer'}, {'id': 11159, 'synset': 'splint.n.02', 'name': 'splint'}, {'id': 11160, 'synset': 'split_rail.n.01', 'name': 'split_rail'}, {'id': 11161, 'synset': 'spode.n.02', 'name': 'Spode'}, {'id': 11162, 'synset': 'spoiler.n.05', 'name': 'spoiler'}, {'id': 11163, 'synset': 'spoiler.n.04', 'name': 'spoiler'}, {'id': 11164, 'synset': 'spoke.n.01', 'name': 'spoke'}, {'id': 11165, 'synset': 'spokeshave.n.01', 'name': 'spokeshave'}, {'id': 11166, 'synset': 'sponge_cloth.n.01', 'name': 'sponge_cloth'}, {'id': 11167, 'synset': 'sponge_mop.n.01', 'name': 'sponge_mop'}, {'id': 11168, 'synset': 'spoon.n.03', 'name': 'spoon'}, {'id': 11169, 'synset': 'spork.n.01', 'name': 'Spork'}, {'id': 11170, 'synset': 'sporran.n.01', 'name': 'sporran'}, {'id': 11171, 'synset': 'sport_kite.n.01', 'name': 'sport_kite'}, {'id': 11172, 'synset': 'sports_car.n.01', 'name': 'sports_car'}, {'id': 11173, 'synset': 'sports_equipment.n.01', 'name': 'sports_equipment'}, {'id': 11174, 'synset': 'sports_implement.n.01', 'name': 'sports_implement'}, {'id': 11175, 'synset': 'sport_utility.n.01', 'name': 'sport_utility'}, {'id': 11176, 'synset': 'spot.n.07', 'name': 'spot'}, {'id': 11177, 'synset': 'spot_weld.n.01', 'name': 'spot_weld'}, {'id': 11178, 'synset': 'spouter.n.02', 'name': 'spouter'}, {'id': 11179, 'synset': 'sprag.n.01', 'name': 'sprag'}, {'id': 11180, 'synset': 'spray_gun.n.01', 'name': 'spray_gun'}, {'id': 11181, 'synset': 'spray_paint.n.01', 'name': 'spray_paint'}, {'id': 11182, 'synset': 'spreader.n.01', 'name': 'spreader'}, {'id': 11183, 'synset': 'sprig.n.02', 'name': 'sprig'}, {'id': 11184, 'synset': 'spring.n.02', 'name': 'spring'}, {'id': 11185, 'synset': 'spring_balance.n.01', 'name': 'spring_balance'}, {'id': 11186, 'synset': 'springboard.n.01', 'name': 'springboard'}, {'id': 11187, 'synset': 'sprinkler.n.01', 'name': 'sprinkler'}, {'id': 11188, 'synset': 'sprinkler_system.n.01', 'name': 'sprinkler_system'}, {'id': 11189, 'synset': 'sprit.n.01', 'name': 'sprit'}, {'id': 11190, 'synset': 'spritsail.n.01', 'name': 'spritsail'}, {'id': 11191, 'synset': 'sprocket.n.02', 'name': 'sprocket'}, {'id': 11192, 'synset': 'sprocket.n.01', 'name': 'sprocket'}, {'id': 11193, 'synset': 'spun_yarn.n.01', 'name': 'spun_yarn'}, {'id': 11194, 'synset': 'spur.n.04', 'name': 'spur'}, {'id': 11195, 'synset': 'spur_gear.n.01', 'name': 'spur_gear'}, {'id': 11196, 'synset': 'sputnik.n.01', 'name': 'sputnik'}, {'id': 11197, 'synset': 'spy_satellite.n.01', 'name': 'spy_satellite'}, {'id': 11198, 'synset': 'squad_room.n.01', 'name': 'squad_room'}, {'id': 11199, 'synset': 'square.n.08', 'name': 'square'}, {'id': 11200, 'synset': 'square_knot.n.01', 'name': 'square_knot'}, {'id': 11201, 'synset': 'square-rigger.n.01', 'name': 'square-rigger'}, {'id': 11202, 'synset': 'square_sail.n.01', 'name': 'square_sail'}, {'id': 11203, 'synset': 'squash_ball.n.01', 'name': 'squash_ball'}, {'id': 11204, 'synset': 'squash_racket.n.01', 'name': 'squash_racket'}, {'id': 11205, 'synset': 'squawk_box.n.01', 'name': 'squawk_box'}, {'id': 11206, 'synset': 'squeegee.n.01', 'name': 'squeegee'}, {'id': 11207, 'synset': 'squeezer.n.01', 'name': 'squeezer'}, {'id': 11208, 'synset': 'squelch_circuit.n.01', 'name': 'squelch_circuit'}, {'id': 11209, 'synset': 'squinch.n.01', 'name': 'squinch'}, {'id': 11210, 'synset': 'stabilizer.n.03', 'name': 'stabilizer'}, {'id': 11211, 'synset': 'stabilizer.n.02', 'name': 'stabilizer'}, {'id': 11212, 'synset': 'stabilizer_bar.n.01', 'name': 'stabilizer_bar'}, {'id': 11213, 'synset': 'stable.n.01', 'name': 'stable'}, {'id': 11214, 'synset': 'stable_gear.n.01', 'name': 'stable_gear'}, {'id': 11215, 'synset': 'stabling.n.01', 'name': 'stabling'}, {'id': 11216, 'synset': 'stacks.n.02', 'name': 'stacks'}, {'id': 11217, 'synset': 'staddle.n.01', 'name': 'staddle'}, {'id': 11218, 'synset': 'stadium.n.01', 'name': 'stadium'}, {'id': 11219, 'synset': 'stage.n.03', 'name': 'stage'}, {'id': 11220, 'synset': 'stained-glass_window.n.01', 'name': 'stained-glass_window'}, {'id': 11221, 'synset': 'stair-carpet.n.01', 'name': 'stair-carpet'}, {'id': 11222, 'synset': 'stair-rod.n.01', 'name': 'stair-rod'}, {'id': 11223, 'synset': 'stairwell.n.01', 'name': 'stairwell'}, {'id': 11224, 'synset': 'stake.n.05', 'name': 'stake'}, {'id': 11225, 'synset': 'stall.n.03', 'name': 'stall'}, {'id': 11226, 'synset': 'stall.n.01', 'name': 'stall'}, {'id': 11227, 'synset': 'stamp.n.08', 'name': 'stamp'}, {'id': 11228, 'synset': 'stamp_mill.n.01', 'name': 'stamp_mill'}, {'id': 11229, 'synset': 'stamping_machine.n.01', 'name': 'stamping_machine'}, {'id': 11230, 'synset': 'stanchion.n.01', 'name': 'stanchion'}, {'id': 11231, 'synset': 'stand.n.04', 'name': 'stand'}, {'id': 11232, 'synset': 'standard.n.05', 'name': 'standard'}, {'id': 11233, 'synset': 'standard_cell.n.01', 'name': 'standard_cell'}, {'id': 11234, 'synset': 'standard_transmission.n.01', 'name': 'standard_transmission'}, {'id': 11235, 'synset': 'standing_press.n.01', 'name': 'standing_press'}, {'id': 11236, 'synset': 'stanhope.n.01', 'name': 'stanhope'}, {'id': 11237, 'synset': 'stanley_steamer.n.01', 'name': 'Stanley_Steamer'}, {'id': 11238, 'synset': 'staple.n.05', 'name': 'staple'}, {'id': 11239, 'synset': 'staple.n.04', 'name': 'staple'}, {'id': 11240, 'synset': 'staple_gun.n.01', 'name': 'staple_gun'}, {'id': 11241, 'synset': 'starship.n.01', 'name': 'starship'}, {'id': 11242, 'synset': 'starter.n.01', 'name': 'starter'}, {'id': 11243, 'synset': 'starting_gate.n.01', 'name': 'starting_gate'}, {'id': 11244, 'synset': 'stassano_furnace.n.01', 'name': 'Stassano_furnace'}, {'id': 11245, 'synset': 'statehouse.n.01', 'name': 'Statehouse'}, {'id': 11246, 'synset': 'stately_home.n.01', 'name': 'stately_home'}, {'id': 11247, 'synset': 'state_prison.n.01', 'name': 'state_prison'}, {'id': 11248, 'synset': 'stateroom.n.01', 'name': 'stateroom'}, {'id': 11249, 'synset': 'static_tube.n.01', 'name': 'static_tube'}, {'id': 11250, 'synset': 'station.n.01', 'name': 'station'}, {'id': 11251, 'synset': 'stator.n.01', 'name': 'stator'}, {'id': 11252, 'synset': 'stay.n.05', 'name': 'stay'}, {'id': 11253, 'synset': 'staysail.n.01', 'name': 'staysail'}, {'id': 11254, 'synset': 'steakhouse.n.01', 'name': 'steakhouse'}, {'id': 11255, 'synset': 'stealth_aircraft.n.01', 'name': 'stealth_aircraft'}, {'id': 11256, 'synset': 'stealth_bomber.n.01', 'name': 'stealth_bomber'}, {'id': 11257, 'synset': 'stealth_fighter.n.01', 'name': 'stealth_fighter'}, {'id': 11258, 'synset': 'steam_bath.n.01', 'name': 'steam_bath'}, {'id': 11259, 'synset': 'steamboat.n.01', 'name': 'steamboat'}, {'id': 11260, 'synset': 'steam_chest.n.01', 'name': 'steam_chest'}, {'id': 11261, 'synset': 'steam_engine.n.01', 'name': 'steam_engine'}, {'id': 11262, 'synset': 'steamer.n.03', 'name': 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'name': 'stomach_pump'}, {'id': 11324, 'synset': 'stone_wall.n.01', 'name': 'stone_wall'}, {'id': 11325, 'synset': 'stoneware.n.01', 'name': 'stoneware'}, {'id': 11326, 'synset': 'stonework.n.01', 'name': 'stonework'}, {'id': 11327, 'synset': 'stoop.n.03', 'name': 'stoop'}, {'id': 11328, 'synset': 'stop_bath.n.01', 'name': 'stop_bath'}, {'id': 11329, 'synset': 'stopcock.n.01', 'name': 'stopcock'}, {'id': 11330, 'synset': 'stopper_knot.n.01', 'name': 'stopper_knot'}, {'id': 11331, 'synset': 'stopwatch.n.01', 'name': 'stopwatch'}, {'id': 11332, 'synset': 'storage_battery.n.01', 'name': 'storage_battery'}, {'id': 11333, 'synset': 'storage_cell.n.01', 'name': 'storage_cell'}, {'id': 11334, 'synset': 'storage_ring.n.01', 'name': 'storage_ring'}, {'id': 11335, 'synset': 'storage_space.n.01', 'name': 'storage_space'}, {'id': 11336, 'synset': 'storeroom.n.01', 'name': 'storeroom'}, {'id': 11337, 'synset': 'storm_cellar.n.01', 'name': 'storm_cellar'}, {'id': 11338, 'synset': 'storm_door.n.01', 'name': 'storm_door'}, {'id': 11339, 'synset': 'storm_window.n.01', 'name': 'storm_window'}, {'id': 11340, 'synset': 'stoup.n.02', 'name': 'stoup'}, {'id': 11341, 'synset': 'stoup.n.01', 'name': 'stoup'}, {'id': 11342, 'synset': 'stove.n.02', 'name': 'stove'}, {'id': 11343, 'synset': 'stove_bolt.n.01', 'name': 'stove_bolt'}, {'id': 11344, 'synset': 'stovepipe.n.01', 'name': 'stovepipe'}, {'id': 11345, 'synset': 'stovepipe_iron.n.01', 'name': 'stovepipe_iron'}, {'id': 11346, 'synset': 'stradavarius.n.01', 'name': 'Stradavarius'}, {'id': 11347, 'synset': 'straight_chair.n.01', 'name': 'straight_chair'}, {'id': 11348, 'synset': 'straightedge.n.01', 'name': 'straightedge'}, {'id': 11349, 'synset': 'straightener.n.01', 'name': 'straightener'}, {'id': 11350, 'synset': 'straight_flute.n.01', 'name': 'straight_flute'}, {'id': 11351, 'synset': 'straight_pin.n.01', 'name': 'straight_pin'}, {'id': 11352, 'synset': 'straight_razor.n.01', 'name': 'straight_razor'}, {'id': 11353, 'synset': 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'synset': 'student_lamp.n.01', 'name': 'student_lamp'}, {'id': 11384, 'synset': 'student_union.n.01', 'name': 'student_union'}, {'id': 11385, 'synset': 'stud_finder.n.01', 'name': 'stud_finder'}, {'id': 11386, 'synset': 'studio_apartment.n.01', 'name': 'studio_apartment'}, {'id': 11387, 'synset': 'studio_couch.n.01', 'name': 'studio_couch'}, {'id': 11388, 'synset': 'study.n.05', 'name': 'study'}, {'id': 11389, 'synset': 'study_hall.n.02', 'name': 'study_hall'}, {'id': 11390, 'synset': 'stuffing_nut.n.01', 'name': 'stuffing_nut'}, {'id': 11391, 'synset': 'stump.n.03', 'name': 'stump'}, {'id': 11392, 'synset': 'stun_gun.n.01', 'name': 'stun_gun'}, {'id': 11393, 'synset': 'stupa.n.01', 'name': 'stupa'}, {'id': 11394, 'synset': 'sty.n.02', 'name': 'sty'}, {'id': 11395, 'synset': 'stylus.n.01', 'name': 'stylus'}, {'id': 11396, 'synset': 'sub-assembly.n.01', 'name': 'sub-assembly'}, {'id': 11397, 'synset': 'subcompact.n.01', 'name': 'subcompact'}, {'id': 11398, 'synset': 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{'id': 11456, 'synset': 'surfboat.n.01', 'name': 'surfboat'}, {'id': 11457, 'synset': 'surcoat.n.01', 'name': 'surcoat'}, {'id': 11458, 'synset': "surgeon's_knot.n.01", 'name': "surgeon's_knot"}, {'id': 11459, 'synset': 'surgery.n.02', 'name': 'surgery'}, {'id': 11460, 'synset': 'surge_suppressor.n.01', 'name': 'surge_suppressor'}, {'id': 11461, 'synset': 'surgical_dressing.n.01', 'name': 'surgical_dressing'}, {'id': 11462, 'synset': 'surgical_instrument.n.01', 'name': 'surgical_instrument'}, {'id': 11463, 'synset': 'surgical_knife.n.01', 'name': 'surgical_knife'}, {'id': 11464, 'synset': 'surplice.n.01', 'name': 'surplice'}, {'id': 11465, 'synset': 'surrey.n.02', 'name': 'surrey'}, {'id': 11466, 'synset': 'surtout.n.01', 'name': 'surtout'}, {'id': 11467, 'synset': 'surveillance_system.n.01', 'name': 'surveillance_system'}, {'id': 11468, 'synset': 'surveying_instrument.n.01', 'name': 'surveying_instrument'}, {'id': 11469, 'synset': "surveyor's_level.n.01", 'name': "surveyor's_level"}, {'id': 11470, 'synset': 'sushi_bar.n.01', 'name': 'sushi_bar'}, {'id': 11471, 'synset': 'suspension.n.05', 'name': 'suspension'}, {'id': 11472, 'synset': 'suspension_bridge.n.01', 'name': 'suspension_bridge'}, {'id': 11473, 'synset': 'suspensory.n.01', 'name': 'suspensory'}, {'id': 11474, 'synset': 'sustaining_pedal.n.01', 'name': 'sustaining_pedal'}, {'id': 11475, 'synset': 'suture.n.02', 'name': 'suture'}, {'id': 11476, 'synset': 'swab.n.01', 'name': 'swab'}, {'id': 11477, 'synset': 'swaddling_clothes.n.01', 'name': 'swaddling_clothes'}, {'id': 11478, 'synset': 'swag.n.03', 'name': 'swag'}, {'id': 11479, 'synset': 'swage_block.n.01', 'name': 'swage_block'}, {'id': 11480, 'synset': 'swagger_stick.n.01', 'name': 'swagger_stick'}, {'id': 11481, 'synset': 'swallow-tailed_coat.n.01', 'name': 'swallow-tailed_coat'}, {'id': 11482, 'synset': 'swamp_buggy.n.01', 'name': 'swamp_buggy'}, {'id': 11483, 'synset': "swan's_down.n.01", 'name': "swan's_down"}, {'id': 11484, 'synset': 'swathe.n.01', 'name': 'swathe'}, {'id': 11485, 'synset': 'swatter.n.01', 'name': 'swatter'}, {'id': 11486, 'synset': 'sweat_bag.n.01', 'name': 'sweat_bag'}, {'id': 11487, 'synset': 'sweatband.n.01', 'name': 'sweatband'}, {'id': 11488, 'synset': 'sweatshop.n.01', 'name': 'sweatshop'}, {'id': 11489, 'synset': 'sweat_suit.n.01', 'name': 'sweat_suit'}, {'id': 11490, 'synset': 'sweep.n.04', 'name': 'sweep'}, {'id': 11491, 'synset': 'sweep_hand.n.01', 'name': 'sweep_hand'}, {'id': 11492, 'synset': 'swimming_trunks.n.01', 'name': 'swimming_trunks'}, {'id': 11493, 'synset': 'swing.n.02', 'name': 'swing'}, {'id': 11494, 'synset': 'swing_door.n.01', 'name': 'swing_door'}, {'id': 11495, 'synset': 'switch.n.01', 'name': 'switch'}, {'id': 11496, 'synset': 'switchblade.n.01', 'name': 'switchblade'}, {'id': 11497, 'synset': 'switch_engine.n.01', 'name': 'switch_engine'}, {'id': 11498, 'synset': 'swivel.n.01', 'name': 'swivel'}, {'id': 11499, 'synset': 'swivel_chair.n.01', 'name': 'swivel_chair'}, {'id': 11500, 'synset': 'swizzle_stick.n.01', 'name': 'swizzle_stick'}, {'id': 11501, 'synset': 'sword_cane.n.01', 'name': 'sword_cane'}, {'id': 11502, 'synset': 's_wrench.n.01', 'name': 'S_wrench'}, {'id': 11503, 'synset': 'synagogue.n.01', 'name': 'synagogue'}, {'id': 11504, 'synset': 'synchrocyclotron.n.01', 'name': 'synchrocyclotron'}, {'id': 11505, 'synset': 'synchroflash.n.01', 'name': 'synchroflash'}, {'id': 11506, 'synset': 'synchromesh.n.01', 'name': 'synchromesh'}, {'id': 11507, 'synset': 'synchronous_converter.n.01', 'name': 'synchronous_converter'}, {'id': 11508, 'synset': 'synchronous_motor.n.01', 'name': 'synchronous_motor'}, {'id': 11509, 'synset': 'synchrotron.n.01', 'name': 'synchrotron'}, {'id': 11510, 'synset': 'synchroscope.n.01', 'name': 'synchroscope'}, {'id': 11511, 'synset': 'synthesizer.n.02', 'name': 'synthesizer'}, {'id': 11512, 'synset': 'system.n.01', 'name': 'system'}, {'id': 11513, 'synset': 'tabard.n.01', 'name': 'tabard'}, {'id': 11514, 'synset': 'tabernacle.n.02', 'name': 'Tabernacle'}, {'id': 11515, 'synset': 'tabi.n.01', 'name': 'tabi'}, {'id': 11516, 'synset': 'tab_key.n.01', 'name': 'tab_key'}, {'id': 11517, 'synset': 'table.n.03', 'name': 'table'}, {'id': 11518, 'synset': 'tablefork.n.01', 'name': 'tablefork'}, {'id': 11519, 'synset': 'table_knife.n.01', 'name': 'table_knife'}, {'id': 11520, 'synset': 'table_saw.n.01', 'name': 'table_saw'}, {'id': 11521, 'synset': 'tablespoon.n.02', 'name': 'tablespoon'}, {'id': 11522, 'synset': 'tablet-armed_chair.n.01', 'name': 'tablet-armed_chair'}, {'id': 11523, 'synset': 'table-tennis_racquet.n.01', 'name': 'table-tennis_racquet'}, {'id': 11524, 'synset': 'tabletop.n.01', 'name': 'tabletop'}, {'id': 11525, 'synset': 'tableware.n.01', 'name': 'tableware'}, {'id': 11526, 'synset': 'tabor.n.01', 'name': 'tabor'}, {'id': 11527, 'synset': 'taboret.n.01', 'name': 'taboret'}, {'id': 11528, 'synset': 'tachistoscope.n.01', 'name': 'tachistoscope'}, {'id': 11529, 'synset': 'tachograph.n.01', 'name': 'tachograph'}, {'id': 11530, 'synset': 'tachymeter.n.01', 'name': 'tachymeter'}, {'id': 11531, 'synset': 'tack.n.02', 'name': 'tack'}, {'id': 11532, 'synset': 'tack_hammer.n.01', 'name': 'tack_hammer'}, {'id': 11533, 'synset': 'taffeta.n.01', 'name': 'taffeta'}, {'id': 11534, 'synset': 'taffrail.n.01', 'name': 'taffrail'}, {'id': 11535, 'synset': 'tailgate.n.01', 'name': 'tailgate'}, {'id': 11536, 'synset': 'tailor-made.n.01', 'name': 'tailor-made'}, {'id': 11537, 'synset': "tailor's_chalk.n.01", 'name': "tailor's_chalk"}, {'id': 11538, 'synset': 'tailpipe.n.01', 'name': 'tailpipe'}, {'id': 11539, 'synset': 'tail_rotor.n.01', 'name': 'tail_rotor'}, {'id': 11540, 'synset': 'tailstock.n.01', 'name': 'tailstock'}, {'id': 11541, 'synset': 'take-up.n.01', 'name': 'take-up'}, {'id': 11542, 'synset': 'talaria.n.01', 'name': 'talaria'}, {'id': 11543, 'synset': 'talcum.n.02', 'name': 'talcum'}, {'id': 11544, 'synset': 'tam.n.01', 'name': 'tam'}, {'id': 11545, 'synset': 'tambour.n.02', 'name': 'tambour'}, {'id': 11546, 'synset': 'tambour.n.01', 'name': 'tambour'}, {'id': 11547, 'synset': 'tammy.n.01', 'name': 'tammy'}, {'id': 11548, 'synset': 'tamp.n.01', 'name': 'tamp'}, {'id': 11549, 'synset': 'tampax.n.01', 'name': 'Tampax'}, {'id': 11550, 'synset': 'tampion.n.01', 'name': 'tampion'}, {'id': 11551, 'synset': 'tampon.n.01', 'name': 'tampon'}, {'id': 11552, 'synset': 'tandoor.n.01', 'name': 'tandoor'}, {'id': 11553, 'synset': 'tangram.n.01', 'name': 'tangram'}, {'id': 11554, 'synset': 'tankard.n.01', 'name': 'tankard'}, {'id': 11555, 'synset': 'tank_car.n.01', 'name': 'tank_car'}, {'id': 11556, 'synset': 'tank_destroyer.n.01', 'name': 'tank_destroyer'}, {'id': 11557, 'synset': 'tank_engine.n.01', 'name': 'tank_engine'}, {'id': 11558, 'synset': 'tanker_plane.n.01', 'name': 'tanker_plane'}, {'id': 11559, 'synset': 'tank_shell.n.01', 'name': 'tank_shell'}, {'id': 11560, 'synset': 'tannoy.n.01', 'name': 'tannoy'}, {'id': 11561, 'synset': 'tap.n.06', 'name': 'tap'}, {'id': 11562, 'synset': 'tapa.n.02', 'name': 'tapa'}, {'id': 11563, 'synset': 'tape.n.02', 'name': 'tape'}, {'id': 11564, 'synset': 'tape_deck.n.01', 'name': 'tape_deck'}, {'id': 11565, 'synset': 'tape_drive.n.01', 'name': 'tape_drive'}, {'id': 11566, 'synset': 'tape_player.n.01', 'name': 'tape_player'}, {'id': 11567, 'synset': 'tape_recorder.n.01', 'name': 'tape_recorder'}, {'id': 11568, 'synset': 'taper_file.n.01', 'name': 'taper_file'}, {'id': 11569, 'synset': 'tappet.n.01', 'name': 'tappet'}, {'id': 11570, 'synset': 'tap_wrench.n.01', 'name': 'tap_wrench'}, {'id': 11571, 'synset': 'tare.n.05', 'name': 'tare'}, {'id': 11572, 'synset': 'target.n.04', 'name': 'target'}, {'id': 11573, 'synset': 'target_acquisition_system.n.01', 'name': 'target_acquisition_system'}, {'id': 11574, 'synset': 'tarmacadam.n.02', 'name': 'tarmacadam'}, {'id': 11575, 'synset': 'tasset.n.01', 'name': 'tasset'}, {'id': 11576, 'synset': 'tattoo.n.02', 'name': 'tattoo'}, {'id': 11577, 'synset': 'tavern.n.01', 'name': 'tavern'}, {'id': 11578, 'synset': 'tawse.n.01', 'name': 'tawse'}, {'id': 11579, 'synset': 'taximeter.n.01', 'name': 'taximeter'}, {'id': 11580, 'synset': 't-bar_lift.n.01', 'name': 'T-bar_lift'}, {'id': 11581, 'synset': 'tea_bag.n.02', 'name': 'tea_bag'}, {'id': 11582, 'synset': 'tea_ball.n.01', 'name': 'tea_ball'}, {'id': 11583, 'synset': 'tea_cart.n.01', 'name': 'tea_cart'}, {'id': 11584, 'synset': 'tea_chest.n.01', 'name': 'tea_chest'}, {'id': 11585, 'synset': 'teaching_aid.n.01', 'name': 'teaching_aid'}, {'id': 11586, 'synset': 'tea_gown.n.01', 'name': 'tea_gown'}, {'id': 11587, 'synset': 'tea_maker.n.01', 'name': 'tea_maker'}, {'id': 11588, 'synset': 'teashop.n.01', 'name': 'teashop'}, {'id': 11589, 'synset': 'teaspoon.n.02', 'name': 'teaspoon'}, {'id': 11590, 'synset': 'tea-strainer.n.01', 'name': 'tea-strainer'}, {'id': 11591, 'synset': 'tea_table.n.01', 'name': 'tea_table'}, {'id': 11592, 'synset': 'tea_tray.n.01', 'name': 'tea_tray'}, {'id': 11593, 'synset': 'tea_urn.n.01', 'name': 'tea_urn'}, {'id': 11594, 'synset': 'tee.n.03', 'name': 'tee'}, {'id': 11595, 'synset': 'tee_hinge.n.01', 'name': 'tee_hinge'}, {'id': 11596, 'synset': 'telecom_hotel.n.01', 'name': 'telecom_hotel'}, {'id': 11597, 'synset': 'telecommunication_system.n.01', 'name': 'telecommunication_system'}, {'id': 11598, 'synset': 'telegraph.n.01', 'name': 'telegraph'}, {'id': 11599, 'synset': 'telegraph_key.n.01', 'name': 'telegraph_key'}, {'id': 11600, 'synset': 'telemeter.n.01', 'name': 'telemeter'}, {'id': 11601, 'synset': 'telephone_bell.n.01', 'name': 'telephone_bell'}, {'id': 11602, 'synset': 'telephone_cord.n.01', 'name': 'telephone_cord'}, {'id': 11603, 'synset': 'telephone_jack.n.01', 'name': 'telephone_jack'}, {'id': 11604, 'synset': 'telephone_line.n.02', 'name': 'telephone_line'}, {'id': 11605, 'synset': 'telephone_plug.n.01', 'name': 'telephone_plug'}, {'id': 11606, 'synset': 'telephone_receiver.n.01', 'name': 'telephone_receiver'}, {'id': 11607, 'synset': 'telephone_system.n.01', 'name': 'telephone_system'}, {'id': 11608, 'synset': 'telephone_wire.n.01', 'name': 'telephone_wire'}, {'id': 11609, 'synset': 'teleprompter.n.01', 'name': 'Teleprompter'}, {'id': 11610, 'synset': 'telescope.n.01', 'name': 'telescope'}, {'id': 11611, 'synset': 'telescopic_sight.n.01', 'name': 'telescopic_sight'}, {'id': 11612, 'synset': 'telethermometer.n.01', 'name': 'telethermometer'}, {'id': 11613, 'synset': 'teletypewriter.n.01', 'name': 'teletypewriter'}, {'id': 11614, 'synset': 'television.n.02', 'name': 'television'}, {'id': 11615, 'synset': 'television_antenna.n.01', 'name': 'television_antenna'}, {'id': 11616, 'synset': 'television_equipment.n.01', 'name': 'television_equipment'}, {'id': 11617, 'synset': 'television_monitor.n.01', 'name': 'television_monitor'}, {'id': 11618, 'synset': 'television_room.n.01', 'name': 'television_room'}, {'id': 11619, 'synset': 'television_transmitter.n.01', 'name': 'television_transmitter'}, {'id': 11620, 'synset': 'telpher.n.01', 'name': 'telpher'}, {'id': 11621, 'synset': 'telpherage.n.01', 'name': 'telpherage'}, {'id': 11622, 'synset': 'tempera.n.01', 'name': 'tempera'}, {'id': 11623, 'synset': 'temple.n.01', 'name': 'temple'}, {'id': 11624, 'synset': 'temple.n.03', 'name': 'temple'}, {'id': 11625, 'synset': 'temporary_hookup.n.01', 'name': 'temporary_hookup'}, {'id': 11626, 'synset': 'tender.n.06', 'name': 'tender'}, {'id': 11627, 'synset': 'tender.n.05', 'name': 'tender'}, {'id': 11628, 'synset': 'tender.n.04', 'name': 'tender'}, {'id': 11629, 'synset': 'tenement.n.01', 'name': 'tenement'}, {'id': 11630, 'synset': 'tennis_camp.n.01', 'name': 'tennis_camp'}, {'id': 11631, 'synset': 'tenon.n.01', 'name': 'tenon'}, {'id': 11632, 'synset': 'tenor_drum.n.01', 'name': 'tenor_drum'}, {'id': 11633, 'synset': 'tenoroon.n.01', 'name': 'tenoroon'}, {'id': 11634, 'synset': 'tenpenny_nail.n.01', 'name': 'tenpenny_nail'}, {'id': 11635, 'synset': 'tenpin.n.01', 'name': 'tenpin'}, {'id': 11636, 'synset': 'tensimeter.n.01', 'name': 'tensimeter'}, {'id': 11637, 'synset': 'tensiometer.n.03', 'name': 'tensiometer'}, {'id': 11638, 'synset': 'tensiometer.n.02', 'name': 'tensiometer'}, {'id': 11639, 'synset': 'tensiometer.n.01', 'name': 'tensiometer'}, {'id': 11640, 'synset': 'tent.n.01', 'name': 'tent'}, {'id': 11641, 'synset': 'tenter.n.01', 'name': 'tenter'}, {'id': 11642, 'synset': 'tenterhook.n.01', 'name': 'tenterhook'}, {'id': 11643, 'synset': 'tent-fly.n.01', 'name': 'tent-fly'}, {'id': 11644, 'synset': 'tent_peg.n.01', 'name': 'tent_peg'}, {'id': 11645, 'synset': 'tepee.n.01', 'name': 'tepee'}, {'id': 11646, 'synset': 'terminal.n.02', 'name': 'terminal'}, {'id': 11647, 'synset': 'terminal.n.04', 'name': 'terminal'}, {'id': 11648, 'synset': 'terraced_house.n.01', 'name': 'terraced_house'}, {'id': 11649, 'synset': 'terra_cotta.n.01', 'name': 'terra_cotta'}, {'id': 11650, 'synset': 'terrarium.n.01', 'name': 'terrarium'}, {'id': 11651, 'synset': 'terra_sigillata.n.01', 'name': 'terra_sigillata'}, {'id': 11652, 'synset': 'terry.n.02', 'name': 'terry'}, {'id': 11653, 'synset': 'tesla_coil.n.01', 'name': 'Tesla_coil'}, {'id': 11654, 'synset': 'tessera.n.01', 'name': 'tessera'}, {'id': 11655, 'synset': 'test_equipment.n.01', 'name': 'test_equipment'}, {'id': 11656, 'synset': 'test_rocket.n.01', 'name': 'test_rocket'}, {'id': 11657, 'synset': 'test_room.n.01', 'name': 'test_room'}, {'id': 11658, 'synset': 'testudo.n.01', 'name': 'testudo'}, {'id': 11659, 'synset': 'tetraskelion.n.01', 'name': 'tetraskelion'}, {'id': 11660, 'synset': 'tetrode.n.01', 'name': 'tetrode'}, {'id': 11661, 'synset': 'textile_machine.n.01', 'name': 'textile_machine'}, {'id': 11662, 'synset': 'textile_mill.n.01', 'name': 'textile_mill'}, {'id': 11663, 'synset': 'thatch.n.04', 'name': 'thatch'}, {'id': 11664, 'synset': 'theater.n.01', 'name': 'theater'}, {'id': 11665, 'synset': 'theater_curtain.n.01', 'name': 'theater_curtain'}, {'id': 11666, 'synset': 'theater_light.n.01', 'name': 'theater_light'}, {'id': 11667, 'synset': 'theodolite.n.01', 'name': 'theodolite'}, {'id': 11668, 'synset': 'theremin.n.01', 'name': 'theremin'}, {'id': 11669, 'synset': 'thermal_printer.n.01', 'name': 'thermal_printer'}, {'id': 11670, 'synset': 'thermal_reactor.n.01', 'name': 'thermal_reactor'}, {'id': 11671, 'synset': 'thermocouple.n.01', 'name': 'thermocouple'}, {'id': 11672, 'synset': 'thermoelectric_thermometer.n.01', 'name': 'thermoelectric_thermometer'}, {'id': 11673, 'synset': 'thermograph.n.02', 'name': 'thermograph'}, {'id': 11674, 'synset': 'thermograph.n.01', 'name': 'thermograph'}, {'id': 11675, 'synset': 'thermohydrometer.n.01', 'name': 'thermohydrometer'}, {'id': 11676, 'synset': 'thermojunction.n.01', 'name': 'thermojunction'}, {'id': 11677, 'synset': 'thermonuclear_reactor.n.01', 'name': 'thermonuclear_reactor'}, {'id': 11678, 'synset': 'thermopile.n.01', 'name': 'thermopile'}, {'id': 11679, 'synset': 'thigh_pad.n.01', 'name': 'thigh_pad'}, {'id': 11680, 'synset': 'thill.n.01', 'name': 'thill'}, {'id': 11681, 'synset': 'thinning_shears.n.01', 'name': 'thinning_shears'}, {'id': 11682, 'synset': 'third_base.n.01', 'name': 'third_base'}, {'id': 11683, 'synset': 'third_gear.n.01', 'name': 'third_gear'}, {'id': 11684, 'synset': 'third_rail.n.01', 'name': 'third_rail'}, {'id': 11685, 'synset': 'thong.n.03', 'name': 'thong'}, {'id': 11686, 'synset': 'thong.n.02', 'name': 'thong'}, {'id': 11687, 'synset': 'three-centered_arch.n.01', 'name': 'three-centered_arch'}, {'id': 11688, 'synset': 'three-decker.n.02', 'name': 'three-decker'}, {'id': 11689, 'synset': 'three-dimensional_radar.n.01', 'name': 'three-dimensional_radar'}, {'id': 11690, 'synset': 'three-piece_suit.n.01', 'name': 'three-piece_suit'}, {'id': 11691, 'synset': 'three-quarter_binding.n.01', 'name': 'three-quarter_binding'}, {'id': 11692, 'synset': 'three-way_switch.n.01', 'name': 'three-way_switch'}, {'id': 11693, 'synset': 'thresher.n.01', 'name': 'thresher'}, {'id': 11694, 'synset': 'threshing_floor.n.01', 'name': 'threshing_floor'}, {'id': 11695, 'synset': 'thriftshop.n.01', 'name': 'thriftshop'}, {'id': 11696, 'synset': 'throat_protector.n.01', 'name': 'throat_protector'}, {'id': 11697, 'synset': 'throne.n.01', 'name': 'throne'}, {'id': 11698, 'synset': 'thrust_bearing.n.01', 'name': 'thrust_bearing'}, {'id': 11699, 'synset': 'thruster.n.02', 'name': 'thruster'}, {'id': 11700, 'synset': 'thumb.n.02', 'name': 'thumb'}, {'id': 11701, 'synset': 'thumbhole.n.02', 'name': 'thumbhole'}, {'id': 11702, 'synset': 'thumbscrew.n.02', 'name': 'thumbscrew'}, {'id': 11703, 'synset': 'thumbstall.n.01', 'name': 'thumbstall'}, {'id': 11704, 'synset': 'thunderer.n.02', 'name': 'thunderer'}, {'id': 11705, 'synset': 'thwart.n.01', 'name': 'thwart'}, {'id': 11706, 'synset': 'ticking.n.02', 'name': 'ticking'}, {'id': 11707, 'synset': 'tickler_coil.n.01', 'name': 'tickler_coil'}, {'id': 11708, 'synset': 'tie.n.04', 'name': 'tie'}, {'id': 11709, 'synset': 'tie.n.08', 'name': 'tie'}, {'id': 11710, 'synset': 'tie_rack.n.01', 'name': 'tie_rack'}, {'id': 11711, 'synset': 'tie_rod.n.01', 'name': 'tie_rod'}, {'id': 11712, 'synset': 'tile.n.01', 'name': 'tile'}, {'id': 11713, 'synset': 'tile_cutter.n.01', 'name': 'tile_cutter'}, {'id': 11714, 'synset': 'tile_roof.n.01', 'name': 'tile_roof'}, {'id': 11715, 'synset': 'tiller.n.03', 'name': 'tiller'}, {'id': 11716, 'synset': 'tilter.n.02', 'name': 'tilter'}, {'id': 11717, 'synset': 'tilt-top_table.n.01', 'name': 'tilt-top_table'}, {'id': 11718, 'synset': 'timber.n.02', 'name': 'timber'}, {'id': 11719, 'synset': 'timber.n.03', 'name': 'timber'}, {'id': 11720, 'synset': 'timber_hitch.n.01', 'name': 'timber_hitch'}, {'id': 11721, 'synset': 'timbrel.n.01', 'name': 'timbrel'}, {'id': 11722, 'synset': 'time_bomb.n.02', 'name': 'time_bomb'}, {'id': 11723, 'synset': 'time_capsule.n.01', 'name': 'time_capsule'}, {'id': 11724, 'synset': 'time_clock.n.01', 'name': 'time_clock'}, {'id': 11725, 'synset': 'time-delay_measuring_instrument.n.01', 'name': 'time-delay_measuring_instrument'}, {'id': 11726, 'synset': 'time-fuse.n.01', 'name': 'time-fuse'}, {'id': 11727, 'synset': 'timepiece.n.01', 'name': 'timepiece'}, {'id': 11728, 'synset': 'timer.n.03', 'name': 'timer'}, {'id': 11729, 'synset': 'time-switch.n.01', 'name': 'time-switch'}, {'id': 11730, 'synset': 'tin.n.02', 'name': 'tin'}, {'id': 11731, 'synset': 'tinderbox.n.02', 'name': 'tinderbox'}, {'id': 11732, 'synset': 'tine.n.01', 'name': 'tine'}, {'id': 11733, 'synset': 'tippet.n.01', 'name': 'tippet'}, {'id': 11734, 'synset': 'tire_chain.n.01', 'name': 'tire_chain'}, {'id': 11735, 'synset': 'tire_iron.n.01', 'name': 'tire_iron'}, {'id': 11736, 'synset': 'titfer.n.01', 'name': 'titfer'}, {'id': 11737, 'synset': 'tithe_barn.n.01', 'name': 'tithe_barn'}, {'id': 11738, 'synset': 'titrator.n.01', 'name': 'titrator'}, {'id': 11739, 'synset': 'toasting_fork.n.01', 'name': 'toasting_fork'}, {'id': 11740, 'synset': 'toastrack.n.01', 'name': 'toastrack'}, {'id': 11741, 'synset': 'tobacco_pouch.n.01', 'name': 'tobacco_pouch'}, {'id': 11742, 'synset': 'tobacco_shop.n.01', 'name': 'tobacco_shop'}, {'id': 11743, 'synset': 'toboggan.n.01', 'name': 'toboggan'}, {'id': 11744, 'synset': 'toby.n.01', 'name': 'toby'}, {'id': 11745, 'synset': 'tocsin.n.02', 'name': 'tocsin'}, {'id': 11746, 'synset': 'toe.n.02', 'name': 'toe'}, {'id': 11747, 'synset': 'toecap.n.01', 'name': 'toecap'}, {'id': 11748, 'synset': 'toehold.n.02', 'name': 'toehold'}, {'id': 11749, 'synset': 'toga.n.01', 'name': 'toga'}, {'id': 11750, 'synset': 'toga_virilis.n.01', 'name': 'toga_virilis'}, {'id': 11751, 'synset': 'toggle.n.03', 'name': 'toggle'}, {'id': 11752, 'synset': 'toggle_bolt.n.01', 'name': 'toggle_bolt'}, {'id': 11753, 'synset': 'toggle_joint.n.01', 'name': 'toggle_joint'}, {'id': 11754, 'synset': 'toggle_switch.n.01', 'name': 'toggle_switch'}, {'id': 11755, 'synset': 'togs.n.01', 'name': 'togs'}, {'id': 11756, 'synset': 'toilet.n.01', 'name': 'toilet'}, {'id': 11757, 'synset': 'toilet_bag.n.01', 'name': 'toilet_bag'}, {'id': 11758, 'synset': 'toilet_bowl.n.01', 'name': 'toilet_bowl'}, {'id': 11759, 'synset': 'toilet_kit.n.01', 'name': 'toilet_kit'}, {'id': 11760, 'synset': 'toilet_powder.n.01', 'name': 'toilet_powder'}, {'id': 11761, 'synset': 'toiletry.n.01', 'name': 'toiletry'}, {'id': 11762, 'synset': 'toilet_seat.n.01', 'name': 'toilet_seat'}, {'id': 11763, 'synset': 'toilet_water.n.01', 'name': 'toilet_water'}, {'id': 11764, 'synset': 'tokamak.n.01', 'name': 'tokamak'}, {'id': 11765, 'synset': 'token.n.03', 'name': 'token'}, {'id': 11766, 'synset': 'tollbooth.n.01', 'name': 'tollbooth'}, {'id': 11767, 'synset': 'toll_bridge.n.01', 'name': 'toll_bridge'}, {'id': 11768, 'synset': 'tollgate.n.01', 'name': 'tollgate'}, {'id': 11769, 'synset': 'toll_line.n.01', 'name': 'toll_line'}, {'id': 11770, 'synset': 'tomahawk.n.01', 'name': 'tomahawk'}, {'id': 11771, 'synset': 'tommy_gun.n.01', 'name': 'Tommy_gun'}, {'id': 11772, 'synset': 'tomograph.n.01', 'name': 'tomograph'}, {'id': 11773, 'synset': 'tone_arm.n.01', 'name': 'tone_arm'}, {'id': 11774, 'synset': 'toner.n.03', 'name': 'toner'}, {'id': 11775, 'synset': 'tongue.n.07', 'name': 'tongue'}, {'id': 11776, 'synset': 'tongue_and_groove_joint.n.01', 'name': 'tongue_and_groove_joint'}, {'id': 11777, 'synset': 'tongue_depressor.n.01', 'name': 'tongue_depressor'}, {'id': 11778, 'synset': 'tonometer.n.01', 'name': 'tonometer'}, {'id': 11779, 'synset': 'tool.n.01', 'name': 'tool'}, {'id': 11780, 'synset': 'tool_bag.n.01', 'name': 'tool_bag'}, {'id': 11781, 'synset': 'toolshed.n.01', 'name': 'toolshed'}, {'id': 11782, 'synset': 'tooth.n.02', 'name': 'tooth'}, {'id': 11783, 'synset': 'tooth.n.05', 'name': 'tooth'}, {'id': 11784, 'synset': 'top.n.10', 'name': 'top'}, {'id': 11785, 'synset': 'topgallant.n.02', 'name': 'topgallant'}, {'id': 11786, 'synset': 'topgallant.n.01', 'name': 'topgallant'}, {'id': 11787, 'synset': 'topiary.n.01', 'name': 'topiary'}, {'id': 11788, 'synset': 'topknot.n.01', 'name': 'topknot'}, {'id': 11789, 'synset': 'topmast.n.01', 'name': 'topmast'}, {'id': 11790, 'synset': 'topper.n.05', 'name': 'topper'}, {'id': 11791, 'synset': 'topsail.n.01', 'name': 'topsail'}, {'id': 11792, 'synset': 'toque.n.01', 'name': 'toque'}, {'id': 11793, 'synset': 'torch.n.01', 'name': 'torch'}, {'id': 11794, 'synset': 'torpedo.n.06', 'name': 'torpedo'}, {'id': 11795, 'synset': 'torpedo.n.05', 'name': 'torpedo'}, {'id': 11796, 'synset': 'torpedo.n.03', 'name': 'torpedo'}, {'id': 11797, 'synset': 'torpedo_boat.n.01', 'name': 'torpedo_boat'}, {'id': 11798, 'synset': 'torpedo-boat_destroyer.n.01', 'name': 'torpedo-boat_destroyer'}, {'id': 11799, 'synset': 'torpedo_tube.n.01', 'name': 'torpedo_tube'}, {'id': 11800, 'synset': 'torque_converter.n.01', 'name': 'torque_converter'}, {'id': 11801, 'synset': 'torque_wrench.n.01', 'name': 'torque_wrench'}, {'id': 11802, 'synset': 'torture_chamber.n.01', 'name': 'torture_chamber'}, {'id': 11803, 'synset': 'totem_pole.n.01', 'name': 'totem_pole'}, {'id': 11804, 'synset': 'touch_screen.n.01', 'name': 'touch_screen'}, {'id': 11805, 'synset': 'toupee.n.01', 'name': 'toupee'}, {'id': 11806, 'synset': 'touring_car.n.01', 'name': 'touring_car'}, {'id': 11807, 'synset': 'tourist_class.n.01', 'name': 'tourist_class'}, {'id': 11808, 'synset': 'toweling.n.01', 'name': 'toweling'}, {'id': 11809, 'synset': 'towel_rail.n.01', 'name': 'towel_rail'}, {'id': 11810, 'synset': 'tower.n.01', 'name': 'tower'}, {'id': 11811, 'synset': 'town_hall.n.01', 'name': 'town_hall'}, {'id': 11812, 'synset': 'towpath.n.01', 'name': 'towpath'}, {'id': 11813, 'synset': 'toy_box.n.01', 'name': 'toy_box'}, {'id': 11814, 'synset': 'toyshop.n.01', 'name': 'toyshop'}, {'id': 11815, 'synset': 'trace_detector.n.01', 'name': 'trace_detector'}, {'id': 11816, 'synset': 'track.n.09', 'name': 'track'}, {'id': 11817, 'synset': 'track.n.08', 'name': 'track'}, {'id': 11818, 'synset': 'trackball.n.01', 'name': 'trackball'}, {'id': 11819, 'synset': 'tracked_vehicle.n.01', 'name': 'tracked_vehicle'}, {'id': 11820, 'synset': 'tract_house.n.01', 'name': 'tract_house'}, {'id': 11821, 'synset': 'tract_housing.n.01', 'name': 'tract_housing'}, {'id': 11822, 'synset': 'traction_engine.n.01', 'name': 'traction_engine'}, {'id': 11823, 'synset': 'tractor.n.02', 'name': 'tractor'}, {'id': 11824, 'synset': 'trailer.n.04', 'name': 'trailer'}, {'id': 11825, 'synset': 'trailer.n.03', 'name': 'trailer'}, {'id': 11826, 'synset': 'trailer_camp.n.01', 'name': 'trailer_camp'}, {'id': 11827, 'synset': 'trailing_edge.n.01', 'name': 'trailing_edge'}, {'id': 11828, 'synset': 'tramline.n.01', 'name': 'tramline'}, {'id': 11829, 'synset': 'trammel.n.02', 'name': 'trammel'}, {'id': 11830, 'synset': 'tramp_steamer.n.01', 'name': 'tramp_steamer'}, {'id': 11831, 'synset': 'tramway.n.01', 'name': 'tramway'}, {'id': 11832, 'synset': 'transdermal_patch.n.01', 'name': 'transdermal_patch'}, {'id': 11833, 'synset': 'transept.n.01', 'name': 'transept'}, {'id': 11834, 'synset': 'transformer.n.01', 'name': 'transformer'}, {'id': 11835, 'synset': 'transistor.n.01', 'name': 'transistor'}, {'id': 11836, 'synset': 'transit_instrument.n.01', 'name': 'transit_instrument'}, {'id': 11837, 'synset': 'transmission.n.05', 'name': 'transmission'}, {'id': 11838, 'synset': 'transmission_shaft.n.01', 'name': 'transmission_shaft'}, {'id': 11839, 'synset': 'transmitter.n.03', 'name': 'transmitter'}, {'id': 11840, 'synset': 'transom.n.02', 'name': 'transom'}, {'id': 11841, 'synset': 'transom.n.01', 'name': 'transom'}, {'id': 11842, 'synset': 'transponder.n.01', 'name': 'transponder'}, {'id': 11843, 'synset': 'transporter.n.02', 'name': 'transporter'}, {'id': 11844, 'synset': 'transporter.n.01', 'name': 'transporter'}, {'id': 11845, 'synset': 'transport_ship.n.01', 'name': 'transport_ship'}, {'id': 11846, 'synset': 'trap.n.01', 'name': 'trap'}, {'id': 11847, 'synset': 'trap_door.n.01', 'name': 'trap_door'}, {'id': 11848, 'synset': 'trapeze.n.01', 'name': 'trapeze'}, {'id': 11849, 'synset': 'trave.n.01', 'name': 'trave'}, {'id': 11850, 'synset': 'travel_iron.n.01', 'name': 'travel_iron'}, {'id': 11851, 'synset': 'trawl.n.02', 'name': 'trawl'}, {'id': 11852, 'synset': 'trawl.n.01', 'name': 'trawl'}, {'id': 11853, 'synset': 'trawler.n.02', 'name': 'trawler'}, {'id': 11854, 'synset': 'tray_cloth.n.01', 'name': 'tray_cloth'}, {'id': 11855, 'synset': 'tread.n.04', 'name': 'tread'}, {'id': 11856, 'synset': 'tread.n.03', 'name': 'tread'}, {'id': 11857, 'synset': 'treadmill.n.02', 'name': 'treadmill'}, {'id': 11858, 'synset': 'treadmill.n.01', 'name': 'treadmill'}, {'id': 11859, 'synset': 'treasure_chest.n.01', 'name': 'treasure_chest'}, {'id': 11860, 'synset': 'treasure_ship.n.01', 'name': 'treasure_ship'}, {'id': 11861, 'synset': 'treenail.n.01', 'name': 'treenail'}, {'id': 11862, 'synset': 'trefoil_arch.n.01', 'name': 'trefoil_arch'}, {'id': 11863, 'synset': 'trellis.n.01', 'name': 'trellis'}, {'id': 11864, 'synset': 'trench.n.01', 'name': 'trench'}, {'id': 11865, 'synset': 'trench_knife.n.01', 'name': 'trench_knife'}, {'id': 11866, 'synset': 'trepan.n.02', 'name': 'trepan'}, {'id': 11867, 'synset': 'trepan.n.01', 'name': 'trepan'}, {'id': 11868, 'synset': 'trestle.n.02', 'name': 'trestle'}, {'id': 11869, 'synset': 'trestle.n.01', 'name': 'trestle'}, {'id': 11870, 'synset': 'trestle_bridge.n.01', 'name': 'trestle_bridge'}, {'id': 11871, 'synset': 'trestle_table.n.01', 'name': 'trestle_table'}, {'id': 11872, 'synset': 'trestlework.n.01', 'name': 'trestlework'}, {'id': 11873, 'synset': 'trews.n.01', 'name': 'trews'}, {'id': 11874, 'synset': 'trial_balloon.n.02', 'name': 'trial_balloon'}, {'id': 11875, 'synset': 'triangle.n.04', 'name': 'triangle'}, {'id': 11876, 'synset': 'triclinium.n.02', 'name': 'triclinium'}, {'id': 11877, 'synset': 'triclinium.n.01', 'name': 'triclinium'}, {'id': 11878, 'synset': 'tricorn.n.01', 'name': 'tricorn'}, {'id': 11879, 'synset': 'tricot.n.01', 'name': 'tricot'}, {'id': 11880, 'synset': 'trident.n.01', 'name': 'trident'}, {'id': 11881, 'synset': 'trigger.n.02', 'name': 'trigger'}, {'id': 11882, 'synset': 'trimaran.n.01', 'name': 'trimaran'}, {'id': 11883, 'synset': 'trimmer.n.02', 'name': 'trimmer'}, {'id': 11884, 'synset': 'trimmer_arch.n.01', 'name': 'trimmer_arch'}, {'id': 11885, 'synset': 'triode.n.01', 'name': 'triode'}, {'id': 11886, 'synset': 'triptych.n.01', 'name': 'triptych'}, {'id': 11887, 'synset': 'trip_wire.n.02', 'name': 'trip_wire'}, {'id': 11888, 'synset': 'trireme.n.01', 'name': 'trireme'}, {'id': 11889, 'synset': 'triskelion.n.01', 'name': 'triskelion'}, {'id': 11890, 'synset': 'triumphal_arch.n.01', 'name': 'triumphal_arch'}, {'id': 11891, 'synset': 'trivet.n.02', 'name': 'trivet'}, {'id': 11892, 'synset': 'trivet.n.01', 'name': 'trivet'}, {'id': 11893, 'synset': 'troika.n.01', 'name': 'troika'}, {'id': 11894, 'synset': 'troll.n.03', 'name': 'troll'}, {'id': 11895, 'synset': 'trolleybus.n.01', 'name': 'trolleybus'}, {'id': 11896, 'synset': 'trombone.n.01', 'name': 'trombone'}, {'id': 11897, 'synset': 'troop_carrier.n.01', 'name': 'troop_carrier'}, {'id': 11898, 'synset': 'troopship.n.01', 'name': 'troopship'}, {'id': 11899, 'synset': 'trophy_case.n.01', 'name': 'trophy_case'}, {'id': 11900, 'synset': 'trough.n.05', 'name': 'trough'}, {'id': 11901, 'synset': 'trouser.n.02', 'name': 'trouser'}, {'id': 11902, 'synset': 'trouser_cuff.n.01', 'name': 'trouser_cuff'}, {'id': 11903, 'synset': 'trouser_press.n.01', 'name': 'trouser_press'}, {'id': 11904, 'synset': 'trousseau.n.01', 'name': 'trousseau'}, {'id': 11905, 'synset': 'trowel.n.01', 'name': 'trowel'}, {'id': 11906, 'synset': 'trumpet_arch.n.01', 'name': 'trumpet_arch'}, {'id': 11907, 'synset': 'truncheon.n.01', 'name': 'truncheon'}, {'id': 11908, 'synset': 'trundle_bed.n.01', 'name': 'trundle_bed'}, {'id': 11909, 'synset': 'trunk_hose.n.01', 'name': 'trunk_hose'}, {'id': 11910, 'synset': 'trunk_lid.n.01', 'name': 'trunk_lid'}, {'id': 11911, 'synset': 'trunk_line.n.02', 'name': 'trunk_line'}, {'id': 11912, 'synset': 'truss.n.02', 'name': 'truss'}, {'id': 11913, 'synset': 'truss_bridge.n.01', 'name': 'truss_bridge'}, {'id': 11914, 'synset': 'try_square.n.01', 'name': 'try_square'}, {'id': 11915, 'synset': 't-square.n.01', 'name': 'T-square'}, {'id': 11916, 'synset': 'tube.n.02', 'name': 'tube'}, {'id': 11917, 'synset': 'tuck_box.n.01', 'name': 'tuck_box'}, {'id': 11918, 'synset': 'tucker.n.04', 'name': 'tucker'}, {'id': 11919, 'synset': 'tucker-bag.n.01', 'name': 'tucker-bag'}, {'id': 11920, 'synset': 'tuck_shop.n.01', 'name': 'tuck_shop'}, {'id': 11921, 'synset': 'tudor_arch.n.01', 'name': 'Tudor_arch'}, {'id': 11922, 'synset': 'tudung.n.01', 'name': 'tudung'}, {'id': 11923, 'synset': 'tugboat.n.01', 'name': 'tugboat'}, {'id': 11924, 'synset': 'tulle.n.01', 'name': 'tulle'}, {'id': 11925, 'synset': 'tumble-dryer.n.01', 'name': 'tumble-dryer'}, {'id': 11926, 'synset': 'tumbler.n.02', 'name': 'tumbler'}, {'id': 11927, 'synset': 'tumbrel.n.01', 'name': 'tumbrel'}, {'id': 11928, 'synset': 'tun.n.01', 'name': 'tun'}, {'id': 11929, 'synset': 'tunic.n.02', 'name': 'tunic'}, {'id': 11930, 'synset': 'tuning_fork.n.01', 'name': 'tuning_fork'}, {'id': 11931, 'synset': 'tupik.n.01', 'name': 'tupik'}, {'id': 11932, 'synset': 'turbine.n.01', 'name': 'turbine'}, {'id': 11933, 'synset': 'turbogenerator.n.01', 'name': 'turbogenerator'}, {'id': 11934, 'synset': 'tureen.n.01', 'name': 'tureen'}, {'id': 11935, 'synset': 'turkish_bath.n.01', 'name': 'Turkish_bath'}, {'id': 11936, 'synset': 'turkish_towel.n.01', 'name': 'Turkish_towel'}, {'id': 11937, 'synset': "turk's_head.n.01", 'name': "Turk's_head"}, {'id': 11938, 'synset': 'turnbuckle.n.01', 'name': 'turnbuckle'}, {'id': 11939, 'synset': 'turner.n.08', 'name': 'turner'}, {'id': 11940, 'synset': 'turnery.n.01', 'name': 'turnery'}, {'id': 11941, 'synset': 'turnpike.n.01', 'name': 'turnpike'}, {'id': 11942, 'synset': 'turnspit.n.01', 'name': 'turnspit'}, {'id': 11943, 'synset': 'turnstile.n.01', 'name': 'turnstile'}, {'id': 11944, 'synset': 'turntable.n.01', 'name': 'turntable'}, {'id': 11945, 'synset': 'turntable.n.02', 'name': 'turntable'}, {'id': 11946, 'synset': 'turret.n.01', 'name': 'turret'}, {'id': 11947, 'synset': 'turret_clock.n.01', 'name': 'turret_clock'}, {'id': 11948, 'synset': 'tweed.n.01', 'name': 'tweed'}, {'id': 11949, 'synset': 'tweeter.n.01', 'name': 'tweeter'}, {'id': 11950, 'synset': 'twenty-two.n.02', 'name': 'twenty-two'}, {'id': 11951, 'synset': 'twenty-two_pistol.n.01', 'name': 'twenty-two_pistol'}, {'id': 11952, 'synset': 'twenty-two_rifle.n.01', 'name': 'twenty-two_rifle'}, {'id': 11953, 'synset': 'twill.n.02', 'name': 'twill'}, {'id': 11954, 'synset': 'twill.n.01', 'name': 'twill'}, {'id': 11955, 'synset': 'twin_bed.n.01', 'name': 'twin_bed'}, {'id': 11956, 'synset': 'twinjet.n.01', 'name': 'twinjet'}, {'id': 11957, 'synset': 'twist_bit.n.01', 'name': 'twist_bit'}, {'id': 11958, 'synset': 'two-by-four.n.01', 'name': 'two-by-four'}, {'id': 11959, 'synset': 'two-man_tent.n.01', 'name': 'two-man_tent'}, {'id': 11960, 'synset': 'two-piece.n.01', 'name': 'two-piece'}, {'id': 11961, 'synset': 'typesetting_machine.n.01', 'name': 'typesetting_machine'}, {'id': 11962, 'synset': 'typewriter_carriage.n.01', 'name': 'typewriter_carriage'}, {'id': 11963, 'synset': 'typewriter_keyboard.n.01', 'name': 'typewriter_keyboard'}, {'id': 11964, 'synset': 'tyrolean.n.02', 'name': 'tyrolean'}, {'id': 11965, 'synset': 'uke.n.01', 'name': 'uke'}, {'id': 11966, 'synset': 'ulster.n.02', 'name': 'ulster'}, {'id': 11967, 'synset': 'ultracentrifuge.n.01', 'name': 'ultracentrifuge'}, {'id': 11968, 'synset': 'ultramicroscope.n.01', 'name': 'ultramicroscope'}, {'id': 11969, 'synset': 'ultrasuede.n.01', 'name': 'Ultrasuede'}, {'id': 11970, 'synset': 'ultraviolet_lamp.n.01', 'name': 'ultraviolet_lamp'}, {'id': 11971, 'synset': 'umbrella_tent.n.01', 'name': 'umbrella_tent'}, {'id': 11972, 'synset': 'undercarriage.n.01', 'name': 'undercarriage'}, {'id': 11973, 'synset': 'undercoat.n.01', 'name': 'undercoat'}, {'id': 11974, 'synset': 'undergarment.n.01', 'name': 'undergarment'}, {'id': 11975, 'synset': 'underpants.n.01', 'name': 'underpants'}, {'id': 11976, 'synset': 'undies.n.01', 'name': 'undies'}, {'id': 11977, 'synset': 'uneven_parallel_bars.n.01', 'name': 'uneven_parallel_bars'}, {'id': 11978, 'synset': 'uniform.n.01', 'name': 'uniform'}, {'id': 11979, 'synset': 'universal_joint.n.01', 'name': 'universal_joint'}, {'id': 11980, 'synset': 'university.n.02', 'name': 'university'}, {'id': 11981, 'synset': 'upholstery.n.01', 'name': 'upholstery'}, {'id': 11982, 'synset': 'upholstery_material.n.01', 'name': 'upholstery_material'}, {'id': 11983, 'synset': 'upholstery_needle.n.01', 'name': 'upholstery_needle'}, {'id': 11984, 'synset': 'uplift.n.02', 'name': 'uplift'}, {'id': 11985, 'synset': 'upper_berth.n.01', 'name': 'upper_berth'}, {'id': 11986, 'synset': 'upright.n.02', 'name': 'upright'}, {'id': 11987, 'synset': 'upset.n.04', 'name': 'upset'}, {'id': 11988, 'synset': 'upstairs.n.01', 'name': 'upstairs'}, {'id': 11989, 'synset': 'urceole.n.01', 'name': 'urceole'}, {'id': 11990, 'synset': 'urn.n.02', 'name': 'urn'}, {'id': 11991, 'synset': 'used-car.n.01', 'name': 'used-car'}, {'id': 11992, 'synset': 'utensil.n.01', 'name': 'utensil'}, {'id': 11993, 'synset': 'uzi.n.01', 'name': 'Uzi'}, {'id': 11994, 'synset': 'vacation_home.n.01', 'name': 'vacation_home'}, {'id': 11995, 'synset': 'vacuum_chamber.n.01', 'name': 'vacuum_chamber'}, {'id': 11996, 'synset': 'vacuum_flask.n.01', 'name': 'vacuum_flask'}, {'id': 11997, 'synset': 'vacuum_gauge.n.01', 'name': 'vacuum_gauge'}, {'id': 11998, 'synset': 'valenciennes.n.02', 'name': 'Valenciennes'}, {'id': 11999, 'synset': 'valise.n.01', 'name': 'valise'}, {'id': 12000, 'synset': 'valve.n.03', 'name': 'valve'}, {'id': 12001, 'synset': 'valve.n.02', 'name': 'valve'}, {'id': 12002, 'synset': 'valve-in-head_engine.n.01', 'name': 'valve-in-head_engine'}, {'id': 12003, 'synset': 'vambrace.n.01', 'name': 'vambrace'}, {'id': 12004, 'synset': 'van.n.05', 'name': 'van'}, {'id': 12005, 'synset': 'van.n.04', 'name': 'van'}, {'id': 12006, 'synset': 'vane.n.02', 'name': 'vane'}, {'id': 12007, 'synset': 'vaporizer.n.01', 'name': 'vaporizer'}, {'id': 12008, 'synset': 'variable-pitch_propeller.n.01', 'name': 'variable-pitch_propeller'}, {'id': 12009, 'synset': 'variometer.n.01', 'name': 'variometer'}, {'id': 12010, 'synset': 'varnish.n.01', 'name': 'varnish'}, {'id': 12011, 'synset': 'vault.n.03', 'name': 'vault'}, {'id': 12012, 'synset': 'vault.n.02', 'name': 'vault'}, {'id': 12013, 'synset': 'vaulting_horse.n.01', 'name': 'vaulting_horse'}, {'id': 12014, 'synset': 'vehicle.n.01', 'name': 'vehicle'}, {'id': 12015, 'synset': 'velcro.n.01', 'name': 'Velcro'}, {'id': 12016, 'synset': 'velocipede.n.01', 'name': 'velocipede'}, {'id': 12017, 'synset': 'velour.n.01', 'name': 'velour'}, {'id': 12018, 'synset': 'velvet.n.01', 'name': 'velvet'}, {'id': 12019, 'synset': 'velveteen.n.01', 'name': 'velveteen'}, {'id': 12020, 'synset': 'veneer.n.01', 'name': 'veneer'}, {'id': 12021, 'synset': 'venetian_blind.n.01', 'name': 'Venetian_blind'}, {'id': 12022, 'synset': 'venn_diagram.n.01', 'name': 'Venn_diagram'}, {'id': 12023, 'synset': 'ventilation.n.02', 'name': 'ventilation'}, {'id': 12024, 'synset': 'ventilation_shaft.n.01', 'name': 'ventilation_shaft'}, {'id': 12025, 'synset': 'ventilator.n.01', 'name': 'ventilator'}, {'id': 12026, 'synset': 'veranda.n.01', 'name': 'veranda'}, {'id': 12027, 'synset': 'verdigris.n.02', 'name': 'verdigris'}, {'id': 12028, 'synset': 'vernier_caliper.n.01', 'name': 'vernier_caliper'}, {'id': 12029, 'synset': 'vernier_scale.n.01', 'name': 'vernier_scale'}, {'id': 12030, 'synset': 'vertical_file.n.01', 'name': 'vertical_file'}, {'id': 12031, 'synset': 'vertical_stabilizer.n.01', 'name': 'vertical_stabilizer'}, {'id': 12032, 'synset': 'vertical_tail.n.01', 'name': 'vertical_tail'}, {'id': 12033, 'synset': 'very_pistol.n.01', 'name': 'Very_pistol'}, {'id': 12034, 'synset': 'vessel.n.02', 'name': 'vessel'}, {'id': 12035, 'synset': 'vessel.n.03', 'name': 'vessel'}, {'id': 12036, 'synset': 'vestiture.n.01', 'name': 'vestiture'}, {'id': 12037, 'synset': 'vestment.n.01', 'name': 'vestment'}, {'id': 12038, 'synset': 'vest_pocket.n.01', 'name': 'vest_pocket'}, {'id': 12039, 'synset': 'vestry.n.02', 'name': 'vestry'}, {'id': 12040, 'synset': 'viaduct.n.01', 'name': 'viaduct'}, {'id': 12041, 'synset': 'vibraphone.n.01', 'name': 'vibraphone'}, {'id': 12042, 'synset': 'vibrator.n.02', 'name': 'vibrator'}, {'id': 12043, 'synset': 'vibrator.n.01', 'name': 'vibrator'}, {'id': 12044, 'synset': 'victrola.n.01', 'name': 'Victrola'}, {'id': 12045, 'synset': 'vicuna.n.02', 'name': 'vicuna'}, {'id': 12046, 'synset': 'videocassette.n.01', 'name': 'videocassette'}, {'id': 12047, 'synset': 'videocassette_recorder.n.01', 'name': 'videocassette_recorder'}, {'id': 12048, 'synset': 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'synset': 'walking_shoe.n.01', 'name': 'walking_shoe'}, {'id': 12095, 'synset': 'walkman.n.01', 'name': 'Walkman'}, {'id': 12096, 'synset': 'walk-up_apartment.n.01', 'name': 'walk-up_apartment'}, {'id': 12097, 'synset': 'wall.n.01', 'name': 'wall'}, {'id': 12098, 'synset': 'wall.n.07', 'name': 'wall'}, {'id': 12099, 'synset': 'wall_tent.n.01', 'name': 'wall_tent'}, {'id': 12100, 'synset': 'wall_unit.n.01', 'name': 'wall_unit'}, {'id': 12101, 'synset': 'wand.n.01', 'name': 'wand'}, {'id': 12102, 'synset': 'wankel_engine.n.01', 'name': 'Wankel_engine'}, {'id': 12103, 'synset': 'ward.n.03', 'name': 'ward'}, {'id': 12104, 'synset': 'wardroom.n.01', 'name': 'wardroom'}, {'id': 12105, 'synset': 'warehouse.n.01', 'name': 'warehouse'}, {'id': 12106, 'synset': 'warming_pan.n.01', 'name': 'warming_pan'}, {'id': 12107, 'synset': 'war_paint.n.02', 'name': 'war_paint'}, {'id': 12108, 'synset': 'warplane.n.01', 'name': 'warplane'}, {'id': 12109, 'synset': 'war_room.n.01', 'name': 'war_room'}, {'id': 12110, 'synset': 'warship.n.01', 'name': 'warship'}, {'id': 12111, 'synset': 'wash.n.01', 'name': 'wash'}, {'id': 12112, 'synset': 'wash-and-wear.n.01', 'name': 'wash-and-wear'}, {'id': 12113, 'synset': 'washbasin.n.02', 'name': 'washbasin'}, {'id': 12114, 'synset': 'washboard.n.02', 'name': 'washboard'}, {'id': 12115, 'synset': 'washboard.n.01', 'name': 'washboard'}, {'id': 12116, 'synset': 'washer.n.02', 'name': 'washer'}, {'id': 12117, 'synset': 'washhouse.n.01', 'name': 'washhouse'}, {'id': 12118, 'synset': 'washroom.n.01', 'name': 'washroom'}, {'id': 12119, 'synset': 'washstand.n.01', 'name': 'washstand'}, {'id': 12120, 'synset': 'washtub.n.01', 'name': 'washtub'}, {'id': 12121, 'synset': 'wastepaper_basket.n.01', 'name': 'wastepaper_basket'}, {'id': 12122, 'synset': 'watch_cap.n.01', 'name': 'watch_cap'}, {'id': 12123, 'synset': 'watch_case.n.01', 'name': 'watch_case'}, {'id': 12124, 'synset': 'watch_glass.n.01', 'name': 'watch_glass'}, {'id': 12125, 'synset': 'watchtower.n.01', 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'name': 'X-ray_film'}, {'id': 12325, 'synset': 'x-ray_machine.n.01', 'name': 'X-ray_machine'}, {'id': 12326, 'synset': 'x-ray_tube.n.01', 'name': 'X-ray_tube'}, {'id': 12327, 'synset': 'yacht_chair.n.01', 'name': 'yacht_chair'}, {'id': 12328, 'synset': 'yagi.n.01', 'name': 'yagi'}, {'id': 12329, 'synset': 'yard.n.09', 'name': 'yard'}, {'id': 12330, 'synset': 'yard.n.08', 'name': 'yard'}, {'id': 12331, 'synset': 'yardarm.n.01', 'name': 'yardarm'}, {'id': 12332, 'synset': 'yard_marker.n.01', 'name': 'yard_marker'}, {'id': 12333, 'synset': 'yardstick.n.02', 'name': 'yardstick'}, {'id': 12334, 'synset': 'yarmulke.n.01', 'name': 'yarmulke'}, {'id': 12335, 'synset': 'yashmak.n.01', 'name': 'yashmak'}, {'id': 12336, 'synset': 'yataghan.n.01', 'name': 'yataghan'}, {'id': 12337, 'synset': 'yawl.n.02', 'name': 'yawl'}, {'id': 12338, 'synset': 'yawl.n.01', 'name': 'yawl'}, {'id': 12339, 'synset': 'yoke.n.01', 'name': 'yoke'}, {'id': 12340, 'synset': 'yoke.n.06', 'name': 'yoke'}, {'id': 12341, 'synset': 'yurt.n.01', 'name': 'yurt'}, {'id': 12342, 'synset': 'zamboni.n.01', 'name': 'Zamboni'}, {'id': 12343, 'synset': 'zero.n.04', 'name': 'zero'}, {'id': 12344, 'synset': 'ziggurat.n.01', 'name': 'ziggurat'}, {'id': 12345, 'synset': 'zill.n.01', 'name': 'zill'}, {'id': 12346, 'synset': 'zip_gun.n.01', 'name': 'zip_gun'}, {'id': 12347, 'synset': 'zither.n.01', 'name': 'zither'}, {'id': 12348, 'synset': 'zoot_suit.n.01', 'name': 'zoot_suit'}, {'id': 12349, 'synset': 'shading.n.01', 'name': 'shading'}, {'id': 12350, 'synset': 'grain.n.10', 'name': 'grain'}, {'id': 12351, 'synset': 'wood_grain.n.01', 'name': 'wood_grain'}, {'id': 12352, 'synset': 'graining.n.01', 'name': 'graining'}, {'id': 12353, 'synset': 'marbleization.n.01', 'name': 'marbleization'}, {'id': 12354, 'synset': 'light.n.07', 'name': 'light'}, {'id': 12355, 'synset': 'aura.n.02', 'name': 'aura'}, {'id': 12356, 'synset': 'sunniness.n.01', 'name': 'sunniness'}, {'id': 12357, 'synset': 'glint.n.02', 'name': 'glint'}, {'id': 12358, 'synset': 'opalescence.n.01', 'name': 'opalescence'}, {'id': 12359, 'synset': 'polish.n.01', 'name': 'polish'}, {'id': 12360, 'synset': 'primary_color_for_pigments.n.01', 'name': 'primary_color_for_pigments'}, {'id': 12361, 'synset': 'primary_color_for_light.n.01', 'name': 'primary_color_for_light'}, {'id': 12362, 'synset': 'colorlessness.n.01', 'name': 'colorlessness'}, {'id': 12363, 'synset': 'mottle.n.01', 'name': 'mottle'}, {'id': 12364, 'synset': 'achromia.n.01', 'name': 'achromia'}, {'id': 12365, 'synset': 'shade.n.02', 'name': 'shade'}, {'id': 12366, 'synset': 'chromatic_color.n.01', 'name': 'chromatic_color'}, {'id': 12367, 'synset': 'black.n.01', 'name': 'black'}, {'id': 12368, 'synset': 'coal_black.n.01', 'name': 'coal_black'}, {'id': 12369, 'synset': 'alabaster.n.03', 'name': 'alabaster'}, {'id': 12370, 'synset': 'bone.n.03', 'name': 'bone'}, {'id': 12371, 'synset': 'gray.n.01', 'name': 'gray'}, {'id': 12372, 'synset': 'ash_grey.n.01', 'name': 'ash_grey'}, {'id': 12373, 'synset': 'charcoal.n.03', 'name': 'charcoal'}, {'id': 12374, 'synset': 'sanguine.n.01', 'name': 'sanguine'}, {'id': 12375, 'synset': 'turkey_red.n.01', 'name': 'Turkey_red'}, {'id': 12376, 'synset': 'crimson.n.01', 'name': 'crimson'}, {'id': 12377, 'synset': 'dark_red.n.01', 'name': 'dark_red'}, {'id': 12378, 'synset': 'claret.n.01', 'name': 'claret'}, {'id': 12379, 'synset': 'fuschia.n.01', 'name': 'fuschia'}, {'id': 12380, 'synset': 'maroon.n.02', 'name': 'maroon'}, {'id': 12381, 'synset': 'orange.n.02', 'name': 'orange'}, {'id': 12382, 'synset': 'reddish_orange.n.01', 'name': 'reddish_orange'}, {'id': 12383, 'synset': 'yellow.n.01', 'name': 'yellow'}, {'id': 12384, 'synset': 'gamboge.n.02', 'name': 'gamboge'}, {'id': 12385, 'synset': 'pale_yellow.n.01', 'name': 'pale_yellow'}, {'id': 12386, 'synset': 'green.n.01', 'name': 'green'}, {'id': 12387, 'synset': 'greenishness.n.01', 'name': 'greenishness'}, {'id': 12388, 'synset': 'sea_green.n.01', 'name': 'sea_green'}, {'id': 12389, 'synset': 'sage_green.n.01', 'name': 'sage_green'}, {'id': 12390, 'synset': 'bottle_green.n.01', 'name': 'bottle_green'}, {'id': 12391, 'synset': 'emerald.n.03', 'name': 'emerald'}, {'id': 12392, 'synset': 'olive_green.n.01', 'name': 'olive_green'}, {'id': 12393, 'synset': 'jade_green.n.01', 'name': 'jade_green'}, {'id': 12394, 'synset': 'blue.n.01', 'name': 'blue'}, {'id': 12395, 'synset': 'azure.n.01', 'name': 'azure'}, {'id': 12396, 'synset': 'steel_blue.n.01', 'name': 'steel_blue'}, {'id': 12397, 'synset': 'greenish_blue.n.01', 'name': 'greenish_blue'}, {'id': 12398, 'synset': 'purplish_blue.n.01', 'name': 'purplish_blue'}, {'id': 12399, 'synset': 'purple.n.01', 'name': 'purple'}, {'id': 12400, 'synset': 'tyrian_purple.n.02', 'name': 'Tyrian_purple'}, {'id': 12401, 'synset': 'indigo.n.03', 'name': 'indigo'}, {'id': 12402, 'synset': 'lavender.n.02', 'name': 'lavender'}, {'id': 12403, 'synset': 'reddish_purple.n.01', 'name': 'reddish_purple'}, {'id': 12404, 'synset': 'pink.n.01', 'name': 'pink'}, {'id': 12405, 'synset': 'carnation.n.02', 'name': 'carnation'}, {'id': 12406, 'synset': 'rose.n.03', 'name': 'rose'}, {'id': 12407, 'synset': 'chestnut.n.04', 'name': 'chestnut'}, {'id': 12408, 'synset': 'chocolate.n.03', 'name': 'chocolate'}, {'id': 12409, 'synset': 'light_brown.n.01', 'name': 'light_brown'}, {'id': 12410, 'synset': 'tan.n.02', 'name': 'tan'}, {'id': 12411, 'synset': 'beige.n.01', 'name': 'beige'}, {'id': 12412, 'synset': 'reddish_brown.n.01', 'name': 'reddish_brown'}, {'id': 12413, 'synset': 'brick_red.n.01', 'name': 'brick_red'}, {'id': 12414, 'synset': 'copper.n.04', 'name': 'copper'}, {'id': 12415, 'synset': 'indian_red.n.03', 'name': 'Indian_red'}, {'id': 12416, 'synset': 'puce.n.01', 'name': 'puce'}, {'id': 12417, 'synset': 'olive.n.05', 'name': 'olive'}, {'id': 12418, 'synset': 'ultramarine.n.02', 'name': 'ultramarine'}, {'id': 12419, 'synset': 'complementary_color.n.01', 'name': 'complementary_color'}, {'id': 12420, 'synset': 'pigmentation.n.02', 'name': 'pigmentation'}, {'id': 12421, 'synset': 'complexion.n.01', 'name': 'complexion'}, {'id': 12422, 'synset': 'ruddiness.n.01', 'name': 'ruddiness'}, {'id': 12423, 'synset': 'nonsolid_color.n.01', 'name': 'nonsolid_color'}, {'id': 12424, 'synset': 'aposematic_coloration.n.01', 'name': 'aposematic_coloration'}, {'id': 12425, 'synset': 'cryptic_coloration.n.01', 'name': 'cryptic_coloration'}, {'id': 12426, 'synset': 'ring.n.01', 'name': 'ring'}, {'id': 12427, 'synset': 'center_of_curvature.n.01', 'name': 'center_of_curvature'}, {'id': 12428, 'synset': 'cadaver.n.01', 'name': 'cadaver'}, {'id': 12429, 'synset': 'mandibular_notch.n.01', 'name': 'mandibular_notch'}, {'id': 12430, 'synset': 'rib.n.05', 'name': 'rib'}, {'id': 12431, 'synset': 'skin.n.01', 'name': 'skin'}, {'id': 12432, 'synset': 'skin_graft.n.01', 'name': 'skin_graft'}, {'id': 12433, 'synset': 'epidermal_cell.n.01', 'name': 'epidermal_cell'}, {'id': 12434, 'synset': 'melanocyte.n.01', 'name': 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'pompadour'}, {'id': 12450, 'synset': 'thatch.n.01', 'name': 'thatch'}, {'id': 12451, 'synset': 'soup-strainer.n.01', 'name': 'soup-strainer'}, {'id': 12452, 'synset': 'mustachio.n.01', 'name': 'mustachio'}, {'id': 12453, 'synset': 'walrus_mustache.n.01', 'name': 'walrus_mustache'}, {'id': 12454, 'synset': 'stubble.n.02', 'name': 'stubble'}, {'id': 12455, 'synset': 'vandyke_beard.n.01', 'name': 'vandyke_beard'}, {'id': 12456, 'synset': 'soul_patch.n.01', 'name': 'soul_patch'}, {'id': 12457, 'synset': 'esophageal_smear.n.01', 'name': 'esophageal_smear'}, {'id': 12458, 'synset': 'paraduodenal_smear.n.01', 'name': 'paraduodenal_smear'}, {'id': 12459, 'synset': 'specimen.n.02', 'name': 'specimen'}, {'id': 12460, 'synset': 'punctum.n.01', 'name': 'punctum'}, {'id': 12461, 'synset': 'glenoid_fossa.n.02', 'name': 'glenoid_fossa'}, {'id': 12462, 'synset': 'diastema.n.01', 'name': 'diastema'}, {'id': 12463, 'synset': 'marrow.n.01', 'name': 'marrow'}, {'id': 12464, 'synset': 'mouth.n.01', 'name': 'mouth'}, {'id': 12465, 'synset': 'canthus.n.01', 'name': 'canthus'}, {'id': 12466, 'synset': 'milk.n.02', 'name': 'milk'}, {'id': 12467, 'synset': "mother's_milk.n.01", 'name': "mother's_milk"}, {'id': 12468, 'synset': 'colostrum.n.01', 'name': 'colostrum'}, {'id': 12469, 'synset': 'vein.n.01', 'name': 'vein'}, {'id': 12470, 'synset': 'ganglion_cell.n.01', 'name': 'ganglion_cell'}, {'id': 12471, 'synset': 'x_chromosome.n.01', 'name': 'X_chromosome'}, {'id': 12472, 'synset': 'embryonic_cell.n.01', 'name': 'embryonic_cell'}, {'id': 12473, 'synset': 'myeloblast.n.01', 'name': 'myeloblast'}, {'id': 12474, 'synset': 'sideroblast.n.01', 'name': 'sideroblast'}, {'id': 12475, 'synset': 'osteocyte.n.01', 'name': 'osteocyte'}, {'id': 12476, 'synset': 'megalocyte.n.01', 'name': 'megalocyte'}, {'id': 12477, 'synset': 'leukocyte.n.01', 'name': 'leukocyte'}, {'id': 12478, 'synset': 'histiocyte.n.01', 'name': 'histiocyte'}, {'id': 12479, 'synset': 'fixed_phagocyte.n.01', 'name': 'fixed_phagocyte'}, {'id': 12480, 'synset': 'lymphocyte.n.01', 'name': 'lymphocyte'}, {'id': 12481, 'synset': 'monoblast.n.01', 'name': 'monoblast'}, {'id': 12482, 'synset': 'neutrophil.n.01', 'name': 'neutrophil'}, {'id': 12483, 'synset': 'microphage.n.01', 'name': 'microphage'}, {'id': 12484, 'synset': 'sickle_cell.n.01', 'name': 'sickle_cell'}, {'id': 12485, 'synset': 'siderocyte.n.01', 'name': 'siderocyte'}, {'id': 12486, 'synset': 'spherocyte.n.01', 'name': 'spherocyte'}, {'id': 12487, 'synset': 'ootid.n.01', 'name': 'ootid'}, {'id': 12488, 'synset': 'oocyte.n.01', 'name': 'oocyte'}, {'id': 12489, 'synset': 'spermatid.n.01', 'name': 'spermatid'}, {'id': 12490, 'synset': 'leydig_cell.n.01', 'name': 'Leydig_cell'}, {'id': 12491, 'synset': 'striated_muscle_cell.n.01', 'name': 'striated_muscle_cell'}, {'id': 12492, 'synset': 'smooth_muscle_cell.n.01', 'name': 'smooth_muscle_cell'}, {'id': 12493, 'synset': "ranvier's_nodes.n.01", 'name': "Ranvier's_nodes"}, {'id': 12494, 'synset': 'neuroglia.n.01', 'name': 'neuroglia'}, {'id': 12495, 'synset': 'astrocyte.n.01', 'name': 'astrocyte'}, {'id': 12496, 'synset': 'protoplasmic_astrocyte.n.01', 'name': 'protoplasmic_astrocyte'}, {'id': 12497, 'synset': 'oligodendrocyte.n.01', 'name': 'oligodendrocyte'}, {'id': 12498, 'synset': 'proprioceptor.n.01', 'name': 'proprioceptor'}, {'id': 12499, 'synset': 'dendrite.n.01', 'name': 'dendrite'}, {'id': 12500, 'synset': 'sensory_fiber.n.01', 'name': 'sensory_fiber'}, {'id': 12501, 'synset': 'subarachnoid_space.n.01', 'name': 'subarachnoid_space'}, {'id': 12502, 'synset': 'cerebral_cortex.n.01', 'name': 'cerebral_cortex'}, {'id': 12503, 'synset': 'renal_cortex.n.01', 'name': 'renal_cortex'}, {'id': 12504, 'synset': 'prepuce.n.02', 'name': 'prepuce'}, {'id': 12505, 'synset': 'head.n.01', 'name': 'head'}, {'id': 12506, 'synset': 'scalp.n.01', 'name': 'scalp'}, {'id': 12507, 'synset': 'frontal_eminence.n.01', 'name': 'frontal_eminence'}, {'id': 12508, 'synset': 'suture.n.01', 'name': 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'synset': 'silver_screen.n.01', 'name': 'silver_screen'}, {'id': 12525, 'synset': 'free_press.n.01', 'name': 'free_press'}, {'id': 12526, 'synset': 'press.n.02', 'name': 'press'}, {'id': 12527, 'synset': 'print_media.n.01', 'name': 'print_media'}, {'id': 12528, 'synset': 'storage_medium.n.01', 'name': 'storage_medium'}, {'id': 12529, 'synset': 'magnetic_storage_medium.n.01', 'name': 'magnetic_storage_medium'}, {'id': 12530, 'synset': 'journalism.n.01', 'name': 'journalism'}, {'id': 12531, 'synset': 'fleet_street.n.02', 'name': 'Fleet_Street'}, {'id': 12532, 'synset': 'photojournalism.n.01', 'name': 'photojournalism'}, {'id': 12533, 'synset': 'news_photography.n.01', 'name': 'news_photography'}, {'id': 12534, 'synset': 'rotogravure.n.02', 'name': 'rotogravure'}, {'id': 12535, 'synset': 'daily.n.01', 'name': 'daily'}, {'id': 12536, 'synset': 'gazette.n.01', 'name': 'gazette'}, {'id': 12537, 'synset': 'school_newspaper.n.01', 'name': 'school_newspaper'}, {'id': 12538, 'synset': 'tabloid.n.02', 'name': 'tabloid'}, {'id': 12539, 'synset': 'yellow_journalism.n.01', 'name': 'yellow_journalism'}, {'id': 12540, 'synset': 'telecommunication.n.01', 'name': 'telecommunication'}, {'id': 12541, 'synset': 'telephone.n.02', 'name': 'telephone'}, {'id': 12542, 'synset': 'voice_mail.n.01', 'name': 'voice_mail'}, {'id': 12543, 'synset': 'call.n.01', 'name': 'call'}, {'id': 12544, 'synset': 'call-back.n.01', 'name': 'call-back'}, {'id': 12545, 'synset': 'collect_call.n.01', 'name': 'collect_call'}, {'id': 12546, 'synset': 'call_forwarding.n.01', 'name': 'call_forwarding'}, {'id': 12547, 'synset': 'call-in.n.01', 'name': 'call-in'}, {'id': 12548, 'synset': 'call_waiting.n.01', 'name': 'call_waiting'}, {'id': 12549, 'synset': 'crank_call.n.01', 'name': 'crank_call'}, {'id': 12550, 'synset': 'local_call.n.01', 'name': 'local_call'}, {'id': 12551, 'synset': 'long_distance.n.01', 'name': 'long_distance'}, {'id': 12552, 'synset': 'toll_call.n.01', 'name': 'toll_call'}, {'id': 12553, 'synset': 'wake-up_call.n.02', 'name': 'wake-up_call'}, {'id': 12554, 'synset': 'three-way_calling.n.01', 'name': 'three-way_calling'}, {'id': 12555, 'synset': 'telegraphy.n.01', 'name': 'telegraphy'}, {'id': 12556, 'synset': 'cable.n.01', 'name': 'cable'}, {'id': 12557, 'synset': 'wireless.n.02', 'name': 'wireless'}, {'id': 12558, 'synset': 'radiotelegraph.n.01', 'name': 'radiotelegraph'}, {'id': 12559, 'synset': 'radiotelephone.n.01', 'name': 'radiotelephone'}, {'id': 12560, 'synset': 'broadcasting.n.02', 'name': 'broadcasting'}, {'id': 12561, 'synset': 'rediffusion.n.01', 'name': 'Rediffusion'}, {'id': 12562, 'synset': 'multiplex.n.01', 'name': 'multiplex'}, {'id': 12563, 'synset': 'radio.n.01', 'name': 'radio'}, {'id': 12564, 'synset': 'television.n.01', 'name': 'television'}, {'id': 12565, 'synset': 'cable_television.n.01', 'name': 'cable_television'}, {'id': 12566, 'synset': 'high-definition_television.n.01', 'name': 'high-definition_television'}, {'id': 12567, 'synset': 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{'id': 12674, 'synset': 'meal.n.01', 'name': 'meal'}, {'id': 12675, 'synset': 'potluck.n.01', 'name': 'potluck'}, {'id': 12676, 'synset': 'refection.n.01', 'name': 'refection'}, {'id': 12677, 'synset': 'refreshment.n.01', 'name': 'refreshment'}, {'id': 12678, 'synset': 'breakfast.n.01', 'name': 'breakfast'}, {'id': 12679, 'synset': 'continental_breakfast.n.01', 'name': 'continental_breakfast'}, {'id': 12680, 'synset': 'brunch.n.01', 'name': 'brunch'}, {'id': 12681, 'synset': 'lunch.n.01', 'name': 'lunch'}, {'id': 12682, 'synset': 'business_lunch.n.01', 'name': 'business_lunch'}, {'id': 12683, 'synset': 'high_tea.n.01', 'name': 'high_tea'}, {'id': 12684, 'synset': 'tea.n.02', 'name': 'tea'}, {'id': 12685, 'synset': 'dinner.n.01', 'name': 'dinner'}, {'id': 12686, 'synset': 'supper.n.01', 'name': 'supper'}, {'id': 12687, 'synset': 'buffet.n.02', 'name': 'buffet'}, {'id': 12688, 'synset': 'picnic.n.03', 'name': 'picnic'}, {'id': 12689, 'synset': 'cookout.n.01', 'name': 'cookout'}, {'id': 12690, 'synset': 'barbecue.n.02', 'name': 'barbecue'}, {'id': 12691, 'synset': 'clambake.n.01', 'name': 'clambake'}, {'id': 12692, 'synset': 'fish_fry.n.01', 'name': 'fish_fry'}, {'id': 12693, 'synset': 'bite.n.04', 'name': 'bite'}, {'id': 12694, 'synset': 'nosh.n.01', 'name': 'nosh'}, {'id': 12695, 'synset': 'nosh-up.n.01', 'name': 'nosh-up'}, {'id': 12696, 'synset': "ploughman's_lunch.n.01", 'name': "ploughman's_lunch"}, {'id': 12697, 'synset': 'coffee_break.n.01', 'name': 'coffee_break'}, {'id': 12698, 'synset': 'banquet.n.02', 'name': 'banquet'}, {'id': 12699, 'synset': 'entree.n.01', 'name': 'entree'}, {'id': 12700, 'synset': 'piece_de_resistance.n.02', 'name': 'piece_de_resistance'}, {'id': 12701, 'synset': 'plate.n.08', 'name': 'plate'}, {'id': 12702, 'synset': 'adobo.n.01', 'name': 'adobo'}, {'id': 12703, 'synset': 'side_dish.n.01', 'name': 'side_dish'}, {'id': 12704, 'synset': 'special.n.02', 'name': 'special'}, {'id': 12705, 'synset': 'chicken_casserole.n.01', 'name': 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12736, 'synset': 'gazpacho.n.01', 'name': 'gazpacho'}, {'id': 12737, 'synset': 'gumbo.n.04', 'name': 'gumbo'}, {'id': 12738, 'synset': 'julienne.n.02', 'name': 'julienne'}, {'id': 12739, 'synset': 'marmite.n.01', 'name': 'marmite'}, {'id': 12740, 'synset': 'mock_turtle_soup.n.01', 'name': 'mock_turtle_soup'}, {'id': 12741, 'synset': 'mulligatawny.n.01', 'name': 'mulligatawny'}, {'id': 12742, 'synset': 'oxtail_soup.n.01', 'name': 'oxtail_soup'}, {'id': 12743, 'synset': 'pea_soup.n.01', 'name': 'pea_soup'}, {'id': 12744, 'synset': 'pepper_pot.n.01', 'name': 'pepper_pot'}, {'id': 12745, 'synset': 'petite_marmite.n.01', 'name': 'petite_marmite'}, {'id': 12746, 'synset': 'potage.n.01', 'name': 'potage'}, {'id': 12747, 'synset': 'pottage.n.01', 'name': 'pottage'}, {'id': 12748, 'synset': 'turtle_soup.n.01', 'name': 'turtle_soup'}, {'id': 12749, 'synset': 'eggdrop_soup.n.01', 'name': 'eggdrop_soup'}, {'id': 12750, 'synset': 'chowder.n.01', 'name': 'chowder'}, {'id': 12751, 'synset': 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'synset': 'brown_sugar.n.01', 'name': 'brown_sugar'}, {'id': 12811, 'synset': 'demerara.n.05', 'name': 'demerara'}, {'id': 12812, 'synset': 'sweet.n.03', 'name': 'sweet'}, {'id': 12813, 'synset': 'confectionery.n.01', 'name': 'confectionery'}, {'id': 12814, 'synset': 'confiture.n.01', 'name': 'confiture'}, {'id': 12815, 'synset': 'sweetmeat.n.01', 'name': 'sweetmeat'}, {'id': 12816, 'synset': 'candy.n.01', 'name': 'candy'}, {'id': 12817, 'synset': 'carob_bar.n.01', 'name': 'carob_bar'}, {'id': 12818, 'synset': 'hardbake.n.01', 'name': 'hardbake'}, {'id': 12819, 'synset': 'hard_candy.n.01', 'name': 'hard_candy'}, {'id': 12820, 'synset': 'barley-sugar.n.01', 'name': 'barley-sugar'}, {'id': 12821, 'synset': 'brandyball.n.01', 'name': 'brandyball'}, {'id': 12822, 'synset': 'jawbreaker.n.01', 'name': 'jawbreaker'}, {'id': 12823, 'synset': 'lemon_drop.n.01', 'name': 'lemon_drop'}, {'id': 12824, 'synset': 'sourball.n.01', 'name': 'sourball'}, {'id': 12825, 'synset': 'patty.n.03', 'name': 'patty'}, {'id': 12826, 'synset': 'peppermint_patty.n.01', 'name': 'peppermint_patty'}, {'id': 12827, 'synset': 'bonbon.n.01', 'name': 'bonbon'}, {'id': 12828, 'synset': 'brittle.n.01', 'name': 'brittle'}, {'id': 12829, 'synset': 'peanut_brittle.n.01', 'name': 'peanut_brittle'}, {'id': 12830, 'synset': 'chewing_gum.n.01', 'name': 'chewing_gum'}, {'id': 12831, 'synset': 'gum_ball.n.01', 'name': 'gum_ball'}, {'id': 12832, 'synset': 'butterscotch.n.01', 'name': 'butterscotch'}, {'id': 12833, 'synset': 'candied_fruit.n.01', 'name': 'candied_fruit'}, {'id': 12834, 'synset': 'candied_apple.n.01', 'name': 'candied_apple'}, {'id': 12835, 'synset': 'crystallized_ginger.n.01', 'name': 'crystallized_ginger'}, {'id': 12836, 'synset': 'grapefruit_peel.n.01', 'name': 'grapefruit_peel'}, {'id': 12837, 'synset': 'lemon_peel.n.02', 'name': 'lemon_peel'}, {'id': 12838, 'synset': 'orange_peel.n.02', 'name': 'orange_peel'}, {'id': 12839, 'synset': 'candied_citrus_peel.n.01', 'name': 'candied_citrus_peel'}, {'id': 12840, 'synset': 'candy_corn.n.01', 'name': 'candy_corn'}, {'id': 12841, 'synset': 'caramel.n.01', 'name': 'caramel'}, {'id': 12842, 'synset': 'center.n.14', 'name': 'center'}, {'id': 12843, 'synset': 'comfit.n.01', 'name': 'comfit'}, {'id': 12844, 'synset': 'cotton_candy.n.01', 'name': 'cotton_candy'}, {'id': 12845, 'synset': 'dragee.n.02', 'name': 'dragee'}, {'id': 12846, 'synset': 'dragee.n.01', 'name': 'dragee'}, {'id': 12847, 'synset': 'fondant.n.01', 'name': 'fondant'}, {'id': 12848, 'synset': 'chocolate_fudge.n.01', 'name': 'chocolate_fudge'}, {'id': 12849, 'synset': 'divinity.n.03', 'name': 'divinity'}, {'id': 12850, 'synset': 'penuche.n.01', 'name': 'penuche'}, {'id': 12851, 'synset': 'gumdrop.n.01', 'name': 'gumdrop'}, {'id': 12852, 'synset': 'jujube.n.03', 'name': 'jujube'}, {'id': 12853, 'synset': 'honey_crisp.n.01', 'name': 'honey_crisp'}, {'id': 12854, 'synset': 'horehound.n.02', 'name': 'horehound'}, {'id': 12855, 'synset': 'peppermint.n.03', 'name': 'peppermint'}, {'id': 12856, 'synset': 'kiss.n.03', 'name': 'kiss'}, {'id': 12857, 'synset': 'molasses_kiss.n.01', 'name': 'molasses_kiss'}, {'id': 12858, 'synset': 'meringue_kiss.n.01', 'name': 'meringue_kiss'}, {'id': 12859, 'synset': 'chocolate_kiss.n.01', 'name': 'chocolate_kiss'}, {'id': 12860, 'synset': 'licorice.n.02', 'name': 'licorice'}, {'id': 12861, 'synset': 'life_saver.n.01', 'name': 'Life_Saver'}, {'id': 12862, 'synset': 'lozenge.n.01', 'name': 'lozenge'}, {'id': 12863, 'synset': 'cachou.n.01', 'name': 'cachou'}, {'id': 12864, 'synset': 'cough_drop.n.01', 'name': 'cough_drop'}, {'id': 12865, 'synset': 'marshmallow.n.01', 'name': 'marshmallow'}, {'id': 12866, 'synset': 'marzipan.n.01', 'name': 'marzipan'}, {'id': 12867, 'synset': 'nougat.n.01', 'name': 'nougat'}, {'id': 12868, 'synset': 'nougat_bar.n.01', 'name': 'nougat_bar'}, {'id': 12869, 'synset': 'nut_bar.n.01', 'name': 'nut_bar'}, {'id': 12870, 'synset': 'peanut_bar.n.01', 'name': 'peanut_bar'}, {'id': 12871, 'synset': 'popcorn_ball.n.01', 'name': 'popcorn_ball'}, {'id': 12872, 'synset': 'praline.n.01', 'name': 'praline'}, {'id': 12873, 'synset': 'rock_candy.n.02', 'name': 'rock_candy'}, {'id': 12874, 'synset': 'rock_candy.n.01', 'name': 'rock_candy'}, {'id': 12875, 'synset': 'sugar_candy.n.01', 'name': 'sugar_candy'}, {'id': 12876, 'synset': 'sugarplum.n.01', 'name': 'sugarplum'}, {'id': 12877, 'synset': 'taffy.n.01', 'name': 'taffy'}, {'id': 12878, 'synset': 'molasses_taffy.n.01', 'name': 'molasses_taffy'}, {'id': 12879, 'synset': 'turkish_delight.n.01', 'name': 'Turkish_Delight'}, {'id': 12880, 'synset': 'dessert.n.01', 'name': 'dessert'}, {'id': 12881, 'synset': 'ambrosia.n.04', 'name': 'ambrosia'}, {'id': 12882, 'synset': 'ambrosia.n.03', 'name': 'ambrosia'}, {'id': 12883, 'synset': 'baked_alaska.n.01', 'name': 'baked_Alaska'}, {'id': 12884, 'synset': 'blancmange.n.01', 'name': 'blancmange'}, {'id': 12885, 'synset': 'charlotte.n.02', 'name': 'charlotte'}, {'id': 12886, 'synset': 'compote.n.01', 'name': 'compote'}, {'id': 12887, 'synset': 'dumpling.n.02', 'name': 'dumpling'}, {'id': 12888, 'synset': 'flan.n.01', 'name': 'flan'}, {'id': 12889, 'synset': 'frozen_dessert.n.01', 'name': 'frozen_dessert'}, {'id': 12890, 'synset': 'junket.n.01', 'name': 'junket'}, {'id': 12891, 'synset': 'mousse.n.02', 'name': 'mousse'}, {'id': 12892, 'synset': 'mousse.n.01', 'name': 'mousse'}, {'id': 12893, 'synset': 'pavlova.n.02', 'name': 'pavlova'}, {'id': 12894, 'synset': 'peach_melba.n.01', 'name': 'peach_melba'}, {'id': 12895, 'synset': 'whip.n.03', 'name': 'whip'}, {'id': 12896, 'synset': 'prune_whip.n.01', 'name': 'prune_whip'}, {'id': 12897, 'synset': 'pudding.n.03', 'name': 'pudding'}, {'id': 12898, 'synset': 'pudding.n.02', 'name': 'pudding'}, {'id': 12899, 'synset': 'syllabub.n.02', 'name': 'syllabub'}, {'id': 12900, 'synset': 'tiramisu.n.01', 'name': 'tiramisu'}, {'id': 12901, 'synset': 'trifle.n.01', 'name': 'trifle'}, {'id': 12902, 'synset': 'tipsy_cake.n.01', 'name': 'tipsy_cake'}, {'id': 12903, 'synset': 'jello.n.01', 'name': 'jello'}, {'id': 12904, 'synset': 'apple_dumpling.n.01', 'name': 'apple_dumpling'}, {'id': 12905, 'synset': 'ice.n.05', 'name': 'ice'}, {'id': 12906, 'synset': 'water_ice.n.02', 'name': 'water_ice'}, {'id': 12907, 'synset': 'ice-cream_cone.n.01', 'name': 'ice-cream_cone'}, {'id': 12908, 'synset': 'chocolate_ice_cream.n.01', 'name': 'chocolate_ice_cream'}, {'id': 12909, 'synset': 'neapolitan_ice_cream.n.01', 'name': 'Neapolitan_ice_cream'}, {'id': 12910, 'synset': 'peach_ice_cream.n.01', 'name': 'peach_ice_cream'}, {'id': 12911, 'synset': 'strawberry_ice_cream.n.01', 'name': 'strawberry_ice_cream'}, {'id': 12912, 'synset': 'tutti-frutti.n.01', 'name': 'tutti-frutti'}, {'id': 12913, 'synset': 'vanilla_ice_cream.n.01', 'name': 'vanilla_ice_cream'}, {'id': 12914, 'synset': 'ice_milk.n.01', 'name': 'ice_milk'}, {'id': 12915, 'synset': 'frozen_yogurt.n.01', 'name': 'frozen_yogurt'}, {'id': 12916, 'synset': 'snowball.n.03', 'name': 'snowball'}, {'id': 12917, 'synset': 'snowball.n.02', 'name': 'snowball'}, {'id': 12918, 'synset': 'parfait.n.01', 'name': 'parfait'}, {'id': 12919, 'synset': 'ice-cream_sundae.n.01', 'name': 'ice-cream_sundae'}, {'id': 12920, 'synset': 'split.n.07', 'name': 'split'}, {'id': 12921, 'synset': 'banana_split.n.01', 'name': 'banana_split'}, {'id': 12922, 'synset': 'frozen_pudding.n.01', 'name': 'frozen_pudding'}, {'id': 12923, 'synset': 'frozen_custard.n.01', 'name': 'frozen_custard'}, {'id': 12924, 'synset': 'flummery.n.01', 'name': 'flummery'}, {'id': 12925, 'synset': 'fish_mousse.n.01', 'name': 'fish_mousse'}, {'id': 12926, 'synset': 'chicken_mousse.n.01', 'name': 'chicken_mousse'}, {'id': 12927, 'synset': 'plum_pudding.n.01', 'name': 'plum_pudding'}, {'id': 12928, 'synset': 'carrot_pudding.n.01', 'name': 'carrot_pudding'}, {'id': 12929, 'synset': 'corn_pudding.n.01', 'name': 'corn_pudding'}, {'id': 12930, 'synset': 'steamed_pudding.n.01', 'name': 'steamed_pudding'}, {'id': 12931, 'synset': 'duff.n.01', 'name': 'duff'}, {'id': 12932, 'synset': 'vanilla_pudding.n.01', 'name': 'vanilla_pudding'}, {'id': 12933, 'synset': 'chocolate_pudding.n.01', 'name': 'chocolate_pudding'}, {'id': 12934, 'synset': 'brown_betty.n.01', 'name': 'brown_Betty'}, {'id': 12935, 'synset': 'nesselrode.n.01', 'name': 'Nesselrode'}, {'id': 12936, 'synset': 'pease_pudding.n.01', 'name': 'pease_pudding'}, {'id': 12937, 'synset': 'custard.n.01', 'name': 'custard'}, {'id': 12938, 'synset': 'creme_caramel.n.01', 'name': 'creme_caramel'}, {'id': 12939, 'synset': 'creme_anglais.n.01', 'name': 'creme_anglais'}, {'id': 12940, 'synset': 'creme_brulee.n.01', 'name': 'creme_brulee'}, {'id': 12941, 'synset': 'fruit_custard.n.01', 'name': 'fruit_custard'}, {'id': 12942, 'synset': 'tapioca.n.01', 'name': 'tapioca'}, {'id': 12943, 'synset': 'tapioca_pudding.n.01', 'name': 'tapioca_pudding'}, {'id': 12944, 'synset': 'roly-poly.n.02', 'name': 'roly-poly'}, {'id': 12945, 'synset': 'suet_pudding.n.01', 'name': 'suet_pudding'}, {'id': 12946, 'synset': 'bavarian_cream.n.01', 'name': 'Bavarian_cream'}, {'id': 12947, 'synset': 'maraschino.n.02', 'name': 'maraschino'}, {'id': 12948, 'synset': 'nonpareil.n.02', 'name': 'nonpareil'}, {'id': 12949, 'synset': 'zabaglione.n.01', 'name': 'zabaglione'}, {'id': 12950, 'synset': 'garnish.n.01', 'name': 'garnish'}, {'id': 12951, 'synset': 'pastry.n.01', 'name': 'pastry'}, {'id': 12952, 'synset': 'turnover.n.02', 'name': 'turnover'}, {'id': 12953, 'synset': 'apple_turnover.n.01', 'name': 'apple_turnover'}, {'id': 12954, 'synset': 'knish.n.01', 'name': 'knish'}, {'id': 12955, 'synset': 'pirogi.n.01', 'name': 'pirogi'}, {'id': 12956, 'synset': 'samosa.n.01', 'name': 'samosa'}, {'id': 12957, 'synset': 'timbale.n.01', 'name': 'timbale'}, {'id': 12958, 'synset': 'puff_paste.n.01', 'name': 'puff_paste'}, {'id': 12959, 'synset': 'phyllo.n.01', 'name': 'phyllo'}, {'id': 12960, 'synset': 'puff_batter.n.01', 'name': 'puff_batter'}, {'id': 12961, 'synset': 'ice-cream_cake.n.01', 'name': 'ice-cream_cake'}, {'id': 12962, 'synset': 'fish_cake.n.01', 'name': 'fish_cake'}, {'id': 12963, 'synset': 'fish_stick.n.01', 'name': 'fish_stick'}, {'id': 12964, 'synset': 'conserve.n.01', 'name': 'conserve'}, {'id': 12965, 'synset': 'apple_butter.n.01', 'name': 'apple_butter'}, {'id': 12966, 'synset': 'chowchow.n.02', 'name': 'chowchow'}, {'id': 12967, 'synset': 'lemon_curd.n.01', 'name': 'lemon_curd'}, {'id': 12968, 'synset': 'strawberry_jam.n.01', 'name': 'strawberry_jam'}, {'id': 12969, 'synset': 'jelly.n.02', 'name': 'jelly'}, {'id': 12970, 'synset': 'apple_jelly.n.01', 'name': 'apple_jelly'}, {'id': 12971, 'synset': 'crabapple_jelly.n.01', 'name': 'crabapple_jelly'}, {'id': 12972, 'synset': 'grape_jelly.n.01', 'name': 'grape_jelly'}, {'id': 12973, 'synset': 'marmalade.n.01', 'name': 'marmalade'}, {'id': 12974, 'synset': 'orange_marmalade.n.01', 'name': 'orange_marmalade'}, {'id': 12975, 'synset': 'gelatin_dessert.n.01', 'name': 'gelatin_dessert'}, {'id': 12976, 'synset': 'buffalo_wing.n.01', 'name': 'buffalo_wing'}, {'id': 12977, 'synset': 'barbecued_wing.n.01', 'name': 'barbecued_wing'}, {'id': 12978, 'synset': 'mess.n.03', 'name': 'mess'}, {'id': 12979, 'synset': 'mince.n.01', 'name': 'mince'}, {'id': 12980, 'synset': 'puree.n.01', 'name': 'puree'}, {'id': 12981, 'synset': 'barbecue.n.01', 'name': 'barbecue'}, {'id': 12982, 'synset': 'biryani.n.01', 'name': 'biryani'}, {'id': 12983, 'synset': 'escalope_de_veau_orloff.n.01', 'name': 'escalope_de_veau_Orloff'}, {'id': 12984, 'synset': 'saute.n.01', 'name': 'saute'}, {'id': 12985, 'synset': 'veal_parmesan.n.01', 'name': 'veal_parmesan'}, {'id': 12986, 'synset': 'veal_cordon_bleu.n.01', 'name': 'veal_cordon_bleu'}, {'id': 12987, 'synset': 'margarine.n.01', 'name': 'margarine'}, {'id': 12988, 'synset': 'mincemeat.n.01', 'name': 'mincemeat'}, {'id': 12989, 'synset': 'stuffing.n.01', 'name': 'stuffing'}, {'id': 12990, 'synset': 'turkey_stuffing.n.01', 'name': 'turkey_stuffing'}, {'id': 12991, 'synset': 'oyster_stuffing.n.01', 'name': 'oyster_stuffing'}, {'id': 12992, 'synset': 'forcemeat.n.01', 'name': 'forcemeat'}, {'id': 12993, 'synset': 'anadama_bread.n.01', 'name': 'anadama_bread'}, {'id': 12994, 'synset': 'bap.n.01', 'name': 'bap'}, {'id': 12995, 'synset': 'barmbrack.n.01', 'name': 'barmbrack'}, {'id': 12996, 'synset': 'breadstick.n.01', 'name': 'breadstick'}, {'id': 12997, 'synset': 'grissino.n.01', 'name': 'grissino'}, {'id': 12998, 'synset': 'brown_bread.n.02', 'name': 'brown_bread'}, {'id': 12999, 'synset': 'tea_bread.n.01', 'name': 'tea_bread'}, {'id': 13000, 'synset': 'caraway_seed_bread.n.01', 'name': 'caraway_seed_bread'}, {'id': 13001, 'synset': 'challah.n.01', 'name': 'challah'}, {'id': 13002, 'synset': 'cinnamon_bread.n.01', 'name': 'cinnamon_bread'}, {'id': 13003, 'synset': 'cracked-wheat_bread.n.01', 'name': 'cracked-wheat_bread'}, {'id': 13004, 'synset': 'dark_bread.n.01', 'name': 'dark_bread'}, {'id': 13005, 'synset': 'english_muffin.n.01', 'name': 'English_muffin'}, {'id': 13006, 'synset': 'flatbread.n.01', 'name': 'flatbread'}, {'id': 13007, 'synset': 'garlic_bread.n.01', 'name': 'garlic_bread'}, {'id': 13008, 'synset': 'gluten_bread.n.01', 'name': 'gluten_bread'}, {'id': 13009, 'synset': 'graham_bread.n.01', 'name': 'graham_bread'}, {'id': 13010, 'synset': 'host.n.09', 'name': 'Host'}, {'id': 13011, 'synset': 'flatbrod.n.01', 'name': 'flatbrod'}, {'id': 13012, 'synset': 'bannock.n.01', 'name': 'bannock'}, {'id': 13013, 'synset': 'chapatti.n.01', 'name': 'chapatti'}, {'id': 13014, 'synset': 'loaf_of_bread.n.01', 'name': 'loaf_of_bread'}, {'id': 13015, 'synset': 'french_loaf.n.01', 'name': 'French_loaf'}, {'id': 13016, 'synset': 'matzo.n.01', 'name': 'matzo'}, {'id': 13017, 'synset': 'nan.n.04', 'name': 'nan'}, {'id': 13018, 'synset': 'onion_bread.n.01', 'name': 'onion_bread'}, {'id': 13019, 'synset': 'raisin_bread.n.01', 'name': 'raisin_bread'}, {'id': 13020, 'synset': 'quick_bread.n.01', 'name': 'quick_bread'}, {'id': 13021, 'synset': 'banana_bread.n.01', 'name': 'banana_bread'}, {'id': 13022, 'synset': 'date_bread.n.01', 'name': 'date_bread'}, {'id': 13023, 'synset': 'date-nut_bread.n.01', 'name': 'date-nut_bread'}, {'id': 13024, 'synset': 'nut_bread.n.01', 'name': 'nut_bread'}, {'id': 13025, 'synset': 'oatcake.n.01', 'name': 'oatcake'}, {'id': 13026, 'synset': 'irish_soda_bread.n.01', 'name': 'Irish_soda_bread'}, {'id': 13027, 'synset': 'skillet_bread.n.01', 'name': 'skillet_bread'}, {'id': 13028, 'synset': 'rye_bread.n.01', 'name': 'rye_bread'}, {'id': 13029, 'synset': 'black_bread.n.01', 'name': 'black_bread'}, {'id': 13030, 'synset': 'jewish_rye_bread.n.01', 'name': 'Jewish_rye_bread'}, {'id': 13031, 'synset': 'limpa.n.01', 'name': 'limpa'}, {'id': 13032, 'synset': 'swedish_rye_bread.n.01', 'name': 'Swedish_rye_bread'}, {'id': 13033, 'synset': 'salt-rising_bread.n.01', 'name': 'salt-rising_bread'}, {'id': 13034, 'synset': 'simnel.n.01', 'name': 'simnel'}, {'id': 13035, 'synset': 'sour_bread.n.01', 'name': 'sour_bread'}, {'id': 13036, 'synset': 'wafer.n.03', 'name': 'wafer'}, {'id': 13037, 'synset': 'white_bread.n.01', 'name': 'white_bread'}, {'id': 13038, 'synset': 'french_bread.n.01', 'name': 'French_bread'}, {'id': 13039, 'synset': 'italian_bread.n.01', 'name': 'Italian_bread'}, {'id': 13040, 'synset': 'corn_cake.n.01', 'name': 'corn_cake'}, {'id': 13041, 'synset': 'skillet_corn_bread.n.01', 'name': 'skillet_corn_bread'}, {'id': 13042, 'synset': 'ashcake.n.01', 'name': 'ashcake'}, {'id': 13043, 'synset': 'hoecake.n.01', 'name': 'hoecake'}, {'id': 13044, 'synset': 'cornpone.n.01', 'name': 'cornpone'}, {'id': 13045, 'synset': 'corn_dab.n.01', 'name': 'corn_dab'}, {'id': 13046, 'synset': 'hush_puppy.n.01', 'name': 'hush_puppy'}, {'id': 13047, 'synset': 'johnnycake.n.01', 'name': 'johnnycake'}, {'id': 13048, 'synset': 'shawnee_cake.n.01', 'name': 'Shawnee_cake'}, {'id': 13049, 'synset': 'spoon_bread.n.01', 'name': 'spoon_bread'}, {'id': 13050, 'synset': 'cinnamon_toast.n.01', 'name': 'cinnamon_toast'}, {'id': 13051, 'synset': 'orange_toast.n.01', 'name': 'orange_toast'}, {'id': 13052, 'synset': 'melba_toast.n.01', 'name': 'Melba_toast'}, {'id': 13053, 'synset': 'zwieback.n.01', 'name': 'zwieback'}, {'id': 13054, 'synset': 'frankfurter_bun.n.01', 'name': 'frankfurter_bun'}, {'id': 13055, 'synset': 'hamburger_bun.n.01', 'name': 'hamburger_bun'}, {'id': 13056, 'synset': 'bran_muffin.n.01', 'name': 'bran_muffin'}, {'id': 13057, 'synset': 'corn_muffin.n.01', 'name': 'corn_muffin'}, {'id': 13058, 'synset': 'yorkshire_pudding.n.01', 'name': 'Yorkshire_pudding'}, {'id': 13059, 'synset': 'popover.n.01', 'name': 'popover'}, {'id': 13060, 'synset': 'scone.n.01', 'name': 'scone'}, {'id': 13061, 'synset': 'drop_scone.n.01', 'name': 'drop_scone'}, {'id': 13062, 'synset': 'cross_bun.n.01', 'name': 'cross_bun'}, {'id': 13063, 'synset': 'brioche.n.01', 'name': 'brioche'}, {'id': 13064, 'synset': 'hard_roll.n.01', 'name': 'hard_roll'}, {'id': 13065, 'synset': 'soft_roll.n.01', 'name': 'soft_roll'}, {'id': 13066, 'synset': 'kaiser_roll.n.01', 'name': 'kaiser_roll'}, {'id': 13067, 'synset': 'parker_house_roll.n.01', 'name': 'Parker_House_roll'}, {'id': 13068, 'synset': 'clover-leaf_roll.n.01', 'name': 'clover-leaf_roll'}, {'id': 13069, 'synset': 'onion_roll.n.01', 'name': 'onion_roll'}, {'id': 13070, 'synset': 'bialy.n.01', 'name': 'bialy'}, {'id': 13071, 'synset': 'sweet_roll.n.01', 'name': 'sweet_roll'}, {'id': 13072, 'synset': 'bear_claw.n.01', 'name': 'bear_claw'}, {'id': 13073, 'synset': 'cinnamon_roll.n.01', 'name': 'cinnamon_roll'}, {'id': 13074, 'synset': 'honey_bun.n.01', 'name': 'honey_bun'}, {'id': 13075, 'synset': 'pinwheel_roll.n.01', 'name': 'pinwheel_roll'}, {'id': 13076, 'synset': 'danish.n.02', 'name': 'danish'}, {'id': 13077, 'synset': 'onion_bagel.n.01', 'name': 'onion_bagel'}, {'id': 13078, 'synset': 'biscuit.n.01', 'name': 'biscuit'}, {'id': 13079, 'synset': 'rolled_biscuit.n.01', 'name': 'rolled_biscuit'}, {'id': 13080, 'synset': 'baking-powder_biscuit.n.01', 'name': 'baking-powder_biscuit'}, {'id': 13081, 'synset': 'buttermilk_biscuit.n.01', 'name': 'buttermilk_biscuit'}, {'id': 13082, 'synset': 'shortcake.n.01', 'name': 'shortcake'}, {'id': 13083, 'synset': 'hardtack.n.01', 'name': 'hardtack'}, {'id': 13084, 'synset': 'saltine.n.01', 'name': 'saltine'}, {'id': 13085, 'synset': 'soda_cracker.n.01', 'name': 'soda_cracker'}, {'id': 13086, 'synset': 'oyster_cracker.n.01', 'name': 'oyster_cracker'}, {'id': 13087, 'synset': 'water_biscuit.n.01', 'name': 'water_biscuit'}, {'id': 13088, 'synset': 'graham_cracker.n.01', 'name': 'graham_cracker'}, {'id': 13089, 'synset': 'soft_pretzel.n.01', 'name': 'soft_pretzel'}, {'id': 13090, 'synset': 'sandwich_plate.n.01', 'name': 'sandwich_plate'}, {'id': 13091, 'synset': 'butty.n.01', 'name': 'butty'}, {'id': 13092, 'synset': 'ham_sandwich.n.01', 'name': 'ham_sandwich'}, {'id': 13093, 'synset': 'chicken_sandwich.n.01', 'name': 'chicken_sandwich'}, {'id': 13094, 'synset': 'club_sandwich.n.01', 'name': 'club_sandwich'}, {'id': 13095, 'synset': 'open-face_sandwich.n.01', 'name': 'open-face_sandwich'}, {'id': 13096, 'synset': 'cheeseburger.n.01', 'name': 'cheeseburger'}, {'id': 13097, 'synset': 'tunaburger.n.01', 'name': 'tunaburger'}, {'id': 13098, 'synset': 'hotdog.n.02', 'name': 'hotdog'}, {'id': 13099, 'synset': 'sloppy_joe.n.01', 'name': 'Sloppy_Joe'}, {'id': 13100, 'synset': 'bomber.n.03', 'name': 'bomber'}, {'id': 13101, 'synset': 'gyro.n.01', 'name': 'gyro'}, {'id': 13102, 'synset': 'bacon-lettuce-tomato_sandwich.n.01', 'name': 'bacon-lettuce-tomato_sandwich'}, {'id': 13103, 'synset': 'reuben.n.02', 'name': 'Reuben'}, {'id': 13104, 'synset': 'western.n.02', 'name': 'western'}, {'id': 13105, 'synset': 'wrap.n.02', 'name': 'wrap'}, {'id': 13106, 'synset': 'spaghetti.n.01', 'name': 'spaghetti'}, {'id': 13107, 'synset': 'hasty_pudding.n.01', 'name': 'hasty_pudding'}, {'id': 13108, 'synset': 'gruel.n.01', 'name': 'gruel'}, {'id': 13109, 'synset': 'congee.n.01', 'name': 'congee'}, {'id': 13110, 'synset': 'skilly.n.01', 'name': 'skilly'}, {'id': 13111, 'synset': 'edible_fruit.n.01', 'name': 'edible_fruit'}, {'id': 13112, 'synset': 'vegetable.n.01', 'name': 'vegetable'}, {'id': 13113, 'synset': 'julienne.n.01', 'name': 'julienne'}, {'id': 13114, 'synset': 'raw_vegetable.n.01', 'name': 'raw_vegetable'}, {'id': 13115, 'synset': 'crudites.n.01', 'name': 'crudites'}, {'id': 13116, 'synset': 'celery_stick.n.01', 'name': 'celery_stick'}, {'id': 13117, 'synset': 'legume.n.03', 'name': 'legume'}, {'id': 13118, 'synset': 'pulse.n.04', 'name': 'pulse'}, {'id': 13119, 'synset': 'potherb.n.01', 'name': 'potherb'}, {'id': 13120, 'synset': 'greens.n.01', 'name': 'greens'}, {'id': 13121, 'synset': 'chop-suey_greens.n.02', 'name': 'chop-suey_greens'}, {'id': 13122, 'synset': 'solanaceous_vegetable.n.01', 'name': 'solanaceous_vegetable'}, {'id': 13123, 'synset': 'root_vegetable.n.01', 'name': 'root_vegetable'}, {'id': 13124, 'synset': 'baked_potato.n.01', 'name': 'baked_potato'}, {'id': 13125, 'synset': 'french_fries.n.01', 'name': 'french_fries'}, {'id': 13126, 'synset': 'home_fries.n.01', 'name': 'home_fries'}, {'id': 13127, 'synset': 'jacket_potato.n.01', 'name': 'jacket_potato'}, {'id': 13128, 'synset': 'potato_skin.n.01', 'name': 'potato_skin'}, {'id': 13129, 'synset': 'uruguay_potato.n.02', 'name': 'Uruguay_potato'}, {'id': 13130, 'synset': 'yam.n.04', 'name': 'yam'}, {'id': 13131, 'synset': 'yam.n.03', 'name': 'yam'}, {'id': 13132, 'synset': 'snack_food.n.01', 'name': 'snack_food'}, {'id': 13133, 'synset': 'corn_chip.n.01', 'name': 'corn_chip'}, {'id': 13134, 'synset': 'tortilla_chip.n.01', 'name': 'tortilla_chip'}, {'id': 13135, 'synset': 'nacho.n.01', 'name': 'nacho'}, {'id': 13136, 'synset': 'pieplant.n.01', 'name': 'pieplant'}, {'id': 13137, 'synset': 'cruciferous_vegetable.n.01', 'name': 'cruciferous_vegetable'}, {'id': 13138, 'synset': 'mustard.n.03', 'name': 'mustard'}, {'id': 13139, 'synset': 'cabbage.n.01', 'name': 'cabbage'}, {'id': 13140, 'synset': 'kale.n.03', 'name': 'kale'}, {'id': 13141, 'synset': 'collards.n.01', 'name': 'collards'}, {'id': 13142, 'synset': 'chinese_cabbage.n.02', 'name': 'Chinese_cabbage'}, {'id': 13143, 'synset': 'bok_choy.n.02', 'name': 'bok_choy'}, {'id': 13144, 'synset': 'head_cabbage.n.02', 'name': 'head_cabbage'}, {'id': 13145, 'synset': 'red_cabbage.n.02', 'name': 'red_cabbage'}, {'id': 13146, 'synset': 'savoy_cabbage.n.02', 'name': 'savoy_cabbage'}, {'id': 13147, 'synset': 'broccoli.n.02', 'name': 'broccoli'}, {'id': 13148, 'synset': 'broccoli_rabe.n.02', 'name': 'broccoli_rabe'}, {'id': 13149, 'synset': 'squash.n.02', 'name': 'squash'}, {'id': 13150, 'synset': 'summer_squash.n.02', 'name': 'summer_squash'}, {'id': 13151, 'synset': 'yellow_squash.n.02', 'name': 'yellow_squash'}, {'id': 13152, 'synset': 'crookneck.n.01', 'name': 'crookneck'}, {'id': 13153, 'synset': 'marrow.n.04', 'name': 'marrow'}, {'id': 13154, 'synset': 'cocozelle.n.02', 'name': 'cocozelle'}, {'id': 13155, 'synset': 'pattypan_squash.n.02', 'name': 'pattypan_squash'}, {'id': 13156, 'synset': 'spaghetti_squash.n.02', 'name': 'spaghetti_squash'}, {'id': 13157, 'synset': 'winter_squash.n.02', 'name': 'winter_squash'}, {'id': 13158, 'synset': 'acorn_squash.n.02', 'name': 'acorn_squash'}, {'id': 13159, 'synset': 'butternut_squash.n.02', 'name': 'butternut_squash'}, {'id': 13160, 'synset': 'hubbard_squash.n.02', 'name': 'hubbard_squash'}, {'id': 13161, 'synset': 'turban_squash.n.02', 'name': 'turban_squash'}, {'id': 13162, 'synset': 'buttercup_squash.n.02', 'name': 'buttercup_squash'}, {'id': 13163, 'synset': 'cushaw.n.02', 'name': 'cushaw'}, {'id': 13164, 'synset': 'winter_crookneck_squash.n.02', 'name': 'winter_crookneck_squash'}, {'id': 13165, 'synset': 'gherkin.n.02', 'name': 'gherkin'}, {'id': 13166, 'synset': 'artichoke_heart.n.01', 'name': 'artichoke_heart'}, {'id': 13167, 'synset': 'jerusalem_artichoke.n.03', 'name': 'Jerusalem_artichoke'}, {'id': 13168, 'synset': 'bamboo_shoot.n.01', 'name': 'bamboo_shoot'}, {'id': 13169, 'synset': 'sprout.n.02', 'name': 'sprout'}, {'id': 13170, 'synset': 'bean_sprout.n.01', 'name': 'bean_sprout'}, {'id': 13171, 'synset': 'alfalfa_sprout.n.01', 'name': 'alfalfa_sprout'}, {'id': 13172, 'synset': 'beet.n.02', 'name': 'beet'}, {'id': 13173, 'synset': 'beet_green.n.01', 'name': 'beet_green'}, {'id': 13174, 'synset': 'sugar_beet.n.02', 'name': 'sugar_beet'}, {'id': 13175, 'synset': 'mangel-wurzel.n.02', 'name': 'mangel-wurzel'}, {'id': 13176, 'synset': 'chard.n.02', 'name': 'chard'}, {'id': 13177, 'synset': 'pepper.n.04', 'name': 'pepper'}, {'id': 13178, 'synset': 'sweet_pepper.n.02', 'name': 'sweet_pepper'}, {'id': 13179, 'synset': 'green_pepper.n.01', 'name': 'green_pepper'}, {'id': 13180, 'synset': 'globe_pepper.n.01', 'name': 'globe_pepper'}, {'id': 13181, 'synset': 'pimento.n.02', 'name': 'pimento'}, {'id': 13182, 'synset': 'hot_pepper.n.02', 'name': 'hot_pepper'}, {'id': 13183, 'synset': 'jalapeno.n.02', 'name': 'jalapeno'}, {'id': 13184, 'synset': 'chipotle.n.01', 'name': 'chipotle'}, {'id': 13185, 'synset': 'cayenne.n.03', 'name': 'cayenne'}, {'id': 13186, 'synset': 'tabasco.n.03', 'name': 'tabasco'}, {'id': 13187, 'synset': 'onion.n.03', 'name': 'onion'}, {'id': 13188, 'synset': 'bermuda_onion.n.01', 'name': 'Bermuda_onion'}, {'id': 13189, 'synset': 'vidalia_onion.n.01', 'name': 'Vidalia_onion'}, {'id': 13190, 'synset': 'spanish_onion.n.01', 'name': 'Spanish_onion'}, {'id': 13191, 'synset': 'purple_onion.n.01', 'name': 'purple_onion'}, {'id': 13192, 'synset': 'leek.n.02', 'name': 'leek'}, {'id': 13193, 'synset': 'shallot.n.03', 'name': 'shallot'}, {'id': 13194, 'synset': 'salad_green.n.01', 'name': 'salad_green'}, {'id': 13195, 'synset': 'lettuce.n.03', 'name': 'lettuce'}, {'id': 13196, 'synset': 'butterhead_lettuce.n.01', 'name': 'butterhead_lettuce'}, {'id': 13197, 'synset': 'buttercrunch.n.01', 'name': 'buttercrunch'}, {'id': 13198, 'synset': 'bibb_lettuce.n.01', 'name': 'Bibb_lettuce'}, {'id': 13199, 'synset': 'boston_lettuce.n.01', 'name': 'Boston_lettuce'}, {'id': 13200, 'synset': 'crisphead_lettuce.n.01', 'name': 'crisphead_lettuce'}, {'id': 13201, 'synset': 'cos.n.02', 'name': 'cos'}, {'id': 13202, 'synset': 'leaf_lettuce.n.02', 'name': 'leaf_lettuce'}, {'id': 13203, 'synset': 'celtuce.n.02', 'name': 'celtuce'}, {'id': 13204, 'synset': 'bean.n.01', 'name': 'bean'}, {'id': 13205, 'synset': 'goa_bean.n.02', 'name': 'goa_bean'}, {'id': 13206, 'synset': 'lentil.n.01', 'name': 'lentil'}, {'id': 13207, 'synset': 'green_pea.n.01', 'name': 'green_pea'}, {'id': 13208, 'synset': 'marrowfat_pea.n.01', 'name': 'marrowfat_pea'}, {'id': 13209, 'synset': 'snow_pea.n.02', 'name': 'snow_pea'}, {'id': 13210, 'synset': 'sugar_snap_pea.n.02', 'name': 'sugar_snap_pea'}, {'id': 13211, 'synset': 'split-pea.n.01', 'name': 'split-pea'}, {'id': 13212, 'synset': 'chickpea.n.03', 'name': 'chickpea'}, {'id': 13213, 'synset': 'cajan_pea.n.02', 'name': 'cajan_pea'}, {'id': 13214, 'synset': 'field_pea.n.03', 'name': 'field_pea'}, {'id': 13215, 'synset': 'mushy_peas.n.01', 'name': 'mushy_peas'}, {'id': 13216, 'synset': 'black-eyed_pea.n.03', 'name': 'black-eyed_pea'}, {'id': 13217, 'synset': 'common_bean.n.02', 'name': 'common_bean'}, {'id': 13218, 'synset': 'kidney_bean.n.02', 'name': 'kidney_bean'}, {'id': 13219, 'synset': 'navy_bean.n.01', 'name': 'navy_bean'}, {'id': 13220, 'synset': 'pinto_bean.n.01', 'name': 'pinto_bean'}, {'id': 13221, 'synset': 'frijole.n.02', 'name': 'frijole'}, {'id': 13222, 'synset': 'black_bean.n.01', 'name': 'black_bean'}, {'id': 13223, 'synset': 'fresh_bean.n.01', 'name': 'fresh_bean'}, {'id': 13224, 'synset': 'flageolet.n.01', 'name': 'flageolet'}, {'id': 13225, 'synset': 'green_bean.n.01', 'name': 'green_bean'}, {'id': 13226, 'synset': 'snap_bean.n.01', 'name': 'snap_bean'}, {'id': 13227, 'synset': 'string_bean.n.01', 'name': 'string_bean'}, {'id': 13228, 'synset': 'kentucky_wonder.n.01', 'name': 'Kentucky_wonder'}, {'id': 13229, 'synset': 'scarlet_runner.n.03', 'name': 'scarlet_runner'}, {'id': 13230, 'synset': 'haricot_vert.n.01', 'name': 'haricot_vert'}, {'id': 13231, 'synset': 'wax_bean.n.02', 'name': 'wax_bean'}, {'id': 13232, 'synset': 'shell_bean.n.02', 'name': 'shell_bean'}, {'id': 13233, 'synset': 'lima_bean.n.03', 'name': 'lima_bean'}, {'id': 13234, 'synset': 'fordhooks.n.01', 'name': 'Fordhooks'}, {'id': 13235, 'synset': 'sieva_bean.n.02', 'name': 'sieva_bean'}, {'id': 13236, 'synset': 'fava_bean.n.02', 'name': 'fava_bean'}, {'id': 13237, 'synset': 'soy.n.04', 'name': 'soy'}, {'id': 13238, 'synset': 'green_soybean.n.01', 'name': 'green_soybean'}, {'id': 13239, 'synset': 'field_soybean.n.01', 'name': 'field_soybean'}, {'id': 13240, 'synset': 'cardoon.n.02', 'name': 'cardoon'}, {'id': 13241, 'synset': 'carrot.n.03', 'name': 'carrot'}, {'id': 13242, 'synset': 'carrot_stick.n.01', 'name': 'carrot_stick'}, {'id': 13243, 'synset': 'celery.n.02', 'name': 'celery'}, {'id': 13244, 'synset': 'pascal_celery.n.01', 'name': 'pascal_celery'}, {'id': 13245, 'synset': 'celeriac.n.02', 'name': 'celeriac'}, {'id': 13246, 'synset': 'chicory.n.04', 'name': 'chicory'}, {'id': 13247, 'synset': 'radicchio.n.01', 'name': 'radicchio'}, {'id': 13248, 'synset': 'coffee_substitute.n.01', 'name': 'coffee_substitute'}, {'id': 13249, 'synset': 'chicory.n.03', 'name': 'chicory'}, {'id': 13250, 'synset': 'postum.n.01', 'name': 'Postum'}, {'id': 13251, 'synset': 'chicory_escarole.n.01', 'name': 'chicory_escarole'}, {'id': 13252, 'synset': 'belgian_endive.n.01', 'name': 'Belgian_endive'}, {'id': 13253, 'synset': 'sweet_corn.n.02', 'name': 'sweet_corn'}, {'id': 13254, 'synset': 'hominy.n.01', 'name': 'hominy'}, {'id': 13255, 'synset': 'lye_hominy.n.01', 'name': 'lye_hominy'}, {'id': 13256, 'synset': 'pearl_hominy.n.01', 'name': 'pearl_hominy'}, {'id': 13257, 'synset': 'popcorn.n.02', 'name': 'popcorn'}, {'id': 13258, 'synset': 'cress.n.02', 'name': 'cress'}, {'id': 13259, 'synset': 'watercress.n.02', 'name': 'watercress'}, {'id': 13260, 'synset': 'garden_cress.n.01', 'name': 'garden_cress'}, {'id': 13261, 'synset': 'winter_cress.n.02', 'name': 'winter_cress'}, {'id': 13262, 'synset': 'dandelion_green.n.02', 'name': 'dandelion_green'}, {'id': 13263, 'synset': 'gumbo.n.03', 'name': 'gumbo'}, {'id': 13264, 'synset': 'kohlrabi.n.02', 'name': 'kohlrabi'}, {'id': 13265, 'synset': "lamb's-quarter.n.01", 'name': "lamb's-quarter"}, {'id': 13266, 'synset': 'wild_spinach.n.03', 'name': 'wild_spinach'}, {'id': 13267, 'synset': 'beefsteak_tomato.n.01', 'name': 'beefsteak_tomato'}, {'id': 13268, 'synset': 'cherry_tomato.n.02', 'name': 'cherry_tomato'}, {'id': 13269, 'synset': 'plum_tomato.n.02', 'name': 'plum_tomato'}, {'id': 13270, 'synset': 'tomatillo.n.03', 'name': 'tomatillo'}, {'id': 13271, 'synset': 'mushroom.n.05', 'name': 'mushroom'}, {'id': 13272, 'synset': 'stuffed_mushroom.n.01', 'name': 'stuffed_mushroom'}, {'id': 13273, 'synset': 'salsify.n.03', 'name': 'salsify'}, {'id': 13274, 'synset': 'oyster_plant.n.03', 'name': 'oyster_plant'}, {'id': 13275, 'synset': 'scorzonera.n.02', 'name': 'scorzonera'}, {'id': 13276, 'synset': 'parsnip.n.03', 'name': 'parsnip'}, {'id': 13277, 'synset': 'radish.n.01', 'name': 'radish'}, {'id': 13278, 'synset': 'turnip.n.02', 'name': 'turnip'}, {'id': 13279, 'synset': 'white_turnip.n.02', 'name': 'white_turnip'}, {'id': 13280, 'synset': 'rutabaga.n.01', 'name': 'rutabaga'}, {'id': 13281, 'synset': 'turnip_greens.n.01', 'name': 'turnip_greens'}, {'id': 13282, 'synset': 'sorrel.n.04', 'name': 'sorrel'}, {'id': 13283, 'synset': 'french_sorrel.n.02', 'name': 'French_sorrel'}, {'id': 13284, 'synset': 'spinach.n.02', 'name': 'spinach'}, {'id': 13285, 'synset': 'taro.n.03', 'name': 'taro'}, {'id': 13286, 'synset': 'truffle.n.02', 'name': 'truffle'}, {'id': 13287, 'synset': 'edible_nut.n.01', 'name': 'edible_nut'}, {'id': 13288, 'synset': 'bunya_bunya.n.02', 'name': 'bunya_bunya'}, {'id': 13289, 'synset': 'peanut.n.04', 'name': 'peanut'}, {'id': 13290, 'synset': 'freestone.n.01', 'name': 'freestone'}, {'id': 13291, 'synset': 'cling.n.01', 'name': 'cling'}, {'id': 13292, 'synset': 'windfall.n.01', 'name': 'windfall'}, {'id': 13293, 'synset': 'crab_apple.n.03', 'name': 'crab_apple'}, {'id': 13294, 'synset': 'eating_apple.n.01', 'name': 'eating_apple'}, {'id': 13295, 'synset': 'baldwin.n.03', 'name': 'Baldwin'}, {'id': 13296, 'synset': 'cortland.n.01', 'name': 'Cortland'}, {'id': 13297, 'synset': "cox's_orange_pippin.n.01", 'name': "Cox's_Orange_Pippin"}, {'id': 13298, 'synset': 'delicious.n.01', 'name': 'Delicious'}, {'id': 13299, 'synset': 'golden_delicious.n.01', 'name': 'Golden_Delicious'}, {'id': 13300, 'synset': 'red_delicious.n.01', 'name': 'Red_Delicious'}, {'id': 13301, 'synset': 'empire.n.05', 'name': 'Empire'}, {'id': 13302, 'synset': "grimes'_golden.n.01", 'name': "Grimes'_golden"}, {'id': 13303, 'synset': 'jonathan.n.01', 'name': 'Jonathan'}, {'id': 13304, 'synset': 'mcintosh.n.01', 'name': 'McIntosh'}, {'id': 13305, 'synset': 'macoun.n.01', 'name': 'Macoun'}, {'id': 13306, 'synset': 'northern_spy.n.01', 'name': 'Northern_Spy'}, {'id': 13307, 'synset': 'pearmain.n.01', 'name': 'Pearmain'}, {'id': 13308, 'synset': 'pippin.n.01', 'name': 'Pippin'}, {'id': 13309, 'synset': 'prima.n.01', 'name': 'Prima'}, {'id': 13310, 'synset': 'stayman.n.01', 'name': 'Stayman'}, {'id': 13311, 'synset': 'winesap.n.01', 'name': 'Winesap'}, {'id': 13312, 'synset': 'stayman_winesap.n.01', 'name': 'Stayman_Winesap'}, {'id': 13313, 'synset': 'cooking_apple.n.01', 'name': 'cooking_apple'}, {'id': 13314, 'synset': "bramley's_seedling.n.01", 'name': "Bramley's_Seedling"}, {'id': 13315, 'synset': 'granny_smith.n.01', 'name': 'Granny_Smith'}, {'id': 13316, 'synset': "lane's_prince_albert.n.01", 'name': "Lane's_Prince_Albert"}, {'id': 13317, 'synset': 'newtown_wonder.n.01', 'name': 'Newtown_Wonder'}, {'id': 13318, 'synset': 'rome_beauty.n.01', 'name': 'Rome_Beauty'}, {'id': 13319, 'synset': 'berry.n.01', 'name': 'berry'}, {'id': 13320, 'synset': 'bilberry.n.03', 'name': 'bilberry'}, {'id': 13321, 'synset': 'huckleberry.n.03', 'name': 'huckleberry'}, {'id': 13322, 'synset': 'wintergreen.n.03', 'name': 'wintergreen'}, {'id': 13323, 'synset': 'cranberry.n.02', 'name': 'cranberry'}, {'id': 13324, 'synset': 'lingonberry.n.02', 'name': 'lingonberry'}, {'id': 13325, 'synset': 'currant.n.01', 'name': 'currant'}, {'id': 13326, 'synset': 'gooseberry.n.02', 'name': 'gooseberry'}, {'id': 13327, 'synset': 'black_currant.n.02', 'name': 'black_currant'}, {'id': 13328, 'synset': 'red_currant.n.02', 'name': 'red_currant'}, {'id': 13329, 'synset': 'boysenberry.n.02', 'name': 'boysenberry'}, {'id': 13330, 'synset': 'dewberry.n.02', 'name': 'dewberry'}, {'id': 13331, 'synset': 'loganberry.n.02', 'name': 'loganberry'}, {'id': 13332, 'synset': 'saskatoon.n.02', 'name': 'saskatoon'}, {'id': 13333, 'synset': 'sugarberry.n.02', 'name': 'sugarberry'}, {'id': 13334, 'synset': 'acerola.n.02', 'name': 'acerola'}, {'id': 13335, 'synset': 'carambola.n.02', 'name': 'carambola'}, {'id': 13336, 'synset': 'ceriman.n.02', 'name': 'ceriman'}, {'id': 13337, 'synset': 'carissa_plum.n.01', 'name': 'carissa_plum'}, {'id': 13338, 'synset': 'citrus.n.01', 'name': 'citrus'}, {'id': 13339, 'synset': 'temple_orange.n.02', 'name': 'temple_orange'}, {'id': 13340, 'synset': 'clementine.n.02', 'name': 'clementine'}, {'id': 13341, 'synset': 'satsuma.n.02', 'name': 'satsuma'}, {'id': 13342, 'synset': 'tangerine.n.02', 'name': 'tangerine'}, {'id': 13343, 'synset': 'tangelo.n.02', 'name': 'tangelo'}, {'id': 13344, 'synset': 'bitter_orange.n.02', 'name': 'bitter_orange'}, {'id': 13345, 'synset': 'sweet_orange.n.01', 'name': 'sweet_orange'}, {'id': 13346, 'synset': 'jaffa_orange.n.01', 'name': 'Jaffa_orange'}, {'id': 13347, 'synset': 'navel_orange.n.01', 'name': 'navel_orange'}, {'id': 13348, 'synset': 'valencia_orange.n.01', 'name': 'Valencia_orange'}, {'id': 13349, 'synset': 'kumquat.n.02', 'name': 'kumquat'}, {'id': 13350, 'synset': 'key_lime.n.01', 'name': 'key_lime'}, {'id': 13351, 'synset': 'grapefruit.n.02', 'name': 'grapefruit'}, {'id': 13352, 'synset': 'pomelo.n.02', 'name': 'pomelo'}, {'id': 13353, 'synset': 'citrange.n.02', 'name': 'citrange'}, {'id': 13354, 'synset': 'citron.n.01', 'name': 'citron'}, {'id': 13355, 'synset': 'jordan_almond.n.02', 'name': 'Jordan_almond'}, {'id': 13356, 'synset': 'nectarine.n.02', 'name': 'nectarine'}, {'id': 13357, 'synset': 'pitahaya.n.02', 'name': 'pitahaya'}, {'id': 13358, 'synset': 'plum.n.02', 'name': 'plum'}, {'id': 13359, 'synset': 'damson.n.01', 'name': 'damson'}, {'id': 13360, 'synset': 'greengage.n.01', 'name': 'greengage'}, {'id': 13361, 'synset': 'beach_plum.n.02', 'name': 'beach_plum'}, {'id': 13362, 'synset': 'sloe.n.03', 'name': 'sloe'}, {'id': 13363, 'synset': 'victoria_plum.n.01', 'name': 'Victoria_plum'}, {'id': 13364, 'synset': 'dried_fruit.n.01', 'name': 'dried_fruit'}, {'id': 13365, 'synset': 'dried_apricot.n.01', 'name': 'dried_apricot'}, {'id': 13366, 'synset': 'raisin.n.01', 'name': 'raisin'}, {'id': 13367, 'synset': 'seedless_raisin.n.01', 'name': 'seedless_raisin'}, {'id': 13368, 'synset': 'seeded_raisin.n.01', 'name': 'seeded_raisin'}, {'id': 13369, 'synset': 'currant.n.03', 'name': 'currant'}, {'id': 13370, 'synset': 'anchovy_pear.n.02', 'name': 'anchovy_pear'}, {'id': 13371, 'synset': 'passion_fruit.n.01', 'name': 'passion_fruit'}, {'id': 13372, 'synset': 'granadilla.n.04', 'name': 'granadilla'}, {'id': 13373, 'synset': 'sweet_calabash.n.02', 'name': 'sweet_calabash'}, {'id': 13374, 'synset': 'bell_apple.n.01', 'name': 'bell_apple'}, {'id': 13375, 'synset': 'breadfruit.n.02', 'name': 'breadfruit'}, {'id': 13376, 'synset': 'jackfruit.n.02', 'name': 'jackfruit'}, {'id': 13377, 'synset': 'cacao_bean.n.01', 'name': 'cacao_bean'}, {'id': 13378, 'synset': 'cocoa.n.02', 'name': 'cocoa'}, {'id': 13379, 'synset': 'canistel.n.02', 'name': 'canistel'}, {'id': 13380, 'synset': 'melon_ball.n.01', 'name': 'melon_ball'}, {'id': 13381, 'synset': 'muskmelon.n.02', 'name': 'muskmelon'}, {'id': 13382, 'synset': 'winter_melon.n.02', 'name': 'winter_melon'}, {'id': 13383, 'synset': 'honeydew.n.01', 'name': 'honeydew'}, {'id': 13384, 'synset': 'persian_melon.n.02', 'name': 'Persian_melon'}, {'id': 13385, 'synset': 'net_melon.n.02', 'name': 'net_melon'}, {'id': 13386, 'synset': 'casaba.n.01', 'name': 'casaba'}, {'id': 13387, 'synset': 'sweet_cherry.n.02', 'name': 'sweet_cherry'}, {'id': 13388, 'synset': 'bing_cherry.n.01', 'name': 'bing_cherry'}, {'id': 13389, 'synset': 'heart_cherry.n.02', 'name': 'heart_cherry'}, {'id': 13390, 'synset': 'blackheart.n.02', 'name': 'blackheart'}, {'id': 13391, 'synset': 'capulin.n.02', 'name': 'capulin'}, {'id': 13392, 'synset': 'sour_cherry.n.03', 'name': 'sour_cherry'}, {'id': 13393, 'synset': 'amarelle.n.02', 'name': 'amarelle'}, {'id': 13394, 'synset': 'morello.n.02', 'name': 'morello'}, {'id': 13395, 'synset': 'cocoa_plum.n.02', 'name': 'cocoa_plum'}, {'id': 13396, 'synset': 'gherkin.n.01', 'name': 'gherkin'}, {'id': 13397, 'synset': 'fox_grape.n.02', 'name': 'fox_grape'}, {'id': 13398, 'synset': 'concord_grape.n.01', 'name': 'Concord_grape'}, {'id': 13399, 'synset': 'catawba.n.02', 'name': 'Catawba'}, {'id': 13400, 'synset': 'muscadine.n.02', 'name': 'muscadine'}, {'id': 13401, 'synset': 'scuppernong.n.01', 'name': 'scuppernong'}, {'id': 13402, 'synset': 'slipskin_grape.n.01', 'name': 'slipskin_grape'}, {'id': 13403, 'synset': 'vinifera_grape.n.02', 'name': 'vinifera_grape'}, {'id': 13404, 'synset': 'emperor.n.02', 'name': 'emperor'}, {'id': 13405, 'synset': 'muscat.n.04', 'name': 'muscat'}, {'id': 13406, 'synset': 'ribier.n.01', 'name': 'ribier'}, {'id': 13407, 'synset': 'sultana.n.01', 'name': 'sultana'}, {'id': 13408, 'synset': 'tokay.n.02', 'name': 'Tokay'}, {'id': 13409, 'synset': 'flame_tokay.n.01', 'name': 'flame_tokay'}, {'id': 13410, 'synset': 'thompson_seedless.n.01', 'name': 'Thompson_Seedless'}, {'id': 13411, 'synset': 'custard_apple.n.02', 'name': 'custard_apple'}, {'id': 13412, 'synset': 'cherimoya.n.02', 'name': 'cherimoya'}, {'id': 13413, 'synset': 'soursop.n.02', 'name': 'soursop'}, {'id': 13414, 'synset': 'sweetsop.n.02', 'name': 'sweetsop'}, {'id': 13415, 'synset': 'ilama.n.02', 'name': 'ilama'}, {'id': 13416, 'synset': 'pond_apple.n.02', 'name': 'pond_apple'}, {'id': 13417, 'synset': 'papaw.n.02', 'name': 'papaw'}, {'id': 13418, 'synset': 'kai_apple.n.01', 'name': 'kai_apple'}, {'id': 13419, 'synset': 'ketembilla.n.02', 'name': 'ketembilla'}, {'id': 13420, 'synset': 'ackee.n.01', 'name': 'ackee'}, {'id': 13421, 'synset': 'durian.n.02', 'name': 'durian'}, {'id': 13422, 'synset': 'feijoa.n.02', 'name': 'feijoa'}, {'id': 13423, 'synset': 'genip.n.02', 'name': 'genip'}, {'id': 13424, 'synset': 'genipap.n.01', 'name': 'genipap'}, {'id': 13425, 'synset': 'loquat.n.02', 'name': 'loquat'}, {'id': 13426, 'synset': 'mangosteen.n.02', 'name': 'mangosteen'}, {'id': 13427, 'synset': 'mango.n.02', 'name': 'mango'}, {'id': 13428, 'synset': 'sapodilla.n.02', 'name': 'sapodilla'}, {'id': 13429, 'synset': 'sapote.n.02', 'name': 'sapote'}, {'id': 13430, 'synset': 'tamarind.n.02', 'name': 'tamarind'}, {'id': 13431, 'synset': 'elderberry.n.02', 'name': 'elderberry'}, {'id': 13432, 'synset': 'guava.n.03', 'name': 'guava'}, {'id': 13433, 'synset': 'mombin.n.02', 'name': 'mombin'}, {'id': 13434, 'synset': 'hog_plum.n.04', 'name': 'hog_plum'}, {'id': 13435, 'synset': 'hog_plum.n.03', 'name': 'hog_plum'}, {'id': 13436, 'synset': 'jaboticaba.n.02', 'name': 'jaboticaba'}, {'id': 13437, 'synset': 'jujube.n.02', 'name': 'jujube'}, {'id': 13438, 'synset': 'litchi.n.02', 'name': 'litchi'}, {'id': 13439, 'synset': 'longanberry.n.02', 'name': 'longanberry'}, {'id': 13440, 'synset': 'mamey.n.02', 'name': 'mamey'}, {'id': 13441, 'synset': 'marang.n.02', 'name': 'marang'}, {'id': 13442, 'synset': 'medlar.n.04', 'name': 'medlar'}, {'id': 13443, 'synset': 'medlar.n.03', 'name': 'medlar'}, {'id': 13444, 'synset': 'mulberry.n.02', 'name': 'mulberry'}, {'id': 13445, 'synset': 'olive.n.04', 'name': 'olive'}, {'id': 13446, 'synset': 'black_olive.n.01', 'name': 'black_olive'}, {'id': 13447, 'synset': 'green_olive.n.01', 'name': 'green_olive'}, {'id': 13448, 'synset': 'bosc.n.01', 'name': 'bosc'}, {'id': 13449, 'synset': 'anjou.n.02', 'name': 'anjou'}, {'id': 13450, 'synset': 'bartlett.n.03', 'name': 'bartlett'}, {'id': 13451, 'synset': 'seckel.n.01', 'name': 'seckel'}, {'id': 13452, 'synset': 'plantain.n.03', 'name': 'plantain'}, {'id': 13453, 'synset': 'plumcot.n.02', 'name': 'plumcot'}, {'id': 13454, 'synset': 'pomegranate.n.02', 'name': 'pomegranate'}, {'id': 13455, 'synset': 'prickly_pear.n.02', 'name': 'prickly_pear'}, {'id': 13456, 'synset': 'barbados_gooseberry.n.02', 'name': 'Barbados_gooseberry'}, {'id': 13457, 'synset': 'quandong.n.04', 'name': 'quandong'}, {'id': 13458, 'synset': 'quandong_nut.n.01', 'name': 'quandong_nut'}, {'id': 13459, 'synset': 'quince.n.02', 'name': 'quince'}, {'id': 13460, 'synset': 'rambutan.n.02', 'name': 'rambutan'}, {'id': 13461, 'synset': 'pulasan.n.02', 'name': 'pulasan'}, {'id': 13462, 'synset': 'rose_apple.n.02', 'name': 'rose_apple'}, {'id': 13463, 'synset': 'sorb.n.01', 'name': 'sorb'}, {'id': 13464, 'synset': 'sour_gourd.n.02', 'name': 'sour_gourd'}, {'id': 13465, 'synset': 'edible_seed.n.01', 'name': 'edible_seed'}, {'id': 13466, 'synset': 'pumpkin_seed.n.01', 'name': 'pumpkin_seed'}, {'id': 13467, 'synset': 'betel_nut.n.01', 'name': 'betel_nut'}, {'id': 13468, 'synset': 'beechnut.n.01', 'name': 'beechnut'}, {'id': 13469, 'synset': 'walnut.n.01', 'name': 'walnut'}, {'id': 13470, 'synset': 'black_walnut.n.02', 'name': 'black_walnut'}, {'id': 13471, 'synset': 'english_walnut.n.02', 'name': 'English_walnut'}, {'id': 13472, 'synset': 'brazil_nut.n.02', 'name': 'brazil_nut'}, {'id': 13473, 'synset': 'butternut.n.02', 'name': 'butternut'}, {'id': 13474, 'synset': 'souari_nut.n.02', 'name': 'souari_nut'}, {'id': 13475, 'synset': 'cashew.n.02', 'name': 'cashew'}, {'id': 13476, 'synset': 'chestnut.n.03', 'name': 'chestnut'}, {'id': 13477, 'synset': 'chincapin.n.01', 'name': 'chincapin'}, {'id': 13478, 'synset': 'hazelnut.n.02', 'name': 'hazelnut'}, {'id': 13479, 'synset': 'coconut_milk.n.02', 'name': 'coconut_milk'}, {'id': 13480, 'synset': 'grugru_nut.n.01', 'name': 'grugru_nut'}, {'id': 13481, 'synset': 'hickory_nut.n.01', 'name': 'hickory_nut'}, {'id': 13482, 'synset': 'cola_extract.n.01', 'name': 'cola_extract'}, {'id': 13483, 'synset': 'macadamia_nut.n.02', 'name': 'macadamia_nut'}, {'id': 13484, 'synset': 'pecan.n.03', 'name': 'pecan'}, {'id': 13485, 'synset': 'pine_nut.n.01', 'name': 'pine_nut'}, {'id': 13486, 'synset': 'pistachio.n.02', 'name': 'pistachio'}, {'id': 13487, 'synset': 'sunflower_seed.n.01', 'name': 'sunflower_seed'}, {'id': 13488, 'synset': 'anchovy_paste.n.01', 'name': 'anchovy_paste'}, {'id': 13489, 'synset': 'rollmops.n.01', 'name': 'rollmops'}, {'id': 13490, 'synset': 'feed.n.01', 'name': 'feed'}, {'id': 13491, 'synset': 'cattle_cake.n.01', 'name': 'cattle_cake'}, {'id': 13492, 'synset': 'creep_feed.n.01', 'name': 'creep_feed'}, {'id': 13493, 'synset': 'fodder.n.02', 'name': 'fodder'}, {'id': 13494, 'synset': 'feed_grain.n.01', 'name': 'feed_grain'}, {'id': 13495, 'synset': 'eatage.n.01', 'name': 'eatage'}, {'id': 13496, 'synset': 'silage.n.01', 'name': 'silage'}, {'id': 13497, 'synset': 'oil_cake.n.01', 'name': 'oil_cake'}, {'id': 13498, 'synset': 'oil_meal.n.01', 'name': 'oil_meal'}, {'id': 13499, 'synset': 'alfalfa.n.02', 'name': 'alfalfa'}, {'id': 13500, 'synset': 'broad_bean.n.03', 'name': 'broad_bean'}, {'id': 13501, 'synset': 'hay.n.01', 'name': 'hay'}, {'id': 13502, 'synset': 'timothy.n.03', 'name': 'timothy'}, {'id': 13503, 'synset': 'stover.n.01', 'name': 'stover'}, {'id': 13504, 'synset': 'grain.n.02', 'name': 'grain'}, {'id': 13505, 'synset': 'grist.n.01', 'name': 'grist'}, {'id': 13506, 'synset': 'groats.n.01', 'name': 'groats'}, {'id': 13507, 'synset': 'millet.n.03', 'name': 'millet'}, {'id': 13508, 'synset': 'barley.n.01', 'name': 'barley'}, {'id': 13509, 'synset': 'pearl_barley.n.01', 'name': 'pearl_barley'}, {'id': 13510, 'synset': 'buckwheat.n.02', 'name': 'buckwheat'}, {'id': 13511, 'synset': 'bulgur.n.01', 'name': 'bulgur'}, {'id': 13512, 'synset': 'wheat.n.02', 'name': 'wheat'}, {'id': 13513, 'synset': 'cracked_wheat.n.01', 'name': 'cracked_wheat'}, {'id': 13514, 'synset': 'stodge.n.01', 'name': 'stodge'}, {'id': 13515, 'synset': 'wheat_germ.n.01', 'name': 'wheat_germ'}, {'id': 13516, 'synset': 'oat.n.02', 'name': 'oat'}, {'id': 13517, 'synset': 'rice.n.01', 'name': 'rice'}, {'id': 13518, 'synset': 'brown_rice.n.01', 'name': 'brown_rice'}, {'id': 13519, 'synset': 'white_rice.n.01', 'name': 'white_rice'}, {'id': 13520, 'synset': 'wild_rice.n.02', 'name': 'wild_rice'}, {'id': 13521, 'synset': 'paddy.n.03', 'name': 'paddy'}, {'id': 13522, 'synset': 'slop.n.01', 'name': 'slop'}, {'id': 13523, 'synset': 'mash.n.02', 'name': 'mash'}, {'id': 13524, 'synset': 'chicken_feed.n.01', 'name': 'chicken_feed'}, {'id': 13525, 'synset': 'cud.n.01', 'name': 'cud'}, {'id': 13526, 'synset': 'bird_feed.n.01', 'name': 'bird_feed'}, {'id': 13527, 'synset': 'petfood.n.01', 'name': 'petfood'}, {'id': 13528, 'synset': 'dog_food.n.01', 'name': 'dog_food'}, {'id': 13529, 'synset': 'cat_food.n.01', 'name': 'cat_food'}, {'id': 13530, 'synset': 'canary_seed.n.01', 'name': 'canary_seed'}, {'id': 13531, 'synset': 'tossed_salad.n.01', 'name': 'tossed_salad'}, {'id': 13532, 'synset': 'green_salad.n.01', 'name': 'green_salad'}, {'id': 13533, 'synset': 'caesar_salad.n.01', 'name': 'Caesar_salad'}, {'id': 13534, 'synset': 'salmagundi.n.02', 'name': 'salmagundi'}, {'id': 13535, 'synset': 'salad_nicoise.n.01', 'name': 'salad_nicoise'}, {'id': 13536, 'synset': 'combination_salad.n.01', 'name': 'combination_salad'}, {'id': 13537, 'synset': "chef's_salad.n.01", 'name': "chef's_salad"}, {'id': 13538, 'synset': 'potato_salad.n.01', 'name': 'potato_salad'}, {'id': 13539, 'synset': 'pasta_salad.n.01', 'name': 'pasta_salad'}, {'id': 13540, 'synset': 'macaroni_salad.n.01', 'name': 'macaroni_salad'}, {'id': 13541, 'synset': 'fruit_salad.n.01', 'name': 'fruit_salad'}, {'id': 13542, 'synset': 'waldorf_salad.n.01', 'name': 'Waldorf_salad'}, {'id': 13543, 'synset': 'crab_louis.n.01', 'name': 'crab_Louis'}, {'id': 13544, 'synset': 'herring_salad.n.01', 'name': 'herring_salad'}, {'id': 13545, 'synset': 'tuna_fish_salad.n.01', 'name': 'tuna_fish_salad'}, {'id': 13546, 'synset': 'chicken_salad.n.01', 'name': 'chicken_salad'}, {'id': 13547, 'synset': 'aspic.n.01', 'name': 'aspic'}, {'id': 13548, 'synset': 'molded_salad.n.01', 'name': 'molded_salad'}, {'id': 13549, 'synset': 'tabbouleh.n.01', 'name': 'tabbouleh'}, {'id': 13550, 'synset': 'ingredient.n.03', 'name': 'ingredient'}, {'id': 13551, 'synset': 'flavorer.n.01', 'name': 'flavorer'}, {'id': 13552, 'synset': 'bouillon_cube.n.01', 'name': 'bouillon_cube'}, {'id': 13553, 'synset': 'herb.n.02', 'name': 'herb'}, {'id': 13554, 'synset': 'fines_herbes.n.01', 'name': 'fines_herbes'}, {'id': 13555, 'synset': 'spice.n.02', 'name': 'spice'}, {'id': 13556, 'synset': 'spearmint_oil.n.01', 'name': 'spearmint_oil'}, {'id': 13557, 'synset': 'lemon_oil.n.01', 'name': 'lemon_oil'}, {'id': 13558, 'synset': 'wintergreen_oil.n.01', 'name': 'wintergreen_oil'}, {'id': 13559, 'synset': 'salt.n.02', 'name': 'salt'}, {'id': 13560, 'synset': 'celery_salt.n.01', 'name': 'celery_salt'}, {'id': 13561, 'synset': 'onion_salt.n.01', 'name': 'onion_salt'}, {'id': 13562, 'synset': 'seasoned_salt.n.01', 'name': 'seasoned_salt'}, {'id': 13563, 'synset': 'sour_salt.n.01', 'name': 'sour_salt'}, {'id': 13564, 'synset': 'five_spice_powder.n.01', 'name': 'five_spice_powder'}, {'id': 13565, 'synset': 'allspice.n.03', 'name': 'allspice'}, {'id': 13566, 'synset': 'cinnamon.n.03', 'name': 'cinnamon'}, {'id': 13567, 'synset': 'stick_cinnamon.n.01', 'name': 'stick_cinnamon'}, {'id': 13568, 'synset': 'clove.n.04', 'name': 'clove'}, {'id': 13569, 'synset': 'cumin.n.02', 'name': 'cumin'}, {'id': 13570, 'synset': 'fennel.n.04', 'name': 'fennel'}, {'id': 13571, 'synset': 'ginger.n.02', 'name': 'ginger'}, {'id': 13572, 'synset': 'mace.n.03', 'name': 'mace'}, {'id': 13573, 'synset': 'nutmeg.n.02', 'name': 'nutmeg'}, {'id': 13574, 'synset': 'black_pepper.n.02', 'name': 'black_pepper'}, {'id': 13575, 'synset': 'white_pepper.n.02', 'name': 'white_pepper'}, {'id': 13576, 'synset': 'sassafras.n.02', 'name': 'sassafras'}, {'id': 13577, 'synset': 'basil.n.03', 'name': 'basil'}, {'id': 13578, 'synset': 'bay_leaf.n.01', 'name': 'bay_leaf'}, {'id': 13579, 'synset': 'borage.n.02', 'name': 'borage'}, {'id': 13580, 'synset': 'hyssop.n.02', 'name': 'hyssop'}, {'id': 13581, 'synset': 'caraway.n.02', 'name': 'caraway'}, {'id': 13582, 'synset': 'chervil.n.02', 'name': 'chervil'}, {'id': 13583, 'synset': 'chives.n.02', 'name': 'chives'}, {'id': 13584, 'synset': 'comfrey.n.02', 'name': 'comfrey'}, {'id': 13585, 'synset': 'coriander.n.03', 'name': 'coriander'}, {'id': 13586, 'synset': 'coriander.n.02', 'name': 'coriander'}, {'id': 13587, 'synset': 'costmary.n.02', 'name': 'costmary'}, {'id': 13588, 'synset': 'fennel.n.03', 'name': 'fennel'}, {'id': 13589, 'synset': 'fennel.n.02', 'name': 'fennel'}, {'id': 13590, 'synset': 'fennel_seed.n.01', 'name': 'fennel_seed'}, {'id': 13591, 'synset': 'fenugreek.n.02', 'name': 'fenugreek'}, {'id': 13592, 'synset': 'clove.n.03', 'name': 'clove'}, {'id': 13593, 'synset': 'garlic_chive.n.02', 'name': 'garlic_chive'}, {'id': 13594, 'synset': 'lemon_balm.n.02', 'name': 'lemon_balm'}, {'id': 13595, 'synset': 'lovage.n.02', 'name': 'lovage'}, {'id': 13596, 'synset': 'marjoram.n.02', 'name': 'marjoram'}, {'id': 13597, 'synset': 'mint.n.04', 'name': 'mint'}, {'id': 13598, 'synset': 'mustard_seed.n.01', 'name': 'mustard_seed'}, {'id': 13599, 'synset': 'mustard.n.02', 'name': 'mustard'}, {'id': 13600, 'synset': 'chinese_mustard.n.02', 'name': 'Chinese_mustard'}, {'id': 13601, 'synset': 'nasturtium.n.03', 'name': 'nasturtium'}, {'id': 13602, 'synset': 'parsley.n.02', 'name': 'parsley'}, {'id': 13603, 'synset': 'salad_burnet.n.02', 'name': 'salad_burnet'}, {'id': 13604, 'synset': 'rosemary.n.02', 'name': 'rosemary'}, {'id': 13605, 'synset': 'rue.n.02', 'name': 'rue'}, {'id': 13606, 'synset': 'sage.n.02', 'name': 'sage'}, {'id': 13607, 'synset': 'clary_sage.n.02', 'name': 'clary_sage'}, {'id': 13608, 'synset': 'savory.n.03', 'name': 'savory'}, {'id': 13609, 'synset': 'summer_savory.n.02', 'name': 'summer_savory'}, {'id': 13610, 'synset': 'winter_savory.n.02', 'name': 'winter_savory'}, {'id': 13611, 'synset': 'sweet_woodruff.n.02', 'name': 'sweet_woodruff'}, {'id': 13612, 'synset': 'sweet_cicely.n.03', 'name': 'sweet_cicely'}, {'id': 13613, 'synset': 'tarragon.n.02', 'name': 'tarragon'}, {'id': 13614, 'synset': 'thyme.n.02', 'name': 'thyme'}, {'id': 13615, 'synset': 'turmeric.n.02', 'name': 'turmeric'}, {'id': 13616, 'synset': 'caper.n.02', 'name': 'caper'}, {'id': 13617, 'synset': 'catsup.n.01', 'name': 'catsup'}, {'id': 13618, 'synset': 'cardamom.n.02', 'name': 'cardamom'}, {'id': 13619, 'synset': 'chili_powder.n.01', 'name': 'chili_powder'}, {'id': 13620, 'synset': 'chili_sauce.n.01', 'name': 'chili_sauce'}, {'id': 13621, 'synset': 'chutney.n.01', 'name': 'chutney'}, {'id': 13622, 'synset': 'steak_sauce.n.01', 'name': 'steak_sauce'}, {'id': 13623, 'synset': 'taco_sauce.n.01', 'name': 'taco_sauce'}, {'id': 13624, 'synset': 'mint_sauce.n.01', 'name': 'mint_sauce'}, {'id': 13625, 'synset': 'cranberry_sauce.n.01', 'name': 'cranberry_sauce'}, {'id': 13626, 'synset': 'curry_powder.n.01', 'name': 'curry_powder'}, {'id': 13627, 'synset': 'curry.n.01', 'name': 'curry'}, {'id': 13628, 'synset': 'lamb_curry.n.01', 'name': 'lamb_curry'}, {'id': 13629, 'synset': 'duck_sauce.n.01', 'name': 'duck_sauce'}, {'id': 13630, 'synset': 'horseradish.n.03', 'name': 'horseradish'}, {'id': 13631, 'synset': 'marinade.n.01', 'name': 'marinade'}, {'id': 13632, 'synset': 'paprika.n.02', 'name': 'paprika'}, {'id': 13633, 'synset': 'spanish_paprika.n.01', 'name': 'Spanish_paprika'}, {'id': 13634, 'synset': 'dill_pickle.n.01', 'name': 'dill_pickle'}, {'id': 13635, 'synset': 'bread_and_butter_pickle.n.01', 'name': 'bread_and_butter_pickle'}, {'id': 13636, 'synset': 'pickle_relish.n.01', 'name': 'pickle_relish'}, {'id': 13637, 'synset': 'piccalilli.n.01', 'name': 'piccalilli'}, {'id': 13638, 'synset': 'sweet_pickle.n.01', 'name': 'sweet_pickle'}, {'id': 13639, 'synset': 'soy_sauce.n.01', 'name': 'soy_sauce'}, {'id': 13640, 'synset': 'tomato_paste.n.01', 'name': 'tomato_paste'}, {'id': 13641, 'synset': 'angelica.n.03', 'name': 'angelica'}, {'id': 13642, 'synset': 'angelica.n.02', 'name': 'angelica'}, {'id': 13643, 'synset': 'almond_extract.n.01', 'name': 'almond_extract'}, {'id': 13644, 'synset': 'anise.n.02', 'name': 'anise'}, {'id': 13645, 'synset': 'chinese_anise.n.02', 'name': 'Chinese_anise'}, {'id': 13646, 'synset': 'juniper_berries.n.01', 'name': 'juniper_berries'}, {'id': 13647, 'synset': 'saffron.n.02', 'name': 'saffron'}, {'id': 13648, 'synset': 'sesame_seed.n.01', 'name': 'sesame_seed'}, {'id': 13649, 'synset': 'caraway_seed.n.01', 'name': 'caraway_seed'}, {'id': 13650, 'synset': 'poppy_seed.n.01', 'name': 'poppy_seed'}, {'id': 13651, 'synset': 'dill.n.02', 'name': 'dill'}, {'id': 13652, 'synset': 'dill_seed.n.01', 'name': 'dill_seed'}, {'id': 13653, 'synset': 'celery_seed.n.01', 'name': 'celery_seed'}, {'id': 13654, 'synset': 'lemon_extract.n.01', 'name': 'lemon_extract'}, {'id': 13655, 'synset': 'monosodium_glutamate.n.01', 'name': 'monosodium_glutamate'}, {'id': 13656, 'synset': 'vanilla_bean.n.01', 'name': 'vanilla_bean'}, {'id': 13657, 'synset': 'cider_vinegar.n.01', 'name': 'cider_vinegar'}, {'id': 13658, 'synset': 'wine_vinegar.n.01', 'name': 'wine_vinegar'}, {'id': 13659, 'synset': 'sauce.n.01', 'name': 'sauce'}, {'id': 13660, 'synset': 'anchovy_sauce.n.01', 'name': 'anchovy_sauce'}, {'id': 13661, 'synset': 'hard_sauce.n.01', 'name': 'hard_sauce'}, {'id': 13662, 'synset': 'horseradish_sauce.n.01', 'name': 'horseradish_sauce'}, {'id': 13663, 'synset': 'bolognese_pasta_sauce.n.01', 'name': 'bolognese_pasta_sauce'}, {'id': 13664, 'synset': 'carbonara.n.01', 'name': 'carbonara'}, {'id': 13665, 'synset': 'tomato_sauce.n.01', 'name': 'tomato_sauce'}, {'id': 13666, 'synset': 'tartare_sauce.n.01', 'name': 'tartare_sauce'}, {'id': 13667, 'synset': 'wine_sauce.n.01', 'name': 'wine_sauce'}, {'id': 13668, 'synset': 'marchand_de_vin.n.01', 'name': 'marchand_de_vin'}, {'id': 13669, 'synset': 'bread_sauce.n.01', 'name': 'bread_sauce'}, {'id': 13670, 'synset': 'plum_sauce.n.01', 'name': 'plum_sauce'}, {'id': 13671, 'synset': 'peach_sauce.n.01', 'name': 'peach_sauce'}, {'id': 13672, 'synset': 'apricot_sauce.n.01', 'name': 'apricot_sauce'}, {'id': 13673, 'synset': 'pesto.n.01', 'name': 'pesto'}, {'id': 13674, 'synset': 'ravigote.n.01', 'name': 'ravigote'}, {'id': 13675, 'synset': 'remoulade_sauce.n.01', 'name': 'remoulade_sauce'}, {'id': 13676, 'synset': 'dressing.n.01', 'name': 'dressing'}, {'id': 13677, 'synset': 'sauce_louis.n.01', 'name': 'sauce_Louis'}, {'id': 13678, 'synset': 'bleu_cheese_dressing.n.01', 'name': 'bleu_cheese_dressing'}, {'id': 13679, 'synset': 'blue_cheese_dressing.n.01', 'name': 'blue_cheese_dressing'}, {'id': 13680, 'synset': 'french_dressing.n.01', 'name': 'French_dressing'}, {'id': 13681, 'synset': 'lorenzo_dressing.n.01', 'name': 'Lorenzo_dressing'}, {'id': 13682, 'synset': 'anchovy_dressing.n.01', 'name': 'anchovy_dressing'}, {'id': 13683, 'synset': 'italian_dressing.n.01', 'name': 'Italian_dressing'}, {'id': 13684, 'synset': 'half-and-half_dressing.n.01', 'name': 'half-and-half_dressing'}, {'id': 13685, 'synset': 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'name': 'Chinese_brown_sauce'}, {'id': 13700, 'synset': 'blanc.n.01', 'name': 'blanc'}, {'id': 13701, 'synset': 'cheese_sauce.n.01', 'name': 'cheese_sauce'}, {'id': 13702, 'synset': 'chocolate_sauce.n.01', 'name': 'chocolate_sauce'}, {'id': 13703, 'synset': 'hot-fudge_sauce.n.01', 'name': 'hot-fudge_sauce'}, {'id': 13704, 'synset': 'cocktail_sauce.n.01', 'name': 'cocktail_sauce'}, {'id': 13705, 'synset': 'colbert.n.01', 'name': 'Colbert'}, {'id': 13706, 'synset': 'white_sauce.n.01', 'name': 'white_sauce'}, {'id': 13707, 'synset': 'cream_sauce.n.01', 'name': 'cream_sauce'}, {'id': 13708, 'synset': 'mornay_sauce.n.01', 'name': 'Mornay_sauce'}, {'id': 13709, 'synset': 'demiglace.n.01', 'name': 'demiglace'}, {'id': 13710, 'synset': 'gravy.n.02', 'name': 'gravy'}, {'id': 13711, 'synset': 'gravy.n.01', 'name': 'gravy'}, {'id': 13712, 'synset': 'spaghetti_sauce.n.01', 'name': 'spaghetti_sauce'}, {'id': 13713, 'synset': 'marinara.n.01', 'name': 'marinara'}, {'id': 13714, 'synset': 'mole.n.03', 'name': 'mole'}, {'id': 13715, 'synset': "hunter's_sauce.n.01", 'name': "hunter's_sauce"}, {'id': 13716, 'synset': 'mushroom_sauce.n.01', 'name': 'mushroom_sauce'}, {'id': 13717, 'synset': 'mustard_sauce.n.01', 'name': 'mustard_sauce'}, {'id': 13718, 'synset': 'nantua.n.01', 'name': 'Nantua'}, {'id': 13719, 'synset': 'hungarian_sauce.n.01', 'name': 'Hungarian_sauce'}, {'id': 13720, 'synset': 'pepper_sauce.n.01', 'name': 'pepper_sauce'}, {'id': 13721, 'synset': 'roux.n.01', 'name': 'roux'}, {'id': 13722, 'synset': 'smitane.n.01', 'name': 'Smitane'}, {'id': 13723, 'synset': 'soubise.n.01', 'name': 'Soubise'}, {'id': 13724, 'synset': 'lyonnaise_sauce.n.01', 'name': 'Lyonnaise_sauce'}, {'id': 13725, 'synset': 'veloute.n.01', 'name': 'veloute'}, {'id': 13726, 'synset': 'allemande.n.01', 'name': 'allemande'}, {'id': 13727, 'synset': 'caper_sauce.n.01', 'name': 'caper_sauce'}, {'id': 13728, 'synset': 'poulette.n.01', 'name': 'poulette'}, {'id': 13729, 'synset': 'curry_sauce.n.01', 'name': 'curry_sauce'}, {'id': 13730, 'synset': 'worcester_sauce.n.01', 'name': 'Worcester_sauce'}, {'id': 13731, 'synset': 'coconut_milk.n.01', 'name': 'coconut_milk'}, {'id': 13732, 'synset': 'egg_white.n.01', 'name': 'egg_white'}, {'id': 13733, 'synset': 'hard-boiled_egg.n.01', 'name': 'hard-boiled_egg'}, {'id': 13734, 'synset': 'easter_egg.n.02', 'name': 'Easter_egg'}, {'id': 13735, 'synset': 'easter_egg.n.01', 'name': 'Easter_egg'}, {'id': 13736, 'synset': 'chocolate_egg.n.01', 'name': 'chocolate_egg'}, {'id': 13737, 'synset': 'candy_egg.n.01', 'name': 'candy_egg'}, {'id': 13738, 'synset': 'poached_egg.n.01', 'name': 'poached_egg'}, {'id': 13739, 'synset': 'scrambled_eggs.n.01', 'name': 'scrambled_eggs'}, {'id': 13740, 'synset': 'deviled_egg.n.01', 'name': 'deviled_egg'}, {'id': 13741, 'synset': 'shirred_egg.n.01', 'name': 'shirred_egg'}, {'id': 13742, 'synset': 'firm_omelet.n.01', 'name': 'firm_omelet'}, {'id': 13743, 'synset': 'french_omelet.n.01', 'name': 'French_omelet'}, {'id': 13744, 'synset': 'fluffy_omelet.n.01', 'name': 'fluffy_omelet'}, {'id': 13745, 'synset': 'western_omelet.n.01', 'name': 'western_omelet'}, {'id': 13746, 'synset': 'souffle.n.01', 'name': 'souffle'}, {'id': 13747, 'synset': 'fried_egg.n.01', 'name': 'fried_egg'}, {'id': 13748, 'synset': 'dairy_product.n.01', 'name': 'dairy_product'}, {'id': 13749, 'synset': 'milk.n.04', 'name': 'milk'}, {'id': 13750, 'synset': 'sour_milk.n.01', 'name': 'sour_milk'}, {'id': 13751, 'synset': 'formula.n.06', 'name': 'formula'}, {'id': 13752, 'synset': 'pasteurized_milk.n.01', 'name': 'pasteurized_milk'}, {'id': 13753, 'synset': "cows'_milk.n.01", 'name': "cows'_milk"}, {'id': 13754, 'synset': "yak's_milk.n.01", 'name': "yak's_milk"}, {'id': 13755, 'synset': "goats'_milk.n.01", 'name': "goats'_milk"}, {'id': 13756, 'synset': 'acidophilus_milk.n.01', 'name': 'acidophilus_milk'}, {'id': 13757, 'synset': 'raw_milk.n.01', 'name': 'raw_milk'}, {'id': 13758, 'synset': 'scalded_milk.n.01', 'name': 'scalded_milk'}, {'id': 13759, 'synset': 'homogenized_milk.n.01', 'name': 'homogenized_milk'}, {'id': 13760, 'synset': 'certified_milk.n.01', 'name': 'certified_milk'}, {'id': 13761, 'synset': 'powdered_milk.n.01', 'name': 'powdered_milk'}, {'id': 13762, 'synset': 'nonfat_dry_milk.n.01', 'name': 'nonfat_dry_milk'}, {'id': 13763, 'synset': 'evaporated_milk.n.01', 'name': 'evaporated_milk'}, {'id': 13764, 'synset': 'condensed_milk.n.01', 'name': 'condensed_milk'}, {'id': 13765, 'synset': 'skim_milk.n.01', 'name': 'skim_milk'}, {'id': 13766, 'synset': 'semi-skimmed_milk.n.01', 'name': 'semi-skimmed_milk'}, {'id': 13767, 'synset': 'whole_milk.n.01', 'name': 'whole_milk'}, {'id': 13768, 'synset': 'low-fat_milk.n.01', 'name': 'low-fat_milk'}, {'id': 13769, 'synset': 'buttermilk.n.01', 'name': 'buttermilk'}, {'id': 13770, 'synset': 'cream.n.02', 'name': 'cream'}, {'id': 13771, 'synset': 'clotted_cream.n.01', 'name': 'clotted_cream'}, {'id': 13772, 'synset': 'double_creme.n.01', 'name': 'double_creme'}, {'id': 13773, 'synset': 'half-and-half.n.01', 'name': 'half-and-half'}, {'id': 13774, 'synset': 'heavy_cream.n.01', 'name': 'heavy_cream'}, {'id': 13775, 'synset': 'light_cream.n.01', 'name': 'light_cream'}, {'id': 13776, 'synset': 'whipping_cream.n.01', 'name': 'whipping_cream'}, {'id': 13777, 'synset': 'clarified_butter.n.01', 'name': 'clarified_butter'}, {'id': 13778, 'synset': 'ghee.n.01', 'name': 'ghee'}, {'id': 13779, 'synset': 'brown_butter.n.01', 'name': 'brown_butter'}, {'id': 13780, 'synset': 'meuniere_butter.n.01', 'name': 'Meuniere_butter'}, {'id': 13781, 'synset': 'blueberry_yogurt.n.01', 'name': 'blueberry_yogurt'}, {'id': 13782, 'synset': 'raita.n.01', 'name': 'raita'}, {'id': 13783, 'synset': 'whey.n.02', 'name': 'whey'}, {'id': 13784, 'synset': 'curd.n.02', 'name': 'curd'}, {'id': 13785, 'synset': 'curd.n.01', 'name': 'curd'}, {'id': 13786, 'synset': 'clabber.n.01', 'name': 'clabber'}, {'id': 13787, 'synset': 'cheese.n.01', 'name': 'cheese'}, {'id': 13788, 'synset': 'paring.n.02', 'name': 'paring'}, {'id': 13789, 'synset': 'cream_cheese.n.01', 'name': 'cream_cheese'}, {'id': 13790, 'synset': 'double_cream.n.01', 'name': 'double_cream'}, {'id': 13791, 'synset': 'mascarpone.n.01', 'name': 'mascarpone'}, {'id': 13792, 'synset': 'triple_cream.n.01', 'name': 'triple_cream'}, {'id': 13793, 'synset': 'cottage_cheese.n.01', 'name': 'cottage_cheese'}, {'id': 13794, 'synset': 'process_cheese.n.01', 'name': 'process_cheese'}, {'id': 13795, 'synset': 'bleu.n.01', 'name': 'bleu'}, {'id': 13796, 'synset': 'stilton.n.01', 'name': 'Stilton'}, {'id': 13797, 'synset': 'roquefort.n.01', 'name': 'Roquefort'}, {'id': 13798, 'synset': 'gorgonzola.n.01', 'name': 'gorgonzola'}, {'id': 13799, 'synset': 'danish_blue.n.01', 'name': 'Danish_blue'}, {'id': 13800, 'synset': 'bavarian_blue.n.01', 'name': 'Bavarian_blue'}, {'id': 13801, 'synset': 'brie.n.01', 'name': 'Brie'}, {'id': 13802, 'synset': 'brick_cheese.n.01', 'name': 'brick_cheese'}, {'id': 13803, 'synset': 'camembert.n.01', 'name': 'Camembert'}, {'id': 13804, 'synset': 'cheddar.n.02', 'name': 'cheddar'}, {'id': 13805, 'synset': 'rat_cheese.n.01', 'name': 'rat_cheese'}, {'id': 13806, 'synset': 'cheshire_cheese.n.01', 'name': 'Cheshire_cheese'}, {'id': 13807, 'synset': 'double_gloucester.n.01', 'name': 'double_Gloucester'}, {'id': 13808, 'synset': 'edam.n.01', 'name': 'Edam'}, {'id': 13809, 'synset': 'goat_cheese.n.01', 'name': 'goat_cheese'}, {'id': 13810, 'synset': 'gouda.n.01', 'name': 'Gouda'}, {'id': 13811, 'synset': 'grated_cheese.n.01', 'name': 'grated_cheese'}, {'id': 13812, 'synset': 'hand_cheese.n.01', 'name': 'hand_cheese'}, {'id': 13813, 'synset': 'liederkranz.n.01', 'name': 'Liederkranz'}, {'id': 13814, 'synset': 'limburger.n.01', 'name': 'Limburger'}, {'id': 13815, 'synset': 'mozzarella.n.01', 'name': 'mozzarella'}, {'id': 13816, 'synset': 'muenster.n.01', 'name': 'Muenster'}, {'id': 13817, 'synset': 'parmesan.n.01', 'name': 'Parmesan'}, {'id': 13818, 'synset': 'quark_cheese.n.01', 'name': 'quark_cheese'}, {'id': 13819, 'synset': 'ricotta.n.01', 'name': 'ricotta'}, {'id': 13820, 'synset': 'swiss_cheese.n.01', 'name': 'Swiss_cheese'}, {'id': 13821, 'synset': 'emmenthal.n.01', 'name': 'Emmenthal'}, {'id': 13822, 'synset': 'gruyere.n.01', 'name': 'Gruyere'}, {'id': 13823, 'synset': 'sapsago.n.01', 'name': 'sapsago'}, {'id': 13824, 'synset': 'velveeta.n.01', 'name': 'Velveeta'}, {'id': 13825, 'synset': 'nut_butter.n.01', 'name': 'nut_butter'}, {'id': 13826, 'synset': 'marshmallow_fluff.n.01', 'name': 'marshmallow_fluff'}, {'id': 13827, 'synset': 'onion_butter.n.01', 'name': 'onion_butter'}, {'id': 13828, 'synset': 'pimento_butter.n.01', 'name': 'pimento_butter'}, {'id': 13829, 'synset': 'shrimp_butter.n.01', 'name': 'shrimp_butter'}, {'id': 13830, 'synset': 'lobster_butter.n.01', 'name': 'lobster_butter'}, {'id': 13831, 'synset': 'yak_butter.n.01', 'name': 'yak_butter'}, {'id': 13832, 'synset': 'spread.n.05', 'name': 'spread'}, {'id': 13833, 'synset': 'cheese_spread.n.01', 'name': 'cheese_spread'}, {'id': 13834, 'synset': 'anchovy_butter.n.01', 'name': 'anchovy_butter'}, {'id': 13835, 'synset': 'fishpaste.n.01', 'name': 'fishpaste'}, {'id': 13836, 'synset': 'garlic_butter.n.01', 'name': 'garlic_butter'}, {'id': 13837, 'synset': 'miso.n.01', 'name': 'miso'}, {'id': 13838, 'synset': 'wasabi.n.02', 'name': 'wasabi'}, {'id': 13839, 'synset': 'snail_butter.n.01', 'name': 'snail_butter'}, {'id': 13840, 'synset': 'pate.n.01', 'name': 'pate'}, {'id': 13841, 'synset': 'duck_pate.n.01', 'name': 'duck_pate'}, {'id': 13842, 'synset': 'foie_gras.n.01', 'name': 'foie_gras'}, {'id': 13843, 'synset': 'tapenade.n.01', 'name': 'tapenade'}, {'id': 13844, 'synset': 'tahini.n.01', 'name': 'tahini'}, {'id': 13845, 'synset': 'sweetening.n.01', 'name': 'sweetening'}, {'id': 13846, 'synset': 'aspartame.n.01', 'name': 'aspartame'}, {'id': 13847, 'synset': 'saccharin.n.01', 'name': 'saccharin'}, {'id': 13848, 'synset': 'sugar.n.01', 'name': 'sugar'}, {'id': 13849, 'synset': 'syrup.n.01', 'name': 'syrup'}, {'id': 13850, 'synset': 'sugar_syrup.n.01', 'name': 'sugar_syrup'}, {'id': 13851, 'synset': 'molasses.n.01', 'name': 'molasses'}, {'id': 13852, 'synset': 'sorghum.n.03', 'name': 'sorghum'}, {'id': 13853, 'synset': 'treacle.n.01', 'name': 'treacle'}, {'id': 13854, 'synset': 'grenadine.n.01', 'name': 'grenadine'}, {'id': 13855, 'synset': 'maple_syrup.n.01', 'name': 'maple_syrup'}, {'id': 13856, 'synset': 'corn_syrup.n.01', 'name': 'corn_syrup'}, {'id': 13857, 'synset': 'miraculous_food.n.01', 'name': 'miraculous_food'}, {'id': 13858, 'synset': 'dough.n.01', 'name': 'dough'}, {'id': 13859, 'synset': 'bread_dough.n.01', 'name': 'bread_dough'}, {'id': 13860, 'synset': 'pancake_batter.n.01', 'name': 'pancake_batter'}, {'id': 13861, 'synset': 'fritter_batter.n.01', 'name': 'fritter_batter'}, {'id': 13862, 'synset': 'coq_au_vin.n.01', 'name': 'coq_au_vin'}, {'id': 13863, 'synset': 'chicken_provencale.n.01', 'name': 'chicken_provencale'}, {'id': 13864, 'synset': 'chicken_and_rice.n.01', 'name': 'chicken_and_rice'}, {'id': 13865, 'synset': 'moo_goo_gai_pan.n.01', 'name': 'moo_goo_gai_pan'}, {'id': 13866, 'synset': 'arroz_con_pollo.n.01', 'name': 'arroz_con_pollo'}, {'id': 13867, 'synset': 'bacon_and_eggs.n.02', 'name': 'bacon_and_eggs'}, {'id': 13868, 'synset': 'barbecued_spareribs.n.01', 'name': 'barbecued_spareribs'}, {'id': 13869, 'synset': 'beef_bourguignonne.n.01', 'name': 'beef_Bourguignonne'}, {'id': 13870, 'synset': 'beef_wellington.n.01', 'name': 'beef_Wellington'}, {'id': 13871, 'synset': 'bitok.n.01', 'name': 'bitok'}, {'id': 13872, 'synset': 'boiled_dinner.n.01', 'name': 'boiled_dinner'}, {'id': 13873, 'synset': 'boston_baked_beans.n.01', 'name': 'Boston_baked_beans'}, {'id': 13874, 'synset': 'bubble_and_squeak.n.01', 'name': 'bubble_and_squeak'}, {'id': 13875, 'synset': 'pasta.n.01', 'name': 'pasta'}, {'id': 13876, 'synset': 'cannelloni.n.01', 'name': 'cannelloni'}, {'id': 13877, 'synset': 'carbonnade_flamande.n.01', 'name': 'carbonnade_flamande'}, {'id': 13878, 'synset': 'cheese_souffle.n.01', 'name': 'cheese_souffle'}, {'id': 13879, 'synset': 'chicken_marengo.n.01', 'name': 'chicken_Marengo'}, {'id': 13880, 'synset': 'chicken_cordon_bleu.n.01', 'name': 'chicken_cordon_bleu'}, {'id': 13881, 'synset': 'maryland_chicken.n.01', 'name': 'Maryland_chicken'}, {'id': 13882, 'synset': 'chicken_paprika.n.01', 'name': 'chicken_paprika'}, {'id': 13883, 'synset': 'chicken_tetrazzini.n.01', 'name': 'chicken_Tetrazzini'}, {'id': 13884, 'synset': 'tetrazzini.n.01', 'name': 'Tetrazzini'}, {'id': 13885, 'synset': 'chicken_kiev.n.01', 'name': 'chicken_Kiev'}, {'id': 13886, 'synset': 'chili.n.01', 'name': 'chili'}, {'id': 13887, 'synset': 'chili_dog.n.01', 'name': 'chili_dog'}, {'id': 13888, 'synset': 'chop_suey.n.01', 'name': 'chop_suey'}, {'id': 13889, 'synset': 'chow_mein.n.01', 'name': 'chow_mein'}, {'id': 13890, 'synset': 'codfish_ball.n.01', 'name': 'codfish_ball'}, {'id': 13891, 'synset': 'coquille.n.01', 'name': 'coquille'}, {'id': 13892, 'synset': 'coquilles_saint-jacques.n.01', 'name': 'coquilles_Saint-Jacques'}, {'id': 13893, 'synset': 'croquette.n.01', 'name': 'croquette'}, {'id': 13894, 'synset': 'cottage_pie.n.01', 'name': 'cottage_pie'}, {'id': 13895, 'synset': 'rissole.n.01', 'name': 'rissole'}, {'id': 13896, 'synset': 'dolmas.n.01', 'name': 'dolmas'}, {'id': 13897, 'synset': 'egg_foo_yong.n.01', 'name': 'egg_foo_yong'}, {'id': 13898, 'synset': 'eggs_benedict.n.01', 'name': 'eggs_Benedict'}, {'id': 13899, 'synset': 'enchilada.n.01', 'name': 'enchilada'}, {'id': 13900, 'synset': 'falafel.n.01', 'name': 'falafel'}, {'id': 13901, 'synset': 'fish_and_chips.n.01', 'name': 'fish_and_chips'}, {'id': 13902, 'synset': 'fondue.n.02', 'name': 'fondue'}, {'id': 13903, 'synset': 'cheese_fondue.n.01', 'name': 'cheese_fondue'}, {'id': 13904, 'synset': 'chocolate_fondue.n.01', 'name': 'chocolate_fondue'}, {'id': 13905, 'synset': 'fondue.n.01', 'name': 'fondue'}, {'id': 13906, 'synset': 'beef_fondue.n.01', 'name': 'beef_fondue'}, {'id': 13907, 'synset': 'fried_rice.n.01', 'name': 'fried_rice'}, {'id': 13908, 'synset': 'frittata.n.01', 'name': 'frittata'}, {'id': 13909, 'synset': 'frog_legs.n.01', 'name': 'frog_legs'}, {'id': 13910, 'synset': 'galantine.n.01', 'name': 'galantine'}, {'id': 13911, 'synset': 'gefilte_fish.n.01', 'name': 'gefilte_fish'}, {'id': 13912, 'synset': 'haggis.n.01', 'name': 'haggis'}, {'id': 13913, 'synset': 'ham_and_eggs.n.01', 'name': 'ham_and_eggs'}, {'id': 13914, 'synset': 'hash.n.01', 'name': 'hash'}, {'id': 13915, 'synset': 'corned_beef_hash.n.01', 'name': 'corned_beef_hash'}, {'id': 13916, 'synset': 'jambalaya.n.01', 'name': 'jambalaya'}, {'id': 13917, 'synset': 'kabob.n.01', 'name': 'kabob'}, {'id': 13918, 'synset': 'kedgeree.n.01', 'name': 'kedgeree'}, {'id': 13919, 'synset': 'souvlaki.n.01', 'name': 'souvlaki'}, {'id': 13920, 'synset': 'seafood_newburg.n.01', 'name': 'seafood_Newburg'}, {'id': 13921, 'synset': 'lobster_newburg.n.01', 'name': 'lobster_Newburg'}, {'id': 13922, 'synset': 'shrimp_newburg.n.01', 'name': 'shrimp_Newburg'}, {'id': 13923, 'synset': 'newburg_sauce.n.01', 'name': 'Newburg_sauce'}, {'id': 13924, 'synset': 'lobster_thermidor.n.01', 'name': 'lobster_thermidor'}, {'id': 13925, 'synset': 'lutefisk.n.01', 'name': 'lutefisk'}, {'id': 13926, 'synset': 'macaroni_and_cheese.n.01', 'name': 'macaroni_and_cheese'}, {'id': 13927, 'synset': 'macedoine.n.01', 'name': 'macedoine'}, {'id': 13928, 'synset': 'porcupine_ball.n.01', 'name': 'porcupine_ball'}, {'id': 13929, 'synset': 'swedish_meatball.n.01', 'name': 'Swedish_meatball'}, {'id': 13930, 'synset': 'meat_loaf.n.01', 'name': 'meat_loaf'}, {'id': 13931, 'synset': 'moussaka.n.01', 'name': 'moussaka'}, {'id': 13932, 'synset': 'osso_buco.n.01', 'name': 'osso_buco'}, {'id': 13933, 'synset': 'marrow.n.03', 'name': 'marrow'}, {'id': 13934, 'synset': 'pheasant_under_glass.n.01', 'name': 'pheasant_under_glass'}, {'id': 13935, 'synset': 'pigs_in_blankets.n.01', 'name': 'pigs_in_blankets'}, {'id': 13936, 'synset': 'pilaf.n.01', 'name': 'pilaf'}, {'id': 13937, 'synset': 'bulgur_pilaf.n.01', 'name': 'bulgur_pilaf'}, {'id': 13938, 'synset': 'sausage_pizza.n.01', 'name': 'sausage_pizza'}, {'id': 13939, 'synset': 'pepperoni_pizza.n.01', 'name': 'pepperoni_pizza'}, {'id': 13940, 'synset': 'cheese_pizza.n.01', 'name': 'cheese_pizza'}, {'id': 13941, 'synset': 'anchovy_pizza.n.01', 'name': 'anchovy_pizza'}, {'id': 13942, 'synset': 'sicilian_pizza.n.01', 'name': 'Sicilian_pizza'}, {'id': 13943, 'synset': 'poi.n.01', 'name': 'poi'}, {'id': 13944, 'synset': 'pork_and_beans.n.01', 'name': 'pork_and_beans'}, {'id': 13945, 'synset': 'porridge.n.01', 'name': 'porridge'}, {'id': 13946, 'synset': 'oatmeal.n.01', 'name': 'oatmeal'}, {'id': 13947, 'synset': 'loblolly.n.01', 'name': 'loblolly'}, {'id': 13948, 'synset': 'potpie.n.01', 'name': 'potpie'}, {'id': 13949, 'synset': 'rijsttaffel.n.01', 'name': 'rijsttaffel'}, {'id': 13950, 'synset': 'risotto.n.01', 'name': 'risotto'}, {'id': 13951, 'synset': 'roulade.n.01', 'name': 'roulade'}, {'id': 13952, 'synset': 'fish_loaf.n.01', 'name': 'fish_loaf'}, {'id': 13953, 'synset': 'salmon_loaf.n.01', 'name': 'salmon_loaf'}, {'id': 13954, 'synset': 'salisbury_steak.n.01', 'name': 'Salisbury_steak'}, {'id': 13955, 'synset': 'sauerbraten.n.01', 'name': 'sauerbraten'}, {'id': 13956, 'synset': 'sauerkraut.n.01', 'name': 'sauerkraut'}, {'id': 13957, 'synset': 'scallopine.n.01', 'name': 'scallopine'}, {'id': 13958, 'synset': 'veal_scallopini.n.01', 'name': 'veal_scallopini'}, {'id': 13959, 'synset': 'scampi.n.01', 'name': 'scampi'}, {'id': 13960, 'synset': 'scotch_egg.n.01', 'name': 'Scotch_egg'}, {'id': 13961, 'synset': 'scotch_woodcock.n.01', 'name': 'Scotch_woodcock'}, {'id': 13962, 'synset': 'scrapple.n.01', 'name': 'scrapple'}, {'id': 13963, 'synset': 'spaghetti_and_meatballs.n.01', 'name': 'spaghetti_and_meatballs'}, {'id': 13964, 'synset': 'spanish_rice.n.01', 'name': 'Spanish_rice'}, {'id': 13965, 'synset': 'steak_tartare.n.01', 'name': 'steak_tartare'}, {'id': 13966, 'synset': 'pepper_steak.n.02', 'name': 'pepper_steak'}, {'id': 13967, 'synset': 'steak_au_poivre.n.01', 'name': 'steak_au_poivre'}, {'id': 13968, 'synset': 'beef_stroganoff.n.01', 'name': 'beef_Stroganoff'}, {'id': 13969, 'synset': 'stuffed_cabbage.n.01', 'name': 'stuffed_cabbage'}, {'id': 13970, 'synset': 'kishke.n.01', 'name': 'kishke'}, {'id': 13971, 'synset': 'stuffed_peppers.n.01', 'name': 'stuffed_peppers'}, {'id': 13972, 'synset': 'stuffed_tomato.n.02', 'name': 'stuffed_tomato'}, {'id': 13973, 'synset': 'stuffed_tomato.n.01', 'name': 'stuffed_tomato'}, {'id': 13974, 'synset': 'succotash.n.01', 'name': 'succotash'}, {'id': 13975, 'synset': 'sukiyaki.n.01', 'name': 'sukiyaki'}, {'id': 13976, 'synset': 'sashimi.n.01', 'name': 'sashimi'}, {'id': 13977, 'synset': 'swiss_steak.n.01', 'name': 'Swiss_steak'}, {'id': 13978, 'synset': 'tamale.n.02', 'name': 'tamale'}, {'id': 13979, 'synset': 'tamale_pie.n.01', 'name': 'tamale_pie'}, {'id': 13980, 'synset': 'tempura.n.01', 'name': 'tempura'}, {'id': 13981, 'synset': 'teriyaki.n.01', 'name': 'teriyaki'}, {'id': 13982, 'synset': 'terrine.n.01', 'name': 'terrine'}, {'id': 13983, 'synset': 'welsh_rarebit.n.01', 'name': 'Welsh_rarebit'}, {'id': 13984, 'synset': 'schnitzel.n.01', 'name': 'schnitzel'}, {'id': 13985, 'synset': 'chicken_taco.n.01', 'name': 'chicken_taco'}, {'id': 13986, 'synset': 'beef_burrito.n.01', 'name': 'beef_burrito'}, {'id': 13987, 'synset': 'tostada.n.01', 'name': 'tostada'}, {'id': 13988, 'synset': 'bean_tostada.n.01', 'name': 'bean_tostada'}, {'id': 13989, 'synset': 'refried_beans.n.01', 'name': 'refried_beans'}, {'id': 13990, 'synset': 'beverage.n.01', 'name': 'beverage'}, {'id': 13991, 'synset': 'wish-wash.n.01', 'name': 'wish-wash'}, {'id': 13992, 'synset': 'concoction.n.01', 'name': 'concoction'}, {'id': 13993, 'synset': 'mix.n.01', 'name': 'mix'}, {'id': 13994, 'synset': 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{'id': 14011, 'synset': 'lager.n.02', 'name': 'lager'}, {'id': 14012, 'synset': 'light_beer.n.01', 'name': 'light_beer'}, {'id': 14013, 'synset': 'oktoberfest.n.01', 'name': 'Oktoberfest'}, {'id': 14014, 'synset': 'pilsner.n.01', 'name': 'Pilsner'}, {'id': 14015, 'synset': 'shebeen.n.01', 'name': 'shebeen'}, {'id': 14016, 'synset': 'weissbier.n.01', 'name': 'Weissbier'}, {'id': 14017, 'synset': 'weizenbock.n.01', 'name': 'Weizenbock'}, {'id': 14018, 'synset': 'malt.n.03', 'name': 'malt'}, {'id': 14019, 'synset': 'wort.n.02', 'name': 'wort'}, {'id': 14020, 'synset': 'malt.n.02', 'name': 'malt'}, {'id': 14021, 'synset': 'ale.n.01', 'name': 'ale'}, {'id': 14022, 'synset': 'bitter.n.01', 'name': 'bitter'}, {'id': 14023, 'synset': 'burton.n.03', 'name': 'Burton'}, {'id': 14024, 'synset': 'pale_ale.n.01', 'name': 'pale_ale'}, {'id': 14025, 'synset': 'porter.n.07', 'name': 'porter'}, {'id': 14026, 'synset': 'stout.n.01', 'name': 'stout'}, {'id': 14027, 'synset': 'guinness.n.02', 'name': 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'sauvignon_blanc.n.02', 'name': 'Sauvignon_blanc'}, {'id': 14060, 'synset': 'california_wine.n.01', 'name': 'California_wine'}, {'id': 14061, 'synset': 'cotes_de_provence.n.01', 'name': 'Cotes_de_Provence'}, {'id': 14062, 'synset': 'dessert_wine.n.01', 'name': 'dessert_wine'}, {'id': 14063, 'synset': 'dubonnet.n.01', 'name': 'Dubonnet'}, {'id': 14064, 'synset': 'jug_wine.n.01', 'name': 'jug_wine'}, {'id': 14065, 'synset': 'macon.n.02', 'name': 'macon'}, {'id': 14066, 'synset': 'moselle.n.01', 'name': 'Moselle'}, {'id': 14067, 'synset': 'muscadet.n.02', 'name': 'Muscadet'}, {'id': 14068, 'synset': 'plonk.n.01', 'name': 'plonk'}, {'id': 14069, 'synset': 'retsina.n.01', 'name': 'retsina'}, {'id': 14070, 'synset': 'rhine_wine.n.01', 'name': 'Rhine_wine'}, {'id': 14071, 'synset': 'riesling.n.02', 'name': 'Riesling'}, {'id': 14072, 'synset': 'liebfraumilch.n.01', 'name': 'liebfraumilch'}, {'id': 14073, 'synset': 'rhone_wine.n.01', 'name': 'Rhone_wine'}, {'id': 14074, 'synset': 'rioja.n.01', 'name': 'Rioja'}, {'id': 14075, 'synset': 'sack.n.04', 'name': 'sack'}, {'id': 14076, 'synset': 'saint_emilion.n.01', 'name': 'Saint_Emilion'}, {'id': 14077, 'synset': 'soave.n.01', 'name': 'Soave'}, {'id': 14078, 'synset': 'zinfandel.n.02', 'name': 'zinfandel'}, {'id': 14079, 'synset': 'sauterne.n.01', 'name': 'Sauterne'}, {'id': 14080, 'synset': 'straw_wine.n.01', 'name': 'straw_wine'}, {'id': 14081, 'synset': 'table_wine.n.01', 'name': 'table_wine'}, {'id': 14082, 'synset': 'tokay.n.01', 'name': 'Tokay'}, {'id': 14083, 'synset': 'vin_ordinaire.n.01', 'name': 'vin_ordinaire'}, {'id': 14084, 'synset': 'vermouth.n.01', 'name': 'vermouth'}, {'id': 14085, 'synset': 'sweet_vermouth.n.01', 'name': 'sweet_vermouth'}, {'id': 14086, 'synset': 'dry_vermouth.n.01', 'name': 'dry_vermouth'}, {'id': 14087, 'synset': 'chenin_blanc.n.02', 'name': 'Chenin_blanc'}, {'id': 14088, 'synset': 'verdicchio.n.02', 'name': 'Verdicchio'}, {'id': 14089, 'synset': 'vouvray.n.01', 'name': 'Vouvray'}, {'id': 14090, 'synset': 'yquem.n.01', 'name': 'Yquem'}, {'id': 14091, 'synset': 'generic.n.01', 'name': 'generic'}, {'id': 14092, 'synset': 'varietal.n.01', 'name': 'varietal'}, {'id': 14093, 'synset': 'fortified_wine.n.01', 'name': 'fortified_wine'}, {'id': 14094, 'synset': 'madeira.n.03', 'name': 'Madeira'}, {'id': 14095, 'synset': 'malmsey.n.01', 'name': 'malmsey'}, {'id': 14096, 'synset': 'port.n.02', 'name': 'port'}, {'id': 14097, 'synset': 'sherry.n.01', 'name': 'sherry'}, {'id': 14098, 'synset': 'marsala.n.01', 'name': 'Marsala'}, {'id': 14099, 'synset': 'muscat.n.03', 'name': 'muscat'}, {'id': 14100, 'synset': 'neutral_spirits.n.01', 'name': 'neutral_spirits'}, {'id': 14101, 'synset': 'aqua_vitae.n.01', 'name': 'aqua_vitae'}, {'id': 14102, 'synset': 'eau_de_vie.n.01', 'name': 'eau_de_vie'}, {'id': 14103, 'synset': 'moonshine.n.02', 'name': 'moonshine'}, {'id': 14104, 'synset': 'bathtub_gin.n.01', 'name': 'bathtub_gin'}, {'id': 14105, 'synset': 'aquavit.n.01', 'name': 'aquavit'}, {'id': 14106, 'synset': 'arrack.n.01', 'name': 'arrack'}, {'id': 14107, 'synset': 'bitters.n.01', 'name': 'bitters'}, {'id': 14108, 'synset': 'brandy.n.01', 'name': 'brandy'}, {'id': 14109, 'synset': 'applejack.n.01', 'name': 'applejack'}, {'id': 14110, 'synset': 'calvados.n.01', 'name': 'Calvados'}, {'id': 14111, 'synset': 'armagnac.n.01', 'name': 'Armagnac'}, {'id': 14112, 'synset': 'cognac.n.01', 'name': 'Cognac'}, {'id': 14113, 'synset': 'grappa.n.01', 'name': 'grappa'}, {'id': 14114, 'synset': 'kirsch.n.01', 'name': 'kirsch'}, {'id': 14115, 'synset': 'slivovitz.n.01', 'name': 'slivovitz'}, {'id': 14116, 'synset': 'gin.n.01', 'name': 'gin'}, {'id': 14117, 'synset': 'sloe_gin.n.01', 'name': 'sloe_gin'}, {'id': 14118, 'synset': 'geneva.n.02', 'name': 'geneva'}, {'id': 14119, 'synset': 'grog.n.01', 'name': 'grog'}, {'id': 14120, 'synset': 'ouzo.n.01', 'name': 'ouzo'}, {'id': 14121, 'synset': 'rum.n.01', 'name': 'rum'}, {'id': 14122, 'synset': 'demerara.n.04', 'name': 'demerara'}, {'id': 14123, 'synset': 'jamaica_rum.n.01', 'name': 'Jamaica_rum'}, {'id': 14124, 'synset': 'schnapps.n.01', 'name': 'schnapps'}, {'id': 14125, 'synset': 'pulque.n.01', 'name': 'pulque'}, {'id': 14126, 'synset': 'mescal.n.02', 'name': 'mescal'}, {'id': 14127, 'synset': 'whiskey.n.01', 'name': 'whiskey'}, {'id': 14128, 'synset': 'blended_whiskey.n.01', 'name': 'blended_whiskey'}, {'id': 14129, 'synset': 'bourbon.n.02', 'name': 'bourbon'}, {'id': 14130, 'synset': 'corn_whiskey.n.01', 'name': 'corn_whiskey'}, {'id': 14131, 'synset': 'firewater.n.01', 'name': 'firewater'}, {'id': 14132, 'synset': 'irish.n.02', 'name': 'Irish'}, {'id': 14133, 'synset': 'poteen.n.01', 'name': 'poteen'}, {'id': 14134, 'synset': 'rye.n.03', 'name': 'rye'}, {'id': 14135, 'synset': 'scotch.n.02', 'name': 'Scotch'}, {'id': 14136, 'synset': 'sour_mash.n.02', 'name': 'sour_mash'}, {'id': 14137, 'synset': 'liqueur.n.01', 'name': 'liqueur'}, {'id': 14138, 'synset': 'absinth.n.01', 'name': 'absinth'}, {'id': 14139, 'synset': 'amaretto.n.01', 'name': 'amaretto'}, {'id': 14140, 'synset': 'anisette.n.01', 'name': 'anisette'}, {'id': 14141, 'synset': 'benedictine.n.02', 'name': 'benedictine'}, {'id': 14142, 'synset': 'chartreuse.n.01', 'name': 'Chartreuse'}, {'id': 14143, 'synset': 'coffee_liqueur.n.01', 'name': 'coffee_liqueur'}, {'id': 14144, 'synset': 'creme_de_cacao.n.01', 'name': 'creme_de_cacao'}, {'id': 14145, 'synset': 'creme_de_menthe.n.01', 'name': 'creme_de_menthe'}, {'id': 14146, 'synset': 'creme_de_fraise.n.01', 'name': 'creme_de_fraise'}, {'id': 14147, 'synset': 'drambuie.n.01', 'name': 'Drambuie'}, {'id': 14148, 'synset': 'galliano.n.01', 'name': 'Galliano'}, {'id': 14149, 'synset': 'orange_liqueur.n.01', 'name': 'orange_liqueur'}, {'id': 14150, 'synset': 'curacao.n.02', 'name': 'curacao'}, {'id': 14151, 'synset': 'triple_sec.n.01', 'name': 'triple_sec'}, {'id': 14152, 'synset': 'grand_marnier.n.01', 'name': 'Grand_Marnier'}, {'id': 14153, 'synset': 'kummel.n.01', 'name': 'kummel'}, {'id': 14154, 'synset': 'maraschino.n.01', 'name': 'maraschino'}, {'id': 14155, 'synset': 'pastis.n.01', 'name': 'pastis'}, {'id': 14156, 'synset': 'pernod.n.01', 'name': 'Pernod'}, {'id': 14157, 'synset': 'pousse-cafe.n.01', 'name': 'pousse-cafe'}, {'id': 14158, 'synset': 'kahlua.n.01', 'name': 'Kahlua'}, {'id': 14159, 'synset': 'ratafia.n.01', 'name': 'ratafia'}, {'id': 14160, 'synset': 'sambuca.n.01', 'name': 'sambuca'}, {'id': 14161, 'synset': 'mixed_drink.n.01', 'name': 'mixed_drink'}, {'id': 14162, 'synset': 'cocktail.n.01', 'name': 'cocktail'}, {'id': 14163, 'synset': 'dom_pedro.n.01', 'name': 'Dom_Pedro'}, {'id': 14164, 'synset': 'highball.n.01', 'name': 'highball'}, {'id': 14165, 'synset': 'mixer.n.02', 'name': 'mixer'}, {'id': 14166, 'synset': 'bishop.n.02', 'name': 'bishop'}, {'id': 14167, 'synset': 'bloody_mary.n.02', 'name': 'Bloody_Mary'}, {'id': 14168, 'synset': 'virgin_mary.n.02', 'name': 'Virgin_Mary'}, {'id': 14169, 'synset': 'bullshot.n.01', 'name': 'bullshot'}, {'id': 14170, 'synset': 'cobbler.n.02', 'name': 'cobbler'}, {'id': 14171, 'synset': 'collins.n.02', 'name': 'collins'}, {'id': 14172, 'synset': 'cooler.n.02', 'name': 'cooler'}, {'id': 14173, 'synset': 'refresher.n.02', 'name': 'refresher'}, {'id': 14174, 'synset': 'daiquiri.n.01', 'name': 'daiquiri'}, {'id': 14175, 'synset': 'strawberry_daiquiri.n.01', 'name': 'strawberry_daiquiri'}, {'id': 14176, 'synset': 'nada_daiquiri.n.01', 'name': 'NADA_daiquiri'}, {'id': 14177, 'synset': 'spritzer.n.01', 'name': 'spritzer'}, {'id': 14178, 'synset': 'flip.n.02', 'name': 'flip'}, {'id': 14179, 'synset': 'gimlet.n.01', 'name': 'gimlet'}, {'id': 14180, 'synset': 'gin_and_tonic.n.01', 'name': 'gin_and_tonic'}, {'id': 14181, 'synset': 'grasshopper.n.02', 'name': 'grasshopper'}, {'id': 14182, 'synset': 'harvey_wallbanger.n.01', 'name': 'Harvey_Wallbanger'}, {'id': 14183, 'synset': 'julep.n.01', 'name': 'julep'}, {'id': 14184, 'synset': 'manhattan.n.02', 'name': 'manhattan'}, {'id': 14185, 'synset': 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{'id': 14216, 'synset': 'mocha.n.02', 'name': 'mocha'}, {'id': 14217, 'synset': 'cassareep.n.01', 'name': 'cassareep'}, {'id': 14218, 'synset': 'turkish_coffee.n.01', 'name': 'Turkish_coffee'}, {'id': 14219, 'synset': 'hard_cider.n.01', 'name': 'hard_cider'}, {'id': 14220, 'synset': 'scrumpy.n.01', 'name': 'scrumpy'}, {'id': 14221, 'synset': 'sweet_cider.n.01', 'name': 'sweet_cider'}, {'id': 14222, 'synset': 'mulled_cider.n.01', 'name': 'mulled_cider'}, {'id': 14223, 'synset': 'perry.n.04', 'name': 'perry'}, {'id': 14224, 'synset': 'rotgut.n.01', 'name': 'rotgut'}, {'id': 14225, 'synset': 'slug.n.05', 'name': 'slug'}, {'id': 14226, 'synset': 'criollo.n.02', 'name': 'criollo'}, {'id': 14227, 'synset': 'juice.n.01', 'name': 'juice'}, {'id': 14228, 'synset': 'nectar.n.02', 'name': 'nectar'}, {'id': 14229, 'synset': 'apple_juice.n.01', 'name': 'apple_juice'}, {'id': 14230, 'synset': 'cranberry_juice.n.01', 'name': 'cranberry_juice'}, {'id': 14231, 'synset': 'grape_juice.n.01', 'name': 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{'id': 14262, 'synset': 'tonic.n.01', 'name': 'tonic'}, {'id': 14263, 'synset': 'coffee_bean.n.01', 'name': 'coffee_bean'}, {'id': 14264, 'synset': 'coffee.n.01', 'name': 'coffee'}, {'id': 14265, 'synset': 'cafe_royale.n.01', 'name': 'cafe_royale'}, {'id': 14266, 'synset': 'fruit_punch.n.01', 'name': 'fruit_punch'}, {'id': 14267, 'synset': 'milk_punch.n.01', 'name': 'milk_punch'}, {'id': 14268, 'synset': 'mimosa.n.03', 'name': 'mimosa'}, {'id': 14269, 'synset': 'pina_colada.n.01', 'name': 'pina_colada'}, {'id': 14270, 'synset': 'punch.n.02', 'name': 'punch'}, {'id': 14271, 'synset': 'cup.n.06', 'name': 'cup'}, {'id': 14272, 'synset': 'champagne_cup.n.01', 'name': 'champagne_cup'}, {'id': 14273, 'synset': 'claret_cup.n.01', 'name': 'claret_cup'}, {'id': 14274, 'synset': 'wassail.n.01', 'name': 'wassail'}, {'id': 14275, 'synset': "planter's_punch.n.01", 'name': "planter's_punch"}, {'id': 14276, 'synset': 'white_russian.n.02', 'name': 'White_Russian'}, {'id': 14277, 'synset': 'fish_house_punch.n.01', 'name': 'fish_house_punch'}, {'id': 14278, 'synset': 'may_wine.n.01', 'name': 'May_wine'}, {'id': 14279, 'synset': 'eggnog.n.01', 'name': 'eggnog'}, {'id': 14280, 'synset': 'cassiri.n.01', 'name': 'cassiri'}, {'id': 14281, 'synset': 'spruce_beer.n.01', 'name': 'spruce_beer'}, {'id': 14282, 'synset': 'rickey.n.01', 'name': 'rickey'}, {'id': 14283, 'synset': 'gin_rickey.n.01', 'name': 'gin_rickey'}, {'id': 14284, 'synset': 'tea.n.05', 'name': 'tea'}, {'id': 14285, 'synset': 'tea.n.01', 'name': 'tea'}, {'id': 14286, 'synset': 'tea-like_drink.n.01', 'name': 'tea-like_drink'}, {'id': 14287, 'synset': 'cambric_tea.n.01', 'name': 'cambric_tea'}, {'id': 14288, 'synset': 'cuppa.n.01', 'name': 'cuppa'}, {'id': 14289, 'synset': 'herb_tea.n.01', 'name': 'herb_tea'}, {'id': 14290, 'synset': 'tisane.n.01', 'name': 'tisane'}, {'id': 14291, 'synset': 'camomile_tea.n.01', 'name': 'camomile_tea'}, {'id': 14292, 'synset': 'ice_tea.n.01', 'name': 'ice_tea'}, {'id': 14293, 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'name': 'ice_water'}, {'id': 14309, 'synset': 'soda_water.n.01', 'name': 'soda_water'}, {'id': 14310, 'synset': 'mineral_water.n.01', 'name': 'mineral_water'}, {'id': 14311, 'synset': 'seltzer.n.01', 'name': 'seltzer'}, {'id': 14312, 'synset': 'vichy_water.n.01', 'name': 'Vichy_water'}, {'id': 14313, 'synset': 'perishable.n.01', 'name': 'perishable'}, {'id': 14314, 'synset': 'couscous.n.01', 'name': 'couscous'}, {'id': 14315, 'synset': 'ramekin.n.01', 'name': 'ramekin'}, {'id': 14316, 'synset': 'multivitamin.n.01', 'name': 'multivitamin'}, {'id': 14317, 'synset': 'vitamin_pill.n.01', 'name': 'vitamin_pill'}, {'id': 14318, 'synset': 'soul_food.n.01', 'name': 'soul_food'}, {'id': 14319, 'synset': 'mold.n.06', 'name': 'mold'}, {'id': 14320, 'synset': 'people.n.01', 'name': 'people'}, {'id': 14321, 'synset': 'collection.n.01', 'name': 'collection'}, {'id': 14322, 'synset': 'book.n.07', 'name': 'book'}, {'id': 14323, 'synset': 'library.n.02', 'name': 'library'}, {'id': 14324, 'synset': 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'synset': 'benthos.n.01', 'name': 'benthos'}, {'id': 14371, 'synset': 'goldfield.n.01', 'name': 'goldfield'}, {'id': 14372, 'synset': 'grainfield.n.01', 'name': 'grainfield'}, {'id': 14373, 'synset': 'half-mast.n.01', 'name': 'half-mast'}, {'id': 14374, 'synset': 'hemline.n.01', 'name': 'hemline'}, {'id': 14375, 'synset': 'heronry.n.01', 'name': 'heronry'}, {'id': 14376, 'synset': 'hipline.n.02', 'name': 'hipline'}, {'id': 14377, 'synset': 'hipline.n.01', 'name': 'hipline'}, {'id': 14378, 'synset': 'hole-in-the-wall.n.01', 'name': 'hole-in-the-wall'}, {'id': 14379, 'synset': 'junkyard.n.01', 'name': 'junkyard'}, {'id': 14380, 'synset': 'isoclinic_line.n.01', 'name': 'isoclinic_line'}, {'id': 14381, 'synset': 'littoral.n.01', 'name': 'littoral'}, {'id': 14382, 'synset': 'magnetic_pole.n.01', 'name': 'magnetic_pole'}, {'id': 14383, 'synset': 'grassland.n.01', 'name': 'grassland'}, {'id': 14384, 'synset': 'mecca.n.02', 'name': 'mecca'}, {'id': 14385, 'synset': "observer's_meridian.n.01", 'name': "observer's_meridian"}, {'id': 14386, 'synset': 'prime_meridian.n.01', 'name': 'prime_meridian'}, {'id': 14387, 'synset': 'nombril.n.01', 'name': 'nombril'}, {'id': 14388, 'synset': 'no-parking_zone.n.01', 'name': 'no-parking_zone'}, {'id': 14389, 'synset': 'outdoors.n.01', 'name': 'outdoors'}, {'id': 14390, 'synset': 'fairground.n.01', 'name': 'fairground'}, {'id': 14391, 'synset': 'pasture.n.01', 'name': 'pasture'}, {'id': 14392, 'synset': 'perihelion.n.01', 'name': 'perihelion'}, {'id': 14393, 'synset': 'periselene.n.01', 'name': 'periselene'}, {'id': 14394, 'synset': 'locus_of_infection.n.01', 'name': 'locus_of_infection'}, {'id': 14395, 'synset': 'kasbah.n.01', 'name': 'kasbah'}, {'id': 14396, 'synset': 'waterfront.n.01', 'name': 'waterfront'}, {'id': 14397, 'synset': 'resort.n.01', 'name': 'resort'}, {'id': 14398, 'synset': 'resort_area.n.01', 'name': 'resort_area'}, {'id': 14399, 'synset': 'rough.n.01', 'name': 'rough'}, {'id': 14400, 'synset': 'ashram.n.02', 'name': 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{'id': 14481, 'synset': 'comet.n.01', 'name': 'comet'}, {'id': 14482, 'synset': 'continental_glacier.n.01', 'name': 'continental_glacier'}, {'id': 14483, 'synset': 'coral_reef.n.01', 'name': 'coral_reef'}, {'id': 14484, 'synset': 'cove.n.02', 'name': 'cove'}, {'id': 14485, 'synset': 'crag.n.01', 'name': 'crag'}, {'id': 14486, 'synset': 'crater.n.03', 'name': 'crater'}, {'id': 14487, 'synset': 'cultivated_land.n.01', 'name': 'cultivated_land'}, {'id': 14488, 'synset': 'dale.n.01', 'name': 'dale'}, {'id': 14489, 'synset': 'defile.n.01', 'name': 'defile'}, {'id': 14490, 'synset': 'delta.n.01', 'name': 'delta'}, {'id': 14491, 'synset': 'descent.n.05', 'name': 'descent'}, {'id': 14492, 'synset': 'diapir.n.01', 'name': 'diapir'}, {'id': 14493, 'synset': 'divot.n.02', 'name': 'divot'}, {'id': 14494, 'synset': 'divot.n.01', 'name': 'divot'}, {'id': 14495, 'synset': 'down.n.04', 'name': 'down'}, {'id': 14496, 'synset': 'downhill.n.01', 'name': 'downhill'}, {'id': 14497, 'synset': 'draw.n.01', 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14660, 'synset': 'volcanic_crater.n.01', 'name': 'volcanic_crater'}, {'id': 14661, 'synset': 'volcano.n.02', 'name': 'volcano'}, {'id': 14662, 'synset': 'wadi.n.01', 'name': 'wadi'}, {'id': 14663, 'synset': 'wall.n.05', 'name': 'wall'}, {'id': 14664, 'synset': 'warren.n.03', 'name': 'warren'}, {'id': 14665, 'synset': "wasp's_nest.n.01", 'name': "wasp's_nest"}, {'id': 14666, 'synset': 'watercourse.n.01', 'name': 'watercourse'}, {'id': 14667, 'synset': 'waterside.n.01', 'name': 'waterside'}, {'id': 14668, 'synset': 'water_table.n.01', 'name': 'water_table'}, {'id': 14669, 'synset': 'whinstone.n.01', 'name': 'whinstone'}, {'id': 14670, 'synset': 'wormcast.n.02', 'name': 'wormcast'}, {'id': 14671, 'synset': 'xenolith.n.01', 'name': 'xenolith'}, {'id': 14672, 'synset': 'circe.n.01', 'name': 'Circe'}, {'id': 14673, 'synset': 'gryphon.n.01', 'name': 'gryphon'}, {'id': 14674, 'synset': 'spiritual_leader.n.01', 'name': 'spiritual_leader'}, {'id': 14675, 'synset': 'messiah.n.01', 'name': 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14788, 'synset': 'tutelo.n.01', 'name': 'Tutelo'}, {'id': 14789, 'synset': 'yana.n.01', 'name': 'Yana'}, {'id': 14790, 'synset': 'yavapai.n.01', 'name': 'Yavapai'}, {'id': 14791, 'synset': 'yokuts.n.02', 'name': 'Yokuts'}, {'id': 14792, 'synset': 'yuma.n.01', 'name': 'Yuma'}, {'id': 14793, 'synset': 'gadaba.n.01', 'name': 'Gadaba'}, {'id': 14794, 'synset': 'kolam.n.01', 'name': 'Kolam'}, {'id': 14795, 'synset': 'kui.n.01', 'name': 'Kui'}, {'id': 14796, 'synset': 'toda.n.01', 'name': 'Toda'}, {'id': 14797, 'synset': 'tulu.n.01', 'name': 'Tulu'}, {'id': 14798, 'synset': 'gujarati.n.01', 'name': 'Gujarati'}, {'id': 14799, 'synset': 'kashmiri.n.01', 'name': 'Kashmiri'}, {'id': 14800, 'synset': 'punjabi.n.01', 'name': 'Punjabi'}, {'id': 14801, 'synset': 'slav.n.01', 'name': 'Slav'}, {'id': 14802, 'synset': 'anabaptist.n.01', 'name': 'Anabaptist'}, {'id': 14803, 'synset': 'adventist.n.01', 'name': 'Adventist'}, {'id': 14804, 'synset': 'gentile.n.03', 'name': 'gentile'}, {'id': 14805, 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'synset': 'frank.n.01', 'name': 'Frank'}, {'id': 14822, 'synset': 'afghan.n.02', 'name': 'Afghan'}, {'id': 14823, 'synset': 'albanian.n.01', 'name': 'Albanian'}, {'id': 14824, 'synset': 'algerian.n.01', 'name': 'Algerian'}, {'id': 14825, 'synset': 'altaic.n.01', 'name': 'Altaic'}, {'id': 14826, 'synset': 'andorran.n.01', 'name': 'Andorran'}, {'id': 14827, 'synset': 'angolan.n.01', 'name': 'Angolan'}, {'id': 14828, 'synset': 'anguillan.n.01', 'name': 'Anguillan'}, {'id': 14829, 'synset': 'austrian.n.01', 'name': 'Austrian'}, {'id': 14830, 'synset': 'bahamian.n.01', 'name': 'Bahamian'}, {'id': 14831, 'synset': 'bahraini.n.01', 'name': 'Bahraini'}, {'id': 14832, 'synset': 'basotho.n.01', 'name': 'Basotho'}, {'id': 14833, 'synset': 'herero.n.01', 'name': 'Herero'}, {'id': 14834, 'synset': 'luba.n.01', 'name': 'Luba'}, {'id': 14835, 'synset': 'barbadian.n.01', 'name': 'Barbadian'}, {'id': 14836, 'synset': 'bolivian.n.01', 'name': 'Bolivian'}, {'id': 14837, 'synset': 'bornean.n.01', 'name': 'Bornean'}, {'id': 14838, 'synset': 'carioca.n.01', 'name': 'Carioca'}, {'id': 14839, 'synset': 'tupi.n.01', 'name': 'Tupi'}, {'id': 14840, 'synset': 'bruneian.n.01', 'name': 'Bruneian'}, {'id': 14841, 'synset': 'bulgarian.n.01', 'name': 'Bulgarian'}, {'id': 14842, 'synset': 'byelorussian.n.01', 'name': 'Byelorussian'}, {'id': 14843, 'synset': 'cameroonian.n.01', 'name': 'Cameroonian'}, {'id': 14844, 'synset': 'canadian.n.01', 'name': 'Canadian'}, {'id': 14845, 'synset': 'french_canadian.n.01', 'name': 'French_Canadian'}, {'id': 14846, 'synset': 'central_american.n.01', 'name': 'Central_American'}, {'id': 14847, 'synset': 'chilean.n.01', 'name': 'Chilean'}, {'id': 14848, 'synset': 'congolese.n.01', 'name': 'Congolese'}, {'id': 14849, 'synset': 'cypriot.n.01', 'name': 'Cypriot'}, {'id': 14850, 'synset': 'dane.n.01', 'name': 'Dane'}, {'id': 14851, 'synset': 'djiboutian.n.01', 'name': 'Djiboutian'}, {'id': 14852, 'synset': 'britisher.n.01', 'name': 'Britisher'}, {'id': 14853, 'synset': 'english_person.n.01', 'name': 'English_person'}, {'id': 14854, 'synset': 'englishwoman.n.01', 'name': 'Englishwoman'}, {'id': 14855, 'synset': 'anglo-saxon.n.02', 'name': 'Anglo-Saxon'}, {'id': 14856, 'synset': 'angle.n.03', 'name': 'Angle'}, {'id': 14857, 'synset': 'west_saxon.n.01', 'name': 'West_Saxon'}, {'id': 14858, 'synset': 'lombard.n.01', 'name': 'Lombard'}, {'id': 14859, 'synset': 'limey.n.01', 'name': 'limey'}, {'id': 14860, 'synset': 'cantabrigian.n.01', 'name': 'Cantabrigian'}, {'id': 14861, 'synset': 'cornishman.n.01', 'name': 'Cornishman'}, {'id': 14862, 'synset': 'cornishwoman.n.01', 'name': 'Cornishwoman'}, {'id': 14863, 'synset': 'lancastrian.n.02', 'name': 'Lancastrian'}, {'id': 14864, 'synset': 'lancastrian.n.01', 'name': 'Lancastrian'}, {'id': 14865, 'synset': 'geordie.n.01', 'name': 'Geordie'}, {'id': 14866, 'synset': 'oxonian.n.01', 'name': 'Oxonian'}, {'id': 14867, 'synset': 'ethiopian.n.01', 'name': 'Ethiopian'}, {'id': 14868, 'synset': 'amhara.n.01', 'name': 'Amhara'}, {'id': 14869, 'synset': 'eritrean.n.01', 'name': 'Eritrean'}, {'id': 14870, 'synset': 'finn.n.01', 'name': 'Finn'}, {'id': 14871, 'synset': 'komi.n.01', 'name': 'Komi'}, {'id': 14872, 'synset': 'livonian.n.01', 'name': 'Livonian'}, {'id': 14873, 'synset': 'lithuanian.n.01', 'name': 'Lithuanian'}, {'id': 14874, 'synset': 'selkup.n.01', 'name': 'Selkup'}, {'id': 14875, 'synset': 'parisian.n.01', 'name': 'Parisian'}, {'id': 14876, 'synset': 'parisienne.n.01', 'name': 'Parisienne'}, {'id': 14877, 'synset': 'creole.n.02', 'name': 'Creole'}, {'id': 14878, 'synset': 'creole.n.01', 'name': 'Creole'}, {'id': 14879, 'synset': 'gabonese.n.01', 'name': 'Gabonese'}, {'id': 14880, 'synset': 'greek.n.02', 'name': 'Greek'}, {'id': 14881, 'synset': 'dorian.n.01', 'name': 'Dorian'}, {'id': 14882, 'synset': 'athenian.n.01', 'name': 'Athenian'}, {'id': 14883, 'synset': 'laconian.n.01', 'name': 'Laconian'}, {'id': 14884, 'synset': 'guyanese.n.01', 'name': 'Guyanese'}, {'id': 14885, 'synset': 'haitian.n.01', 'name': 'Haitian'}, {'id': 14886, 'synset': 'malay.n.01', 'name': 'Malay'}, {'id': 14887, 'synset': 'moro.n.01', 'name': 'Moro'}, {'id': 14888, 'synset': 'netherlander.n.01', 'name': 'Netherlander'}, {'id': 14889, 'synset': 'icelander.n.01', 'name': 'Icelander'}, {'id': 14890, 'synset': 'iraqi.n.01', 'name': 'Iraqi'}, {'id': 14891, 'synset': 'irishman.n.01', 'name': 'Irishman'}, {'id': 14892, 'synset': 'irishwoman.n.01', 'name': 'Irishwoman'}, {'id': 14893, 'synset': 'dubliner.n.01', 'name': 'Dubliner'}, {'id': 14894, 'synset': 'italian.n.01', 'name': 'Italian'}, {'id': 14895, 'synset': 'roman.n.01', 'name': 'Roman'}, {'id': 14896, 'synset': 'sabine.n.02', 'name': 'Sabine'}, {'id': 14897, 'synset': 'japanese.n.01', 'name': 'Japanese'}, {'id': 14898, 'synset': 'jordanian.n.01', 'name': 'Jordanian'}, {'id': 14899, 'synset': 'korean.n.01', 'name': 'Korean'}, {'id': 14900, 'synset': 'kenyan.n.01', 'name': 'Kenyan'}, {'id': 14901, 'synset': 'lao.n.01', 'name': 'Lao'}, {'id': 14902, 'synset': 'lapp.n.01', 'name': 'Lapp'}, {'id': 14903, 'synset': 'latin_american.n.01', 'name': 'Latin_American'}, {'id': 14904, 'synset': 'lebanese.n.01', 'name': 'Lebanese'}, {'id': 14905, 'synset': 'levantine.n.01', 'name': 'Levantine'}, {'id': 14906, 'synset': 'liberian.n.01', 'name': 'Liberian'}, {'id': 14907, 'synset': 'luxemburger.n.01', 'name': 'Luxemburger'}, {'id': 14908, 'synset': 'macedonian.n.01', 'name': 'Macedonian'}, {'id': 14909, 'synset': 'sabahan.n.01', 'name': 'Sabahan'}, {'id': 14910, 'synset': 'mexican.n.01', 'name': 'Mexican'}, {'id': 14911, 'synset': 'chicano.n.01', 'name': 'Chicano'}, {'id': 14912, 'synset': 'mexican-american.n.01', 'name': 'Mexican-American'}, {'id': 14913, 'synset': 'namibian.n.01', 'name': 'Namibian'}, {'id': 14914, 'synset': 'nauruan.n.01', 'name': 'Nauruan'}, {'id': 14915, 'synset': 'gurkha.n.02', 'name': 'Gurkha'}, {'id': 14916, 'synset': 'new_zealander.n.01', 'name': 'New_Zealander'}, {'id': 14917, 'synset': 'nicaraguan.n.01', 'name': 'Nicaraguan'}, {'id': 14918, 'synset': 'nigerian.n.01', 'name': 'Nigerian'}, {'id': 14919, 'synset': 'hausa.n.01', 'name': 'Hausa'}, {'id': 14920, 'synset': 'north_american.n.01', 'name': 'North_American'}, {'id': 14921, 'synset': 'nova_scotian.n.01', 'name': 'Nova_Scotian'}, {'id': 14922, 'synset': 'omani.n.01', 'name': 'Omani'}, {'id': 14923, 'synset': 'pakistani.n.01', 'name': 'Pakistani'}, {'id': 14924, 'synset': 'brahui.n.01', 'name': 'Brahui'}, {'id': 14925, 'synset': 'south_american_indian.n.01', 'name': 'South_American_Indian'}, {'id': 14926, 'synset': 'carib.n.01', 'name': 'Carib'}, {'id': 14927, 'synset': 'filipino.n.01', 'name': 'Filipino'}, {'id': 14928, 'synset': 'polynesian.n.01', 'name': 'Polynesian'}, {'id': 14929, 'synset': 'qatari.n.01', 'name': 'Qatari'}, {'id': 14930, 'synset': 'romanian.n.01', 'name': 'Romanian'}, {'id': 14931, 'synset': 'muscovite.n.02', 'name': 'Muscovite'}, {'id': 14932, 'synset': 'georgian.n.02', 'name': 'Georgian'}, {'id': 14933, 'synset': 'sarawakian.n.01', 'name': 'Sarawakian'}, {'id': 14934, 'synset': 'scandinavian.n.01', 'name': 'Scandinavian'}, {'id': 14935, 'synset': 'senegalese.n.01', 'name': 'Senegalese'}, {'id': 14936, 'synset': 'slovene.n.01', 'name': 'Slovene'}, {'id': 14937, 'synset': 'south_african.n.01', 'name': 'South_African'}, {'id': 14938, 'synset': 'south_american.n.01', 'name': 'South_American'}, {'id': 14939, 'synset': 'sudanese.n.01', 'name': 'Sudanese'}, {'id': 14940, 'synset': 'syrian.n.01', 'name': 'Syrian'}, {'id': 14941, 'synset': 'tahitian.n.01', 'name': 'Tahitian'}, {'id': 14942, 'synset': 'tanzanian.n.01', 'name': 'Tanzanian'}, {'id': 14943, 'synset': 'tibetan.n.02', 'name': 'Tibetan'}, {'id': 14944, 'synset': 'togolese.n.01', 'name': 'Togolese'}, {'id': 14945, 'synset': 'tuareg.n.01', 'name': 'Tuareg'}, {'id': 14946, 'synset': 'turki.n.01', 'name': 'Turki'}, {'id': 14947, 'synset': 'chuvash.n.01', 'name': 'Chuvash'}, {'id': 14948, 'synset': 'turkoman.n.01', 'name': 'Turkoman'}, {'id': 14949, 'synset': 'uzbek.n.01', 'name': 'Uzbek'}, {'id': 14950, 'synset': 'ugandan.n.01', 'name': 'Ugandan'}, {'id': 14951, 'synset': 'ukranian.n.01', 'name': 'Ukranian'}, {'id': 14952, 'synset': 'yakut.n.01', 'name': 'Yakut'}, {'id': 14953, 'synset': 'tungus.n.01', 'name': 'Tungus'}, {'id': 14954, 'synset': 'igbo.n.01', 'name': 'Igbo'}, {'id': 14955, 'synset': 'american.n.03', 'name': 'American'}, {'id': 14956, 'synset': 'anglo-american.n.01', 'name': 'Anglo-American'}, {'id': 14957, 'synset': 'alaska_native.n.01', 'name': 'Alaska_Native'}, {'id': 14958, 'synset': 'arkansan.n.01', 'name': 'Arkansan'}, {'id': 14959, 'synset': 'carolinian.n.01', 'name': 'Carolinian'}, {'id': 14960, 'synset': 'coloradan.n.01', 'name': 'Coloradan'}, {'id': 14961, 'synset': 'connecticuter.n.01', 'name': 'Connecticuter'}, {'id': 14962, 'synset': 'delawarean.n.01', 'name': 'Delawarean'}, {'id': 14963, 'synset': 'floridian.n.01', 'name': 'Floridian'}, {'id': 14964, 'synset': 'german_american.n.01', 'name': 'German_American'}, {'id': 14965, 'synset': 'illinoisan.n.01', 'name': 'Illinoisan'}, {'id': 14966, 'synset': 'mainer.n.01', 'name': 'Mainer'}, {'id': 14967, 'synset': 'marylander.n.01', 'name': 'Marylander'}, {'id': 14968, 'synset': 'minnesotan.n.01', 'name': 'Minnesotan'}, {'id': 14969, 'synset': 'nebraskan.n.01', 'name': 'Nebraskan'}, {'id': 14970, 'synset': 'new_hampshirite.n.01', 'name': 'New_Hampshirite'}, {'id': 14971, 'synset': 'new_jerseyan.n.01', 'name': 'New_Jerseyan'}, {'id': 14972, 'synset': 'new_yorker.n.01', 'name': 'New_Yorker'}, {'id': 14973, 'synset': 'north_carolinian.n.01', 'name': 'North_Carolinian'}, {'id': 14974, 'synset': 'oregonian.n.01', 'name': 'Oregonian'}, {'id': 14975, 'synset': 'pennsylvanian.n.02', 'name': 'Pennsylvanian'}, {'id': 14976, 'synset': 'texan.n.01', 'name': 'Texan'}, {'id': 14977, 'synset': 'utahan.n.01', 'name': 'Utahan'}, {'id': 14978, 'synset': 'uruguayan.n.01', 'name': 'Uruguayan'}, {'id': 14979, 'synset': 'vietnamese.n.01', 'name': 'Vietnamese'}, {'id': 14980, 'synset': 'gambian.n.01', 'name': 'Gambian'}, {'id': 14981, 'synset': 'east_german.n.01', 'name': 'East_German'}, {'id': 14982, 'synset': 'berliner.n.01', 'name': 'Berliner'}, {'id': 14983, 'synset': 'prussian.n.01', 'name': 'Prussian'}, {'id': 14984, 'synset': 'ghanian.n.01', 'name': 'Ghanian'}, {'id': 14985, 'synset': 'guinean.n.01', 'name': 'Guinean'}, {'id': 14986, 'synset': 'papuan.n.01', 'name': 'Papuan'}, {'id': 14987, 'synset': 'walloon.n.01', 'name': 'Walloon'}, {'id': 14988, 'synset': 'yemeni.n.01', 'name': 'Yemeni'}, {'id': 14989, 'synset': 'yugoslav.n.01', 'name': 'Yugoslav'}, {'id': 14990, 'synset': 'serbian.n.01', 'name': 'Serbian'}, {'id': 14991, 'synset': 'xhosa.n.01', 'name': 'Xhosa'}, {'id': 14992, 'synset': 'zairese.n.01', 'name': 'Zairese'}, {'id': 14993, 'synset': 'zimbabwean.n.01', 'name': 'Zimbabwean'}, {'id': 14994, 'synset': 'zulu.n.01', 'name': 'Zulu'}, {'id': 14995, 'synset': 'gemini.n.01', 'name': 'Gemini'}, {'id': 14996, 'synset': 'sagittarius.n.01', 'name': 'Sagittarius'}, {'id': 14997, 'synset': 'pisces.n.02', 'name': 'Pisces'}, {'id': 14998, 'synset': 'abbe.n.01', 'name': 'abbe'}, {'id': 14999, 'synset': 'abbess.n.01', 'name': 'abbess'}, {'id': 15000, 'synset': 'abnegator.n.01', 'name': 'abnegator'}, {'id': 15001, 'synset': 'abridger.n.01', 'name': 'abridger'}, {'id': 15002, 'synset': 'abstractor.n.01', 'name': 'abstractor'}, {'id': 15003, 'synset': 'absconder.n.01', 'name': 'absconder'}, {'id': 15004, 'synset': 'absolver.n.01', 'name': 'absolver'}, {'id': 15005, 'synset': 'abecedarian.n.01', 'name': 'abecedarian'}, {'id': 15006, 'synset': 'aberrant.n.01', 'name': 'aberrant'}, {'id': 15007, 'synset': 'abettor.n.01', 'name': 'abettor'}, {'id': 15008, 'synset': 'abhorrer.n.01', 'name': 'abhorrer'}, {'id': 15009, 'synset': 'abomination.n.01', 'name': 'abomination'}, {'id': 15010, 'synset': 'abseiler.n.01', 'name': 'abseiler'}, {'id': 15011, 'synset': 'abstainer.n.01', 'name': 'abstainer'}, {'id': 15012, 'synset': 'academic_administrator.n.01', 'name': 'academic_administrator'}, {'id': 15013, 'synset': 'academician.n.01', 'name': 'academician'}, {'id': 15014, 'synset': 'accessory_before_the_fact.n.01', 'name': 'accessory_before_the_fact'}, {'id': 15015, 'synset': 'companion.n.03', 'name': 'companion'}, {'id': 15016, 'synset': 'accompanist.n.01', 'name': 'accompanist'}, {'id': 15017, 'synset': 'accomplice.n.01', 'name': 'accomplice'}, {'id': 15018, 'synset': 'account_executive.n.01', 'name': 'account_executive'}, {'id': 15019, 'synset': 'accused.n.01', 'name': 'accused'}, {'id': 15020, 'synset': 'accuser.n.01', 'name': 'accuser'}, {'id': 15021, 'synset': 'acid_head.n.01', 'name': 'acid_head'}, {'id': 15022, 'synset': 'acquaintance.n.03', 'name': 'acquaintance'}, {'id': 15023, 'synset': 'acquirer.n.01', 'name': 'acquirer'}, {'id': 15024, 'synset': 'aerialist.n.01', 'name': 'aerialist'}, {'id': 15025, 'synset': 'action_officer.n.01', 'name': 'action_officer'}, {'id': 15026, 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15042, 'synset': 'aeronautical_engineer.n.01', 'name': 'aeronautical_engineer'}, {'id': 15043, 'synset': 'affiliate.n.01', 'name': 'affiliate'}, {'id': 15044, 'synset': 'affluent.n.01', 'name': 'affluent'}, {'id': 15045, 'synset': 'aficionado.n.02', 'name': 'aficionado'}, {'id': 15046, 'synset': 'buck_sergeant.n.01', 'name': 'buck_sergeant'}, {'id': 15047, 'synset': 'agent-in-place.n.01', 'name': 'agent-in-place'}, {'id': 15048, 'synset': 'aggravator.n.01', 'name': 'aggravator'}, {'id': 15049, 'synset': 'agitator.n.01', 'name': 'agitator'}, {'id': 15050, 'synset': 'agnostic.n.02', 'name': 'agnostic'}, {'id': 15051, 'synset': 'agnostic.n.01', 'name': 'agnostic'}, {'id': 15052, 'synset': 'agonist.n.02', 'name': 'agonist'}, {'id': 15053, 'synset': 'agony_aunt.n.01', 'name': 'agony_aunt'}, {'id': 15054, 'synset': 'agriculturist.n.01', 'name': 'agriculturist'}, {'id': 15055, 'synset': 'air_attache.n.01', 'name': 'air_attache'}, {'id': 15056, 'synset': 'air_force_officer.n.01', 'name': 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'synset': 'wanton.n.01', 'name': 'wanton'}, {'id': 17437, 'synset': 'warrantee.n.02', 'name': 'warrantee'}, {'id': 17438, 'synset': 'warrantee.n.01', 'name': 'warrantee'}, {'id': 17439, 'synset': 'washer.n.01', 'name': 'washer'}, {'id': 17440, 'synset': 'washerman.n.01', 'name': 'washerman'}, {'id': 17441, 'synset': 'washwoman.n.01', 'name': 'washwoman'}, {'id': 17442, 'synset': 'wassailer.n.01', 'name': 'wassailer'}, {'id': 17443, 'synset': 'wastrel.n.01', 'name': 'wastrel'}, {'id': 17444, 'synset': 'wave.n.09', 'name': 'Wave'}, {'id': 17445, 'synset': 'weatherman.n.01', 'name': 'weatherman'}, {'id': 17446, 'synset': 'weekend_warrior.n.02', 'name': 'weekend_warrior'}, {'id': 17447, 'synset': 'weeder.n.01', 'name': 'weeder'}, {'id': 17448, 'synset': 'welder.n.01', 'name': 'welder'}, {'id': 17449, 'synset': 'welfare_case.n.01', 'name': 'welfare_case'}, {'id': 17450, 'synset': 'westerner.n.01', 'name': 'westerner'}, {'id': 17451, 'synset': 'west-sider.n.01', 'name': 'West-sider'}, {'id': 17452, 'synset': 'wetter.n.02', 'name': 'wetter'}, {'id': 17453, 'synset': 'whaler.n.01', 'name': 'whaler'}, {'id': 17454, 'synset': 'whig.n.02', 'name': 'Whig'}, {'id': 17455, 'synset': 'whiner.n.01', 'name': 'whiner'}, {'id': 17456, 'synset': 'whipper-in.n.01', 'name': 'whipper-in'}, {'id': 17457, 'synset': 'whisperer.n.01', 'name': 'whisperer'}, {'id': 17458, 'synset': 'whiteface.n.02', 'name': 'whiteface'}, {'id': 17459, 'synset': 'carmelite.n.01', 'name': 'Carmelite'}, {'id': 17460, 'synset': 'augustinian.n.01', 'name': 'Augustinian'}, {'id': 17461, 'synset': 'white_hope.n.01', 'name': 'white_hope'}, {'id': 17462, 'synset': 'white_supremacist.n.01', 'name': 'white_supremacist'}, {'id': 17463, 'synset': 'whoremaster.n.02', 'name': 'whoremaster'}, {'id': 17464, 'synset': 'whoremaster.n.01', 'name': 'whoremaster'}, {'id': 17465, 'synset': 'widow.n.01', 'name': 'widow'}, {'id': 17466, 'synset': 'wife.n.01', 'name': 'wife'}, {'id': 17467, 'synset': 'wiggler.n.01', 'name': 'wiggler'}, {'id': 17468, 'synset': 'wimp.n.01', 'name': 'wimp'}, {'id': 17469, 'synset': 'wing_commander.n.01', 'name': 'wing_commander'}, {'id': 17470, 'synset': 'winger.n.01', 'name': 'winger'}, {'id': 17471, 'synset': 'winner.n.02', 'name': 'winner'}, {'id': 17472, 'synset': 'winner.n.01', 'name': 'winner'}, {'id': 17473, 'synset': 'window_dresser.n.01', 'name': 'window_dresser'}, {'id': 17474, 'synset': 'winker.n.01', 'name': 'winker'}, {'id': 17475, 'synset': 'wiper.n.01', 'name': 'wiper'}, {'id': 17476, 'synset': 'wireman.n.01', 'name': 'wireman'}, {'id': 17477, 'synset': 'wise_guy.n.01', 'name': 'wise_guy'}, {'id': 17478, 'synset': 'witch_doctor.n.01', 'name': 'witch_doctor'}, {'id': 17479, 'synset': 'withdrawer.n.05', 'name': 'withdrawer'}, {'id': 17480, 'synset': 'withdrawer.n.01', 'name': 'withdrawer'}, {'id': 17481, 'synset': 'woman.n.01', 'name': 'woman'}, {'id': 17482, 'synset': 'woman.n.02', 'name': 'woman'}, {'id': 17483, 'synset': 'wonder_boy.n.01', 'name': 'wonder_boy'}, {'id': 17484, 'synset': 'wonderer.n.01', 'name': 'wonderer'}, {'id': 17485, 'synset': 'working_girl.n.01', 'name': 'working_girl'}, {'id': 17486, 'synset': 'workman.n.01', 'name': 'workman'}, {'id': 17487, 'synset': 'workmate.n.01', 'name': 'workmate'}, {'id': 17488, 'synset': 'worldling.n.01', 'name': 'worldling'}, {'id': 17489, 'synset': 'worshiper.n.01', 'name': 'worshiper'}, {'id': 17490, 'synset': 'worthy.n.01', 'name': 'worthy'}, {'id': 17491, 'synset': 'wrecker.n.01', 'name': 'wrecker'}, {'id': 17492, 'synset': 'wright.n.07', 'name': 'wright'}, {'id': 17493, 'synset': 'write-in_candidate.n.01', 'name': 'write-in_candidate'}, {'id': 17494, 'synset': 'writer.n.01', 'name': 'writer'}, {'id': 17495, 'synset': 'wykehamist.n.01', 'name': 'Wykehamist'}, {'id': 17496, 'synset': 'yakuza.n.01', 'name': 'yakuza'}, {'id': 17497, 'synset': 'yard_bird.n.01', 'name': 'yard_bird'}, {'id': 17498, 'synset': 'yardie.n.01', 'name': 'yardie'}, {'id': 17499, 'synset': 'yardman.n.01', 'name': 'yardman'}, {'id': 17500, 'synset': 'yardmaster.n.01', 'name': 'yardmaster'}, {'id': 17501, 'synset': 'yenta.n.02', 'name': 'yenta'}, {'id': 17502, 'synset': 'yogi.n.02', 'name': 'yogi'}, {'id': 17503, 'synset': 'young_buck.n.01', 'name': 'young_buck'}, {'id': 17504, 'synset': 'young_turk.n.02', 'name': 'young_Turk'}, {'id': 17505, 'synset': 'young_turk.n.01', 'name': 'Young_Turk'}, {'id': 17506, 'synset': 'zionist.n.01', 'name': 'Zionist'}, {'id': 17507, 'synset': 'zoo_keeper.n.01', 'name': 'zoo_keeper'}, {'id': 17508, 'synset': 'genet.n.01', 'name': 'Genet'}, {'id': 17509, 'synset': 'kennan.n.01', 'name': 'Kennan'}, {'id': 17510, 'synset': 'munro.n.01', 'name': 'Munro'}, {'id': 17511, 'synset': 'popper.n.01', 'name': 'Popper'}, {'id': 17512, 'synset': 'stoker.n.01', 'name': 'Stoker'}, {'id': 17513, 'synset': 'townes.n.01', 'name': 'Townes'}, {'id': 17514, 'synset': 'dust_storm.n.01', 'name': 'dust_storm'}, {'id': 17515, 'synset': 'parhelion.n.01', 'name': 'parhelion'}, {'id': 17516, 'synset': 'snow.n.01', 'name': 'snow'}, {'id': 17517, 'synset': 'facula.n.01', 'name': 'facula'}, {'id': 17518, 'synset': 'wave.n.08', 'name': 'wave'}, {'id': 17519, 'synset': 'microflora.n.01', 'name': 'microflora'}, {'id': 17520, 'synset': 'wilding.n.01', 'name': 'wilding'}, {'id': 17521, 'synset': 'semi-climber.n.01', 'name': 'semi-climber'}, {'id': 17522, 'synset': 'volva.n.01', 'name': 'volva'}, {'id': 17523, 'synset': 'basidiocarp.n.01', 'name': 'basidiocarp'}, {'id': 17524, 'synset': 'domatium.n.01', 'name': 'domatium'}, {'id': 17525, 'synset': 'apomict.n.01', 'name': 'apomict'}, {'id': 17526, 'synset': 'aquatic.n.01', 'name': 'aquatic'}, {'id': 17527, 'synset': 'bryophyte.n.01', 'name': 'bryophyte'}, {'id': 17528, 'synset': 'acrocarp.n.01', 'name': 'acrocarp'}, {'id': 17529, 'synset': 'sphagnum.n.01', 'name': 'sphagnum'}, {'id': 17530, 'synset': 'liverwort.n.01', 'name': 'liverwort'}, {'id': 17531, 'synset': 'hepatica.n.02', 'name': 'hepatica'}, {'id': 17532, 'synset': 'pecopteris.n.01', 'name': 'pecopteris'}, {'id': 17533, 'synset': 'pteridophyte.n.01', 'name': 'pteridophyte'}, {'id': 17534, 'synset': 'fern.n.01', 'name': 'fern'}, {'id': 17535, 'synset': 'fern_ally.n.01', 'name': 'fern_ally'}, {'id': 17536, 'synset': 'spore.n.01', 'name': 'spore'}, {'id': 17537, 'synset': 'carpospore.n.01', 'name': 'carpospore'}, {'id': 17538, 'synset': 'chlamydospore.n.01', 'name': 'chlamydospore'}, {'id': 17539, 'synset': 'conidium.n.01', 'name': 'conidium'}, {'id': 17540, 'synset': 'oospore.n.01', 'name': 'oospore'}, {'id': 17541, 'synset': 'tetraspore.n.01', 'name': 'tetraspore'}, {'id': 17542, 'synset': 'zoospore.n.01', 'name': 'zoospore'}, {'id': 17543, 'synset': 'cryptogam.n.01', 'name': 'cryptogam'}, {'id': 17544, 'synset': 'spermatophyte.n.01', 'name': 'spermatophyte'}, {'id': 17545, 'synset': 'seedling.n.01', 'name': 'seedling'}, {'id': 17546, 'synset': 'annual.n.01', 'name': 'annual'}, {'id': 17547, 'synset': 'biennial.n.01', 'name': 'biennial'}, {'id': 17548, 'synset': 'perennial.n.01', 'name': 'perennial'}, {'id': 17549, 'synset': 'hygrophyte.n.01', 'name': 'hygrophyte'}, {'id': 17550, 'synset': 'gymnosperm.n.01', 'name': 'gymnosperm'}, {'id': 17551, 'synset': 'gnetum.n.01', 'name': 'gnetum'}, {'id': 17552, 'synset': 'catha_edulis.n.01', 'name': 'Catha_edulis'}, {'id': 17553, 'synset': 'ephedra.n.01', 'name': 'ephedra'}, {'id': 17554, 'synset': 'mahuang.n.01', 'name': 'mahuang'}, {'id': 17555, 'synset': 'welwitschia.n.01', 'name': 'welwitschia'}, {'id': 17556, 'synset': 'cycad.n.01', 'name': 'cycad'}, {'id': 17557, 'synset': 'sago_palm.n.02', 'name': 'sago_palm'}, {'id': 17558, 'synset': 'false_sago.n.01', 'name': 'false_sago'}, {'id': 17559, 'synset': 'zamia.n.01', 'name': 'zamia'}, {'id': 17560, 'synset': 'coontie.n.01', 'name': 'coontie'}, {'id': 17561, 'synset': 'ceratozamia.n.01', 'name': 'ceratozamia'}, {'id': 17562, 'synset': 'dioon.n.01', 'name': 'dioon'}, {'id': 17563, 'synset': 'encephalartos.n.01', 'name': 'encephalartos'}, {'id': 17564, 'synset': 'kaffir_bread.n.01', 'name': 'kaffir_bread'}, {'id': 17565, 'synset': 'macrozamia.n.01', 'name': 'macrozamia'}, {'id': 17566, 'synset': 'burrawong.n.01', 'name': 'burrawong'}, {'id': 17567, 'synset': 'pine.n.01', 'name': 'pine'}, {'id': 17568, 'synset': 'pinon.n.01', 'name': 'pinon'}, {'id': 17569, 'synset': 'nut_pine.n.01', 'name': 'nut_pine'}, {'id': 17570, 'synset': 'pinon_pine.n.01', 'name': 'pinon_pine'}, {'id': 17571, 'synset': 'rocky_mountain_pinon.n.01', 'name': 'Rocky_mountain_pinon'}, {'id': 17572, 'synset': 'single-leaf.n.01', 'name': 'single-leaf'}, {'id': 17573, 'synset': 'bishop_pine.n.01', 'name': 'bishop_pine'}, {'id': 17574, 'synset': 'california_single-leaf_pinyon.n.01', 'name': 'California_single-leaf_pinyon'}, {'id': 17575, 'synset': "parry's_pinyon.n.01", 'name': "Parry's_pinyon"}, {'id': 17576, 'synset': 'spruce_pine.n.04', 'name': 'spruce_pine'}, {'id': 17577, 'synset': 'black_pine.n.05', 'name': 'black_pine'}, {'id': 17578, 'synset': 'pitch_pine.n.02', 'name': 'pitch_pine'}, {'id': 17579, 'synset': 'pond_pine.n.01', 'name': 'pond_pine'}, {'id': 17580, 'synset': 'stone_pine.n.01', 'name': 'stone_pine'}, {'id': 17581, 'synset': 'swiss_pine.n.01', 'name': 'Swiss_pine'}, {'id': 17582, 'synset': 'cembra_nut.n.01', 'name': 'cembra_nut'}, {'id': 17583, 'synset': 'swiss_mountain_pine.n.01', 'name': 'Swiss_mountain_pine'}, {'id': 17584, 'synset': 'ancient_pine.n.01', 'name': 'ancient_pine'}, {'id': 17585, 'synset': 'white_pine.n.01', 'name': 'white_pine'}, {'id': 17586, 'synset': 'american_white_pine.n.01', 'name': 'American_white_pine'}, {'id': 17587, 'synset': 'western_white_pine.n.01', 'name': 'western_white_pine'}, {'id': 17588, 'synset': 'southwestern_white_pine.n.01', 'name': 'southwestern_white_pine'}, {'id': 17589, 'synset': 'limber_pine.n.01', 'name': 'limber_pine'}, {'id': 17590, 'synset': 'whitebark_pine.n.01', 'name': 'whitebark_pine'}, {'id': 17591, 'synset': 'yellow_pine.n.01', 'name': 'yellow_pine'}, {'id': 17592, 'synset': 'ponderosa.n.01', 'name': 'ponderosa'}, {'id': 17593, 'synset': 'jeffrey_pine.n.01', 'name': 'Jeffrey_pine'}, {'id': 17594, 'synset': 'shore_pine.n.01', 'name': 'shore_pine'}, {'id': 17595, 'synset': 'sierra_lodgepole_pine.n.01', 'name': 'Sierra_lodgepole_pine'}, {'id': 17596, 'synset': 'loblolly_pine.n.01', 'name': 'loblolly_pine'}, {'id': 17597, 'synset': 'jack_pine.n.01', 'name': 'jack_pine'}, {'id': 17598, 'synset': 'swamp_pine.n.01', 'name': 'swamp_pine'}, {'id': 17599, 'synset': 'longleaf_pine.n.01', 'name': 'longleaf_pine'}, {'id': 17600, 'synset': 'shortleaf_pine.n.01', 'name': 'shortleaf_pine'}, {'id': 17601, 'synset': 'red_pine.n.02', 'name': 'red_pine'}, {'id': 17602, 'synset': 'scotch_pine.n.01', 'name': 'Scotch_pine'}, {'id': 17603, 'synset': 'scrub_pine.n.01', 'name': 'scrub_pine'}, {'id': 17604, 'synset': 'monterey_pine.n.01', 'name': 'Monterey_pine'}, {'id': 17605, 'synset': 'bristlecone_pine.n.01', 'name': 'bristlecone_pine'}, {'id': 17606, 'synset': 'table-mountain_pine.n.01', 'name': 'table-mountain_pine'}, {'id': 17607, 'synset': 'knobcone_pine.n.01', 'name': 'knobcone_pine'}, {'id': 17608, 'synset': 'japanese_red_pine.n.01', 'name': 'Japanese_red_pine'}, {'id': 17609, 'synset': 'japanese_black_pine.n.01', 'name': 'Japanese_black_pine'}, {'id': 17610, 'synset': 'torrey_pine.n.01', 'name': 'Torrey_pine'}, {'id': 17611, 'synset': 'larch.n.02', 'name': 'larch'}, {'id': 17612, 'synset': 'american_larch.n.01', 'name': 'American_larch'}, {'id': 17613, 'synset': 'western_larch.n.01', 'name': 'western_larch'}, {'id': 17614, 'synset': 'subalpine_larch.n.01', 'name': 'subalpine_larch'}, {'id': 17615, 'synset': 'european_larch.n.01', 'name': 'European_larch'}, {'id': 17616, 'synset': 'siberian_larch.n.01', 'name': 'Siberian_larch'}, {'id': 17617, 'synset': 'golden_larch.n.01', 'name': 'golden_larch'}, {'id': 17618, 'synset': 'fir.n.02', 'name': 'fir'}, {'id': 17619, 'synset': 'silver_fir.n.01', 'name': 'silver_fir'}, {'id': 17620, 'synset': 'amabilis_fir.n.01', 'name': 'amabilis_fir'}, {'id': 17621, 'synset': 'european_silver_fir.n.01', 'name': 'European_silver_fir'}, {'id': 17622, 'synset': 'white_fir.n.01', 'name': 'white_fir'}, {'id': 17623, 'synset': 'balsam_fir.n.01', 'name': 'balsam_fir'}, {'id': 17624, 'synset': 'fraser_fir.n.01', 'name': 'Fraser_fir'}, {'id': 17625, 'synset': 'lowland_fir.n.01', 'name': 'lowland_fir'}, {'id': 17626, 'synset': 'alpine_fir.n.01', 'name': 'Alpine_fir'}, {'id': 17627, 'synset': 'santa_lucia_fir.n.01', 'name': 'Santa_Lucia_fir'}, {'id': 17628, 'synset': 'cedar.n.03', 'name': 'cedar'}, {'id': 17629, 'synset': 'cedar_of_lebanon.n.01', 'name': 'cedar_of_Lebanon'}, {'id': 17630, 'synset': 'deodar.n.01', 'name': 'deodar'}, {'id': 17631, 'synset': 'atlas_cedar.n.01', 'name': 'Atlas_cedar'}, {'id': 17632, 'synset': 'spruce.n.02', 'name': 'spruce'}, {'id': 17633, 'synset': 'norway_spruce.n.01', 'name': 'Norway_spruce'}, {'id': 17634, 'synset': 'weeping_spruce.n.01', 'name': 'weeping_spruce'}, {'id': 17635, 'synset': 'engelmann_spruce.n.01', 'name': 'Engelmann_spruce'}, {'id': 17636, 'synset': 'white_spruce.n.01', 'name': 'white_spruce'}, {'id': 17637, 'synset': 'black_spruce.n.01', 'name': 'black_spruce'}, {'id': 17638, 'synset': 'siberian_spruce.n.01', 'name': 'Siberian_spruce'}, {'id': 17639, 'synset': 'sitka_spruce.n.01', 'name': 'Sitka_spruce'}, {'id': 17640, 'synset': 'oriental_spruce.n.01', 'name': 'oriental_spruce'}, {'id': 17641, 'synset': 'colorado_spruce.n.01', 'name': 'Colorado_spruce'}, {'id': 17642, 'synset': 'red_spruce.n.01', 'name': 'red_spruce'}, {'id': 17643, 'synset': 'hemlock.n.04', 'name': 'hemlock'}, {'id': 17644, 'synset': 'eastern_hemlock.n.01', 'name': 'eastern_hemlock'}, {'id': 17645, 'synset': 'carolina_hemlock.n.01', 'name': 'Carolina_hemlock'}, {'id': 17646, 'synset': 'mountain_hemlock.n.01', 'name': 'mountain_hemlock'}, {'id': 17647, 'synset': 'western_hemlock.n.01', 'name': 'western_hemlock'}, {'id': 17648, 'synset': 'douglas_fir.n.02', 'name': 'douglas_fir'}, {'id': 17649, 'synset': 'green_douglas_fir.n.01', 'name': 'green_douglas_fir'}, {'id': 17650, 'synset': 'big-cone_spruce.n.01', 'name': 'big-cone_spruce'}, {'id': 17651, 'synset': 'cathaya.n.01', 'name': 'Cathaya'}, {'id': 17652, 'synset': 'cedar.n.01', 'name': 'cedar'}, {'id': 17653, 'synset': 'cypress.n.02', 'name': 'cypress'}, {'id': 17654, 'synset': 'gowen_cypress.n.01', 'name': 'gowen_cypress'}, {'id': 17655, 'synset': 'pygmy_cypress.n.01', 'name': 'pygmy_cypress'}, {'id': 17656, 'synset': 'santa_cruz_cypress.n.01', 'name': 'Santa_Cruz_cypress'}, {'id': 17657, 'synset': 'arizona_cypress.n.01', 'name': 'Arizona_cypress'}, {'id': 17658, 'synset': 'guadalupe_cypress.n.01', 'name': 'Guadalupe_cypress'}, {'id': 17659, 'synset': 'monterey_cypress.n.01', 'name': 'Monterey_cypress'}, {'id': 17660, 'synset': 'mexican_cypress.n.01', 'name': 'Mexican_cypress'}, {'id': 17661, 'synset': 'italian_cypress.n.01', 'name': 'Italian_cypress'}, {'id': 17662, 'synset': 'king_william_pine.n.01', 'name': 'King_William_pine'}, {'id': 17663, 'synset': 'chilean_cedar.n.01', 'name': 'Chilean_cedar'}, {'id': 17664, 'synset': 'incense_cedar.n.02', 'name': 'incense_cedar'}, {'id': 17665, 'synset': 'southern_white_cedar.n.01', 'name': 'southern_white_cedar'}, {'id': 17666, 'synset': 'oregon_cedar.n.01', 'name': 'Oregon_cedar'}, {'id': 17667, 'synset': 'yellow_cypress.n.01', 'name': 'yellow_cypress'}, {'id': 17668, 'synset': 'japanese_cedar.n.01', 'name': 'Japanese_cedar'}, {'id': 17669, 'synset': 'juniper_berry.n.01', 'name': 'juniper_berry'}, {'id': 17670, 'synset': 'incense_cedar.n.01', 'name': 'incense_cedar'}, {'id': 17671, 'synset': 'kawaka.n.01', 'name': 'kawaka'}, {'id': 17672, 'synset': 'pahautea.n.01', 'name': 'pahautea'}, {'id': 17673, 'synset': 'metasequoia.n.01', 'name': 'metasequoia'}, {'id': 17674, 'synset': 'arborvitae.n.01', 'name': 'arborvitae'}, {'id': 17675, 'synset': 'western_red_cedar.n.01', 'name': 'western_red_cedar'}, {'id': 17676, 'synset': 'american_arborvitae.n.01', 'name': 'American_arborvitae'}, {'id': 17677, 'synset': 'oriental_arborvitae.n.01', 'name': 'Oriental_arborvitae'}, {'id': 17678, 'synset': 'hiba_arborvitae.n.01', 'name': 'hiba_arborvitae'}, {'id': 17679, 'synset': 'keteleeria.n.01', 'name': 'keteleeria'}, {'id': 17680, 'synset': 'wollemi_pine.n.01', 'name': 'Wollemi_pine'}, {'id': 17681, 'synset': 'araucaria.n.01', 'name': 'araucaria'}, {'id': 17682, 'synset': 'monkey_puzzle.n.01', 'name': 'monkey_puzzle'}, {'id': 17683, 'synset': 'norfolk_island_pine.n.01', 'name': 'norfolk_island_pine'}, {'id': 17684, 'synset': 'new_caledonian_pine.n.01', 'name': 'new_caledonian_pine'}, {'id': 17685, 'synset': 'bunya_bunya.n.01', 'name': 'bunya_bunya'}, {'id': 17686, 'synset': 'hoop_pine.n.01', 'name': 'hoop_pine'}, {'id': 17687, 'synset': 'kauri_pine.n.01', 'name': 'kauri_pine'}, {'id': 17688, 'synset': 'kauri.n.02', 'name': 'kauri'}, {'id': 17689, 'synset': 'amboina_pine.n.01', 'name': 'amboina_pine'}, {'id': 17690, 'synset': 'dundathu_pine.n.01', 'name': 'dundathu_pine'}, {'id': 17691, 'synset': 'red_kauri.n.01', 'name': 'red_kauri'}, {'id': 17692, 'synset': 'plum-yew.n.01', 'name': 'plum-yew'}, {'id': 17693, 'synset': 'california_nutmeg.n.01', 'name': 'California_nutmeg'}, {'id': 17694, 'synset': 'stinking_cedar.n.01', 'name': 'stinking_cedar'}, {'id': 17695, 'synset': 'celery_pine.n.01', 'name': 'celery_pine'}, {'id': 17696, 'synset': 'celery_top_pine.n.01', 'name': 'celery_top_pine'}, {'id': 17697, 'synset': 'tanekaha.n.01', 'name': 'tanekaha'}, {'id': 17698, 'synset': 'alpine_celery_pine.n.01', 'name': 'Alpine_celery_pine'}, {'id': 17699, 'synset': 'yellowwood.n.02', 'name': 'yellowwood'}, {'id': 17700, 'synset': 'gymnospermous_yellowwood.n.01', 'name': 'gymnospermous_yellowwood'}, {'id': 17701, 'synset': 'podocarp.n.01', 'name': 'podocarp'}, {'id': 17702, 'synset': 'yacca.n.01', 'name': 'yacca'}, {'id': 17703, 'synset': 'brown_pine.n.01', 'name': 'brown_pine'}, {'id': 17704, 'synset': 'cape_yellowwood.n.01', 'name': 'cape_yellowwood'}, {'id': 17705, 'synset': 'south-african_yellowwood.n.01', 'name': 'South-African_yellowwood'}, {'id': 17706, 'synset': 'alpine_totara.n.01', 'name': 'alpine_totara'}, {'id': 17707, 'synset': 'totara.n.01', 'name': 'totara'}, {'id': 17708, 'synset': 'common_yellowwood.n.01', 'name': 'common_yellowwood'}, {'id': 17709, 'synset': 'kahikatea.n.01', 'name': 'kahikatea'}, {'id': 17710, 'synset': 'rimu.n.01', 'name': 'rimu'}, {'id': 17711, 'synset': 'tarwood.n.02', 'name': 'tarwood'}, {'id': 17712, 'synset': 'common_sickle_pine.n.01', 'name': 'common_sickle_pine'}, {'id': 17713, 'synset': 'yellow-leaf_sickle_pine.n.01', 'name': 'yellow-leaf_sickle_pine'}, {'id': 17714, 'synset': 'tarwood.n.01', 'name': 'tarwood'}, {'id': 17715, 'synset': 'westland_pine.n.01', 'name': 'westland_pine'}, {'id': 17716, 'synset': 'huon_pine.n.01', 'name': 'huon_pine'}, {'id': 17717, 'synset': 'chilean_rimu.n.01', 'name': 'Chilean_rimu'}, {'id': 17718, 'synset': 'mountain_rimu.n.01', 'name': 'mountain_rimu'}, {'id': 17719, 'synset': 'nagi.n.01', 'name': 'nagi'}, {'id': 17720, 'synset': 'miro.n.01', 'name': 'miro'}, {'id': 17721, 'synset': 'matai.n.01', 'name': 'matai'}, {'id': 17722, 'synset': 'plum-fruited_yew.n.01', 'name': 'plum-fruited_yew'}, {'id': 17723, 'synset': 'prince_albert_yew.n.01', 'name': 'Prince_Albert_yew'}, {'id': 17724, 'synset': 'sundacarpus_amara.n.01', 'name': 'Sundacarpus_amara'}, {'id': 17725, 'synset': 'japanese_umbrella_pine.n.01', 'name': 'Japanese_umbrella_pine'}, {'id': 17726, 'synset': 'yew.n.02', 'name': 'yew'}, {'id': 17727, 'synset': 'old_world_yew.n.01', 'name': 'Old_World_yew'}, {'id': 17728, 'synset': 'pacific_yew.n.01', 'name': 'Pacific_yew'}, {'id': 17729, 'synset': 'japanese_yew.n.01', 'name': 'Japanese_yew'}, {'id': 17730, 'synset': 'florida_yew.n.01', 'name': 'Florida_yew'}, {'id': 17731, 'synset': 'new_caledonian_yew.n.01', 'name': 'New_Caledonian_yew'}, {'id': 17732, 'synset': 'white-berry_yew.n.01', 'name': 'white-berry_yew'}, {'id': 17733, 'synset': 'ginkgo.n.01', 'name': 'ginkgo'}, {'id': 17734, 'synset': 'angiosperm.n.01', 'name': 'angiosperm'}, {'id': 17735, 'synset': 'dicot.n.01', 'name': 'dicot'}, {'id': 17736, 'synset': 'monocot.n.01', 'name': 'monocot'}, {'id': 17737, 'synset': 'floret.n.01', 'name': 'floret'}, {'id': 17738, 'synset': 'flower.n.01', 'name': 'flower'}, {'id': 17739, 'synset': 'bloomer.n.01', 'name': 'bloomer'}, {'id': 17740, 'synset': 'wildflower.n.01', 'name': 'wildflower'}, {'id': 17741, 'synset': 'apetalous_flower.n.01', 'name': 'apetalous_flower'}, {'id': 17742, 'synset': 'inflorescence.n.02', 'name': 'inflorescence'}, {'id': 17743, 'synset': 'rosebud.n.01', 'name': 'rosebud'}, {'id': 17744, 'synset': 'gynostegium.n.01', 'name': 'gynostegium'}, {'id': 17745, 'synset': 'pollinium.n.01', 'name': 'pollinium'}, {'id': 17746, 'synset': 'pistil.n.01', 'name': 'pistil'}, {'id': 17747, 'synset': 'gynobase.n.01', 'name': 'gynobase'}, {'id': 17748, 'synset': 'gynophore.n.01', 'name': 'gynophore'}, {'id': 17749, 'synset': 'stylopodium.n.01', 'name': 'stylopodium'}, {'id': 17750, 'synset': 'carpophore.n.01', 'name': 'carpophore'}, {'id': 17751, 'synset': 'cornstalk.n.01', 'name': 'cornstalk'}, {'id': 17752, 'synset': 'petiolule.n.01', 'name': 'petiolule'}, {'id': 17753, 'synset': 'mericarp.n.01', 'name': 'mericarp'}, {'id': 17754, 'synset': 'micropyle.n.01', 'name': 'micropyle'}, {'id': 17755, 'synset': 'germ_tube.n.01', 'name': 'germ_tube'}, {'id': 17756, 'synset': 'pollen_tube.n.01', 'name': 'pollen_tube'}, {'id': 17757, 'synset': 'gemma.n.01', 'name': 'gemma'}, {'id': 17758, 'synset': 'galbulus.n.01', 'name': 'galbulus'}, {'id': 17759, 'synset': 'nectary.n.01', 'name': 'nectary'}, {'id': 17760, 'synset': 'pericarp.n.01', 'name': 'pericarp'}, {'id': 17761, 'synset': 'epicarp.n.01', 'name': 'epicarp'}, {'id': 17762, 'synset': 'mesocarp.n.01', 'name': 'mesocarp'}, {'id': 17763, 'synset': 'pip.n.03', 'name': 'pip'}, {'id': 17764, 'synset': 'silique.n.01', 'name': 'silique'}, {'id': 17765, 'synset': 'cataphyll.n.01', 'name': 'cataphyll'}, {'id': 17766, 'synset': 'perisperm.n.01', 'name': 'perisperm'}, {'id': 17767, 'synset': 'monocarp.n.01', 'name': 'monocarp'}, {'id': 17768, 'synset': 'sporophyte.n.01', 'name': 'sporophyte'}, {'id': 17769, 'synset': 'gametophyte.n.01', 'name': 'gametophyte'}, {'id': 17770, 'synset': 'megasporangium.n.01', 'name': 'megasporangium'}, {'id': 17771, 'synset': 'microspore.n.01', 'name': 'microspore'}, {'id': 17772, 'synset': 'microsporangium.n.01', 'name': 'microsporangium'}, {'id': 17773, 'synset': 'microsporophyll.n.01', 'name': 'microsporophyll'}, {'id': 17774, 'synset': 'archespore.n.01', 'name': 'archespore'}, {'id': 17775, 'synset': 'bonduc_nut.n.01', 'name': 'bonduc_nut'}, {'id': 17776, 'synset': "job's_tears.n.01", 'name': "Job's_tears"}, {'id': 17777, 'synset': 'oilseed.n.01', 'name': 'oilseed'}, {'id': 17778, 'synset': 'castor_bean.n.01', 'name': 'castor_bean'}, {'id': 17779, 'synset': 'cottonseed.n.01', 'name': 'cottonseed'}, {'id': 17780, 'synset': 'candlenut.n.02', 'name': 'candlenut'}, {'id': 17781, 'synset': 'peach_pit.n.01', 'name': 'peach_pit'}, {'id': 17782, 'synset': 'hypanthium.n.01', 'name': 'hypanthium'}, {'id': 17783, 'synset': 'petal.n.01', 'name': 'petal'}, {'id': 17784, 'synset': 'corolla.n.01', 'name': 'corolla'}, {'id': 17785, 'synset': 'lip.n.02', 'name': 'lip'}, {'id': 17786, 'synset': 'perianth.n.01', 'name': 'perianth'}, {'id': 17787, 'synset': 'thistledown.n.01', 'name': 'thistledown'}, {'id': 17788, 'synset': 'custard_apple.n.01', 'name': 'custard_apple'}, {'id': 17789, 'synset': 'cherimoya.n.01', 'name': 'cherimoya'}, {'id': 17790, 'synset': 'ilama.n.01', 'name': 'ilama'}, {'id': 17791, 'synset': 'soursop.n.01', 'name': 'soursop'}, {'id': 17792, 'synset': "bullock's_heart.n.01", 'name': "bullock's_heart"}, {'id': 17793, 'synset': 'sweetsop.n.01', 'name': 'sweetsop'}, {'id': 17794, 'synset': 'pond_apple.n.01', 'name': 'pond_apple'}, {'id': 17795, 'synset': 'pawpaw.n.02', 'name': 'pawpaw'}, {'id': 17796, 'synset': 'ilang-ilang.n.02', 'name': 'ilang-ilang'}, {'id': 17797, 'synset': 'lancewood.n.02', 'name': 'lancewood'}, {'id': 17798, 'synset': 'guinea_pepper.n.02', 'name': 'Guinea_pepper'}, {'id': 17799, 'synset': 'barberry.n.01', 'name': 'barberry'}, {'id': 17800, 'synset': 'american_barberry.n.01', 'name': 'American_barberry'}, {'id': 17801, 'synset': 'common_barberry.n.01', 'name': 'common_barberry'}, {'id': 17802, 'synset': 'japanese_barberry.n.01', 'name': 'Japanese_barberry'}, {'id': 17803, 'synset': 'oregon_grape.n.02', 'name': 'Oregon_grape'}, {'id': 17804, 'synset': 'oregon_grape.n.01', 'name': 'Oregon_grape'}, {'id': 17805, 'synset': 'mayapple.n.01', 'name': 'mayapple'}, {'id': 17806, 'synset': 'may_apple.n.01', 'name': 'May_apple'}, {'id': 17807, 'synset': 'allspice.n.02', 'name': 'allspice'}, {'id': 17808, 'synset': 'carolina_allspice.n.01', 'name': 'Carolina_allspice'}, {'id': 17809, 'synset': 'spicebush.n.02', 'name': 'spicebush'}, {'id': 17810, 'synset': 'katsura_tree.n.01', 'name': 'katsura_tree'}, {'id': 17811, 'synset': 'laurel.n.01', 'name': 'laurel'}, {'id': 17812, 'synset': 'true_laurel.n.01', 'name': 'true_laurel'}, {'id': 17813, 'synset': 'camphor_tree.n.01', 'name': 'camphor_tree'}, {'id': 17814, 'synset': 'cinnamon.n.02', 'name': 'cinnamon'}, {'id': 17815, 'synset': 'cassia.n.03', 'name': 'cassia'}, {'id': 17816, 'synset': 'cassia_bark.n.01', 'name': 'cassia_bark'}, {'id': 17817, 'synset': 'saigon_cinnamon.n.01', 'name': 'Saigon_cinnamon'}, {'id': 17818, 'synset': 'cinnamon_bark.n.01', 'name': 'cinnamon_bark'}, {'id': 17819, 'synset': 'spicebush.n.01', 'name': 'spicebush'}, {'id': 17820, 'synset': 'avocado.n.02', 'name': 'avocado'}, {'id': 17821, 'synset': 'laurel-tree.n.01', 'name': 'laurel-tree'}, {'id': 17822, 'synset': 'sassafras.n.01', 'name': 'sassafras'}, {'id': 17823, 'synset': 'california_laurel.n.01', 'name': 'California_laurel'}, {'id': 17824, 'synset': 'anise_tree.n.01', 'name': 'anise_tree'}, {'id': 17825, 'synset': 'purple_anise.n.01', 'name': 'purple_anise'}, {'id': 17826, 'synset': 'star_anise.n.02', 'name': 'star_anise'}, {'id': 17827, 'synset': 'star_anise.n.01', 'name': 'star_anise'}, {'id': 17828, 'synset': 'magnolia.n.02', 'name': 'magnolia'}, {'id': 17829, 'synset': 'southern_magnolia.n.01', 'name': 'southern_magnolia'}, {'id': 17830, 'synset': 'umbrella_tree.n.02', 'name': 'umbrella_tree'}, {'id': 17831, 'synset': 'earleaved_umbrella_tree.n.01', 'name': 'earleaved_umbrella_tree'}, {'id': 17832, 'synset': 'cucumber_tree.n.01', 'name': 'cucumber_tree'}, {'id': 17833, 'synset': 'large-leaved_magnolia.n.01', 'name': 'large-leaved_magnolia'}, {'id': 17834, 'synset': 'saucer_magnolia.n.01', 'name': 'saucer_magnolia'}, {'id': 17835, 'synset': 'star_magnolia.n.01', 'name': 'star_magnolia'}, {'id': 17836, 'synset': 'sweet_bay.n.01', 'name': 'sweet_bay'}, {'id': 17837, 'synset': 'manglietia.n.01', 'name': 'manglietia'}, {'id': 17838, 'synset': 'tulip_tree.n.01', 'name': 'tulip_tree'}, {'id': 17839, 'synset': 'moonseed.n.01', 'name': 'moonseed'}, {'id': 17840, 'synset': 'common_moonseed.n.01', 'name': 'common_moonseed'}, {'id': 17841, 'synset': 'carolina_moonseed.n.01', 'name': 'Carolina_moonseed'}, {'id': 17842, 'synset': 'nutmeg.n.01', 'name': 'nutmeg'}, {'id': 17843, 'synset': 'water_nymph.n.02', 'name': 'water_nymph'}, {'id': 17844, 'synset': 'european_white_lily.n.01', 'name': 'European_white_lily'}, {'id': 17845, 'synset': 'southern_spatterdock.n.01', 'name': 'southern_spatterdock'}, {'id': 17846, 'synset': 'lotus.n.01', 'name': 'lotus'}, {'id': 17847, 'synset': 'water_chinquapin.n.01', 'name': 'water_chinquapin'}, {'id': 17848, 'synset': 'water-shield.n.02', 'name': 'water-shield'}, {'id': 17849, 'synset': 'water-shield.n.01', 'name': 'water-shield'}, {'id': 17850, 'synset': 'peony.n.01', 'name': 'peony'}, {'id': 17851, 'synset': 'buttercup.n.01', 'name': 'buttercup'}, {'id': 17852, 'synset': 'meadow_buttercup.n.01', 'name': 'meadow_buttercup'}, {'id': 17853, 'synset': 'water_crowfoot.n.01', 'name': 'water_crowfoot'}, {'id': 17854, 'synset': 'lesser_celandine.n.01', 'name': 'lesser_celandine'}, {'id': 17855, 'synset': 'lesser_spearwort.n.01', 'name': 'lesser_spearwort'}, {'id': 17856, 'synset': 'greater_spearwort.n.01', 'name': 'greater_spearwort'}, {'id': 17857, 'synset': 'western_buttercup.n.01', 'name': 'western_buttercup'}, {'id': 17858, 'synset': 'creeping_buttercup.n.01', 'name': 'creeping_buttercup'}, {'id': 17859, 'synset': 'cursed_crowfoot.n.01', 'name': 'cursed_crowfoot'}, {'id': 17860, 'synset': 'aconite.n.01', 'name': 'aconite'}, {'id': 17861, 'synset': 'monkshood.n.01', 'name': 'monkshood'}, {'id': 17862, 'synset': 'wolfsbane.n.01', 'name': 'wolfsbane'}, {'id': 17863, 'synset': 'baneberry.n.02', 'name': 'baneberry'}, {'id': 17864, 'synset': 'baneberry.n.01', 'name': 'baneberry'}, {'id': 17865, 'synset': 'red_baneberry.n.01', 'name': 'red_baneberry'}, {'id': 17866, 'synset': "pheasant's-eye.n.01", 'name': "pheasant's-eye"}, {'id': 17867, 'synset': 'anemone.n.01', 'name': 'anemone'}, {'id': 17868, 'synset': 'alpine_anemone.n.01', 'name': 'Alpine_anemone'}, {'id': 17869, 'synset': 'canada_anemone.n.01', 'name': 'Canada_anemone'}, {'id': 17870, 'synset': 'thimbleweed.n.01', 'name': 'thimbleweed'}, {'id': 17871, 'synset': 'wood_anemone.n.02', 'name': 'wood_anemone'}, {'id': 17872, 'synset': 'wood_anemone.n.01', 'name': 'wood_anemone'}, {'id': 17873, 'synset': 'longheaded_thimbleweed.n.01', 'name': 'longheaded_thimbleweed'}, {'id': 17874, 'synset': 'snowdrop_anemone.n.01', 'name': 'snowdrop_anemone'}, {'id': 17875, 'synset': 'virginia_thimbleweed.n.01', 'name': 'Virginia_thimbleweed'}, {'id': 17876, 'synset': 'rue_anemone.n.01', 'name': 'rue_anemone'}, {'id': 17877, 'synset': 'columbine.n.01', 'name': 'columbine'}, {'id': 17878, 'synset': 'meeting_house.n.01', 'name': 'meeting_house'}, {'id': 17879, 'synset': 'blue_columbine.n.01', 'name': 'blue_columbine'}, {'id': 17880, 'synset': "granny's_bonnets.n.01", 'name': "granny's_bonnets"}, {'id': 17881, 'synset': 'marsh_marigold.n.01', 'name': 'marsh_marigold'}, {'id': 17882, 'synset': 'american_bugbane.n.01', 'name': 'American_bugbane'}, {'id': 17883, 'synset': 'black_cohosh.n.01', 'name': 'black_cohosh'}, {'id': 17884, 'synset': 'fetid_bugbane.n.01', 'name': 'fetid_bugbane'}, {'id': 17885, 'synset': 'clematis.n.01', 'name': 'clematis'}, {'id': 17886, 'synset': 'pine_hyacinth.n.01', 'name': 'pine_hyacinth'}, {'id': 17887, 'synset': 'blue_jasmine.n.01', 'name': 'blue_jasmine'}, {'id': 17888, 'synset': 'golden_clematis.n.01', 'name': 'golden_clematis'}, {'id': 17889, 'synset': 'scarlet_clematis.n.01', 'name': 'scarlet_clematis'}, {'id': 17890, 'synset': 'leather_flower.n.02', 'name': 'leather_flower'}, {'id': 17891, 'synset': 'leather_flower.n.01', 'name': 'leather_flower'}, {'id': 17892, 'synset': "virgin's_bower.n.01", 'name': "virgin's_bower"}, {'id': 17893, 'synset': 'purple_clematis.n.01', 'name': 'purple_clematis'}, {'id': 17894, 'synset': 'goldthread.n.01', 'name': 'goldthread'}, {'id': 17895, 'synset': 'rocket_larkspur.n.01', 'name': 'rocket_larkspur'}, {'id': 17896, 'synset': 'delphinium.n.01', 'name': 'delphinium'}, {'id': 17897, 'synset': 'larkspur.n.01', 'name': 'larkspur'}, {'id': 17898, 'synset': 'winter_aconite.n.01', 'name': 'winter_aconite'}, {'id': 17899, 'synset': 'lenten_rose.n.01', 'name': 'lenten_rose'}, {'id': 17900, 'synset': 'green_hellebore.n.01', 'name': 'green_hellebore'}, {'id': 17901, 'synset': 'hepatica.n.01', 'name': 'hepatica'}, {'id': 17902, 'synset': 'goldenseal.n.01', 'name': 'goldenseal'}, {'id': 17903, 'synset': 'false_rue_anemone.n.01', 'name': 'false_rue_anemone'}, {'id': 17904, 'synset': 'giant_buttercup.n.01', 'name': 'giant_buttercup'}, {'id': 17905, 'synset': 'nigella.n.01', 'name': 'nigella'}, {'id': 17906, 'synset': 'love-in-a-mist.n.03', 'name': 'love-in-a-mist'}, {'id': 17907, 'synset': 'fennel_flower.n.01', 'name': 'fennel_flower'}, {'id': 17908, 'synset': 'black_caraway.n.01', 'name': 'black_caraway'}, {'id': 17909, 'synset': 'pasqueflower.n.01', 'name': 'pasqueflower'}, {'id': 17910, 'synset': 'meadow_rue.n.01', 'name': 'meadow_rue'}, {'id': 17911, 'synset': 'false_bugbane.n.01', 'name': 'false_bugbane'}, {'id': 17912, 'synset': 'globeflower.n.01', 'name': 'globeflower'}, {'id': 17913, 'synset': "winter's_bark.n.02", 'name': "winter's_bark"}, {'id': 17914, 'synset': 'pepper_shrub.n.01', 'name': 'pepper_shrub'}, {'id': 17915, 'synset': 'sweet_gale.n.01', 'name': 'sweet_gale'}, {'id': 17916, 'synset': 'wax_myrtle.n.01', 'name': 'wax_myrtle'}, {'id': 17917, 'synset': 'bay_myrtle.n.01', 'name': 'bay_myrtle'}, {'id': 17918, 'synset': 'bayberry.n.02', 'name': 'bayberry'}, {'id': 17919, 'synset': 'sweet_fern.n.02', 'name': 'sweet_fern'}, {'id': 17920, 'synset': 'corkwood.n.01', 'name': 'corkwood'}, {'id': 17921, 'synset': 'jointed_rush.n.01', 'name': 'jointed_rush'}, {'id': 17922, 'synset': 'toad_rush.n.01', 'name': 'toad_rush'}, {'id': 17923, 'synset': 'slender_rush.n.01', 'name': 'slender_rush'}, {'id': 17924, 'synset': 'zebrawood.n.02', 'name': 'zebrawood'}, {'id': 17925, 'synset': 'connarus_guianensis.n.01', 'name': 'Connarus_guianensis'}, {'id': 17926, 'synset': 'legume.n.01', 'name': 'legume'}, {'id': 17927, 'synset': 'peanut.n.01', 'name': 'peanut'}, {'id': 17928, 'synset': 'granadilla_tree.n.01', 'name': 'granadilla_tree'}, {'id': 17929, 'synset': 'arariba.n.01', 'name': 'arariba'}, {'id': 17930, 'synset': 'tonka_bean.n.01', 'name': 'tonka_bean'}, {'id': 17931, 'synset': 'courbaril.n.01', 'name': 'courbaril'}, {'id': 17932, 'synset': 'melilotus.n.01', 'name': 'melilotus'}, {'id': 17933, 'synset': 'darling_pea.n.01', 'name': 'darling_pea'}, {'id': 17934, 'synset': 'smooth_darling_pea.n.01', 'name': 'smooth_darling_pea'}, {'id': 17935, 'synset': 'clover.n.01', 'name': 'clover'}, {'id': 17936, 'synset': 'alpine_clover.n.01', 'name': 'alpine_clover'}, {'id': 17937, 'synset': 'hop_clover.n.02', 'name': 'hop_clover'}, {'id': 17938, 'synset': 'crimson_clover.n.01', 'name': 'crimson_clover'}, {'id': 17939, 'synset': 'red_clover.n.01', 'name': 'red_clover'}, {'id': 17940, 'synset': 'buffalo_clover.n.02', 'name': 'buffalo_clover'}, {'id': 17941, 'synset': 'white_clover.n.01', 'name': 'white_clover'}, {'id': 17942, 'synset': 'mimosa.n.02', 'name': 'mimosa'}, {'id': 17943, 'synset': 'acacia.n.01', 'name': 'acacia'}, {'id': 17944, 'synset': 'shittah.n.01', 'name': 'shittah'}, {'id': 17945, 'synset': 'wattle.n.03', 'name': 'wattle'}, {'id': 17946, 'synset': 'black_wattle.n.01', 'name': 'black_wattle'}, {'id': 17947, 'synset': 'gidgee.n.01', 'name': 'gidgee'}, {'id': 17948, 'synset': 'catechu.n.02', 'name': 'catechu'}, {'id': 17949, 'synset': 'silver_wattle.n.01', 'name': 'silver_wattle'}, {'id': 17950, 'synset': 'huisache.n.01', 'name': 'huisache'}, {'id': 17951, 'synset': 'lightwood.n.01', 'name': 'lightwood'}, {'id': 17952, 'synset': 'golden_wattle.n.01', 'name': 'golden_wattle'}, {'id': 17953, 'synset': 'fever_tree.n.04', 'name': 'fever_tree'}, {'id': 17954, 'synset': 'coralwood.n.01', 'name': 'coralwood'}, {'id': 17955, 'synset': 'albizzia.n.01', 'name': 'albizzia'}, {'id': 17956, 'synset': 'silk_tree.n.01', 'name': 'silk_tree'}, {'id': 17957, 'synset': 'siris.n.01', 'name': 'siris'}, {'id': 17958, 'synset': 'rain_tree.n.01', 'name': 'rain_tree'}, {'id': 17959, 'synset': 'calliandra.n.01', 'name': 'calliandra'}, {'id': 17960, 'synset': 'conacaste.n.01', 'name': 'conacaste'}, {'id': 17961, 'synset': 'inga.n.01', 'name': 'inga'}, {'id': 17962, 'synset': 'ice-cream_bean.n.01', 'name': 'ice-cream_bean'}, {'id': 17963, 'synset': 'guama.n.01', 'name': 'guama'}, {'id': 17964, 'synset': 'lead_tree.n.01', 'name': 'lead_tree'}, {'id': 17965, 'synset': 'wild_tamarind.n.02', 'name': 'wild_tamarind'}, {'id': 17966, 'synset': 'sabicu.n.02', 'name': 'sabicu'}, {'id': 17967, 'synset': 'nitta_tree.n.01', 'name': 'nitta_tree'}, {'id': 17968, 'synset': 'parkia_javanica.n.01', 'name': 'Parkia_javanica'}, {'id': 17969, 'synset': 'manila_tamarind.n.01', 'name': 'manila_tamarind'}, {'id': 17970, 'synset': "cat's-claw.n.01", 'name': "cat's-claw"}, {'id': 17971, 'synset': 'honey_mesquite.n.01', 'name': 'honey_mesquite'}, {'id': 17972, 'synset': 'algarroba.n.03', 'name': 'algarroba'}, {'id': 17973, 'synset': 'screw_bean.n.02', 'name': 'screw_bean'}, {'id': 17974, 'synset': 'screw_bean.n.01', 'name': 'screw_bean'}, {'id': 17975, 'synset': 'dogbane.n.01', 'name': 'dogbane'}, {'id': 17976, 'synset': 'indian_hemp.n.03', 'name': 'Indian_hemp'}, {'id': 17977, 'synset': "bushman's_poison.n.01", 'name': "bushman's_poison"}, {'id': 17978, 'synset': 'impala_lily.n.01', 'name': 'impala_lily'}, {'id': 17979, 'synset': 'allamanda.n.01', 'name': 'allamanda'}, {'id': 17980, 'synset': 'common_allamanda.n.01', 'name': 'common_allamanda'}, {'id': 17981, 'synset': 'dita.n.01', 'name': 'dita'}, {'id': 17982, 'synset': 'nepal_trumpet_flower.n.01', 'name': 'Nepal_trumpet_flower'}, {'id': 17983, 'synset': 'carissa.n.01', 'name': 'carissa'}, {'id': 17984, 'synset': 'hedge_thorn.n.01', 'name': 'hedge_thorn'}, {'id': 17985, 'synset': 'natal_plum.n.01', 'name': 'natal_plum'}, {'id': 17986, 'synset': 'periwinkle.n.02', 'name': 'periwinkle'}, {'id': 17987, 'synset': 'ivory_tree.n.01', 'name': 'ivory_tree'}, {'id': 17988, 'synset': 'white_dipladenia.n.01', 'name': 'white_dipladenia'}, {'id': 17989, 'synset': 'chilean_jasmine.n.01', 'name': 'Chilean_jasmine'}, {'id': 17990, 'synset': 'oleander.n.01', 'name': 'oleander'}, {'id': 17991, 'synset': 'frangipani.n.01', 'name': 'frangipani'}, {'id': 17992, 'synset': 'west_indian_jasmine.n.01', 'name': 'West_Indian_jasmine'}, {'id': 17993, 'synset': 'rauwolfia.n.02', 'name': 'rauwolfia'}, {'id': 17994, 'synset': 'snakewood.n.01', 'name': 'snakewood'}, {'id': 17995, 'synset': 'strophanthus_kombe.n.01', 'name': 'Strophanthus_kombe'}, {'id': 17996, 'synset': 'yellow_oleander.n.01', 'name': 'yellow_oleander'}, {'id': 17997, 'synset': 'myrtle.n.01', 'name': 'myrtle'}, {'id': 17998, 'synset': 'large_periwinkle.n.01', 'name': 'large_periwinkle'}, {'id': 17999, 'synset': 'arum.n.02', 'name': 'arum'}, {'id': 18000, 'synset': 'cuckoopint.n.01', 'name': 'cuckoopint'}, {'id': 18001, 'synset': 'black_calla.n.01', 'name': 'black_calla'}, {'id': 18002, 'synset': 'calamus.n.02', 'name': 'calamus'}, {'id': 18003, 'synset': 'alocasia.n.01', 'name': 'alocasia'}, {'id': 18004, 'synset': 'giant_taro.n.01', 'name': 'giant_taro'}, {'id': 18005, 'synset': 'amorphophallus.n.01', 'name': 'amorphophallus'}, {'id': 18006, 'synset': 'pungapung.n.01', 'name': 'pungapung'}, {'id': 18007, 'synset': "devil's_tongue.n.01", 'name': "devil's_tongue"}, {'id': 18008, 'synset': 'anthurium.n.01', 'name': 'anthurium'}, {'id': 18009, 'synset': 'flamingo_flower.n.01', 'name': 'flamingo_flower'}, {'id': 18010, 'synset': 'jack-in-the-pulpit.n.01', 'name': 'jack-in-the-pulpit'}, {'id': 18011, 'synset': "friar's-cowl.n.01", 'name': "friar's-cowl"}, {'id': 18012, 'synset': 'caladium.n.01', 'name': 'caladium'}, {'id': 18013, 'synset': 'caladium_bicolor.n.01', 'name': 'Caladium_bicolor'}, {'id': 18014, 'synset': 'wild_calla.n.01', 'name': 'wild_calla'}, {'id': 18015, 'synset': 'taro.n.02', 'name': 'taro'}, {'id': 18016, 'synset': 'taro.n.01', 'name': 'taro'}, {'id': 18017, 'synset': 'cryptocoryne.n.01', 'name': 'cryptocoryne'}, {'id': 18018, 'synset': 'dracontium.n.01', 'name': 'dracontium'}, {'id': 18019, 'synset': 'golden_pothos.n.01', 'name': 'golden_pothos'}, {'id': 18020, 'synset': 'skunk_cabbage.n.02', 'name': 'skunk_cabbage'}, {'id': 18021, 'synset': 'monstera.n.01', 'name': 'monstera'}, {'id': 18022, 'synset': 'ceriman.n.01', 'name': 'ceriman'}, {'id': 18023, 'synset': 'nephthytis.n.01', 'name': 'nephthytis'}, {'id': 18024, 'synset': 'nephthytis_afzelii.n.01', 'name': 'Nephthytis_afzelii'}, {'id': 18025, 'synset': 'arrow_arum.n.01', 'name': 'arrow_arum'}, {'id': 18026, 'synset': 'green_arrow_arum.n.01', 'name': 'green_arrow_arum'}, {'id': 18027, 'synset': 'philodendron.n.01', 'name': 'philodendron'}, {'id': 18028, 'synset': 'pistia.n.01', 'name': 'pistia'}, {'id': 18029, 'synset': 'pothos.n.01', 'name': 'pothos'}, {'id': 18030, 'synset': 'spathiphyllum.n.01', 'name': 'spathiphyllum'}, {'id': 18031, 'synset': 'skunk_cabbage.n.01', 'name': 'skunk_cabbage'}, {'id': 18032, 'synset': 'yautia.n.01', 'name': 'yautia'}, {'id': 18033, 'synset': 'calla_lily.n.01', 'name': 'calla_lily'}, {'id': 18034, 'synset': 'pink_calla.n.01', 'name': 'pink_calla'}, {'id': 18035, 'synset': 'golden_calla.n.01', 'name': 'golden_calla'}, {'id': 18036, 'synset': 'duckweed.n.01', 'name': 'duckweed'}, {'id': 18037, 'synset': 'common_duckweed.n.01', 'name': 'common_duckweed'}, {'id': 18038, 'synset': 'star-duckweed.n.01', 'name': 'star-duckweed'}, {'id': 18039, 'synset': 'great_duckweed.n.01', 'name': 'great_duckweed'}, {'id': 18040, 'synset': 'watermeal.n.01', 'name': 'watermeal'}, {'id': 18041, 'synset': 'common_wolffia.n.01', 'name': 'common_wolffia'}, {'id': 18042, 'synset': 'aralia.n.01', 'name': 'aralia'}, {'id': 18043, 'synset': 'american_angelica_tree.n.01', 'name': 'American_angelica_tree'}, {'id': 18044, 'synset': 'american_spikenard.n.01', 'name': 'American_spikenard'}, {'id': 18045, 'synset': 'bristly_sarsaparilla.n.01', 'name': 'bristly_sarsaparilla'}, {'id': 18046, 'synset': 'japanese_angelica_tree.n.01', 'name': 'Japanese_angelica_tree'}, {'id': 18047, 'synset': 'chinese_angelica.n.01', 'name': 'Chinese_angelica'}, {'id': 18048, 'synset': 'ivy.n.01', 'name': 'ivy'}, {'id': 18049, 'synset': 'puka.n.02', 'name': 'puka'}, {'id': 18050, 'synset': 'ginseng.n.02', 'name': 'ginseng'}, {'id': 18051, 'synset': 'ginseng.n.01', 'name': 'ginseng'}, {'id': 18052, 'synset': 'umbrella_tree.n.01', 'name': 'umbrella_tree'}, {'id': 18053, 'synset': 'birthwort.n.01', 'name': 'birthwort'}, {'id': 18054, 'synset': "dutchman's-pipe.n.01", 'name': "Dutchman's-pipe"}, {'id': 18055, 'synset': 'virginia_snakeroot.n.01', 'name': 'Virginia_snakeroot'}, {'id': 18056, 'synset': 'canada_ginger.n.01', 'name': 'Canada_ginger'}, {'id': 18057, 'synset': 'heartleaf.n.02', 'name': 'heartleaf'}, {'id': 18058, 'synset': 'heartleaf.n.01', 'name': 'heartleaf'}, {'id': 18059, 'synset': 'asarabacca.n.01', 'name': 'asarabacca'}, {'id': 18060, 'synset': 'caryophyllaceous_plant.n.01', 'name': 'caryophyllaceous_plant'}, {'id': 18061, 'synset': 'corn_cockle.n.01', 'name': 'corn_cockle'}, {'id': 18062, 'synset': 'sandwort.n.03', 'name': 'sandwort'}, {'id': 18063, 'synset': 'mountain_sandwort.n.01', 'name': 'mountain_sandwort'}, {'id': 18064, 'synset': 'pine-barren_sandwort.n.01', 'name': 'pine-barren_sandwort'}, {'id': 18065, 'synset': 'seabeach_sandwort.n.01', 'name': 'seabeach_sandwort'}, {'id': 18066, 'synset': 'rock_sandwort.n.01', 'name': 'rock_sandwort'}, {'id': 18067, 'synset': 'thyme-leaved_sandwort.n.01', 'name': 'thyme-leaved_sandwort'}, {'id': 18068, 'synset': 'mouse-ear_chickweed.n.01', 'name': 'mouse-ear_chickweed'}, {'id': 18069, 'synset': 'snow-in-summer.n.02', 'name': 'snow-in-summer'}, {'id': 18070, 'synset': 'alpine_mouse-ear.n.01', 'name': 'Alpine_mouse-ear'}, {'id': 18071, 'synset': 'pink.n.02', 'name': 'pink'}, {'id': 18072, 'synset': 'sweet_william.n.01', 'name': 'sweet_William'}, {'id': 18073, 'synset': 'china_pink.n.01', 'name': 'china_pink'}, {'id': 18074, 'synset': 'japanese_pink.n.01', 'name': 'Japanese_pink'}, {'id': 18075, 'synset': 'maiden_pink.n.01', 'name': 'maiden_pink'}, {'id': 18076, 'synset': 'cheddar_pink.n.01', 'name': 'cheddar_pink'}, {'id': 18077, 'synset': 'button_pink.n.01', 'name': 'button_pink'}, {'id': 18078, 'synset': 'cottage_pink.n.01', 'name': 'cottage_pink'}, {'id': 18079, 'synset': 'fringed_pink.n.02', 'name': 'fringed_pink'}, {'id': 18080, 'synset': 'drypis.n.01', 'name': 'drypis'}, {'id': 18081, 'synset': "baby's_breath.n.01", 'name': "baby's_breath"}, {'id': 18082, 'synset': 'coral_necklace.n.01', 'name': 'coral_necklace'}, {'id': 18083, 'synset': 'lychnis.n.01', 'name': 'lychnis'}, {'id': 18084, 'synset': 'ragged_robin.n.01', 'name': 'ragged_robin'}, {'id': 18085, 'synset': 'scarlet_lychnis.n.01', 'name': 'scarlet_lychnis'}, {'id': 18086, 'synset': 'mullein_pink.n.01', 'name': 'mullein_pink'}, {'id': 18087, 'synset': 'sandwort.n.02', 'name': 'sandwort'}, {'id': 18088, 'synset': 'sandwort.n.01', 'name': 'sandwort'}, {'id': 18089, 'synset': 'soapwort.n.01', 'name': 'soapwort'}, {'id': 18090, 'synset': 'knawel.n.01', 'name': 'knawel'}, {'id': 18091, 'synset': 'silene.n.01', 'name': 'silene'}, {'id': 18092, 'synset': 'moss_campion.n.01', 'name': 'moss_campion'}, {'id': 18093, 'synset': 'wild_pink.n.02', 'name': 'wild_pink'}, {'id': 18094, 'synset': 'red_campion.n.01', 'name': 'red_campion'}, {'id': 18095, 'synset': 'white_campion.n.01', 'name': 'white_campion'}, {'id': 18096, 'synset': 'fire_pink.n.01', 'name': 'fire_pink'}, {'id': 18097, 'synset': 'bladder_campion.n.01', 'name': 'bladder_campion'}, {'id': 18098, 'synset': 'corn_spurry.n.01', 'name': 'corn_spurry'}, {'id': 18099, 'synset': 'sand_spurry.n.01', 'name': 'sand_spurry'}, {'id': 18100, 'synset': 'chickweed.n.01', 'name': 'chickweed'}, {'id': 18101, 'synset': 'common_chickweed.n.01', 'name': 'common_chickweed'}, {'id': 18102, 'synset': 'cowherb.n.01', 'name': 'cowherb'}, {'id': 18103, 'synset': 'hottentot_fig.n.01', 'name': 'Hottentot_fig'}, {'id': 18104, 'synset': 'livingstone_daisy.n.01', 'name': 'livingstone_daisy'}, {'id': 18105, 'synset': 'fig_marigold.n.01', 'name': 'fig_marigold'}, {'id': 18106, 'synset': 'ice_plant.n.01', 'name': 'ice_plant'}, {'id': 18107, 'synset': 'new_zealand_spinach.n.01', 'name': 'New_Zealand_spinach'}, {'id': 18108, 'synset': 'amaranth.n.02', 'name': 'amaranth'}, {'id': 18109, 'synset': 'amaranth.n.01', 'name': 'amaranth'}, {'id': 18110, 'synset': 'tumbleweed.n.04', 'name': 'tumbleweed'}, {'id': 18111, 'synset': "prince's-feather.n.02", 'name': "prince's-feather"}, {'id': 18112, 'synset': 'pigweed.n.02', 'name': 'pigweed'}, {'id': 18113, 'synset': 'thorny_amaranth.n.01', 'name': 'thorny_amaranth'}, {'id': 18114, 'synset': 'alligator_weed.n.01', 'name': 'alligator_weed'}, {'id': 18115, 'synset': 'cockscomb.n.01', 'name': 'cockscomb'}, {'id': 18116, 'synset': 'cottonweed.n.02', 'name': 'cottonweed'}, {'id': 18117, 'synset': 'globe_amaranth.n.01', 'name': 'globe_amaranth'}, {'id': 18118, 'synset': 'bloodleaf.n.01', 'name': 'bloodleaf'}, {'id': 18119, 'synset': 'saltwort.n.02', 'name': 'saltwort'}, {'id': 18120, 'synset': "lamb's-quarters.n.01", 'name': "lamb's-quarters"}, {'id': 18121, 'synset': 'good-king-henry.n.01', 'name': 'good-king-henry'}, {'id': 18122, 'synset': 'jerusalem_oak.n.01', 'name': 'Jerusalem_oak'}, {'id': 18123, 'synset': 'oak-leaved_goosefoot.n.01', 'name': 'oak-leaved_goosefoot'}, {'id': 18124, 'synset': 'sowbane.n.01', 'name': 'sowbane'}, {'id': 18125, 'synset': 'nettle-leaved_goosefoot.n.01', 'name': 'nettle-leaved_goosefoot'}, {'id': 18126, 'synset': 'red_goosefoot.n.01', 'name': 'red_goosefoot'}, {'id': 18127, 'synset': 'stinking_goosefoot.n.01', 'name': 'stinking_goosefoot'}, {'id': 18128, 'synset': 'orach.n.01', 'name': 'orach'}, {'id': 18129, 'synset': 'saltbush.n.01', 'name': 'saltbush'}, {'id': 18130, 'synset': 'garden_orache.n.01', 'name': 'garden_orache'}, {'id': 18131, 'synset': 'desert_holly.n.01', 'name': 'desert_holly'}, {'id': 18132, 'synset': 'quail_bush.n.01', 'name': 'quail_bush'}, {'id': 18133, 'synset': 'beet.n.01', 'name': 'beet'}, {'id': 18134, 'synset': 'beetroot.n.01', 'name': 'beetroot'}, {'id': 18135, 'synset': 'chard.n.01', 'name': 'chard'}, {'id': 18136, 'synset': 'mangel-wurzel.n.01', 'name': 'mangel-wurzel'}, {'id': 18137, 'synset': 'winged_pigweed.n.01', 'name': 'winged_pigweed'}, {'id': 18138, 'synset': 'halogeton.n.01', 'name': 'halogeton'}, {'id': 18139, 'synset': 'glasswort.n.02', 'name': 'glasswort'}, {'id': 18140, 'synset': 'saltwort.n.01', 'name': 'saltwort'}, {'id': 18141, 'synset': 'russian_thistle.n.01', 'name': 'Russian_thistle'}, {'id': 18142, 'synset': 'greasewood.n.01', 'name': 'greasewood'}, {'id': 18143, 'synset': 'scarlet_musk_flower.n.01', 'name': 'scarlet_musk_flower'}, {'id': 18144, 'synset': 'sand_verbena.n.01', 'name': 'sand_verbena'}, {'id': 18145, 'synset': 'sweet_sand_verbena.n.01', 'name': 'sweet_sand_verbena'}, {'id': 18146, 'synset': 'yellow_sand_verbena.n.01', 'name': 'yellow_sand_verbena'}, {'id': 18147, 'synset': 'beach_pancake.n.01', 'name': 'beach_pancake'}, {'id': 18148, 'synset': 'beach_sand_verbena.n.01', 'name': 'beach_sand_verbena'}, {'id': 18149, 'synset': 'desert_sand_verbena.n.01', 'name': 'desert_sand_verbena'}, {'id': 18150, 'synset': "trailing_four_o'clock.n.01", 'name': "trailing_four_o'clock"}, {'id': 18151, 'synset': 'bougainvillea.n.01', 'name': 'bougainvillea'}, {'id': 18152, 'synset': 'umbrellawort.n.01', 'name': 'umbrellawort'}, {'id': 18153, 'synset': "four_o'clock.n.01", 'name': "four_o'clock"}, {'id': 18154, 'synset': "common_four-o'clock.n.01", 'name': "common_four-o'clock"}, {'id': 18155, 'synset': "california_four_o'clock.n.01", 'name': "California_four_o'clock"}, {'id': 18156, 'synset': "sweet_four_o'clock.n.01", 'name': "sweet_four_o'clock"}, {'id': 18157, 'synset': "desert_four_o'clock.n.01", 'name': "desert_four_o'clock"}, {'id': 18158, 'synset': "mountain_four_o'clock.n.01", 'name': "mountain_four_o'clock"}, {'id': 18159, 'synset': 'cockspur.n.02', 'name': 'cockspur'}, {'id': 18160, 'synset': 'rattail_cactus.n.01', 'name': 'rattail_cactus'}, {'id': 18161, 'synset': 'saguaro.n.01', 'name': 'saguaro'}, {'id': 18162, 'synset': 'night-blooming_cereus.n.03', 'name': 'night-blooming_cereus'}, {'id': 18163, 'synset': 'echinocactus.n.01', 'name': 'echinocactus'}, {'id': 18164, 'synset': 'hedgehog_cactus.n.01', 'name': 'hedgehog_cactus'}, {'id': 18165, 'synset': 'golden_barrel_cactus.n.01', 'name': 'golden_barrel_cactus'}, {'id': 18166, 'synset': 'hedgehog_cereus.n.01', 'name': 'hedgehog_cereus'}, {'id': 18167, 'synset': 'rainbow_cactus.n.01', 'name': 'rainbow_cactus'}, {'id': 18168, 'synset': 'epiphyllum.n.01', 'name': 'epiphyllum'}, {'id': 18169, 'synset': 'barrel_cactus.n.01', 'name': 'barrel_cactus'}, {'id': 18170, 'synset': 'night-blooming_cereus.n.02', 'name': 'night-blooming_cereus'}, {'id': 18171, 'synset': 'chichipe.n.01', 'name': 'chichipe'}, {'id': 18172, 'synset': 'mescal.n.01', 'name': 'mescal'}, {'id': 18173, 'synset': 'mescal_button.n.01', 'name': 'mescal_button'}, {'id': 18174, 'synset': 'mammillaria.n.01', 'name': 'mammillaria'}, {'id': 18175, 'synset': 'feather_ball.n.01', 'name': 'feather_ball'}, {'id': 18176, 'synset': 'garambulla.n.01', 'name': 'garambulla'}, {'id': 18177, 'synset': "knowlton's_cactus.n.01", 'name': "Knowlton's_cactus"}, {'id': 18178, 'synset': 'nopal.n.02', 'name': 'nopal'}, {'id': 18179, 'synset': 'prickly_pear.n.01', 'name': 'prickly_pear'}, {'id': 18180, 'synset': 'cholla.n.01', 'name': 'cholla'}, {'id': 18181, 'synset': 'nopal.n.01', 'name': 'nopal'}, {'id': 18182, 'synset': 'tuna.n.01', 'name': 'tuna'}, {'id': 18183, 'synset': 'barbados_gooseberry.n.01', 'name': 'Barbados_gooseberry'}, {'id': 18184, 'synset': 'mistletoe_cactus.n.01', 'name': 'mistletoe_cactus'}, {'id': 18185, 'synset': 'christmas_cactus.n.01', 'name': 'Christmas_cactus'}, {'id': 18186, 'synset': 'night-blooming_cereus.n.01', 'name': 'night-blooming_cereus'}, {'id': 18187, 'synset': 'crab_cactus.n.01', 'name': 'crab_cactus'}, {'id': 18188, 'synset': 'pokeweed.n.01', 'name': 'pokeweed'}, {'id': 18189, 'synset': 'indian_poke.n.02', 'name': 'Indian_poke'}, {'id': 18190, 'synset': 'poke.n.01', 'name': 'poke'}, {'id': 18191, 'synset': 'ombu.n.01', 'name': 'ombu'}, {'id': 18192, 'synset': 'bloodberry.n.01', 'name': 'bloodberry'}, {'id': 18193, 'synset': 'portulaca.n.01', 'name': 'portulaca'}, {'id': 18194, 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'synset': 'garlic_mustard.n.01', 'name': 'garlic_mustard'}, {'id': 18223, 'synset': 'alyssum.n.01', 'name': 'alyssum'}, {'id': 18224, 'synset': 'rose_of_jericho.n.02', 'name': 'rose_of_Jericho'}, {'id': 18225, 'synset': 'arabidopsis_thaliana.n.01', 'name': 'Arabidopsis_thaliana'}, {'id': 18226, 'synset': 'arabidopsis_lyrata.n.01', 'name': 'Arabidopsis_lyrata'}, {'id': 18227, 'synset': 'rock_cress.n.01', 'name': 'rock_cress'}, {'id': 18228, 'synset': 'sicklepod.n.02', 'name': 'sicklepod'}, {'id': 18229, 'synset': 'tower_mustard.n.01', 'name': 'tower_mustard'}, {'id': 18230, 'synset': 'horseradish.n.01', 'name': 'horseradish'}, {'id': 18231, 'synset': 'winter_cress.n.01', 'name': 'winter_cress'}, {'id': 18232, 'synset': 'yellow_rocket.n.01', 'name': 'yellow_rocket'}, {'id': 18233, 'synset': 'hoary_alison.n.01', 'name': 'hoary_alison'}, {'id': 18234, 'synset': 'buckler_mustard.n.01', 'name': 'buckler_mustard'}, {'id': 18235, 'synset': 'wild_cabbage.n.01', 'name': 'wild_cabbage'}, {'id': 18236, 'synset': 'cabbage.n.03', 'name': 'cabbage'}, {'id': 18237, 'synset': 'head_cabbage.n.01', 'name': 'head_cabbage'}, {'id': 18238, 'synset': 'savoy_cabbage.n.01', 'name': 'savoy_cabbage'}, {'id': 18239, 'synset': 'brussels_sprout.n.01', 'name': 'brussels_sprout'}, {'id': 18240, 'synset': 'cauliflower.n.01', 'name': 'cauliflower'}, {'id': 18241, 'synset': 'collard.n.01', 'name': 'collard'}, {'id': 18242, 'synset': 'kohlrabi.n.01', 'name': 'kohlrabi'}, {'id': 18243, 'synset': 'turnip_plant.n.01', 'name': 'turnip_plant'}, {'id': 18244, 'synset': 'rutabaga.n.02', 'name': 'rutabaga'}, {'id': 18245, 'synset': 'broccoli_raab.n.01', 'name': 'broccoli_raab'}, {'id': 18246, 'synset': 'mustard.n.01', 'name': 'mustard'}, {'id': 18247, 'synset': 'chinese_mustard.n.01', 'name': 'chinese_mustard'}, {'id': 18248, 'synset': 'bok_choy.n.01', 'name': 'bok_choy'}, {'id': 18249, 'synset': 'rape.n.01', 'name': 'rape'}, {'id': 18250, 'synset': 'rapeseed.n.01', 'name': 'rapeseed'}, {'id': 18251, 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'synset': 'pennycress.n.01', 'name': 'pennycress'}, {'id': 18294, 'synset': 'field_pennycress.n.01', 'name': 'field_pennycress'}, {'id': 18295, 'synset': 'fringepod.n.01', 'name': 'fringepod'}, {'id': 18296, 'synset': 'bladderpod.n.02', 'name': 'bladderpod'}, {'id': 18297, 'synset': 'wasabi.n.01', 'name': 'wasabi'}, {'id': 18298, 'synset': 'poppy.n.01', 'name': 'poppy'}, {'id': 18299, 'synset': 'iceland_poppy.n.02', 'name': 'Iceland_poppy'}, {'id': 18300, 'synset': 'western_poppy.n.01', 'name': 'western_poppy'}, {'id': 18301, 'synset': 'prickly_poppy.n.02', 'name': 'prickly_poppy'}, {'id': 18302, 'synset': 'iceland_poppy.n.01', 'name': 'Iceland_poppy'}, {'id': 18303, 'synset': 'oriental_poppy.n.01', 'name': 'oriental_poppy'}, {'id': 18304, 'synset': 'corn_poppy.n.01', 'name': 'corn_poppy'}, {'id': 18305, 'synset': 'opium_poppy.n.01', 'name': 'opium_poppy'}, {'id': 18306, 'synset': 'prickly_poppy.n.01', 'name': 'prickly_poppy'}, {'id': 18307, 'synset': 'mexican_poppy.n.01', 'name': 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18349, 'synset': 'mayweed.n.01', 'name': 'mayweed'}, {'id': 18350, 'synset': 'yellow_chamomile.n.01', 'name': 'yellow_chamomile'}, {'id': 18351, 'synset': 'corn_chamomile.n.01', 'name': 'corn_chamomile'}, {'id': 18352, 'synset': 'woolly_daisy.n.01', 'name': 'woolly_daisy'}, {'id': 18353, 'synset': 'burdock.n.01', 'name': 'burdock'}, {'id': 18354, 'synset': 'great_burdock.n.01', 'name': 'great_burdock'}, {'id': 18355, 'synset': 'african_daisy.n.03', 'name': 'African_daisy'}, {'id': 18356, 'synset': 'blue-eyed_african_daisy.n.01', 'name': 'blue-eyed_African_daisy'}, {'id': 18357, 'synset': 'marguerite.n.02', 'name': 'marguerite'}, {'id': 18358, 'synset': 'silversword.n.01', 'name': 'silversword'}, {'id': 18359, 'synset': 'arnica.n.02', 'name': 'arnica'}, {'id': 18360, 'synset': 'heartleaf_arnica.n.01', 'name': 'heartleaf_arnica'}, {'id': 18361, 'synset': 'arnica_montana.n.01', 'name': 'Arnica_montana'}, {'id': 18362, 'synset': 'lamb_succory.n.01', 'name': 'lamb_succory'}, {'id': 18363, 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18391, 'synset': 'prairie_aster.n.01', 'name': 'prairie_aster'}, {'id': 18392, 'synset': 'annual_salt-marsh_aster.n.01', 'name': 'annual_salt-marsh_aster'}, {'id': 18393, 'synset': 'aromatic_aster.n.01', 'name': 'aromatic_aster'}, {'id': 18394, 'synset': 'arrow_leaved_aster.n.01', 'name': 'arrow_leaved_aster'}, {'id': 18395, 'synset': 'azure_aster.n.01', 'name': 'azure_aster'}, {'id': 18396, 'synset': 'bog_aster.n.01', 'name': 'bog_aster'}, {'id': 18397, 'synset': 'crooked-stemmed_aster.n.01', 'name': 'crooked-stemmed_aster'}, {'id': 18398, 'synset': 'eastern_silvery_aster.n.01', 'name': 'Eastern_silvery_aster'}, {'id': 18399, 'synset': 'flat-topped_white_aster.n.01', 'name': 'flat-topped_white_aster'}, {'id': 18400, 'synset': 'late_purple_aster.n.01', 'name': 'late_purple_aster'}, {'id': 18401, 'synset': 'panicled_aster.n.01', 'name': 'panicled_aster'}, {'id': 18402, 'synset': 'perennial_salt_marsh_aster.n.01', 'name': 'perennial_salt_marsh_aster'}, {'id': 18403, 'synset': 'purple-stemmed_aster.n.01', 'name': 'purple-stemmed_aster'}, {'id': 18404, 'synset': 'rough-leaved_aster.n.01', 'name': 'rough-leaved_aster'}, {'id': 18405, 'synset': 'rush_aster.n.01', 'name': 'rush_aster'}, {'id': 18406, 'synset': "schreiber's_aster.n.01", 'name': "Schreiber's_aster"}, {'id': 18407, 'synset': 'small_white_aster.n.01', 'name': 'small_white_aster'}, {'id': 18408, 'synset': 'smooth_aster.n.01', 'name': 'smooth_aster'}, {'id': 18409, 'synset': 'southern_aster.n.01', 'name': 'southern_aster'}, {'id': 18410, 'synset': 'starved_aster.n.01', 'name': 'starved_aster'}, {'id': 18411, 'synset': "tradescant's_aster.n.01", 'name': "tradescant's_aster"}, {'id': 18412, 'synset': 'wavy-leaved_aster.n.01', 'name': 'wavy-leaved_aster'}, {'id': 18413, 'synset': 'western_silvery_aster.n.01', 'name': 'Western_silvery_aster'}, {'id': 18414, 'synset': 'willow_aster.n.01', 'name': 'willow_aster'}, {'id': 18415, 'synset': 'ayapana.n.01', 'name': 'ayapana'}, {'id': 18416, 'synset': 'mule_fat.n.01', 'name': 'mule_fat'}, {'id': 18417, 'synset': 'balsamroot.n.01', 'name': 'balsamroot'}, {'id': 18418, 'synset': 'daisy.n.01', 'name': 'daisy'}, {'id': 18419, 'synset': 'common_daisy.n.01', 'name': 'common_daisy'}, {'id': 18420, 'synset': 'bur_marigold.n.01', 'name': 'bur_marigold'}, {'id': 18421, 'synset': 'spanish_needles.n.02', 'name': 'Spanish_needles'}, {'id': 18422, 'synset': 'tickseed_sunflower.n.01', 'name': 'tickseed_sunflower'}, {'id': 18423, 'synset': 'european_beggar-ticks.n.01', 'name': 'European_beggar-ticks'}, {'id': 18424, 'synset': 'slender_knapweed.n.01', 'name': 'slender_knapweed'}, {'id': 18425, 'synset': 'false_chamomile.n.01', 'name': 'false_chamomile'}, {'id': 18426, 'synset': 'swan_river_daisy.n.01', 'name': 'Swan_River_daisy'}, {'id': 18427, 'synset': 'woodland_oxeye.n.01', 'name': 'woodland_oxeye'}, {'id': 18428, 'synset': 'indian_plantain.n.01', 'name': 'Indian_plantain'}, {'id': 18429, 'synset': 'calendula.n.01', 'name': 'calendula'}, {'id': 18430, 'synset': 'common_marigold.n.01', 'name': 'common_marigold'}, {'id': 18431, 'synset': 'china_aster.n.01', 'name': 'China_aster'}, {'id': 18432, 'synset': 'thistle.n.01', 'name': 'thistle'}, {'id': 18433, 'synset': 'welted_thistle.n.01', 'name': 'welted_thistle'}, {'id': 18434, 'synset': 'musk_thistle.n.01', 'name': 'musk_thistle'}, {'id': 18435, 'synset': 'carline_thistle.n.01', 'name': 'carline_thistle'}, {'id': 18436, 'synset': 'stemless_carline_thistle.n.01', 'name': 'stemless_carline_thistle'}, {'id': 18437, 'synset': 'common_carline_thistle.n.01', 'name': 'common_carline_thistle'}, {'id': 18438, 'synset': 'safflower.n.01', 'name': 'safflower'}, {'id': 18439, 'synset': 'safflower_seed.n.01', 'name': 'safflower_seed'}, {'id': 18440, 'synset': 'catananche.n.01', 'name': 'catananche'}, {'id': 18441, 'synset': 'blue_succory.n.01', 'name': 'blue_succory'}, {'id': 18442, 'synset': 'centaury.n.02', 'name': 'centaury'}, {'id': 18443, 'synset': 'dusty_miller.n.03', 'name': 'dusty_miller'}, {'id': 18444, 'synset': 'cornflower.n.02', 'name': 'cornflower'}, {'id': 18445, 'synset': 'star-thistle.n.01', 'name': 'star-thistle'}, {'id': 18446, 'synset': 'knapweed.n.01', 'name': 'knapweed'}, {'id': 18447, 'synset': 'sweet_sultan.n.02', 'name': 'sweet_sultan'}, {'id': 18448, 'synset': 'great_knapweed.n.01', 'name': 'great_knapweed'}, {'id': 18449, 'synset': "barnaby's_thistle.n.01", 'name': "Barnaby's_thistle"}, {'id': 18450, 'synset': 'chamomile.n.01', 'name': 'chamomile'}, {'id': 18451, 'synset': 'chaenactis.n.01', 'name': 'chaenactis'}, {'id': 18452, 'synset': 'chrysanthemum.n.02', 'name': 'chrysanthemum'}, {'id': 18453, 'synset': 'corn_marigold.n.01', 'name': 'corn_marigold'}, {'id': 18454, 'synset': 'crown_daisy.n.01', 'name': 'crown_daisy'}, {'id': 18455, 'synset': 'chop-suey_greens.n.01', 'name': 'chop-suey_greens'}, {'id': 18456, 'synset': 'golden_aster.n.01', 'name': 'golden_aster'}, {'id': 18457, 'synset': 'maryland_golden_aster.n.01', 'name': 'Maryland_golden_aster'}, {'id': 18458, 'synset': 'goldenbush.n.02', 'name': 'goldenbush'}, {'id': 18459, 'synset': 'rabbit_brush.n.01', 'name': 'rabbit_brush'}, {'id': 18460, 'synset': 'chicory.n.02', 'name': 'chicory'}, {'id': 18461, 'synset': 'endive.n.01', 'name': 'endive'}, {'id': 18462, 'synset': 'chicory.n.01', 'name': 'chicory'}, {'id': 18463, 'synset': 'plume_thistle.n.01', 'name': 'plume_thistle'}, {'id': 18464, 'synset': 'canada_thistle.n.01', 'name': 'Canada_thistle'}, {'id': 18465, 'synset': 'field_thistle.n.01', 'name': 'field_thistle'}, {'id': 18466, 'synset': 'woolly_thistle.n.02', 'name': 'woolly_thistle'}, {'id': 18467, 'synset': 'european_woolly_thistle.n.01', 'name': 'European_woolly_thistle'}, {'id': 18468, 'synset': 'melancholy_thistle.n.01', 'name': 'melancholy_thistle'}, {'id': 18469, 'synset': 'brook_thistle.n.01', 'name': 'brook_thistle'}, {'id': 18470, 'synset': 'bull_thistle.n.01', 'name': 'bull_thistle'}, {'id': 18471, 'synset': 'blessed_thistle.n.02', 'name': 'blessed_thistle'}, {'id': 18472, 'synset': 'mistflower.n.01', 'name': 'mistflower'}, {'id': 18473, 'synset': 'horseweed.n.02', 'name': 'horseweed'}, {'id': 18474, 'synset': 'coreopsis.n.01', 'name': 'coreopsis'}, {'id': 18475, 'synset': 'giant_coreopsis.n.01', 'name': 'giant_coreopsis'}, {'id': 18476, 'synset': 'sea_dahlia.n.01', 'name': 'sea_dahlia'}, {'id': 18477, 'synset': 'calliopsis.n.01', 'name': 'calliopsis'}, {'id': 18478, 'synset': 'cosmos.n.02', 'name': 'cosmos'}, {'id': 18479, 'synset': 'brass_buttons.n.01', 'name': 'brass_buttons'}, {'id': 18480, 'synset': 'billy_buttons.n.01', 'name': 'billy_buttons'}, {'id': 18481, 'synset': "hawk's-beard.n.01", 'name': "hawk's-beard"}, {'id': 18482, 'synset': 'artichoke.n.01', 'name': 'artichoke'}, {'id': 18483, 'synset': 'cardoon.n.01', 'name': 'cardoon'}, {'id': 18484, 'synset': 'dahlia.n.01', 'name': 'dahlia'}, {'id': 18485, 'synset': 'german_ivy.n.01', 'name': 'German_ivy'}, {'id': 18486, 'synset': "florist's_chrysanthemum.n.01", 'name': "florist's_chrysanthemum"}, {'id': 18487, 'synset': 'cape_marigold.n.01', 'name': 'cape_marigold'}, {'id': 18488, 'synset': "leopard's-bane.n.01", 'name': "leopard's-bane"}, {'id': 18489, 'synset': 'coneflower.n.03', 'name': 'coneflower'}, {'id': 18490, 'synset': 'globe_thistle.n.01', 'name': 'globe_thistle'}, {'id': 18491, 'synset': "elephant's-foot.n.02", 'name': "elephant's-foot"}, {'id': 18492, 'synset': 'tassel_flower.n.01', 'name': 'tassel_flower'}, {'id': 18493, 'synset': 'brittlebush.n.01', 'name': 'brittlebush'}, {'id': 18494, 'synset': 'sunray.n.02', 'name': 'sunray'}, {'id': 18495, 'synset': 'engelmannia.n.01', 'name': 'engelmannia'}, {'id': 18496, 'synset': 'fireweed.n.02', 'name': 'fireweed'}, {'id': 18497, 'synset': 'fleabane.n.02', 'name': 'fleabane'}, {'id': 18498, 'synset': 'blue_fleabane.n.01', 'name': 'blue_fleabane'}, {'id': 18499, 'synset': 'daisy_fleabane.n.01', 'name': 'daisy_fleabane'}, {'id': 18500, 'synset': 'orange_daisy.n.01', 'name': 'orange_daisy'}, {'id': 18501, 'synset': 'spreading_fleabane.n.01', 'name': 'spreading_fleabane'}, {'id': 18502, 'synset': 'seaside_daisy.n.01', 'name': 'seaside_daisy'}, {'id': 18503, 'synset': 'philadelphia_fleabane.n.01', 'name': 'Philadelphia_fleabane'}, {'id': 18504, 'synset': "robin's_plantain.n.01", 'name': "robin's_plantain"}, {'id': 18505, 'synset': 'showy_daisy.n.01', 'name': 'showy_daisy'}, {'id': 18506, 'synset': 'woolly_sunflower.n.01', 'name': 'woolly_sunflower'}, {'id': 18507, 'synset': 'golden_yarrow.n.01', 'name': 'golden_yarrow'}, {'id': 18508, 'synset': 'dog_fennel.n.01', 'name': 'dog_fennel'}, {'id': 18509, 'synset': 'joe-pye_weed.n.02', 'name': 'Joe-Pye_weed'}, {'id': 18510, 'synset': 'boneset.n.02', 'name': 'boneset'}, {'id': 18511, 'synset': 'joe-pye_weed.n.01', 'name': 'Joe-Pye_weed'}, {'id': 18512, 'synset': 'blue_daisy.n.01', 'name': 'blue_daisy'}, {'id': 18513, 'synset': 'kingfisher_daisy.n.01', 'name': 'kingfisher_daisy'}, {'id': 18514, 'synset': 'cotton_rose.n.02', 'name': 'cotton_rose'}, {'id': 18515, 'synset': 'herba_impia.n.01', 'name': 'herba_impia'}, {'id': 18516, 'synset': 'gaillardia.n.01', 'name': 'gaillardia'}, {'id': 18517, 'synset': 'gazania.n.01', 'name': 'gazania'}, {'id': 18518, 'synset': 'treasure_flower.n.01', 'name': 'treasure_flower'}, {'id': 18519, 'synset': 'african_daisy.n.02', 'name': 'African_daisy'}, {'id': 18520, 'synset': 'barberton_daisy.n.01', 'name': 'Barberton_daisy'}, {'id': 18521, 'synset': 'desert_sunflower.n.01', 'name': 'desert_sunflower'}, {'id': 18522, 'synset': 'cudweed.n.01', 'name': 'cudweed'}, {'id': 18523, 'synset': 'chafeweed.n.01', 'name': 'chafeweed'}, {'id': 18524, 'synset': 'gumweed.n.01', 'name': 'gumweed'}, {'id': 18525, 'synset': 'grindelia_robusta.n.01', 'name': 'Grindelia_robusta'}, {'id': 18526, 'synset': 'curlycup_gumweed.n.01', 'name': 'curlycup_gumweed'}, {'id': 18527, 'synset': 'little-head_snakeweed.n.01', 'name': 'little-head_snakeweed'}, {'id': 18528, 'synset': 'rabbitweed.n.01', 'name': 'rabbitweed'}, {'id': 18529, 'synset': 'broomweed.n.01', 'name': 'broomweed'}, {'id': 18530, 'synset': 'velvet_plant.n.02', 'name': 'velvet_plant'}, {'id': 18531, 'synset': 'goldenbush.n.01', 'name': 'goldenbush'}, {'id': 18532, 'synset': 'camphor_daisy.n.01', 'name': 'camphor_daisy'}, {'id': 18533, 'synset': 'yellow_spiny_daisy.n.01', 'name': 'yellow_spiny_daisy'}, {'id': 18534, 'synset': 'hoary_golden_bush.n.01', 'name': 'hoary_golden_bush'}, {'id': 18535, 'synset': 'sneezeweed.n.01', 'name': 'sneezeweed'}, {'id': 18536, 'synset': 'orange_sneezeweed.n.01', 'name': 'orange_sneezeweed'}, {'id': 18537, 'synset': 'rosilla.n.01', 'name': 'rosilla'}, {'id': 18538, 'synset': 'swamp_sunflower.n.01', 'name': 'swamp_sunflower'}, {'id': 18539, 'synset': 'common_sunflower.n.01', 'name': 'common_sunflower'}, {'id': 18540, 'synset': 'giant_sunflower.n.01', 'name': 'giant_sunflower'}, {'id': 18541, 'synset': 'showy_sunflower.n.01', 'name': 'showy_sunflower'}, {'id': 18542, 'synset': "maximilian's_sunflower.n.01", 'name': "Maximilian's_sunflower"}, {'id': 18543, 'synset': 'prairie_sunflower.n.01', 'name': 'prairie_sunflower'}, {'id': 18544, 'synset': 'jerusalem_artichoke.n.02', 'name': 'Jerusalem_artichoke'}, {'id': 18545, 'synset': 'jerusalem_artichoke.n.01', 'name': 'Jerusalem_artichoke'}, {'id': 18546, 'synset': 'strawflower.n.03', 'name': 'strawflower'}, {'id': 18547, 'synset': 'heliopsis.n.01', 'name': 'heliopsis'}, {'id': 18548, 'synset': 'strawflower.n.02', 'name': 'strawflower'}, {'id': 18549, 'synset': 'hairy_golden_aster.n.01', 'name': 'hairy_golden_aster'}, {'id': 18550, 'synset': 'hawkweed.n.02', 'name': 'hawkweed'}, {'id': 18551, 'synset': 'rattlesnake_weed.n.01', 'name': 'rattlesnake_weed'}, {'id': 18552, 'synset': 'alpine_coltsfoot.n.01', 'name': 'alpine_coltsfoot'}, {'id': 18553, 'synset': 'alpine_gold.n.01', 'name': 'alpine_gold'}, {'id': 18554, 'synset': 'dwarf_hulsea.n.01', 'name': 'dwarf_hulsea'}, {'id': 18555, 'synset': "cat's-ear.n.02", 'name': "cat's-ear"}, {'id': 18556, 'synset': 'inula.n.01', 'name': 'inula'}, {'id': 18557, 'synset': 'marsh_elder.n.01', 'name': 'marsh_elder'}, {'id': 18558, 'synset': 'burweed_marsh_elder.n.01', 'name': 'burweed_marsh_elder'}, {'id': 18559, 'synset': 'krigia.n.01', 'name': 'krigia'}, {'id': 18560, 'synset': 'dwarf_dandelion.n.01', 'name': 'dwarf_dandelion'}, {'id': 18561, 'synset': 'garden_lettuce.n.01', 'name': 'garden_lettuce'}, {'id': 18562, 'synset': 'cos_lettuce.n.01', 'name': 'cos_lettuce'}, {'id': 18563, 'synset': 'leaf_lettuce.n.01', 'name': 'leaf_lettuce'}, {'id': 18564, 'synset': 'celtuce.n.01', 'name': 'celtuce'}, {'id': 18565, 'synset': 'prickly_lettuce.n.01', 'name': 'prickly_lettuce'}, {'id': 18566, 'synset': 'goldfields.n.01', 'name': 'goldfields'}, {'id': 18567, 'synset': 'tidytips.n.01', 'name': 'tidytips'}, {'id': 18568, 'synset': 'hawkbit.n.01', 'name': 'hawkbit'}, {'id': 18569, 'synset': 'fall_dandelion.n.01', 'name': 'fall_dandelion'}, {'id': 18570, 'synset': 'edelweiss.n.01', 'name': 'edelweiss'}, {'id': 18571, 'synset': 'oxeye_daisy.n.02', 'name': 'oxeye_daisy'}, {'id': 18572, 'synset': 'oxeye_daisy.n.01', 'name': 'oxeye_daisy'}, {'id': 18573, 'synset': 'shasta_daisy.n.01', 'name': 'shasta_daisy'}, {'id': 18574, 'synset': 'pyrenees_daisy.n.01', 'name': 'Pyrenees_daisy'}, {'id': 18575, 'synset': 'north_island_edelweiss.n.01', 'name': 'north_island_edelweiss'}, {'id': 18576, 'synset': 'blazing_star.n.02', 'name': 'blazing_star'}, {'id': 18577, 'synset': 'dotted_gayfeather.n.01', 'name': 'dotted_gayfeather'}, {'id': 18578, 'synset': 'dense_blazing_star.n.01', 'name': 'dense_blazing_star'}, {'id': 18579, 'synset': 'texas_star.n.02', 'name': 'Texas_star'}, {'id': 18580, 'synset': 'african_daisy.n.01', 'name': 'African_daisy'}, {'id': 18581, 'synset': 'tahoka_daisy.n.01', 'name': 'tahoka_daisy'}, {'id': 18582, 'synset': 'sticky_aster.n.01', 'name': 'sticky_aster'}, {'id': 18583, 'synset': 'mojave_aster.n.01', 'name': 'Mojave_aster'}, {'id': 18584, 'synset': 'tarweed.n.01', 'name': 'tarweed'}, {'id': 18585, 'synset': 'sweet_false_chamomile.n.01', 'name': 'sweet_false_chamomile'}, {'id': 18586, 'synset': 'pineapple_weed.n.01', 'name': 'pineapple_weed'}, {'id': 18587, 'synset': 'climbing_hempweed.n.01', 'name': 'climbing_hempweed'}, {'id': 18588, 'synset': 'mutisia.n.01', 'name': 'mutisia'}, {'id': 18589, 'synset': 'rattlesnake_root.n.02', 'name': 'rattlesnake_root'}, {'id': 18590, 'synset': 'white_lettuce.n.01', 'name': 'white_lettuce'}, {'id': 18591, 'synset': 'daisybush.n.01', 'name': 'daisybush'}, {'id': 18592, 'synset': 'new_zealand_daisybush.n.01', 'name': 'New_Zealand_daisybush'}, {'id': 18593, 'synset': 'cotton_thistle.n.01', 'name': 'cotton_thistle'}, {'id': 18594, 'synset': 'othonna.n.01', 'name': 'othonna'}, {'id': 18595, 'synset': 'cascade_everlasting.n.01', 'name': 'cascade_everlasting'}, {'id': 18596, 'synset': 'butterweed.n.02', 'name': 'butterweed'}, {'id': 18597, 'synset': 'american_feverfew.n.01', 'name': 'American_feverfew'}, {'id': 18598, 'synset': 'cineraria.n.01', 'name': 'cineraria'}, {'id': 18599, 'synset': "florest's_cineraria.n.01", 'name': "florest's_cineraria"}, {'id': 18600, 'synset': 'butterbur.n.01', 'name': 'butterbur'}, {'id': 18601, 'synset': 'winter_heliotrope.n.01', 'name': 'winter_heliotrope'}, {'id': 18602, 'synset': 'sweet_coltsfoot.n.01', 'name': 'sweet_coltsfoot'}, {'id': 18603, 'synset': 'oxtongue.n.01', 'name': 'oxtongue'}, {'id': 18604, 'synset': 'hawkweed.n.01', 'name': 'hawkweed'}, {'id': 18605, 'synset': 'mouse-ear_hawkweed.n.01', 'name': 'mouse-ear_hawkweed'}, {'id': 18606, 'synset': 'stevia.n.02', 'name': 'stevia'}, {'id': 18607, 'synset': 'rattlesnake_root.n.01', 'name': 'rattlesnake_root'}, {'id': 18608, 'synset': 'fleabane.n.01', 'name': 'fleabane'}, {'id': 18609, 'synset': 'sheep_plant.n.01', 'name': 'sheep_plant'}, {'id': 18610, 'synset': 'coneflower.n.02', 'name': 'coneflower'}, {'id': 18611, 'synset': 'mexican_hat.n.01', 'name': 'Mexican_hat'}, {'id': 18612, 'synset': 'long-head_coneflower.n.01', 'name': 'long-head_coneflower'}, {'id': 18613, 'synset': 'prairie_coneflower.n.01', 'name': 'prairie_coneflower'}, {'id': 18614, 'synset': 'swan_river_everlasting.n.01', 'name': 'Swan_River_everlasting'}, {'id': 18615, 'synset': 'coneflower.n.01', 'name': 'coneflower'}, {'id': 18616, 'synset': 'black-eyed_susan.n.03', 'name': 'black-eyed_Susan'}, {'id': 18617, 'synset': 'cutleaved_coneflower.n.01', 'name': 'cutleaved_coneflower'}, {'id': 18618, 'synset': 'golden_glow.n.01', 'name': 'golden_glow'}, {'id': 18619, 'synset': 'lavender_cotton.n.01', 'name': 'lavender_cotton'}, {'id': 18620, 'synset': 'creeping_zinnia.n.01', 'name': 'creeping_zinnia'}, {'id': 18621, 'synset': 'golden_thistle.n.01', 'name': 'golden_thistle'}, {'id': 18622, 'synset': 'spanish_oyster_plant.n.01', 'name': 'Spanish_oyster_plant'}, {'id': 18623, 'synset': 'nodding_groundsel.n.01', 'name': 'nodding_groundsel'}, {'id': 18624, 'synset': 'dusty_miller.n.02', 'name': 'dusty_miller'}, {'id': 18625, 'synset': 'butterweed.n.01', 'name': 'butterweed'}, {'id': 18626, 'synset': 'ragwort.n.01', 'name': 'ragwort'}, {'id': 18627, 'synset': 'arrowleaf_groundsel.n.01', 'name': 'arrowleaf_groundsel'}, {'id': 18628, 'synset': 'black_salsify.n.01', 'name': 'black_salsify'}, {'id': 18629, 'synset': 'white-topped_aster.n.01', 'name': 'white-topped_aster'}, {'id': 18630, 'synset': 'narrow-leaved_white-topped_aster.n.01', 'name': 'narrow-leaved_white-topped_aster'}, {'id': 18631, 'synset': 'silver_sage.n.01', 'name': 'silver_sage'}, {'id': 18632, 'synset': 'sea_wormwood.n.01', 'name': 'sea_wormwood'}, {'id': 18633, 'synset': 'sawwort.n.01', 'name': 'sawwort'}, {'id': 18634, 'synset': 'rosinweed.n.01', 'name': 'rosinweed'}, {'id': 18635, 'synset': 'milk_thistle.n.02', 'name': 'milk_thistle'}, {'id': 18636, 'synset': 'goldenrod.n.01', 'name': 'goldenrod'}, {'id': 18637, 'synset': 'silverrod.n.01', 'name': 'silverrod'}, {'id': 18638, 'synset': 'meadow_goldenrod.n.01', 'name': 'meadow_goldenrod'}, {'id': 18639, 'synset': 'missouri_goldenrod.n.01', 'name': 'Missouri_goldenrod'}, {'id': 18640, 'synset': 'alpine_goldenrod.n.01', 'name': 'alpine_goldenrod'}, {'id': 18641, 'synset': 'grey_goldenrod.n.01', 'name': 'grey_goldenrod'}, {'id': 18642, 'synset': 'blue_mountain_tea.n.01', 'name': 'Blue_Mountain_tea'}, {'id': 18643, 'synset': "dyer's_weed.n.01", 'name': "dyer's_weed"}, {'id': 18644, 'synset': 'seaside_goldenrod.n.01', 'name': 'seaside_goldenrod'}, {'id': 18645, 'synset': 'narrow_goldenrod.n.01', 'name': 'narrow_goldenrod'}, {'id': 18646, 'synset': "boott's_goldenrod.n.01", 'name': "Boott's_goldenrod"}, {'id': 18647, 'synset': "elliott's_goldenrod.n.01", 'name': "Elliott's_goldenrod"}, {'id': 18648, 'synset': 'ohio_goldenrod.n.01', 'name': 'Ohio_goldenrod'}, {'id': 18649, 'synset': 'rough-stemmed_goldenrod.n.01', 'name': 'rough-stemmed_goldenrod'}, {'id': 18650, 'synset': 'showy_goldenrod.n.01', 'name': 'showy_goldenrod'}, {'id': 18651, 'synset': 'tall_goldenrod.n.01', 'name': 'tall_goldenrod'}, {'id': 18652, 'synset': 'zigzag_goldenrod.n.01', 'name': 'zigzag_goldenrod'}, {'id': 18653, 'synset': 'sow_thistle.n.01', 'name': 'sow_thistle'}, {'id': 18654, 'synset': 'milkweed.n.02', 'name': 'milkweed'}, {'id': 18655, 'synset': 'stevia.n.01', 'name': 'stevia'}, {'id': 18656, 'synset': "stokes'_aster.n.01", 'name': "stokes'_aster"}, {'id': 18657, 'synset': 'marigold.n.01', 'name': 'marigold'}, {'id': 18658, 'synset': 'african_marigold.n.01', 'name': 'African_marigold'}, {'id': 18659, 'synset': 'french_marigold.n.01', 'name': 'French_marigold'}, {'id': 18660, 'synset': 'painted_daisy.n.01', 'name': 'painted_daisy'}, {'id': 18661, 'synset': 'pyrethrum.n.02', 'name': 'pyrethrum'}, {'id': 18662, 'synset': 'northern_dune_tansy.n.01', 'name': 'northern_dune_tansy'}, {'id': 18663, 'synset': 'feverfew.n.01', 'name': 'feverfew'}, {'id': 18664, 'synset': 'dusty_miller.n.01', 'name': 'dusty_miller'}, {'id': 18665, 'synset': 'tansy.n.01', 'name': 'tansy'}, {'id': 18666, 'synset': 'dandelion.n.01', 'name': 'dandelion'}, {'id': 18667, 'synset': 'common_dandelion.n.01', 'name': 'common_dandelion'}, {'id': 18668, 'synset': 'dandelion_green.n.01', 'name': 'dandelion_green'}, {'id': 18669, 'synset': 'russian_dandelion.n.01', 'name': 'Russian_dandelion'}, {'id': 18670, 'synset': 'stemless_hymenoxys.n.01', 'name': 'stemless_hymenoxys'}, {'id': 18671, 'synset': 'mexican_sunflower.n.01', 'name': 'Mexican_sunflower'}, {'id': 18672, 'synset': 'easter_daisy.n.01', 'name': 'Easter_daisy'}, {'id': 18673, 'synset': 'yellow_salsify.n.01', 'name': 'yellow_salsify'}, {'id': 18674, 'synset': 'salsify.n.02', 'name': 'salsify'}, {'id': 18675, 'synset': 'meadow_salsify.n.01', 'name': 'meadow_salsify'}, {'id': 18676, 'synset': 'scentless_camomile.n.01', 'name': 'scentless_camomile'}, {'id': 18677, 'synset': 'turfing_daisy.n.01', 'name': 'turfing_daisy'}, {'id': 18678, 'synset': 'coltsfoot.n.02', 'name': 'coltsfoot'}, {'id': 18679, 'synset': 'ursinia.n.01', 'name': 'ursinia'}, {'id': 18680, 'synset': 'crownbeard.n.01', 'name': 'crownbeard'}, {'id': 18681, 'synset': 'wingstem.n.01', 'name': 'wingstem'}, {'id': 18682, 'synset': 'cowpen_daisy.n.01', 'name': 'cowpen_daisy'}, {'id': 18683, 'synset': 'gravelweed.n.01', 'name': 'gravelweed'}, {'id': 18684, 'synset': 'virginia_crownbeard.n.01', 'name': 'Virginia_crownbeard'}, {'id': 18685, 'synset': 'ironweed.n.01', 'name': 'ironweed'}, {'id': 18686, 'synset': "mule's_ears.n.01", 'name': "mule's_ears"}, {'id': 18687, 'synset': "white-rayed_mule's_ears.n.01", 'name': "white-rayed_mule's_ears"}, {'id': 18688, 'synset': 'cocklebur.n.01', 'name': 'cocklebur'}, {'id': 18689, 'synset': 'xeranthemum.n.01', 'name': 'xeranthemum'}, {'id': 18690, 'synset': 'immortelle.n.01', 'name': 'immortelle'}, {'id': 18691, 'synset': 'zinnia.n.01', 'name': 'zinnia'}, {'id': 18692, 'synset': 'white_zinnia.n.01', 'name': 'white_zinnia'}, {'id': 18693, 'synset': 'little_golden_zinnia.n.01', 'name': 'little_golden_zinnia'}, {'id': 18694, 'synset': 'blazing_star.n.01', 'name': 'blazing_star'}, {'id': 18695, 'synset': 'bartonia.n.01', 'name': 'bartonia'}, {'id': 18696, 'synset': 'achene.n.01', 'name': 'achene'}, {'id': 18697, 'synset': 'samara.n.01', 'name': 'samara'}, {'id': 18698, 'synset': 'campanula.n.01', 'name': 'campanula'}, {'id': 18699, 'synset': 'creeping_bellflower.n.01', 'name': 'creeping_bellflower'}, {'id': 18700, 'synset': 'canterbury_bell.n.02', 'name': 'Canterbury_bell'}, {'id': 18701, 'synset': 'tall_bellflower.n.01', 'name': 'tall_bellflower'}, {'id': 18702, 'synset': 'marsh_bellflower.n.01', 'name': 'marsh_bellflower'}, {'id': 18703, 'synset': 'clustered_bellflower.n.01', 'name': 'clustered_bellflower'}, {'id': 18704, 'synset': 'peach_bells.n.01', 'name': 'peach_bells'}, {'id': 18705, 'synset': 'chimney_plant.n.01', 'name': 'chimney_plant'}, {'id': 18706, 'synset': 'rampion.n.01', 'name': 'rampion'}, {'id': 18707, 'synset': 'tussock_bellflower.n.01', 'name': 'tussock_bellflower'}, {'id': 18708, 'synset': 'orchid.n.01', 'name': 'orchid'}, {'id': 18709, 'synset': 'orchis.n.01', 'name': 'orchis'}, {'id': 18710, 'synset': 'male_orchis.n.01', 'name': 'male_orchis'}, {'id': 18711, 'synset': 'butterfly_orchid.n.05', 'name': 'butterfly_orchid'}, {'id': 18712, 'synset': 'showy_orchis.n.01', 'name': 'showy_orchis'}, {'id': 18713, 'synset': 'aerides.n.01', 'name': 'aerides'}, {'id': 18714, 'synset': 'angrecum.n.01', 'name': 'angrecum'}, {'id': 18715, 'synset': 'jewel_orchid.n.01', 'name': 'jewel_orchid'}, {'id': 18716, 'synset': 'puttyroot.n.01', 'name': 'puttyroot'}, {'id': 18717, 'synset': 'arethusa.n.01', 'name': 'arethusa'}, {'id': 18718, 'synset': 'bog_rose.n.01', 'name': 'bog_rose'}, {'id': 18719, 'synset': 'bletia.n.01', 'name': 'bletia'}, {'id': 18720, 'synset': 'bletilla_striata.n.01', 'name': 'Bletilla_striata'}, {'id': 18721, 'synset': 'brassavola.n.01', 'name': 'brassavola'}, {'id': 18722, 'synset': 'spider_orchid.n.03', 'name': 'spider_orchid'}, {'id': 18723, 'synset': 'spider_orchid.n.02', 'name': 'spider_orchid'}, {'id': 18724, 'synset': 'caladenia.n.01', 'name': 'caladenia'}, {'id': 18725, 'synset': 'calanthe.n.01', 'name': 'calanthe'}, {'id': 18726, 'synset': 'grass_pink.n.01', 'name': 'grass_pink'}, {'id': 18727, 'synset': 'calypso.n.01', 'name': 'calypso'}, {'id': 18728, 'synset': 'cattleya.n.01', 'name': 'cattleya'}, {'id': 18729, 'synset': 'helleborine.n.03', 'name': 'helleborine'}, {'id': 18730, 'synset': 'red_helleborine.n.01', 'name': 'red_helleborine'}, {'id': 18731, 'synset': 'spreading_pogonia.n.01', 'name': 'spreading_pogonia'}, {'id': 18732, 'synset': 'rosebud_orchid.n.01', 'name': 'rosebud_orchid'}, {'id': 18733, 'synset': 'satyr_orchid.n.01', 'name': 'satyr_orchid'}, {'id': 18734, 'synset': 'frog_orchid.n.02', 'name': 'frog_orchid'}, {'id': 18735, 'synset': 'coelogyne.n.01', 'name': 'coelogyne'}, {'id': 18736, 'synset': 'coral_root.n.01', 'name': 'coral_root'}, {'id': 18737, 'synset': 'spotted_coral_root.n.01', 'name': 'spotted_coral_root'}, {'id': 18738, 'synset': 'striped_coral_root.n.01', 'name': 'striped_coral_root'}, {'id': 18739, 'synset': 'early_coral_root.n.01', 'name': 'early_coral_root'}, {'id': 18740, 'synset': 'swan_orchid.n.01', 'name': 'swan_orchid'}, {'id': 18741, 'synset': 'cymbid.n.01', 'name': 'cymbid'}, {'id': 18742, 'synset': 'cypripedia.n.01', 'name': 'cypripedia'}, {'id': 18743, 'synset': "lady's_slipper.n.01", 'name': "lady's_slipper"}, {'id': 18744, 'synset': 'moccasin_flower.n.01', 'name': 'moccasin_flower'}, {'id': 18745, 'synset': "common_lady's-slipper.n.01", 'name': "common_lady's-slipper"}, {'id': 18746, 'synset': "ram's-head.n.01", 'name': "ram's-head"}, {'id': 18747, 'synset': "yellow_lady's_slipper.n.01", 'name': "yellow_lady's_slipper"}, {'id': 18748, 'synset': "large_yellow_lady's_slipper.n.01", 'name': "large_yellow_lady's_slipper"}, {'id': 18749, 'synset': "california_lady's_slipper.n.01", 'name': "California_lady's_slipper"}, {'id': 18750, 'synset': "clustered_lady's_slipper.n.01", 'name': "clustered_lady's_slipper"}, {'id': 18751, 'synset': "mountain_lady's_slipper.n.01", 'name': "mountain_lady's_slipper"}, {'id': 18752, 'synset': 'marsh_orchid.n.01', 'name': 'marsh_orchid'}, {'id': 18753, 'synset': 'common_spotted_orchid.n.01', 'name': 'common_spotted_orchid'}, {'id': 18754, 'synset': 'dendrobium.n.01', 'name': 'dendrobium'}, {'id': 18755, 'synset': 'disa.n.01', 'name': 'disa'}, {'id': 18756, 'synset': 'phantom_orchid.n.01', 'name': 'phantom_orchid'}, {'id': 18757, 'synset': 'tulip_orchid.n.01', 'name': 'tulip_orchid'}, {'id': 18758, 'synset': 'butterfly_orchid.n.04', 'name': 'butterfly_orchid'}, {'id': 18759, 'synset': 'butterfly_orchid.n.03', 'name': 'butterfly_orchid'}, {'id': 18760, 'synset': 'epidendron.n.01', 'name': 'epidendron'}, {'id': 18761, 'synset': 'helleborine.n.02', 'name': 'helleborine'}, {'id': 18762, 'synset': 'epipactis_helleborine.n.01', 'name': 'Epipactis_helleborine'}, {'id': 18763, 'synset': 'stream_orchid.n.01', 'name': 'stream_orchid'}, {'id': 18764, 'synset': 'tongueflower.n.01', 'name': 'tongueflower'}, {'id': 18765, 'synset': 'rattlesnake_plantain.n.01', 'name': 'rattlesnake_plantain'}, {'id': 18766, 'synset': 'fragrant_orchid.n.01', 'name': 'fragrant_orchid'}, {'id': 18767, 'synset': 'short-spurred_fragrant_orchid.n.01', 'name': 'short-spurred_fragrant_orchid'}, {'id': 18768, 'synset': 'fringed_orchis.n.01', 'name': 'fringed_orchis'}, {'id': 18769, 'synset': 'frog_orchid.n.01', 'name': 'frog_orchid'}, {'id': 18770, 'synset': 'rein_orchid.n.01', 'name': 'rein_orchid'}, {'id': 18771, 'synset': 'bog_rein_orchid.n.01', 'name': 'bog_rein_orchid'}, {'id': 18772, 'synset': 'white_fringed_orchis.n.01', 'name': 'white_fringed_orchis'}, {'id': 18773, 'synset': 'elegant_habenaria.n.01', 'name': 'elegant_Habenaria'}, {'id': 18774, 'synset': 'purple-fringed_orchid.n.02', 'name': 'purple-fringed_orchid'}, {'id': 18775, 'synset': 'coastal_rein_orchid.n.01', 'name': 'coastal_rein_orchid'}, {'id': 18776, 'synset': "hooker's_orchid.n.01", 'name': "Hooker's_orchid"}, {'id': 18777, 'synset': 'ragged_orchid.n.01', 'name': 'ragged_orchid'}, {'id': 18778, 'synset': 'prairie_orchid.n.01', 'name': 'prairie_orchid'}, {'id': 18779, 'synset': 'snowy_orchid.n.01', 'name': 'snowy_orchid'}, {'id': 18780, 'synset': 'round-leaved_rein_orchid.n.01', 'name': 'round-leaved_rein_orchid'}, {'id': 18781, 'synset': 'purple_fringeless_orchid.n.01', 'name': 'purple_fringeless_orchid'}, {'id': 18782, 'synset': 'purple-fringed_orchid.n.01', 'name': 'purple-fringed_orchid'}, {'id': 18783, 'synset': 'alaska_rein_orchid.n.01', 'name': 'Alaska_rein_orchid'}, {'id': 18784, 'synset': 'crested_coral_root.n.01', 'name': 'crested_coral_root'}, {'id': 18785, 'synset': 'texas_purple_spike.n.01', 'name': 'Texas_purple_spike'}, {'id': 18786, 'synset': 'lizard_orchid.n.01', 'name': 'lizard_orchid'}, {'id': 18787, 'synset': 'laelia.n.01', 'name': 'laelia'}, {'id': 18788, 'synset': 'liparis.n.01', 'name': 'liparis'}, {'id': 18789, 'synset': 'twayblade.n.02', 'name': 'twayblade'}, {'id': 18790, 'synset': 'fen_orchid.n.01', 'name': 'fen_orchid'}, {'id': 18791, 'synset': 'broad-leaved_twayblade.n.01', 'name': 'broad-leaved_twayblade'}, {'id': 18792, 'synset': 'lesser_twayblade.n.01', 'name': 'lesser_twayblade'}, {'id': 18793, 'synset': 'twayblade.n.01', 'name': 'twayblade'}, {'id': 18794, 'synset': "green_adder's_mouth.n.01", 'name': "green_adder's_mouth"}, {'id': 18795, 'synset': 'masdevallia.n.01', 'name': 'masdevallia'}, {'id': 18796, 'synset': 'maxillaria.n.01', 'name': 'maxillaria'}, {'id': 18797, 'synset': 'pansy_orchid.n.01', 'name': 'pansy_orchid'}, {'id': 18798, 'synset': 'odontoglossum.n.01', 'name': 'odontoglossum'}, {'id': 18799, 'synset': 'oncidium.n.01', 'name': 'oncidium'}, {'id': 18800, 'synset': 'bee_orchid.n.01', 'name': 'bee_orchid'}, {'id': 18801, 'synset': 'fly_orchid.n.02', 'name': 'fly_orchid'}, {'id': 18802, 'synset': 'spider_orchid.n.01', 'name': 'spider_orchid'}, {'id': 18803, 'synset': 'early_spider_orchid.n.01', 'name': 'early_spider_orchid'}, {'id': 18804, 'synset': "venus'_slipper.n.01", 'name': "Venus'_slipper"}, {'id': 18805, 'synset': 'phaius.n.01', 'name': 'phaius'}, {'id': 18806, 'synset': 'moth_orchid.n.01', 'name': 'moth_orchid'}, {'id': 18807, 'synset': 'butterfly_plant.n.01', 'name': 'butterfly_plant'}, {'id': 18808, 'synset': 'rattlesnake_orchid.n.01', 'name': 'rattlesnake_orchid'}, {'id': 18809, 'synset': 'lesser_butterfly_orchid.n.01', 'name': 'lesser_butterfly_orchid'}, {'id': 18810, 'synset': 'greater_butterfly_orchid.n.01', 'name': 'greater_butterfly_orchid'}, {'id': 18811, 'synset': 'prairie_white-fringed_orchid.n.01', 'name': 'prairie_white-fringed_orchid'}, {'id': 18812, 'synset': 'tangle_orchid.n.01', 'name': 'tangle_orchid'}, {'id': 18813, 'synset': 'indian_crocus.n.01', 'name': 'Indian_crocus'}, {'id': 18814, 'synset': 'pleurothallis.n.01', 'name': 'pleurothallis'}, {'id': 18815, 'synset': 'pogonia.n.01', 'name': 'pogonia'}, {'id': 18816, 'synset': 'butterfly_orchid.n.01', 'name': 'butterfly_orchid'}, {'id': 18817, 'synset': 'psychopsis_krameriana.n.01', 'name': 'Psychopsis_krameriana'}, {'id': 18818, 'synset': 'psychopsis_papilio.n.01', 'name': 'Psychopsis_papilio'}, {'id': 18819, 'synset': 'helmet_orchid.n.01', 'name': 'helmet_orchid'}, {'id': 18820, 'synset': 'foxtail_orchid.n.01', 'name': 'foxtail_orchid'}, {'id': 18821, 'synset': 'orange-blossom_orchid.n.01', 'name': 'orange-blossom_orchid'}, {'id': 18822, 'synset': 'sobralia.n.01', 'name': 'sobralia'}, {'id': 18823, 'synset': "ladies'_tresses.n.01", 'name': "ladies'_tresses"}, {'id': 18824, 'synset': 'screw_augur.n.01', 'name': 'screw_augur'}, {'id': 18825, 'synset': "hooded_ladies'_tresses.n.01", 'name': "hooded_ladies'_tresses"}, {'id': 18826, 'synset': "western_ladies'_tresses.n.01", 'name': "western_ladies'_tresses"}, {'id': 18827, 'synset': "european_ladies'_tresses.n.01", 'name': "European_ladies'_tresses"}, {'id': 18828, 'synset': 'stanhopea.n.01', 'name': 'stanhopea'}, {'id': 18829, 'synset': 'stelis.n.01', 'name': 'stelis'}, {'id': 18830, 'synset': 'fly_orchid.n.01', 'name': 'fly_orchid'}, {'id': 18831, 'synset': 'vanda.n.01', 'name': 'vanda'}, {'id': 18832, 'synset': 'blue_orchid.n.01', 'name': 'blue_orchid'}, {'id': 18833, 'synset': 'vanilla.n.01', 'name': 'vanilla'}, {'id': 18834, 'synset': 'vanilla_orchid.n.01', 'name': 'vanilla_orchid'}, {'id': 18835, 'synset': 'yam.n.02', 'name': 'yam'}, {'id': 18836, 'synset': 'yam.n.01', 'name': 'yam'}, {'id': 18837, 'synset': 'white_yam.n.01', 'name': 'white_yam'}, {'id': 18838, 'synset': 'cinnamon_vine.n.01', 'name': 'cinnamon_vine'}, {'id': 18839, 'synset': "elephant's-foot.n.01", 'name': "elephant's-foot"}, {'id': 18840, 'synset': 'wild_yam.n.01', 'name': 'wild_yam'}, {'id': 18841, 'synset': 'cush-cush.n.01', 'name': 'cush-cush'}, {'id': 18842, 'synset': 'black_bryony.n.01', 'name': 'black_bryony'}, {'id': 18843, 'synset': 'primrose.n.01', 'name': 'primrose'}, {'id': 18844, 'synset': 'english_primrose.n.01', 'name': 'English_primrose'}, {'id': 18845, 'synset': 'cowslip.n.01', 'name': 'cowslip'}, {'id': 18846, 'synset': 'oxlip.n.01', 'name': 'oxlip'}, {'id': 18847, 'synset': 'chinese_primrose.n.01', 'name': 'Chinese_primrose'}, {'id': 18848, 'synset': 'polyanthus.n.01', 'name': 'polyanthus'}, {'id': 18849, 'synset': 'pimpernel.n.02', 'name': 'pimpernel'}, {'id': 18850, 'synset': 'scarlet_pimpernel.n.01', 'name': 'scarlet_pimpernel'}, {'id': 18851, 'synset': 'bog_pimpernel.n.01', 'name': 'bog_pimpernel'}, {'id': 18852, 'synset': 'chaffweed.n.01', 'name': 'chaffweed'}, {'id': 18853, 'synset': 'cyclamen.n.01', 'name': 'cyclamen'}, {'id': 18854, 'synset': 'sowbread.n.01', 'name': 'sowbread'}, {'id': 18855, 'synset': 'sea_milkwort.n.01', 'name': 'sea_milkwort'}, {'id': 18856, 'synset': 'featherfoil.n.01', 'name': 'featherfoil'}, {'id': 18857, 'synset': 'water_gillyflower.n.01', 'name': 'water_gillyflower'}, {'id': 18858, 'synset': 'water_violet.n.01', 'name': 'water_violet'}, {'id': 18859, 'synset': 'loosestrife.n.02', 'name': 'loosestrife'}, {'id': 18860, 'synset': 'gooseneck_loosestrife.n.01', 'name': 'gooseneck_loosestrife'}, {'id': 18861, 'synset': 'yellow_pimpernel.n.01', 'name': 'yellow_pimpernel'}, {'id': 18862, 'synset': 'fringed_loosestrife.n.01', 'name': 'fringed_loosestrife'}, {'id': 18863, 'synset': 'moneywort.n.01', 'name': 'moneywort'}, {'id': 18864, 'synset': 'swamp_candles.n.01', 'name': 'swamp_candles'}, {'id': 18865, 'synset': 'whorled_loosestrife.n.01', 'name': 'whorled_loosestrife'}, {'id': 18866, 'synset': 'water_pimpernel.n.01', 'name': 'water_pimpernel'}, {'id': 18867, 'synset': 'brookweed.n.02', 'name': 'brookweed'}, {'id': 18868, 'synset': 'brookweed.n.01', 'name': 'brookweed'}, {'id': 18869, 'synset': 'coralberry.n.02', 'name': 'coralberry'}, {'id': 18870, 'synset': 'marlberry.n.01', 'name': 'marlberry'}, {'id': 18871, 'synset': 'plumbago.n.02', 'name': 'plumbago'}, {'id': 18872, 'synset': 'leadwort.n.01', 'name': 'leadwort'}, {'id': 18873, 'synset': 'thrift.n.01', 'name': 'thrift'}, {'id': 18874, 'synset': 'sea_lavender.n.01', 'name': 'sea_lavender'}, {'id': 18875, 'synset': 'barbasco.n.01', 'name': 'barbasco'}, {'id': 18876, 'synset': 'gramineous_plant.n.01', 'name': 'gramineous_plant'}, {'id': 18877, 'synset': 'grass.n.01', 'name': 'grass'}, {'id': 18878, 'synset': 'midgrass.n.01', 'name': 'midgrass'}, {'id': 18879, 'synset': 'shortgrass.n.01', 'name': 'shortgrass'}, {'id': 18880, 'synset': 'sword_grass.n.01', 'name': 'sword_grass'}, {'id': 18881, 'synset': 'tallgrass.n.01', 'name': 'tallgrass'}, {'id': 18882, 'synset': 'herbage.n.01', 'name': 'herbage'}, {'id': 18883, 'synset': 'goat_grass.n.01', 'name': 'goat_grass'}, {'id': 18884, 'synset': 'wheatgrass.n.01', 'name': 'wheatgrass'}, {'id': 18885, 'synset': 'crested_wheatgrass.n.01', 'name': 'crested_wheatgrass'}, {'id': 18886, 'synset': 'bearded_wheatgrass.n.01', 'name': 'bearded_wheatgrass'}, {'id': 18887, 'synset': 'western_wheatgrass.n.01', 'name': 'western_wheatgrass'}, {'id': 18888, 'synset': 'intermediate_wheatgrass.n.01', 'name': 'intermediate_wheatgrass'}, {'id': 18889, 'synset': 'slender_wheatgrass.n.01', 'name': 'slender_wheatgrass'}, {'id': 18890, 'synset': 'velvet_bent.n.01', 'name': 'velvet_bent'}, {'id': 18891, 'synset': 'cloud_grass.n.01', 'name': 'cloud_grass'}, {'id': 18892, 'synset': 'meadow_foxtail.n.01', 'name': 'meadow_foxtail'}, {'id': 18893, 'synset': 'foxtail.n.01', 'name': 'foxtail'}, {'id': 18894, 'synset': 'broom_grass.n.01', 'name': 'broom_grass'}, {'id': 18895, 'synset': 'broom_sedge.n.01', 'name': 'broom_sedge'}, {'id': 18896, 'synset': 'tall_oat_grass.n.01', 'name': 'tall_oat_grass'}, {'id': 18897, 'synset': 'toetoe.n.02', 'name': 'toetoe'}, {'id': 18898, 'synset': 'oat.n.01', 'name': 'oat'}, {'id': 18899, 'synset': 'cereal_oat.n.01', 'name': 'cereal_oat'}, {'id': 18900, 'synset': 'wild_oat.n.01', 'name': 'wild_oat'}, {'id': 18901, 'synset': 'slender_wild_oat.n.01', 'name': 'slender_wild_oat'}, {'id': 18902, 'synset': 'wild_red_oat.n.01', 'name': 'wild_red_oat'}, {'id': 18903, 'synset': 'brome.n.01', 'name': 'brome'}, {'id': 18904, 'synset': 'chess.n.01', 'name': 'chess'}, {'id': 18905, 'synset': 'field_brome.n.01', 'name': 'field_brome'}, {'id': 18906, 'synset': 'grama.n.01', 'name': 'grama'}, {'id': 18907, 'synset': 'black_grama.n.01', 'name': 'black_grama'}, {'id': 18908, 'synset': 'buffalo_grass.n.02', 'name': 'buffalo_grass'}, {'id': 18909, 'synset': 'reed_grass.n.01', 'name': 'reed_grass'}, {'id': 18910, 'synset': 'feather_reed_grass.n.01', 'name': 'feather_reed_grass'}, {'id': 18911, 'synset': 'australian_reed_grass.n.01', 'name': 'Australian_reed_grass'}, {'id': 18912, 'synset': 'burgrass.n.01', 'name': 'burgrass'}, {'id': 18913, 'synset': 'buffel_grass.n.01', 'name': 'buffel_grass'}, {'id': 18914, 'synset': 'rhodes_grass.n.01', 'name': 'Rhodes_grass'}, {'id': 18915, 'synset': 'pampas_grass.n.01', 'name': 'pampas_grass'}, {'id': 18916, 'synset': 'giant_star_grass.n.01', 'name': 'giant_star_grass'}, {'id': 18917, 'synset': 'orchard_grass.n.01', 'name': 'orchard_grass'}, {'id': 18918, 'synset': 'egyptian_grass.n.01', 'name': 'Egyptian_grass'}, {'id': 18919, 'synset': 'crabgrass.n.01', 'name': 'crabgrass'}, {'id': 18920, 'synset': 'smooth_crabgrass.n.01', 'name': 'smooth_crabgrass'}, {'id': 18921, 'synset': 'large_crabgrass.n.01', 'name': 'large_crabgrass'}, {'id': 18922, 'synset': 'barnyard_grass.n.01', 'name': 'barnyard_grass'}, {'id': 18923, 'synset': 'japanese_millet.n.01', 'name': 'Japanese_millet'}, {'id': 18924, 'synset': 'yardgrass.n.01', 'name': 'yardgrass'}, {'id': 18925, 'synset': 'finger_millet.n.01', 'name': 'finger_millet'}, {'id': 18926, 'synset': 'lyme_grass.n.01', 'name': 'lyme_grass'}, {'id': 18927, 'synset': 'wild_rye.n.01', 'name': 'wild_rye'}, {'id': 18928, 'synset': 'giant_ryegrass.n.01', 'name': 'giant_ryegrass'}, {'id': 18929, 'synset': 'sea_lyme_grass.n.01', 'name': 'sea_lyme_grass'}, {'id': 18930, 'synset': 'canada_wild_rye.n.01', 'name': 'Canada_wild_rye'}, {'id': 18931, 'synset': 'teff.n.01', 'name': 'teff'}, {'id': 18932, 'synset': 'weeping_love_grass.n.01', 'name': 'weeping_love_grass'}, {'id': 18933, 'synset': 'plume_grass.n.01', 'name': 'plume_grass'}, {'id': 18934, 'synset': 'ravenna_grass.n.01', 'name': 'Ravenna_grass'}, {'id': 18935, 'synset': 'fescue.n.01', 'name': 'fescue'}, {'id': 18936, 'synset': 'reed_meadow_grass.n.01', 'name': 'reed_meadow_grass'}, {'id': 18937, 'synset': 'velvet_grass.n.01', 'name': 'velvet_grass'}, {'id': 18938, 'synset': 'creeping_soft_grass.n.01', 'name': 'creeping_soft_grass'}, {'id': 18939, 'synset': 'barleycorn.n.01', 'name': 'barleycorn'}, {'id': 18940, 'synset': 'barley_grass.n.01', 'name': 'barley_grass'}, {'id': 18941, 'synset': 'little_barley.n.01', 'name': 'little_barley'}, {'id': 18942, 'synset': 'rye_grass.n.01', 'name': 'rye_grass'}, {'id': 18943, 'synset': 'perennial_ryegrass.n.01', 'name': 'perennial_ryegrass'}, {'id': 18944, 'synset': 'italian_ryegrass.n.01', 'name': 'Italian_ryegrass'}, {'id': 18945, 'synset': 'darnel.n.01', 'name': 'darnel'}, {'id': 18946, 'synset': 'nimblewill.n.01', 'name': 'nimblewill'}, {'id': 18947, 'synset': 'cultivated_rice.n.01', 'name': 'cultivated_rice'}, {'id': 18948, 'synset': 'ricegrass.n.01', 'name': 'ricegrass'}, {'id': 18949, 'synset': 'smilo.n.01', 'name': 'smilo'}, {'id': 18950, 'synset': 'switch_grass.n.01', 'name': 'switch_grass'}, {'id': 18951, 'synset': 'broomcorn_millet.n.01', 'name': 'broomcorn_millet'}, {'id': 18952, 'synset': 'goose_grass.n.03', 'name': 'goose_grass'}, {'id': 18953, 'synset': 'dallisgrass.n.01', 'name': 'dallisgrass'}, {'id': 18954, 'synset': 'bahia_grass.n.01', 'name': 'Bahia_grass'}, {'id': 18955, 'synset': 'knotgrass.n.01', 'name': 'knotgrass'}, {'id': 18956, 'synset': 'fountain_grass.n.01', 'name': 'fountain_grass'}, {'id': 18957, 'synset': 'reed_canary_grass.n.01', 'name': 'reed_canary_grass'}, {'id': 18958, 'synset': 'canary_grass.n.01', 'name': 'canary_grass'}, {'id': 18959, 'synset': 'timothy.n.01', 'name': 'timothy'}, {'id': 18960, 'synset': 'bluegrass.n.01', 'name': 'bluegrass'}, {'id': 18961, 'synset': 'meadowgrass.n.01', 'name': 'meadowgrass'}, {'id': 18962, 'synset': 'wood_meadowgrass.n.01', 'name': 'wood_meadowgrass'}, {'id': 18963, 'synset': 'noble_cane.n.01', 'name': 'noble_cane'}, {'id': 18964, 'synset': 'munj.n.01', 'name': 'munj'}, {'id': 18965, 'synset': 'broom_beard_grass.n.01', 'name': 'broom_beard_grass'}, {'id': 18966, 'synset': 'bluestem.n.01', 'name': 'bluestem'}, {'id': 18967, 'synset': 'rye.n.02', 'name': 'rye'}, {'id': 18968, 'synset': 'bristlegrass.n.01', 'name': 'bristlegrass'}, {'id': 18969, 'synset': 'giant_foxtail.n.01', 'name': 'giant_foxtail'}, {'id': 18970, 'synset': 'yellow_bristlegrass.n.01', 'name': 'yellow_bristlegrass'}, {'id': 18971, 'synset': 'green_bristlegrass.n.01', 'name': 'green_bristlegrass'}, {'id': 18972, 'synset': 'siberian_millet.n.01', 'name': 'Siberian_millet'}, {'id': 18973, 'synset': 'german_millet.n.01', 'name': 'German_millet'}, {'id': 18974, 'synset': 'millet.n.01', 'name': 'millet'}, {'id': 18975, 'synset': 'rattan.n.02', 'name': 'rattan'}, {'id': 18976, 'synset': 'malacca.n.01', 'name': 'malacca'}, {'id': 18977, 'synset': 'reed.n.01', 'name': 'reed'}, {'id': 18978, 'synset': 'sorghum.n.01', 'name': 'sorghum'}, {'id': 18979, 'synset': 'grain_sorghum.n.01', 'name': 'grain_sorghum'}, {'id': 18980, 'synset': 'durra.n.01', 'name': 'durra'}, {'id': 18981, 'synset': 'feterita.n.01', 'name': 'feterita'}, {'id': 18982, 'synset': 'hegari.n.01', 'name': 'hegari'}, {'id': 18983, 'synset': 'kaoliang.n.01', 'name': 'kaoliang'}, {'id': 18984, 'synset': 'milo.n.01', 'name': 'milo'}, {'id': 18985, 'synset': 'shallu.n.01', 'name': 'shallu'}, {'id': 18986, 'synset': 'broomcorn.n.01', 'name': 'broomcorn'}, {'id': 18987, 'synset': 'cordgrass.n.01', 'name': 'cordgrass'}, {'id': 18988, 'synset': 'salt_reed_grass.n.01', 'name': 'salt_reed_grass'}, {'id': 18989, 'synset': 'prairie_cordgrass.n.01', 'name': 'prairie_cordgrass'}, {'id': 18990, 'synset': 'smut_grass.n.01', 'name': 'smut_grass'}, {'id': 18991, 'synset': 'sand_dropseed.n.01', 'name': 'sand_dropseed'}, {'id': 18992, 'synset': 'rush_grass.n.01', 'name': 'rush_grass'}, {'id': 18993, 'synset': 'st._augustine_grass.n.01', 'name': 'St._Augustine_grass'}, {'id': 18994, 'synset': 'grain.n.08', 'name': 'grain'}, {'id': 18995, 'synset': 'cereal.n.01', 'name': 'cereal'}, {'id': 18996, 'synset': 'wheat.n.01', 'name': 'wheat'}, {'id': 18997, 'synset': 'wheat_berry.n.01', 'name': 'wheat_berry'}, {'id': 18998, 'synset': 'durum.n.01', 'name': 'durum'}, {'id': 18999, 'synset': 'spelt.n.01', 'name': 'spelt'}, {'id': 19000, 'synset': 'emmer.n.01', 'name': 'emmer'}, {'id': 19001, 'synset': 'wild_wheat.n.01', 'name': 'wild_wheat'}, {'id': 19002, 'synset': 'corn.n.01', 'name': 'corn'}, {'id': 19003, 'synset': 'mealie.n.01', 'name': 'mealie'}, {'id': 19004, 'synset': 'corn.n.02', 'name': 'corn'}, {'id': 19005, 'synset': 'dent_corn.n.01', 'name': 'dent_corn'}, {'id': 19006, 'synset': 'flint_corn.n.01', 'name': 'flint_corn'}, {'id': 19007, 'synset': 'popcorn.n.01', 'name': 'popcorn'}, {'id': 19008, 'synset': 'zoysia.n.01', 'name': 'zoysia'}, {'id': 19009, 'synset': 'manila_grass.n.01', 'name': 'Manila_grass'}, {'id': 19010, 'synset': 'korean_lawn_grass.n.01', 'name': 'Korean_lawn_grass'}, {'id': 19011, 'synset': 'common_bamboo.n.01', 'name': 'common_bamboo'}, {'id': 19012, 'synset': 'giant_bamboo.n.01', 'name': 'giant_bamboo'}, {'id': 19013, 'synset': 'umbrella_plant.n.03', 'name': 'umbrella_plant'}, {'id': 19014, 'synset': 'chufa.n.01', 'name': 'chufa'}, {'id': 19015, 'synset': 'galingale.n.01', 'name': 'galingale'}, {'id': 19016, 'synset': 'nutgrass.n.01', 'name': 'nutgrass'}, {'id': 19017, 'synset': 'sand_sedge.n.01', 'name': 'sand_sedge'}, {'id': 19018, 'synset': 'cypress_sedge.n.01', 'name': 'cypress_sedge'}, {'id': 19019, 'synset': 'cotton_grass.n.01', 'name': 'cotton_grass'}, {'id': 19020, 'synset': 'common_cotton_grass.n.01', 'name': 'common_cotton_grass'}, {'id': 19021, 'synset': 'hardstem_bulrush.n.01', 'name': 'hardstem_bulrush'}, {'id': 19022, 'synset': 'wool_grass.n.01', 'name': 'wool_grass'}, {'id': 19023, 'synset': 'spike_rush.n.01', 'name': 'spike_rush'}, {'id': 19024, 'synset': 'water_chestnut.n.02', 'name': 'water_chestnut'}, {'id': 19025, 'synset': 'needle_spike_rush.n.01', 'name': 'needle_spike_rush'}, {'id': 19026, 'synset': 'creeping_spike_rush.n.01', 'name': 'creeping_spike_rush'}, {'id': 19027, 'synset': 'pandanus.n.02', 'name': 'pandanus'}, {'id': 19028, 'synset': 'textile_screw_pine.n.01', 'name': 'textile_screw_pine'}, {'id': 19029, 'synset': 'cattail.n.01', 'name': 'cattail'}, {'id': 19030, 'synset': "cat's-tail.n.01", 'name': "cat's-tail"}, {'id': 19031, 'synset': 'bur_reed.n.01', 'name': 'bur_reed'}, {'id': 19032, 'synset': 'grain.n.07', 'name': 'grain'}, {'id': 19033, 'synset': 'kernel.n.02', 'name': 'kernel'}, {'id': 19034, 'synset': 'rye.n.01', 'name': 'rye'}, {'id': 19035, 'synset': 'gourd.n.03', 'name': 'gourd'}, {'id': 19036, 'synset': 'pumpkin.n.01', 'name': 'pumpkin'}, {'id': 19037, 'synset': 'squash.n.01', 'name': 'squash'}, {'id': 19038, 'synset': 'summer_squash.n.01', 'name': 'summer_squash'}, {'id': 19039, 'synset': 'yellow_squash.n.01', 'name': 'yellow_squash'}, {'id': 19040, 'synset': 'marrow.n.02', 'name': 'marrow'}, {'id': 19041, 'synset': 'zucchini.n.01', 'name': 'zucchini'}, {'id': 19042, 'synset': 'cocozelle.n.01', 'name': 'cocozelle'}, {'id': 19043, 'synset': 'cymling.n.01', 'name': 'cymling'}, {'id': 19044, 'synset': 'spaghetti_squash.n.01', 'name': 'spaghetti_squash'}, {'id': 19045, 'synset': 'winter_squash.n.01', 'name': 'winter_squash'}, {'id': 19046, 'synset': 'acorn_squash.n.01', 'name': 'acorn_squash'}, {'id': 19047, 'synset': 'hubbard_squash.n.01', 'name': 'hubbard_squash'}, {'id': 19048, 'synset': 'turban_squash.n.01', 'name': 'turban_squash'}, {'id': 19049, 'synset': 'buttercup_squash.n.01', 'name': 'buttercup_squash'}, {'id': 19050, 'synset': 'butternut_squash.n.01', 'name': 'butternut_squash'}, {'id': 19051, 'synset': 'winter_crookneck.n.01', 'name': 'winter_crookneck'}, {'id': 19052, 'synset': 'cushaw.n.01', 'name': 'cushaw'}, {'id': 19053, 'synset': 'prairie_gourd.n.02', 'name': 'prairie_gourd'}, {'id': 19054, 'synset': 'prairie_gourd.n.01', 'name': 'prairie_gourd'}, {'id': 19055, 'synset': 'bryony.n.01', 'name': 'bryony'}, {'id': 19056, 'synset': 'white_bryony.n.01', 'name': 'white_bryony'}, {'id': 19057, 'synset': 'sweet_melon.n.01', 'name': 'sweet_melon'}, {'id': 19058, 'synset': 'cantaloupe.n.01', 'name': 'cantaloupe'}, {'id': 19059, 'synset': 'winter_melon.n.01', 'name': 'winter_melon'}, {'id': 19060, 'synset': 'net_melon.n.01', 'name': 'net_melon'}, {'id': 19061, 'synset': 'cucumber.n.01', 'name': 'cucumber'}, {'id': 19062, 'synset': 'squirting_cucumber.n.01', 'name': 'squirting_cucumber'}, {'id': 19063, 'synset': 'bottle_gourd.n.01', 'name': 'bottle_gourd'}, {'id': 19064, 'synset': 'luffa.n.02', 'name': 'luffa'}, {'id': 19065, 'synset': 'loofah.n.02', 'name': 'loofah'}, {'id': 19066, 'synset': 'angled_loofah.n.01', 'name': 'angled_loofah'}, {'id': 19067, 'synset': 'loofa.n.01', 'name': 'loofa'}, {'id': 19068, 'synset': 'balsam_apple.n.01', 'name': 'balsam_apple'}, {'id': 19069, 'synset': 'balsam_pear.n.01', 'name': 'balsam_pear'}, {'id': 19070, 'synset': 'lobelia.n.01', 'name': 'lobelia'}, {'id': 19071, 'synset': 'water_lobelia.n.01', 'name': 'water_lobelia'}, {'id': 19072, 'synset': 'mallow.n.01', 'name': 'mallow'}, {'id': 19073, 'synset': 'musk_mallow.n.02', 'name': 'musk_mallow'}, {'id': 19074, 'synset': 'common_mallow.n.01', 'name': 'common_mallow'}, {'id': 19075, 'synset': 'okra.n.02', 'name': 'okra'}, {'id': 19076, 'synset': 'okra.n.01', 'name': 'okra'}, {'id': 19077, 'synset': 'abelmosk.n.01', 'name': 'abelmosk'}, {'id': 19078, 'synset': 'flowering_maple.n.01', 'name': 'flowering_maple'}, {'id': 19079, 'synset': 'velvetleaf.n.02', 'name': 'velvetleaf'}, {'id': 19080, 'synset': 'hollyhock.n.02', 'name': 'hollyhock'}, {'id': 19081, 'synset': 'rose_mallow.n.02', 'name': 'rose_mallow'}, {'id': 19082, 'synset': 'althea.n.01', 'name': 'althea'}, {'id': 19083, 'synset': 'marsh_mallow.n.01', 'name': 'marsh_mallow'}, {'id': 19084, 'synset': 'poppy_mallow.n.01', 'name': 'poppy_mallow'}, {'id': 19085, 'synset': 'fringed_poppy_mallow.n.01', 'name': 'fringed_poppy_mallow'}, {'id': 19086, 'synset': 'purple_poppy_mallow.n.01', 'name': 'purple_poppy_mallow'}, {'id': 19087, 'synset': 'clustered_poppy_mallow.n.01', 'name': 'clustered_poppy_mallow'}, {'id': 19088, 'synset': 'sea_island_cotton.n.01', 'name': 'sea_island_cotton'}, {'id': 19089, 'synset': 'levant_cotton.n.01', 'name': 'Levant_cotton'}, {'id': 19090, 'synset': 'upland_cotton.n.01', 'name': 'upland_cotton'}, {'id': 19091, 'synset': 'peruvian_cotton.n.01', 'name': 'Peruvian_cotton'}, {'id': 19092, 'synset': 'wild_cotton.n.01', 'name': 'wild_cotton'}, {'id': 19093, 'synset': 'kenaf.n.02', 'name': 'kenaf'}, {'id': 19094, 'synset': 'sorrel_tree.n.02', 'name': 'sorrel_tree'}, {'id': 19095, 'synset': 'rose_mallow.n.01', 'name': 'rose_mallow'}, {'id': 19096, 'synset': 'cotton_rose.n.01', 'name': 'cotton_rose'}, {'id': 19097, 'synset': 'roselle.n.01', 'name': 'roselle'}, {'id': 19098, 'synset': 'mahoe.n.01', 'name': 'mahoe'}, {'id': 19099, 'synset': 'flower-of-an-hour.n.01', 'name': 'flower-of-an-hour'}, {'id': 19100, 'synset': 'lacebark.n.01', 'name': 'lacebark'}, {'id': 19101, 'synset': 'wild_hollyhock.n.02', 'name': 'wild_hollyhock'}, {'id': 19102, 'synset': 'mountain_hollyhock.n.01', 'name': 'mountain_hollyhock'}, {'id': 19103, 'synset': 'seashore_mallow.n.01', 'name': 'seashore_mallow'}, {'id': 19104, 'synset': 'salt_marsh_mallow.n.01', 'name': 'salt_marsh_mallow'}, {'id': 19105, 'synset': 'chaparral_mallow.n.01', 'name': 'chaparral_mallow'}, {'id': 19106, 'synset': 'malope.n.01', 'name': 'malope'}, {'id': 19107, 'synset': 'false_mallow.n.02', 'name': 'false_mallow'}, {'id': 19108, 'synset': 'waxmallow.n.01', 'name': 'waxmallow'}, {'id': 19109, 'synset': 'glade_mallow.n.01', 'name': 'glade_mallow'}, {'id': 19110, 'synset': 'pavonia.n.01', 'name': 'pavonia'}, {'id': 19111, 'synset': 'ribbon_tree.n.01', 'name': 'ribbon_tree'}, {'id': 19112, 'synset': 'bush_hibiscus.n.01', 'name': 'bush_hibiscus'}, {'id': 19113, 'synset': 'virginia_mallow.n.01', 'name': 'Virginia_mallow'}, {'id': 19114, 'synset': 'queensland_hemp.n.01', 'name': 'Queensland_hemp'}, {'id': 19115, 'synset': 'indian_mallow.n.01', 'name': 'Indian_mallow'}, {'id': 19116, 'synset': 'checkerbloom.n.01', 'name': 'checkerbloom'}, {'id': 19117, 'synset': 'globe_mallow.n.01', 'name': 'globe_mallow'}, {'id': 19118, 'synset': 'prairie_mallow.n.01', 'name': 'prairie_mallow'}, {'id': 19119, 'synset': 'tulipwood_tree.n.01', 'name': 'tulipwood_tree'}, {'id': 19120, 'synset': 'portia_tree.n.01', 'name': 'portia_tree'}, {'id': 19121, 'synset': 'red_silk-cotton_tree.n.01', 'name': 'red_silk-cotton_tree'}, {'id': 19122, 'synset': 'cream-of-tartar_tree.n.01', 'name': 'cream-of-tartar_tree'}, {'id': 19123, 'synset': 'baobab.n.01', 'name': 'baobab'}, {'id': 19124, 'synset': 'kapok.n.02', 'name': 'kapok'}, {'id': 19125, 'synset': 'durian.n.01', 'name': 'durian'}, {'id': 19126, 'synset': 'montezuma.n.01', 'name': 'Montezuma'}, {'id': 19127, 'synset': 'shaving-brush_tree.n.01', 'name': 'shaving-brush_tree'}, {'id': 19128, 'synset': 'quandong.n.03', 'name': 'quandong'}, {'id': 19129, 'synset': 'quandong.n.02', 'name': 'quandong'}, {'id': 19130, 'synset': 'makomako.n.01', 'name': 'makomako'}, {'id': 19131, 'synset': 'jamaican_cherry.n.01', 'name': 'Jamaican_cherry'}, {'id': 19132, 'synset': 'breakax.n.01', 'name': 'breakax'}, {'id': 19133, 'synset': 'sterculia.n.01', 'name': 'sterculia'}, {'id': 19134, 'synset': 'panama_tree.n.01', 'name': 'Panama_tree'}, {'id': 19135, 'synset': 'kalumpang.n.01', 'name': 'kalumpang'}, {'id': 19136, 'synset': 'bottle-tree.n.01', 'name': 'bottle-tree'}, {'id': 19137, 'synset': 'flame_tree.n.04', 'name': 'flame_tree'}, {'id': 19138, 'synset': 'flame_tree.n.03', 'name': 'flame_tree'}, {'id': 19139, 'synset': 'kurrajong.n.01', 'name': 'kurrajong'}, {'id': 19140, 'synset': 'queensland_bottletree.n.01', 'name': 'Queensland_bottletree'}, {'id': 19141, 'synset': 'kola.n.01', 'name': 'kola'}, {'id': 19142, 'synset': 'kola_nut.n.01', 'name': 'kola_nut'}, {'id': 19143, 'synset': 'chinese_parasol_tree.n.01', 'name': 'Chinese_parasol_tree'}, {'id': 19144, 'synset': 'flannelbush.n.01', 'name': 'flannelbush'}, {'id': 19145, 'synset': 'screw_tree.n.01', 'name': 'screw_tree'}, {'id': 19146, 'synset': 'nut-leaved_screw_tree.n.01', 'name': 'nut-leaved_screw_tree'}, {'id': 19147, 'synset': 'red_beech.n.02', 'name': 'red_beech'}, {'id': 19148, 'synset': 'looking_glass_tree.n.01', 'name': 'looking_glass_tree'}, {'id': 19149, 'synset': 'looking-glass_plant.n.01', 'name': 'looking-glass_plant'}, {'id': 19150, 'synset': 'honey_bell.n.01', 'name': 'honey_bell'}, {'id': 19151, 'synset': 'mayeng.n.01', 'name': 'mayeng'}, {'id': 19152, 'synset': 'silver_tree.n.02', 'name': 'silver_tree'}, {'id': 19153, 'synset': 'cacao.n.01', 'name': 'cacao'}, {'id': 19154, 'synset': 'obeche.n.02', 'name': 'obeche'}, {'id': 19155, 'synset': 'linden.n.02', 'name': 'linden'}, {'id': 19156, 'synset': 'american_basswood.n.01', 'name': 'American_basswood'}, {'id': 19157, 'synset': 'small-leaved_linden.n.01', 'name': 'small-leaved_linden'}, {'id': 19158, 'synset': 'white_basswood.n.01', 'name': 'white_basswood'}, {'id': 19159, 'synset': 'japanese_linden.n.01', 'name': 'Japanese_linden'}, {'id': 19160, 'synset': 'silver_lime.n.01', 'name': 'silver_lime'}, {'id': 19161, 'synset': 'corchorus.n.01', 'name': 'corchorus'}, {'id': 19162, 'synset': 'african_hemp.n.02', 'name': 'African_hemp'}, {'id': 19163, 'synset': 'herb.n.01', 'name': 'herb'}, {'id': 19164, 'synset': 'protea.n.01', 'name': 'protea'}, {'id': 19165, 'synset': 'honeypot.n.01', 'name': 'honeypot'}, {'id': 19166, 'synset': 'honeyflower.n.02', 'name': 'honeyflower'}, {'id': 19167, 'synset': 'banksia.n.01', 'name': 'banksia'}, {'id': 19168, 'synset': 'honeysuckle.n.02', 'name': 'honeysuckle'}, {'id': 19169, 'synset': 'smoke_bush.n.02', 'name': 'smoke_bush'}, {'id': 19170, 'synset': 'chilean_firebush.n.01', 'name': 'Chilean_firebush'}, {'id': 19171, 'synset': 'chilean_nut.n.01', 'name': 'Chilean_nut'}, {'id': 19172, 'synset': 'grevillea.n.01', 'name': 'grevillea'}, {'id': 19173, 'synset': 'red-flowered_silky_oak.n.01', 'name': 'red-flowered_silky_oak'}, {'id': 19174, 'synset': 'silky_oak.n.01', 'name': 'silky_oak'}, {'id': 19175, 'synset': 'beefwood.n.05', 'name': 'beefwood'}, {'id': 19176, 'synset': 'cushion_flower.n.01', 'name': 'cushion_flower'}, {'id': 19177, 'synset': 'rewa-rewa.n.01', 'name': 'rewa-rewa'}, {'id': 19178, 'synset': 'honeyflower.n.01', 'name': 'honeyflower'}, {'id': 19179, 'synset': 'silver_tree.n.01', 'name': 'silver_tree'}, {'id': 19180, 'synset': 'lomatia.n.01', 'name': 'lomatia'}, {'id': 19181, 'synset': 'macadamia.n.01', 'name': 'macadamia'}, {'id': 19182, 'synset': 'macadamia_integrifolia.n.01', 'name': 'Macadamia_integrifolia'}, {'id': 19183, 'synset': 'macadamia_nut.n.01', 'name': 'macadamia_nut'}, {'id': 19184, 'synset': 'queensland_nut.n.01', 'name': 'Queensland_nut'}, {'id': 19185, 'synset': 'prickly_ash.n.02', 'name': 'prickly_ash'}, {'id': 19186, 'synset': 'geebung.n.01', 'name': 'geebung'}, {'id': 19187, 'synset': 'wheel_tree.n.01', 'name': 'wheel_tree'}, {'id': 19188, 'synset': 'scrub_beefwood.n.01', 'name': 'scrub_beefwood'}, {'id': 19189, 'synset': 'waratah.n.02', 'name': 'waratah'}, {'id': 19190, 'synset': 'waratah.n.01', 'name': 'waratah'}, {'id': 19191, 'synset': 'casuarina.n.01', 'name': 'casuarina'}, {'id': 19192, 'synset': 'she-oak.n.01', 'name': 'she-oak'}, {'id': 19193, 'synset': 'beefwood.n.03', 'name': 'beefwood'}, {'id': 19194, 'synset': 'australian_pine.n.01', 'name': 'Australian_pine'}, {'id': 19195, 'synset': 'heath.n.01', 'name': 'heath'}, {'id': 19196, 'synset': 'tree_heath.n.02', 'name': 'tree_heath'}, {'id': 19197, 'synset': 'briarroot.n.01', 'name': 'briarroot'}, {'id': 19198, 'synset': 'winter_heath.n.01', 'name': 'winter_heath'}, {'id': 19199, 'synset': 'bell_heather.n.02', 'name': 'bell_heather'}, {'id': 19200, 'synset': 'cornish_heath.n.01', 'name': 'Cornish_heath'}, {'id': 19201, 'synset': 'spanish_heath.n.01', 'name': 'Spanish_heath'}, {'id': 19202, 'synset': "prince-of-wales'-heath.n.01", 'name': "Prince-of-Wales'-heath"}, {'id': 19203, 'synset': 'bog_rosemary.n.01', 'name': 'bog_rosemary'}, {'id': 19204, 'synset': 'marsh_andromeda.n.01', 'name': 'marsh_andromeda'}, {'id': 19205, 'synset': 'madrona.n.01', 'name': 'madrona'}, {'id': 19206, 'synset': 'strawberry_tree.n.01', 'name': 'strawberry_tree'}, {'id': 19207, 'synset': 'bearberry.n.03', 'name': 'bearberry'}, {'id': 19208, 'synset': 'alpine_bearberry.n.01', 'name': 'alpine_bearberry'}, {'id': 19209, 'synset': 'heartleaf_manzanita.n.01', 'name': 'heartleaf_manzanita'}, {'id': 19210, 'synset': 'parry_manzanita.n.01', 'name': 'Parry_manzanita'}, {'id': 19211, 'synset': 'spike_heath.n.01', 'name': 'spike_heath'}, {'id': 19212, 'synset': 'bryanthus.n.01', 'name': 'bryanthus'}, {'id': 19213, 'synset': 'leatherleaf.n.02', 'name': 'leatherleaf'}, {'id': 19214, 'synset': 'connemara_heath.n.01', 'name': 'Connemara_heath'}, {'id': 19215, 'synset': 'trailing_arbutus.n.01', 'name': 'trailing_arbutus'}, {'id': 19216, 'synset': 'creeping_snowberry.n.01', 'name': 'creeping_snowberry'}, {'id': 19217, 'synset': 'salal.n.01', 'name': 'salal'}, {'id': 19218, 'synset': 'huckleberry.n.02', 'name': 'huckleberry'}, {'id': 19219, 'synset': 'black_huckleberry.n.01', 'name': 'black_huckleberry'}, {'id': 19220, 'synset': 'dangleberry.n.01', 'name': 'dangleberry'}, {'id': 19221, 'synset': 'box_huckleberry.n.01', 'name': 'box_huckleberry'}, {'id': 19222, 'synset': 'kalmia.n.01', 'name': 'kalmia'}, {'id': 19223, 'synset': 'mountain_laurel.n.01', 'name': 'mountain_laurel'}, {'id': 19224, 'synset': 'swamp_laurel.n.01', 'name': 'swamp_laurel'}, {'id': 19225, 'synset': "trapper's_tea.n.01", 'name': "trapper's_tea"}, {'id': 19226, 'synset': 'wild_rosemary.n.01', 'name': 'wild_rosemary'}, {'id': 19227, 'synset': 'sand_myrtle.n.01', 'name': 'sand_myrtle'}, {'id': 19228, 'synset': 'leucothoe.n.01', 'name': 'leucothoe'}, {'id': 19229, 'synset': 'dog_laurel.n.01', 'name': 'dog_laurel'}, {'id': 19230, 'synset': 'sweet_bells.n.01', 'name': 'sweet_bells'}, {'id': 19231, 'synset': 'alpine_azalea.n.01', 'name': 'alpine_azalea'}, {'id': 19232, 'synset': 'staggerbush.n.01', 'name': 'staggerbush'}, {'id': 19233, 'synset': 'maleberry.n.01', 'name': 'maleberry'}, {'id': 19234, 'synset': 'fetterbush.n.02', 'name': 'fetterbush'}, {'id': 19235, 'synset': 'false_azalea.n.01', 'name': 'false_azalea'}, {'id': 19236, 'synset': 'minniebush.n.01', 'name': 'minniebush'}, {'id': 19237, 'synset': 'sorrel_tree.n.01', 'name': 'sorrel_tree'}, {'id': 19238, 'synset': 'mountain_heath.n.01', 'name': 'mountain_heath'}, {'id': 19239, 'synset': 'purple_heather.n.01', 'name': 'purple_heather'}, {'id': 19240, 'synset': 'fetterbush.n.01', 'name': 'fetterbush'}, {'id': 19241, 'synset': 'rhododendron.n.01', 'name': 'rhododendron'}, {'id': 19242, 'synset': 'coast_rhododendron.n.01', 'name': 'coast_rhododendron'}, {'id': 19243, 'synset': 'rosebay.n.01', 'name': 'rosebay'}, {'id': 19244, 'synset': 'swamp_azalea.n.01', 'name': 'swamp_azalea'}, {'id': 19245, 'synset': 'azalea.n.01', 'name': 'azalea'}, {'id': 19246, 'synset': 'cranberry.n.01', 'name': 'cranberry'}, {'id': 19247, 'synset': 'american_cranberry.n.01', 'name': 'American_cranberry'}, {'id': 19248, 'synset': 'european_cranberry.n.01', 'name': 'European_cranberry'}, {'id': 19249, 'synset': 'blueberry.n.01', 'name': 'blueberry'}, {'id': 19250, 'synset': 'farkleberry.n.01', 'name': 'farkleberry'}, {'id': 19251, 'synset': 'low-bush_blueberry.n.01', 'name': 'low-bush_blueberry'}, {'id': 19252, 'synset': 'rabbiteye_blueberry.n.01', 'name': 'rabbiteye_blueberry'}, {'id': 19253, 'synset': 'dwarf_bilberry.n.01', 'name': 'dwarf_bilberry'}, {'id': 19254, 'synset': 'evergreen_blueberry.n.01', 'name': 'evergreen_blueberry'}, {'id': 19255, 'synset': 'evergreen_huckleberry.n.01', 'name': 'evergreen_huckleberry'}, {'id': 19256, 'synset': 'bilberry.n.02', 'name': 'bilberry'}, {'id': 19257, 'synset': 'bilberry.n.01', 'name': 'bilberry'}, {'id': 19258, 'synset': 'bog_bilberry.n.01', 'name': 'bog_bilberry'}, {'id': 19259, 'synset': 'dryland_blueberry.n.01', 'name': 'dryland_blueberry'}, {'id': 19260, 'synset': 'grouseberry.n.01', 'name': 'grouseberry'}, {'id': 19261, 'synset': 'deerberry.n.01', 'name': 'deerberry'}, {'id': 19262, 'synset': 'cowberry.n.01', 'name': 'cowberry'}, {'id': 19263, 'synset': 'diapensia.n.01', 'name': 'diapensia'}, {'id': 19264, 'synset': 'galax.n.01', 'name': 'galax'}, {'id': 19265, 'synset': 'pyxie.n.01', 'name': 'pyxie'}, {'id': 19266, 'synset': 'shortia.n.01', 'name': 'shortia'}, {'id': 19267, 'synset': 'oconee_bells.n.01', 'name': 'oconee_bells'}, {'id': 19268, 'synset': 'australian_heath.n.01', 'name': 'Australian_heath'}, {'id': 19269, 'synset': 'epacris.n.01', 'name': 'epacris'}, {'id': 19270, 'synset': 'common_heath.n.02', 'name': 'common_heath'}, {'id': 19271, 'synset': 'common_heath.n.01', 'name': 'common_heath'}, {'id': 19272, 'synset': 'port_jackson_heath.n.01', 'name': 'Port_Jackson_heath'}, {'id': 19273, 'synset': 'native_cranberry.n.01', 'name': 'native_cranberry'}, {'id': 19274, 'synset': 'pink_fivecorner.n.01', 'name': 'pink_fivecorner'}, {'id': 19275, 'synset': 'wintergreen.n.01', 'name': 'wintergreen'}, {'id': 19276, 'synset': 'false_wintergreen.n.01', 'name': 'false_wintergreen'}, {'id': 19277, 'synset': 'lesser_wintergreen.n.01', 'name': 'lesser_wintergreen'}, {'id': 19278, 'synset': 'wild_lily_of_the_valley.n.02', 'name': 'wild_lily_of_the_valley'}, {'id': 19279, 'synset': 'wild_lily_of_the_valley.n.01', 'name': 'wild_lily_of_the_valley'}, {'id': 19280, 'synset': 'pipsissewa.n.01', 'name': 'pipsissewa'}, {'id': 19281, 'synset': 'love-in-winter.n.01', 'name': 'love-in-winter'}, {'id': 19282, 'synset': 'one-flowered_wintergreen.n.01', 'name': 'one-flowered_wintergreen'}, {'id': 19283, 'synset': 'indian_pipe.n.01', 'name': 'Indian_pipe'}, {'id': 19284, 'synset': 'pinesap.n.01', 'name': 'pinesap'}, {'id': 19285, 'synset': 'beech.n.01', 'name': 'beech'}, {'id': 19286, 'synset': 'common_beech.n.01', 'name': 'common_beech'}, {'id': 19287, 'synset': 'copper_beech.n.01', 'name': 'copper_beech'}, {'id': 19288, 'synset': 'american_beech.n.01', 'name': 'American_beech'}, {'id': 19289, 'synset': 'weeping_beech.n.01', 'name': 'weeping_beech'}, {'id': 19290, 'synset': 'japanese_beech.n.01', 'name': 'Japanese_beech'}, {'id': 19291, 'synset': 'chestnut.n.02', 'name': 'chestnut'}, {'id': 19292, 'synset': 'american_chestnut.n.01', 'name': 'American_chestnut'}, {'id': 19293, 'synset': 'european_chestnut.n.01', 'name': 'European_chestnut'}, {'id': 19294, 'synset': 'chinese_chestnut.n.01', 'name': 'Chinese_chestnut'}, {'id': 19295, 'synset': 'japanese_chestnut.n.01', 'name': 'Japanese_chestnut'}, {'id': 19296, 'synset': 'allegheny_chinkapin.n.01', 'name': 'Allegheny_chinkapin'}, {'id': 19297, 'synset': 'ozark_chinkapin.n.01', 'name': 'Ozark_chinkapin'}, {'id': 19298, 'synset': 'oak_chestnut.n.01', 'name': 'oak_chestnut'}, {'id': 19299, 'synset': 'giant_chinkapin.n.01', 'name': 'giant_chinkapin'}, {'id': 19300, 'synset': 'dwarf_golden_chinkapin.n.01', 'name': 'dwarf_golden_chinkapin'}, {'id': 19301, 'synset': 'tanbark_oak.n.01', 'name': 'tanbark_oak'}, {'id': 19302, 'synset': 'japanese_oak.n.02', 'name': 'Japanese_oak'}, {'id': 19303, 'synset': 'southern_beech.n.01', 'name': 'southern_beech'}, {'id': 19304, 'synset': 'myrtle_beech.n.01', 'name': 'myrtle_beech'}, {'id': 19305, 'synset': 'coigue.n.01', 'name': 'Coigue'}, {'id': 19306, 'synset': 'new_zealand_beech.n.01', 'name': 'New_Zealand_beech'}, {'id': 19307, 'synset': 'silver_beech.n.01', 'name': 'silver_beech'}, {'id': 19308, 'synset': 'roble_beech.n.01', 'name': 'roble_beech'}, {'id': 19309, 'synset': 'rauli_beech.n.01', 'name': 'rauli_beech'}, {'id': 19310, 'synset': 'black_beech.n.01', 'name': 'black_beech'}, {'id': 19311, 'synset': 'hard_beech.n.01', 'name': 'hard_beech'}, {'id': 19312, 'synset': 'acorn.n.01', 'name': 'acorn'}, {'id': 19313, 'synset': 'cupule.n.01', 'name': 'cupule'}, {'id': 19314, 'synset': 'oak.n.02', 'name': 'oak'}, {'id': 19315, 'synset': 'live_oak.n.01', 'name': 'live_oak'}, {'id': 19316, 'synset': 'coast_live_oak.n.01', 'name': 'coast_live_oak'}, {'id': 19317, 'synset': 'white_oak.n.01', 'name': 'white_oak'}, {'id': 19318, 'synset': 'american_white_oak.n.01', 'name': 'American_white_oak'}, {'id': 19319, 'synset': 'arizona_white_oak.n.01', 'name': 'Arizona_white_oak'}, {'id': 19320, 'synset': 'swamp_white_oak.n.01', 'name': 'swamp_white_oak'}, {'id': 19321, 'synset': 'european_turkey_oak.n.01', 'name': 'European_turkey_oak'}, {'id': 19322, 'synset': 'canyon_oak.n.01', 'name': 'canyon_oak'}, {'id': 19323, 'synset': 'scarlet_oak.n.01', 'name': 'scarlet_oak'}, {'id': 19324, 'synset': 'jack_oak.n.02', 'name': 'jack_oak'}, {'id': 19325, 'synset': 'red_oak.n.01', 'name': 'red_oak'}, {'id': 19326, 'synset': 'southern_red_oak.n.01', 'name': 'southern_red_oak'}, {'id': 19327, 'synset': 'oregon_white_oak.n.01', 'name': 'Oregon_white_oak'}, {'id': 19328, 'synset': 'holm_oak.n.02', 'name': 'holm_oak'}, {'id': 19329, 'synset': 'bear_oak.n.01', 'name': 'bear_oak'}, {'id': 19330, 'synset': 'shingle_oak.n.01', 'name': 'shingle_oak'}, {'id': 19331, 'synset': 'bluejack_oak.n.01', 'name': 'bluejack_oak'}, {'id': 19332, 'synset': 'california_black_oak.n.01', 'name': 'California_black_oak'}, {'id': 19333, 'synset': 'american_turkey_oak.n.01', 'name': 'American_turkey_oak'}, {'id': 19334, 'synset': 'laurel_oak.n.01', 'name': 'laurel_oak'}, {'id': 19335, 'synset': 'california_white_oak.n.01', 'name': 'California_white_oak'}, {'id': 19336, 'synset': 'overcup_oak.n.01', 'name': 'overcup_oak'}, {'id': 19337, 'synset': 'bur_oak.n.01', 'name': 'bur_oak'}, {'id': 19338, 'synset': 'scrub_oak.n.01', 'name': 'scrub_oak'}, {'id': 19339, 'synset': 'blackjack_oak.n.01', 'name': 'blackjack_oak'}, {'id': 19340, 'synset': 'swamp_chestnut_oak.n.01', 'name': 'swamp_chestnut_oak'}, {'id': 19341, 'synset': 'japanese_oak.n.01', 'name': 'Japanese_oak'}, {'id': 19342, 'synset': 'chestnut_oak.n.01', 'name': 'chestnut_oak'}, {'id': 19343, 'synset': 'chinquapin_oak.n.01', 'name': 'chinquapin_oak'}, {'id': 19344, 'synset': 'myrtle_oak.n.01', 'name': 'myrtle_oak'}, {'id': 19345, 'synset': 'water_oak.n.01', 'name': 'water_oak'}, {'id': 19346, 'synset': 'nuttall_oak.n.01', 'name': 'Nuttall_oak'}, {'id': 19347, 'synset': 'durmast.n.01', 'name': 'durmast'}, {'id': 19348, 'synset': 'basket_oak.n.01', 'name': 'basket_oak'}, {'id': 19349, 'synset': 'pin_oak.n.01', 'name': 'pin_oak'}, {'id': 19350, 'synset': 'willow_oak.n.01', 'name': 'willow_oak'}, {'id': 19351, 'synset': 'dwarf_chinkapin_oak.n.01', 'name': 'dwarf_chinkapin_oak'}, {'id': 19352, 'synset': 'common_oak.n.01', 'name': 'common_oak'}, {'id': 19353, 'synset': 'northern_red_oak.n.01', 'name': 'northern_red_oak'}, {'id': 19354, 'synset': 'shumard_oak.n.01', 'name': 'Shumard_oak'}, {'id': 19355, 'synset': 'post_oak.n.01', 'name': 'post_oak'}, {'id': 19356, 'synset': 'cork_oak.n.01', 'name': 'cork_oak'}, {'id': 19357, 'synset': 'spanish_oak.n.01', 'name': 'Spanish_oak'}, {'id': 19358, 'synset': 'huckleberry_oak.n.01', 'name': 'huckleberry_oak'}, {'id': 19359, 'synset': 'chinese_cork_oak.n.01', 'name': 'Chinese_cork_oak'}, {'id': 19360, 'synset': 'black_oak.n.01', 'name': 'black_oak'}, {'id': 19361, 'synset': 'southern_live_oak.n.01', 'name': 'southern_live_oak'}, {'id': 19362, 'synset': 'interior_live_oak.n.01', 'name': 'interior_live_oak'}, {'id': 19363, 'synset': 'mast.n.02', 'name': 'mast'}, {'id': 19364, 'synset': 'birch.n.02', 'name': 'birch'}, {'id': 19365, 'synset': 'yellow_birch.n.01', 'name': 'yellow_birch'}, {'id': 19366, 'synset': 'american_white_birch.n.01', 'name': 'American_white_birch'}, {'id': 19367, 'synset': 'grey_birch.n.01', 'name': 'grey_birch'}, {'id': 19368, 'synset': 'silver_birch.n.01', 'name': 'silver_birch'}, {'id': 19369, 'synset': 'downy_birch.n.01', 'name': 'downy_birch'}, {'id': 19370, 'synset': 'black_birch.n.02', 'name': 'black_birch'}, {'id': 19371, 'synset': 'sweet_birch.n.01', 'name': 'sweet_birch'}, {'id': 19372, 'synset': 'yukon_white_birch.n.01', 'name': 'Yukon_white_birch'}, {'id': 19373, 'synset': 'swamp_birch.n.01', 'name': 'swamp_birch'}, {'id': 19374, 'synset': 'newfoundland_dwarf_birch.n.01', 'name': 'Newfoundland_dwarf_birch'}, {'id': 19375, 'synset': 'alder.n.02', 'name': 'alder'}, {'id': 19376, 'synset': 'common_alder.n.01', 'name': 'common_alder'}, {'id': 19377, 'synset': 'grey_alder.n.01', 'name': 'grey_alder'}, {'id': 19378, 'synset': 'seaside_alder.n.01', 'name': 'seaside_alder'}, {'id': 19379, 'synset': 'white_alder.n.01', 'name': 'white_alder'}, {'id': 19380, 'synset': 'red_alder.n.01', 'name': 'red_alder'}, {'id': 19381, 'synset': 'speckled_alder.n.01', 'name': 'speckled_alder'}, {'id': 19382, 'synset': 'smooth_alder.n.01', 'name': 'smooth_alder'}, {'id': 19383, 'synset': 'green_alder.n.02', 'name': 'green_alder'}, {'id': 19384, 'synset': 'green_alder.n.01', 'name': 'green_alder'}, {'id': 19385, 'synset': 'hornbeam.n.01', 'name': 'hornbeam'}, {'id': 19386, 'synset': 'european_hornbeam.n.01', 'name': 'European_hornbeam'}, {'id': 19387, 'synset': 'american_hornbeam.n.01', 'name': 'American_hornbeam'}, {'id': 19388, 'synset': 'hop_hornbeam.n.01', 'name': 'hop_hornbeam'}, {'id': 19389, 'synset': 'old_world_hop_hornbeam.n.01', 'name': 'Old_World_hop_hornbeam'}, {'id': 19390, 'synset': 'eastern_hop_hornbeam.n.01', 'name': 'Eastern_hop_hornbeam'}, {'id': 19391, 'synset': 'hazelnut.n.01', 'name': 'hazelnut'}, {'id': 19392, 'synset': 'american_hazel.n.01', 'name': 'American_hazel'}, {'id': 19393, 'synset': 'cobnut.n.01', 'name': 'cobnut'}, {'id': 19394, 'synset': 'beaked_hazelnut.n.01', 'name': 'beaked_hazelnut'}, {'id': 19395, 'synset': 'centaury.n.01', 'name': 'centaury'}, {'id': 19396, 'synset': 'rosita.n.01', 'name': 'rosita'}, {'id': 19397, 'synset': 'lesser_centaury.n.01', 'name': 'lesser_centaury'}, {'id': 19398, 'synset': 'seaside_centaury.n.01', 'name': 'seaside_centaury'}, {'id': 19399, 'synset': 'slender_centaury.n.01', 'name': 'slender_centaury'}, {'id': 19400, 'synset': 'prairie_gentian.n.01', 'name': 'prairie_gentian'}, {'id': 19401, 'synset': 'persian_violet.n.01', 'name': 'Persian_violet'}, {'id': 19402, 'synset': 'columbo.n.01', 'name': 'columbo'}, {'id': 19403, 'synset': 'gentian.n.01', 'name': 'gentian'}, {'id': 19404, 'synset': 'gentianella.n.02', 'name': 'gentianella'}, {'id': 19405, 'synset': 'closed_gentian.n.02', 'name': 'closed_gentian'}, {'id': 19406, 'synset': "explorer's_gentian.n.01", 'name': "explorer's_gentian"}, {'id': 19407, 'synset': 'closed_gentian.n.01', 'name': 'closed_gentian'}, {'id': 19408, 'synset': 'great_yellow_gentian.n.01', 'name': 'great_yellow_gentian'}, {'id': 19409, 'synset': 'marsh_gentian.n.01', 'name': 'marsh_gentian'}, {'id': 19410, 'synset': 'soapwort_gentian.n.01', 'name': 'soapwort_gentian'}, {'id': 19411, 'synset': 'striped_gentian.n.01', 'name': 'striped_gentian'}, {'id': 19412, 'synset': 'agueweed.n.01', 'name': 'agueweed'}, {'id': 19413, 'synset': 'felwort.n.01', 'name': 'felwort'}, {'id': 19414, 'synset': 'fringed_gentian.n.01', 'name': 'fringed_gentian'}, {'id': 19415, 'synset': 'gentianopsis_crinita.n.01', 'name': 'Gentianopsis_crinita'}, {'id': 19416, 'synset': 'gentianopsis_detonsa.n.01', 'name': 'Gentianopsis_detonsa'}, {'id': 19417, 'synset': 'gentianopsid_procera.n.01', 'name': 'Gentianopsid_procera'}, {'id': 19418, 'synset': 'gentianopsis_thermalis.n.01', 'name': 'Gentianopsis_thermalis'}, {'id': 19419, 'synset': 'tufted_gentian.n.01', 'name': 'tufted_gentian'}, {'id': 19420, 'synset': 'spurred_gentian.n.01', 'name': 'spurred_gentian'}, {'id': 19421, 'synset': 'sabbatia.n.01', 'name': 'sabbatia'}, {'id': 19422, 'synset': 'toothbrush_tree.n.01', 'name': 'toothbrush_tree'}, {'id': 19423, 'synset': 'olive_tree.n.01', 'name': 'olive_tree'}, {'id': 19424, 'synset': 'olive.n.02', 'name': 'olive'}, {'id': 19425, 'synset': 'olive.n.01', 'name': 'olive'}, {'id': 19426, 'synset': 'black_maire.n.01', 'name': 'black_maire'}, {'id': 19427, 'synset': 'white_maire.n.01', 'name': 'white_maire'}, {'id': 19428, 'synset': 'fringe_tree.n.01', 'name': 'fringe_tree'}, {'id': 19429, 'synset': 'fringe_bush.n.01', 'name': 'fringe_bush'}, {'id': 19430, 'synset': 'forestiera.n.01', 'name': 'forestiera'}, {'id': 19431, 'synset': 'forsythia.n.01', 'name': 'forsythia'}, {'id': 19432, 'synset': 'ash.n.02', 'name': 'ash'}, {'id': 19433, 'synset': 'white_ash.n.02', 'name': 'white_ash'}, {'id': 19434, 'synset': 'swamp_ash.n.01', 'name': 'swamp_ash'}, {'id': 19435, 'synset': 'flowering_ash.n.03', 'name': 'flowering_ash'}, {'id': 19436, 'synset': 'european_ash.n.01', 'name': 'European_ash'}, {'id': 19437, 'synset': 'oregon_ash.n.01', 'name': 'Oregon_ash'}, {'id': 19438, 'synset': 'black_ash.n.01', 'name': 'black_ash'}, {'id': 19439, 'synset': 'manna_ash.n.01', 'name': 'manna_ash'}, {'id': 19440, 'synset': 'red_ash.n.01', 'name': 'red_ash'}, {'id': 19441, 'synset': 'green_ash.n.01', 'name': 'green_ash'}, {'id': 19442, 'synset': 'blue_ash.n.01', 'name': 'blue_ash'}, {'id': 19443, 'synset': 'mountain_ash.n.03', 'name': 'mountain_ash'}, {'id': 19444, 'synset': 'pumpkin_ash.n.01', 'name': 'pumpkin_ash'}, {'id': 19445, 'synset': 'arizona_ash.n.01', 'name': 'Arizona_ash'}, {'id': 19446, 'synset': 'jasmine.n.01', 'name': 'jasmine'}, {'id': 19447, 'synset': 'primrose_jasmine.n.01', 'name': 'primrose_jasmine'}, {'id': 19448, 'synset': 'winter_jasmine.n.01', 'name': 'winter_jasmine'}, {'id': 19449, 'synset': 'common_jasmine.n.01', 'name': 'common_jasmine'}, {'id': 19450, 'synset': 'privet.n.01', 'name': 'privet'}, {'id': 19451, 'synset': 'amur_privet.n.01', 'name': 'Amur_privet'}, {'id': 19452, 'synset': 'japanese_privet.n.01', 'name': 'Japanese_privet'}, {'id': 19453, 'synset': 'ligustrum_obtusifolium.n.01', 'name': 'Ligustrum_obtusifolium'}, {'id': 19454, 'synset': 'common_privet.n.01', 'name': 'common_privet'}, {'id': 19455, 'synset': 'devilwood.n.01', 'name': 'devilwood'}, {'id': 19456, 'synset': 'mock_privet.n.01', 'name': 'mock_privet'}, {'id': 19457, 'synset': 'lilac.n.01', 'name': 'lilac'}, {'id': 19458, 'synset': 'himalayan_lilac.n.01', 'name': 'Himalayan_lilac'}, {'id': 19459, 'synset': 'persian_lilac.n.02', 'name': 'Persian_lilac'}, {'id': 19460, 'synset': 'japanese_tree_lilac.n.01', 'name': 'Japanese_tree_lilac'}, {'id': 19461, 'synset': 'japanese_lilac.n.01', 'name': 'Japanese_lilac'}, {'id': 19462, 'synset': 'common_lilac.n.01', 'name': 'common_lilac'}, {'id': 19463, 'synset': 'bloodwort.n.01', 'name': 'bloodwort'}, {'id': 19464, 'synset': 'kangaroo_paw.n.01', 'name': 'kangaroo_paw'}, {'id': 19465, 'synset': 'virginian_witch_hazel.n.01', 'name': 'Virginian_witch_hazel'}, {'id': 19466, 'synset': 'vernal_witch_hazel.n.01', 'name': 'vernal_witch_hazel'}, {'id': 19467, 'synset': 'winter_hazel.n.01', 'name': 'winter_hazel'}, {'id': 19468, 'synset': 'fothergilla.n.01', 'name': 'fothergilla'}, {'id': 19469, 'synset': 'liquidambar.n.02', 'name': 'liquidambar'}, {'id': 19470, 'synset': 'sweet_gum.n.03', 'name': 'sweet_gum'}, {'id': 19471, 'synset': 'iron_tree.n.01', 'name': 'iron_tree'}, {'id': 19472, 'synset': 'walnut.n.03', 'name': 'walnut'}, {'id': 19473, 'synset': 'california_black_walnut.n.01', 'name': 'California_black_walnut'}, {'id': 19474, 'synset': 'butternut.n.01', 'name': 'butternut'}, {'id': 19475, 'synset': 'black_walnut.n.01', 'name': 'black_walnut'}, {'id': 19476, 'synset': 'english_walnut.n.01', 'name': 'English_walnut'}, {'id': 19477, 'synset': 'hickory.n.02', 'name': 'hickory'}, {'id': 19478, 'synset': 'water_hickory.n.01', 'name': 'water_hickory'}, {'id': 19479, 'synset': 'pignut.n.01', 'name': 'pignut'}, {'id': 19480, 'synset': 'bitternut.n.01', 'name': 'bitternut'}, {'id': 19481, 'synset': 'pecan.n.02', 'name': 'pecan'}, {'id': 19482, 'synset': 'big_shellbark.n.01', 'name': 'big_shellbark'}, {'id': 19483, 'synset': 'nutmeg_hickory.n.01', 'name': 'nutmeg_hickory'}, {'id': 19484, 'synset': 'shagbark.n.01', 'name': 'shagbark'}, {'id': 19485, 'synset': 'mockernut.n.01', 'name': 'mockernut'}, {'id': 19486, 'synset': 'wing_nut.n.01', 'name': 'wing_nut'}, {'id': 19487, 'synset': 'caucasian_walnut.n.01', 'name': 'Caucasian_walnut'}, {'id': 19488, 'synset': 'dhawa.n.01', 'name': 'dhawa'}, {'id': 19489, 'synset': 'combretum.n.01', 'name': 'combretum'}, {'id': 19490, 'synset': 'hiccup_nut.n.01', 'name': 'hiccup_nut'}, {'id': 19491, 'synset': 'bush_willow.n.02', 'name': 'bush_willow'}, {'id': 19492, 'synset': 'bush_willow.n.01', 'name': 'bush_willow'}, {'id': 19493, 'synset': 'button_tree.n.01', 'name': 'button_tree'}, {'id': 19494, 'synset': 'white_mangrove.n.02', 'name': 'white_mangrove'}, {'id': 19495, 'synset': 'oleaster.n.01', 'name': 'oleaster'}, {'id': 19496, 'synset': 'water_milfoil.n.01', 'name': 'water_milfoil'}, {'id': 19497, 'synset': 'anchovy_pear.n.01', 'name': 'anchovy_pear'}, {'id': 19498, 'synset': 'brazil_nut.n.01', 'name': 'brazil_nut'}, {'id': 19499, 'synset': 'loosestrife.n.01', 'name': 'loosestrife'}, {'id': 19500, 'synset': 'purple_loosestrife.n.01', 'name': 'purple_loosestrife'}, {'id': 19501, 'synset': 'grass_poly.n.01', 'name': 'grass_poly'}, {'id': 19502, 'synset': 'crape_myrtle.n.01', 'name': 'crape_myrtle'}, {'id': 19503, 'synset': "queen's_crape_myrtle.n.01", 'name': "Queen's_crape_myrtle"}, {'id': 19504, 'synset': 'myrtaceous_tree.n.01', 'name': 'myrtaceous_tree'}, {'id': 19505, 'synset': 'myrtle.n.02', 'name': 'myrtle'}, {'id': 19506, 'synset': 'common_myrtle.n.01', 'name': 'common_myrtle'}, {'id': 19507, 'synset': 'bayberry.n.01', 'name': 'bayberry'}, {'id': 19508, 'synset': 'allspice.n.01', 'name': 'allspice'}, {'id': 19509, 'synset': 'allspice_tree.n.01', 'name': 'allspice_tree'}, {'id': 19510, 'synset': 'sour_cherry.n.02', 'name': 'sour_cherry'}, {'id': 19511, 'synset': 'nakedwood.n.02', 'name': 'nakedwood'}, {'id': 19512, 'synset': 'surinam_cherry.n.02', 'name': 'Surinam_cherry'}, {'id': 19513, 'synset': 'rose_apple.n.01', 'name': 'rose_apple'}, {'id': 19514, 'synset': 'feijoa.n.01', 'name': 'feijoa'}, {'id': 19515, 'synset': 'jaboticaba.n.01', 'name': 'jaboticaba'}, {'id': 19516, 'synset': 'guava.n.02', 'name': 'guava'}, {'id': 19517, 'synset': 'guava.n.01', 'name': 'guava'}, {'id': 19518, 'synset': 'cattley_guava.n.01', 'name': 'cattley_guava'}, {'id': 19519, 'synset': 'brazilian_guava.n.01', 'name': 'Brazilian_guava'}, {'id': 19520, 'synset': 'gum_tree.n.01', 'name': 'gum_tree'}, {'id': 19521, 'synset': 'eucalyptus.n.02', 'name': 'eucalyptus'}, {'id': 19522, 'synset': 'flooded_gum.n.01', 'name': 'flooded_gum'}, {'id': 19523, 'synset': 'mallee.n.01', 'name': 'mallee'}, {'id': 19524, 'synset': 'stringybark.n.01', 'name': 'stringybark'}, {'id': 19525, 'synset': 'smoothbark.n.01', 'name': 'smoothbark'}, {'id': 19526, 'synset': 'red_gum.n.03', 'name': 'red_gum'}, {'id': 19527, 'synset': 'red_gum.n.02', 'name': 'red_gum'}, {'id': 19528, 'synset': 'river_red_gum.n.01', 'name': 'river_red_gum'}, {'id': 19529, 'synset': 'mountain_swamp_gum.n.01', 'name': 'mountain_swamp_gum'}, {'id': 19530, 'synset': 'snow_gum.n.01', 'name': 'snow_gum'}, {'id': 19531, 'synset': 'alpine_ash.n.01', 'name': 'alpine_ash'}, {'id': 19532, 'synset': 'white_mallee.n.01', 'name': 'white_mallee'}, {'id': 19533, 'synset': 'white_stringybark.n.01', 'name': 'white_stringybark'}, {'id': 19534, 'synset': 'white_mountain_ash.n.01', 'name': 'white_mountain_ash'}, {'id': 19535, 'synset': 'blue_gum.n.01', 'name': 'blue_gum'}, {'id': 19536, 'synset': 'rose_gum.n.01', 'name': 'rose_gum'}, {'id': 19537, 'synset': 'cider_gum.n.01', 'name': 'cider_gum'}, {'id': 19538, 'synset': 'swamp_gum.n.01', 'name': 'swamp_gum'}, {'id': 19539, 'synset': 'spotted_gum.n.01', 'name': 'spotted_gum'}, {'id': 19540, 'synset': 'lemon-scented_gum.n.01', 'name': 'lemon-scented_gum'}, {'id': 19541, 'synset': 'black_mallee.n.01', 'name': 'black_mallee'}, {'id': 19542, 'synset': 'forest_red_gum.n.01', 'name': 'forest_red_gum'}, {'id': 19543, 'synset': 'mountain_ash.n.02', 'name': 'mountain_ash'}, {'id': 19544, 'synset': 'manna_gum.n.01', 'name': 'manna_gum'}, {'id': 19545, 'synset': 'clove.n.02', 'name': 'clove'}, {'id': 19546, 'synset': 'clove.n.01', 'name': 'clove'}, {'id': 19547, 'synset': 'tupelo.n.02', 'name': 'tupelo'}, {'id': 19548, 'synset': 'water_gum.n.01', 'name': 'water_gum'}, {'id': 19549, 'synset': 'sour_gum.n.01', 'name': 'sour_gum'}, {'id': 19550, 'synset': "enchanter's_nightshade.n.01", 'name': "enchanter's_nightshade"}, {'id': 19551, 'synset': 'circaea_lutetiana.n.01', 'name': 'Circaea_lutetiana'}, {'id': 19552, 'synset': 'willowherb.n.01', 'name': 'willowherb'}, {'id': 19553, 'synset': 'fireweed.n.01', 'name': 'fireweed'}, {'id': 19554, 'synset': 'california_fuchsia.n.01', 'name': 'California_fuchsia'}, {'id': 19555, 'synset': 'fuchsia.n.01', 'name': 'fuchsia'}, {'id': 19556, 'synset': "lady's-eardrop.n.01", 'name': "lady's-eardrop"}, {'id': 19557, 'synset': 'evening_primrose.n.01', 'name': 'evening_primrose'}, {'id': 19558, 'synset': 'common_evening_primrose.n.01', 'name': 'common_evening_primrose'}, {'id': 19559, 'synset': 'sundrops.n.01', 'name': 'sundrops'}, {'id': 19560, 'synset': 'missouri_primrose.n.01', 'name': 'Missouri_primrose'}, {'id': 19561, 'synset': 'pomegranate.n.01', 'name': 'pomegranate'}, {'id': 19562, 'synset': 'mangrove.n.01', 'name': 'mangrove'}, {'id': 19563, 'synset': 'daphne.n.01', 'name': 'daphne'}, {'id': 19564, 'synset': 'garland_flower.n.01', 'name': 'garland_flower'}, {'id': 19565, 'synset': 'spurge_laurel.n.01', 'name': 'spurge_laurel'}, {'id': 19566, 'synset': 'mezereon.n.01', 'name': 'mezereon'}, {'id': 19567, 'synset': 'indian_rhododendron.n.01', 'name': 'Indian_rhododendron'}, {'id': 19568, 'synset': 'medinilla_magnifica.n.01', 'name': 'Medinilla_magnifica'}, {'id': 19569, 'synset': 'deer_grass.n.01', 'name': 'deer_grass'}, {'id': 19570, 'synset': 'canna.n.01', 'name': 'canna'}, {'id': 19571, 'synset': 'achira.n.01', 'name': 'achira'}, {'id': 19572, 'synset': 'arrowroot.n.02', 'name': 'arrowroot'}, {'id': 19573, 'synset': 'banana.n.01', 'name': 'banana'}, {'id': 19574, 'synset': 'dwarf_banana.n.01', 'name': 'dwarf_banana'}, {'id': 19575, 'synset': 'japanese_banana.n.01', 'name': 'Japanese_banana'}, {'id': 19576, 'synset': 'plantain.n.02', 'name': 'plantain'}, {'id': 19577, 'synset': 'edible_banana.n.01', 'name': 'edible_banana'}, {'id': 19578, 'synset': 'abaca.n.02', 'name': 'abaca'}, {'id': 19579, 'synset': 'abyssinian_banana.n.01', 'name': 'Abyssinian_banana'}, {'id': 19580, 'synset': 'ginger.n.01', 'name': 'ginger'}, {'id': 19581, 'synset': 'common_ginger.n.01', 'name': 'common_ginger'}, {'id': 19582, 'synset': 'turmeric.n.01', 'name': 'turmeric'}, {'id': 19583, 'synset': 'galangal.n.01', 'name': 'galangal'}, {'id': 19584, 'synset': 'shellflower.n.02', 'name': 'shellflower'}, {'id': 19585, 'synset': 'grains_of_paradise.n.01', 'name': 'grains_of_paradise'}, {'id': 19586, 'synset': 'cardamom.n.01', 'name': 'cardamom'}, {'id': 19587, 'synset': 'begonia.n.01', 'name': 'begonia'}, {'id': 19588, 'synset': 'fibrous-rooted_begonia.n.01', 'name': 'fibrous-rooted_begonia'}, {'id': 19589, 'synset': 'tuberous_begonia.n.01', 'name': 'tuberous_begonia'}, {'id': 19590, 'synset': 'rhizomatous_begonia.n.01', 'name': 'rhizomatous_begonia'}, {'id': 19591, 'synset': 'christmas_begonia.n.01', 'name': 'Christmas_begonia'}, {'id': 19592, 'synset': 'angel-wing_begonia.n.01', 'name': 'angel-wing_begonia'}, {'id': 19593, 'synset': 'beefsteak_begonia.n.01', 'name': 'beefsteak_begonia'}, {'id': 19594, 'synset': 'star_begonia.n.01', 'name': 'star_begonia'}, {'id': 19595, 'synset': 'rex_begonia.n.01', 'name': 'rex_begonia'}, {'id': 19596, 'synset': 'wax_begonia.n.01', 'name': 'wax_begonia'}, {'id': 19597, 'synset': 'socotra_begonia.n.01', 'name': 'Socotra_begonia'}, {'id': 19598, 'synset': 'hybrid_tuberous_begonia.n.01', 'name': 'hybrid_tuberous_begonia'}, {'id': 19599, 'synset': 'dillenia.n.01', 'name': 'dillenia'}, {'id': 19600, 'synset': 'guinea_gold_vine.n.01', 'name': 'guinea_gold_vine'}, {'id': 19601, 'synset': 'poon.n.02', 'name': 'poon'}, {'id': 19602, 'synset': 'calaba.n.01', 'name': 'calaba'}, {'id': 19603, 'synset': 'maria.n.02', 'name': 'Maria'}, {'id': 19604, 'synset': 'laurelwood.n.01', 'name': 'laurelwood'}, {'id': 19605, 'synset': 'alexandrian_laurel.n.01', 'name': 'Alexandrian_laurel'}, {'id': 19606, 'synset': 'clusia.n.01', 'name': 'clusia'}, {'id': 19607, 'synset': 'wild_fig.n.02', 'name': 'wild_fig'}, {'id': 19608, 'synset': 'waxflower.n.02', 'name': 'waxflower'}, {'id': 19609, 'synset': 'pitch_apple.n.01', 'name': 'pitch_apple'}, {'id': 19610, 'synset': 'mangosteen.n.01', 'name': 'mangosteen'}, {'id': 19611, 'synset': 'gamboge_tree.n.01', 'name': 'gamboge_tree'}, {'id': 19612, 'synset': "st_john's_wort.n.01", 'name': "St_John's_wort"}, {'id': 19613, 'synset': "common_st_john's_wort.n.01", 'name': "common_St_John's_wort"}, {'id': 19614, 'synset': "great_st_john's_wort.n.01", 'name': "great_St_John's_wort"}, {'id': 19615, 'synset': "creeping_st_john's_wort.n.01", 'name': "creeping_St_John's_wort"}, {'id': 19616, 'synset': "low_st_andrew's_cross.n.01", 'name': "low_St_Andrew's_cross"}, {'id': 19617, 'synset': 'klammath_weed.n.01', 'name': 'klammath_weed'}, {'id': 19618, 'synset': "shrubby_st_john's_wort.n.01", 'name': "shrubby_St_John's_wort"}, {'id': 19619, 'synset': "st_peter's_wort.n.01", 'name': "St_Peter's_wort"}, {'id': 19620, 'synset': "marsh_st-john's_wort.n.01", 'name': "marsh_St-John's_wort"}, {'id': 19621, 'synset': 'mammee_apple.n.01', 'name': 'mammee_apple'}, {'id': 19622, 'synset': 'rose_chestnut.n.01', 'name': 'rose_chestnut'}, {'id': 19623, 'synset': 'bower_actinidia.n.01', 'name': 'bower_actinidia'}, {'id': 19624, 'synset': 'chinese_gooseberry.n.01', 'name': 'Chinese_gooseberry'}, {'id': 19625, 'synset': 'silvervine.n.01', 'name': 'silvervine'}, {'id': 19626, 'synset': 'wild_cinnamon.n.01', 'name': 'wild_cinnamon'}, {'id': 19627, 'synset': 'papaya.n.01', 'name': 'papaya'}, {'id': 19628, 'synset': 'souari.n.01', 'name': 'souari'}, {'id': 19629, 'synset': 'rockrose.n.02', 'name': 'rockrose'}, {'id': 19630, 'synset': 'white-leaved_rockrose.n.01', 'name': 'white-leaved_rockrose'}, {'id': 19631, 'synset': 'common_gum_cistus.n.01', 'name': 'common_gum_cistus'}, {'id': 19632, 'synset': 'frostweed.n.01', 'name': 'frostweed'}, {'id': 19633, 'synset': 'dipterocarp.n.01', 'name': 'dipterocarp'}, {'id': 19634, 'synset': 'red_lauan.n.02', 'name': 'red_lauan'}, {'id': 19635, 'synset': "governor's_plum.n.01", 'name': "governor's_plum"}, {'id': 19636, 'synset': 'kei_apple.n.01', 'name': 'kei_apple'}, {'id': 19637, 'synset': 'ketembilla.n.01', 'name': 'ketembilla'}, {'id': 19638, 'synset': 'chaulmoogra.n.01', 'name': 'chaulmoogra'}, {'id': 19639, 'synset': 'wild_peach.n.01', 'name': 'wild_peach'}, {'id': 19640, 'synset': 'candlewood.n.01', 'name': 'candlewood'}, {'id': 19641, 'synset': 'boojum_tree.n.01', 'name': 'boojum_tree'}, {'id': 19642, 'synset': "bird's-eye_bush.n.01", 'name': "bird's-eye_bush"}, {'id': 19643, 'synset': 'granadilla.n.03', 'name': 'granadilla'}, {'id': 19644, 'synset': 'granadilla.n.02', 'name': 'granadilla'}, {'id': 19645, 'synset': 'granadilla.n.01', 'name': 'granadilla'}, {'id': 19646, 'synset': 'maypop.n.01', 'name': 'maypop'}, {'id': 19647, 'synset': 'jamaica_honeysuckle.n.01', 'name': 'Jamaica_honeysuckle'}, {'id': 19648, 'synset': 'banana_passion_fruit.n.01', 'name': 'banana_passion_fruit'}, {'id': 19649, 'synset': 'sweet_calabash.n.01', 'name': 'sweet_calabash'}, {'id': 19650, 'synset': 'love-in-a-mist.n.01', 'name': 'love-in-a-mist'}, {'id': 19651, 'synset': 'reseda.n.01', 'name': 'reseda'}, {'id': 19652, 'synset': 'mignonette.n.01', 'name': 'mignonette'}, {'id': 19653, 'synset': "dyer's_rocket.n.01", 'name': "dyer's_rocket"}, {'id': 19654, 'synset': 'false_tamarisk.n.01', 'name': 'false_tamarisk'}, {'id': 19655, 'synset': 'halophyte.n.01', 'name': 'halophyte'}, {'id': 19656, 'synset': 'viola.n.01', 'name': 'viola'}, {'id': 19657, 'synset': 'violet.n.01', 'name': 'violet'}, {'id': 19658, 'synset': 'field_pansy.n.01', 'name': 'field_pansy'}, {'id': 19659, 'synset': 'american_dog_violet.n.01', 'name': 'American_dog_violet'}, {'id': 19660, 'synset': 'dog_violet.n.01', 'name': 'dog_violet'}, {'id': 19661, 'synset': 'horned_violet.n.01', 'name': 'horned_violet'}, {'id': 19662, 'synset': 'two-eyed_violet.n.01', 'name': 'two-eyed_violet'}, {'id': 19663, 'synset': "bird's-foot_violet.n.01", 'name': "bird's-foot_violet"}, {'id': 19664, 'synset': 'downy_yellow_violet.n.01', 'name': 'downy_yellow_violet'}, {'id': 19665, 'synset': 'long-spurred_violet.n.01', 'name': 'long-spurred_violet'}, {'id': 19666, 'synset': 'pale_violet.n.01', 'name': 'pale_violet'}, {'id': 19667, 'synset': 'hedge_violet.n.01', 'name': 'hedge_violet'}, {'id': 19668, 'synset': 'nettle.n.01', 'name': 'nettle'}, {'id': 19669, 'synset': 'stinging_nettle.n.01', 'name': 'stinging_nettle'}, {'id': 19670, 'synset': 'roman_nettle.n.01', 'name': 'Roman_nettle'}, {'id': 19671, 'synset': 'ramie.n.01', 'name': 'ramie'}, {'id': 19672, 'synset': 'wood_nettle.n.01', 'name': 'wood_nettle'}, {'id': 19673, 'synset': 'australian_nettle.n.01', 'name': 'Australian_nettle'}, {'id': 19674, 'synset': 'pellitory-of-the-wall.n.01', 'name': 'pellitory-of-the-wall'}, {'id': 19675, 'synset': 'richweed.n.02', 'name': 'richweed'}, {'id': 19676, 'synset': 'artillery_plant.n.01', 'name': 'artillery_plant'}, {'id': 19677, 'synset': 'friendship_plant.n.01', 'name': 'friendship_plant'}, {'id': 19678, 'synset': 'queensland_grass-cloth_plant.n.01', 'name': 'Queensland_grass-cloth_plant'}, {'id': 19679, 'synset': 'pipturus_albidus.n.01', 'name': 'Pipturus_albidus'}, {'id': 19680, 'synset': 'cannabis.n.01', 'name': 'cannabis'}, {'id': 19681, 'synset': 'indian_hemp.n.01', 'name': 'Indian_hemp'}, {'id': 19682, 'synset': 'mulberry.n.01', 'name': 'mulberry'}, {'id': 19683, 'synset': 'white_mulberry.n.01', 'name': 'white_mulberry'}, {'id': 19684, 'synset': 'black_mulberry.n.01', 'name': 'black_mulberry'}, {'id': 19685, 'synset': 'red_mulberry.n.01', 'name': 'red_mulberry'}, {'id': 19686, 'synset': 'osage_orange.n.01', 'name': 'osage_orange'}, {'id': 19687, 'synset': 'breadfruit.n.01', 'name': 'breadfruit'}, {'id': 19688, 'synset': 'jackfruit.n.01', 'name': 'jackfruit'}, {'id': 19689, 'synset': 'marang.n.01', 'name': 'marang'}, {'id': 19690, 'synset': 'fig_tree.n.01', 'name': 'fig_tree'}, {'id': 19691, 'synset': 'fig.n.02', 'name': 'fig'}, {'id': 19692, 'synset': 'caprifig.n.01', 'name': 'caprifig'}, {'id': 19693, 'synset': 'golden_fig.n.01', 'name': 'golden_fig'}, {'id': 19694, 'synset': 'banyan.n.01', 'name': 'banyan'}, {'id': 19695, 'synset': 'pipal.n.01', 'name': 'pipal'}, {'id': 19696, 'synset': 'india-rubber_tree.n.01', 'name': 'India-rubber_tree'}, {'id': 19697, 'synset': 'mistletoe_fig.n.01', 'name': 'mistletoe_fig'}, {'id': 19698, 'synset': 'port_jackson_fig.n.01', 'name': 'Port_Jackson_fig'}, {'id': 19699, 'synset': 'sycamore.n.04', 'name': 'sycamore'}, {'id': 19700, 'synset': 'paper_mulberry.n.01', 'name': 'paper_mulberry'}, {'id': 19701, 'synset': 'trumpetwood.n.01', 'name': 'trumpetwood'}, {'id': 19702, 'synset': 'elm.n.01', 'name': 'elm'}, {'id': 19703, 'synset': 'winged_elm.n.01', 'name': 'winged_elm'}, {'id': 19704, 'synset': 'american_elm.n.01', 'name': 'American_elm'}, {'id': 19705, 'synset': 'smooth-leaved_elm.n.01', 'name': 'smooth-leaved_elm'}, {'id': 19706, 'synset': 'cedar_elm.n.01', 'name': 'cedar_elm'}, {'id': 19707, 'synset': 'witch_elm.n.01', 'name': 'witch_elm'}, {'id': 19708, 'synset': 'dutch_elm.n.01', 'name': 'Dutch_elm'}, {'id': 19709, 'synset': 'huntingdon_elm.n.01', 'name': 'Huntingdon_elm'}, {'id': 19710, 'synset': 'water_elm.n.01', 'name': 'water_elm'}, {'id': 19711, 'synset': 'chinese_elm.n.02', 'name': 'Chinese_elm'}, {'id': 19712, 'synset': 'english_elm.n.01', 'name': 'English_elm'}, {'id': 19713, 'synset': 'siberian_elm.n.01', 'name': 'Siberian_elm'}, {'id': 19714, 'synset': 'slippery_elm.n.01', 'name': 'slippery_elm'}, {'id': 19715, 'synset': 'jersey_elm.n.01', 'name': 'Jersey_elm'}, {'id': 19716, 'synset': 'september_elm.n.01', 'name': 'September_elm'}, {'id': 19717, 'synset': 'rock_elm.n.01', 'name': 'rock_elm'}, {'id': 19718, 'synset': 'hackberry.n.01', 'name': 'hackberry'}, {'id': 19719, 'synset': 'european_hackberry.n.01', 'name': 'European_hackberry'}, {'id': 19720, 'synset': 'american_hackberry.n.01', 'name': 'American_hackberry'}, {'id': 19721, 'synset': 'sugarberry.n.01', 'name': 'sugarberry'}, {'id': 19722, 'synset': 'iridaceous_plant.n.01', 'name': 'iridaceous_plant'}, {'id': 19723, 'synset': 'bearded_iris.n.01', 'name': 'bearded_iris'}, {'id': 19724, 'synset': 'beardless_iris.n.01', 'name': 'beardless_iris'}, {'id': 19725, 'synset': 'orrisroot.n.01', 'name': 'orrisroot'}, {'id': 19726, 'synset': 'dwarf_iris.n.02', 'name': 'dwarf_iris'}, {'id': 19727, 'synset': 'dutch_iris.n.02', 'name': 'Dutch_iris'}, {'id': 19728, 'synset': 'florentine_iris.n.01', 'name': 'Florentine_iris'}, {'id': 19729, 'synset': 'stinking_iris.n.01', 'name': 'stinking_iris'}, {'id': 19730, 'synset': 'german_iris.n.02', 'name': 'German_iris'}, {'id': 19731, 'synset': 'japanese_iris.n.01', 'name': 'Japanese_iris'}, {'id': 19732, 'synset': 'german_iris.n.01', 'name': 'German_iris'}, {'id': 19733, 'synset': 'dalmatian_iris.n.01', 'name': 'Dalmatian_iris'}, {'id': 19734, 'synset': 'persian_iris.n.01', 'name': 'Persian_iris'}, {'id': 19735, 'synset': 'dutch_iris.n.01', 'name': 'Dutch_iris'}, {'id': 19736, 'synset': 'dwarf_iris.n.01', 'name': 'dwarf_iris'}, {'id': 19737, 'synset': 'spanish_iris.n.01', 'name': 'Spanish_iris'}, {'id': 19738, 'synset': 'blackberry-lily.n.01', 'name': 'blackberry-lily'}, {'id': 19739, 'synset': 'crocus.n.01', 'name': 'crocus'}, {'id': 19740, 'synset': 'saffron.n.01', 'name': 'saffron'}, {'id': 19741, 'synset': 'corn_lily.n.01', 'name': 'corn_lily'}, {'id': 19742, 'synset': 'blue-eyed_grass.n.01', 'name': 'blue-eyed_grass'}, {'id': 19743, 'synset': 'wandflower.n.01', 'name': 'wandflower'}, {'id': 19744, 'synset': 'amaryllis.n.01', 'name': 'amaryllis'}, {'id': 19745, 'synset': 'salsilla.n.02', 'name': 'salsilla'}, {'id': 19746, 'synset': 'salsilla.n.01', 'name': 'salsilla'}, {'id': 19747, 'synset': 'blood_lily.n.01', 'name': 'blood_lily'}, {'id': 19748, 'synset': 'cape_tulip.n.01', 'name': 'Cape_tulip'}, {'id': 19749, 'synset': 'hippeastrum.n.01', 'name': 'hippeastrum'}, {'id': 19750, 'synset': 'narcissus.n.01', 'name': 'narcissus'}, {'id': 19751, 'synset': 'daffodil.n.01', 'name': 'daffodil'}, {'id': 19752, 'synset': 'jonquil.n.01', 'name': 'jonquil'}, {'id': 19753, 'synset': 'jonquil.n.02', 'name': 'jonquil'}, {'id': 19754, 'synset': 'jacobean_lily.n.01', 'name': 'Jacobean_lily'}, {'id': 19755, 'synset': 'liliaceous_plant.n.01', 'name': 'liliaceous_plant'}, {'id': 19756, 'synset': 'mountain_lily.n.01', 'name': 'mountain_lily'}, {'id': 19757, 'synset': 'canada_lily.n.01', 'name': 'Canada_lily'}, {'id': 19758, 'synset': 'tiger_lily.n.02', 'name': 'tiger_lily'}, {'id': 19759, 'synset': 'columbia_tiger_lily.n.01', 'name': 'Columbia_tiger_lily'}, {'id': 19760, 'synset': 'tiger_lily.n.01', 'name': 'tiger_lily'}, {'id': 19761, 'synset': 'easter_lily.n.01', 'name': 'Easter_lily'}, {'id': 19762, 'synset': 'coast_lily.n.01', 'name': 'coast_lily'}, {'id': 19763, 'synset': "turk's-cap.n.02", 'name': "Turk's-cap"}, {'id': 19764, 'synset': 'michigan_lily.n.01', 'name': 'Michigan_lily'}, {'id': 19765, 'synset': 'leopard_lily.n.01', 'name': 'leopard_lily'}, {'id': 19766, 'synset': "turk's-cap.n.01", 'name': "Turk's-cap"}, {'id': 19767, 'synset': 'african_lily.n.01', 'name': 'African_lily'}, {'id': 19768, 'synset': 'colicroot.n.01', 'name': 'colicroot'}, {'id': 19769, 'synset': 'ague_root.n.01', 'name': 'ague_root'}, {'id': 19770, 'synset': 'yellow_colicroot.n.01', 'name': 'yellow_colicroot'}, {'id': 19771, 'synset': 'alliaceous_plant.n.01', 'name': 'alliaceous_plant'}, {'id': 19772, 'synset': "hooker's_onion.n.01", 'name': "Hooker's_onion"}, {'id': 19773, 'synset': 'wild_leek.n.02', 'name': 'wild_leek'}, {'id': 19774, 'synset': 'canada_garlic.n.01', 'name': 'Canada_garlic'}, {'id': 19775, 'synset': 'keeled_garlic.n.01', 'name': 'keeled_garlic'}, {'id': 19776, 'synset': 'shallot.n.02', 'name': 'shallot'}, {'id': 19777, 'synset': 'nodding_onion.n.01', 'name': 'nodding_onion'}, {'id': 19778, 'synset': 'welsh_onion.n.01', 'name': 'Welsh_onion'}, {'id': 19779, 'synset': 'red-skinned_onion.n.01', 'name': 'red-skinned_onion'}, {'id': 19780, 'synset': 'daffodil_garlic.n.01', 'name': 'daffodil_garlic'}, {'id': 19781, 'synset': 'few-flowered_leek.n.01', 'name': 'few-flowered_leek'}, {'id': 19782, 'synset': 'garlic.n.01', 'name': 'garlic'}, {'id': 19783, 'synset': 'sand_leek.n.01', 'name': 'sand_leek'}, {'id': 19784, 'synset': 'chives.n.01', 'name': 'chives'}, {'id': 19785, 'synset': 'crow_garlic.n.01', 'name': 'crow_garlic'}, {'id': 19786, 'synset': 'wild_garlic.n.01', 'name': 'wild_garlic'}, {'id': 19787, 'synset': 'garlic_chive.n.01', 'name': 'garlic_chive'}, {'id': 19788, 'synset': 'round-headed_leek.n.01', 'name': 'round-headed_leek'}, {'id': 19789, 'synset': 'three-cornered_leek.n.01', 'name': 'three-cornered_leek'}, {'id': 19790, 'synset': 'cape_aloe.n.01', 'name': 'cape_aloe'}, {'id': 19791, 'synset': 'kniphofia.n.01', 'name': 'kniphofia'}, {'id': 19792, 'synset': 'poker_plant.n.01', 'name': 'poker_plant'}, {'id': 19793, 'synset': 'red-hot_poker.n.01', 'name': 'red-hot_poker'}, {'id': 19794, 'synset': 'fly_poison.n.01', 'name': 'fly_poison'}, {'id': 19795, 'synset': 'amber_lily.n.01', 'name': 'amber_lily'}, {'id': 19796, 'synset': 'asparagus.n.01', 'name': 'asparagus'}, {'id': 19797, 'synset': 'asparagus_fern.n.01', 'name': 'asparagus_fern'}, {'id': 19798, 'synset': 'smilax.n.02', 'name': 'smilax'}, {'id': 19799, 'synset': 'asphodel.n.01', 'name': 'asphodel'}, {'id': 19800, 'synset': "jacob's_rod.n.01", 'name': "Jacob's_rod"}, {'id': 19801, 'synset': 'aspidistra.n.01', 'name': 'aspidistra'}, {'id': 19802, 'synset': 'coral_drops.n.01', 'name': 'coral_drops'}, {'id': 19803, 'synset': 'christmas_bells.n.01', 'name': 'Christmas_bells'}, {'id': 19804, 'synset': 'climbing_onion.n.01', 'name': 'climbing_onion'}, {'id': 19805, 'synset': 'mariposa.n.01', 'name': 'mariposa'}, {'id': 19806, 'synset': 'globe_lily.n.01', 'name': 'globe_lily'}, {'id': 19807, 'synset': "cat's-ear.n.01", 'name': "cat's-ear"}, {'id': 19808, 'synset': 'white_globe_lily.n.01', 'name': 'white_globe_lily'}, {'id': 19809, 'synset': 'yellow_globe_lily.n.01', 'name': 'yellow_globe_lily'}, {'id': 19810, 'synset': 'rose_globe_lily.n.01', 'name': 'rose_globe_lily'}, {'id': 19811, 'synset': 'star_tulip.n.01', 'name': 'star_tulip'}, {'id': 19812, 'synset': 'desert_mariposa_tulip.n.01', 'name': 'desert_mariposa_tulip'}, {'id': 19813, 'synset': 'yellow_mariposa_tulip.n.01', 'name': 'yellow_mariposa_tulip'}, {'id': 19814, 'synset': 'sagebrush_mariposa_tulip.n.01', 'name': 'sagebrush_mariposa_tulip'}, {'id': 19815, 'synset': 'sego_lily.n.01', 'name': 'sego_lily'}, {'id': 19816, 'synset': 'camas.n.01', 'name': 'camas'}, {'id': 19817, 'synset': 'common_camas.n.01', 'name': 'common_camas'}, {'id': 19818, 'synset': "leichtlin's_camas.n.01", 'name': "Leichtlin's_camas"}, {'id': 19819, 'synset': 'wild_hyacinth.n.02', 'name': 'wild_hyacinth'}, {'id': 19820, 'synset': 'dogtooth_violet.n.01', 'name': 'dogtooth_violet'}, {'id': 19821, 'synset': 'white_dogtooth_violet.n.01', 'name': 'white_dogtooth_violet'}, {'id': 19822, 'synset': "yellow_adder's_tongue.n.01", 'name': "yellow_adder's_tongue"}, {'id': 19823, 'synset': 'european_dogtooth.n.01', 'name': 'European_dogtooth'}, {'id': 19824, 'synset': 'fawn_lily.n.01', 'name': 'fawn_lily'}, {'id': 19825, 'synset': 'glacier_lily.n.01', 'name': 'glacier_lily'}, {'id': 19826, 'synset': 'avalanche_lily.n.01', 'name': 'avalanche_lily'}, {'id': 19827, 'synset': 'fritillary.n.01', 'name': 'fritillary'}, {'id': 19828, 'synset': 'mission_bells.n.02', 'name': 'mission_bells'}, {'id': 19829, 'synset': 'mission_bells.n.01', 'name': 'mission_bells'}, {'id': 19830, 'synset': 'stink_bell.n.01', 'name': 'stink_bell'}, {'id': 19831, 'synset': 'crown_imperial.n.01', 'name': 'crown_imperial'}, {'id': 19832, 'synset': 'white_fritillary.n.01', 'name': 'white_fritillary'}, {'id': 19833, 'synset': "snake's_head_fritillary.n.01", 'name': "snake's_head_fritillary"}, {'id': 19834, 'synset': 'adobe_lily.n.01', 'name': 'adobe_lily'}, {'id': 19835, 'synset': 'scarlet_fritillary.n.01', 'name': 'scarlet_fritillary'}, {'id': 19836, 'synset': 'tulip.n.01', 'name': 'tulip'}, {'id': 19837, 'synset': 'dwarf_tulip.n.01', 'name': 'dwarf_tulip'}, {'id': 19838, 'synset': 'lady_tulip.n.01', 'name': 'lady_tulip'}, {'id': 19839, 'synset': 'tulipa_gesneriana.n.01', 'name': 'Tulipa_gesneriana'}, {'id': 19840, 'synset': 'cottage_tulip.n.01', 'name': 'cottage_tulip'}, {'id': 19841, 'synset': 'darwin_tulip.n.01', 'name': 'Darwin_tulip'}, {'id': 19842, 'synset': 'gloriosa.n.01', 'name': 'gloriosa'}, {'id': 19843, 'synset': 'lemon_lily.n.01', 'name': 'lemon_lily'}, {'id': 19844, 'synset': 'common_hyacinth.n.01', 'name': 'common_hyacinth'}, {'id': 19845, 'synset': 'roman_hyacinth.n.01', 'name': 'Roman_hyacinth'}, {'id': 19846, 'synset': 'summer_hyacinth.n.01', 'name': 'summer_hyacinth'}, {'id': 19847, 'synset': 'star-of-bethlehem.n.01', 'name': 'star-of-Bethlehem'}, {'id': 19848, 'synset': 'bath_asparagus.n.01', 'name': 'bath_asparagus'}, {'id': 19849, 'synset': 'grape_hyacinth.n.01', 'name': 'grape_hyacinth'}, {'id': 19850, 'synset': 'common_grape_hyacinth.n.01', 'name': 'common_grape_hyacinth'}, {'id': 19851, 'synset': 'tassel_hyacinth.n.01', 'name': 'tassel_hyacinth'}, {'id': 19852, 'synset': 'scilla.n.01', 'name': 'scilla'}, {'id': 19853, 'synset': 'spring_squill.n.01', 'name': 'spring_squill'}, {'id': 19854, 'synset': 'false_asphodel.n.01', 'name': 'false_asphodel'}, {'id': 19855, 'synset': 'scotch_asphodel.n.01', 'name': 'Scotch_asphodel'}, {'id': 19856, 'synset': 'sea_squill.n.01', 'name': 'sea_squill'}, {'id': 19857, 'synset': 'squill.n.01', 'name': 'squill'}, {'id': 19858, 'synset': "butcher's_broom.n.01", 'name': "butcher's_broom"}, {'id': 19859, 'synset': 'bog_asphodel.n.01', 'name': 'bog_asphodel'}, {'id': 19860, 'synset': 'european_bog_asphodel.n.01', 'name': 'European_bog_asphodel'}, {'id': 19861, 'synset': 'american_bog_asphodel.n.01', 'name': 'American_bog_asphodel'}, {'id': 19862, 'synset': 'hellebore.n.01', 'name': 'hellebore'}, {'id': 19863, 'synset': 'white_hellebore.n.01', 'name': 'white_hellebore'}, {'id': 19864, 'synset': 'squaw_grass.n.01', 'name': 'squaw_grass'}, {'id': 19865, 'synset': 'death_camas.n.01', 'name': 'death_camas'}, {'id': 19866, 'synset': 'alkali_grass.n.01', 'name': 'alkali_grass'}, {'id': 19867, 'synset': 'white_camas.n.01', 'name': 'white_camas'}, {'id': 19868, 'synset': 'poison_camas.n.01', 'name': 'poison_camas'}, {'id': 19869, 'synset': 'grassy_death_camas.n.01', 'name': 'grassy_death_camas'}, {'id': 19870, 'synset': 'prairie_wake-robin.n.01', 'name': 'prairie_wake-robin'}, {'id': 19871, 'synset': 'dwarf-white_trillium.n.01', 'name': 'dwarf-white_trillium'}, {'id': 19872, 'synset': 'herb_paris.n.01', 'name': 'herb_Paris'}, {'id': 19873, 'synset': 'sarsaparilla.n.01', 'name': 'sarsaparilla'}, {'id': 19874, 'synset': 'bullbrier.n.01', 'name': 'bullbrier'}, {'id': 19875, 'synset': 'rough_bindweed.n.01', 'name': 'rough_bindweed'}, {'id': 19876, 'synset': 'clintonia.n.01', 'name': 'clintonia'}, {'id': 19877, 'synset': 'false_lily_of_the_valley.n.02', 'name': 'false_lily_of_the_valley'}, {'id': 19878, 'synset': 'false_lily_of_the_valley.n.01', 'name': 'false_lily_of_the_valley'}, {'id': 19879, 'synset': "solomon's-seal.n.01", 'name': "Solomon's-seal"}, {'id': 19880, 'synset': "great_solomon's-seal.n.01", 'name': "great_Solomon's-seal"}, {'id': 19881, 'synset': 'bellwort.n.01', 'name': 'bellwort'}, {'id': 19882, 'synset': 'strawflower.n.01', 'name': 'strawflower'}, {'id': 19883, 'synset': 'pia.n.01', 'name': 'pia'}, {'id': 19884, 'synset': 'agave.n.01', 'name': 'agave'}, {'id': 19885, 'synset': 'american_agave.n.01', 'name': 'American_agave'}, {'id': 19886, 'synset': 'sisal.n.02', 'name': 'sisal'}, {'id': 19887, 'synset': 'maguey.n.02', 'name': 'maguey'}, {'id': 19888, 'synset': 'maguey.n.01', 'name': 'maguey'}, {'id': 19889, 'synset': 'agave_tequilana.n.01', 'name': 'Agave_tequilana'}, {'id': 19890, 'synset': 'cabbage_tree.n.03', 'name': 'cabbage_tree'}, {'id': 19891, 'synset': 'dracaena.n.01', 'name': 'dracaena'}, {'id': 19892, 'synset': 'tuberose.n.01', 'name': 'tuberose'}, {'id': 19893, 'synset': 'sansevieria.n.01', 'name': 'sansevieria'}, {'id': 19894, 'synset': 'african_bowstring_hemp.n.01', 'name': 'African_bowstring_hemp'}, {'id': 19895, 'synset': 'ceylon_bowstring_hemp.n.01', 'name': 'Ceylon_bowstring_hemp'}, {'id': 19896, 'synset': "mother-in-law's_tongue.n.01", 'name': "mother-in-law's_tongue"}, {'id': 19897, 'synset': 'spanish_bayonet.n.02', 'name': 'Spanish_bayonet'}, {'id': 19898, 'synset': 'spanish_bayonet.n.01', 'name': 'Spanish_bayonet'}, {'id': 19899, 'synset': 'joshua_tree.n.01', 'name': 'Joshua_tree'}, {'id': 19900, 'synset': 'soapweed.n.01', 'name': 'soapweed'}, {'id': 19901, 'synset': "adam's_needle.n.01", 'name': "Adam's_needle"}, {'id': 19902, 'synset': 'bear_grass.n.02', 'name': 'bear_grass'}, {'id': 19903, 'synset': 'spanish_dagger.n.01', 'name': 'Spanish_dagger'}, {'id': 19904, 'synset': "our_lord's_candle.n.01", 'name': "Our_Lord's_candle"}, {'id': 19905, 'synset': 'water_shamrock.n.01', 'name': 'water_shamrock'}, {'id': 19906, 'synset': 'butterfly_bush.n.01', 'name': 'butterfly_bush'}, {'id': 19907, 'synset': 'yellow_jasmine.n.01', 'name': 'yellow_jasmine'}, {'id': 19908, 'synset': 'flax.n.02', 'name': 'flax'}, {'id': 19909, 'synset': 'calabar_bean.n.01', 'name': 'calabar_bean'}, {'id': 19910, 'synset': 'bonduc.n.02', 'name': 'bonduc'}, {'id': 19911, 'synset': 'divi-divi.n.02', 'name': 'divi-divi'}, {'id': 19912, 'synset': 'mysore_thorn.n.01', 'name': 'Mysore_thorn'}, {'id': 19913, 'synset': 'brazilian_ironwood.n.01', 'name': 'brazilian_ironwood'}, {'id': 19914, 'synset': 'bird_of_paradise.n.01', 'name': 'bird_of_paradise'}, {'id': 19915, 'synset': 'shingle_tree.n.01', 'name': 'shingle_tree'}, {'id': 19916, 'synset': 'mountain_ebony.n.01', 'name': 'mountain_ebony'}, {'id': 19917, 'synset': 'msasa.n.01', 'name': 'msasa'}, {'id': 19918, 'synset': 'cassia.n.01', 'name': 'cassia'}, {'id': 19919, 'synset': 'golden_shower_tree.n.01', 'name': 'golden_shower_tree'}, {'id': 19920, 'synset': 'pink_shower.n.01', 'name': 'pink_shower'}, {'id': 19921, 'synset': 'rainbow_shower.n.01', 'name': 'rainbow_shower'}, {'id': 19922, 'synset': 'horse_cassia.n.01', 'name': 'horse_cassia'}, {'id': 19923, 'synset': 'carob.n.02', 'name': 'carob'}, {'id': 19924, 'synset': 'carob.n.01', 'name': 'carob'}, {'id': 19925, 'synset': 'paloverde.n.01', 'name': 'paloverde'}, {'id': 19926, 'synset': 'royal_poinciana.n.01', 'name': 'royal_poinciana'}, {'id': 19927, 'synset': 'locust_tree.n.01', 'name': 'locust_tree'}, {'id': 19928, 'synset': 'water_locust.n.01', 'name': 'water_locust'}, {'id': 19929, 'synset': 'honey_locust.n.01', 'name': 'honey_locust'}, {'id': 19930, 'synset': 'kentucky_coffee_tree.n.01', 'name': 'Kentucky_coffee_tree'}, {'id': 19931, 'synset': 'logwood.n.02', 'name': 'logwood'}, {'id': 19932, 'synset': 'jerusalem_thorn.n.03', 'name': 'Jerusalem_thorn'}, {'id': 19933, 'synset': 'palo_verde.n.01', 'name': 'palo_verde'}, {'id': 19934, 'synset': 'dalmatian_laburnum.n.01', 'name': 'Dalmatian_laburnum'}, {'id': 19935, 'synset': 'senna.n.01', 'name': 'senna'}, {'id': 19936, 'synset': 'avaram.n.01', 'name': 'avaram'}, {'id': 19937, 'synset': 'alexandria_senna.n.01', 'name': 'Alexandria_senna'}, {'id': 19938, 'synset': 'wild_senna.n.01', 'name': 'wild_senna'}, {'id': 19939, 'synset': 'sicklepod.n.01', 'name': 'sicklepod'}, {'id': 19940, 'synset': 'coffee_senna.n.01', 'name': 'coffee_senna'}, {'id': 19941, 'synset': 'tamarind.n.01', 'name': 'tamarind'}, {'id': 19942, 'synset': 'false_indigo.n.03', 'name': 'false_indigo'}, {'id': 19943, 'synset': 'false_indigo.n.02', 'name': 'false_indigo'}, {'id': 19944, 'synset': 'hog_peanut.n.01', 'name': 'hog_peanut'}, {'id': 19945, 'synset': 'angelim.n.01', 'name': 'angelim'}, {'id': 19946, 'synset': 'cabbage_bark.n.01', 'name': 'cabbage_bark'}, {'id': 19947, 'synset': 'kidney_vetch.n.01', 'name': 'kidney_vetch'}, {'id': 19948, 'synset': 'groundnut.n.01', 'name': 'groundnut'}, {'id': 19949, 'synset': 'rooibos.n.01', 'name': 'rooibos'}, {'id': 19950, 'synset': 'milk_vetch.n.01', 'name': 'milk_vetch'}, {'id': 19951, 'synset': 'alpine_milk_vetch.n.01', 'name': 'alpine_milk_vetch'}, {'id': 19952, 'synset': 'purple_milk_vetch.n.01', 'name': 'purple_milk_vetch'}, {'id': 19953, 'synset': 'camwood.n.01', 'name': 'camwood'}, {'id': 19954, 'synset': 'wild_indigo.n.01', 'name': 'wild_indigo'}, {'id': 19955, 'synset': 'blue_false_indigo.n.01', 'name': 'blue_false_indigo'}, {'id': 19956, 'synset': 'white_false_indigo.n.01', 'name': 'white_false_indigo'}, {'id': 19957, 'synset': 'indigo_broom.n.01', 'name': 'indigo_broom'}, {'id': 19958, 'synset': 'dhak.n.01', 'name': 'dhak'}, {'id': 19959, 'synset': 'pigeon_pea.n.01', 'name': 'pigeon_pea'}, {'id': 19960, 'synset': 'sword_bean.n.01', 'name': 'sword_bean'}, {'id': 19961, 'synset': 'pea_tree.n.01', 'name': 'pea_tree'}, {'id': 19962, 'synset': 'siberian_pea_tree.n.01', 'name': 'Siberian_pea_tree'}, {'id': 19963, 'synset': 'chinese_pea_tree.n.01', 'name': 'Chinese_pea_tree'}, {'id': 19964, 'synset': 'moreton_bay_chestnut.n.01', 'name': 'Moreton_Bay_chestnut'}, {'id': 19965, 'synset': 'butterfly_pea.n.03', 'name': 'butterfly_pea'}, {'id': 19966, 'synset': 'judas_tree.n.01', 'name': 'Judas_tree'}, {'id': 19967, 'synset': 'redbud.n.01', 'name': 'redbud'}, {'id': 19968, 'synset': 'western_redbud.n.01', 'name': 'western_redbud'}, {'id': 19969, 'synset': 'tagasaste.n.01', 'name': 'tagasaste'}, {'id': 19970, 'synset': 'weeping_tree_broom.n.01', 'name': 'weeping_tree_broom'}, {'id': 19971, 'synset': 'flame_pea.n.01', 'name': 'flame_pea'}, {'id': 19972, 'synset': 'chickpea.n.02', 'name': 'chickpea'}, {'id': 19973, 'synset': 'kentucky_yellowwood.n.01', 'name': 'Kentucky_yellowwood'}, {'id': 19974, 'synset': 'glory_pea.n.01', 'name': 'glory_pea'}, {'id': 19975, 'synset': 'desert_pea.n.01', 'name': 'desert_pea'}, {'id': 19976, 'synset': "parrot's_beak.n.01", 'name': "parrot's_beak"}, {'id': 19977, 'synset': 'butterfly_pea.n.02', 'name': 'butterfly_pea'}, {'id': 19978, 'synset': 'blue_pea.n.01', 'name': 'blue_pea'}, {'id': 19979, 'synset': 'telegraph_plant.n.01', 'name': 'telegraph_plant'}, {'id': 19980, 'synset': 'bladder_senna.n.01', 'name': 'bladder_senna'}, {'id': 19981, 'synset': 'axseed.n.01', 'name': 'axseed'}, {'id': 19982, 'synset': 'crotalaria.n.01', 'name': 'crotalaria'}, {'id': 19983, 'synset': 'guar.n.01', 'name': 'guar'}, {'id': 19984, 'synset': 'white_broom.n.01', 'name': 'white_broom'}, {'id': 19985, 'synset': 'common_broom.n.01', 'name': 'common_broom'}, {'id': 19986, 'synset': 'rosewood.n.02', 'name': 'rosewood'}, {'id': 19987, 'synset': 'indian_blackwood.n.01', 'name': 'Indian_blackwood'}, {'id': 19988, 'synset': 'sissoo.n.01', 'name': 'sissoo'}, {'id': 19989, 'synset': 'kingwood.n.02', 'name': 'kingwood'}, {'id': 19990, 'synset': 'brazilian_rosewood.n.01', 'name': 'Brazilian_rosewood'}, {'id': 19991, 'synset': 'cocobolo.n.01', 'name': 'cocobolo'}, {'id': 19992, 'synset': 'blackwood.n.02', 'name': 'blackwood'}, {'id': 19993, 'synset': 'bitter_pea.n.01', 'name': 'bitter_pea'}, {'id': 19994, 'synset': 'derris.n.01', 'name': 'derris'}, {'id': 19995, 'synset': 'derris_root.n.01', 'name': 'derris_root'}, {'id': 19996, 'synset': 'prairie_mimosa.n.01', 'name': 'prairie_mimosa'}, {'id': 19997, 'synset': 'tick_trefoil.n.01', 'name': 'tick_trefoil'}, {'id': 19998, 'synset': 'beggarweed.n.01', 'name': 'beggarweed'}, {'id': 19999, 'synset': 'australian_pea.n.01', 'name': 'Australian_pea'}, {'id': 20000, 'synset': 'coral_tree.n.01', 'name': 'coral_tree'}, {'id': 20001, 'synset': 'kaffir_boom.n.02', 'name': 'kaffir_boom'}, {'id': 20002, 'synset': 'coral_bean_tree.n.01', 'name': 'coral_bean_tree'}, {'id': 20003, 'synset': 'ceibo.n.01', 'name': 'ceibo'}, {'id': 20004, 'synset': 'kaffir_boom.n.01', 'name': 'kaffir_boom'}, {'id': 20005, 'synset': 'indian_coral_tree.n.01', 'name': 'Indian_coral_tree'}, {'id': 20006, 'synset': 'cork_tree.n.02', 'name': 'cork_tree'}, {'id': 20007, 'synset': "goat's_rue.n.02", 'name': "goat's_rue"}, {'id': 20008, 'synset': 'poison_bush.n.01', 'name': 'poison_bush'}, {'id': 20009, 'synset': 'spanish_broom.n.02', 'name': 'Spanish_broom'}, {'id': 20010, 'synset': 'woodwaxen.n.01', 'name': 'woodwaxen'}, {'id': 20011, 'synset': 'chanar.n.01', 'name': 'chanar'}, {'id': 20012, 'synset': 'gliricidia.n.01', 'name': 'gliricidia'}, {'id': 20013, 'synset': 'soy.n.01', 'name': 'soy'}, {'id': 20014, 'synset': 'licorice.n.01', 'name': 'licorice'}, {'id': 20015, 'synset': 'wild_licorice.n.02', 'name': 'wild_licorice'}, {'id': 20016, 'synset': 'licorice_root.n.01', 'name': 'licorice_root'}, {'id': 20017, 'synset': 'western_australia_coral_pea.n.01', 'name': 'Western_Australia_coral_pea'}, {'id': 20018, 'synset': 'sweet_vetch.n.01', 'name': 'sweet_vetch'}, {'id': 20019, 'synset': 'french_honeysuckle.n.02', 'name': 'French_honeysuckle'}, {'id': 20020, 'synset': 'anil.n.02', 'name': 'anil'}, {'id': 20021, 'synset': 'scarlet_runner.n.02', 'name': 'scarlet_runner'}, {'id': 20022, 'synset': 'hyacinth_bean.n.01', 'name': 'hyacinth_bean'}, {'id': 20023, 'synset': 'scotch_laburnum.n.01', 'name': 'Scotch_laburnum'}, {'id': 20024, 'synset': 'vetchling.n.01', 'name': 'vetchling'}, {'id': 20025, 'synset': 'wild_pea.n.01', 'name': 'wild_pea'}, {'id': 20026, 'synset': 'everlasting_pea.n.01', 'name': 'everlasting_pea'}, {'id': 20027, 'synset': 'beach_pea.n.01', 'name': 'beach_pea'}, {'id': 20028, 'synset': 'grass_vetch.n.01', 'name': 'grass_vetch'}, {'id': 20029, 'synset': 'marsh_pea.n.01', 'name': 'marsh_pea'}, {'id': 20030, 'synset': 'common_vetchling.n.01', 'name': 'common_vetchling'}, {'id': 20031, 'synset': 'grass_pea.n.01', 'name': 'grass_pea'}, {'id': 20032, 'synset': 'tangier_pea.n.01', 'name': 'Tangier_pea'}, {'id': 20033, 'synset': 'heath_pea.n.01', 'name': 'heath_pea'}, {'id': 20034, 'synset': 'bicolor_lespediza.n.01', 'name': 'bicolor_lespediza'}, {'id': 20035, 'synset': 'japanese_clover.n.01', 'name': 'japanese_clover'}, {'id': 20036, 'synset': 'korean_lespedeza.n.01', 'name': 'Korean_lespedeza'}, {'id': 20037, 'synset': 'sericea_lespedeza.n.01', 'name': 'sericea_lespedeza'}, {'id': 20038, 'synset': 'lentil.n.03', 'name': 'lentil'}, {'id': 20039, 'synset': 'lentil.n.02', 'name': 'lentil'}, {'id': 20040, 'synset': "prairie_bird's-foot_trefoil.n.01", 'name': "prairie_bird's-foot_trefoil"}, {'id': 20041, 'synset': "bird's_foot_trefoil.n.02", 'name': "bird's_foot_trefoil"}, {'id': 20042, 'synset': 'winged_pea.n.02', 'name': 'winged_pea'}, {'id': 20043, 'synset': 'lupine.n.01', 'name': 'lupine'}, {'id': 20044, 'synset': 'white_lupine.n.01', 'name': 'white_lupine'}, {'id': 20045, 'synset': 'tree_lupine.n.01', 'name': 'tree_lupine'}, {'id': 20046, 'synset': 'wild_lupine.n.01', 'name': 'wild_lupine'}, {'id': 20047, 'synset': 'bluebonnet.n.01', 'name': 'bluebonnet'}, {'id': 20048, 'synset': 'texas_bluebonnet.n.01', 'name': 'Texas_bluebonnet'}, {'id': 20049, 'synset': 'medic.n.01', 'name': 'medic'}, {'id': 20050, 'synset': 'moon_trefoil.n.01', 'name': 'moon_trefoil'}, {'id': 20051, 'synset': 'sickle_alfalfa.n.01', 'name': 'sickle_alfalfa'}, {'id': 20052, 'synset': 'calvary_clover.n.01', 'name': 'Calvary_clover'}, {'id': 20053, 'synset': 'black_medick.n.01', 'name': 'black_medick'}, {'id': 20054, 'synset': 'alfalfa.n.01', 'name': 'alfalfa'}, {'id': 20055, 'synset': 'millettia.n.01', 'name': 'millettia'}, {'id': 20056, 'synset': 'mucuna.n.01', 'name': 'mucuna'}, {'id': 20057, 'synset': 'cowage.n.02', 'name': 'cowage'}, {'id': 20058, 'synset': 'tolu_tree.n.01', 'name': 'tolu_tree'}, {'id': 20059, 'synset': 'peruvian_balsam.n.01', 'name': 'Peruvian_balsam'}, {'id': 20060, 'synset': 'sainfoin.n.01', 'name': 'sainfoin'}, {'id': 20061, 'synset': 'restharrow.n.02', 'name': 'restharrow'}, {'id': 20062, 'synset': 'bead_tree.n.01', 'name': 'bead_tree'}, {'id': 20063, 'synset': 'jumby_bead.n.01', 'name': 'jumby_bead'}, {'id': 20064, 'synset': 'locoweed.n.01', 'name': 'locoweed'}, {'id': 20065, 'synset': 'purple_locoweed.n.01', 'name': 'purple_locoweed'}, {'id': 20066, 'synset': 'tumbleweed.n.01', 'name': 'tumbleweed'}, {'id': 20067, 'synset': 'yam_bean.n.02', 'name': 'yam_bean'}, {'id': 20068, 'synset': 'shamrock_pea.n.01', 'name': 'shamrock_pea'}, {'id': 20069, 'synset': 'pole_bean.n.01', 'name': 'pole_bean'}, {'id': 20070, 'synset': 'kidney_bean.n.01', 'name': 'kidney_bean'}, {'id': 20071, 'synset': 'haricot.n.01', 'name': 'haricot'}, {'id': 20072, 'synset': 'wax_bean.n.01', 'name': 'wax_bean'}, {'id': 20073, 'synset': 'scarlet_runner.n.01', 'name': 'scarlet_runner'}, {'id': 20074, 'synset': 'lima_bean.n.02', 'name': 'lima_bean'}, {'id': 20075, 'synset': 'sieva_bean.n.01', 'name': 'sieva_bean'}, {'id': 20076, 'synset': 'tepary_bean.n.01', 'name': 'tepary_bean'}, {'id': 20077, 'synset': 'chaparral_pea.n.01', 'name': 'chaparral_pea'}, {'id': 20078, 'synset': 'jamaica_dogwood.n.01', 'name': 'Jamaica_dogwood'}, {'id': 20079, 'synset': 'pea.n.02', 'name': 'pea'}, {'id': 20080, 'synset': 'garden_pea.n.01', 'name': 'garden_pea'}, {'id': 20081, 'synset': 'edible-pod_pea.n.01', 'name': 'edible-pod_pea'}, {'id': 20082, 'synset': 'sugar_snap_pea.n.01', 'name': 'sugar_snap_pea'}, {'id': 20083, 'synset': 'field_pea.n.02', 'name': 'field_pea'}, {'id': 20084, 'synset': 'field_pea.n.01', 'name': 'field_pea'}, {'id': 20085, 'synset': 'common_flat_pea.n.01', 'name': 'common_flat_pea'}, {'id': 20086, 'synset': 'quira.n.02', 'name': 'quira'}, {'id': 20087, 'synset': 'roble.n.01', 'name': 'roble'}, {'id': 20088, 'synset': 'panama_redwood_tree.n.01', 'name': 'Panama_redwood_tree'}, {'id': 20089, 'synset': 'indian_beech.n.01', 'name': 'Indian_beech'}, {'id': 20090, 'synset': 'winged_bean.n.01', 'name': 'winged_bean'}, {'id': 20091, 'synset': 'breadroot.n.01', 'name': 'breadroot'}, {'id': 20092, 'synset': 'bloodwood_tree.n.01', 'name': 'bloodwood_tree'}, {'id': 20093, 'synset': 'kino.n.02', 'name': 'kino'}, {'id': 20094, 'synset': 'red_sandalwood.n.02', 'name': 'red_sandalwood'}, {'id': 20095, 'synset': 'kudzu.n.01', 'name': 'kudzu'}, {'id': 20096, 'synset': 'bristly_locust.n.01', 'name': 'bristly_locust'}, {'id': 20097, 'synset': 'black_locust.n.02', 'name': 'black_locust'}, {'id': 20098, 'synset': 'clammy_locust.n.01', 'name': 'clammy_locust'}, {'id': 20099, 'synset': 'carib_wood.n.01', 'name': 'carib_wood'}, {'id': 20100, 'synset': 'colorado_river_hemp.n.01', 'name': 'Colorado_River_hemp'}, {'id': 20101, 'synset': 'scarlet_wisteria_tree.n.01', 'name': 'scarlet_wisteria_tree'}, {'id': 20102, 'synset': 'japanese_pagoda_tree.n.01', 'name': 'Japanese_pagoda_tree'}, {'id': 20103, 'synset': 'mescal_bean.n.01', 'name': 'mescal_bean'}, {'id': 20104, 'synset': 'kowhai.n.01', 'name': 'kowhai'}, {'id': 20105, 'synset': 'jade_vine.n.01', 'name': 'jade_vine'}, {'id': 20106, 'synset': 'hoary_pea.n.01', 'name': 'hoary_pea'}, {'id': 20107, 'synset': 'bastard_indigo.n.01', 'name': 'bastard_indigo'}, {'id': 20108, 'synset': 'catgut.n.01', 'name': 'catgut'}, {'id': 20109, 'synset': 'bush_pea.n.01', 'name': 'bush_pea'}, {'id': 20110, 'synset': 'false_lupine.n.01', 'name': 'false_lupine'}, {'id': 20111, 'synset': 'carolina_lupine.n.01', 'name': 'Carolina_lupine'}, {'id': 20112, 'synset': 'tipu.n.01', 'name': 'tipu'}, {'id': 20113, 'synset': "bird's_foot_trefoil.n.01", 'name': "bird's_foot_trefoil"}, {'id': 20114, 'synset': 'fenugreek.n.01', 'name': 'fenugreek'}, {'id': 20115, 'synset': 'gorse.n.01', 'name': 'gorse'}, {'id': 20116, 'synset': 'vetch.n.01', 'name': 'vetch'}, {'id': 20117, 'synset': 'tufted_vetch.n.01', 'name': 'tufted_vetch'}, {'id': 20118, 'synset': 'broad_bean.n.01', 'name': 'broad_bean'}, {'id': 20119, 'synset': 'bitter_betch.n.01', 'name': 'bitter_betch'}, {'id': 20120, 'synset': 'bush_vetch.n.01', 'name': 'bush_vetch'}, {'id': 20121, 'synset': 'moth_bean.n.01', 'name': 'moth_bean'}, {'id': 20122, 'synset': 'snailflower.n.01', 'name': 'snailflower'}, {'id': 20123, 'synset': 'mung.n.01', 'name': 'mung'}, {'id': 20124, 'synset': 'cowpea.n.02', 'name': 'cowpea'}, {'id': 20125, 'synset': 'cowpea.n.01', 'name': 'cowpea'}, {'id': 20126, 'synset': 'asparagus_bean.n.01', 'name': 'asparagus_bean'}, {'id': 20127, 'synset': 'swamp_oak.n.01', 'name': 'swamp_oak'}, {'id': 20128, 'synset': 'keurboom.n.02', 'name': 'keurboom'}, {'id': 20129, 'synset': 'keurboom.n.01', 'name': 'keurboom'}, {'id': 20130, 'synset': 'japanese_wistaria.n.01', 'name': 'Japanese_wistaria'}, {'id': 20131, 'synset': 'chinese_wistaria.n.01', 'name': 'Chinese_wistaria'}, {'id': 20132, 'synset': 'american_wistaria.n.01', 'name': 'American_wistaria'}, {'id': 20133, 'synset': 'silky_wisteria.n.01', 'name': 'silky_wisteria'}, {'id': 20134, 'synset': 'palm.n.03', 'name': 'palm'}, {'id': 20135, 'synset': 'sago_palm.n.01', 'name': 'sago_palm'}, {'id': 20136, 'synset': 'feather_palm.n.01', 'name': 'feather_palm'}, {'id': 20137, 'synset': 'fan_palm.n.01', 'name': 'fan_palm'}, {'id': 20138, 'synset': 'palmetto.n.01', 'name': 'palmetto'}, {'id': 20139, 'synset': 'coyol.n.01', 'name': 'coyol'}, {'id': 20140, 'synset': 'grugru.n.01', 'name': 'grugru'}, {'id': 20141, 'synset': 'areca.n.01', 'name': 'areca'}, {'id': 20142, 'synset': 'betel_palm.n.01', 'name': 'betel_palm'}, {'id': 20143, 'synset': 'sugar_palm.n.01', 'name': 'sugar_palm'}, {'id': 20144, 'synset': 'piassava_palm.n.01', 'name': 'piassava_palm'}, {'id': 20145, 'synset': 'coquilla_nut.n.01', 'name': 'coquilla_nut'}, {'id': 20146, 'synset': 'palmyra.n.01', 'name': 'palmyra'}, {'id': 20147, 'synset': 'calamus.n.01', 'name': 'calamus'}, {'id': 20148, 'synset': 'rattan.n.01', 'name': 'rattan'}, {'id': 20149, 'synset': 'lawyer_cane.n.01', 'name': 'lawyer_cane'}, {'id': 20150, 'synset': 'fishtail_palm.n.01', 'name': 'fishtail_palm'}, {'id': 20151, 'synset': 'wine_palm.n.01', 'name': 'wine_palm'}, {'id': 20152, 'synset': 'wax_palm.n.03', 'name': 'wax_palm'}, {'id': 20153, 'synset': 'coconut.n.03', 'name': 'coconut'}, {'id': 20154, 'synset': 'carnauba.n.02', 'name': 'carnauba'}, {'id': 20155, 'synset': 'caranday.n.01', 'name': 'caranday'}, {'id': 20156, 'synset': 'corozo.n.01', 'name': 'corozo'}, {'id': 20157, 'synset': 'gebang_palm.n.01', 'name': 'gebang_palm'}, {'id': 20158, 'synset': 'latanier.n.01', 'name': 'latanier'}, {'id': 20159, 'synset': 'talipot.n.01', 'name': 'talipot'}, {'id': 20160, 'synset': 'oil_palm.n.01', 'name': 'oil_palm'}, {'id': 20161, 'synset': 'african_oil_palm.n.01', 'name': 'African_oil_palm'}, {'id': 20162, 'synset': 'american_oil_palm.n.01', 'name': 'American_oil_palm'}, {'id': 20163, 'synset': 'palm_nut.n.01', 'name': 'palm_nut'}, {'id': 20164, 'synset': 'cabbage_palm.n.04', 'name': 'cabbage_palm'}, {'id': 20165, 'synset': 'cabbage_palm.n.03', 'name': 'cabbage_palm'}, {'id': 20166, 'synset': 'true_sago_palm.n.01', 'name': 'true_sago_palm'}, {'id': 20167, 'synset': 'nipa_palm.n.01', 'name': 'nipa_palm'}, {'id': 20168, 'synset': 'babassu.n.01', 'name': 'babassu'}, {'id': 20169, 'synset': 'babassu_nut.n.01', 'name': 'babassu_nut'}, {'id': 20170, 'synset': 'cohune_palm.n.01', 'name': 'cohune_palm'}, {'id': 20171, 'synset': 'cohune_nut.n.01', 'name': 'cohune_nut'}, {'id': 20172, 'synset': 'date_palm.n.01', 'name': 'date_palm'}, {'id': 20173, 'synset': 'ivory_palm.n.01', 'name': 'ivory_palm'}, {'id': 20174, 'synset': 'raffia_palm.n.01', 'name': 'raffia_palm'}, {'id': 20175, 'synset': 'bamboo_palm.n.02', 'name': 'bamboo_palm'}, {'id': 20176, 'synset': 'lady_palm.n.01', 'name': 'lady_palm'}, {'id': 20177, 'synset': 'miniature_fan_palm.n.01', 'name': 'miniature_fan_palm'}, {'id': 20178, 'synset': 'reed_rhapis.n.01', 'name': 'reed_rhapis'}, {'id': 20179, 'synset': 'royal_palm.n.01', 'name': 'royal_palm'}, {'id': 20180, 'synset': 'cabbage_palm.n.02', 'name': 'cabbage_palm'}, {'id': 20181, 'synset': 'cabbage_palmetto.n.01', 'name': 'cabbage_palmetto'}, {'id': 20182, 'synset': 'saw_palmetto.n.01', 'name': 'saw_palmetto'}, {'id': 20183, 'synset': 'thatch_palm.n.01', 'name': 'thatch_palm'}, {'id': 20184, 'synset': 'key_palm.n.01', 'name': 'key_palm'}, {'id': 20185, 'synset': 'english_plantain.n.01', 'name': 'English_plantain'}, {'id': 20186, 'synset': 'broad-leaved_plantain.n.02', 'name': 'broad-leaved_plantain'}, {'id': 20187, 'synset': 'hoary_plantain.n.02', 'name': 'hoary_plantain'}, {'id': 20188, 'synset': 'fleawort.n.01', 'name': 'fleawort'}, {'id': 20189, 'synset': "rugel's_plantain.n.01", 'name': "rugel's_plantain"}, {'id': 20190, 'synset': 'hoary_plantain.n.01', 'name': 'hoary_plantain'}, {'id': 20191, 'synset': 'buckwheat.n.01', 'name': 'buckwheat'}, {'id': 20192, 'synset': "prince's-feather.n.01", 'name': "prince's-feather"}, {'id': 20193, 'synset': 'eriogonum.n.01', 'name': 'eriogonum'}, {'id': 20194, 'synset': 'umbrella_plant.n.02', 'name': 'umbrella_plant'}, {'id': 20195, 'synset': 'wild_buckwheat.n.01', 'name': 'wild_buckwheat'}, {'id': 20196, 'synset': 'rhubarb.n.02', 'name': 'rhubarb'}, {'id': 20197, 'synset': 'himalayan_rhubarb.n.01', 'name': 'Himalayan_rhubarb'}, {'id': 20198, 'synset': 'pie_plant.n.01', 'name': 'pie_plant'}, {'id': 20199, 'synset': 'chinese_rhubarb.n.01', 'name': 'Chinese_rhubarb'}, {'id': 20200, 'synset': 'sour_dock.n.01', 'name': 'sour_dock'}, {'id': 20201, 'synset': 'sheep_sorrel.n.01', 'name': 'sheep_sorrel'}, {'id': 20202, 'synset': 'bitter_dock.n.01', 'name': 'bitter_dock'}, {'id': 20203, 'synset': 'french_sorrel.n.01', 'name': 'French_sorrel'}, {'id': 20204, 'synset': 'yellow-eyed_grass.n.01', 'name': 'yellow-eyed_grass'}, {'id': 20205, 'synset': 'commelina.n.01', 'name': 'commelina'}, {'id': 20206, 'synset': 'spiderwort.n.01', 'name': 'spiderwort'}, {'id': 20207, 'synset': 'pineapple.n.01', 'name': 'pineapple'}, {'id': 20208, 'synset': 'pipewort.n.01', 'name': 'pipewort'}, {'id': 20209, 'synset': 'water_hyacinth.n.01', 'name': 'water_hyacinth'}, {'id': 20210, 'synset': 'water_star_grass.n.01', 'name': 'water_star_grass'}, {'id': 20211, 'synset': 'naiad.n.01', 'name': 'naiad'}, {'id': 20212, 'synset': 'water_plantain.n.01', 'name': 'water_plantain'}, {'id': 20213, 'synset': 'narrow-leaved_water_plantain.n.01', 'name': 'narrow-leaved_water_plantain'}, {'id': 20214, 'synset': 'hydrilla.n.01', 'name': 'hydrilla'}, {'id': 20215, 'synset': 'american_frogbit.n.01', 'name': 'American_frogbit'}, {'id': 20216, 'synset': 'waterweed.n.01', 'name': 'waterweed'}, {'id': 20217, 'synset': 'canadian_pondweed.n.01', 'name': 'Canadian_pondweed'}, {'id': 20218, 'synset': 'tape_grass.n.01', 'name': 'tape_grass'}, {'id': 20219, 'synset': 'pondweed.n.01', 'name': 'pondweed'}, {'id': 20220, 'synset': 'curled_leaf_pondweed.n.01', 'name': 'curled_leaf_pondweed'}, {'id': 20221, 'synset': 'loddon_pondweed.n.01', 'name': 'loddon_pondweed'}, {'id': 20222, 'synset': "frog's_lettuce.n.01", 'name': "frog's_lettuce"}, {'id': 20223, 'synset': 'arrow_grass.n.01', 'name': 'arrow_grass'}, {'id': 20224, 'synset': 'horned_pondweed.n.01', 'name': 'horned_pondweed'}, {'id': 20225, 'synset': 'eelgrass.n.01', 'name': 'eelgrass'}, {'id': 20226, 'synset': 'rose.n.01', 'name': 'rose'}, {'id': 20227, 'synset': 'hip.n.05', 'name': 'hip'}, {'id': 20228, 'synset': 'banksia_rose.n.01', 'name': 'banksia_rose'}, {'id': 20229, 'synset': 'damask_rose.n.01', 'name': 'damask_rose'}, {'id': 20230, 'synset': 'sweetbrier.n.01', 'name': 'sweetbrier'}, {'id': 20231, 'synset': 'cherokee_rose.n.01', 'name': 'Cherokee_rose'}, {'id': 20232, 'synset': 'musk_rose.n.01', 'name': 'musk_rose'}, {'id': 20233, 'synset': 'agrimonia.n.01', 'name': 'agrimonia'}, {'id': 20234, 'synset': 'harvest-lice.n.01', 'name': 'harvest-lice'}, {'id': 20235, 'synset': 'fragrant_agrimony.n.01', 'name': 'fragrant_agrimony'}, {'id': 20236, 'synset': 'alderleaf_juneberry.n.01', 'name': 'alderleaf_Juneberry'}, {'id': 20237, 'synset': 'flowering_quince.n.01', 'name': 'flowering_quince'}, {'id': 20238, 'synset': 'japonica.n.02', 'name': 'japonica'}, {'id': 20239, 'synset': 'coco_plum.n.01', 'name': 'coco_plum'}, {'id': 20240, 'synset': 'cotoneaster.n.01', 'name': 'cotoneaster'}, {'id': 20241, 'synset': 'cotoneaster_dammeri.n.01', 'name': 'Cotoneaster_dammeri'}, {'id': 20242, 'synset': 'cotoneaster_horizontalis.n.01', 'name': 'Cotoneaster_horizontalis'}, {'id': 20243, 'synset': 'parsley_haw.n.01', 'name': 'parsley_haw'}, {'id': 20244, 'synset': 'scarlet_haw.n.01', 'name': 'scarlet_haw'}, {'id': 20245, 'synset': 'blackthorn.n.02', 'name': 'blackthorn'}, {'id': 20246, 'synset': 'cockspur_thorn.n.01', 'name': 'cockspur_thorn'}, {'id': 20247, 'synset': 'mayhaw.n.01', 'name': 'mayhaw'}, {'id': 20248, 'synset': 'red_haw.n.02', 'name': 'red_haw'}, {'id': 20249, 'synset': 'red_haw.n.01', 'name': 'red_haw'}, {'id': 20250, 'synset': 'quince.n.01', 'name': 'quince'}, {'id': 20251, 'synset': 'mountain_avens.n.01', 'name': 'mountain_avens'}, {'id': 20252, 'synset': 'loquat.n.01', 'name': 'loquat'}, {'id': 20253, 'synset': 'beach_strawberry.n.01', 'name': 'beach_strawberry'}, {'id': 20254, 'synset': 'virginia_strawberry.n.01', 'name': 'Virginia_strawberry'}, {'id': 20255, 'synset': 'avens.n.01', 'name': 'avens'}, {'id': 20256, 'synset': 'yellow_avens.n.02', 'name': 'yellow_avens'}, {'id': 20257, 'synset': 'yellow_avens.n.01', 'name': 'yellow_avens'}, {'id': 20258, 'synset': 'prairie_smoke.n.01', 'name': 'prairie_smoke'}, {'id': 20259, 'synset': 'bennet.n.01', 'name': 'bennet'}, {'id': 20260, 'synset': 'toyon.n.01', 'name': 'toyon'}, {'id': 20261, 'synset': 'apple_tree.n.01', 'name': 'apple_tree'}, {'id': 20262, 'synset': 'apple.n.02', 'name': 'apple'}, {'id': 20263, 'synset': 'wild_apple.n.01', 'name': 'wild_apple'}, {'id': 20264, 'synset': 'crab_apple.n.01', 'name': 'crab_apple'}, {'id': 20265, 'synset': 'siberian_crab.n.01', 'name': 'Siberian_crab'}, {'id': 20266, 'synset': 'wild_crab.n.01', 'name': 'wild_crab'}, {'id': 20267, 'synset': 'american_crab_apple.n.01', 'name': 'American_crab_apple'}, {'id': 20268, 'synset': 'oregon_crab_apple.n.01', 'name': 'Oregon_crab_apple'}, {'id': 20269, 'synset': 'southern_crab_apple.n.01', 'name': 'Southern_crab_apple'}, {'id': 20270, 'synset': 'iowa_crab.n.01', 'name': 'Iowa_crab'}, {'id': 20271, 'synset': 'bechtel_crab.n.01', 'name': 'Bechtel_crab'}, {'id': 20272, 'synset': 'medlar.n.02', 'name': 'medlar'}, {'id': 20273, 'synset': 'cinquefoil.n.01', 'name': 'cinquefoil'}, {'id': 20274, 'synset': 'silverweed.n.02', 'name': 'silverweed'}, {'id': 20275, 'synset': 'salad_burnet.n.01', 'name': 'salad_burnet'}, {'id': 20276, 'synset': 'plum.n.01', 'name': 'plum'}, {'id': 20277, 'synset': 'wild_plum.n.01', 'name': 'wild_plum'}, {'id': 20278, 'synset': 'allegheny_plum.n.01', 'name': 'Allegheny_plum'}, {'id': 20279, 'synset': 'american_red_plum.n.01', 'name': 'American_red_plum'}, {'id': 20280, 'synset': 'chickasaw_plum.n.01', 'name': 'chickasaw_plum'}, {'id': 20281, 'synset': 'beach_plum.n.01', 'name': 'beach_plum'}, {'id': 20282, 'synset': 'common_plum.n.01', 'name': 'common_plum'}, {'id': 20283, 'synset': 'bullace.n.01', 'name': 'bullace'}, {'id': 20284, 'synset': 'damson_plum.n.02', 'name': 'damson_plum'}, {'id': 20285, 'synset': 'big-tree_plum.n.01', 'name': 'big-tree_plum'}, {'id': 20286, 'synset': 'canada_plum.n.01', 'name': 'Canada_plum'}, {'id': 20287, 'synset': 'plumcot.n.01', 'name': 'plumcot'}, {'id': 20288, 'synset': 'apricot.n.01', 'name': 'apricot'}, {'id': 20289, 'synset': 'japanese_apricot.n.01', 'name': 'Japanese_apricot'}, {'id': 20290, 'synset': 'common_apricot.n.01', 'name': 'common_apricot'}, {'id': 20291, 'synset': 'purple_apricot.n.01', 'name': 'purple_apricot'}, {'id': 20292, 'synset': 'cherry.n.02', 'name': 'cherry'}, {'id': 20293, 'synset': 'wild_cherry.n.02', 'name': 'wild_cherry'}, {'id': 20294, 'synset': 'wild_cherry.n.01', 'name': 'wild_cherry'}, {'id': 20295, 'synset': 'sweet_cherry.n.01', 'name': 'sweet_cherry'}, {'id': 20296, 'synset': 'heart_cherry.n.01', 'name': 'heart_cherry'}, {'id': 20297, 'synset': 'gean.n.01', 'name': 'gean'}, {'id': 20298, 'synset': 'capulin.n.01', 'name': 'capulin'}, {'id': 20299, 'synset': 'cherry_laurel.n.02', 'name': 'cherry_laurel'}, {'id': 20300, 'synset': 'cherry_plum.n.01', 'name': 'cherry_plum'}, {'id': 20301, 'synset': 'sour_cherry.n.01', 'name': 'sour_cherry'}, {'id': 20302, 'synset': 'amarelle.n.01', 'name': 'amarelle'}, {'id': 20303, 'synset': 'morello.n.01', 'name': 'morello'}, {'id': 20304, 'synset': 'marasca.n.01', 'name': 'marasca'}, {'id': 20305, 'synset': 'almond_tree.n.01', 'name': 'almond_tree'}, {'id': 20306, 'synset': 'almond.n.01', 'name': 'almond'}, {'id': 20307, 'synset': 'bitter_almond.n.01', 'name': 'bitter_almond'}, {'id': 20308, 'synset': 'jordan_almond.n.01', 'name': 'jordan_almond'}, {'id': 20309, 'synset': 'dwarf_flowering_almond.n.01', 'name': 'dwarf_flowering_almond'}, {'id': 20310, 'synset': 'holly-leaved_cherry.n.01', 'name': 'holly-leaved_cherry'}, {'id': 20311, 'synset': 'fuji.n.01', 'name': 'fuji'}, {'id': 20312, 'synset': 'flowering_almond.n.02', 'name': 'flowering_almond'}, {'id': 20313, 'synset': 'cherry_laurel.n.01', 'name': 'cherry_laurel'}, {'id': 20314, 'synset': 'catalina_cherry.n.01', 'name': 'Catalina_cherry'}, {'id': 20315, 'synset': 'bird_cherry.n.01', 'name': 'bird_cherry'}, {'id': 20316, 'synset': 'hagberry_tree.n.01', 'name': 'hagberry_tree'}, {'id': 20317, 'synset': 'hagberry.n.01', 'name': 'hagberry'}, {'id': 20318, 'synset': 'pin_cherry.n.01', 'name': 'pin_cherry'}, {'id': 20319, 'synset': 'peach.n.01', 'name': 'peach'}, {'id': 20320, 'synset': 'nectarine.n.01', 'name': 'nectarine'}, {'id': 20321, 'synset': 'sand_cherry.n.01', 'name': 'sand_cherry'}, {'id': 20322, 'synset': 'japanese_plum.n.01', 'name': 'Japanese_plum'}, {'id': 20323, 'synset': 'black_cherry.n.01', 'name': 'black_cherry'}, {'id': 20324, 'synset': 'flowering_cherry.n.01', 'name': 'flowering_cherry'}, {'id': 20325, 'synset': 'oriental_cherry.n.01', 'name': 'oriental_cherry'}, {'id': 20326, 'synset': 'japanese_flowering_cherry.n.01', 'name': 'Japanese_flowering_cherry'}, {'id': 20327, 'synset': 'sierra_plum.n.01', 'name': 'Sierra_plum'}, {'id': 20328, 'synset': 'rosebud_cherry.n.01', 'name': 'rosebud_cherry'}, {'id': 20329, 'synset': 'russian_almond.n.01', 'name': 'Russian_almond'}, {'id': 20330, 'synset': 'flowering_almond.n.01', 'name': 'flowering_almond'}, {'id': 20331, 'synset': 'chokecherry.n.02', 'name': 'chokecherry'}, {'id': 20332, 'synset': 'chokecherry.n.01', 'name': 'chokecherry'}, {'id': 20333, 'synset': 'western_chokecherry.n.01', 'name': 'western_chokecherry'}, {'id': 20334, 'synset': 'pyracantha.n.01', 'name': 'Pyracantha'}, {'id': 20335, 'synset': 'pear.n.02', 'name': 'pear'}, {'id': 20336, 'synset': 'fruit_tree.n.01', 'name': 'fruit_tree'}, {'id': 20337, 'synset': 'bramble_bush.n.01', 'name': 'bramble_bush'}, {'id': 20338, 'synset': 'lawyerbush.n.01', 'name': 'lawyerbush'}, {'id': 20339, 'synset': 'stone_bramble.n.01', 'name': 'stone_bramble'}, {'id': 20340, 'synset': 'sand_blackberry.n.01', 'name': 'sand_blackberry'}, {'id': 20341, 'synset': 'boysenberry.n.01', 'name': 'boysenberry'}, {'id': 20342, 'synset': 'loganberry.n.01', 'name': 'loganberry'}, {'id': 20343, 'synset': 'american_dewberry.n.02', 'name': 'American_dewberry'}, {'id': 20344, 'synset': 'northern_dewberry.n.01', 'name': 'Northern_dewberry'}, {'id': 20345, 'synset': 'southern_dewberry.n.01', 'name': 'Southern_dewberry'}, {'id': 20346, 'synset': 'swamp_dewberry.n.01', 'name': 'swamp_dewberry'}, {'id': 20347, 'synset': 'european_dewberry.n.01', 'name': 'European_dewberry'}, {'id': 20348, 'synset': 'raspberry.n.01', 'name': 'raspberry'}, {'id': 20349, 'synset': 'wild_raspberry.n.01', 'name': 'wild_raspberry'}, {'id': 20350, 'synset': 'american_raspberry.n.01', 'name': 'American_raspberry'}, {'id': 20351, 'synset': 'black_raspberry.n.01', 'name': 'black_raspberry'}, {'id': 20352, 'synset': 'salmonberry.n.03', 'name': 'salmonberry'}, {'id': 20353, 'synset': 'salmonberry.n.02', 'name': 'salmonberry'}, {'id': 20354, 'synset': 'wineberry.n.01', 'name': 'wineberry'}, {'id': 20355, 'synset': 'mountain_ash.n.01', 'name': 'mountain_ash'}, {'id': 20356, 'synset': 'rowan.n.01', 'name': 'rowan'}, {'id': 20357, 'synset': 'rowanberry.n.01', 'name': 'rowanberry'}, {'id': 20358, 'synset': 'american_mountain_ash.n.01', 'name': 'American_mountain_ash'}, {'id': 20359, 'synset': 'western_mountain_ash.n.01', 'name': 'Western_mountain_ash'}, {'id': 20360, 'synset': 'service_tree.n.01', 'name': 'service_tree'}, {'id': 20361, 'synset': 'wild_service_tree.n.01', 'name': 'wild_service_tree'}, {'id': 20362, 'synset': 'spirea.n.02', 'name': 'spirea'}, {'id': 20363, 'synset': 'bridal_wreath.n.02', 'name': 'bridal_wreath'}, {'id': 20364, 'synset': 'madderwort.n.01', 'name': 'madderwort'}, {'id': 20365, 'synset': 'indian_madder.n.01', 'name': 'Indian_madder'}, {'id': 20366, 'synset': 'madder.n.01', 'name': 'madder'}, {'id': 20367, 'synset': 'woodruff.n.02', 'name': 'woodruff'}, {'id': 20368, 'synset': 'dagame.n.01', 'name': 'dagame'}, {'id': 20369, 'synset': 'blolly.n.01', 'name': 'blolly'}, {'id': 20370, 'synset': 'coffee.n.02', 'name': 'coffee'}, {'id': 20371, 'synset': 'arabian_coffee.n.01', 'name': 'Arabian_coffee'}, {'id': 20372, 'synset': 'liberian_coffee.n.01', 'name': 'Liberian_coffee'}, {'id': 20373, 'synset': 'robusta_coffee.n.01', 'name': 'robusta_coffee'}, {'id': 20374, 'synset': 'cinchona.n.02', 'name': 'cinchona'}, {'id': 20375, 'synset': 'cartagena_bark.n.01', 'name': 'Cartagena_bark'}, {'id': 20376, 'synset': 'calisaya.n.01', 'name': 'calisaya'}, {'id': 20377, 'synset': 'cinchona_tree.n.01', 'name': 'cinchona_tree'}, {'id': 20378, 'synset': 'cinchona.n.01', 'name': 'cinchona'}, {'id': 20379, 'synset': 'bedstraw.n.01', 'name': 'bedstraw'}, {'id': 20380, 'synset': 'sweet_woodruff.n.01', 'name': 'sweet_woodruff'}, {'id': 20381, 'synset': 'northern_bedstraw.n.01', 'name': 'Northern_bedstraw'}, {'id': 20382, 'synset': 'yellow_bedstraw.n.01', 'name': 'yellow_bedstraw'}, {'id': 20383, 'synset': 'wild_licorice.n.01', 'name': 'wild_licorice'}, {'id': 20384, 'synset': 'cleavers.n.01', 'name': 'cleavers'}, {'id': 20385, 'synset': 'wild_madder.n.01', 'name': 'wild_madder'}, {'id': 20386, 'synset': 'cape_jasmine.n.01', 'name': 'cape_jasmine'}, {'id': 20387, 'synset': 'genipa.n.01', 'name': 'genipa'}, {'id': 20388, 'synset': 'genipap_fruit.n.01', 'name': 'genipap_fruit'}, {'id': 20389, 'synset': 'hamelia.n.01', 'name': 'hamelia'}, {'id': 20390, 'synset': 'scarlet_bush.n.01', 'name': 'scarlet_bush'}, {'id': 20391, 'synset': 'lemonwood.n.02', 'name': 'lemonwood'}, {'id': 20392, 'synset': 'negro_peach.n.01', 'name': 'negro_peach'}, {'id': 20393, 'synset': 'wild_medlar.n.01', 'name': 'wild_medlar'}, {'id': 20394, 'synset': 'spanish_tamarind.n.01', 'name': 'Spanish_tamarind'}, {'id': 20395, 'synset': 'abelia.n.01', 'name': 'abelia'}, {'id': 20396, 'synset': 'bush_honeysuckle.n.02', 'name': 'bush_honeysuckle'}, {'id': 20397, 'synset': 'american_twinflower.n.01', 'name': 'American_twinflower'}, {'id': 20398, 'synset': 'honeysuckle.n.01', 'name': 'honeysuckle'}, {'id': 20399, 'synset': 'american_fly_honeysuckle.n.01', 'name': 'American_fly_honeysuckle'}, {'id': 20400, 'synset': 'italian_honeysuckle.n.01', 'name': 'Italian_honeysuckle'}, {'id': 20401, 'synset': 'yellow_honeysuckle.n.01', 'name': 'yellow_honeysuckle'}, {'id': 20402, 'synset': 'hairy_honeysuckle.n.01', 'name': 'hairy_honeysuckle'}, {'id': 20403, 'synset': 'japanese_honeysuckle.n.01', 'name': 'Japanese_honeysuckle'}, {'id': 20404, 'synset': "hall's_honeysuckle.n.01", 'name': "Hall's_honeysuckle"}, {'id': 20405, 'synset': "morrow's_honeysuckle.n.01", 'name': "Morrow's_honeysuckle"}, {'id': 20406, 'synset': 'woodbine.n.02', 'name': 'woodbine'}, {'id': 20407, 'synset': 'trumpet_honeysuckle.n.01', 'name': 'trumpet_honeysuckle'}, {'id': 20408, 'synset': 'european_fly_honeysuckle.n.01', 'name': 'European_fly_honeysuckle'}, {'id': 20409, 'synset': 'swamp_fly_honeysuckle.n.01', 'name': 'swamp_fly_honeysuckle'}, {'id': 20410, 'synset': 'snowberry.n.01', 'name': 'snowberry'}, {'id': 20411, 'synset': 'coralberry.n.01', 'name': 'coralberry'}, {'id': 20412, 'synset': 'blue_elder.n.01', 'name': 'blue_elder'}, {'id': 20413, 'synset': 'dwarf_elder.n.01', 'name': 'dwarf_elder'}, {'id': 20414, 'synset': 'american_red_elder.n.01', 'name': 'American_red_elder'}, {'id': 20415, 'synset': 'european_red_elder.n.01', 'name': 'European_red_elder'}, {'id': 20416, 'synset': 'feverroot.n.01', 'name': 'feverroot'}, {'id': 20417, 'synset': 'cranberry_bush.n.01', 'name': 'cranberry_bush'}, {'id': 20418, 'synset': 'wayfaring_tree.n.01', 'name': 'wayfaring_tree'}, {'id': 20419, 'synset': 'guelder_rose.n.01', 'name': 'guelder_rose'}, {'id': 20420, 'synset': 'arrow_wood.n.01', 'name': 'arrow_wood'}, {'id': 20421, 'synset': 'black_haw.n.02', 'name': 'black_haw'}, {'id': 20422, 'synset': 'weigela.n.01', 'name': 'weigela'}, {'id': 20423, 'synset': 'teasel.n.01', 'name': 'teasel'}, {'id': 20424, 'synset': 'common_teasel.n.01', 'name': 'common_teasel'}, {'id': 20425, 'synset': "fuller's_teasel.n.01", 'name': "fuller's_teasel"}, {'id': 20426, 'synset': 'wild_teasel.n.01', 'name': 'wild_teasel'}, {'id': 20427, 'synset': 'scabious.n.01', 'name': 'scabious'}, {'id': 20428, 'synset': 'sweet_scabious.n.01', 'name': 'sweet_scabious'}, {'id': 20429, 'synset': 'field_scabious.n.01', 'name': 'field_scabious'}, {'id': 20430, 'synset': 'jewelweed.n.01', 'name': 'jewelweed'}, {'id': 20431, 'synset': 'geranium.n.01', 'name': 'geranium'}, {'id': 20432, 'synset': 'cranesbill.n.01', 'name': 'cranesbill'}, {'id': 20433, 'synset': 'wild_geranium.n.01', 'name': 'wild_geranium'}, {'id': 20434, 'synset': 'meadow_cranesbill.n.01', 'name': 'meadow_cranesbill'}, {'id': 20435, 'synset': "richardson's_geranium.n.01", 'name': "Richardson's_geranium"}, {'id': 20436, 'synset': 'herb_robert.n.01', 'name': 'herb_robert'}, {'id': 20437, 'synset': 'sticky_geranium.n.01', 'name': 'sticky_geranium'}, {'id': 20438, 'synset': "dove's_foot_geranium.n.01", 'name': "dove's_foot_geranium"}, {'id': 20439, 'synset': 'rose_geranium.n.01', 'name': 'rose_geranium'}, {'id': 20440, 'synset': 'fish_geranium.n.01', 'name': 'fish_geranium'}, {'id': 20441, 'synset': 'ivy_geranium.n.01', 'name': 'ivy_geranium'}, {'id': 20442, 'synset': 'apple_geranium.n.01', 'name': 'apple_geranium'}, {'id': 20443, 'synset': 'lemon_geranium.n.01', 'name': 'lemon_geranium'}, {'id': 20444, 'synset': 'storksbill.n.01', 'name': 'storksbill'}, {'id': 20445, 'synset': 'musk_clover.n.01', 'name': 'musk_clover'}, {'id': 20446, 'synset': 'incense_tree.n.01', 'name': 'incense_tree'}, {'id': 20447, 'synset': 'elephant_tree.n.01', 'name': 'elephant_tree'}, {'id': 20448, 'synset': 'gumbo-limbo.n.01', 'name': 'gumbo-limbo'}, {'id': 20449, 'synset': 'boswellia_carteri.n.01', 'name': 'Boswellia_carteri'}, {'id': 20450, 'synset': 'salai.n.01', 'name': 'salai'}, {'id': 20451, 'synset': 'balm_of_gilead.n.03', 'name': 'balm_of_gilead'}, {'id': 20452, 'synset': 'myrrh_tree.n.01', 'name': 'myrrh_tree'}, {'id': 20453, 'synset': 'protium_heptaphyllum.n.01', 'name': 'Protium_heptaphyllum'}, {'id': 20454, 'synset': 'protium_guianense.n.01', 'name': 'Protium_guianense'}, {'id': 20455, 'synset': 'water_starwort.n.01', 'name': 'water_starwort'}, {'id': 20456, 'synset': 'barbados_cherry.n.01', 'name': 'barbados_cherry'}, {'id': 20457, 'synset': 'mahogany.n.02', 'name': 'mahogany'}, {'id': 20458, 'synset': 'chinaberry.n.02', 'name': 'chinaberry'}, {'id': 20459, 'synset': 'neem.n.01', 'name': 'neem'}, {'id': 20460, 'synset': 'neem_seed.n.01', 'name': 'neem_seed'}, {'id': 20461, 'synset': 'spanish_cedar.n.01', 'name': 'Spanish_cedar'}, {'id': 20462, 'synset': 'satinwood.n.03', 'name': 'satinwood'}, {'id': 20463, 'synset': 'african_scented_mahogany.n.01', 'name': 'African_scented_mahogany'}, {'id': 20464, 'synset': 'silver_ash.n.01', 'name': 'silver_ash'}, {'id': 20465, 'synset': 'native_beech.n.01', 'name': 'native_beech'}, {'id': 20466, 'synset': 'bunji-bunji.n.01', 'name': 'bunji-bunji'}, {'id': 20467, 'synset': 'african_mahogany.n.01', 'name': 'African_mahogany'}, {'id': 20468, 'synset': 'lanseh_tree.n.01', 'name': 'lanseh_tree'}, {'id': 20469, 'synset': 'true_mahogany.n.01', 'name': 'true_mahogany'}, {'id': 20470, 'synset': 'honduras_mahogany.n.01', 'name': 'Honduras_mahogany'}, {'id': 20471, 'synset': 'philippine_mahogany.n.02', 'name': 'Philippine_mahogany'}, {'id': 20472, 'synset': 'caracolito.n.01', 'name': 'caracolito'}, {'id': 20473, 'synset': 'common_wood_sorrel.n.01', 'name': 'common_wood_sorrel'}, {'id': 20474, 'synset': 'bermuda_buttercup.n.01', 'name': 'Bermuda_buttercup'}, {'id': 20475, 'synset': 'creeping_oxalis.n.01', 'name': 'creeping_oxalis'}, {'id': 20476, 'synset': 'goatsfoot.n.01', 'name': 'goatsfoot'}, {'id': 20477, 'synset': 'violet_wood_sorrel.n.01', 'name': 'violet_wood_sorrel'}, {'id': 20478, 'synset': 'oca.n.01', 'name': 'oca'}, {'id': 20479, 'synset': 'carambola.n.01', 'name': 'carambola'}, {'id': 20480, 'synset': 'bilimbi.n.01', 'name': 'bilimbi'}, {'id': 20481, 'synset': 'milkwort.n.01', 'name': 'milkwort'}, {'id': 20482, 'synset': 'senega.n.02', 'name': 'senega'}, {'id': 20483, 'synset': 'orange_milkwort.n.01', 'name': 'orange_milkwort'}, {'id': 20484, 'synset': 'flowering_wintergreen.n.01', 'name': 'flowering_wintergreen'}, {'id': 20485, 'synset': 'seneca_snakeroot.n.01', 'name': 'Seneca_snakeroot'}, {'id': 20486, 'synset': 'common_milkwort.n.01', 'name': 'common_milkwort'}, {'id': 20487, 'synset': 'rue.n.01', 'name': 'rue'}, {'id': 20488, 'synset': 'citrus.n.02', 'name': 'citrus'}, {'id': 20489, 'synset': 'orange.n.03', 'name': 'orange'}, {'id': 20490, 'synset': 'sour_orange.n.01', 'name': 'sour_orange'}, {'id': 20491, 'synset': 'bergamot.n.01', 'name': 'bergamot'}, {'id': 20492, 'synset': 'pomelo.n.01', 'name': 'pomelo'}, {'id': 20493, 'synset': 'citron.n.02', 'name': 'citron'}, {'id': 20494, 'synset': 'grapefruit.n.01', 'name': 'grapefruit'}, {'id': 20495, 'synset': 'mandarin.n.01', 'name': 'mandarin'}, {'id': 20496, 'synset': 'tangerine.n.01', 'name': 'tangerine'}, {'id': 20497, 'synset': 'satsuma.n.01', 'name': 'satsuma'}, {'id': 20498, 'synset': 'sweet_orange.n.02', 'name': 'sweet_orange'}, {'id': 20499, 'synset': 'temple_orange.n.01', 'name': 'temple_orange'}, {'id': 20500, 'synset': 'tangelo.n.01', 'name': 'tangelo'}, {'id': 20501, 'synset': 'rangpur.n.01', 'name': 'rangpur'}, {'id': 20502, 'synset': 'lemon.n.03', 'name': 'lemon'}, {'id': 20503, 'synset': 'sweet_lemon.n.01', 'name': 'sweet_lemon'}, {'id': 20504, 'synset': 'lime.n.04', 'name': 'lime'}, {'id': 20505, 'synset': 'citrange.n.01', 'name': 'citrange'}, {'id': 20506, 'synset': 'fraxinella.n.01', 'name': 'fraxinella'}, {'id': 20507, 'synset': 'kumquat.n.01', 'name': 'kumquat'}, {'id': 20508, 'synset': 'marumi.n.01', 'name': 'marumi'}, {'id': 20509, 'synset': 'nagami.n.01', 'name': 'nagami'}, {'id': 20510, 'synset': 'cork_tree.n.01', 'name': 'cork_tree'}, {'id': 20511, 'synset': 'trifoliate_orange.n.01', 'name': 'trifoliate_orange'}, {'id': 20512, 'synset': 'prickly_ash.n.01', 'name': 'prickly_ash'}, {'id': 20513, 'synset': 'toothache_tree.n.01', 'name': 'toothache_tree'}, {'id': 20514, 'synset': "hercules'-club.n.01", 'name': "Hercules'-club"}, {'id': 20515, 'synset': 'bitterwood_tree.n.01', 'name': 'bitterwood_tree'}, {'id': 20516, 'synset': 'marupa.n.01', 'name': 'marupa'}, {'id': 20517, 'synset': 'paradise_tree.n.01', 'name': 'paradise_tree'}, {'id': 20518, 'synset': 'ailanthus.n.01', 'name': 'ailanthus'}, {'id': 20519, 'synset': 'tree_of_heaven.n.01', 'name': 'tree_of_heaven'}, {'id': 20520, 'synset': 'wild_mango.n.01', 'name': 'wild_mango'}, {'id': 20521, 'synset': 'pepper_tree.n.02', 'name': 'pepper_tree'}, {'id': 20522, 'synset': 'jamaica_quassia.n.02', 'name': 'Jamaica_quassia'}, {'id': 20523, 'synset': 'quassia.n.02', 'name': 'quassia'}, {'id': 20524, 'synset': 'nasturtium.n.01', 'name': 'nasturtium'}, {'id': 20525, 'synset': 'garden_nasturtium.n.01', 'name': 'garden_nasturtium'}, {'id': 20526, 'synset': 'bush_nasturtium.n.01', 'name': 'bush_nasturtium'}, {'id': 20527, 'synset': 'canarybird_flower.n.01', 'name': 'canarybird_flower'}, {'id': 20528, 'synset': 'bean_caper.n.01', 'name': 'bean_caper'}, {'id': 20529, 'synset': 'palo_santo.n.01', 'name': 'palo_santo'}, {'id': 20530, 'synset': 'lignum_vitae.n.02', 'name': 'lignum_vitae'}, {'id': 20531, 'synset': 'creosote_bush.n.01', 'name': 'creosote_bush'}, {'id': 20532, 'synset': 'caltrop.n.01', 'name': 'caltrop'}, {'id': 20533, 'synset': 'willow.n.01', 'name': 'willow'}, {'id': 20534, 'synset': 'osier.n.02', 'name': 'osier'}, {'id': 20535, 'synset': 'white_willow.n.01', 'name': 'white_willow'}, {'id': 20536, 'synset': 'silver_willow.n.01', 'name': 'silver_willow'}, {'id': 20537, 'synset': 'golden_willow.n.01', 'name': 'golden_willow'}, {'id': 20538, 'synset': 'cricket-bat_willow.n.01', 'name': 'cricket-bat_willow'}, {'id': 20539, 'synset': 'arctic_willow.n.01', 'name': 'arctic_willow'}, {'id': 20540, 'synset': 'weeping_willow.n.01', 'name': 'weeping_willow'}, {'id': 20541, 'synset': 'wisconsin_weeping_willow.n.01', 'name': 'Wisconsin_weeping_willow'}, {'id': 20542, 'synset': 'pussy_willow.n.01', 'name': 'pussy_willow'}, {'id': 20543, 'synset': 'sallow.n.01', 'name': 'sallow'}, {'id': 20544, 'synset': 'goat_willow.n.01', 'name': 'goat_willow'}, {'id': 20545, 'synset': 'peachleaf_willow.n.01', 'name': 'peachleaf_willow'}, {'id': 20546, 'synset': 'almond_willow.n.01', 'name': 'almond_willow'}, {'id': 20547, 'synset': 'hoary_willow.n.01', 'name': 'hoary_willow'}, {'id': 20548, 'synset': 'crack_willow.n.01', 'name': 'crack_willow'}, {'id': 20549, 'synset': 'prairie_willow.n.01', 'name': 'prairie_willow'}, {'id': 20550, 'synset': 'dwarf_willow.n.01', 'name': 'dwarf_willow'}, {'id': 20551, 'synset': 'grey_willow.n.01', 'name': 'grey_willow'}, {'id': 20552, 'synset': 'arroyo_willow.n.01', 'name': 'arroyo_willow'}, {'id': 20553, 'synset': 'shining_willow.n.01', 'name': 'shining_willow'}, {'id': 20554, 'synset': 'swamp_willow.n.01', 'name': 'swamp_willow'}, {'id': 20555, 'synset': 'bay_willow.n.01', 'name': 'bay_willow'}, {'id': 20556, 'synset': 'purple_willow.n.01', 'name': 'purple_willow'}, {'id': 20557, 'synset': 'balsam_willow.n.01', 'name': 'balsam_willow'}, {'id': 20558, 'synset': 'creeping_willow.n.01', 'name': 'creeping_willow'}, {'id': 20559, 'synset': 'sitka_willow.n.01', 'name': 'Sitka_willow'}, {'id': 20560, 'synset': 'dwarf_grey_willow.n.01', 'name': 'dwarf_grey_willow'}, {'id': 20561, 'synset': 'bearberry_willow.n.01', 'name': 'bearberry_willow'}, {'id': 20562, 'synset': 'common_osier.n.01', 'name': 'common_osier'}, {'id': 20563, 'synset': 'poplar.n.02', 'name': 'poplar'}, {'id': 20564, 'synset': 'balsam_poplar.n.01', 'name': 'balsam_poplar'}, {'id': 20565, 'synset': 'white_poplar.n.01', 'name': 'white_poplar'}, {'id': 20566, 'synset': 'grey_poplar.n.01', 'name': 'grey_poplar'}, {'id': 20567, 'synset': 'black_poplar.n.01', 'name': 'black_poplar'}, {'id': 20568, 'synset': 'lombardy_poplar.n.01', 'name': 'Lombardy_poplar'}, {'id': 20569, 'synset': 'cottonwood.n.01', 'name': 'cottonwood'}, {'id': 20570, 'synset': 'eastern_cottonwood.n.01', 'name': 'Eastern_cottonwood'}, {'id': 20571, 'synset': 'black_cottonwood.n.02', 'name': 'black_cottonwood'}, {'id': 20572, 'synset': 'swamp_cottonwood.n.01', 'name': 'swamp_cottonwood'}, {'id': 20573, 'synset': 'aspen.n.01', 'name': 'aspen'}, {'id': 20574, 'synset': 'quaking_aspen.n.01', 'name': 'quaking_aspen'}, {'id': 20575, 'synset': 'american_quaking_aspen.n.01', 'name': 'American_quaking_aspen'}, {'id': 20576, 'synset': 'canadian_aspen.n.01', 'name': 'Canadian_aspen'}, {'id': 20577, 'synset': 'sandalwood_tree.n.01', 'name': 'sandalwood_tree'}, {'id': 20578, 'synset': 'quandong.n.01', 'name': 'quandong'}, {'id': 20579, 'synset': 'rabbitwood.n.01', 'name': 'rabbitwood'}, {'id': 20580, 'synset': 'loranthaceae.n.01', 'name': 'Loranthaceae'}, {'id': 20581, 'synset': 'mistletoe.n.03', 'name': 'mistletoe'}, {'id': 20582, 'synset': 'american_mistletoe.n.02', 'name': 'American_mistletoe'}, {'id': 20583, 'synset': 'mistletoe.n.02', 'name': 'mistletoe'}, {'id': 20584, 'synset': 'american_mistletoe.n.01', 'name': 'American_mistletoe'}, {'id': 20585, 'synset': 'aalii.n.01', 'name': 'aalii'}, {'id': 20586, 'synset': 'soapberry.n.01', 'name': 'soapberry'}, {'id': 20587, 'synset': 'wild_china_tree.n.01', 'name': 'wild_China_tree'}, {'id': 20588, 'synset': 'china_tree.n.01', 'name': 'China_tree'}, {'id': 20589, 'synset': 'akee.n.01', 'name': 'akee'}, {'id': 20590, 'synset': 'soapberry_vine.n.01', 'name': 'soapberry_vine'}, {'id': 20591, 'synset': 'heartseed.n.01', 'name': 'heartseed'}, {'id': 20592, 'synset': 'balloon_vine.n.01', 'name': 'balloon_vine'}, {'id': 20593, 'synset': 'longan.n.01', 'name': 'longan'}, {'id': 20594, 'synset': 'harpullia.n.01', 'name': 'harpullia'}, {'id': 20595, 'synset': 'harpulla.n.01', 'name': 'harpulla'}, {'id': 20596, 'synset': 'moreton_bay_tulipwood.n.01', 'name': 'Moreton_Bay_tulipwood'}, {'id': 20597, 'synset': 'litchi.n.01', 'name': 'litchi'}, {'id': 20598, 'synset': 'spanish_lime.n.01', 'name': 'Spanish_lime'}, {'id': 20599, 'synset': 'rambutan.n.01', 'name': 'rambutan'}, {'id': 20600, 'synset': 'pulasan.n.01', 'name': 'pulasan'}, {'id': 20601, 'synset': 'pachysandra.n.01', 'name': 'pachysandra'}, {'id': 20602, 'synset': 'allegheny_spurge.n.01', 'name': 'Allegheny_spurge'}, {'id': 20603, 'synset': 'bittersweet.n.02', 'name': 'bittersweet'}, {'id': 20604, 'synset': 'spindle_tree.n.01', 'name': 'spindle_tree'}, {'id': 20605, 'synset': 'winged_spindle_tree.n.01', 'name': 'winged_spindle_tree'}, {'id': 20606, 'synset': 'wahoo.n.02', 'name': 'wahoo'}, {'id': 20607, 'synset': 'strawberry_bush.n.01', 'name': 'strawberry_bush'}, {'id': 20608, 'synset': 'evergreen_bittersweet.n.01', 'name': 'evergreen_bittersweet'}, {'id': 20609, 'synset': 'cyrilla.n.01', 'name': 'cyrilla'}, {'id': 20610, 'synset': 'titi.n.01', 'name': 'titi'}, {'id': 20611, 'synset': 'crowberry.n.01', 'name': 'crowberry'}, {'id': 20612, 'synset': 'maple.n.02', 'name': 'maple'}, {'id': 20613, 'synset': 'silver_maple.n.01', 'name': 'silver_maple'}, {'id': 20614, 'synset': 'sugar_maple.n.01', 'name': 'sugar_maple'}, {'id': 20615, 'synset': 'red_maple.n.01', 'name': 'red_maple'}, {'id': 20616, 'synset': 'moosewood.n.01', 'name': 'moosewood'}, {'id': 20617, 'synset': 'oregon_maple.n.01', 'name': 'Oregon_maple'}, {'id': 20618, 'synset': 'dwarf_maple.n.01', 'name': 'dwarf_maple'}, {'id': 20619, 'synset': 'mountain_maple.n.01', 'name': 'mountain_maple'}, {'id': 20620, 'synset': 'vine_maple.n.01', 'name': 'vine_maple'}, {'id': 20621, 'synset': 'hedge_maple.n.01', 'name': 'hedge_maple'}, {'id': 20622, 'synset': 'norway_maple.n.01', 'name': 'Norway_maple'}, {'id': 20623, 'synset': 'sycamore.n.03', 'name': 'sycamore'}, {'id': 20624, 'synset': 'box_elder.n.01', 'name': 'box_elder'}, {'id': 20625, 'synset': 'california_box_elder.n.01', 'name': 'California_box_elder'}, {'id': 20626, 'synset': 'pointed-leaf_maple.n.01', 'name': 'pointed-leaf_maple'}, {'id': 20627, 'synset': 'japanese_maple.n.02', 'name': 'Japanese_maple'}, {'id': 20628, 'synset': 'japanese_maple.n.01', 'name': 'Japanese_maple'}, {'id': 20629, 'synset': 'holly.n.01', 'name': 'holly'}, {'id': 20630, 'synset': 'chinese_holly.n.01', 'name': 'Chinese_holly'}, {'id': 20631, 'synset': 'bearberry.n.02', 'name': 'bearberry'}, {'id': 20632, 'synset': 'inkberry.n.01', 'name': 'inkberry'}, {'id': 20633, 'synset': 'mate.n.07', 'name': 'mate'}, {'id': 20634, 'synset': 'american_holly.n.01', 'name': 'American_holly'}, {'id': 20635, 'synset': 'low_gallberry_holly.n.01', 'name': 'low_gallberry_holly'}, {'id': 20636, 'synset': 'tall_gallberry_holly.n.01', 'name': 'tall_gallberry_holly'}, {'id': 20637, 'synset': 'yaupon_holly.n.01', 'name': 'yaupon_holly'}, {'id': 20638, 'synset': 'deciduous_holly.n.01', 'name': 'deciduous_holly'}, {'id': 20639, 'synset': 'juneberry_holly.n.01', 'name': 'juneberry_holly'}, {'id': 20640, 'synset': 'largeleaf_holly.n.01', 'name': 'largeleaf_holly'}, {'id': 20641, 'synset': 'geogia_holly.n.01', 'name': 'Geogia_holly'}, {'id': 20642, 'synset': 'common_winterberry_holly.n.01', 'name': 'common_winterberry_holly'}, {'id': 20643, 'synset': 'smooth_winterberry_holly.n.01', 'name': 'smooth_winterberry_holly'}, {'id': 20644, 'synset': 'cashew.n.01', 'name': 'cashew'}, {'id': 20645, 'synset': 'goncalo_alves.n.01', 'name': 'goncalo_alves'}, {'id': 20646, 'synset': 'venetian_sumac.n.01', 'name': 'Venetian_sumac'}, {'id': 20647, 'synset': 'laurel_sumac.n.01', 'name': 'laurel_sumac'}, {'id': 20648, 'synset': 'mango.n.01', 'name': 'mango'}, {'id': 20649, 'synset': 'pistachio.n.01', 'name': 'pistachio'}, {'id': 20650, 'synset': 'terebinth.n.01', 'name': 'terebinth'}, {'id': 20651, 'synset': 'mastic.n.03', 'name': 'mastic'}, {'id': 20652, 'synset': 'australian_sumac.n.01', 'name': 'Australian_sumac'}, {'id': 20653, 'synset': 'sumac.n.02', 'name': 'sumac'}, {'id': 20654, 'synset': 'smooth_sumac.n.01', 'name': 'smooth_sumac'}, {'id': 20655, 'synset': 'sugar-bush.n.01', 'name': 'sugar-bush'}, {'id': 20656, 'synset': 'staghorn_sumac.n.01', 'name': 'staghorn_sumac'}, {'id': 20657, 'synset': 'squawbush.n.01', 'name': 'squawbush'}, {'id': 20658, 'synset': 'aroeira_blanca.n.01', 'name': 'aroeira_blanca'}, {'id': 20659, 'synset': 'pepper_tree.n.01', 'name': 'pepper_tree'}, {'id': 20660, 'synset': 'brazilian_pepper_tree.n.01', 'name': 'Brazilian_pepper_tree'}, {'id': 20661, 'synset': 'hog_plum.n.01', 'name': 'hog_plum'}, {'id': 20662, 'synset': 'mombin.n.01', 'name': 'mombin'}, {'id': 20663, 'synset': 'poison_ash.n.01', 'name': 'poison_ash'}, {'id': 20664, 'synset': 'poison_ivy.n.02', 'name': 'poison_ivy'}, {'id': 20665, 'synset': 'western_poison_oak.n.01', 'name': 'western_poison_oak'}, {'id': 20666, 'synset': 'eastern_poison_oak.n.01', 'name': 'eastern_poison_oak'}, {'id': 20667, 'synset': 'varnish_tree.n.02', 'name': 'varnish_tree'}, {'id': 20668, 'synset': 'horse_chestnut.n.01', 'name': 'horse_chestnut'}, {'id': 20669, 'synset': 'buckeye.n.01', 'name': 'buckeye'}, {'id': 20670, 'synset': 'sweet_buckeye.n.01', 'name': 'sweet_buckeye'}, {'id': 20671, 'synset': 'ohio_buckeye.n.01', 'name': 'Ohio_buckeye'}, {'id': 20672, 'synset': 'dwarf_buckeye.n.01', 'name': 'dwarf_buckeye'}, {'id': 20673, 'synset': 'red_buckeye.n.01', 'name': 'red_buckeye'}, {'id': 20674, 'synset': 'particolored_buckeye.n.01', 'name': 'particolored_buckeye'}, {'id': 20675, 'synset': 'ebony.n.03', 'name': 'ebony'}, {'id': 20676, 'synset': 'marblewood.n.02', 'name': 'marblewood'}, {'id': 20677, 'synset': 'marblewood.n.01', 'name': 'marblewood'}, {'id': 20678, 'synset': 'persimmon.n.01', 'name': 'persimmon'}, {'id': 20679, 'synset': 'japanese_persimmon.n.01', 'name': 'Japanese_persimmon'}, {'id': 20680, 'synset': 'american_persimmon.n.01', 'name': 'American_persimmon'}, {'id': 20681, 'synset': 'date_plum.n.01', 'name': 'date_plum'}, {'id': 20682, 'synset': 'buckthorn.n.02', 'name': 'buckthorn'}, {'id': 20683, 'synset': 'southern_buckthorn.n.01', 'name': 'southern_buckthorn'}, {'id': 20684, 'synset': 'false_buckthorn.n.01', 'name': 'false_buckthorn'}, {'id': 20685, 'synset': 'star_apple.n.01', 'name': 'star_apple'}, {'id': 20686, 'synset': 'satinleaf.n.01', 'name': 'satinleaf'}, {'id': 20687, 'synset': 'balata.n.02', 'name': 'balata'}, {'id': 20688, 'synset': 'sapodilla.n.01', 'name': 'sapodilla'}, {'id': 20689, 'synset': 'gutta-percha_tree.n.02', 'name': 'gutta-percha_tree'}, {'id': 20690, 'synset': 'gutta-percha_tree.n.01', 'name': 'gutta-percha_tree'}, {'id': 20691, 'synset': 'canistel.n.01', 'name': 'canistel'}, {'id': 20692, 'synset': 'marmalade_tree.n.01', 'name': 'marmalade_tree'}, {'id': 20693, 'synset': 'sweetleaf.n.01', 'name': 'sweetleaf'}, {'id': 20694, 'synset': 'asiatic_sweetleaf.n.01', 'name': 'Asiatic_sweetleaf'}, {'id': 20695, 'synset': 'styrax.n.01', 'name': 'styrax'}, {'id': 20696, 'synset': 'snowbell.n.01', 'name': 'snowbell'}, {'id': 20697, 'synset': 'japanese_snowbell.n.01', 'name': 'Japanese_snowbell'}, {'id': 20698, 'synset': 'texas_snowbell.n.01', 'name': 'Texas_snowbell'}, {'id': 20699, 'synset': 'silver-bell_tree.n.01', 'name': 'silver-bell_tree'}, {'id': 20700, 'synset': 'carnivorous_plant.n.01', 'name': 'carnivorous_plant'}, {'id': 20701, 'synset': 'pitcher_plant.n.01', 'name': 'pitcher_plant'}, {'id': 20702, 'synset': 'common_pitcher_plant.n.01', 'name': 'common_pitcher_plant'}, {'id': 20703, 'synset': 'hooded_pitcher_plant.n.01', 'name': 'hooded_pitcher_plant'}, {'id': 20704, 'synset': "huntsman's_horn.n.01", 'name': "huntsman's_horn"}, {'id': 20705, 'synset': 'tropical_pitcher_plant.n.01', 'name': 'tropical_pitcher_plant'}, {'id': 20706, 'synset': 'sundew.n.01', 'name': 'sundew'}, {'id': 20707, 'synset': "venus's_flytrap.n.01", 'name': "Venus's_flytrap"}, {'id': 20708, 'synset': 'waterwheel_plant.n.01', 'name': 'waterwheel_plant'}, {'id': 20709, 'synset': 'drosophyllum_lusitanicum.n.01', 'name': 'Drosophyllum_lusitanicum'}, {'id': 20710, 'synset': 'roridula.n.01', 'name': 'roridula'}, {'id': 20711, 'synset': 'australian_pitcher_plant.n.01', 'name': 'Australian_pitcher_plant'}, {'id': 20712, 'synset': 'sedum.n.01', 'name': 'sedum'}, {'id': 20713, 'synset': 'stonecrop.n.01', 'name': 'stonecrop'}, {'id': 20714, 'synset': 'rose-root.n.01', 'name': 'rose-root'}, {'id': 20715, 'synset': 'orpine.n.01', 'name': 'orpine'}, {'id': 20716, 'synset': 'pinwheel.n.01', 'name': 'pinwheel'}, {'id': 20717, 'synset': 'christmas_bush.n.01', 'name': 'Christmas_bush'}, {'id': 20718, 'synset': 'hortensia.n.01', 'name': 'hortensia'}, {'id': 20719, 'synset': 'fall-blooming_hydrangea.n.01', 'name': 'fall-blooming_hydrangea'}, {'id': 20720, 'synset': 'carpenteria.n.01', 'name': 'carpenteria'}, {'id': 20721, 'synset': 'decumary.n.01', 'name': 'decumary'}, {'id': 20722, 'synset': 'deutzia.n.01', 'name': 'deutzia'}, {'id': 20723, 'synset': 'philadelphus.n.01', 'name': 'philadelphus'}, {'id': 20724, 'synset': 'mock_orange.n.01', 'name': 'mock_orange'}, {'id': 20725, 'synset': 'saxifrage.n.01', 'name': 'saxifrage'}, {'id': 20726, 'synset': 'yellow_mountain_saxifrage.n.01', 'name': 'yellow_mountain_saxifrage'}, {'id': 20727, 'synset': 'meadow_saxifrage.n.01', 'name': 'meadow_saxifrage'}, {'id': 20728, 'synset': 'mossy_saxifrage.n.01', 'name': 'mossy_saxifrage'}, {'id': 20729, 'synset': 'western_saxifrage.n.01', 'name': 'western_saxifrage'}, {'id': 20730, 'synset': 'purple_saxifrage.n.01', 'name': 'purple_saxifrage'}, {'id': 20731, 'synset': 'star_saxifrage.n.01', 'name': 'star_saxifrage'}, {'id': 20732, 'synset': 'strawberry_geranium.n.01', 'name': 'strawberry_geranium'}, {'id': 20733, 'synset': 'astilbe.n.01', 'name': 'astilbe'}, {'id': 20734, 'synset': 'false_goatsbeard.n.01', 'name': 'false_goatsbeard'}, {'id': 20735, 'synset': 'dwarf_astilbe.n.01', 'name': 'dwarf_astilbe'}, {'id': 20736, 'synset': 'spirea.n.01', 'name': 'spirea'}, {'id': 20737, 'synset': 'bergenia.n.01', 'name': 'bergenia'}, {'id': 20738, 'synset': 'coast_boykinia.n.01', 'name': 'coast_boykinia'}, {'id': 20739, 'synset': 'golden_saxifrage.n.01', 'name': 'golden_saxifrage'}, {'id': 20740, 'synset': 'umbrella_plant.n.01', 'name': 'umbrella_plant'}, {'id': 20741, 'synset': 'bridal_wreath.n.01', 'name': 'bridal_wreath'}, {'id': 20742, 'synset': 'alumroot.n.01', 'name': 'alumroot'}, {'id': 20743, 'synset': 'coralbells.n.01', 'name': 'coralbells'}, {'id': 20744, 'synset': 'leatherleaf_saxifrage.n.01', 'name': 'leatherleaf_saxifrage'}, {'id': 20745, 'synset': 'woodland_star.n.01', 'name': 'woodland_star'}, {'id': 20746, 'synset': 'prairie_star.n.01', 'name': 'prairie_star'}, {'id': 20747, 'synset': 'miterwort.n.01', 'name': 'miterwort'}, {'id': 20748, 'synset': "five-point_bishop's_cap.n.01", 'name': "five-point_bishop's_cap"}, {'id': 20749, 'synset': 'parnassia.n.01', 'name': 'parnassia'}, {'id': 20750, 'synset': 'bog_star.n.01', 'name': 'bog_star'}, {'id': 20751, 'synset': 'fringed_grass_of_parnassus.n.01', 'name': 'fringed_grass_of_Parnassus'}, {'id': 20752, 'synset': 'false_alumroot.n.01', 'name': 'false_alumroot'}, {'id': 20753, 'synset': 'foamflower.n.01', 'name': 'foamflower'}, {'id': 20754, 'synset': 'false_miterwort.n.01', 'name': 'false_miterwort'}, {'id': 20755, 'synset': 'pickaback_plant.n.01', 'name': 'pickaback_plant'}, {'id': 20756, 'synset': 'currant.n.02', 'name': 'currant'}, {'id': 20757, 'synset': 'black_currant.n.01', 'name': 'black_currant'}, {'id': 20758, 'synset': 'white_currant.n.01', 'name': 'white_currant'}, {'id': 20759, 'synset': 'gooseberry.n.01', 'name': 'gooseberry'}, {'id': 20760, 'synset': 'plane_tree.n.01', 'name': 'plane_tree'}, {'id': 20761, 'synset': 'london_plane.n.01', 'name': 'London_plane'}, {'id': 20762, 'synset': 'american_sycamore.n.01', 'name': 'American_sycamore'}, {'id': 20763, 'synset': 'oriental_plane.n.01', 'name': 'oriental_plane'}, {'id': 20764, 'synset': 'california_sycamore.n.01', 'name': 'California_sycamore'}, {'id': 20765, 'synset': 'arizona_sycamore.n.01', 'name': 'Arizona_sycamore'}, {'id': 20766, 'synset': 'greek_valerian.n.01', 'name': 'Greek_valerian'}, {'id': 20767, 'synset': "northern_jacob's_ladder.n.01", 'name': "northern_Jacob's_ladder"}, {'id': 20768, 'synset': 'skunkweed.n.01', 'name': 'skunkweed'}, {'id': 20769, 'synset': 'phlox.n.01', 'name': 'phlox'}, {'id': 20770, 'synset': 'moss_pink.n.02', 'name': 'moss_pink'}, {'id': 20771, 'synset': 'evening-snow.n.01', 'name': 'evening-snow'}, {'id': 20772, 'synset': 'acanthus.n.01', 'name': 'acanthus'}, {'id': 20773, 'synset': "bear's_breech.n.01", 'name': "bear's_breech"}, {'id': 20774, 'synset': 'caricature_plant.n.01', 'name': 'caricature_plant'}, {'id': 20775, 'synset': 'black-eyed_susan.n.01', 'name': 'black-eyed_Susan'}, {'id': 20776, 'synset': 'catalpa.n.01', 'name': 'catalpa'}, {'id': 20777, 'synset': 'catalpa_bignioides.n.01', 'name': 'Catalpa_bignioides'}, {'id': 20778, 'synset': 'catalpa_speciosa.n.01', 'name': 'Catalpa_speciosa'}, {'id': 20779, 'synset': 'desert_willow.n.01', 'name': 'desert_willow'}, {'id': 20780, 'synset': 'calabash.n.02', 'name': 'calabash'}, {'id': 20781, 'synset': 'calabash.n.01', 'name': 'calabash'}, {'id': 20782, 'synset': 'borage.n.01', 'name': 'borage'}, {'id': 20783, 'synset': 'common_amsinckia.n.01', 'name': 'common_amsinckia'}, {'id': 20784, 'synset': 'anchusa.n.01', 'name': 'anchusa'}, {'id': 20785, 'synset': 'bugloss.n.01', 'name': 'bugloss'}, {'id': 20786, 'synset': 'cape_forget-me-not.n.02', 'name': 'cape_forget-me-not'}, {'id': 20787, 'synset': 'cape_forget-me-not.n.01', 'name': 'cape_forget-me-not'}, {'id': 20788, 'synset': 'spanish_elm.n.02', 'name': 'Spanish_elm'}, {'id': 20789, 'synset': 'princewood.n.01', 'name': 'princewood'}, {'id': 20790, 'synset': 'chinese_forget-me-not.n.01', 'name': 'Chinese_forget-me-not'}, {'id': 20791, 'synset': "hound's-tongue.n.02", 'name': "hound's-tongue"}, {'id': 20792, 'synset': "hound's-tongue.n.01", 'name': "hound's-tongue"}, {'id': 20793, 'synset': 'blueweed.n.01', 'name': 'blueweed'}, {'id': 20794, 'synset': "beggar's_lice.n.01", 'name': "beggar's_lice"}, {'id': 20795, 'synset': 'gromwell.n.01', 'name': 'gromwell'}, {'id': 20796, 'synset': 'puccoon.n.01', 'name': 'puccoon'}, {'id': 20797, 'synset': 'virginia_bluebell.n.01', 'name': 'Virginia_bluebell'}, {'id': 20798, 'synset': 'garden_forget-me-not.n.01', 'name': 'garden_forget-me-not'}, {'id': 20799, 'synset': 'forget-me-not.n.01', 'name': 'forget-me-not'}, {'id': 20800, 'synset': 'false_gromwell.n.01', 'name': 'false_gromwell'}, {'id': 20801, 'synset': 'comfrey.n.01', 'name': 'comfrey'}, {'id': 20802, 'synset': 'common_comfrey.n.01', 'name': 'common_comfrey'}, {'id': 20803, 'synset': 'convolvulus.n.01', 'name': 'convolvulus'}, {'id': 20804, 'synset': 'bindweed.n.01', 'name': 'bindweed'}, {'id': 20805, 'synset': 'field_bindweed.n.01', 'name': 'field_bindweed'}, {'id': 20806, 'synset': 'scammony.n.03', 'name': 'scammony'}, {'id': 20807, 'synset': 'silverweed.n.01', 'name': 'silverweed'}, {'id': 20808, 'synset': 'dodder.n.01', 'name': 'dodder'}, {'id': 20809, 'synset': 'dichondra.n.01', 'name': 'dichondra'}, {'id': 20810, 'synset': 'cypress_vine.n.01', 'name': 'cypress_vine'}, {'id': 20811, 'synset': 'moonflower.n.01', 'name': 'moonflower'}, {'id': 20812, 'synset': 'wild_potato_vine.n.01', 'name': 'wild_potato_vine'}, {'id': 20813, 'synset': 'red_morning-glory.n.01', 'name': 'red_morning-glory'}, {'id': 20814, 'synset': 'man-of-the-earth.n.01', 'name': 'man-of-the-earth'}, {'id': 20815, 'synset': 'scammony.n.01', 'name': 'scammony'}, {'id': 20816, 'synset': 'japanese_morning_glory.n.01', 'name': 'Japanese_morning_glory'}, {'id': 20817, 'synset': 'imperial_japanese_morning_glory.n.01', 'name': 'imperial_Japanese_morning_glory'}, {'id': 20818, 'synset': 'gesneriad.n.01', 'name': 'gesneriad'}, {'id': 20819, 'synset': 'gesneria.n.01', 'name': 'gesneria'}, {'id': 20820, 'synset': 'achimenes.n.01', 'name': 'achimenes'}, {'id': 20821, 'synset': 'aeschynanthus.n.01', 'name': 'aeschynanthus'}, {'id': 20822, 'synset': 'lace-flower_vine.n.01', 'name': 'lace-flower_vine'}, {'id': 20823, 'synset': 'columnea.n.01', 'name': 'columnea'}, {'id': 20824, 'synset': 'episcia.n.01', 'name': 'episcia'}, {'id': 20825, 'synset': 'gloxinia.n.01', 'name': 'gloxinia'}, {'id': 20826, 'synset': 'canterbury_bell.n.01', 'name': 'Canterbury_bell'}, {'id': 20827, 'synset': 'kohleria.n.01', 'name': 'kohleria'}, {'id': 20828, 'synset': 'african_violet.n.01', 'name': 'African_violet'}, {'id': 20829, 'synset': 'streptocarpus.n.01', 'name': 'streptocarpus'}, {'id': 20830, 'synset': 'cape_primrose.n.01', 'name': 'Cape_primrose'}, {'id': 20831, 'synset': 'waterleaf.n.01', 'name': 'waterleaf'}, {'id': 20832, 'synset': 'virginia_waterleaf.n.01', 'name': 'Virginia_waterleaf'}, {'id': 20833, 'synset': 'yellow_bells.n.01', 'name': 'yellow_bells'}, {'id': 20834, 'synset': 'yerba_santa.n.01', 'name': 'yerba_santa'}, {'id': 20835, 'synset': 'nemophila.n.01', 'name': 'nemophila'}, {'id': 20836, 'synset': 'baby_blue-eyes.n.01', 'name': 'baby_blue-eyes'}, {'id': 20837, 'synset': 'five-spot.n.02', 'name': 'five-spot'}, {'id': 20838, 'synset': 'scorpionweed.n.01', 'name': 'scorpionweed'}, {'id': 20839, 'synset': 'california_bluebell.n.02', 'name': 'California_bluebell'}, {'id': 20840, 'synset': 'california_bluebell.n.01', 'name': 'California_bluebell'}, {'id': 20841, 'synset': 'fiddleneck.n.01', 'name': 'fiddleneck'}, {'id': 20842, 'synset': 'fiesta_flower.n.01', 'name': 'fiesta_flower'}, {'id': 20843, 'synset': 'basil_thyme.n.01', 'name': 'basil_thyme'}, {'id': 20844, 'synset': 'giant_hyssop.n.01', 'name': 'giant_hyssop'}, {'id': 20845, 'synset': 'yellow_giant_hyssop.n.01', 'name': 'yellow_giant_hyssop'}, {'id': 20846, 'synset': 'anise_hyssop.n.01', 'name': 'anise_hyssop'}, {'id': 20847, 'synset': 'mexican_hyssop.n.01', 'name': 'Mexican_hyssop'}, {'id': 20848, 'synset': 'bugle.n.02', 'name': 'bugle'}, {'id': 20849, 'synset': 'creeping_bugle.n.01', 'name': 'creeping_bugle'}, {'id': 20850, 'synset': 'erect_bugle.n.01', 'name': 'erect_bugle'}, {'id': 20851, 'synset': 'pyramid_bugle.n.01', 'name': 'pyramid_bugle'}, {'id': 20852, 'synset': 'wood_mint.n.01', 'name': 'wood_mint'}, {'id': 20853, 'synset': 'hairy_wood_mint.n.01', 'name': 'hairy_wood_mint'}, {'id': 20854, 'synset': 'downy_wood_mint.n.01', 'name': 'downy_wood_mint'}, {'id': 20855, 'synset': 'calamint.n.01', 'name': 'calamint'}, {'id': 20856, 'synset': 'common_calamint.n.01', 'name': 'common_calamint'}, {'id': 20857, 'synset': 'large-flowered_calamint.n.01', 'name': 'large-flowered_calamint'}, {'id': 20858, 'synset': 'lesser_calamint.n.01', 'name': 'lesser_calamint'}, {'id': 20859, 'synset': 'wild_basil.n.01', 'name': 'wild_basil'}, {'id': 20860, 'synset': 'horse_balm.n.01', 'name': 'horse_balm'}, {'id': 20861, 'synset': 'coleus.n.01', 'name': 'coleus'}, {'id': 20862, 'synset': 'country_borage.n.01', 'name': 'country_borage'}, {'id': 20863, 'synset': 'painted_nettle.n.01', 'name': 'painted_nettle'}, {'id': 20864, 'synset': 'apalachicola_rosemary.n.01', 'name': 'Apalachicola_rosemary'}, {'id': 20865, 'synset': 'dragonhead.n.01', 'name': 'dragonhead'}, {'id': 20866, 'synset': 'elsholtzia.n.01', 'name': 'elsholtzia'}, {'id': 20867, 'synset': 'hemp_nettle.n.01', 'name': 'hemp_nettle'}, {'id': 20868, 'synset': 'ground_ivy.n.01', 'name': 'ground_ivy'}, {'id': 20869, 'synset': 'pennyroyal.n.02', 'name': 'pennyroyal'}, {'id': 20870, 'synset': 'hyssop.n.01', 'name': 'hyssop'}, {'id': 20871, 'synset': 'dead_nettle.n.02', 'name': 'dead_nettle'}, {'id': 20872, 'synset': 'white_dead_nettle.n.01', 'name': 'white_dead_nettle'}, {'id': 20873, 'synset': 'henbit.n.01', 'name': 'henbit'}, {'id': 20874, 'synset': 'english_lavender.n.01', 'name': 'English_lavender'}, {'id': 20875, 'synset': 'french_lavender.n.02', 'name': 'French_lavender'}, {'id': 20876, 'synset': 'spike_lavender.n.01', 'name': 'spike_lavender'}, {'id': 20877, 'synset': 'dagga.n.01', 'name': 'dagga'}, {'id': 20878, 'synset': "lion's-ear.n.01", 'name': "lion's-ear"}, {'id': 20879, 'synset': 'motherwort.n.01', 'name': 'motherwort'}, {'id': 20880, 'synset': 'pitcher_sage.n.02', 'name': 'pitcher_sage'}, {'id': 20881, 'synset': 'bugleweed.n.01', 'name': 'bugleweed'}, {'id': 20882, 'synset': 'water_horehound.n.01', 'name': 'water_horehound'}, {'id': 20883, 'synset': 'gipsywort.n.01', 'name': 'gipsywort'}, {'id': 20884, 'synset': 'origanum.n.01', 'name': 'origanum'}, {'id': 20885, 'synset': 'oregano.n.01', 'name': 'oregano'}, {'id': 20886, 'synset': 'sweet_marjoram.n.01', 'name': 'sweet_marjoram'}, {'id': 20887, 'synset': 'horehound.n.01', 'name': 'horehound'}, {'id': 20888, 'synset': 'common_horehound.n.01', 'name': 'common_horehound'}, {'id': 20889, 'synset': 'lemon_balm.n.01', 'name': 'lemon_balm'}, {'id': 20890, 'synset': 'corn_mint.n.01', 'name': 'corn_mint'}, {'id': 20891, 'synset': 'water-mint.n.01', 'name': 'water-mint'}, {'id': 20892, 'synset': 'bergamot_mint.n.02', 'name': 'bergamot_mint'}, {'id': 20893, 'synset': 'horsemint.n.03', 'name': 'horsemint'}, {'id': 20894, 'synset': 'peppermint.n.01', 'name': 'peppermint'}, {'id': 20895, 'synset': 'spearmint.n.01', 'name': 'spearmint'}, {'id': 20896, 'synset': 'apple_mint.n.01', 'name': 'apple_mint'}, {'id': 20897, 'synset': 'pennyroyal.n.01', 'name': 'pennyroyal'}, {'id': 20898, 'synset': 'yerba_buena.n.01', 'name': 'yerba_buena'}, {'id': 20899, 'synset': 'molucca_balm.n.01', 'name': 'molucca_balm'}, {'id': 20900, 'synset': 'monarda.n.01', 'name': 'monarda'}, {'id': 20901, 'synset': 'bee_balm.n.02', 'name': 'bee_balm'}, {'id': 20902, 'synset': 'horsemint.n.02', 'name': 'horsemint'}, {'id': 20903, 'synset': 'bee_balm.n.01', 'name': 'bee_balm'}, {'id': 20904, 'synset': 'lemon_mint.n.01', 'name': 'lemon_mint'}, {'id': 20905, 'synset': 'plains_lemon_monarda.n.01', 'name': 'plains_lemon_monarda'}, {'id': 20906, 'synset': 'basil_balm.n.01', 'name': 'basil_balm'}, {'id': 20907, 'synset': 'mustang_mint.n.01', 'name': 'mustang_mint'}, {'id': 20908, 'synset': 'catmint.n.01', 'name': 'catmint'}, {'id': 20909, 'synset': 'basil.n.01', 'name': 'basil'}, {'id': 20910, 'synset': 'beefsteak_plant.n.01', 'name': 'beefsteak_plant'}, {'id': 20911, 'synset': 'phlomis.n.01', 'name': 'phlomis'}, {'id': 20912, 'synset': 'jerusalem_sage.n.01', 'name': 'Jerusalem_sage'}, {'id': 20913, 'synset': 'physostegia.n.01', 'name': 'physostegia'}, {'id': 20914, 'synset': 'plectranthus.n.01', 'name': 'plectranthus'}, {'id': 20915, 'synset': 'patchouli.n.01', 'name': 'patchouli'}, {'id': 20916, 'synset': 'self-heal.n.01', 'name': 'self-heal'}, {'id': 20917, 'synset': 'mountain_mint.n.01', 'name': 'mountain_mint'}, {'id': 20918, 'synset': 'rosemary.n.01', 'name': 'rosemary'}, {'id': 20919, 'synset': 'clary_sage.n.01', 'name': 'clary_sage'}, {'id': 20920, 'synset': 'purple_sage.n.01', 'name': 'purple_sage'}, {'id': 20921, 'synset': 'cancerweed.n.01', 'name': 'cancerweed'}, {'id': 20922, 'synset': 'common_sage.n.01', 'name': 'common_sage'}, {'id': 20923, 'synset': 'meadow_clary.n.01', 'name': 'meadow_clary'}, {'id': 20924, 'synset': 'clary.n.01', 'name': 'clary'}, {'id': 20925, 'synset': 'pitcher_sage.n.01', 'name': 'pitcher_sage'}, {'id': 20926, 'synset': 'mexican_mint.n.01', 'name': 'Mexican_mint'}, {'id': 20927, 'synset': 'wild_sage.n.01', 'name': 'wild_sage'}, {'id': 20928, 'synset': 'savory.n.01', 'name': 'savory'}, {'id': 20929, 'synset': 'summer_savory.n.01', 'name': 'summer_savory'}, {'id': 20930, 'synset': 'winter_savory.n.01', 'name': 'winter_savory'}, {'id': 20931, 'synset': 'skullcap.n.02', 'name': 'skullcap'}, {'id': 20932, 'synset': 'blue_pimpernel.n.01', 'name': 'blue_pimpernel'}, {'id': 20933, 'synset': 'hedge_nettle.n.02', 'name': 'hedge_nettle'}, {'id': 20934, 'synset': 'hedge_nettle.n.01', 'name': 'hedge_nettle'}, {'id': 20935, 'synset': 'germander.n.01', 'name': 'germander'}, {'id': 20936, 'synset': 'american_germander.n.01', 'name': 'American_germander'}, {'id': 20937, 'synset': 'cat_thyme.n.01', 'name': 'cat_thyme'}, {'id': 20938, 'synset': 'wood_sage.n.01', 'name': 'wood_sage'}, {'id': 20939, 'synset': 'thyme.n.01', 'name': 'thyme'}, {'id': 20940, 'synset': 'common_thyme.n.01', 'name': 'common_thyme'}, {'id': 20941, 'synset': 'wild_thyme.n.01', 'name': 'wild_thyme'}, {'id': 20942, 'synset': 'blue_curls.n.01', 'name': 'blue_curls'}, {'id': 20943, 'synset': 'turpentine_camphor_weed.n.01', 'name': 'turpentine_camphor_weed'}, {'id': 20944, 'synset': 'bastard_pennyroyal.n.01', 'name': 'bastard_pennyroyal'}, {'id': 20945, 'synset': 'bladderwort.n.01', 'name': 'bladderwort'}, {'id': 20946, 'synset': 'butterwort.n.01', 'name': 'butterwort'}, {'id': 20947, 'synset': 'genlisea.n.01', 'name': 'genlisea'}, {'id': 20948, 'synset': 'martynia.n.01', 'name': 'martynia'}, {'id': 20949, 'synset': 'common_unicorn_plant.n.01', 'name': 'common_unicorn_plant'}, {'id': 20950, 'synset': "sand_devil's_claw.n.01", 'name': "sand_devil's_claw"}, {'id': 20951, 'synset': 'sweet_unicorn_plant.n.01', 'name': 'sweet_unicorn_plant'}, {'id': 20952, 'synset': 'figwort.n.01', 'name': 'figwort'}, {'id': 20953, 'synset': 'snapdragon.n.01', 'name': 'snapdragon'}, {'id': 20954, 'synset': 'white_snapdragon.n.01', 'name': 'white_snapdragon'}, {'id': 20955, 'synset': 'yellow_twining_snapdragon.n.01', 'name': 'yellow_twining_snapdragon'}, {'id': 20956, 'synset': 'mediterranean_snapdragon.n.01', 'name': 'Mediterranean_snapdragon'}, {'id': 20957, 'synset': 'kitten-tails.n.01', 'name': 'kitten-tails'}, {'id': 20958, 'synset': 'alpine_besseya.n.01', 'name': 'Alpine_besseya'}, {'id': 20959, 'synset': 'false_foxglove.n.02', 'name': 'false_foxglove'}, {'id': 20960, 'synset': 'false_foxglove.n.01', 'name': 'false_foxglove'}, {'id': 20961, 'synset': 'calceolaria.n.01', 'name': 'calceolaria'}, {'id': 20962, 'synset': 'indian_paintbrush.n.02', 'name': 'Indian_paintbrush'}, {'id': 20963, 'synset': 'desert_paintbrush.n.01', 'name': 'desert_paintbrush'}, {'id': 20964, 'synset': 'giant_red_paintbrush.n.01', 'name': 'giant_red_paintbrush'}, {'id': 20965, 'synset': 'great_plains_paintbrush.n.01', 'name': 'great_plains_paintbrush'}, {'id': 20966, 'synset': 'sulfur_paintbrush.n.01', 'name': 'sulfur_paintbrush'}, {'id': 20967, 'synset': 'shellflower.n.01', 'name': 'shellflower'}, {'id': 20968, 'synset': 'maiden_blue-eyed_mary.n.01', 'name': 'maiden_blue-eyed_Mary'}, {'id': 20969, 'synset': 'blue-eyed_mary.n.01', 'name': 'blue-eyed_Mary'}, {'id': 20970, 'synset': 'foxglove.n.01', 'name': 'foxglove'}, {'id': 20971, 'synset': 'common_foxglove.n.01', 'name': 'common_foxglove'}, {'id': 20972, 'synset': 'yellow_foxglove.n.01', 'name': 'yellow_foxglove'}, {'id': 20973, 'synset': 'gerardia.n.01', 'name': 'gerardia'}, {'id': 20974, 'synset': 'blue_toadflax.n.01', 'name': 'blue_toadflax'}, {'id': 20975, 'synset': 'toadflax.n.01', 'name': 'toadflax'}, {'id': 20976, 'synset': 'golden-beard_penstemon.n.01', 'name': 'golden-beard_penstemon'}, {'id': 20977, 'synset': 'scarlet_bugler.n.01', 'name': 'scarlet_bugler'}, {'id': 20978, 'synset': 'red_shrubby_penstemon.n.01', 'name': 'red_shrubby_penstemon'}, {'id': 20979, 'synset': 'platte_river_penstemon.n.01', 'name': 'Platte_River_penstemon'}, {'id': 20980, 'synset': 'hot-rock_penstemon.n.01', 'name': 'hot-rock_penstemon'}, {'id': 20981, 'synset': "jones'_penstemon.n.01", 'name': "Jones'_penstemon"}, {'id': 20982, 'synset': 'shrubby_penstemon.n.01', 'name': 'shrubby_penstemon'}, {'id': 20983, 'synset': 'narrow-leaf_penstemon.n.01', 'name': 'narrow-leaf_penstemon'}, {'id': 20984, 'synset': 'balloon_flower.n.01', 'name': 'balloon_flower'}, {'id': 20985, 'synset': "parry's_penstemon.n.01", 'name': "Parry's_penstemon"}, {'id': 20986, 'synset': 'rock_penstemon.n.01', 'name': 'rock_penstemon'}, {'id': 20987, 'synset': "rydberg's_penstemon.n.01", 'name': "Rydberg's_penstemon"}, {'id': 20988, 'synset': 'cascade_penstemon.n.01', 'name': 'cascade_penstemon'}, {'id': 20989, 'synset': "whipple's_penstemon.n.01", 'name': "Whipple's_penstemon"}, {'id': 20990, 'synset': 'moth_mullein.n.01', 'name': 'moth_mullein'}, {'id': 20991, 'synset': 'white_mullein.n.01', 'name': 'white_mullein'}, {'id': 20992, 'synset': 'purple_mullein.n.01', 'name': 'purple_mullein'}, {'id': 20993, 'synset': 'common_mullein.n.01', 'name': 'common_mullein'}, {'id': 20994, 'synset': 'veronica.n.01', 'name': 'veronica'}, {'id': 20995, 'synset': 'field_speedwell.n.01', 'name': 'field_speedwell'}, {'id': 20996, 'synset': 'brooklime.n.02', 'name': 'brooklime'}, {'id': 20997, 'synset': 'corn_speedwell.n.01', 'name': 'corn_speedwell'}, {'id': 20998, 'synset': 'brooklime.n.01', 'name': 'brooklime'}, {'id': 20999, 'synset': 'germander_speedwell.n.01', 'name': 'germander_speedwell'}, {'id': 21000, 'synset': 'water_speedwell.n.01', 'name': 'water_speedwell'}, {'id': 21001, 'synset': 'common_speedwell.n.01', 'name': 'common_speedwell'}, {'id': 21002, 'synset': 'purslane_speedwell.n.01', 'name': 'purslane_speedwell'}, {'id': 21003, 'synset': 'thyme-leaved_speedwell.n.01', 'name': 'thyme-leaved_speedwell'}, {'id': 21004, 'synset': 'nightshade.n.01', 'name': 'nightshade'}, {'id': 21005, 'synset': 'horse_nettle.n.01', 'name': 'horse_nettle'}, {'id': 21006, 'synset': 'african_holly.n.01', 'name': 'African_holly'}, {'id': 21007, 'synset': 'potato_vine.n.02', 'name': 'potato_vine'}, {'id': 21008, 'synset': 'garden_huckleberry.n.01', 'name': 'garden_huckleberry'}, {'id': 21009, 'synset': 'naranjilla.n.01', 'name': 'naranjilla'}, {'id': 21010, 'synset': 'potato_vine.n.01', 'name': 'potato_vine'}, {'id': 21011, 'synset': 'potato_tree.n.01', 'name': 'potato_tree'}, {'id': 21012, 'synset': 'belladonna.n.01', 'name': 'belladonna'}, {'id': 21013, 'synset': 'bush_violet.n.01', 'name': 'bush_violet'}, {'id': 21014, 'synset': 'lady-of-the-night.n.01', 'name': 'lady-of-the-night'}, {'id': 21015, 'synset': "angel's_trumpet.n.02", 'name': "angel's_trumpet"}, {'id': 21016, 'synset': "angel's_trumpet.n.01", 'name': "angel's_trumpet"}, {'id': 21017, 'synset': "red_angel's_trumpet.n.01", 'name': "red_angel's_trumpet"}, {'id': 21018, 'synset': 'cone_pepper.n.01', 'name': 'cone_pepper'}, {'id': 21019, 'synset': 'bird_pepper.n.01', 'name': 'bird_pepper'}, {'id': 21020, 'synset': 'day_jessamine.n.01', 'name': 'day_jessamine'}, {'id': 21021, 'synset': 'night_jasmine.n.01', 'name': 'night_jasmine'}, {'id': 21022, 'synset': 'tree_tomato.n.01', 'name': 'tree_tomato'}, {'id': 21023, 'synset': 'thorn_apple.n.01', 'name': 'thorn_apple'}, {'id': 21024, 'synset': 'jimsonweed.n.01', 'name': 'jimsonweed'}, {'id': 21025, 'synset': 'pichi.n.01', 'name': 'pichi'}, {'id': 21026, 'synset': 'henbane.n.01', 'name': 'henbane'}, {'id': 21027, 'synset': 'egyptian_henbane.n.01', 'name': 'Egyptian_henbane'}, {'id': 21028, 'synset': 'matrimony_vine.n.01', 'name': 'matrimony_vine'}, {'id': 21029, 'synset': 'common_matrimony_vine.n.01', 'name': 'common_matrimony_vine'}, {'id': 21030, 'synset': 'christmasberry.n.01', 'name': 'Christmasberry'}, {'id': 21031, 'synset': 'plum_tomato.n.01', 'name': 'plum_tomato'}, {'id': 21032, 'synset': 'mandrake.n.02', 'name': 'mandrake'}, {'id': 21033, 'synset': 'mandrake_root.n.01', 'name': 'mandrake_root'}, {'id': 21034, 'synset': 'apple_of_peru.n.01', 'name': 'apple_of_Peru'}, {'id': 21035, 'synset': 'flowering_tobacco.n.01', 'name': 'flowering_tobacco'}, {'id': 21036, 'synset': 'common_tobacco.n.01', 'name': 'common_tobacco'}, {'id': 21037, 'synset': 'wild_tobacco.n.01', 'name': 'wild_tobacco'}, {'id': 21038, 'synset': 'cupflower.n.02', 'name': 'cupflower'}, {'id': 21039, 'synset': 'whitecup.n.01', 'name': 'whitecup'}, {'id': 21040, 'synset': 'petunia.n.01', 'name': 'petunia'}, {'id': 21041, 'synset': 'large_white_petunia.n.01', 'name': 'large_white_petunia'}, {'id': 21042, 'synset': 'violet-flowered_petunia.n.01', 'name': 'violet-flowered_petunia'}, {'id': 21043, 'synset': 'hybrid_petunia.n.01', 'name': 'hybrid_petunia'}, {'id': 21044, 'synset': 'cape_gooseberry.n.01', 'name': 'cape_gooseberry'}, {'id': 21045, 'synset': 'strawberry_tomato.n.01', 'name': 'strawberry_tomato'}, {'id': 21046, 'synset': 'tomatillo.n.02', 'name': 'tomatillo'}, {'id': 21047, 'synset': 'tomatillo.n.01', 'name': 'tomatillo'}, {'id': 21048, 'synset': 'yellow_henbane.n.01', 'name': 'yellow_henbane'}, {'id': 21049, 'synset': "cock's_eggs.n.01", 'name': "cock's_eggs"}, {'id': 21050, 'synset': 'salpiglossis.n.01', 'name': 'salpiglossis'}, {'id': 21051, 'synset': 'painted_tongue.n.01', 'name': 'painted_tongue'}, {'id': 21052, 'synset': 'butterfly_flower.n.01', 'name': 'butterfly_flower'}, {'id': 21053, 'synset': 'scopolia_carniolica.n.01', 'name': 'Scopolia_carniolica'}, {'id': 21054, 'synset': 'chalice_vine.n.01', 'name': 'chalice_vine'}, {'id': 21055, 'synset': 'verbena.n.01', 'name': 'verbena'}, {'id': 21056, 'synset': 'lantana.n.01', 'name': 'lantana'}, {'id': 21057, 'synset': 'black_mangrove.n.02', 'name': 'black_mangrove'}, {'id': 21058, 'synset': 'white_mangrove.n.01', 'name': 'white_mangrove'}, {'id': 21059, 'synset': 'black_mangrove.n.01', 'name': 'black_mangrove'}, {'id': 21060, 'synset': 'teak.n.02', 'name': 'teak'}, {'id': 21061, 'synset': 'spurge.n.01', 'name': 'spurge'}, {'id': 21062, 'synset': 'sun_spurge.n.01', 'name': 'sun_spurge'}, {'id': 21063, 'synset': 'petty_spurge.n.01', 'name': 'petty_spurge'}, {'id': 21064, 'synset': "medusa's_head.n.01", 'name': "medusa's_head"}, {'id': 21065, 'synset': 'wild_spurge.n.01', 'name': 'wild_spurge'}, {'id': 21066, 'synset': 'snow-on-the-mountain.n.01', 'name': 'snow-on-the-mountain'}, {'id': 21067, 'synset': 'cypress_spurge.n.01', 'name': 'cypress_spurge'}, {'id': 21068, 'synset': 'leafy_spurge.n.01', 'name': 'leafy_spurge'}, {'id': 21069, 'synset': 'hairy_spurge.n.01', 'name': 'hairy_spurge'}, {'id': 21070, 'synset': 'poinsettia.n.01', 'name': 'poinsettia'}, {'id': 21071, 'synset': 'japanese_poinsettia.n.01', 'name': 'Japanese_poinsettia'}, {'id': 21072, 'synset': 'fire-on-the-mountain.n.01', 'name': 'fire-on-the-mountain'}, {'id': 21073, 'synset': 'wood_spurge.n.01', 'name': 'wood_spurge'}, {'id': 21074, 'synset': 'dwarf_spurge.n.01', 'name': 'dwarf_spurge'}, {'id': 21075, 'synset': 'scarlet_plume.n.01', 'name': 'scarlet_plume'}, {'id': 21076, 'synset': 'naboom.n.01', 'name': 'naboom'}, {'id': 21077, 'synset': 'crown_of_thorns.n.02', 'name': 'crown_of_thorns'}, {'id': 21078, 'synset': 'toothed_spurge.n.01', 'name': 'toothed_spurge'}, {'id': 21079, 'synset': 'three-seeded_mercury.n.01', 'name': 'three-seeded_mercury'}, {'id': 21080, 'synset': 'croton.n.02', 'name': 'croton'}, {'id': 21081, 'synset': 'cascarilla.n.01', 'name': 'cascarilla'}, {'id': 21082, 'synset': 'cascarilla_bark.n.01', 'name': 'cascarilla_bark'}, {'id': 21083, 'synset': 'castor-oil_plant.n.01', 'name': 'castor-oil_plant'}, {'id': 21084, 'synset': 'spurge_nettle.n.01', 'name': 'spurge_nettle'}, {'id': 21085, 'synset': 'physic_nut.n.01', 'name': 'physic_nut'}, {'id': 21086, 'synset': 'para_rubber_tree.n.01', 'name': 'Para_rubber_tree'}, {'id': 21087, 'synset': 'cassava.n.03', 'name': 'cassava'}, {'id': 21088, 'synset': 'bitter_cassava.n.01', 'name': 'bitter_cassava'}, {'id': 21089, 'synset': 'cassava.n.02', 'name': 'cassava'}, {'id': 21090, 'synset': 'sweet_cassava.n.01', 'name': 'sweet_cassava'}, {'id': 21091, 'synset': 'candlenut.n.01', 'name': 'candlenut'}, {'id': 21092, 'synset': 'tung_tree.n.01', 'name': 'tung_tree'}, {'id': 21093, 'synset': 'slipper_spurge.n.01', 'name': 'slipper_spurge'}, {'id': 21094, 'synset': 'candelilla.n.01', 'name': 'candelilla'}, {'id': 21095, 'synset': 'jewbush.n.01', 'name': 'Jewbush'}, {'id': 21096, 'synset': 'jumping_bean.n.01', 'name': 'jumping_bean'}, {'id': 21097, 'synset': 'camellia.n.01', 'name': 'camellia'}, {'id': 21098, 'synset': 'japonica.n.01', 'name': 'japonica'}, {'id': 21099, 'synset': 'umbellifer.n.01', 'name': 'umbellifer'}, {'id': 21100, 'synset': 'wild_parsley.n.01', 'name': 'wild_parsley'}, {'id': 21101, 'synset': "fool's_parsley.n.01", 'name': "fool's_parsley"}, {'id': 21102, 'synset': 'dill.n.01', 'name': 'dill'}, {'id': 21103, 'synset': 'angelica.n.01', 'name': 'angelica'}, {'id': 21104, 'synset': 'garden_angelica.n.01', 'name': 'garden_angelica'}, {'id': 21105, 'synset': 'wild_angelica.n.01', 'name': 'wild_angelica'}, {'id': 21106, 'synset': 'chervil.n.01', 'name': 'chervil'}, {'id': 21107, 'synset': 'cow_parsley.n.01', 'name': 'cow_parsley'}, {'id': 21108, 'synset': 'wild_celery.n.01', 'name': 'wild_celery'}, {'id': 21109, 'synset': 'astrantia.n.01', 'name': 'astrantia'}, {'id': 21110, 'synset': 'greater_masterwort.n.01', 'name': 'greater_masterwort'}, {'id': 21111, 'synset': 'caraway.n.01', 'name': 'caraway'}, {'id': 21112, 'synset': 'whorled_caraway.n.01', 'name': 'whorled_caraway'}, {'id': 21113, 'synset': 'water_hemlock.n.01', 'name': 'water_hemlock'}, {'id': 21114, 'synset': 'spotted_cowbane.n.01', 'name': 'spotted_cowbane'}, {'id': 21115, 'synset': 'hemlock.n.02', 'name': 'hemlock'}, {'id': 21116, 'synset': 'earthnut.n.02', 'name': 'earthnut'}, {'id': 21117, 'synset': 'cumin.n.01', 'name': 'cumin'}, {'id': 21118, 'synset': 'wild_carrot.n.01', 'name': 'wild_carrot'}, {'id': 21119, 'synset': 'eryngo.n.01', 'name': 'eryngo'}, {'id': 21120, 'synset': 'sea_holly.n.01', 'name': 'sea_holly'}, {'id': 21121, 'synset': 'button_snakeroot.n.02', 'name': 'button_snakeroot'}, {'id': 21122, 'synset': 'rattlesnake_master.n.01', 'name': 'rattlesnake_master'}, {'id': 21123, 'synset': 'fennel.n.01', 'name': 'fennel'}, {'id': 21124, 'synset': 'common_fennel.n.01', 'name': 'common_fennel'}, {'id': 21125, 'synset': 'florence_fennel.n.01', 'name': 'Florence_fennel'}, {'id': 21126, 'synset': 'cow_parsnip.n.01', 'name': 'cow_parsnip'}, {'id': 21127, 'synset': 'lovage.n.01', 'name': 'lovage'}, {'id': 21128, 'synset': 'sweet_cicely.n.01', 'name': 'sweet_cicely'}, {'id': 21129, 'synset': 'water_fennel.n.01', 'name': 'water_fennel'}, {'id': 21130, 'synset': 'parsnip.n.02', 'name': 'parsnip'}, {'id': 21131, 'synset': 'cultivated_parsnip.n.01', 'name': 'cultivated_parsnip'}, {'id': 21132, 'synset': 'wild_parsnip.n.01', 'name': 'wild_parsnip'}, {'id': 21133, 'synset': 'parsley.n.01', 'name': 'parsley'}, {'id': 21134, 'synset': 'italian_parsley.n.01', 'name': 'Italian_parsley'}, {'id': 21135, 'synset': 'hamburg_parsley.n.01', 'name': 'Hamburg_parsley'}, {'id': 21136, 'synset': 'anise.n.01', 'name': 'anise'}, {'id': 21137, 'synset': 'sanicle.n.01', 'name': 'sanicle'}, {'id': 21138, 'synset': 'purple_sanicle.n.01', 'name': 'purple_sanicle'}, {'id': 21139, 'synset': 'european_sanicle.n.01', 'name': 'European_sanicle'}, {'id': 21140, 'synset': 'water_parsnip.n.01', 'name': 'water_parsnip'}, {'id': 21141, 'synset': 'greater_water_parsnip.n.01', 'name': 'greater_water_parsnip'}, {'id': 21142, 'synset': 'skirret.n.01', 'name': 'skirret'}, {'id': 21143, 'synset': 'dogwood.n.01', 'name': 'dogwood'}, {'id': 21144, 'synset': 'common_white_dogwood.n.01', 'name': 'common_white_dogwood'}, {'id': 21145, 'synset': 'red_osier.n.01', 'name': 'red_osier'}, {'id': 21146, 'synset': 'silky_dogwood.n.02', 'name': 'silky_dogwood'}, {'id': 21147, 'synset': 'silky_cornel.n.01', 'name': 'silky_cornel'}, {'id': 21148, 'synset': 'common_european_dogwood.n.01', 'name': 'common_European_dogwood'}, {'id': 21149, 'synset': 'bunchberry.n.01', 'name': 'bunchberry'}, {'id': 21150, 'synset': 'cornelian_cherry.n.01', 'name': 'cornelian_cherry'}, {'id': 21151, 'synset': 'puka.n.01', 'name': 'puka'}, {'id': 21152, 'synset': 'kapuka.n.01', 'name': 'kapuka'}, {'id': 21153, 'synset': 'valerian.n.01', 'name': 'valerian'}, {'id': 21154, 'synset': 'common_valerian.n.01', 'name': 'common_valerian'}, {'id': 21155, 'synset': 'common_corn_salad.n.01', 'name': 'common_corn_salad'}, {'id': 21156, 'synset': 'red_valerian.n.01', 'name': 'red_valerian'}, {'id': 21157, 'synset': 'filmy_fern.n.02', 'name': 'filmy_fern'}, {'id': 21158, 'synset': 'bristle_fern.n.01', 'name': 'bristle_fern'}, {'id': 21159, 'synset': "hare's-foot_bristle_fern.n.01", 'name': "hare's-foot_bristle_fern"}, {'id': 21160, 'synset': 'killarney_fern.n.01', 'name': 'Killarney_fern'}, {'id': 21161, 'synset': 'kidney_fern.n.01', 'name': 'kidney_fern'}, {'id': 21162, 'synset': 'flowering_fern.n.02', 'name': 'flowering_fern'}, {'id': 21163, 'synset': 'royal_fern.n.01', 'name': 'royal_fern'}, {'id': 21164, 'synset': 'interrupted_fern.n.01', 'name': 'interrupted_fern'}, {'id': 21165, 'synset': 'crape_fern.n.01', 'name': 'crape_fern'}, {'id': 21166, 'synset': 'crepe_fern.n.01', 'name': 'crepe_fern'}, {'id': 21167, 'synset': 'curly_grass.n.01', 'name': 'curly_grass'}, {'id': 21168, 'synset': 'pine_fern.n.01', 'name': 'pine_fern'}, {'id': 21169, 'synset': 'climbing_fern.n.01', 'name': 'climbing_fern'}, {'id': 21170, 'synset': 'creeping_fern.n.01', 'name': 'creeping_fern'}, {'id': 21171, 'synset': 'climbing_maidenhair.n.01', 'name': 'climbing_maidenhair'}, {'id': 21172, 'synset': 'scented_fern.n.02', 'name': 'scented_fern'}, {'id': 21173, 'synset': 'clover_fern.n.01', 'name': 'clover_fern'}, {'id': 21174, 'synset': 'nardoo.n.01', 'name': 'nardoo'}, {'id': 21175, 'synset': 'water_clover.n.01', 'name': 'water_clover'}, {'id': 21176, 'synset': 'pillwort.n.01', 'name': 'pillwort'}, {'id': 21177, 'synset': 'regnellidium.n.01', 'name': 'regnellidium'}, {'id': 21178, 'synset': 'floating-moss.n.01', 'name': 'floating-moss'}, {'id': 21179, 'synset': 'mosquito_fern.n.01', 'name': 'mosquito_fern'}, {'id': 21180, 'synset': "adder's_tongue.n.01", 'name': "adder's_tongue"}, {'id': 21181, 'synset': 'ribbon_fern.n.03', 'name': 'ribbon_fern'}, {'id': 21182, 'synset': 'grape_fern.n.01', 'name': 'grape_fern'}, {'id': 21183, 'synset': 'daisyleaf_grape_fern.n.01', 'name': 'daisyleaf_grape_fern'}, {'id': 21184, 'synset': 'leathery_grape_fern.n.01', 'name': 'leathery_grape_fern'}, {'id': 21185, 'synset': 'rattlesnake_fern.n.01', 'name': 'rattlesnake_fern'}, {'id': 21186, 'synset': 'flowering_fern.n.01', 'name': 'flowering_fern'}, {'id': 21187, 'synset': 'powdery_mildew.n.01', 'name': 'powdery_mildew'}, {'id': 21188, 'synset': 'dutch_elm_fungus.n.01', 'name': 'Dutch_elm_fungus'}, {'id': 21189, 'synset': 'ergot.n.02', 'name': 'ergot'}, {'id': 21190, 'synset': 'rye_ergot.n.01', 'name': 'rye_ergot'}, {'id': 21191, 'synset': 'black_root_rot_fungus.n.01', 'name': 'black_root_rot_fungus'}, {'id': 21192, 'synset': "dead-man's-fingers.n.01", 'name': "dead-man's-fingers"}, {'id': 21193, 'synset': 'sclerotinia.n.01', 'name': 'sclerotinia'}, {'id': 21194, 'synset': 'brown_cup.n.01', 'name': 'brown_cup'}, {'id': 21195, 'synset': 'earthball.n.01', 'name': 'earthball'}, {'id': 21196, 'synset': 'scleroderma_citrinum.n.01', 'name': 'Scleroderma_citrinum'}, {'id': 21197, 'synset': 'scleroderma_flavidium.n.01', 'name': 'Scleroderma_flavidium'}, {'id': 21198, 'synset': 'scleroderma_bovista.n.01', 'name': 'Scleroderma_bovista'}, {'id': 21199, 'synset': 'podaxaceae.n.01', 'name': 'Podaxaceae'}, {'id': 21200, 'synset': 'stalked_puffball.n.02', 'name': 'stalked_puffball'}, {'id': 21201, 'synset': 'stalked_puffball.n.01', 'name': 'stalked_puffball'}, {'id': 21202, 'synset': 'false_truffle.n.01', 'name': 'false_truffle'}, {'id': 21203, 'synset': 'rhizopogon_idahoensis.n.01', 'name': 'Rhizopogon_idahoensis'}, {'id': 21204, 'synset': 'truncocolumella_citrina.n.01', 'name': 'Truncocolumella_citrina'}, {'id': 21205, 'synset': 'mucor.n.01', 'name': 'mucor'}, {'id': 21206, 'synset': 'rhizopus.n.01', 'name': 'rhizopus'}, {'id': 21207, 'synset': 'bread_mold.n.01', 'name': 'bread_mold'}, {'id': 21208, 'synset': 'slime_mold.n.01', 'name': 'slime_mold'}, {'id': 21209, 'synset': 'true_slime_mold.n.01', 'name': 'true_slime_mold'}, {'id': 21210, 'synset': 'cellular_slime_mold.n.01', 'name': 'cellular_slime_mold'}, {'id': 21211, 'synset': 'dictostylium.n.01', 'name': 'dictostylium'}, {'id': 21212, 'synset': 'pond-scum_parasite.n.01', 'name': 'pond-scum_parasite'}, {'id': 21213, 'synset': 'potato_wart_fungus.n.01', 'name': 'potato_wart_fungus'}, {'id': 21214, 'synset': 'white_fungus.n.01', 'name': 'white_fungus'}, {'id': 21215, 'synset': 'water_mold.n.01', 'name': 'water_mold'}, {'id': 21216, 'synset': 'downy_mildew.n.01', 'name': 'downy_mildew'}, {'id': 21217, 'synset': 'blue_mold_fungus.n.01', 'name': 'blue_mold_fungus'}, {'id': 21218, 'synset': 'onion_mildew.n.01', 'name': 'onion_mildew'}, {'id': 21219, 'synset': 'tobacco_mildew.n.01', 'name': 'tobacco_mildew'}, {'id': 21220, 'synset': 'white_rust.n.01', 'name': 'white_rust'}, {'id': 21221, 'synset': 'pythium.n.01', 'name': 'pythium'}, {'id': 21222, 'synset': 'damping_off_fungus.n.01', 'name': 'damping_off_fungus'}, {'id': 21223, 'synset': 'phytophthora_citrophthora.n.01', 'name': 'Phytophthora_citrophthora'}, {'id': 21224, 'synset': 'phytophthora_infestans.n.01', 'name': 'Phytophthora_infestans'}, {'id': 21225, 'synset': 'clubroot_fungus.n.01', 'name': 'clubroot_fungus'}, {'id': 21226, 'synset': 'geglossaceae.n.01', 'name': 'Geglossaceae'}, {'id': 21227, 'synset': 'sarcosomataceae.n.01', 'name': 'Sarcosomataceae'}, {'id': 21228, 'synset': 'rufous_rubber_cup.n.01', 'name': 'Rufous_rubber_cup'}, {'id': 21229, 'synset': "devil's_cigar.n.01", 'name': "devil's_cigar"}, {'id': 21230, 'synset': "devil's_urn.n.01", 'name': "devil's_urn"}, {'id': 21231, 'synset': 'truffle.n.01', 'name': 'truffle'}, {'id': 21232, 'synset': 'club_fungus.n.01', 'name': 'club_fungus'}, {'id': 21233, 'synset': 'coral_fungus.n.01', 'name': 'coral_fungus'}, {'id': 21234, 'synset': 'tooth_fungus.n.01', 'name': 'tooth_fungus'}, {'id': 21235, 'synset': 'lichen.n.02', 'name': 'lichen'}, {'id': 21236, 'synset': 'ascolichen.n.01', 'name': 'ascolichen'}, {'id': 21237, 'synset': 'basidiolichen.n.01', 'name': 'basidiolichen'}, {'id': 21238, 'synset': 'lecanora.n.01', 'name': 'lecanora'}, {'id': 21239, 'synset': 'manna_lichen.n.01', 'name': 'manna_lichen'}, {'id': 21240, 'synset': 'archil.n.02', 'name': 'archil'}, {'id': 21241, 'synset': 'roccella.n.01', 'name': 'roccella'}, {'id': 21242, 'synset': 'beard_lichen.n.01', 'name': 'beard_lichen'}, {'id': 21243, 'synset': 'horsehair_lichen.n.01', 'name': 'horsehair_lichen'}, {'id': 21244, 'synset': 'reindeer_moss.n.01', 'name': 'reindeer_moss'}, {'id': 21245, 'synset': 'crottle.n.01', 'name': 'crottle'}, {'id': 21246, 'synset': 'iceland_moss.n.01', 'name': 'Iceland_moss'}, {'id': 21247, 'synset': 'fungus.n.01', 'name': 'fungus'}, {'id': 21248, 'synset': 'promycelium.n.01', 'name': 'promycelium'}, {'id': 21249, 'synset': 'true_fungus.n.01', 'name': 'true_fungus'}, {'id': 21250, 'synset': 'basidiomycete.n.01', 'name': 'basidiomycete'}, {'id': 21251, 'synset': 'mushroom.n.03', 'name': 'mushroom'}, {'id': 21252, 'synset': 'agaric.n.02', 'name': 'agaric'}, {'id': 21253, 'synset': 'mushroom.n.01', 'name': 'mushroom'}, {'id': 21254, 'synset': 'toadstool.n.01', 'name': 'toadstool'}, {'id': 21255, 'synset': 'horse_mushroom.n.01', 'name': 'horse_mushroom'}, {'id': 21256, 'synset': 'meadow_mushroom.n.01', 'name': 'meadow_mushroom'}, {'id': 21257, 'synset': 'shiitake.n.01', 'name': 'shiitake'}, {'id': 21258, 'synset': 'scaly_lentinus.n.01', 'name': 'scaly_lentinus'}, {'id': 21259, 'synset': 'royal_agaric.n.01', 'name': 'royal_agaric'}, {'id': 21260, 'synset': 'false_deathcap.n.01', 'name': 'false_deathcap'}, {'id': 21261, 'synset': 'fly_agaric.n.01', 'name': 'fly_agaric'}, {'id': 21262, 'synset': 'death_cap.n.01', 'name': 'death_cap'}, {'id': 21263, 'synset': 'blushing_mushroom.n.01', 'name': 'blushing_mushroom'}, {'id': 21264, 'synset': 'destroying_angel.n.01', 'name': 'destroying_angel'}, {'id': 21265, 'synset': 'chanterelle.n.01', 'name': 'chanterelle'}, {'id': 21266, 'synset': 'floccose_chanterelle.n.01', 'name': 'floccose_chanterelle'}, {'id': 21267, 'synset': "pig's_ears.n.01", 'name': "pig's_ears"}, {'id': 21268, 'synset': 'cinnabar_chanterelle.n.01', 'name': 'cinnabar_chanterelle'}, {'id': 21269, 'synset': 'jack-o-lantern_fungus.n.01', 'name': 'jack-o-lantern_fungus'}, {'id': 21270, 'synset': 'inky_cap.n.01', 'name': 'inky_cap'}, {'id': 21271, 'synset': 'shaggymane.n.01', 'name': 'shaggymane'}, {'id': 21272, 'synset': 'milkcap.n.01', 'name': 'milkcap'}, {'id': 21273, 'synset': 'fairy-ring_mushroom.n.01', 'name': 'fairy-ring_mushroom'}, {'id': 21274, 'synset': 'fairy_ring.n.01', 'name': 'fairy_ring'}, {'id': 21275, 'synset': 'oyster_mushroom.n.01', 'name': 'oyster_mushroom'}, {'id': 21276, 'synset': 'olive-tree_agaric.n.01', 'name': 'olive-tree_agaric'}, {'id': 21277, 'synset': 'pholiota_astragalina.n.01', 'name': 'Pholiota_astragalina'}, {'id': 21278, 'synset': 'pholiota_aurea.n.01', 'name': 'Pholiota_aurea'}, {'id': 21279, 'synset': 'pholiota_destruens.n.01', 'name': 'Pholiota_destruens'}, {'id': 21280, 'synset': 'pholiota_flammans.n.01', 'name': 'Pholiota_flammans'}, {'id': 21281, 'synset': 'pholiota_flavida.n.01', 'name': 'Pholiota_flavida'}, {'id': 21282, 'synset': 'nameko.n.01', 'name': 'nameko'}, {'id': 21283, 'synset': 'pholiota_squarrosa-adiposa.n.01', 'name': 'Pholiota_squarrosa-adiposa'}, {'id': 21284, 'synset': 'pholiota_squarrosa.n.01', 'name': 'Pholiota_squarrosa'}, {'id': 21285, 'synset': 'pholiota_squarrosoides.n.01', 'name': 'Pholiota_squarrosoides'}, {'id': 21286, 'synset': 'stropharia_ambigua.n.01', 'name': 'Stropharia_ambigua'}, {'id': 21287, 'synset': 'stropharia_hornemannii.n.01', 'name': 'Stropharia_hornemannii'}, {'id': 21288, 'synset': 'stropharia_rugoso-annulata.n.01', 'name': 'Stropharia_rugoso-annulata'}, {'id': 21289, 'synset': 'gill_fungus.n.01', 'name': 'gill_fungus'}, {'id': 21290, 'synset': 'entoloma_lividum.n.01', 'name': 'Entoloma_lividum'}, {'id': 21291, 'synset': 'entoloma_aprile.n.01', 'name': 'Entoloma_aprile'}, {'id': 21292, 'synset': 'chlorophyllum_molybdites.n.01', 'name': 'Chlorophyllum_molybdites'}, {'id': 21293, 'synset': 'lepiota.n.01', 'name': 'lepiota'}, {'id': 21294, 'synset': 'parasol_mushroom.n.01', 'name': 'parasol_mushroom'}, {'id': 21295, 'synset': 'poisonous_parasol.n.01', 'name': 'poisonous_parasol'}, {'id': 21296, 'synset': 'lepiota_naucina.n.01', 'name': 'Lepiota_naucina'}, {'id': 21297, 'synset': 'lepiota_rhacodes.n.01', 'name': 'Lepiota_rhacodes'}, {'id': 21298, 'synset': 'american_parasol.n.01', 'name': 'American_parasol'}, {'id': 21299, 'synset': 'lepiota_rubrotincta.n.01', 'name': 'Lepiota_rubrotincta'}, {'id': 21300, 'synset': 'lepiota_clypeolaria.n.01', 'name': 'Lepiota_clypeolaria'}, {'id': 21301, 'synset': 'onion_stem.n.01', 'name': 'onion_stem'}, {'id': 21302, 'synset': 'pink_disease_fungus.n.01', 'name': 'pink_disease_fungus'}, {'id': 21303, 'synset': 'bottom_rot_fungus.n.01', 'name': 'bottom_rot_fungus'}, {'id': 21304, 'synset': 'potato_fungus.n.01', 'name': 'potato_fungus'}, {'id': 21305, 'synset': 'coffee_fungus.n.01', 'name': 'coffee_fungus'}, {'id': 21306, 'synset': 'blewits.n.01', 'name': 'blewits'}, {'id': 21307, 'synset': 'sandy_mushroom.n.01', 'name': 'sandy_mushroom'}, {'id': 21308, 'synset': 'tricholoma_pessundatum.n.01', 'name': 'Tricholoma_pessundatum'}, {'id': 21309, 'synset': 'tricholoma_sejunctum.n.01', 'name': 'Tricholoma_sejunctum'}, {'id': 21310, 'synset': 'man-on-a-horse.n.01', 'name': 'man-on-a-horse'}, {'id': 21311, 'synset': 'tricholoma_venenata.n.01', 'name': 'Tricholoma_venenata'}, {'id': 21312, 'synset': 'tricholoma_pardinum.n.01', 'name': 'Tricholoma_pardinum'}, {'id': 21313, 'synset': 'tricholoma_vaccinum.n.01', 'name': 'Tricholoma_vaccinum'}, {'id': 21314, 'synset': 'tricholoma_aurantium.n.01', 'name': 'Tricholoma_aurantium'}, {'id': 21315, 'synset': 'volvaria_bombycina.n.01', 'name': 'Volvaria_bombycina'}, {'id': 21316, 'synset': 'pluteus_aurantiorugosus.n.01', 'name': 'Pluteus_aurantiorugosus'}, {'id': 21317, 'synset': 'pluteus_magnus.n.01', 'name': 'Pluteus_magnus'}, {'id': 21318, 'synset': 'deer_mushroom.n.01', 'name': 'deer_mushroom'}, {'id': 21319, 'synset': 'straw_mushroom.n.01', 'name': 'straw_mushroom'}, {'id': 21320, 'synset': 'volvariella_bombycina.n.01', 'name': 'Volvariella_bombycina'}, {'id': 21321, 'synset': 'clitocybe_clavipes.n.01', 'name': 'Clitocybe_clavipes'}, {'id': 21322, 'synset': 'clitocybe_dealbata.n.01', 'name': 'Clitocybe_dealbata'}, {'id': 21323, 'synset': 'clitocybe_inornata.n.01', 'name': 'Clitocybe_inornata'}, {'id': 21324, 'synset': 'clitocybe_robusta.n.01', 'name': 'Clitocybe_robusta'}, {'id': 21325, 'synset': 'clitocybe_irina.n.01', 'name': 'Clitocybe_irina'}, {'id': 21326, 'synset': 'clitocybe_subconnexa.n.01', 'name': 'Clitocybe_subconnexa'}, {'id': 21327, 'synset': 'winter_mushroom.n.01', 'name': 'winter_mushroom'}, {'id': 21328, 'synset': 'mycelium.n.01', 'name': 'mycelium'}, {'id': 21329, 'synset': 'sclerotium.n.02', 'name': 'sclerotium'}, {'id': 21330, 'synset': 'sac_fungus.n.01', 'name': 'sac_fungus'}, {'id': 21331, 'synset': 'ascomycete.n.01', 'name': 'ascomycete'}, {'id': 21332, 'synset': 'clavicipitaceae.n.01', 'name': 'Clavicipitaceae'}, {'id': 21333, 'synset': 'grainy_club.n.01', 'name': 'grainy_club'}, {'id': 21334, 'synset': 'yeast.n.02', 'name': 'yeast'}, {'id': 21335, 'synset': "baker's_yeast.n.01", 'name': "baker's_yeast"}, {'id': 21336, 'synset': "wine-maker's_yeast.n.01", 'name': "wine-maker's_yeast"}, {'id': 21337, 'synset': 'aspergillus_fumigatus.n.01', 'name': 'Aspergillus_fumigatus'}, {'id': 21338, 'synset': 'brown_root_rot_fungus.n.01', 'name': 'brown_root_rot_fungus'}, {'id': 21339, 'synset': 'discomycete.n.01', 'name': 'discomycete'}, {'id': 21340, 'synset': 'leotia_lubrica.n.01', 'name': 'Leotia_lubrica'}, {'id': 21341, 'synset': 'mitrula_elegans.n.01', 'name': 'Mitrula_elegans'}, {'id': 21342, 'synset': 'sarcoscypha_coccinea.n.01', 'name': 'Sarcoscypha_coccinea'}, {'id': 21343, 'synset': 'caloscypha_fulgens.n.01', 'name': 'Caloscypha_fulgens'}, {'id': 21344, 'synset': 'aleuria_aurantia.n.01', 'name': 'Aleuria_aurantia'}, {'id': 21345, 'synset': 'elf_cup.n.01', 'name': 'elf_cup'}, {'id': 21346, 'synset': 'peziza_domicilina.n.01', 'name': 'Peziza_domicilina'}, {'id': 21347, 'synset': 'blood_cup.n.01', 'name': 'blood_cup'}, {'id': 21348, 'synset': 'urnula_craterium.n.01', 'name': 'Urnula_craterium'}, {'id': 21349, 'synset': 'galiella_rufa.n.01', 'name': 'Galiella_rufa'}, {'id': 21350, 'synset': 'jafnea_semitosta.n.01', 'name': 'Jafnea_semitosta'}, {'id': 21351, 'synset': 'morel.n.01', 'name': 'morel'}, {'id': 21352, 'synset': 'common_morel.n.01', 'name': 'common_morel'}, {'id': 21353, 'synset': 'disciotis_venosa.n.01', 'name': 'Disciotis_venosa'}, {'id': 21354, 'synset': 'verpa.n.01', 'name': 'Verpa'}, {'id': 21355, 'synset': 'verpa_bohemica.n.01', 'name': 'Verpa_bohemica'}, {'id': 21356, 'synset': 'verpa_conica.n.01', 'name': 'Verpa_conica'}, {'id': 21357, 'synset': 'black_morel.n.01', 'name': 'black_morel'}, {'id': 21358, 'synset': 'morchella_crassipes.n.01', 'name': 'Morchella_crassipes'}, {'id': 21359, 'synset': 'morchella_semilibera.n.01', 'name': 'Morchella_semilibera'}, {'id': 21360, 'synset': 'wynnea_americana.n.01', 'name': 'Wynnea_americana'}, {'id': 21361, 'synset': 'wynnea_sparassoides.n.01', 'name': 'Wynnea_sparassoides'}, {'id': 21362, 'synset': 'false_morel.n.01', 'name': 'false_morel'}, {'id': 21363, 'synset': 'lorchel.n.01', 'name': 'lorchel'}, {'id': 21364, 'synset': 'helvella.n.01', 'name': 'helvella'}, {'id': 21365, 'synset': 'helvella_crispa.n.01', 'name': 'Helvella_crispa'}, {'id': 21366, 'synset': 'helvella_acetabulum.n.01', 'name': 'Helvella_acetabulum'}, {'id': 21367, 'synset': 'helvella_sulcata.n.01', 'name': 'Helvella_sulcata'}, {'id': 21368, 'synset': 'discina.n.01', 'name': 'discina'}, {'id': 21369, 'synset': 'gyromitra.n.01', 'name': 'gyromitra'}, {'id': 21370, 'synset': 'gyromitra_californica.n.01', 'name': 'Gyromitra_californica'}, {'id': 21371, 'synset': 'gyromitra_sphaerospora.n.01', 'name': 'Gyromitra_sphaerospora'}, {'id': 21372, 'synset': 'gyromitra_esculenta.n.01', 'name': 'Gyromitra_esculenta'}, {'id': 21373, 'synset': 'gyromitra_infula.n.01', 'name': 'Gyromitra_infula'}, {'id': 21374, 'synset': 'gyromitra_fastigiata.n.01', 'name': 'Gyromitra_fastigiata'}, {'id': 21375, 'synset': 'gyromitra_gigas.n.01', 'name': 'Gyromitra_gigas'}, {'id': 21376, 'synset': 'gasteromycete.n.01', 'name': 'gasteromycete'}, {'id': 21377, 'synset': 'stinkhorn.n.01', 'name': 'stinkhorn'}, {'id': 21378, 'synset': 'common_stinkhorn.n.01', 'name': 'common_stinkhorn'}, {'id': 21379, 'synset': 'phallus_ravenelii.n.01', 'name': 'Phallus_ravenelii'}, {'id': 21380, 'synset': 'dog_stinkhorn.n.01', 'name': 'dog_stinkhorn'}, {'id': 21381, 'synset': 'calostoma_lutescens.n.01', 'name': 'Calostoma_lutescens'}, {'id': 21382, 'synset': 'calostoma_cinnabarina.n.01', 'name': 'Calostoma_cinnabarina'}, {'id': 21383, 'synset': 'calostoma_ravenelii.n.01', 'name': 'Calostoma_ravenelii'}, {'id': 21384, 'synset': 'stinky_squid.n.01', 'name': 'stinky_squid'}, {'id': 21385, 'synset': 'puffball.n.01', 'name': 'puffball'}, {'id': 21386, 'synset': 'giant_puffball.n.01', 'name': 'giant_puffball'}, {'id': 21387, 'synset': 'earthstar.n.01', 'name': 'earthstar'}, {'id': 21388, 'synset': 'geastrum_coronatum.n.01', 'name': 'Geastrum_coronatum'}, {'id': 21389, 'synset': 'radiigera_fuscogleba.n.01', 'name': 'Radiigera_fuscogleba'}, {'id': 21390, 'synset': 'astreus_pteridis.n.01', 'name': 'Astreus_pteridis'}, {'id': 21391, 'synset': 'astreus_hygrometricus.n.01', 'name': 'Astreus_hygrometricus'}, {'id': 21392, 'synset': "bird's-nest_fungus.n.01", 'name': "bird's-nest_fungus"}, {'id': 21393, 'synset': 'gastrocybe_lateritia.n.01', 'name': 'Gastrocybe_lateritia'}, {'id': 21394, 'synset': 'macowanites_americanus.n.01', 'name': 'Macowanites_americanus'}, {'id': 21395, 'synset': 'polypore.n.01', 'name': 'polypore'}, {'id': 21396, 'synset': 'bracket_fungus.n.01', 'name': 'bracket_fungus'}, {'id': 21397, 'synset': 'albatrellus_dispansus.n.01', 'name': 'Albatrellus_dispansus'}, {'id': 21398, 'synset': 'albatrellus_ovinus.n.01', 'name': 'Albatrellus_ovinus'}, {'id': 21399, 'synset': 'neolentinus_ponderosus.n.01', 'name': 'Neolentinus_ponderosus'}, {'id': 21400, 'synset': 'oligoporus_leucospongia.n.01', 'name': 'Oligoporus_leucospongia'}, {'id': 21401, 'synset': 'polyporus_tenuiculus.n.01', 'name': 'Polyporus_tenuiculus'}, {'id': 21402, 'synset': 'hen-of-the-woods.n.01', 'name': 'hen-of-the-woods'}, {'id': 21403, 'synset': 'polyporus_squamosus.n.01', 'name': 'Polyporus_squamosus'}, {'id': 21404, 'synset': 'beefsteak_fungus.n.01', 'name': 'beefsteak_fungus'}, {'id': 21405, 'synset': 'agaric.n.01', 'name': 'agaric'}, {'id': 21406, 'synset': 'bolete.n.01', 'name': 'bolete'}, {'id': 21407, 'synset': 'boletus_chrysenteron.n.01', 'name': 'Boletus_chrysenteron'}, {'id': 21408, 'synset': 'boletus_edulis.n.01', 'name': 'Boletus_edulis'}, {'id': 21409, 'synset': "frost's_bolete.n.01", 'name': "Frost's_bolete"}, {'id': 21410, 'synset': 'boletus_luridus.n.01', 'name': 'Boletus_luridus'}, {'id': 21411, 'synset': 'boletus_mirabilis.n.01', 'name': 'Boletus_mirabilis'}, {'id': 21412, 'synset': 'boletus_pallidus.n.01', 'name': 'Boletus_pallidus'}, {'id': 21413, 'synset': 'boletus_pulcherrimus.n.01', 'name': 'Boletus_pulcherrimus'}, {'id': 21414, 'synset': 'boletus_pulverulentus.n.01', 'name': 'Boletus_pulverulentus'}, {'id': 21415, 'synset': 'boletus_roxanae.n.01', 'name': 'Boletus_roxanae'}, {'id': 21416, 'synset': 'boletus_subvelutipes.n.01', 'name': 'Boletus_subvelutipes'}, {'id': 21417, 'synset': 'boletus_variipes.n.01', 'name': 'Boletus_variipes'}, {'id': 21418, 'synset': 'boletus_zelleri.n.01', 'name': 'Boletus_zelleri'}, {'id': 21419, 'synset': 'fuscoboletinus_paluster.n.01', 'name': 'Fuscoboletinus_paluster'}, {'id': 21420, 'synset': 'fuscoboletinus_serotinus.n.01', 'name': 'Fuscoboletinus_serotinus'}, {'id': 21421, 'synset': 'leccinum_fibrillosum.n.01', 'name': 'Leccinum_fibrillosum'}, {'id': 21422, 'synset': 'suillus_albivelatus.n.01', 'name': 'Suillus_albivelatus'}, {'id': 21423, 'synset': 'old-man-of-the-woods.n.01', 'name': 'old-man-of-the-woods'}, {'id': 21424, 'synset': 'boletellus_russellii.n.01', 'name': 'Boletellus_russellii'}, {'id': 21425, 'synset': 'jelly_fungus.n.01', 'name': 'jelly_fungus'}, {'id': 21426, 'synset': 'snow_mushroom.n.01', 'name': 'snow_mushroom'}, {'id': 21427, 'synset': "witches'_butter.n.01", 'name': "witches'_butter"}, {'id': 21428, 'synset': 'tremella_foliacea.n.01', 'name': 'Tremella_foliacea'}, {'id': 21429, 'synset': 'tremella_reticulata.n.01', 'name': 'Tremella_reticulata'}, {'id': 21430, 'synset': "jew's-ear.n.01", 'name': "Jew's-ear"}, {'id': 21431, 'synset': 'rust.n.04', 'name': 'rust'}, {'id': 21432, 'synset': 'aecium.n.01', 'name': 'aecium'}, {'id': 21433, 'synset': 'flax_rust.n.01', 'name': 'flax_rust'}, {'id': 21434, 'synset': 'blister_rust.n.02', 'name': 'blister_rust'}, {'id': 21435, 'synset': 'wheat_rust.n.01', 'name': 'wheat_rust'}, {'id': 21436, 'synset': 'apple_rust.n.01', 'name': 'apple_rust'}, {'id': 21437, 'synset': 'smut.n.03', 'name': 'smut'}, {'id': 21438, 'synset': 'covered_smut.n.01', 'name': 'covered_smut'}, {'id': 21439, 'synset': 'loose_smut.n.02', 'name': 'loose_smut'}, {'id': 21440, 'synset': 'cornsmut.n.01', 'name': 'cornsmut'}, {'id': 21441, 'synset': 'boil_smut.n.01', 'name': 'boil_smut'}, {'id': 21442, 'synset': 'sphacelotheca.n.01', 'name': 'Sphacelotheca'}, {'id': 21443, 'synset': 'head_smut.n.01', 'name': 'head_smut'}, {'id': 21444, 'synset': 'bunt.n.04', 'name': 'bunt'}, {'id': 21445, 'synset': 'bunt.n.03', 'name': 'bunt'}, {'id': 21446, 'synset': 'onion_smut.n.01', 'name': 'onion_smut'}, {'id': 21447, 'synset': 'flag_smut_fungus.n.01', 'name': 'flag_smut_fungus'}, {'id': 21448, 'synset': 'wheat_flag_smut.n.01', 'name': 'wheat_flag_smut'}, {'id': 21449, 'synset': 'felt_fungus.n.01', 'name': 'felt_fungus'}, {'id': 21450, 'synset': 'waxycap.n.01', 'name': 'waxycap'}, {'id': 21451, 'synset': 'hygrocybe_acutoconica.n.01', 'name': 'Hygrocybe_acutoconica'}, {'id': 21452, 'synset': 'hygrophorus_borealis.n.01', 'name': 'Hygrophorus_borealis'}, {'id': 21453, 'synset': 'hygrophorus_caeruleus.n.01', 'name': 'Hygrophorus_caeruleus'}, {'id': 21454, 'synset': 'hygrophorus_inocybiformis.n.01', 'name': 'Hygrophorus_inocybiformis'}, {'id': 21455, 'synset': 'hygrophorus_kauffmanii.n.01', 'name': 'Hygrophorus_kauffmanii'}, {'id': 21456, 'synset': 'hygrophorus_marzuolus.n.01', 'name': 'Hygrophorus_marzuolus'}, {'id': 21457, 'synset': 'hygrophorus_purpurascens.n.01', 'name': 'Hygrophorus_purpurascens'}, {'id': 21458, 'synset': 'hygrophorus_russula.n.01', 'name': 'Hygrophorus_russula'}, {'id': 21459, 'synset': 'hygrophorus_sordidus.n.01', 'name': 'Hygrophorus_sordidus'}, {'id': 21460, 'synset': 'hygrophorus_tennesseensis.n.01', 'name': 'Hygrophorus_tennesseensis'}, {'id': 21461, 'synset': 'hygrophorus_turundus.n.01', 'name': 'Hygrophorus_turundus'}, {'id': 21462, 'synset': 'neohygrophorus_angelesianus.n.01', 'name': 'Neohygrophorus_angelesianus'}, {'id': 21463, 'synset': 'cortinarius_armillatus.n.01', 'name': 'Cortinarius_armillatus'}, {'id': 21464, 'synset': 'cortinarius_atkinsonianus.n.01', 'name': 'Cortinarius_atkinsonianus'}, {'id': 21465, 'synset': 'cortinarius_corrugatus.n.01', 'name': 'Cortinarius_corrugatus'}, {'id': 21466, 'synset': 'cortinarius_gentilis.n.01', 'name': 'Cortinarius_gentilis'}, {'id': 21467, 'synset': 'cortinarius_mutabilis.n.01', 'name': 'Cortinarius_mutabilis'}, {'id': 21468, 'synset': 'cortinarius_semisanguineus.n.01', 'name': 'Cortinarius_semisanguineus'}, {'id': 21469, 'synset': 'cortinarius_subfoetidus.n.01', 'name': 'Cortinarius_subfoetidus'}, {'id': 21470, 'synset': 'cortinarius_violaceus.n.01', 'name': 'Cortinarius_violaceus'}, {'id': 21471, 'synset': 'gymnopilus_spectabilis.n.01', 'name': 'Gymnopilus_spectabilis'}, {'id': 21472, 'synset': 'gymnopilus_validipes.n.01', 'name': 'Gymnopilus_validipes'}, {'id': 21473, 'synset': 'gymnopilus_ventricosus.n.01', 'name': 'Gymnopilus_ventricosus'}, {'id': 21474, 'synset': 'mold.n.05', 'name': 'mold'}, {'id': 21475, 'synset': 'mildew.n.02', 'name': 'mildew'}, {'id': 21476, 'synset': 'verticillium.n.01', 'name': 'verticillium'}, {'id': 21477, 'synset': 'monilia.n.01', 'name': 'monilia'}, {'id': 21478, 'synset': 'candida.n.01', 'name': 'candida'}, {'id': 21479, 'synset': 'candida_albicans.n.01', 'name': 'Candida_albicans'}, {'id': 21480, 'synset': 'blastomycete.n.01', 'name': 'blastomycete'}, {'id': 21481, 'synset': 'yellow_spot_fungus.n.01', 'name': 'yellow_spot_fungus'}, {'id': 21482, 'synset': 'green_smut_fungus.n.01', 'name': 'green_smut_fungus'}, {'id': 21483, 'synset': 'dry_rot.n.02', 'name': 'dry_rot'}, {'id': 21484, 'synset': 'rhizoctinia.n.01', 'name': 'rhizoctinia'}, {'id': 21485, 'synset': 'houseplant.n.01', 'name': 'houseplant'}, {'id': 21486, 'synset': 'bedder.n.01', 'name': 'bedder'}, {'id': 21487, 'synset': 'succulent.n.01', 'name': 'succulent'}, {'id': 21488, 'synset': 'cultivar.n.01', 'name': 'cultivar'}, {'id': 21489, 'synset': 'weed.n.01', 'name': 'weed'}, {'id': 21490, 'synset': 'wort.n.01', 'name': 'wort'}, {'id': 21491, 'synset': 'brier.n.02', 'name': 'brier'}, {'id': 21492, 'synset': 'aril.n.01', 'name': 'aril'}, {'id': 21493, 'synset': 'sporophyll.n.01', 'name': 'sporophyll'}, {'id': 21494, 'synset': 'sporangium.n.01', 'name': 'sporangium'}, {'id': 21495, 'synset': 'sporangiophore.n.01', 'name': 'sporangiophore'}, {'id': 21496, 'synset': 'ascus.n.01', 'name': 'ascus'}, {'id': 21497, 'synset': 'ascospore.n.01', 'name': 'ascospore'}, {'id': 21498, 'synset': 'arthrospore.n.02', 'name': 'arthrospore'}, {'id': 21499, 'synset': 'eusporangium.n.01', 'name': 'eusporangium'}, {'id': 21500, 'synset': 'tetrasporangium.n.01', 'name': 'tetrasporangium'}, {'id': 21501, 'synset': 'gametangium.n.01', 'name': 'gametangium'}, {'id': 21502, 'synset': 'sorus.n.02', 'name': 'sorus'}, {'id': 21503, 'synset': 'sorus.n.01', 'name': 'sorus'}, {'id': 21504, 'synset': 'partial_veil.n.01', 'name': 'partial_veil'}, {'id': 21505, 'synset': 'lignum.n.01', 'name': 'lignum'}, {'id': 21506, 'synset': 'vascular_ray.n.01', 'name': 'vascular_ray'}, {'id': 21507, 'synset': 'phloem.n.01', 'name': 'phloem'}, {'id': 21508, 'synset': 'evergreen.n.01', 'name': 'evergreen'}, {'id': 21509, 'synset': 'deciduous_plant.n.01', 'name': 'deciduous_plant'}, {'id': 21510, 'synset': 'poisonous_plant.n.01', 'name': 'poisonous_plant'}, {'id': 21511, 'synset': 'vine.n.01', 'name': 'vine'}, {'id': 21512, 'synset': 'creeper.n.01', 'name': 'creeper'}, {'id': 21513, 'synset': 'tendril.n.01', 'name': 'tendril'}, {'id': 21514, 'synset': 'root_climber.n.01', 'name': 'root_climber'}, {'id': 21515, 'synset': 'lignosae.n.01', 'name': 'lignosae'}, {'id': 21516, 'synset': 'arborescent_plant.n.01', 'name': 'arborescent_plant'}, {'id': 21517, 'synset': 'snag.n.02', 'name': 'snag'}, {'id': 21518, 'synset': 'tree.n.01', 'name': 'tree'}, {'id': 21519, 'synset': 'timber_tree.n.01', 'name': 'timber_tree'}, {'id': 21520, 'synset': 'treelet.n.01', 'name': 'treelet'}, {'id': 21521, 'synset': 'arbor.n.01', 'name': 'arbor'}, {'id': 21522, 'synset': 'bean_tree.n.01', 'name': 'bean_tree'}, {'id': 21523, 'synset': 'pollard.n.01', 'name': 'pollard'}, {'id': 21524, 'synset': 'sapling.n.01', 'name': 'sapling'}, {'id': 21525, 'synset': 'shade_tree.n.01', 'name': 'shade_tree'}, {'id': 21526, 'synset': 'gymnospermous_tree.n.01', 'name': 'gymnospermous_tree'}, {'id': 21527, 'synset': 'conifer.n.01', 'name': 'conifer'}, {'id': 21528, 'synset': 'angiospermous_tree.n.01', 'name': 'angiospermous_tree'}, {'id': 21529, 'synset': 'nut_tree.n.01', 'name': 'nut_tree'}, {'id': 21530, 'synset': 'spice_tree.n.01', 'name': 'spice_tree'}, {'id': 21531, 'synset': 'fever_tree.n.01', 'name': 'fever_tree'}, {'id': 21532, 'synset': 'stump.n.01', 'name': 'stump'}, {'id': 21533, 'synset': 'bonsai.n.01', 'name': 'bonsai'}, {'id': 21534, 'synset': 'ming_tree.n.02', 'name': 'ming_tree'}, {'id': 21535, 'synset': 'ming_tree.n.01', 'name': 'ming_tree'}, {'id': 21536, 'synset': 'undershrub.n.01', 'name': 'undershrub'}, {'id': 21537, 'synset': 'subshrub.n.01', 'name': 'subshrub'}, {'id': 21538, 'synset': 'bramble.n.01', 'name': 'bramble'}, {'id': 21539, 'synset': 'liana.n.01', 'name': 'liana'}, {'id': 21540, 'synset': 'geophyte.n.01', 'name': 'geophyte'}, {'id': 21541, 'synset': 'desert_plant.n.01', 'name': 'desert_plant'}, {'id': 21542, 'synset': 'mesophyte.n.01', 'name': 'mesophyte'}, {'id': 21543, 'synset': 'marsh_plant.n.01', 'name': 'marsh_plant'}, {'id': 21544, 'synset': 'hemiepiphyte.n.01', 'name': 'hemiepiphyte'}, {'id': 21545, 'synset': 'strangler.n.01', 'name': 'strangler'}, {'id': 21546, 'synset': 'lithophyte.n.01', 'name': 'lithophyte'}, {'id': 21547, 'synset': 'saprobe.n.01', 'name': 'saprobe'}, {'id': 21548, 'synset': 'autophyte.n.01', 'name': 'autophyte'}, {'id': 21549, 'synset': 'root.n.01', 'name': 'root'}, {'id': 21550, 'synset': 'taproot.n.01', 'name': 'taproot'}, {'id': 21551, 'synset': 'prop_root.n.01', 'name': 'prop_root'}, {'id': 21552, 'synset': 'prophyll.n.01', 'name': 'prophyll'}, {'id': 21553, 'synset': 'rootstock.n.02', 'name': 'rootstock'}, {'id': 21554, 'synset': 'quickset.n.01', 'name': 'quickset'}, {'id': 21555, 'synset': 'stolon.n.01', 'name': 'stolon'}, {'id': 21556, 'synset': 'tuberous_plant.n.01', 'name': 'tuberous_plant'}, {'id': 21557, 'synset': 'rhizome.n.01', 'name': 'rhizome'}, {'id': 21558, 'synset': 'rachis.n.01', 'name': 'rachis'}, {'id': 21559, 'synset': 'caudex.n.02', 'name': 'caudex'}, {'id': 21560, 'synset': 'cladode.n.01', 'name': 'cladode'}, {'id': 21561, 'synset': 'receptacle.n.02', 'name': 'receptacle'}, {'id': 21562, 'synset': 'scape.n.01', 'name': 'scape'}, {'id': 21563, 'synset': 'umbel.n.01', 'name': 'umbel'}, {'id': 21564, 'synset': 'petiole.n.01', 'name': 'petiole'}, {'id': 21565, 'synset': 'peduncle.n.02', 'name': 'peduncle'}, {'id': 21566, 'synset': 'pedicel.n.01', 'name': 'pedicel'}, {'id': 21567, 'synset': 'flower_cluster.n.01', 'name': 'flower_cluster'}, {'id': 21568, 'synset': 'raceme.n.01', 'name': 'raceme'}, {'id': 21569, 'synset': 'panicle.n.01', 'name': 'panicle'}, {'id': 21570, 'synset': 'thyrse.n.01', 'name': 'thyrse'}, {'id': 21571, 'synset': 'cyme.n.01', 'name': 'cyme'}, {'id': 21572, 'synset': 'cymule.n.01', 'name': 'cymule'}, {'id': 21573, 'synset': 'glomerule.n.01', 'name': 'glomerule'}, {'id': 21574, 'synset': 'scorpioid_cyme.n.01', 'name': 'scorpioid_cyme'}, {'id': 21575, 'synset': 'ear.n.05', 'name': 'ear'}, {'id': 21576, 'synset': 'spadix.n.01', 'name': 'spadix'}, {'id': 21577, 'synset': 'bulbous_plant.n.01', 'name': 'bulbous_plant'}, {'id': 21578, 'synset': 'bulbil.n.01', 'name': 'bulbil'}, {'id': 21579, 'synset': 'cormous_plant.n.01', 'name': 'cormous_plant'}, {'id': 21580, 'synset': 'fruit.n.01', 'name': 'fruit'}, {'id': 21581, 'synset': 'fruitlet.n.01', 'name': 'fruitlet'}, {'id': 21582, 'synset': 'seed.n.01', 'name': 'seed'}, {'id': 21583, 'synset': 'bean.n.02', 'name': 'bean'}, {'id': 21584, 'synset': 'nut.n.01', 'name': 'nut'}, {'id': 21585, 'synset': 'nutlet.n.01', 'name': 'nutlet'}, {'id': 21586, 'synset': 'kernel.n.01', 'name': 'kernel'}, {'id': 21587, 'synset': 'syconium.n.01', 'name': 'syconium'}, {'id': 21588, 'synset': 'berry.n.02', 'name': 'berry'}, {'id': 21589, 'synset': 'aggregate_fruit.n.01', 'name': 'aggregate_fruit'}, {'id': 21590, 'synset': 'simple_fruit.n.01', 'name': 'simple_fruit'}, {'id': 21591, 'synset': 'acinus.n.01', 'name': 'acinus'}, {'id': 21592, 'synset': 'drupe.n.01', 'name': 'drupe'}, {'id': 21593, 'synset': 'drupelet.n.01', 'name': 'drupelet'}, {'id': 21594, 'synset': 'pome.n.01', 'name': 'pome'}, {'id': 21595, 'synset': 'pod.n.02', 'name': 'pod'}, {'id': 21596, 'synset': 'loment.n.01', 'name': 'loment'}, {'id': 21597, 'synset': 'pyxidium.n.01', 'name': 'pyxidium'}, {'id': 21598, 'synset': 'husk.n.02', 'name': 'husk'}, {'id': 21599, 'synset': 'cornhusk.n.01', 'name': 'cornhusk'}, {'id': 21600, 'synset': 'pod.n.01', 'name': 'pod'}, {'id': 21601, 'synset': 'accessory_fruit.n.01', 'name': 'accessory_fruit'}, {'id': 21602, 'synset': 'buckthorn.n.01', 'name': 'buckthorn'}, {'id': 21603, 'synset': 'buckthorn_berry.n.01', 'name': 'buckthorn_berry'}, {'id': 21604, 'synset': 'cascara_buckthorn.n.01', 'name': 'cascara_buckthorn'}, {'id': 21605, 'synset': 'cascara.n.01', 'name': 'cascara'}, {'id': 21606, 'synset': 'carolina_buckthorn.n.01', 'name': 'Carolina_buckthorn'}, {'id': 21607, 'synset': 'coffeeberry.n.01', 'name': 'coffeeberry'}, {'id': 21608, 'synset': 'redberry.n.01', 'name': 'redberry'}, {'id': 21609, 'synset': 'nakedwood.n.01', 'name': 'nakedwood'}, {'id': 21610, 'synset': 'jujube.n.01', 'name': 'jujube'}, {'id': 21611, 'synset': "christ's-thorn.n.01", 'name': "Christ's-thorn"}, {'id': 21612, 'synset': 'hazel.n.01', 'name': 'hazel'}, {'id': 21613, 'synset': 'fox_grape.n.01', 'name': 'fox_grape'}, {'id': 21614, 'synset': 'muscadine.n.01', 'name': 'muscadine'}, {'id': 21615, 'synset': 'vinifera.n.01', 'name': 'vinifera'}, {'id': 21616, 'synset': 'pinot_blanc.n.01', 'name': 'Pinot_blanc'}, {'id': 21617, 'synset': 'sauvignon_grape.n.01', 'name': 'Sauvignon_grape'}, {'id': 21618, 'synset': 'sauvignon_blanc.n.01', 'name': 'Sauvignon_blanc'}, {'id': 21619, 'synset': 'muscadet.n.01', 'name': 'Muscadet'}, {'id': 21620, 'synset': 'riesling.n.01', 'name': 'Riesling'}, {'id': 21621, 'synset': 'zinfandel.n.01', 'name': 'Zinfandel'}, {'id': 21622, 'synset': 'chenin_blanc.n.01', 'name': 'Chenin_blanc'}, {'id': 21623, 'synset': 'malvasia.n.01', 'name': 'malvasia'}, {'id': 21624, 'synset': 'verdicchio.n.01', 'name': 'Verdicchio'}, {'id': 21625, 'synset': 'boston_ivy.n.01', 'name': 'Boston_ivy'}, {'id': 21626, 'synset': 'virginia_creeper.n.01', 'name': 'Virginia_creeper'}, {'id': 21627, 'synset': 'true_pepper.n.01', 'name': 'true_pepper'}, {'id': 21628, 'synset': 'betel.n.01', 'name': 'betel'}, {'id': 21629, 'synset': 'cubeb.n.01', 'name': 'cubeb'}, {'id': 21630, 'synset': 'schizocarp.n.01', 'name': 'schizocarp'}, {'id': 21631, 'synset': 'peperomia.n.01', 'name': 'peperomia'}, {'id': 21632, 'synset': 'watermelon_begonia.n.01', 'name': 'watermelon_begonia'}, {'id': 21633, 'synset': 'yerba_mansa.n.01', 'name': 'yerba_mansa'}, {'id': 21634, 'synset': 'pinna.n.01', 'name': 'pinna'}, {'id': 21635, 'synset': 'frond.n.01', 'name': 'frond'}, {'id': 21636, 'synset': 'bract.n.01', 'name': 'bract'}, {'id': 21637, 'synset': 'bracteole.n.01', 'name': 'bracteole'}, {'id': 21638, 'synset': 'involucre.n.01', 'name': 'involucre'}, {'id': 21639, 'synset': 'glume.n.01', 'name': 'glume'}, {'id': 21640, 'synset': 'palmate_leaf.n.01', 'name': 'palmate_leaf'}, {'id': 21641, 'synset': 'pinnate_leaf.n.01', 'name': 'pinnate_leaf'}, {'id': 21642, 'synset': 'bijugate_leaf.n.01', 'name': 'bijugate_leaf'}, {'id': 21643, 'synset': 'decompound_leaf.n.01', 'name': 'decompound_leaf'}, {'id': 21644, 'synset': 'acuminate_leaf.n.01', 'name': 'acuminate_leaf'}, {'id': 21645, 'synset': 'deltoid_leaf.n.01', 'name': 'deltoid_leaf'}, {'id': 21646, 'synset': 'ensiform_leaf.n.01', 'name': 'ensiform_leaf'}, {'id': 21647, 'synset': 'linear_leaf.n.01', 'name': 'linear_leaf'}, {'id': 21648, 'synset': 'lyrate_leaf.n.01', 'name': 'lyrate_leaf'}, {'id': 21649, 'synset': 'obtuse_leaf.n.01', 'name': 'obtuse_leaf'}, {'id': 21650, 'synset': 'oblanceolate_leaf.n.01', 'name': 'oblanceolate_leaf'}, {'id': 21651, 'synset': 'pandurate_leaf.n.01', 'name': 'pandurate_leaf'}, {'id': 21652, 'synset': 'reniform_leaf.n.01', 'name': 'reniform_leaf'}, {'id': 21653, 'synset': 'spatulate_leaf.n.01', 'name': 'spatulate_leaf'}, {'id': 21654, 'synset': 'even-pinnate_leaf.n.01', 'name': 'even-pinnate_leaf'}, {'id': 21655, 'synset': 'odd-pinnate_leaf.n.01', 'name': 'odd-pinnate_leaf'}, {'id': 21656, 'synset': 'pedate_leaf.n.01', 'name': 'pedate_leaf'}, {'id': 21657, 'synset': 'crenate_leaf.n.01', 'name': 'crenate_leaf'}, {'id': 21658, 'synset': 'dentate_leaf.n.01', 'name': 'dentate_leaf'}, {'id': 21659, 'synset': 'denticulate_leaf.n.01', 'name': 'denticulate_leaf'}, {'id': 21660, 'synset': 'erose_leaf.n.01', 'name': 'erose_leaf'}, {'id': 21661, 'synset': 'runcinate_leaf.n.01', 'name': 'runcinate_leaf'}, {'id': 21662, 'synset': 'prickly-edged_leaf.n.01', 'name': 'prickly-edged_leaf'}, {'id': 21663, 'synset': 'deadwood.n.01', 'name': 'deadwood'}, {'id': 21664, 'synset': 'haulm.n.01', 'name': 'haulm'}, {'id': 21665, 'synset': 'branchlet.n.01', 'name': 'branchlet'}, {'id': 21666, 'synset': 'osier.n.01', 'name': 'osier'}, {'id': 21667, 'synset': 'giant_scrambling_fern.n.01', 'name': 'giant_scrambling_fern'}, {'id': 21668, 'synset': 'umbrella_fern.n.01', 'name': 'umbrella_fern'}, {'id': 21669, 'synset': 'floating_fern.n.02', 'name': 'floating_fern'}, {'id': 21670, 'synset': 'polypody.n.01', 'name': 'polypody'}, {'id': 21671, 'synset': 'licorice_fern.n.01', 'name': 'licorice_fern'}, {'id': 21672, 'synset': 'grey_polypody.n.01', 'name': 'grey_polypody'}, {'id': 21673, 'synset': 'leatherleaf.n.01', 'name': 'leatherleaf'}, {'id': 21674, 'synset': 'rock_polypody.n.01', 'name': 'rock_polypody'}, {'id': 21675, 'synset': 'common_polypody.n.01', 'name': 'common_polypody'}, {'id': 21676, 'synset': "bear's-paw_fern.n.01", 'name': "bear's-paw_fern"}, {'id': 21677, 'synset': 'strap_fern.n.01', 'name': 'strap_fern'}, {'id': 21678, 'synset': 'florida_strap_fern.n.01', 'name': 'Florida_strap_fern'}, {'id': 21679, 'synset': 'basket_fern.n.02', 'name': 'basket_fern'}, {'id': 21680, 'synset': 'snake_polypody.n.01', 'name': 'snake_polypody'}, {'id': 21681, 'synset': "climbing_bird's_nest_fern.n.01", 'name': "climbing_bird's_nest_fern"}, {'id': 21682, 'synset': 'golden_polypody.n.01', 'name': 'golden_polypody'}, {'id': 21683, 'synset': 'staghorn_fern.n.01', 'name': 'staghorn_fern'}, {'id': 21684, 'synset': 'south_american_staghorn.n.01', 'name': 'South_American_staghorn'}, {'id': 21685, 'synset': 'common_staghorn_fern.n.01', 'name': 'common_staghorn_fern'}, {'id': 21686, 'synset': 'felt_fern.n.01', 'name': 'felt_fern'}, {'id': 21687, 'synset': 'potato_fern.n.02', 'name': 'potato_fern'}, {'id': 21688, 'synset': 'myrmecophyte.n.01', 'name': 'myrmecophyte'}, {'id': 21689, 'synset': 'grass_fern.n.01', 'name': 'grass_fern'}, {'id': 21690, 'synset': 'spleenwort.n.01', 'name': 'spleenwort'}, {'id': 21691, 'synset': 'black_spleenwort.n.01', 'name': 'black_spleenwort'}, {'id': 21692, 'synset': "bird's_nest_fern.n.01", 'name': "bird's_nest_fern"}, {'id': 21693, 'synset': 'ebony_spleenwort.n.01', 'name': 'ebony_spleenwort'}, {'id': 21694, 'synset': 'black-stem_spleenwort.n.01', 'name': 'black-stem_spleenwort'}, {'id': 21695, 'synset': 'walking_fern.n.01', 'name': 'walking_fern'}, {'id': 21696, 'synset': 'green_spleenwort.n.01', 'name': 'green_spleenwort'}, {'id': 21697, 'synset': 'mountain_spleenwort.n.01', 'name': 'mountain_spleenwort'}, {'id': 21698, 'synset': 'lobed_spleenwort.n.01', 'name': 'lobed_spleenwort'}, {'id': 21699, 'synset': 'lanceolate_spleenwort.n.01', 'name': 'lanceolate_spleenwort'}, {'id': 21700, 'synset': "hart's-tongue.n.02", 'name': "hart's-tongue"}, {'id': 21701, 'synset': 'scale_fern.n.01', 'name': 'scale_fern'}, {'id': 21702, 'synset': 'scolopendrium.n.01', 'name': 'scolopendrium'}, {'id': 21703, 'synset': 'deer_fern.n.01', 'name': 'deer_fern'}, {'id': 21704, 'synset': 'doodia.n.01', 'name': 'doodia'}, {'id': 21705, 'synset': 'chain_fern.n.01', 'name': 'chain_fern'}, {'id': 21706, 'synset': 'virginia_chain_fern.n.01', 'name': 'Virginia_chain_fern'}, {'id': 21707, 'synset': 'silver_tree_fern.n.01', 'name': 'silver_tree_fern'}, {'id': 21708, 'synset': 'davallia.n.01', 'name': 'davallia'}, {'id': 21709, 'synset': "hare's-foot_fern.n.01", 'name': "hare's-foot_fern"}, {'id': 21710, 'synset': "canary_island_hare's_foot_fern.n.01", 'name': "Canary_Island_hare's_foot_fern"}, {'id': 21711, 'synset': "squirrel's-foot_fern.n.01", 'name': "squirrel's-foot_fern"}, {'id': 21712, 'synset': 'bracken.n.01', 'name': 'bracken'}, {'id': 21713, 'synset': 'soft_tree_fern.n.01', 'name': 'soft_tree_fern'}, {'id': 21714, 'synset': 'scythian_lamb.n.01', 'name': 'Scythian_lamb'}, {'id': 21715, 'synset': 'false_bracken.n.01', 'name': 'false_bracken'}, {'id': 21716, 'synset': 'thyrsopteris.n.01', 'name': 'thyrsopteris'}, {'id': 21717, 'synset': 'shield_fern.n.01', 'name': 'shield_fern'}, {'id': 21718, 'synset': 'broad_buckler-fern.n.01', 'name': 'broad_buckler-fern'}, {'id': 21719, 'synset': 'fragrant_cliff_fern.n.01', 'name': 'fragrant_cliff_fern'}, {'id': 21720, 'synset': "goldie's_fern.n.01", 'name': "Goldie's_fern"}, {'id': 21721, 'synset': 'wood_fern.n.01', 'name': 'wood_fern'}, {'id': 21722, 'synset': 'male_fern.n.01', 'name': 'male_fern'}, {'id': 21723, 'synset': 'marginal_wood_fern.n.01', 'name': 'marginal_wood_fern'}, {'id': 21724, 'synset': 'mountain_male_fern.n.01', 'name': 'mountain_male_fern'}, {'id': 21725, 'synset': 'lady_fern.n.01', 'name': 'lady_fern'}, {'id': 21726, 'synset': 'alpine_lady_fern.n.01', 'name': 'Alpine_lady_fern'}, {'id': 21727, 'synset': 'silvery_spleenwort.n.02', 'name': 'silvery_spleenwort'}, {'id': 21728, 'synset': 'holly_fern.n.02', 'name': 'holly_fern'}, {'id': 21729, 'synset': 'bladder_fern.n.01', 'name': 'bladder_fern'}, {'id': 21730, 'synset': 'brittle_bladder_fern.n.01', 'name': 'brittle_bladder_fern'}, {'id': 21731, 'synset': 'mountain_bladder_fern.n.01', 'name': 'mountain_bladder_fern'}, {'id': 21732, 'synset': 'bulblet_fern.n.01', 'name': 'bulblet_fern'}, {'id': 21733, 'synset': 'silvery_spleenwort.n.01', 'name': 'silvery_spleenwort'}, {'id': 21734, 'synset': 'oak_fern.n.01', 'name': 'oak_fern'}, {'id': 21735, 'synset': 'limestone_fern.n.01', 'name': 'limestone_fern'}, {'id': 21736, 'synset': 'ostrich_fern.n.01', 'name': 'ostrich_fern'}, {'id': 21737, 'synset': "hart's-tongue.n.01", 'name': "hart's-tongue"}, {'id': 21738, 'synset': 'sensitive_fern.n.01', 'name': 'sensitive_fern'}, {'id': 21739, 'synset': 'christmas_fern.n.01', 'name': 'Christmas_fern'}, {'id': 21740, 'synset': 'holly_fern.n.01', 'name': 'holly_fern'}, {'id': 21741, 'synset': "braun's_holly_fern.n.01", 'name': "Braun's_holly_fern"}, {'id': 21742, 'synset': 'western_holly_fern.n.01', 'name': 'western_holly_fern'}, {'id': 21743, 'synset': 'soft_shield_fern.n.01', 'name': 'soft_shield_fern'}, {'id': 21744, 'synset': 'leather_fern.n.02', 'name': 'leather_fern'}, {'id': 21745, 'synset': 'button_fern.n.02', 'name': 'button_fern'}, {'id': 21746, 'synset': 'indian_button_fern.n.01', 'name': 'Indian_button_fern'}, {'id': 21747, 'synset': 'woodsia.n.01', 'name': 'woodsia'}, {'id': 21748, 'synset': 'rusty_woodsia.n.01', 'name': 'rusty_woodsia'}, {'id': 21749, 'synset': 'alpine_woodsia.n.01', 'name': 'Alpine_woodsia'}, {'id': 21750, 'synset': 'smooth_woodsia.n.01', 'name': 'smooth_woodsia'}, {'id': 21751, 'synset': 'boston_fern.n.01', 'name': 'Boston_fern'}, {'id': 21752, 'synset': 'basket_fern.n.01', 'name': 'basket_fern'}, {'id': 21753, 'synset': 'golden_fern.n.02', 'name': 'golden_fern'}, {'id': 21754, 'synset': 'maidenhair.n.01', 'name': 'maidenhair'}, {'id': 21755, 'synset': 'common_maidenhair.n.01', 'name': 'common_maidenhair'}, {'id': 21756, 'synset': 'american_maidenhair_fern.n.01', 'name': 'American_maidenhair_fern'}, {'id': 21757, 'synset': 'bermuda_maidenhair.n.01', 'name': 'Bermuda_maidenhair'}, {'id': 21758, 'synset': 'brittle_maidenhair.n.01', 'name': 'brittle_maidenhair'}, {'id': 21759, 'synset': 'farley_maidenhair.n.01', 'name': 'Farley_maidenhair'}, {'id': 21760, 'synset': 'annual_fern.n.01', 'name': 'annual_fern'}, {'id': 21761, 'synset': 'lip_fern.n.01', 'name': 'lip_fern'}, {'id': 21762, 'synset': 'smooth_lip_fern.n.01', 'name': 'smooth_lip_fern'}, {'id': 21763, 'synset': 'lace_fern.n.01', 'name': 'lace_fern'}, {'id': 21764, 'synset': 'wooly_lip_fern.n.01', 'name': 'wooly_lip_fern'}, {'id': 21765, 'synset': 'southwestern_lip_fern.n.01', 'name': 'southwestern_lip_fern'}, {'id': 21766, 'synset': 'bamboo_fern.n.01', 'name': 'bamboo_fern'}, {'id': 21767, 'synset': 'american_rock_brake.n.01', 'name': 'American_rock_brake'}, {'id': 21768, 'synset': 'european_parsley_fern.n.01', 'name': 'European_parsley_fern'}, {'id': 21769, 'synset': 'hand_fern.n.01', 'name': 'hand_fern'}, {'id': 21770, 'synset': 'cliff_brake.n.01', 'name': 'cliff_brake'}, {'id': 21771, 'synset': 'coffee_fern.n.01', 'name': 'coffee_fern'}, {'id': 21772, 'synset': 'purple_rock_brake.n.01', 'name': 'purple_rock_brake'}, {'id': 21773, 'synset': "bird's-foot_fern.n.01", 'name': "bird's-foot_fern"}, {'id': 21774, 'synset': 'button_fern.n.01', 'name': 'button_fern'}, {'id': 21775, 'synset': 'silver_fern.n.02', 'name': 'silver_fern'}, {'id': 21776, 'synset': 'golden_fern.n.01', 'name': 'golden_fern'}, {'id': 21777, 'synset': 'gold_fern.n.01', 'name': 'gold_fern'}, {'id': 21778, 'synset': 'pteris_cretica.n.01', 'name': 'Pteris_cretica'}, {'id': 21779, 'synset': 'spider_brake.n.01', 'name': 'spider_brake'}, {'id': 21780, 'synset': 'ribbon_fern.n.01', 'name': 'ribbon_fern'}, {'id': 21781, 'synset': 'potato_fern.n.01', 'name': 'potato_fern'}, {'id': 21782, 'synset': 'angiopteris.n.01', 'name': 'angiopteris'}, {'id': 21783, 'synset': 'skeleton_fork_fern.n.01', 'name': 'skeleton_fork_fern'}, {'id': 21784, 'synset': 'horsetail.n.01', 'name': 'horsetail'}, {'id': 21785, 'synset': 'common_horsetail.n.01', 'name': 'common_horsetail'}, {'id': 21786, 'synset': 'swamp_horsetail.n.01', 'name': 'swamp_horsetail'}, {'id': 21787, 'synset': 'scouring_rush.n.01', 'name': 'scouring_rush'}, {'id': 21788, 'synset': 'marsh_horsetail.n.01', 'name': 'marsh_horsetail'}, {'id': 21789, 'synset': 'wood_horsetail.n.01', 'name': 'wood_horsetail'}, {'id': 21790, 'synset': 'variegated_horsetail.n.01', 'name': 'variegated_horsetail'}, {'id': 21791, 'synset': 'club_moss.n.01', 'name': 'club_moss'}, {'id': 21792, 'synset': 'shining_clubmoss.n.01', 'name': 'shining_clubmoss'}, {'id': 21793, 'synset': 'alpine_clubmoss.n.01', 'name': 'alpine_clubmoss'}, {'id': 21794, 'synset': 'fir_clubmoss.n.01', 'name': 'fir_clubmoss'}, {'id': 21795, 'synset': 'ground_cedar.n.01', 'name': 'ground_cedar'}, {'id': 21796, 'synset': 'ground_fir.n.01', 'name': 'ground_fir'}, {'id': 21797, 'synset': 'foxtail_grass.n.01', 'name': 'foxtail_grass'}, {'id': 21798, 'synset': 'spikemoss.n.01', 'name': 'spikemoss'}, {'id': 21799, 'synset': 'meadow_spikemoss.n.01', 'name': 'meadow_spikemoss'}, {'id': 21800, 'synset': 'desert_selaginella.n.01', 'name': 'desert_selaginella'}, {'id': 21801, 'synset': 'resurrection_plant.n.01', 'name': 'resurrection_plant'}, {'id': 21802, 'synset': 'florida_selaginella.n.01', 'name': 'florida_selaginella'}, {'id': 21803, 'synset': 'quillwort.n.01', 'name': 'quillwort'}, {'id': 21804, 'synset': 'earthtongue.n.01', 'name': 'earthtongue'}, {'id': 21805, 'synset': 'snuffbox_fern.n.01', 'name': 'snuffbox_fern'}, {'id': 21806, 'synset': 'christella.n.01', 'name': 'christella'}, {'id': 21807, 'synset': 'mountain_fern.n.01', 'name': 'mountain_fern'}, {'id': 21808, 'synset': 'new_york_fern.n.01', 'name': 'New_York_fern'}, {'id': 21809, 'synset': 'massachusetts_fern.n.01', 'name': 'Massachusetts_fern'}, {'id': 21810, 'synset': 'beech_fern.n.01', 'name': 'beech_fern'}, {'id': 21811, 'synset': 'broad_beech_fern.n.01', 'name': 'broad_beech_fern'}, {'id': 21812, 'synset': 'long_beech_fern.n.01', 'name': 'long_beech_fern'}, {'id': 21813, 'synset': 'shoestring_fungus.n.01', 'name': 'shoestring_fungus'}, {'id': 21814, 'synset': 'armillaria_caligata.n.01', 'name': 'Armillaria_caligata'}, {'id': 21815, 'synset': 'armillaria_ponderosa.n.01', 'name': 'Armillaria_ponderosa'}, {'id': 21816, 'synset': 'armillaria_zelleri.n.01', 'name': 'Armillaria_zelleri'}, {'id': 21817, 'synset': 'honey_mushroom.n.01', 'name': 'honey_mushroom'}, {'id': 21818, 'synset': 'milkweed.n.01', 'name': 'milkweed'}, {'id': 21819, 'synset': 'white_milkweed.n.01', 'name': 'white_milkweed'}, {'id': 21820, 'synset': 'poke_milkweed.n.01', 'name': 'poke_milkweed'}, {'id': 21821, 'synset': 'swamp_milkweed.n.01', 'name': 'swamp_milkweed'}, {'id': 21822, 'synset': "mead's_milkweed.n.01", 'name': "Mead's_milkweed"}, {'id': 21823, 'synset': 'purple_silkweed.n.01', 'name': 'purple_silkweed'}, {'id': 21824, 'synset': 'showy_milkweed.n.01', 'name': 'showy_milkweed'}, {'id': 21825, 'synset': 'poison_milkweed.n.01', 'name': 'poison_milkweed'}, {'id': 21826, 'synset': 'butterfly_weed.n.01', 'name': 'butterfly_weed'}, {'id': 21827, 'synset': 'whorled_milkweed.n.01', 'name': 'whorled_milkweed'}, {'id': 21828, 'synset': 'cruel_plant.n.01', 'name': 'cruel_plant'}, {'id': 21829, 'synset': 'wax_plant.n.01', 'name': 'wax_plant'}, {'id': 21830, 'synset': 'silk_vine.n.01', 'name': 'silk_vine'}, {'id': 21831, 'synset': 'stapelia.n.01', 'name': 'stapelia'}, {'id': 21832, 'synset': 'stapelias_asterias.n.01', 'name': 'Stapelias_asterias'}, {'id': 21833, 'synset': 'stephanotis.n.01', 'name': 'stephanotis'}, {'id': 21834, 'synset': 'madagascar_jasmine.n.01', 'name': 'Madagascar_jasmine'}, {'id': 21835, 'synset': 'negro_vine.n.01', 'name': 'negro_vine'}, {'id': 21836, 'synset': 'zygospore.n.01', 'name': 'zygospore'}, {'id': 21837, 'synset': 'tree_of_knowledge.n.01', 'name': 'tree_of_knowledge'}, {'id': 21838, 'synset': 'orangery.n.01', 'name': 'orangery'}, {'id': 21839, 'synset': 'pocketbook.n.01', 'name': 'pocketbook'}, {'id': 21840, 'synset': 'shit.n.04', 'name': 'shit'}, {'id': 21841, 'synset': 'cordage.n.01', 'name': 'cordage'}, {'id': 21842, 'synset': 'yard.n.01', 'name': 'yard'}, {'id': 21843, 'synset': 'extremum.n.02', 'name': 'extremum'}, {'id': 21844, 'synset': 'leaf_shape.n.01', 'name': 'leaf_shape'}, {'id': 21845, 'synset': 'equilateral.n.01', 'name': 'equilateral'}, {'id': 21846, 'synset': 'figure.n.06', 'name': 'figure'}, {'id': 21847, 'synset': 'pencil.n.03', 'name': 'pencil'}, {'id': 21848, 'synset': 'plane_figure.n.01', 'name': 'plane_figure'}, {'id': 21849, 'synset': 'solid_figure.n.01', 'name': 'solid_figure'}, {'id': 21850, 'synset': 'line.n.04', 'name': 'line'}, {'id': 21851, 'synset': 'bulb.n.04', 'name': 'bulb'}, {'id': 21852, 'synset': 'convex_shape.n.01', 'name': 'convex_shape'}, {'id': 21853, 'synset': 'concave_shape.n.01', 'name': 'concave_shape'}, {'id': 21854, 'synset': 'cylinder.n.01', 'name': 'cylinder'}, {'id': 21855, 'synset': 'round_shape.n.01', 'name': 'round_shape'}, {'id': 21856, 'synset': 'heart.n.07', 'name': 'heart'}, {'id': 21857, 'synset': 'polygon.n.01', 'name': 'polygon'}, {'id': 21858, 'synset': 'convex_polygon.n.01', 'name': 'convex_polygon'}, {'id': 21859, 'synset': 'concave_polygon.n.01', 'name': 'concave_polygon'}, {'id': 21860, 'synset': 'reentrant_polygon.n.01', 'name': 'reentrant_polygon'}, {'id': 21861, 'synset': 'amorphous_shape.n.01', 'name': 'amorphous_shape'}, {'id': 21862, 'synset': 'closed_curve.n.01', 'name': 'closed_curve'}, {'id': 21863, 'synset': 'simple_closed_curve.n.01', 'name': 'simple_closed_curve'}, {'id': 21864, 'synset': 's-shape.n.01', 'name': 'S-shape'}, {'id': 21865, 'synset': 'wave.n.07', 'name': 'wave'}, {'id': 21866, 'synset': 'extrados.n.01', 'name': 'extrados'}, {'id': 21867, 'synset': 'hook.n.02', 'name': 'hook'}, {'id': 21868, 'synset': 'envelope.n.03', 'name': 'envelope'}, {'id': 21869, 'synset': 'bight.n.02', 'name': 'bight'}, {'id': 21870, 'synset': 'diameter.n.02', 'name': 'diameter'}, {'id': 21871, 'synset': 'cone.n.02', 'name': 'cone'}, {'id': 21872, 'synset': 'funnel.n.01', 'name': 'funnel'}, {'id': 21873, 'synset': 'oblong.n.01', 'name': 'oblong'}, {'id': 21874, 'synset': 'circle.n.01', 'name': 'circle'}, {'id': 21875, 'synset': 'circle.n.03', 'name': 'circle'}, {'id': 21876, 'synset': 'equator.n.02', 'name': 'equator'}, {'id': 21877, 'synset': 'scallop.n.01', 'name': 'scallop'}, {'id': 21878, 'synset': 'ring.n.02', 'name': 'ring'}, {'id': 21879, 'synset': 'loop.n.02', 'name': 'loop'}, {'id': 21880, 'synset': 'bight.n.01', 'name': 'bight'}, {'id': 21881, 'synset': 'helix.n.01', 'name': 'helix'}, {'id': 21882, 'synset': 'element_of_a_cone.n.01', 'name': 'element_of_a_cone'}, {'id': 21883, 'synset': 'element_of_a_cylinder.n.01', 'name': 'element_of_a_cylinder'}, {'id': 21884, 'synset': 'ellipse.n.01', 'name': 'ellipse'}, {'id': 21885, 'synset': 'quadrate.n.02', 'name': 'quadrate'}, {'id': 21886, 'synset': 'triangle.n.01', 'name': 'triangle'}, {'id': 21887, 'synset': 'acute_triangle.n.01', 'name': 'acute_triangle'}, {'id': 21888, 'synset': 'isosceles_triangle.n.01', 'name': 'isosceles_triangle'}, {'id': 21889, 'synset': 'obtuse_triangle.n.01', 'name': 'obtuse_triangle'}, {'id': 21890, 'synset': 'right_triangle.n.01', 'name': 'right_triangle'}, {'id': 21891, 'synset': 'scalene_triangle.n.01', 'name': 'scalene_triangle'}, {'id': 21892, 'synset': 'parallel.n.03', 'name': 'parallel'}, {'id': 21893, 'synset': 'trapezoid.n.01', 'name': 'trapezoid'}, {'id': 21894, 'synset': 'star.n.05', 'name': 'star'}, {'id': 21895, 'synset': 'pentagon.n.03', 'name': 'pentagon'}, {'id': 21896, 'synset': 'hexagon.n.01', 'name': 'hexagon'}, {'id': 21897, 'synset': 'heptagon.n.01', 'name': 'heptagon'}, {'id': 21898, 'synset': 'octagon.n.01', 'name': 'octagon'}, {'id': 21899, 'synset': 'nonagon.n.01', 'name': 'nonagon'}, {'id': 21900, 'synset': 'decagon.n.01', 'name': 'decagon'}, {'id': 21901, 'synset': 'rhombus.n.01', 'name': 'rhombus'}, {'id': 21902, 'synset': 'spherical_polygon.n.01', 'name': 'spherical_polygon'}, {'id': 21903, 'synset': 'spherical_triangle.n.01', 'name': 'spherical_triangle'}, {'id': 21904, 'synset': 'convex_polyhedron.n.01', 'name': 'convex_polyhedron'}, {'id': 21905, 'synset': 'concave_polyhedron.n.01', 'name': 'concave_polyhedron'}, {'id': 21906, 'synset': 'cuboid.n.01', 'name': 'cuboid'}, {'id': 21907, 'synset': 'quadrangular_prism.n.01', 'name': 'quadrangular_prism'}, {'id': 21908, 'synset': 'bell.n.05', 'name': 'bell'}, {'id': 21909, 'synset': 'angular_distance.n.01', 'name': 'angular_distance'}, {'id': 21910, 'synset': 'true_anomaly.n.01', 'name': 'true_anomaly'}, {'id': 21911, 'synset': 'spherical_angle.n.01', 'name': 'spherical_angle'}, {'id': 21912, 'synset': 'angle_of_refraction.n.01', 'name': 'angle_of_refraction'}, {'id': 21913, 'synset': 'acute_angle.n.01', 'name': 'acute_angle'}, {'id': 21914, 'synset': 'groove.n.01', 'name': 'groove'}, {'id': 21915, 'synset': 'rut.n.01', 'name': 'rut'}, {'id': 21916, 'synset': 'bulge.n.01', 'name': 'bulge'}, {'id': 21917, 'synset': 'belly.n.03', 'name': 'belly'}, {'id': 21918, 'synset': 'bow.n.05', 'name': 'bow'}, {'id': 21919, 'synset': 'crescent.n.01', 'name': 'crescent'}, {'id': 21920, 'synset': 'ellipsoid.n.01', 'name': 'ellipsoid'}, {'id': 21921, 'synset': 'hypotenuse.n.01', 'name': 'hypotenuse'}, {'id': 21922, 'synset': 'balance.n.04', 'name': 'balance'}, {'id': 21923, 'synset': 'conformation.n.01', 'name': 'conformation'}, {'id': 21924, 'synset': 'symmetry.n.02', 'name': 'symmetry'}, {'id': 21925, 'synset': 'spheroid.n.01', 'name': 'spheroid'}, {'id': 21926, 'synset': 'spherule.n.01', 'name': 'spherule'}, {'id': 21927, 'synset': 'toroid.n.01', 'name': 'toroid'}, {'id': 21928, 'synset': 'column.n.04', 'name': 'column'}, {'id': 21929, 'synset': 'barrel.n.03', 'name': 'barrel'}, {'id': 21930, 'synset': 'pipe.n.03', 'name': 'pipe'}, {'id': 21931, 'synset': 'pellet.n.01', 'name': 'pellet'}, {'id': 21932, 'synset': 'bolus.n.01', 'name': 'bolus'}, {'id': 21933, 'synset': 'dewdrop.n.01', 'name': 'dewdrop'}, {'id': 21934, 'synset': 'ridge.n.02', 'name': 'ridge'}, {'id': 21935, 'synset': 'rim.n.01', 'name': 'rim'}, {'id': 21936, 'synset': 'taper.n.01', 'name': 'taper'}, {'id': 21937, 'synset': 'boundary.n.02', 'name': 'boundary'}, {'id': 21938, 'synset': 'incisure.n.01', 'name': 'incisure'}, {'id': 21939, 'synset': 'notch.n.01', 'name': 'notch'}, {'id': 21940, 'synset': 'wrinkle.n.01', 'name': 'wrinkle'}, {'id': 21941, 'synset': 'dermatoglyphic.n.01', 'name': 'dermatoglyphic'}, {'id': 21942, 'synset': 'frown_line.n.01', 'name': 'frown_line'}, {'id': 21943, 'synset': 'line_of_life.n.01', 'name': 'line_of_life'}, {'id': 21944, 'synset': 'line_of_heart.n.01', 'name': 'line_of_heart'}, {'id': 21945, 'synset': 'crevice.n.01', 'name': 'crevice'}, {'id': 21946, 'synset': 'cleft.n.01', 'name': 'cleft'}, {'id': 21947, 'synset': 'roulette.n.01', 'name': 'roulette'}, {'id': 21948, 'synset': 'node.n.01', 'name': 'node'}, {'id': 21949, 'synset': 'tree.n.02', 'name': 'tree'}, {'id': 21950, 'synset': 'stemma.n.01', 'name': 'stemma'}, {'id': 21951, 'synset': 'brachium.n.01', 'name': 'brachium'}, {'id': 21952, 'synset': 'fork.n.03', 'name': 'fork'}, {'id': 21953, 'synset': 'block.n.03', 'name': 'block'}, {'id': 21954, 'synset': 'ovoid.n.01', 'name': 'ovoid'}, {'id': 21955, 'synset': 'tetrahedron.n.01', 'name': 'tetrahedron'}, {'id': 21956, 'synset': 'pentahedron.n.01', 'name': 'pentahedron'}, {'id': 21957, 'synset': 'hexahedron.n.01', 'name': 'hexahedron'}, {'id': 21958, 'synset': 'regular_polyhedron.n.01', 'name': 'regular_polyhedron'}, {'id': 21959, 'synset': 'polyhedral_angle.n.01', 'name': 'polyhedral_angle'}, {'id': 21960, 'synset': 'cube.n.01', 'name': 'cube'}, {'id': 21961, 'synset': 'truncated_pyramid.n.01', 'name': 'truncated_pyramid'}, {'id': 21962, 'synset': 'truncated_cone.n.01', 'name': 'truncated_cone'}, {'id': 21963, 'synset': 'tail.n.03', 'name': 'tail'}, {'id': 21964, 'synset': 'tongue.n.03', 'name': 'tongue'}, {'id': 21965, 'synset': 'trapezohedron.n.01', 'name': 'trapezohedron'}, {'id': 21966, 'synset': 'wedge.n.01', 'name': 'wedge'}, {'id': 21967, 'synset': 'keel.n.01', 'name': 'keel'}, {'id': 21968, 'synset': 'place.n.06', 'name': 'place'}, {'id': 21969, 'synset': 'herpes.n.01', 'name': 'herpes'}, {'id': 21970, 'synset': 'chlamydia.n.01', 'name': 'chlamydia'}, {'id': 21971, 'synset': 'wall.n.04', 'name': 'wall'}, {'id': 21972, 'synset': 'micronutrient.n.01', 'name': 'micronutrient'}, {'id': 21973, 'synset': 'chyme.n.01', 'name': 'chyme'}, {'id': 21974, 'synset': 'ragweed_pollen.n.01', 'name': 'ragweed_pollen'}, {'id': 21975, 'synset': 'pina_cloth.n.01', 'name': 'pina_cloth'}, {'id': 21976, 'synset': 'chlorobenzylidenemalononitrile.n.01', 'name': 'chlorobenzylidenemalononitrile'}, {'id': 21977, 'synset': 'carbon.n.01', 'name': 'carbon'}, {'id': 21978, 'synset': 'charcoal.n.01', 'name': 'charcoal'}, {'id': 21979, 'synset': 'rock.n.02', 'name': 'rock'}, {'id': 21980, 'synset': 'gravel.n.01', 'name': 'gravel'}, {'id': 21981, 'synset': 'aflatoxin.n.01', 'name': 'aflatoxin'}, {'id': 21982, 'synset': 'alpha-tocopheral.n.01', 'name': 'alpha-tocopheral'}, {'id': 21983, 'synset': 'leopard.n.01', 'name': 'leopard'}, {'id': 21984, 'synset': 'bricks_and_mortar.n.01', 'name': 'bricks_and_mortar'}, {'id': 21985, 'synset': 'lagging.n.01', 'name': 'lagging'}, {'id': 21986, 'synset': 'hydraulic_cement.n.01', 'name': 'hydraulic_cement'}, {'id': 21987, 'synset': 'choline.n.01', 'name': 'choline'}, {'id': 21988, 'synset': 'concrete.n.01', 'name': 'concrete'}, {'id': 21989, 'synset': 'glass_wool.n.01', 'name': 'glass_wool'}, {'id': 21990, 'synset': 'soil.n.02', 'name': 'soil'}, {'id': 21991, 'synset': 'high_explosive.n.01', 'name': 'high_explosive'}, {'id': 21992, 'synset': 'litter.n.02', 'name': 'litter'}, {'id': 21993, 'synset': 'fish_meal.n.01', 'name': 'fish_meal'}, {'id': 21994, 'synset': 'greek_fire.n.01', 'name': 'Greek_fire'}, {'id': 21995, 'synset': 'culture_medium.n.01', 'name': 'culture_medium'}, {'id': 21996, 'synset': 'agar.n.01', 'name': 'agar'}, {'id': 21997, 'synset': 'blood_agar.n.01', 'name': 'blood_agar'}, {'id': 21998, 'synset': 'hip_tile.n.01', 'name': 'hip_tile'}, {'id': 21999, 'synset': 'hyacinth.n.01', 'name': 'hyacinth'}, {'id': 22000, 'synset': 'hydroxide_ion.n.01', 'name': 'hydroxide_ion'}, {'id': 22001, 'synset': 'ice.n.01', 'name': 'ice'}, {'id': 22002, 'synset': 'inositol.n.01', 'name': 'inositol'}, {'id': 22003, 'synset': 'linoleum.n.01', 'name': 'linoleum'}, {'id': 22004, 'synset': 'lithia_water.n.01', 'name': 'lithia_water'}, {'id': 22005, 'synset': 'lodestone.n.01', 'name': 'lodestone'}, {'id': 22006, 'synset': 'pantothenic_acid.n.01', 'name': 'pantothenic_acid'}, {'id': 22007, 'synset': 'paper.n.01', 'name': 'paper'}, {'id': 22008, 'synset': 'papyrus.n.01', 'name': 'papyrus'}, {'id': 22009, 'synset': 'pantile.n.01', 'name': 'pantile'}, {'id': 22010, 'synset': 'blacktop.n.01', 'name': 'blacktop'}, {'id': 22011, 'synset': 'tarmacadam.n.01', 'name': 'tarmacadam'}, {'id': 22012, 'synset': 'paving.n.01', 'name': 'paving'}, {'id': 22013, 'synset': 'plaster.n.01', 'name': 'plaster'}, {'id': 22014, 'synset': 'poison_gas.n.01', 'name': 'poison_gas'}, {'id': 22015, 'synset': 'ridge_tile.n.01', 'name': 'ridge_tile'}, {'id': 22016, 'synset': 'roughcast.n.01', 'name': 'roughcast'}, {'id': 22017, 'synset': 'sand.n.01', 'name': 'sand'}, {'id': 22018, 'synset': 'spackle.n.01', 'name': 'spackle'}, {'id': 22019, 'synset': 'render.n.01', 'name': 'render'}, {'id': 22020, 'synset': 'wattle_and_daub.n.01', 'name': 'wattle_and_daub'}, {'id': 22021, 'synset': 'stucco.n.01', 'name': 'stucco'}, {'id': 22022, 'synset': 'tear_gas.n.01', 'name': 'tear_gas'}, {'id': 22023, 'synset': 'linseed.n.01', 'name': 'linseed'}, {'id': 22024, 'synset': 'vitamin.n.01', 'name': 'vitamin'}, {'id': 22025, 'synset': 'fat-soluble_vitamin.n.01', 'name': 'fat-soluble_vitamin'}, {'id': 22026, 'synset': 'water-soluble_vitamin.n.01', 'name': 'water-soluble_vitamin'}, {'id': 22027, 'synset': 'vitamin_a.n.01', 'name': 'vitamin_A'}, {'id': 22028, 'synset': 'vitamin_a1.n.01', 'name': 'vitamin_A1'}, {'id': 22029, 'synset': 'vitamin_a2.n.01', 'name': 'vitamin_A2'}, {'id': 22030, 'synset': 'b-complex_vitamin.n.01', 'name': 'B-complex_vitamin'}, {'id': 22031, 'synset': 'vitamin_b1.n.01', 'name': 'vitamin_B1'}, {'id': 22032, 'synset': 'vitamin_b12.n.01', 'name': 'vitamin_B12'}, {'id': 22033, 'synset': 'vitamin_b2.n.01', 'name': 'vitamin_B2'}, {'id': 22034, 'synset': 'vitamin_b6.n.01', 'name': 'vitamin_B6'}, {'id': 22035, 'synset': 'vitamin_bc.n.01', 'name': 'vitamin_Bc'}, {'id': 22036, 'synset': 'niacin.n.01', 'name': 'niacin'}, {'id': 22037, 'synset': 'vitamin_d.n.01', 'name': 'vitamin_D'}, {'id': 22038, 'synset': 'vitamin_e.n.01', 'name': 'vitamin_E'}, {'id': 22039, 'synset': 'biotin.n.01', 'name': 'biotin'}, {'id': 22040, 'synset': 'vitamin_k.n.01', 'name': 'vitamin_K'}, {'id': 22041, 'synset': 'vitamin_k1.n.01', 'name': 'vitamin_K1'}, {'id': 22042, 'synset': 'vitamin_k3.n.01', 'name': 'vitamin_K3'}, {'id': 22043, 'synset': 'vitamin_p.n.01', 'name': 'vitamin_P'}, {'id': 22044, 'synset': 'vitamin_c.n.01', 'name': 'vitamin_C'}, {'id': 22045, 'synset': 'planking.n.01', 'name': 'planking'}, {'id': 22046, 'synset': 'chipboard.n.01', 'name': 'chipboard'}, {'id': 22047, 'synset': 'knothole.n.01', 'name': 'knothole'}] # noqa
\ No newline at end of file
diff --git a/spaces/Detomo/ai-avatar-frontend/src/App.css b/spaces/Detomo/ai-avatar-frontend/src/App.css
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/generate_figures.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/generate_figures.py
deleted file mode 100644
index 45b68b86146198c701a66fb8ba7a363d901d6951..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/generate_figures.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-"""Minimal script for reproducing the figures of the StyleGAN paper using pre-trained generators."""
-
-import os
-import pickle
-import numpy as np
-import PIL.Image
-import dnnlib
-import dnnlib.tflib as tflib
-import config
-
-#----------------------------------------------------------------------------
-# Helpers for loading and using pre-trained generators.
-
-url_ffhq = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl
-url_celebahq = 'https://drive.google.com/uc?id=1MGqJl28pN4t7SAtSrPdSRJSQJqahkzUf' # karras2019stylegan-celebahq-1024x1024.pkl
-url_bedrooms = 'https://drive.google.com/uc?id=1MOSKeGF0FJcivpBI7s63V9YHloUTORiF' # karras2019stylegan-bedrooms-256x256.pkl
-url_cars = 'https://drive.google.com/uc?id=1MJ6iCfNtMIRicihwRorsM3b7mmtmK9c3' # karras2019stylegan-cars-512x384.pkl
-url_cats = 'https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ' # karras2019stylegan-cats-256x256.pkl
-
-synthesis_kwargs = dict(output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True), minibatch_size=8)
-
-_Gs_cache = dict()
-
-def load_Gs(url):
- if url not in _Gs_cache:
- with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
- _G, _D, Gs = pickle.load(f)
- _Gs_cache[url] = Gs
- return _Gs_cache[url]
-
-#----------------------------------------------------------------------------
-# Figures 2, 3, 10, 11, 12: Multi-resolution grid of uncurated result images.
-
-def draw_uncurated_result_figure(png, Gs, cx, cy, cw, ch, rows, lods, seed):
- print(png)
- latents = np.random.RandomState(seed).randn(sum(rows * 2**lod for lod in lods), Gs.input_shape[1])
- images = Gs.run(latents, None, **synthesis_kwargs) # [seed, y, x, rgb]
-
- canvas = PIL.Image.new('RGB', (sum(cw // 2**lod for lod in lods), ch * rows), 'white')
- image_iter = iter(list(images))
- for col, lod in enumerate(lods):
- for row in range(rows * 2**lod):
- image = PIL.Image.fromarray(next(image_iter), 'RGB')
- image = image.crop((cx, cy, cx + cw, cy + ch))
- image = image.resize((cw // 2**lod, ch // 2**lod), PIL.Image.ANTIALIAS)
- canvas.paste(image, (sum(cw // 2**lod for lod in lods[:col]), row * ch // 2**lod))
- canvas.save(png)
-
-#----------------------------------------------------------------------------
-# Figure 3: Style mixing.
-
-def draw_style_mixing_figure(png, Gs, w, h, src_seeds, dst_seeds, style_ranges):
- print(png)
- src_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in src_seeds)
- dst_latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in dst_seeds)
- src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component]
- dst_dlatents = Gs.components.mapping.run(dst_latents, None) # [seed, layer, component]
- src_images = Gs.components.synthesis.run(src_dlatents, randomize_noise=False, **synthesis_kwargs)
- dst_images = Gs.components.synthesis.run(dst_dlatents, randomize_noise=False, **synthesis_kwargs)
-
- canvas = PIL.Image.new('RGB', (w * (len(src_seeds) + 1), h * (len(dst_seeds) + 1)), 'white')
- for col, src_image in enumerate(list(src_images)):
- canvas.paste(PIL.Image.fromarray(src_image, 'RGB'), ((col + 1) * w, 0))
- for row, dst_image in enumerate(list(dst_images)):
- canvas.paste(PIL.Image.fromarray(dst_image, 'RGB'), (0, (row + 1) * h))
- row_dlatents = np.stack([dst_dlatents[row]] * len(src_seeds))
- row_dlatents[:, style_ranges[row]] = src_dlatents[:, style_ranges[row]]
- row_images = Gs.components.synthesis.run(row_dlatents, randomize_noise=False, **synthesis_kwargs)
- for col, image in enumerate(list(row_images)):
- canvas.paste(PIL.Image.fromarray(image, 'RGB'), ((col + 1) * w, (row + 1) * h))
- canvas.save(png)
-
-#----------------------------------------------------------------------------
-# Figure 4: Noise detail.
-
-def draw_noise_detail_figure(png, Gs, w, h, num_samples, seeds):
- print(png)
- canvas = PIL.Image.new('RGB', (w * 3, h * len(seeds)), 'white')
- for row, seed in enumerate(seeds):
- latents = np.stack([np.random.RandomState(seed).randn(Gs.input_shape[1])] * num_samples)
- images = Gs.run(latents, None, truncation_psi=1, **synthesis_kwargs)
- canvas.paste(PIL.Image.fromarray(images[0], 'RGB'), (0, row * h))
- for i in range(4):
- crop = PIL.Image.fromarray(images[i + 1], 'RGB')
- crop = crop.crop((650, 180, 906, 436))
- crop = crop.resize((w//2, h//2), PIL.Image.NEAREST)
- canvas.paste(crop, (w + (i%2) * w//2, row * h + (i//2) * h//2))
- diff = np.std(np.mean(images, axis=3), axis=0) * 4
- diff = np.clip(diff + 0.5, 0, 255).astype(np.uint8)
- canvas.paste(PIL.Image.fromarray(diff, 'L'), (w * 2, row * h))
- canvas.save(png)
-
-#----------------------------------------------------------------------------
-# Figure 5: Noise components.
-
-def draw_noise_components_figure(png, Gs, w, h, seeds, noise_ranges, flips):
- print(png)
- Gsc = Gs.clone()
- noise_vars = [var for name, var in Gsc.components.synthesis.vars.items() if name.startswith('noise')]
- noise_pairs = list(zip(noise_vars, tflib.run(noise_vars))) # [(var, val), ...]
- latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds)
- all_images = []
- for noise_range in noise_ranges:
- tflib.set_vars({var: val * (1 if i in noise_range else 0) for i, (var, val) in enumerate(noise_pairs)})
- range_images = Gsc.run(latents, None, truncation_psi=1, randomize_noise=False, **synthesis_kwargs)
- range_images[flips, :, :] = range_images[flips, :, ::-1]
- all_images.append(list(range_images))
-
- canvas = PIL.Image.new('RGB', (w * 2, h * 2), 'white')
- for col, col_images in enumerate(zip(*all_images)):
- canvas.paste(PIL.Image.fromarray(col_images[0], 'RGB').crop((0, 0, w//2, h)), (col * w, 0))
- canvas.paste(PIL.Image.fromarray(col_images[1], 'RGB').crop((w//2, 0, w, h)), (col * w + w//2, 0))
- canvas.paste(PIL.Image.fromarray(col_images[2], 'RGB').crop((0, 0, w//2, h)), (col * w, h))
- canvas.paste(PIL.Image.fromarray(col_images[3], 'RGB').crop((w//2, 0, w, h)), (col * w + w//2, h))
- canvas.save(png)
-
-#----------------------------------------------------------------------------
-# Figure 8: Truncation trick.
-
-def draw_truncation_trick_figure(png, Gs, w, h, seeds, psis):
- print(png)
- latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds)
- dlatents = Gs.components.mapping.run(latents, None) # [seed, layer, component]
- dlatent_avg = Gs.get_var('dlatent_avg') # [component]
-
- canvas = PIL.Image.new('RGB', (w * len(psis), h * len(seeds)), 'white')
- for row, dlatent in enumerate(list(dlatents)):
- row_dlatents = (dlatent[np.newaxis] - dlatent_avg) * np.reshape(psis, [-1, 1, 1]) + dlatent_avg
- row_images = Gs.components.synthesis.run(row_dlatents, randomize_noise=False, **synthesis_kwargs)
- for col, image in enumerate(list(row_images)):
- canvas.paste(PIL.Image.fromarray(image, 'RGB'), (col * w, row * h))
- canvas.save(png)
-
-#----------------------------------------------------------------------------
-# Main program.
-
-def main():
- tflib.init_tf()
- os.makedirs(config.result_dir, exist_ok=True)
- draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure02-uncurated-ffhq.png'), load_Gs(url_ffhq), cx=0, cy=0, cw=1024, ch=1024, rows=3, lods=[0,1,2,2,3,3], seed=5)
- draw_style_mixing_figure(os.path.join(config.result_dir, 'figure03-style-mixing.png'), load_Gs(url_ffhq), w=1024, h=1024, src_seeds=[639,701,687,615,2268], dst_seeds=[888,829,1898,1733,1614,845], style_ranges=[range(0,4)]*3+[range(4,8)]*2+[range(8,18)])
- draw_noise_detail_figure(os.path.join(config.result_dir, 'figure04-noise-detail.png'), load_Gs(url_ffhq), w=1024, h=1024, num_samples=100, seeds=[1157,1012])
- draw_noise_components_figure(os.path.join(config.result_dir, 'figure05-noise-components.png'), load_Gs(url_ffhq), w=1024, h=1024, seeds=[1967,1555], noise_ranges=[range(0, 18), range(0, 0), range(8, 18), range(0, 8)], flips=[1])
- draw_truncation_trick_figure(os.path.join(config.result_dir, 'figure08-truncation-trick.png'), load_Gs(url_ffhq), w=1024, h=1024, seeds=[91,388], psis=[1, 0.7, 0.5, 0, -0.5, -1])
- draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure10-uncurated-bedrooms.png'), load_Gs(url_bedrooms), cx=0, cy=0, cw=256, ch=256, rows=5, lods=[0,0,1,1,2,2,2], seed=0)
- draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure11-uncurated-cars.png'), load_Gs(url_cars), cx=0, cy=64, cw=512, ch=384, rows=4, lods=[0,1,2,2,3,3], seed=2)
- draw_uncurated_result_figure(os.path.join(config.result_dir, 'figure12-uncurated-cats.png'), load_Gs(url_cats), cx=0, cy=0, cw=256, ch=256, rows=5, lods=[0,0,1,1,2,2,2], seed=1)
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
- main()
-
-#----------------------------------------------------------------------------
diff --git a/spaces/DonngHuang/auto-ai/Dockerfile b/spaces/DonngHuang/auto-ai/Dockerfile
deleted file mode 100644
index c98f2cdf35aed5fe84637c83af9dcdd2384fd668..0000000000000000000000000000000000000000
--- a/spaces/DonngHuang/auto-ai/Dockerfile
+++ /dev/null
@@ -1,10 +0,0 @@
-FROM node:19.1.0-alpine3.16
-# RUN unlink /etc/localtime && ln -s /usr/share/zoneinfo/Etc/GMT-8 /etc/localtime
-RUN apk add curl
-# && curl ipinfo.io
-WORKDIR /app
-ADD . /app
-RUN mv .env.example .env && chmod -R 777 ".env"
-RUN chmod +x "./linux-exec" && \
- chmod +x "./entrypoint.sh"
-CMD ["./entrypoint.sh"]
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/training/__init__.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/training/__init__.py
deleted file mode 100644
index 939e7c6c8f94c4ea1141885c3c3295fe083b06aa..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/stylegan_human/training/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty
diff --git a/spaces/Emmy101/Emer/Dockerfile b/spaces/Emmy101/Emer/Dockerfile
deleted file mode 100644
index 851560c6c43d3d01b47a25e39b1b82460db4b1a1..0000000000000000000000000000000000000000
--- a/spaces/Emmy101/Emer/Dockerfile
+++ /dev/null
@@ -1 +0,0 @@
-FROM python:3.9 WORKDIR /code COPY ./requirements.txt /code/requirements.txt RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt COPY . . CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]
\ No newline at end of file
diff --git a/spaces/Enterprisium/Easy_GUI/lib/infer_pack/transforms.py b/spaces/Enterprisium/Easy_GUI/lib/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/Enterprisium/Easy_GUI/lib/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails="linear",
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == "linear":
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError("{} tails are not implemented.".format(tails))
-
- (
- outputs[inside_interval_mask],
- logabsdet[inside_interval_mask],
- ) = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound,
- right=tail_bound,
- bottom=-tail_bound,
- top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- )
-
- return outputs, logabsdet
-
-
-def rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0.0,
- right=1.0,
- bottom=0.0,
- top=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError("Input to a transform is not within its domain")
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError("Minimal bin width too large for the number of bins")
- if min_bin_height * num_bins > 1.0:
- raise ValueError("Minimal bin height too large for the number of bins")
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- ) + input_heights * (input_delta - input_derivatives)
- b = input_heights * input_derivatives - (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- )
- c = -input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (
- input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
- )
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/Felix123456/bingo/src/pages/api/sydney.ts b/spaces/Felix123456/bingo/src/pages/api/sydney.ts
deleted file mode 100644
index 0e7bbf23d77c2e1a6635185a060eeee58b8c8e66..0000000000000000000000000000000000000000
--- a/spaces/Felix123456/bingo/src/pages/api/sydney.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { NextApiRequest, NextApiResponse } from 'next'
-import { WebSocket, debug } from '@/lib/isomorphic'
-import { BingWebBot } from '@/lib/bots/bing'
-import { websocketUtils } from '@/lib/bots/bing/utils'
-import { WatchDog, createHeaders } from '@/lib/utils'
-
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
- const conversationContext = req.body
- const headers = createHeaders(req.cookies)
- debug(headers)
- res.setHeader('Content-Type', 'text/stream; charset=UTF-8')
-
- const ws = new WebSocket('wss://sydney.bing.com/sydney/ChatHub', {
- headers: {
- ...headers,
- 'accept-language': 'zh-CN,zh;q=0.9',
- 'cache-control': 'no-cache',
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- pragma: 'no-cache',
- }
- })
-
- const closeDog = new WatchDog()
- const timeoutDog = new WatchDog()
- ws.onmessage = (event) => {
- timeoutDog.watch(() => {
- ws.send(websocketUtils.packMessage({ type: 6 }))
- }, 1500)
- closeDog.watch(() => {
- ws.close()
- }, 10000)
- res.write(event.data)
- if (/\{"type":([367])\}/.test(String(event.data))) {
- const type = parseInt(RegExp.$1, 10)
- debug('connection type', type)
- if (type === 3) {
- ws.close()
- } else {
- ws.send(websocketUtils.packMessage({ type }))
- }
- }
- }
-
- ws.onclose = () => {
- timeoutDog.reset()
- closeDog.reset()
- debug('connection close')
- res.end()
- }
-
- await new Promise((resolve) => ws.onopen = resolve)
- ws.send(websocketUtils.packMessage({ protocol: 'json', version: 1 }))
- ws.send(websocketUtils.packMessage({ type: 6 }))
- ws.send(websocketUtils.packMessage(BingWebBot.buildChatRequest(conversationContext!)))
- req.socket.once('close', () => {
- ws.close()
- if (!res.closed) {
- res.end()
- }
- })
-}
diff --git a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Ails.py b/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Ails.py
deleted file mode 100644
index 5feec9e987e3cd2590e2a72b623dc4b90e0cf53d..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/g4f/Provider/Providers/Ails.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-import time
-import json
-import uuid
-import hashlib
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-from datetime import datetime
-
-url: str = 'https://ai.ls'
-model: str = 'gpt-3.5-turbo'
-supports_stream = True
-needs_auth = False
-working = True
-
-
-class Utils:
- def hash(json_data: Dict[str, str]) -> sha256:
-
- base_string: str = '%s:%s:%s:%s' % (
- json_data['t'],
- json_data['m'],
- 'WI,2rU#_r:r~aF4aJ36[.Z(/8Rv93Rf',
- len(json_data['m'])
- )
-
- return hashlib.sha256(base_string.encode()).hexdigest()
-
- def format_timestamp(timestamp: int) -> str:
-
- e = timestamp
- n = e % 10
- r = n + 1 if n % 2 == 0 else n
- return str(e - n + r)
-
-
-def _create_completion(model: str, messages: list, temperature: float = 0.6, stream: bool = False, **kwargs):
-
- headers = {
- 'authority': 'api.caipacity.com',
- 'accept': '*/*',
- 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
- 'authorization': 'Bearer free',
- 'client-id': str(uuid.uuid4()),
- 'client-v': '0.1.249',
- 'content-type': 'application/json',
- 'origin': 'https://ai.ls',
- 'referer': 'https://ai.ls/',
- 'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
- 'sec-ch-ua-mobile': '?0',
- 'sec-ch-ua-platform': '"Windows"',
- 'sec-fetch-dest': 'empty',
- 'sec-fetch-mode': 'cors',
- 'sec-fetch-site': 'cross-site',
- 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
- }
-
- timestamp = Utils.format_timestamp(int(time.time() * 1000))
-
- sig = {
- 'd': datetime.now().strftime('%Y-%m-%d'),
- 't': timestamp,
- 's': Utils.hash({
- 't': timestamp,
- 'm': messages[-1]['content']})}
-
- json_data = json.dumps(separators=(',', ':'), obj={
- 'model': 'gpt-3.5-turbo',
- 'temperature': 0.6,
- 'stream': True,
- 'messages': messages} | sig)
-
- response = requests.post('https://api.caipacity.com/v1/chat/completions',
- headers=headers, data=json_data, stream=True)
-
- for token in response.iter_lines():
- if b'content' in token:
- completion_chunk = json.loads(token.decode().replace('data: ', ''))
- token = completion_chunk['choices'][0]['delta'].get('content')
- if token != None:
- yield token
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/vdecoder/nsf_hifigan/models.py b/spaces/FrankZxShen/so-vits-svc-models-ba/vdecoder/nsf_hifigan/models.py
deleted file mode 100644
index c2c889ec2fbd215702298ba2b7c411c6f5630d80..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-ba/vdecoder/nsf_hifigan/models.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import os
-import json
-from .env import AttrDict
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from .utils import init_weights, get_padding
-
-LRELU_SLOPE = 0.1
-
-
-def load_model(model_path, device='cuda'):
- h = load_config(model_path)
-
- generator = Generator(h).to(device)
-
- cp_dict = torch.load(model_path, map_location=device)
- generator.load_state_dict(cp_dict['generator'])
- generator.eval()
- generator.remove_weight_norm()
- del cp_dict
- return generator, h
-
-def load_config(model_path):
- config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
- with open(config_file) as f:
- data = f.read()
-
- json_config = json.loads(data)
- h = AttrDict(json_config)
- return h
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.h = h
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- xt = c2(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.h = h
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- xt = c(xt)
- x = xt + x
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class SineGen(torch.nn.Module):
- """ Definition of sine generator
- SineGen(samp_rate, harmonic_num = 0,
- sine_amp = 0.1, noise_std = 0.003,
- voiced_threshold = 0,
- flag_for_pulse=False)
- samp_rate: sampling rate in Hz
- harmonic_num: number of harmonic overtones (default 0)
- sine_amp: amplitude of sine-wavefrom (default 0.1)
- noise_std: std of Gaussian noise (default 0.003)
- voiced_thoreshold: F0 threshold for U/V classification (default 0)
- flag_for_pulse: this SinGen is used inside PulseGen (default False)
- Note: when flag_for_pulse is True, the first time step of a voiced
- segment is always sin(np.pi) or cos(0)
- """
-
- def __init__(self, samp_rate, harmonic_num=0,
- sine_amp=0.1, noise_std=0.003,
- voiced_threshold=0):
- super(SineGen, self).__init__()
- self.sine_amp = sine_amp
- self.noise_std = noise_std
- self.harmonic_num = harmonic_num
- self.dim = self.harmonic_num + 1
- self.sampling_rate = samp_rate
- self.voiced_threshold = voiced_threshold
-
- def _f02uv(self, f0):
- # generate uv signal
- uv = torch.ones_like(f0)
- uv = uv * (f0 > self.voiced_threshold)
- return uv
-
- @torch.no_grad()
- def forward(self, f0, upp):
- """ sine_tensor, uv = forward(f0)
- input F0: tensor(batchsize=1, length, dim=1)
- f0 for unvoiced steps should be 0
- output sine_tensor: tensor(batchsize=1, length, dim)
- output uv: tensor(batchsize=1, length, 1)
- """
- f0 = f0.unsqueeze(-1)
- fn = torch.multiply(f0, torch.arange(1, self.dim + 1, device=f0.device).reshape((1, 1, -1)))
- rad_values = (fn / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
- rand_ini = torch.rand(fn.shape[0], fn.shape[2], device=fn.device)
- rand_ini[:, 0] = 0
- rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
- is_half = rad_values.dtype is not torch.float32
- tmp_over_one = torch.cumsum(rad_values.double(), 1) # % 1 #####%1意味着后面的cumsum无法再优化
- if is_half:
- tmp_over_one = tmp_over_one.half()
- else:
- tmp_over_one = tmp_over_one.float()
- tmp_over_one *= upp
- tmp_over_one = F.interpolate(
- tmp_over_one.transpose(2, 1), scale_factor=upp,
- mode='linear', align_corners=True
- ).transpose(2, 1)
- rad_values = F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)
- tmp_over_one %= 1
- tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
- cumsum_shift = torch.zeros_like(rad_values)
- cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
- rad_values = rad_values.double()
- cumsum_shift = cumsum_shift.double()
- sine_waves = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi)
- if is_half:
- sine_waves = sine_waves.half()
- else:
- sine_waves = sine_waves.float()
- sine_waves = sine_waves * self.sine_amp
- uv = self._f02uv(f0)
- uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)
- noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
- noise = noise_amp * torch.randn_like(sine_waves)
- sine_waves = sine_waves * uv + noise
- return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
- """ SourceModule for hn-nsf
- SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0)
- sampling_rate: sampling_rate in Hz
- harmonic_num: number of harmonic above F0 (default: 0)
- sine_amp: amplitude of sine source signal (default: 0.1)
- add_noise_std: std of additive Gaussian noise (default: 0.003)
- note that amplitude of noise in unvoiced is decided
- by sine_amp
- voiced_threshold: threhold to set U/V given F0 (default: 0)
- Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
- F0_sampled (batchsize, length, 1)
- Sine_source (batchsize, length, 1)
- noise_source (batchsize, length 1)
- uv (batchsize, length, 1)
- """
-
- def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
- add_noise_std=0.003, voiced_threshod=0):
- super(SourceModuleHnNSF, self).__init__()
-
- self.sine_amp = sine_amp
- self.noise_std = add_noise_std
-
- # to produce sine waveforms
- self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
- sine_amp, add_noise_std, voiced_threshod)
-
- # to merge source harmonics into a single excitation
- self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
- self.l_tanh = torch.nn.Tanh()
-
- def forward(self, x, upp):
- sine_wavs, uv, _ = self.l_sin_gen(x, upp)
- sine_merge = self.l_tanh(self.l_linear(sine_wavs))
- return sine_merge
-
-
-class Generator(torch.nn.Module):
- def __init__(self, h):
- super(Generator, self).__init__()
- self.h = h
- self.num_kernels = len(h.resblock_kernel_sizes)
- self.num_upsamples = len(h.upsample_rates)
- self.m_source = SourceModuleHnNSF(
- sampling_rate=h.sampling_rate,
- harmonic_num=8
- )
- self.noise_convs = nn.ModuleList()
- self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
- resblock = ResBlock1 if h.resblock == '1' else ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
- c_cur = h.upsample_initial_channel // (2 ** (i + 1))
- self.ups.append(weight_norm(
- ConvTranspose1d(h.upsample_initial_channel // (2 ** i), h.upsample_initial_channel // (2 ** (i + 1)),
- k, u, padding=(k - u) // 2)))
- if i + 1 < len(h.upsample_rates): #
- stride_f0 = int(np.prod(h.upsample_rates[i + 1:]))
- self.noise_convs.append(Conv1d(
- 1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
- else:
- self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
- self.resblocks = nn.ModuleList()
- ch = h.upsample_initial_channel
- for i in range(len(self.ups)):
- ch //= 2
- for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
- self.resblocks.append(resblock(h, ch, k, d))
-
- self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
- self.ups.apply(init_weights)
- self.conv_post.apply(init_weights)
- self.upp = int(np.prod(h.upsample_rates))
-
- def forward(self, x, f0):
- har_source = self.m_source(f0, self.upp).transpose(1, 2)
- x = self.conv_pre(x)
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, LRELU_SLOPE)
- x = self.ups[i](x)
- x_source = self.noise_convs[i](har_source)
- x = x + x_source
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i * self.num_kernels + j](x)
- else:
- xs += self.resblocks[i * self.num_kernels + j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
- remove_weight_norm(self.conv_pre)
- remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, periods=None):
- super(MultiPeriodDiscriminator, self).__init__()
- self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
- self.discriminators = nn.ModuleList()
- for period in self.periods:
- self.discriminators.append(DiscriminatorP(period))
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- fmap_rs.append(fmap_r)
- y_d_gs.append(y_d_g)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 128, 15, 1, padding=7)),
- norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
- norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
- norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
- def __init__(self):
- super(MultiScaleDiscriminator, self).__init__()
- self.discriminators = nn.ModuleList([
- DiscriminatorS(use_spectral_norm=True),
- DiscriminatorS(),
- DiscriminatorS(),
- ])
- self.meanpools = nn.ModuleList([
- AvgPool1d(4, 2, padding=2),
- AvgPool1d(4, 2, padding=2)
- ])
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- if i != 0:
- y = self.meanpools[i - 1](y)
- y_hat = self.meanpools[i - 1](y_hat)
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- fmap_rs.append(fmap_r)
- y_d_gs.append(y_d_g)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- r_loss = torch.mean((1 - dr) ** 2)
- g_loss = torch.mean(dg ** 2)
- loss += (r_loss + g_loss)
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- l = torch.mean((1 - dg) ** 2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
diff --git a/spaces/Froleptan/stablediffusion-infinity/PyPatchMatch/travis.sh b/spaces/Froleptan/stablediffusion-infinity/PyPatchMatch/travis.sh
deleted file mode 100644
index a6ea538775e25b4e9b8c855a38e400c82f9121bf..0000000000000000000000000000000000000000
--- a/spaces/Froleptan/stablediffusion-infinity/PyPatchMatch/travis.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#! /bin/bash
-#
-# travis.sh
-# Copyright (C) 2020 Jiayuan Mao
-#
-# Distributed under terms of the MIT license.
-#
-
-make clean && make
diff --git a/spaces/GigiWasThere/Text/README.md b/spaces/GigiWasThere/Text/README.md
deleted file mode 100644
index 196e8466dc20fbb1750abdc2fc1a65ffe1e2bfad..0000000000000000000000000000000000000000
--- a/spaces/GigiWasThere/Text/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Text
-emoji: 🦀
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
deleted file mode 100644
index 403747f127e0f7a301771e53e75bf0e83a1736c9..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
-# learning policy
-lr_config = dict(step=[28, 34])
-runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py
deleted file mode 100644
index 190309fd42a1b76c12c82fc1acf0511494be5ac3..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
- """Legacy Delta XYWH BBox coder used in MMDet V1.x.
-
- Following the practice in R-CNN [1]_, this coder encodes bbox (x1, y1, x2,
- y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh)
- back to original bbox (x1, y1, x2, y2).
-
- Note:
- The main difference between :class`LegacyDeltaXYWHBBoxCoder` and
- :class:`DeltaXYWHBBoxCoder` is whether ``+ 1`` is used during width and
- height calculation. We suggest to only use this coder when testing with
- MMDet V1.x models.
-
- References:
- .. [1] https://arxiv.org/abs/1311.2524
-
- Args:
- target_means (Sequence[float]): denormalizing means of target for
- delta coordinates
- target_stds (Sequence[float]): denormalizing standard deviation of
- target for delta coordinates
- """
-
- def __init__(self,
- target_means=(0., 0., 0., 0.),
- target_stds=(1., 1., 1., 1.)):
- super(BaseBBoxCoder, self).__init__()
- self.means = target_means
- self.stds = target_stds
-
- def encode(self, bboxes, gt_bboxes):
- """Get box regression transformation deltas that can be used to
- transform the ``bboxes`` into the ``gt_bboxes``.
-
- Args:
- bboxes (torch.Tensor): source boxes, e.g., object proposals.
- gt_bboxes (torch.Tensor): target of the transformation, e.g.,
- ground-truth boxes.
-
- Returns:
- torch.Tensor: Box transformation deltas
- """
- assert bboxes.size(0) == gt_bboxes.size(0)
- assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
- encoded_bboxes = legacy_bbox2delta(bboxes, gt_bboxes, self.means,
- self.stds)
- return encoded_bboxes
-
- def decode(self,
- bboxes,
- pred_bboxes,
- max_shape=None,
- wh_ratio_clip=16 / 1000):
- """Apply transformation `pred_bboxes` to `boxes`.
-
- Args:
- boxes (torch.Tensor): Basic boxes.
- pred_bboxes (torch.Tensor): Encoded boxes with shape
- max_shape (tuple[int], optional): Maximum shape of boxes.
- Defaults to None.
- wh_ratio_clip (float, optional): The allowed ratio between
- width and height.
-
- Returns:
- torch.Tensor: Decoded boxes.
- """
- assert pred_bboxes.size(0) == bboxes.size(0)
- decoded_bboxes = legacy_delta2bbox(bboxes, pred_bboxes, self.means,
- self.stds, max_shape, wh_ratio_clip)
-
- return decoded_bboxes
-
-
-@mmcv.jit(coderize=True)
-def legacy_bbox2delta(proposals,
- gt,
- means=(0., 0., 0., 0.),
- stds=(1., 1., 1., 1.)):
- """Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner.
-
- We usually compute the deltas of x, y, w, h of proposals w.r.t ground
- truth bboxes to get regression target.
- This is the inverse function of `delta2bbox()`
-
- Args:
- proposals (Tensor): Boxes to be transformed, shape (N, ..., 4)
- gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4)
- means (Sequence[float]): Denormalizing means for delta coordinates
- stds (Sequence[float]): Denormalizing standard deviation for delta
- coordinates
-
- Returns:
- Tensor: deltas with shape (N, 4), where columns represent dx, dy,
- dw, dh.
- """
- assert proposals.size() == gt.size()
-
- proposals = proposals.float()
- gt = gt.float()
- px = (proposals[..., 0] + proposals[..., 2]) * 0.5
- py = (proposals[..., 1] + proposals[..., 3]) * 0.5
- pw = proposals[..., 2] - proposals[..., 0] + 1.0
- ph = proposals[..., 3] - proposals[..., 1] + 1.0
-
- gx = (gt[..., 0] + gt[..., 2]) * 0.5
- gy = (gt[..., 1] + gt[..., 3]) * 0.5
- gw = gt[..., 2] - gt[..., 0] + 1.0
- gh = gt[..., 3] - gt[..., 1] + 1.0
-
- dx = (gx - px) / pw
- dy = (gy - py) / ph
- dw = torch.log(gw / pw)
- dh = torch.log(gh / ph)
- deltas = torch.stack([dx, dy, dw, dh], dim=-1)
-
- means = deltas.new_tensor(means).unsqueeze(0)
- stds = deltas.new_tensor(stds).unsqueeze(0)
- deltas = deltas.sub_(means).div_(stds)
-
- return deltas
-
-
-@mmcv.jit(coderize=True)
-def legacy_delta2bbox(rois,
- deltas,
- means=(0., 0., 0., 0.),
- stds=(1., 1., 1., 1.),
- max_shape=None,
- wh_ratio_clip=16 / 1000):
- """Apply deltas to shift/scale base boxes in the MMDet V1.x manner.
-
- Typically the rois are anchor or proposed bounding boxes and the deltas are
- network outputs used to shift/scale those boxes.
- This is the inverse function of `bbox2delta()`
-
- Args:
- rois (Tensor): Boxes to be transformed. Has shape (N, 4)
- deltas (Tensor): Encoded offsets with respect to each roi.
- Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when
- rois is a grid of anchors. Offset encoding follows [1]_.
- means (Sequence[float]): Denormalizing means for delta coordinates
- stds (Sequence[float]): Denormalizing standard deviation for delta
- coordinates
- max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W)
- wh_ratio_clip (float): Maximum aspect ratio for boxes.
-
- Returns:
- Tensor: Boxes with shape (N, 4), where columns represent
- tl_x, tl_y, br_x, br_y.
-
- References:
- .. [1] https://arxiv.org/abs/1311.2524
-
- Example:
- >>> rois = torch.Tensor([[ 0., 0., 1., 1.],
- >>> [ 0., 0., 1., 1.],
- >>> [ 0., 0., 1., 1.],
- >>> [ 5., 5., 5., 5.]])
- >>> deltas = torch.Tensor([[ 0., 0., 0., 0.],
- >>> [ 1., 1., 1., 1.],
- >>> [ 0., 0., 2., -1.],
- >>> [ 0.7, -1.9, -0.5, 0.3]])
- >>> legacy_delta2bbox(rois, deltas, max_shape=(32, 32))
- tensor([[0.0000, 0.0000, 1.5000, 1.5000],
- [0.0000, 0.0000, 5.2183, 5.2183],
- [0.0000, 0.1321, 7.8891, 0.8679],
- [5.3967, 2.4251, 6.0033, 3.7749]])
- """
- means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4)
- stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4)
- denorm_deltas = deltas * stds + means
- dx = denorm_deltas[:, 0::4]
- dy = denorm_deltas[:, 1::4]
- dw = denorm_deltas[:, 2::4]
- dh = denorm_deltas[:, 3::4]
- max_ratio = np.abs(np.log(wh_ratio_clip))
- dw = dw.clamp(min=-max_ratio, max=max_ratio)
- dh = dh.clamp(min=-max_ratio, max=max_ratio)
- # Compute center of each roi
- px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx)
- py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy)
- # Compute width/height of each roi
- pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw)
- ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh)
- # Use exp(network energy) to enlarge/shrink each roi
- gw = pw * dw.exp()
- gh = ph * dh.exp()
- # Use network energy to shift the center of each roi
- gx = px + pw * dx
- gy = py + ph * dy
- # Convert center-xy/width/height to top-left, bottom-right
-
- # The true legacy box coder should +- 0.5 here.
- # However, current implementation improves the performance when testing
- # the models trained in MMDetection 1.X (~0.5 bbox AP, 0.2 mask AP)
- x1 = gx - gw * 0.5
- y1 = gy - gh * 0.5
- x2 = gx + gw * 0.5
- y2 = gy + gh * 0.5
- if max_shape is not None:
- x1 = x1.clamp(min=0, max=max_shape[1] - 1)
- y1 = y1.clamp(min=0, max=max_shape[0] - 1)
- x2 = x2.clamp(min=0, max=max_shape[1] - 1)
- y2 = y2.clamp(min=0, max=max_shape[0] - 1)
- bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas)
- return bboxes
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
deleted file mode 100644
index ebb1a8eaee16de7443ab3e79e02a37340de511d7..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_20k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/__init__.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/__init__.py
deleted file mode 100644
index 39f0957560ff29b9ff0ee630e78972cd3ef187fb..0000000000000000000000000000000000000000
--- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/__init__.py
+++ /dev/null
@@ -1,60 +0,0 @@
-'''
-Netdissect package.
-
-To run dissection:
-
-1. Load up the convolutional model you wish to dissect, and wrap it
- in an InstrumentedModel. Call imodel.retain_layers([layernames,..])
- to analyze a specified set of layers.
-2. Load the segmentation dataset using the BrodenDataset class;
- use the transform_image argument to normalize images to be
- suitable for the model, or the size argument to truncate the dataset.
-3. Write a function to recover the original image (with RGB scaled to
- [0...1]) given a normalized dataset image; ReverseNormalize in this
- package inverts transforms.Normalize for this purpose.
-4. Choose a directory in which to write the output, and call
- dissect(outdir, model, dataset).
-
-Example:
-
- from netdissect import InstrumentedModel, dissect
- from netdissect import BrodenDataset, ReverseNormalize
-
- model = InstrumentedModel(load_my_model())
- model.eval()
- model.cuda()
- model.retain_layers(['conv1', 'conv2', 'conv3', 'conv4', 'conv5'])
- bds = BrodenDataset('dataset/broden1_227',
- transform_image=transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize(IMAGE_MEAN, IMAGE_STDEV)]),
- size=1000)
- dissect('result/dissect', model, bds,
- recover_image=ReverseNormalize(IMAGE_MEAN, IMAGE_STDEV),
- examples_per_unit=10)
-'''
-
-from .dissection import dissect, ReverseNormalize
-from .dissection import ClassifierSegRunner, GeneratorSegRunner
-from .dissection import ImageOnlySegRunner
-from .broden import BrodenDataset, ScaleSegmentation, scatter_batch
-from .segdata import MultiSegmentDataset
-from .nethook import InstrumentedModel
-from .zdataset import z_dataset_for_model, z_sample_for_model, standard_z_sample
-from . import actviz
-from . import progress
-from . import runningstats
-from . import sampler
-
-__all__ = [
- 'dissect', 'ReverseNormalize',
- 'ClassifierSegRunner', 'GeneratorSegRunner', 'ImageOnlySegRunner',
- 'BrodenDataset', 'ScaleSegmentation', 'scatter_batch',
- 'MultiSegmentDataset',
- 'InstrumentedModel',
- 'z_dataset_for_model', 'z_sample_for_model', 'standard_z_sample'
- 'actviz',
- 'progress',
- 'runningstats',
- 'sampler'
-]
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/mt5_summary/fastapi_mt5_summary.py b/spaces/HaloMaster/chinesesummary/fengshen/examples/mt5_summary/fastapi_mt5_summary.py
deleted file mode 100644
index 44adaf8f5855260c683c0bcfe7986ffccc9f25c4..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/examples/mt5_summary/fastapi_mt5_summary.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import os
-import sys
-import uvicorn
-import torch
-from fastapi import Body, FastAPI
-from transformers import T5Tokenizer, MT5ForConditionalGeneration
-import pytorch_lightning as pl
-sys.path.append(os.path.abspath(os.path.join(
- os.path.dirname(__file__), os.path.pardir)))
-os.environ["CUDA_VISIBLE_DEVICES"] = '5'
-os.environ["MASTER_ADDR"] = '127.0.0.1'
-os.environ["MASTER_PORT"] = '6000'
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-print('device')
-pretrain_model_path = '/cognitive_comp/ganruyi/hf_models/google/mt5-large'
-# pretrain_model_path = 'google/mt5-small'
-model_path = '/cognitive_comp/ganruyi/fengshen/mt5_large_summary/ckpt/epoch-0-last.ckpt'
-tokenizer = T5Tokenizer.from_pretrained(pretrain_model_path)
-print('load tokenizer')
-
-
-class MT5FinetuneSummary(pl.LightningModule):
-
- def __init__(self):
- super().__init__()
- self.model = MT5ForConditionalGeneration.from_pretrained(pretrain_model_path)
-
-
-model = MT5FinetuneSummary.load_from_checkpoint(model_path)
-print('load checkpoint')
-model.to(device)
-model.eval()
-app = FastAPI()
-print('server start')
-
-# def flask_gen(text: str, level: float = 0.9, n_sample: int = 5, length: int = 32, is_beam_search=False):
-
-
-@app.post('/mt5_summary')
-async def flask_gen(text: str = Body('', title='原文', embed=True),
- n_sample: int = 5, length: int = 32, is_beam_search=False):
- if len(text) > 128:
- text = text[:128]
- text = 'summary:'+text
- print(text)
- # inputs = tokenizer(text, return_tensors='pt')
- inputs = tokenizer.encode_plus(
- text, max_length=128, padding='max_length', truncation=True, return_tensors='pt')
- # print(inputs)
- if is_beam_search:
- generated_ids = model.model.generate(
- input_ids=inputs['input_ids'].to(device),
- attention_mask=inputs['attention_mask'].to(device),
- max_length=length,
- num_beams=n_sample,
- repetition_penalty=2.5,
- length_penalty=1.0,
- early_stopping=True,
- num_return_sequences=n_sample
- )
- else:
- generated_ids = model.model.generate(
- input_ids=inputs['input_ids'].to(device),
- attention_mask=inputs['attention_mask'].to(device),
- max_length=length,
- do_sample=True,
- temperature=1.0,
- top_p=1.0,
- repetition_penalty=2.5,
- # early_stopping=True,
- num_return_sequences=n_sample
- )
- result = []
- # print(tokenizer.all_special_tokens)
- for sample in generated_ids:
- preds = [tokenizer.decode(sample, skip_special_tokens=True,
- clean_up_tokenization_spaces=True)]
- preds = ''.join(preds)
- # print(preds)
- result.append(preds)
- return result
-
-
-if __name__ == '__main__':
- uvicorn.run(app, host="0.0.0.0", port=6607, log_level="debug")
-# # article = "日前，方舟子发文直指林志颖旗下爱碧丽推销假保健品，引起哗然。调查发现，
-# 爱碧丽没有自己的生产加工厂。其胶原蛋白饮品无核心研发，全部代工生产。号称有“逆生长”功效的爱碧丽“梦幻奇迹限量组”售价>高达1080元，实际成本仅为每瓶4元！"
-# article = '''在北京冬奥会自由式滑雪女子坡面障碍技巧决赛中，中国选手谷爱凌夺得银牌。祝贺谷爱凌！
-# 今天上午，自由式滑雪女子坡面障碍技巧决赛举行。决赛分三轮进行，取选手最佳成绩排名决出奖牌。
-# 第一跳，中国选手谷爱凌获得69.90分。在12位选手中排名第三。完成动作后，谷爱凌又扮了个鬼脸，甚是可爱。
-# 第二轮中，谷爱凌在道具区第三个障碍处失误，落地时摔倒。获得16.98分。网友：摔倒了也没关系，继续加油！
-# 在第二跳失误摔倒的情况下，谷爱凌顶住压力，第三跳稳稳发挥，流畅落地！获得86.23分！此轮比赛，共12位选手参赛，谷爱凌第10位出场。网友：看比赛时我比谷爱凌紧张，加油！'''
- # flask_gen(article, length=30)
diff --git a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/utils/inference/run_gradio.py b/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/utils/inference/run_gradio.py
deleted file mode 100644
index 7d5ccc5e53031c83ea146bc3ac070fd8dedfe9e1..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Odia-TTS/ttsv/utils/inference/run_gradio.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import gradio as gr
-import argparse
-import numpy as np
-from argparse import Namespace
-from .advanced_tts import load_all_models, run_tts_paragraph
-
-
-def hit_tts(textbox, gender, slider_noise_scale, slider_length_sclae, choice_transliteration, choice_number_conversion, choice_split_sentences):
- inputs_to_gradio = {'text' : textbox,
- 'gender' : gender,
- 'noise_scale': slider_noise_scale,
- 'length_scale': slider_length_sclae,
- 'transliteration' : 1 if choice_transliteration else 0,
- 'number_conversion' : 1 if choice_number_conversion else 0,
- 'split_sentences' : 1 if choice_split_sentences else 0
- }
-
- args = Namespace(**inputs_to_gradio)
- args.wav = None
- args.lang = lang
- args.gender = gender
-
- if args.text:
- sr, audio = run_tts_paragraph(args)
- return (sr, audio)
-
-def build_gradio(args):
- global lang
- lang = args.lang
- load_all_models(args)
- textbox = gr.inputs.Textbox(placeholder="Enter Text to run", default="", label="Enter Input Text")
- gender = gr.inputs.Radio(choices = ['Female', 'Male'], default='Female', label='Gender')
- slider_noise_scale = gr.inputs.Slider(minimum=0, maximum=1.0, step=0.001, default=0.667, label='Noise Scale')
- slider_length_sclae = gr.inputs.Slider(minimum=0, maximum=2.0, step=0.1, default=1.0, label='Length Scale')
-
- choice_transliteration = gr.inputs.Checkbox(default=True, label="Transliteration")
- choice_number_conversion = gr.inputs.Checkbox(default=True, label="Number Conversion")
- choice_split_sentences = gr.inputs.Checkbox(default=True, label="Split Sentences")
-
- examples = [['ଭାରତ ମୋର ଦେଶ ଏବଂ ମୁଁ ଜଣେ ଭାରତୀୟ ହୋଇଥିବାରୁ ଗର୍ବିତ |', 'Male', 0.667, 1, 0, 1, 1]]
-
- op = gr.outputs.Audio(type="numpy", label=None)
-
- inputs_to_gradio = [textbox, gender, slider_noise_scale, slider_length_sclae, choice_transliteration, choice_number_conversion, choice_split_sentences]
- iface = gr.Interface(fn=hit_tts, examples = examples, inputs=inputs_to_gradio, outputs=op, theme='huggingface', title='Vakyansh Odia TTS', article = 'Note: Transliteration models may not work well in some scenarios which can hamper the TTS quality, to evaluate the model in better sense it is advisable to provide input in the required language and switch off transliteration. Contact @harveenchadha on twitter for any issues.')
- iface.launch(enable_queue=True)
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("-a", "--acoustic", required=True, type=str)
- parser.add_argument("-v", "--vocoder", required=True, type=str)
- parser.add_argument("-d", "--device", type=str, default="cpu")
- parser.add_argument("-L", "--lang", type=str, required=True)
-
- global lang
-
- args = parser.parse_args()
- lang = args.lang
-
- build_gradio(args)
\ No newline at end of file
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/script/phonetic_sim.py b/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/script/phonetic_sim.py
deleted file mode 100644
index 87f56b63dd38c0f8fd5edf9b6ee5131afd332f31..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/script/phonetic_sim.py
+++ /dev/null
@@ -1,59 +0,0 @@
-#
-# Copyright (c) 2013-present, Anoop Kunchukuttan
-# All rights reserved.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-#
-
-from indicnlp import loader
-from indicnlp import langinfo
-from indicnlp.script.indic_scripts import *
-import numpy as np
-import gzip
-import pandas as pd
-import sys
-
-def equal(v1,v2):
- return 0.0 if np.sum( xor_vectors(v1, v2)) > 0 else 1.0
-
-def dice(v1,v2):
- dotprod=2*float(np.dot( v1, v2.T ))
- return dotprod/float(len(v1)+len(v2))
-
-def jaccard(v1,v2):
- dotprod=float(np.dot( v1, v2.T ))
- return dotprod/float(len(v1)+len(v2)-dotprod)
-
-def cosine(v1,v2):
- dotprod=float(np.dot( v1, v2.T ))
- norm1=float(np.dot( v1, v1.T ))
- norm2=float(np.dot( v2, v2.T ))
- return ((dotprod)/(np.sqrt(norm1*norm2)+0.00001))
-
-def dotprod(v1,v2):
- return float(np.dot( v1, v2.T ))
-
-def sim1(v1,v2,base=5.0):
- return np.power(base,dotprod(v1,v2))
-
-def softmax(v1,v2):
- return sim1(v1,v2,np.e)
-
-def create_similarity_matrix(sim_func,slang,tlang,normalize=True):
-
- dim=langinfo.COORDINATED_RANGE_END_INCLUSIVE-langinfo.COORDINATED_RANGE_START_INCLUSIVE+1
- sim_mat=np.zeros((dim,dim))
-
- for offset1 in range(langinfo.COORDINATED_RANGE_START_INCLUSIVE, langinfo.COORDINATED_RANGE_END_INCLUSIVE+1):
- v1=get_phonetic_feature_vector(offset_to_char(offset1,slang),slang)
- for offset2 in range(langinfo.COORDINATED_RANGE_START_INCLUSIVE, langinfo.COORDINATED_RANGE_END_INCLUSIVE+1):
- v2=get_phonetic_feature_vector(offset_to_char(offset2,tlang),tlang)
- sim_mat[offset1,offset2]=sim_func(v1,v2)
-
- if normalize:
- sums=np.sum(sim_mat, axis=1)
- sim_mat=(sim_mat.transpose()/sums).transpose()
-
- return sim_mat
-
diff --git a/spaces/Hasani/Specific_Object_Recognition_in_the_Wild/README.md b/spaces/Hasani/Specific_Object_Recognition_in_the_Wild/README.md
deleted file mode 100644
index e80168505e00ba6c92b5a166f7679b2af2f43855..0000000000000000000000000000000000000000
--- a/spaces/Hasani/Specific_Object_Recognition_in_the_Wild/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Specific Object Recognition In The Wild
-emoji: ⚡
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HgMenon/Transcribe_V0.2/src/languages.py b/spaces/HgMenon/Transcribe_V0.2/src/languages.py
deleted file mode 100644
index fbad66e4d34119d27d12e3dfecbe99b6fdde4db7..0000000000000000000000000000000000000000
--- a/spaces/HgMenon/Transcribe_V0.2/src/languages.py
+++ /dev/null
@@ -1,147 +0,0 @@
-class Language():
- def __init__(self, code, name):
- self.code = code
- self.name = name
-
- def __str__(self):
- return "Language(code={}, name={})".format(self.code, self.name)
-
-LANGUAGES = [
- Language('en', 'English'),
- Language('zh', 'Chinese'),
- Language('de', 'German'),
- Language('es', 'Spanish'),
- Language('ru', 'Russian'),
- Language('ko', 'Korean'),
- Language('fr', 'French'),
- Language('ja', 'Japanese'),
- Language('pt', 'Portuguese'),
- Language('tr', 'Turkish'),
- Language('pl', 'Polish'),
- Language('ca', 'Catalan'),
- Language('nl', 'Dutch'),
- Language('ar', 'Arabic'),
- Language('sv', 'Swedish'),
- Language('it', 'Italian'),
- Language('id', 'Indonesian'),
- Language('hi', 'Hindi'),
- Language('fi', 'Finnish'),
- Language('vi', 'Vietnamese'),
- Language('he', 'Hebrew'),
- Language('uk', 'Ukrainian'),
- Language('el', 'Greek'),
- Language('ms', 'Malay'),
- Language('cs', 'Czech'),
- Language('ro', 'Romanian'),
- Language('da', 'Danish'),
- Language('hu', 'Hungarian'),
- Language('ta', 'Tamil'),
- Language('no', 'Norwegian'),
- Language('th', 'Thai'),
- Language('ur', 'Urdu'),
- Language('hr', 'Croatian'),
- Language('bg', 'Bulgarian'),
- Language('lt', 'Lithuanian'),
- Language('la', 'Latin'),
- Language('mi', 'Maori'),
- Language('ml', 'Malayalam'),
- Language('cy', 'Welsh'),
- Language('sk', 'Slovak'),
- Language('te', 'Telugu'),
- Language('fa', 'Persian'),
- Language('lv', 'Latvian'),
- Language('bn', 'Bengali'),
- Language('sr', 'Serbian'),
- Language('az', 'Azerbaijani'),
- Language('sl', 'Slovenian'),
- Language('kn', 'Kannada'),
- Language('et', 'Estonian'),
- Language('mk', 'Macedonian'),
- Language('br', 'Breton'),
- Language('eu', 'Basque'),
- Language('is', 'Icelandic'),
- Language('hy', 'Armenian'),
- Language('ne', 'Nepali'),
- Language('mn', 'Mongolian'),
- Language('bs', 'Bosnian'),
- Language('kk', 'Kazakh'),
- Language('sq', 'Albanian'),
- Language('sw', 'Swahili'),
- Language('gl', 'Galician'),
- Language('mr', 'Marathi'),
- Language('pa', 'Punjabi'),
- Language('si', 'Sinhala'),
- Language('km', 'Khmer'),
- Language('sn', 'Shona'),
- Language('yo', 'Yoruba'),
- Language('so', 'Somali'),
- Language('af', 'Afrikaans'),
- Language('oc', 'Occitan'),
- Language('ka', 'Georgian'),
- Language('be', 'Belarusian'),
- Language('tg', 'Tajik'),
- Language('sd', 'Sindhi'),
- Language('gu', 'Gujarati'),
- Language('am', 'Amharic'),
- Language('yi', 'Yiddish'),
- Language('lo', 'Lao'),
- Language('uz', 'Uzbek'),
- Language('fo', 'Faroese'),
- Language('ht', 'Haitian creole'),
- Language('ps', 'Pashto'),
- Language('tk', 'Turkmen'),
- Language('nn', 'Nynorsk'),
- Language('mt', 'Maltese'),
- Language('sa', 'Sanskrit'),
- Language('lb', 'Luxembourgish'),
- Language('my', 'Myanmar'),
- Language('bo', 'Tibetan'),
- Language('tl', 'Tagalog'),
- Language('mg', 'Malagasy'),
- Language('as', 'Assamese'),
- Language('tt', 'Tatar'),
- Language('haw', 'Hawaiian'),
- Language('ln', 'Lingala'),
- Language('ha', 'Hausa'),
- Language('ba', 'Bashkir'),
- Language('jw', 'Javanese'),
- Language('su', 'Sundanese')
-]
-
-_TO_LANGUAGE_CODE = {
- **{language.code: language for language in LANGUAGES},
- "burmese": "my",
- "valencian": "ca",
- "flemish": "nl",
- "haitian": "ht",
- "letzeburgesch": "lb",
- "pushto": "ps",
- "panjabi": "pa",
- "moldavian": "ro",
- "moldovan": "ro",
- "sinhalese": "si",
- "castilian": "es",
-}
-
-_FROM_LANGUAGE_NAME = {
- **{language.name.lower(): language for language in LANGUAGES}
-}
-
-def get_language_from_code(language_code, default=None) -> Language:
- """Return the language name from the language code."""
- return _TO_LANGUAGE_CODE.get(language_code, default)
-
-def get_language_from_name(language, default=None) -> Language:
- """Return the language code from the language name."""
- return _FROM_LANGUAGE_NAME.get(language.lower() if language else None, default)
-
-def get_language_names():
- """Return a list of language names."""
- return [language.name for language in LANGUAGES]
-
-if __name__ == "__main__":
- # Test lookup
- print(get_language_from_code('en'))
- print(get_language_from_name('English'))
-
- print(get_language_names())
\ No newline at end of file
diff --git a/spaces/HgMenon/Transcribe_V0.2/src/vad.py b/spaces/HgMenon/Transcribe_V0.2/src/vad.py
deleted file mode 100644
index e68ee7391e93f539a05d548601f2d87168bb1282..0000000000000000000000000000000000000000
--- a/spaces/HgMenon/Transcribe_V0.2/src/vad.py
+++ /dev/null
@@ -1,568 +0,0 @@
-from abc import ABC, abstractmethod
-from collections import Counter, deque
-import time
-
-from typing import Any, Deque, Iterator, List, Dict
-
-from pprint import pprint
-from src.hooks.progressListener import ProgressListener
-from src.hooks.subTaskProgressListener import SubTaskProgressListener
-from src.hooks.whisperProgressHook import create_progress_listener_handle
-from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache
-
-from src.segments import merge_timestamps
-from src.whisper.abstractWhisperContainer import AbstractWhisperCallback
-
-# Workaround for https://github.com/tensorflow/tensorflow/issues/48797
-try:
- import tensorflow as tf
-except ModuleNotFoundError:
- # Error handling
- pass
-
-import torch
-
-import ffmpeg
-import numpy as np
-
-from src.utils import format_timestamp
-from enum import Enum
-
-class NonSpeechStrategy(Enum):
- """
- Ignore non-speech frames segments.
- """
- SKIP = 1
- """
- Just treat non-speech segments as speech.
- """
- CREATE_SEGMENT = 2
- """
- Expand speech segments into subsequent non-speech segments.
- """
- EXPAND_SEGMENT = 3
-
-# Defaults for Silero
-SPEECH_TRESHOLD = 0.3
-
-# Minimum size of segments to process
-MIN_SEGMENT_DURATION = 1
-
-# The maximum time for texts from old segments to be used in the next segment
-MAX_PROMPT_WINDOW = 0 # seconds (0 = disabled)
-PROMPT_NO_SPEECH_PROB = 0.1 # Do not pass the text from segments with a no speech probability higher than this
-
-VAD_MAX_PROCESSING_CHUNK = 60 * 60 # 60 minutes of audio
-
-class TranscriptionConfig(ABC):
- def __init__(self, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP,
- segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None,
- max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
- self.non_speech_strategy = non_speech_strategy
- self.segment_padding_left = segment_padding_left
- self.segment_padding_right = segment_padding_right
- self.max_silent_period = max_silent_period
- self.max_merge_size = max_merge_size
- self.max_prompt_window = max_prompt_window
- self.initial_segment_index = initial_segment_index
-
-class PeriodicTranscriptionConfig(TranscriptionConfig):
- def __init__(self, periodic_duration: float, non_speech_strategy: NonSpeechStrategy = NonSpeechStrategy.SKIP,
- segment_padding_left: float = None, segment_padding_right = None, max_silent_period: float = None,
- max_merge_size: float = None, max_prompt_window: float = None, initial_segment_index = -1):
- super().__init__(non_speech_strategy, segment_padding_left, segment_padding_right, max_silent_period, max_merge_size, max_prompt_window, initial_segment_index)
- self.periodic_duration = periodic_duration
-
-class AbstractTranscription(ABC):
- def __init__(self, sampling_rate: int = 16000):
- self.sampling_rate = sampling_rate
-
- def get_audio_segment(self, str, start_time: str = None, duration: str = None):
- return load_audio(str, self.sampling_rate, start_time, duration)
-
- def is_transcribe_timestamps_fast(self):
- """
- Determine if get_transcribe_timestamps is fast enough to not need parallelization.
- """
- return False
-
- @abstractmethod
- def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
- """
- Get the start and end timestamps of the sections that should be transcribed by this VAD method.
-
- Parameters
- ----------
- audio: str
- The audio file.
- config: TranscriptionConfig
- The transcription configuration.
-
- Returns
- -------
- A list of start and end timestamps, in fractional seconds.
- """
- return
-
- def get_merged_timestamps(self, timestamps: List[Dict[str, Any]], config: TranscriptionConfig, total_duration: float):
- """
- Get the start and end timestamps of the sections that should be transcribed by this VAD method,
- after merging the given segments using the specified configuration.
-
- Parameters
- ----------
- audio: str
- The audio file.
- config: TranscriptionConfig
- The transcription configuration.
-
- Returns
- -------
- A list of start and end timestamps, in fractional seconds.
- """
- merged = merge_timestamps(timestamps, config.max_silent_period, config.max_merge_size,
- config.segment_padding_left, config.segment_padding_right)
-
- if config.non_speech_strategy != NonSpeechStrategy.SKIP:
- # Expand segments to include the gaps between them
- if (config.non_speech_strategy == NonSpeechStrategy.CREATE_SEGMENT):
- # When we have a prompt window, we create speech segments betwen each segment if we exceed the merge size
- merged = self.fill_gaps(merged, total_duration=total_duration, max_expand_size=config.max_merge_size)
- elif config.non_speech_strategy == NonSpeechStrategy.EXPAND_SEGMENT:
- # With no prompt window, it is better to just expand the segments (this effectively passes the prompt to the next segment)
- merged = self.expand_gaps(merged, total_duration=total_duration)
- else:
- raise Exception("Unknown non-speech strategy: " + str(config.non_speech_strategy))
-
- print("Transcribing non-speech:")
- pprint(merged)
- return merged
-
- def transcribe(self, audio: str, whisperCallable: AbstractWhisperCallback, config: TranscriptionConfig,
- progressListener: ProgressListener = None):
- """
- Transcribe the given audo file.
-
- Parameters
- ----------
- audio: str
- The audio file.
- whisperCallable: WhisperCallback
- A callback object to call to transcribe each segment.
-
- Returns
- -------
- A list of start and end timestamps, in fractional seconds.
- """
-
- try:
- max_audio_duration = self.get_audio_duration(audio, config)
- timestamp_segments = self.get_transcribe_timestamps(audio, config, 0, max_audio_duration)
-
- # Get speech timestamps from full audio file
- merged = self.get_merged_timestamps(timestamp_segments, config, max_audio_duration)
-
- # A deque of transcribed segments that is passed to the next segment as a prompt
- prompt_window = deque()
-
- print("Processing timestamps:")
- pprint(merged)
-
- result = {
- 'text': "",
- 'segments': [],
- 'language': ""
- }
- languageCounter = Counter()
- detected_language = None
-
- segment_index = config.initial_segment_index
-
- # Calculate progress
- progress_start_offset = merged[0]['start'] if len(merged) > 0 else 0
- progress_total_duration = sum([segment['end'] - segment['start'] for segment in merged])
-
- # For each time segment, run whisper
- for segment in merged:
- segment_index += 1
- segment_start = segment['start']
- segment_end = segment['end']
- segment_expand_amount = segment.get('expand_amount', 0)
- segment_gap = segment.get('gap', False)
-
- segment_duration = segment_end - segment_start
-
- if segment_duration < MIN_SEGMENT_DURATION:
- continue
-
- # Audio to run on Whisper
- segment_audio = self.get_audio_segment(audio, start_time = str(segment_start), duration = str(segment_duration))
- # Previous segments to use as a prompt
- segment_prompt = ' '.join([segment['text'] for segment in prompt_window]) if len(prompt_window) > 0 else None
-
- # Detected language
- detected_language = languageCounter.most_common(1)[0][0] if len(languageCounter) > 0 else None
-
- print("Running whisper from ", format_timestamp(segment_start), " to ", format_timestamp(segment_end), ", duration: ",
- segment_duration, "expanded: ", segment_expand_amount, "prompt: ", segment_prompt, "language: ", detected_language)
-
- perf_start_time = time.perf_counter()
-
- scaled_progress_listener = SubTaskProgressListener(progressListener, base_task_total=progress_total_duration,
- sub_task_start=segment_start - progress_start_offset, sub_task_total=segment_duration)
- segment_result = whisperCallable.invoke(segment_audio, segment_index, segment_prompt, detected_language, progress_listener=scaled_progress_listener)
-
- perf_end_time = time.perf_counter()
- print("Whisper took {} seconds".format(perf_end_time - perf_start_time))
-
- adjusted_segments = self.adjust_timestamp(segment_result["segments"], adjust_seconds=segment_start, max_source_time=segment_duration)
-
- # Propagate expand amount to the segments
- if (segment_expand_amount > 0):
- segment_without_expansion = segment_duration - segment_expand_amount
-
- for adjusted_segment in adjusted_segments:
- adjusted_segment_end = adjusted_segment['end']
-
- # Add expand amount if the segment got expanded
- if (adjusted_segment_end > segment_without_expansion):
- adjusted_segment["expand_amount"] = adjusted_segment_end - segment_without_expansion
-
- # Append to output
- result['text'] += segment_result['text']
- result['segments'].extend(adjusted_segments)
-
- # Increment detected language
- if not segment_gap:
- languageCounter[segment_result['language']] += 1
-
- # Update prompt window
- self.__update_prompt_window(prompt_window, adjusted_segments, segment_end, segment_gap, config)
-
- if detected_language is not None:
- result['language'] = detected_language
- finally:
- # Notify progress listener that we are done
- if progressListener is not None:
- progressListener.on_finished()
- return result
-
- def get_audio_duration(self, audio: str, config: TranscriptionConfig):
- return get_audio_duration(audio)
-
- def __update_prompt_window(self, prompt_window: Deque, adjusted_segments: List, segment_end: float, segment_gap: bool, config: TranscriptionConfig):
- if (config.max_prompt_window is not None and config.max_prompt_window > 0):
- # Add segments to the current prompt window (unless it is a speech gap)
- if not segment_gap:
- for segment in adjusted_segments:
- if segment.get('no_speech_prob', 0) <= PROMPT_NO_SPEECH_PROB:
- prompt_window.append(segment)
-
- while (len(prompt_window) > 0):
- first_end_time = prompt_window[0].get('end', 0)
- # Time expanded in the segments should be discounted from the prompt window
- first_expand_time = prompt_window[0].get('expand_amount', 0)
-
- if (first_end_time - first_expand_time < segment_end - config.max_prompt_window):
- prompt_window.popleft()
- else:
- break
-
- def include_gaps(self, segments: Iterator[dict], min_gap_length: float, total_duration: float):
- result = []
- last_end_time = 0
-
- for segment in segments:
- segment_start = float(segment['start'])
- segment_end = float(segment['end'])
-
- if (last_end_time != segment_start):
- delta = segment_start - last_end_time
-
- if (min_gap_length is None or delta >= min_gap_length):
- result.append( { 'start': last_end_time, 'end': segment_start, 'gap': True } )
-
- last_end_time = segment_end
- result.append(segment)
-
- # Also include total duration if specified
- if (total_duration is not None and last_end_time < total_duration):
- delta = total_duration - segment_start
-
- if (min_gap_length is None or delta >= min_gap_length):
- result.append( { 'start': last_end_time, 'end': total_duration, 'gap': True } )
-
- return result
-
- # Expand the end time of each segment to the start of the next segment
- def expand_gaps(self, segments: List[Dict[str, Any]], total_duration: float):
- result = []
-
- if len(segments) == 0:
- return result
-
- # Add gap at the beginning if needed
- if (segments[0]['start'] > 0):
- result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
- for i in range(len(segments) - 1):
- current_segment = segments[i]
- next_segment = segments[i + 1]
-
- delta = next_segment['start'] - current_segment['end']
-
- # Expand if the gap actually exists
- if (delta >= 0):
- current_segment = current_segment.copy()
- current_segment['expand_amount'] = delta
- current_segment['end'] = next_segment['start']
-
- result.append(current_segment)
-
- # Add last segment
- last_segment = segments[-1]
- result.append(last_segment)
-
- # Also include total duration if specified
- if (total_duration is not None):
- last_segment = result[-1]
-
- if (last_segment['end'] < total_duration):
- last_segment = last_segment.copy()
- last_segment['end'] = total_duration
- result[-1] = last_segment
-
- return result
-
- def fill_gaps(self, segments: List[Dict[str, Any]], total_duration: float, max_expand_size: float = None):
- result = []
-
- if len(segments) == 0:
- return result
-
- # Add gap at the beginning if needed
- if (segments[0]['start'] > 0):
- result.append({ 'start': 0, 'end': segments[0]['start'], 'gap': True } )
-
- for i in range(len(segments) - 1):
- expanded = False
- current_segment = segments[i]
- next_segment = segments[i + 1]
-
- delta = next_segment['start'] - current_segment['end']
-
- if (max_expand_size is not None and delta <= max_expand_size):
- # Just expand the current segment
- current_segment = current_segment.copy()
- current_segment['expand_amount'] = delta
- current_segment['end'] = next_segment['start']
- expanded = True
-
- result.append(current_segment)
-
- # Add a gap to the next segment if needed
- if (delta >= 0 and not expanded):
- result.append({ 'start': current_segment['end'], 'end': next_segment['start'], 'gap': True } )
-
- # Add last segment
- last_segment = segments[-1]
- result.append(last_segment)
-
- # Also include total duration if specified
- if (total_duration is not None):
- last_segment = result[-1]
-
- delta = total_duration - last_segment['end']
-
- if (delta > 0):
- if (max_expand_size is not None and delta <= max_expand_size):
- # Expand the last segment
- last_segment = last_segment.copy()
- last_segment['expand_amount'] = delta
- last_segment['end'] = total_duration
- result[-1] = last_segment
- else:
- result.append({ 'start': last_segment['end'], 'end': total_duration, 'gap': True } )
-
- return result
-
- def adjust_timestamp(self, segments: Iterator[dict], adjust_seconds: float, max_source_time: float = None):
- result = []
-
- for segment in segments:
- segment_start = float(segment['start'])
- segment_end = float(segment['end'])
-
- # Filter segments?
- if (max_source_time is not None):
- if (segment_start > max_source_time):
- continue
- segment_end = min(max_source_time, segment_end)
-
- new_segment = segment.copy()
-
- # Add to start and end
- new_segment['start'] = segment_start + adjust_seconds
- new_segment['end'] = segment_end + adjust_seconds
-
- # Handle words
- if ('words' in new_segment):
- for word in new_segment['words']:
- # Adjust start and end
- word['start'] = word['start'] + adjust_seconds
- word['end'] = word['end'] + adjust_seconds
-
- result.append(new_segment)
- return result
-
- def multiply_timestamps(self, timestamps: List[Dict[str, Any]], factor: float):
- result = []
-
- for entry in timestamps:
- start = entry['start']
- end = entry['end']
-
- result.append({
- 'start': start * factor,
- 'end': end * factor
- })
- return result
-
-
-class VadSileroTranscription(AbstractTranscription):
- def __init__(self, sampling_rate: int = 16000, cache: ModelCache = None):
- super().__init__(sampling_rate=sampling_rate)
- self.model = None
- self.cache = cache
- self._initialize_model()
-
- def _initialize_model(self):
- if (self.cache is not None):
- model_key = "VadSileroTranscription"
- self.model, self.get_speech_timestamps = self.cache.get(model_key, self._create_model)
- print("Loaded Silerio model from cache.")
- else:
- self.model, self.get_speech_timestamps = self._create_model()
- print("Created Silerio model")
-
- def _create_model(self):
- model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad')
-
- # Silero does not benefit from multi-threading
- torch.set_num_threads(1) # JIT
- (get_speech_timestamps, _, _, _, _) = utils
-
- return model, get_speech_timestamps
-
- def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, end_time: float):
- result = []
-
- print("Getting timestamps from audio file: {}, start: {}, duration: {}".format(audio, start_time, end_time))
- perf_start_time = time.perf_counter()
-
- # Divide procesisng of audio into chunks
- chunk_start = start_time
-
- while (chunk_start < end_time):
- chunk_duration = min(end_time - chunk_start, VAD_MAX_PROCESSING_CHUNK)
-
- print("Processing VAD in chunk from {} to {}".format(format_timestamp(chunk_start), format_timestamp(chunk_start + chunk_duration)))
- wav = self.get_audio_segment(audio, str(chunk_start), str(chunk_duration))
-
- sample_timestamps = self.get_speech_timestamps(wav, self.model, sampling_rate=self.sampling_rate, threshold=SPEECH_TRESHOLD)
- seconds_timestamps = self.multiply_timestamps(sample_timestamps, factor=1 / self.sampling_rate)
- adjusted = self.adjust_timestamp(seconds_timestamps, adjust_seconds=chunk_start, max_source_time=chunk_start + chunk_duration)
-
- #pprint(adjusted)
-
- result.extend(adjusted)
- chunk_start += chunk_duration
-
- perf_end_time = time.perf_counter()
- print("VAD processing took {} seconds".format(perf_end_time - perf_start_time))
-
- return result
-
- def __getstate__(self):
- # We only need the sampling rate
- return { 'sampling_rate': self.sampling_rate }
-
- def __setstate__(self, state):
- self.sampling_rate = state['sampling_rate']
- self.model = None
- # Use the global cache
- self.cache = GLOBAL_MODEL_CACHE
- self._initialize_model()
-
-# A very simple VAD that just marks every N seconds as speech
-class VadPeriodicTranscription(AbstractTranscription):
- def __init__(self, sampling_rate: int = 16000):
- super().__init__(sampling_rate=sampling_rate)
-
- def is_transcribe_timestamps_fast(self):
- # This is a very fast VAD - no need to parallelize it
- return True
-
- def get_transcribe_timestamps(self, audio: str, config: PeriodicTranscriptionConfig, start_time: float, end_time: float):
- result = []
-
- # Generate a timestamp every N seconds
- start_timestamp = start_time
-
- while (start_timestamp < end_time):
- end_timestamp = min(start_timestamp + config.periodic_duration, end_time)
- segment_duration = end_timestamp - start_timestamp
-
- # Minimum duration is 1 second
- if (segment_duration >= 1):
- result.append( { 'start': start_timestamp, 'end': end_timestamp } )
-
- start_timestamp = end_timestamp
-
- return result
-
-def get_audio_duration(file: str):
- return float(ffmpeg.probe(file)["format"]["duration"])
-
-def load_audio(file: str, sample_rate: int = 16000,
- start_time: str = None, duration: str = None):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- start_time: str
- The start time, using the standard FFMPEG time duration syntax, or None to disable.
-
- duration: str
- The duration, using the standard FFMPEG time duration syntax, or None to disable.
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- inputArgs = {'threads': 0}
-
- if (start_time is not None):
- inputArgs['ss'] = start_time
- if (duration is not None):
- inputArgs['t'] = duration
-
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, **inputArgs)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sample_rate)
- .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}")
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
\ No newline at end of file
diff --git a/spaces/HighCWu/Style2Paints-4.5-Gradio/README.md b/spaces/HighCWu/Style2Paints-4.5-Gradio/README.md
deleted file mode 100644
index 1b09bdd5dd8677cec3e7a5ce71b4a0efa114e815..0000000000000000000000000000000000000000
--- a/spaces/HighCWu/Style2Paints-4.5-Gradio/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Style2Paints 4.5 Gradio
-emoji: 🐨
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-python_version: 3.8
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/fully_sharded_data_parallel.py b/spaces/ICML2022/OFA/fairseq/fairseq/distributed/fully_sharded_data_parallel.py
deleted file mode 100644
index 8a96bfc76516682ac8e2b7e2c3bc2e6aa3d8ef0c..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/fully_sharded_data_parallel.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import contextlib
-from typing import Optional
-
-import torch
-from fairseq.dataclass.configs import DistributedTrainingConfig
-from fairseq.distributed import utils as dist_utils
-
-
-try:
- from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP
-
- has_FSDP = True
-except ImportError:
- FSDP = torch.nn.Module
- has_FSDP = False
-
-
-class FullyShardedDataParallel(FSDP):
- """
- A small wrapper around fairscale's FullyShardedDataParallel (FSDP) with some
- fairseq-specific checkpoint saving/loading logic.
-
- Args:
- use_sharded_state (bool): if True, then ``state_dict`` will return
- ``FSDP.local_state_dict`` and ``load_state_dict`` will call
- ``FSDP.load_local_state_dict``. Otherwise, ``state_dict`` will
- return the full model weights on data parallel rank 0 (empty on
- other ranks) and ``load_state_dict`` will broadcast model weights
- from rank 0 to other ranks.
- """
-
- def __init__(self, *args, use_sharded_state: bool = False, **kwargs):
- if not has_FSDP:
- raise ImportError(
- "Cannot find FullyShardedDataParallel. "
- "Please install fairscale with: pip install fairscale"
- )
- super().__init__(*args, **kwargs)
- self.use_sharded_state = use_sharded_state
-
- @property
- def unwrapped_module(self) -> torch.nn.Module:
- if self.flatten_parameters:
- return self.module.module
- else:
- return self.module
-
- def state_dict(self, destination=None, prefix="", keep_vars=False):
- if self.use_sharded_state:
- return super().local_state_dict(
- destination=destination, prefix=prefix, keep_vars=keep_vars
- )
- else:
- if self.rank == 0:
- return super().state_dict(
- destination=destination, prefix=prefix, keep_vars=keep_vars
- )
- else:
- # We must call state_dict() due to use of communication
- # primitives. But we don't use the result.
- super().state_dict()
- return destination or {}
-
- def load_state_dict(self, state_dict, strict=True, model_cfg=None):
- if self.use_sharded_state:
- return super().load_local_state_dict(state_dict, strict=strict)
- else:
- state_dict = dist_utils.broadcast_object(
- state_dict, src_rank=0, group=self.process_group
- )
- return super().load_state_dict(state_dict, strict=strict)
-
-
-@contextlib.contextmanager
-def fsdp_enable_wrap(cfg: DistributedTrainingConfig):
- try:
- from fairscale.nn import enable_wrap
- except ImportError:
- raise ImportError(
- "Cannot find FullyShardedDataParallel. "
- "Please install fairscale with: pip install fairscale"
- )
- if cfg.memory_efficient_fp16:
- assert cfg.fp16 # memory_efficient_fp16 should imply fp16
- group = dist_utils.get_data_parallel_group()
- if group is None and cfg.distributed_world_size == 1:
- from fairscale.utils.testing import DummyProcessGroup
-
- group = DummyProcessGroup(rank=0, size=1)
- fsdp_config = {
- "process_group": group,
- "reshard_after_forward": not cfg.no_reshard_after_forward,
- "mixed_precision": cfg.fp16 and not cfg.memory_efficient_fp16,
- "fp32_reduce_scatter": cfg.fp32_reduce_scatter,
- "flatten_parameters": True,
- "cpu_offload": cfg.cpu_offload,
- "compute_dtype": torch.float16 if cfg.fp16 else torch.float32,
- "bucket_cap_mb": cfg.bucket_cap_mb,
- "state_dict_device": torch.device("cpu"), # reduce GPU mem usage
- }
- with enable_wrap(
- wrapper_cls=FullyShardedDataParallel,
- use_sharded_state=cfg.use_sharded_state,
- **fsdp_config,
- ):
- yield
-
-
-def fsdp_wrap(module, min_num_params: Optional[int] = None, **kwargs):
- """
- Helper to wrap layers/modules in FSDP. This falls back to a no-op if
- fairscale is not available.
-
- Args:
- module (nn.Module): module to (maybe) wrap
- min_num_params (int, Optional): minimum number of layer params to wrap
- """
- try:
- from fairscale.nn import wrap
-
- if min_num_params is not None:
- num_params = sum(p.numel() for p in module.parameters())
- if num_params >= min_num_params:
- return wrap(module, **kwargs)
- else:
- return module
- else:
- return wrap(module, **kwargs)
- except ImportError:
- return module
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/archs/basicvsrpp_arch.py b/spaces/Iceclear/StableSR/StableSR/basicsr/archs/basicvsrpp_arch.py
deleted file mode 100644
index 2a9952e4b441de0030d665a3db141774184f332f..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/archs/basicvsrpp_arch.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-import warnings
-
-from basicsr.archs.arch_util import flow_warp
-from basicsr.archs.basicvsr_arch import ConvResidualBlocks
-from basicsr.archs.spynet_arch import SpyNet
-from basicsr.ops.dcn import ModulatedDeformConvPack
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-@ARCH_REGISTRY.register()
-class BasicVSRPlusPlus(nn.Module):
- """BasicVSR++ network structure.
-
- Support either x4 upsampling or same size output. Since DCN is used in this
- model, it can only be used with CUDA enabled. If CUDA is not enabled,
- feature alignment will be skipped. Besides, we adopt the official DCN
- implementation and the version of torch need to be higher than 1.9.
-
- ``Paper: BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment``
-
- Args:
- mid_channels (int, optional): Channel number of the intermediate
- features. Default: 64.
- num_blocks (int, optional): The number of residual blocks in each
- propagation branch. Default: 7.
- max_residue_magnitude (int): The maximum magnitude of the offset
- residue (Eq. 6 in paper). Default: 10.
- is_low_res_input (bool, optional): Whether the input is low-resolution
- or not. If False, the output resolution is equal to the input
- resolution. Default: True.
- spynet_path (str): Path to the pretrained weights of SPyNet. Default: None.
- cpu_cache_length (int, optional): When the length of sequence is larger
- than this value, the intermediate features are sent to CPU. This
- saves GPU memory, but slows down the inference speed. You can
- increase this number if you have a GPU with large memory.
- Default: 100.
- """
-
- def __init__(self,
- mid_channels=64,
- num_blocks=7,
- max_residue_magnitude=10,
- is_low_res_input=True,
- spynet_path=None,
- cpu_cache_length=100):
-
- super().__init__()
- self.mid_channels = mid_channels
- self.is_low_res_input = is_low_res_input
- self.cpu_cache_length = cpu_cache_length
-
- # optical flow
- self.spynet = SpyNet(spynet_path)
-
- # feature extraction module
- if is_low_res_input:
- self.feat_extract = ConvResidualBlocks(3, mid_channels, 5)
- else:
- self.feat_extract = nn.Sequential(
- nn.Conv2d(3, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True),
- nn.Conv2d(mid_channels, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True),
- ConvResidualBlocks(mid_channels, mid_channels, 5))
-
- # propagation branches
- self.deform_align = nn.ModuleDict()
- self.backbone = nn.ModuleDict()
- modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2']
- for i, module in enumerate(modules):
- if torch.cuda.is_available():
- self.deform_align[module] = SecondOrderDeformableAlignment(
- 2 * mid_channels,
- mid_channels,
- 3,
- padding=1,
- deformable_groups=16,
- max_residue_magnitude=max_residue_magnitude)
- self.backbone[module] = ConvResidualBlocks((2 + i) * mid_channels, mid_channels, num_blocks)
-
- # upsampling module
- self.reconstruction = ConvResidualBlocks(5 * mid_channels, mid_channels, 5)
-
- self.upconv1 = nn.Conv2d(mid_channels, mid_channels * 4, 3, 1, 1, bias=True)
- self.upconv2 = nn.Conv2d(mid_channels, 64 * 4, 3, 1, 1, bias=True)
-
- self.pixel_shuffle = nn.PixelShuffle(2)
-
- self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1)
- self.conv_last = nn.Conv2d(64, 3, 3, 1, 1)
- self.img_upsample = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False)
-
- # activation function
- self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-
- # check if the sequence is augmented by flipping
- self.is_mirror_extended = False
-
- if len(self.deform_align) > 0:
- self.is_with_alignment = True
- else:
- self.is_with_alignment = False
- warnings.warn('Deformable alignment module is not added. '
- 'Probably your CUDA is not configured correctly. DCN can only '
- 'be used with CUDA enabled. Alignment is skipped now.')
-
- def check_if_mirror_extended(self, lqs):
- """Check whether the input is a mirror-extended sequence.
-
- If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the (t-1-i)-th frame.
-
- Args:
- lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w).
- """
-
- if lqs.size(1) % 2 == 0:
- lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1)
- if torch.norm(lqs_1 - lqs_2.flip(1)) == 0:
- self.is_mirror_extended = True
-
- def compute_flow(self, lqs):
- """Compute optical flow using SPyNet for feature alignment.
-
- Note that if the input is an mirror-extended sequence, 'flows_forward'
- is not needed, since it is equal to 'flows_backward.flip(1)'.
-
- Args:
- lqs (tensor): Input low quality (LQ) sequence with
- shape (n, t, c, h, w).
-
- Return:
- tuple(Tensor): Optical flow. 'flows_forward' corresponds to the flows used for forward-time propagation \
- (current to previous). 'flows_backward' corresponds to the flows used for backward-time \
- propagation (current to next).
- """
-
- n, t, c, h, w = lqs.size()
- lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w)
- lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w)
-
- flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w)
-
- if self.is_mirror_extended: # flows_forward = flows_backward.flip(1)
- flows_forward = flows_backward.flip(1)
- else:
- flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w)
-
- if self.cpu_cache:
- flows_backward = flows_backward.cpu()
- flows_forward = flows_forward.cpu()
-
- return flows_forward, flows_backward
-
- def propagate(self, feats, flows, module_name):
- """Propagate the latent features throughout the sequence.
-
- Args:
- feats dict(list[tensor]): Features from previous branches. Each
- component is a list of tensors with shape (n, c, h, w).
- flows (tensor): Optical flows with shape (n, t - 1, 2, h, w).
- module_name (str): The name of the propgation branches. Can either
- be 'backward_1', 'forward_1', 'backward_2', 'forward_2'.
-
- Return:
- dict(list[tensor]): A dictionary containing all the propagated \
- features. Each key in the dictionary corresponds to a \
- propagation branch, which is represented by a list of tensors.
- """
-
- n, t, _, h, w = flows.size()
-
- frame_idx = range(0, t + 1)
- flow_idx = range(-1, t)
- mapping_idx = list(range(0, len(feats['spatial'])))
- mapping_idx += mapping_idx[::-1]
-
- if 'backward' in module_name:
- frame_idx = frame_idx[::-1]
- flow_idx = frame_idx
-
- feat_prop = flows.new_zeros(n, self.mid_channels, h, w)
- for i, idx in enumerate(frame_idx):
- feat_current = feats['spatial'][mapping_idx[idx]]
- if self.cpu_cache:
- feat_current = feat_current.cuda()
- feat_prop = feat_prop.cuda()
- # second-order deformable alignment
- if i > 0 and self.is_with_alignment:
- flow_n1 = flows[:, flow_idx[i], :, :, :]
- if self.cpu_cache:
- flow_n1 = flow_n1.cuda()
-
- cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1))
-
- # initialize second-order features
- feat_n2 = torch.zeros_like(feat_prop)
- flow_n2 = torch.zeros_like(flow_n1)
- cond_n2 = torch.zeros_like(cond_n1)
-
- if i > 1: # second-order features
- feat_n2 = feats[module_name][-2]
- if self.cpu_cache:
- feat_n2 = feat_n2.cuda()
-
- flow_n2 = flows[:, flow_idx[i - 1], :, :, :]
- if self.cpu_cache:
- flow_n2 = flow_n2.cuda()
-
- flow_n2 = flow_n1 + flow_warp(flow_n2, flow_n1.permute(0, 2, 3, 1))
- cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1))
-
- # flow-guided deformable convolution
- cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1)
- feat_prop = torch.cat([feat_prop, feat_n2], dim=1)
- feat_prop = self.deform_align[module_name](feat_prop, cond, flow_n1, flow_n2)
-
- # concatenate and residual blocks
- feat = [feat_current] + [feats[k][idx] for k in feats if k not in ['spatial', module_name]] + [feat_prop]
- if self.cpu_cache:
- feat = [f.cuda() for f in feat]
-
- feat = torch.cat(feat, dim=1)
- feat_prop = feat_prop + self.backbone[module_name](feat)
- feats[module_name].append(feat_prop)
-
- if self.cpu_cache:
- feats[module_name][-1] = feats[module_name][-1].cpu()
- torch.cuda.empty_cache()
-
- if 'backward' in module_name:
- feats[module_name] = feats[module_name][::-1]
-
- return feats
-
- def upsample(self, lqs, feats):
- """Compute the output image given the features.
-
- Args:
- lqs (tensor): Input low quality (LQ) sequence with
- shape (n, t, c, h, w).
- feats (dict): The features from the propagation branches.
-
- Returns:
- Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).
- """
-
- outputs = []
- num_outputs = len(feats['spatial'])
-
- mapping_idx = list(range(0, num_outputs))
- mapping_idx += mapping_idx[::-1]
-
- for i in range(0, lqs.size(1)):
- hr = [feats[k].pop(0) for k in feats if k != 'spatial']
- hr.insert(0, feats['spatial'][mapping_idx[i]])
- hr = torch.cat(hr, dim=1)
- if self.cpu_cache:
- hr = hr.cuda()
-
- hr = self.reconstruction(hr)
- hr = self.lrelu(self.pixel_shuffle(self.upconv1(hr)))
- hr = self.lrelu(self.pixel_shuffle(self.upconv2(hr)))
- hr = self.lrelu(self.conv_hr(hr))
- hr = self.conv_last(hr)
- if self.is_low_res_input:
- hr += self.img_upsample(lqs[:, i, :, :, :])
- else:
- hr += lqs[:, i, :, :, :]
-
- if self.cpu_cache:
- hr = hr.cpu()
- torch.cuda.empty_cache()
-
- outputs.append(hr)
-
- return torch.stack(outputs, dim=1)
-
- def forward(self, lqs):
- """Forward function for BasicVSR++.
-
- Args:
- lqs (tensor): Input low quality (LQ) sequence with
- shape (n, t, c, h, w).
-
- Returns:
- Tensor: Output HR sequence with shape (n, t, c, 4h, 4w).
- """
-
- n, t, c, h, w = lqs.size()
-
- # whether to cache the features in CPU
- self.cpu_cache = True if t > self.cpu_cache_length else False
-
- if self.is_low_res_input:
- lqs_downsample = lqs.clone()
- else:
- lqs_downsample = F.interpolate(
- lqs.view(-1, c, h, w), scale_factor=0.25, mode='bicubic').view(n, t, c, h // 4, w // 4)
-
- # check whether the input is an extended sequence
- self.check_if_mirror_extended(lqs)
-
- feats = {}
- # compute spatial features
- if self.cpu_cache:
- feats['spatial'] = []
- for i in range(0, t):
- feat = self.feat_extract(lqs[:, i, :, :, :]).cpu()
- feats['spatial'].append(feat)
- torch.cuda.empty_cache()
- else:
- feats_ = self.feat_extract(lqs.view(-1, c, h, w))
- h, w = feats_.shape[2:]
- feats_ = feats_.view(n, t, -1, h, w)
- feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)]
-
- # compute optical flow using the low-res inputs
- assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, (
- 'The height and width of low-res inputs must be at least 64, '
- f'but got {h} and {w}.')
- flows_forward, flows_backward = self.compute_flow(lqs_downsample)
-
- # feature propgation
- for iter_ in [1, 2]:
- for direction in ['backward', 'forward']:
- module = f'{direction}_{iter_}'
-
- feats[module] = []
-
- if direction == 'backward':
- flows = flows_backward
- elif flows_forward is not None:
- flows = flows_forward
- else:
- flows = flows_backward.flip(1)
-
- feats = self.propagate(feats, flows, module)
- if self.cpu_cache:
- del flows
- torch.cuda.empty_cache()
-
- return self.upsample(lqs, feats)
-
-
-class SecondOrderDeformableAlignment(ModulatedDeformConvPack):
- """Second-order deformable alignment module.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int or tuple[int]): Same as nn.Conv2d.
- padding (int or tuple[int]): Same as nn.Conv2d.
- dilation (int or tuple[int]): Same as nn.Conv2d.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- max_residue_magnitude (int): The maximum magnitude of the offset
- residue (Eq. 6 in paper). Default: 10.
- """
-
- def __init__(self, *args, **kwargs):
- self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10)
-
- super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs)
-
- self.conv_offset = nn.Sequential(
- nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1),
- nn.LeakyReLU(negative_slope=0.1, inplace=True),
- nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),
- nn.LeakyReLU(negative_slope=0.1, inplace=True),
- nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1),
- nn.LeakyReLU(negative_slope=0.1, inplace=True),
- nn.Conv2d(self.out_channels, 27 * self.deformable_groups, 3, 1, 1),
- )
-
- self.init_offset()
-
- def init_offset(self):
-
- def _constant_init(module, val, bias=0):
- if hasattr(module, 'weight') and module.weight is not None:
- nn.init.constant_(module.weight, val)
- if hasattr(module, 'bias') and module.bias is not None:
- nn.init.constant_(module.bias, bias)
-
- _constant_init(self.conv_offset[-1], val=0, bias=0)
-
- def forward(self, x, extra_feat, flow_1, flow_2):
- extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1)
- out = self.conv_offset(extra_feat)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
-
- # offset
- offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1))
- offset_1, offset_2 = torch.chunk(offset, 2, dim=1)
- offset_1 = offset_1 + flow_1.flip(1).repeat(1, offset_1.size(1) // 2, 1, 1)
- offset_2 = offset_2 + flow_2.flip(1).repeat(1, offset_2.size(1) // 2, 1, 1)
- offset = torch.cat([offset_1, offset_2], dim=1)
-
- # mask
- mask = torch.sigmoid(mask)
-
- return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding,
- self.dilation, mask)
-
-
-# if __name__ == '__main__':
-# spynet_path = 'experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth'
-# model = BasicVSRPlusPlus(spynet_path=spynet_path).cuda()
-# input = torch.rand(1, 2, 3, 64, 64).cuda()
-# output = model(input)
-# print('===================')
-# print(output.shape)
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/modules/attentions.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/modules/attentions.py
deleted file mode 100644
index f9c11ca4a3acb86bf1abc04d9dcfa82a4ed4061f..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/modules/attentions.py
+++ /dev/null
@@ -1,349 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import modules.commons as commons
-import modules.modules as modules
-from modules.modules import LayerNorm
-
-
-class FFT(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
- proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
- proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
- x = x * x_mask
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
- self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert t_s == t_t, "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert t_s == t_t, "Local attention is only available for self-attention."
- block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
- output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
- output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
- x_flat = x.view([batch, heads, length**2 + length*(length -1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/Illumotion/Koboldcpp/examples/batched-bench/README.md b/spaces/Illumotion/Koboldcpp/examples/batched-bench/README.md
deleted file mode 100644
index 34b343f66d6b95184ef45ddb678765282e19770c..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/examples/batched-bench/README.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# llama.cpp/example/batched-bench
-
-Benchmark the batched decoding performance of `llama.cpp`
-
-## Usage
-
-There are 2 modes of operation:
-
-- `prompt not shared` - each batch has a separate prompt of size `PP` (i.e. `N_KV = B*(PP + TG)`)
-- `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
-
-```bash
-./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ]
-
-# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
-./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99
-
-# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99
-
-# custom set of batches
-./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32
-```
-
-## Sample results
-
-- `PP` - prompt tokens per batch
-- `TG` - generated tokens per batch
-- `B` - number of batches
-- `N_KV` - required KV cache size
-- `T_PP` - prompt processing time (i.e. time to first token)
-- `S_PP` - prompt processing speed (`(B*PP)/T_PP` or `PP/T_PP`)
-- `T_TG` - time to generate all batches
-- `S_TG` - text generation speed (`(B*TG)/T_TG`)
-- `T` - total time
-- `S` - total speed (i.e. all tokens / total time)
-
-| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
-|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-| 128 | 128 | 1 | 256 | 0.108 | 1186.64 | 3.079 | 41.57 | 3.187 | 80.32 |
-| 128 | 128 | 2 | 512 | 0.198 | 1295.19 | 5.029 | 50.90 | 5.227 | 97.95 |
-| 128 | 128 | 4 | 1024 | 0.373 | 1373.96 | 6.878 | 74.44 | 7.251 | 141.23 |
-| 128 | 128 | 8 | 2048 | 0.751 | 1363.27 | 7.344 | 139.43 | 8.095 | 252.99 |
-| 128 | 128 | 16 | 4096 | 1.570 | 1304.68 | 8.455 | 242.23 | 10.024 | 408.60 |
-| 128 | 128 | 32 | 8192 | 3.408 | 1201.73 | 8.801 | 465.40 | 12.209 | 670.96 |
-| 128 | 256 | 1 | 384 | 0.107 | 1196.70 | 6.329 | 40.45 | 6.436 | 59.67 |
-| 128 | 256 | 2 | 768 | 0.194 | 1317.45 | 10.239 | 50.00 | 10.433 | 73.61 |
-| 128 | 256 | 4 | 1536 | 0.366 | 1399.03 | 13.960 | 73.35 | 14.326 | 107.22 |
-| 128 | 256 | 8 | 3072 | 0.751 | 1363.92 | 15.110 | 135.54 | 15.861 | 193.69 |
-| 128 | 256 | 16 | 6144 | 1.569 | 1304.93 | 18.073 | 226.64 | 19.642 | 312.80 |
-| 128 | 256 | 32 | 12288 | 3.409 | 1201.35 | 19.223 | 426.15 | 22.633 | 542.93 |
diff --git a/spaces/Jamel887/Rvc-tio887/app-2.py b/spaces/Jamel887/Rvc-tio887/app-2.py
deleted file mode 100644
index 2ac3c75490ffa9c5724dc745ff51268c6a9327a4..0000000000000000000000000000000000000000
--- a/spaces/Jamel887/Rvc-tio887/app-2.py
+++ /dev/null
@@ -1,518 +0,0 @@
-import os
-import glob
-import json
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-import yt_dlp
-import ffmpeg
-import subprocess
-import sys
-import io
-import wave
-from datetime import datetime
-from fairseq import checkpoint_utils
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"
-
-audio_mode = []
-f0method_mode = []
-f0method_info = ""
-if limitation is True:
- audio_mode = ["Upload audio", "TTS Audio"]
- f0method_mode = ["pm", "harvest"]
- f0method_info = "PM is fast, Harvest is good but extremely slow. (Default: PM)"
-else:
- audio_mode = ["Input path", "Upload audio", "Youtube", "TTS Audio"]
- f0method_mode = ["pm", "harvest", "crepe"]
- f0method_info = "PM is fast, Harvest is good but extremely slow, and Crepe effect is good but requires GPU (Default: PM)"
-
-def create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, file_index):
- def vc_fn(
- vc_audio_mode,
- vc_input,
- vc_upload,
- tts_text,
- tts_voice,
- f0_up_key,
- f0_method,
- index_rate,
- filter_radius,
- resample_sr,
- rms_mix_rate,
- protect,
- ):
- try:
- if vc_audio_mode == "Input path" or "Youtube" and vc_input != "":
- audio, sr = librosa.load(vc_input, sr=16000, mono=True)
- elif vc_audio_mode == "Upload audio":
- if vc_upload is None:
- return "You need to upload an audio", None
- sampling_rate, audio = vc_upload
- duration = audio.shape[0] / sampling_rate
- if duration > 20 and limitation:
- return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- elif vc_audio_mode == "TTS Audio":
- if len(tts_text) > 100 and limitation:
- return "Text is too long", None
- if tts_text is None or tts_voice is None:
- return "You need to enter text and select a voice", None
- asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
- audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
- vc_input = "tts.mp3"
- times = [0, 0, 0]
- f0_up_key = int(f0_up_key)
- audio_opt = vc.pipeline(
- hubert_model,
- net_g,
- 0,
- audio,
- vc_input,
- times,
- f0_up_key,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- f0_file=None,
- )
- info = f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
- print(f"{model_title} | {info}")
- return info, (tgt_sr, audio_opt)
- except:
- info = traceback.format_exc()
- print(info)
- return info, None
- return vc_fn
-
-def load_model():
- categories = []
- with open("weights/folder_info.json", "r", encoding="utf-8") as f:
- folder_info = json.load(f)
- for category_name, category_info in folder_info.items():
- if not category_info['enable']:
- continue
- category_title = category_info['title']
- category_folder = category_info['folder_path']
- description = category_info['description']
- models = []
- with open(f"weights/{category_folder}/model_info.json", "r", encoding="utf-8") as f:
- models_info = json.load(f)
- for character_name, info in models_info.items():
- if not info['enable']:
- continue
- model_title = info['title']
- model_name = info['model_path']
- model_author = info.get("author", None)
- model_cover = f"weights/{category_folder}/{character_name}/{info['cover']}"
- model_index = f"weights/{category_folder}/{character_name}/{info['feature_retrieval_library']}"
- cpt = torch.load(f"weights/{category_folder}/{character_name}/{model_name}", map_location="cpu")
- tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- if_f0 = cpt.get("f0", 1)
- version = cpt.get("version", "v1")
- if version == "v1":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- model_version = "V1"
- elif version == "v2":
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- model_version = "V2"
- del net_g.enc_q
- print(net_g.load_state_dict(cpt["weight"], strict=False))
- net_g.eval().to(config.device)
- if config.is_half:
- net_g = net_g.half()
- else:
- net_g = net_g.float()
- vc = VC(tgt_sr, config)
- print(f"Model loaded: {character_name} / {info['feature_retrieval_library']} | ({model_version})")
- models.append((character_name, model_title, model_author, model_cover, model_version, create_vc_fn(model_title, tgt_sr, net_g, vc, if_f0, version, model_index)))
- categories.append([category_title, category_folder, description, models])
- return categories
-
-def cut_vocal_and_inst(url, audio_provider, split_model):
- if url != "":
- if not os.path.exists("dl_audio"):
- os.mkdir("dl_audio")
- if audio_provider == "Youtube":
- ydl_opts = {
- 'noplaylist': True,
- 'format': 'bestaudio/best',
- 'postprocessors': [{
- 'key': 'FFmpegExtractAudio',
- 'preferredcodec': 'wav',
- }],
- "outtmpl": 'dl_audio/youtube_audio',
- }
- with yt_dlp.YoutubeDL(ydl_opts) as ydl:
- ydl.download([url])
- audio_path = "dl_audio/youtube_audio.wav"
- if split_model == "htdemucs":
- command = f"demucs --two-stems=vocals {audio_path} -o output"
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return "output/htdemucs/youtube_audio/vocals.wav", "output/htdemucs/youtube_audio/no_vocals.wav", audio_path, "output/htdemucs/youtube_audio/vocals.wav"
- else:
- command = f"demucs --two-stems=vocals -n mdx_extra_q {audio_path} -o output"
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return "output/mdx_extra_q/youtube_audio/vocals.wav", "output/mdx_extra_q/youtube_audio/no_vocals.wav", audio_path, "output/mdx_extra_q/youtube_audio/vocals.wav"
- else:
- raise gr.Error("URL Required!")
- return None, None, None, None
-
-def combine_vocal_and_inst(audio_data, audio_volume, split_model):
- if not os.path.exists("output/result"):
- os.mkdir("output/result")
- vocal_path = "output/result/output.wav"
- output_path = "output/result/combine.mp3"
- if split_model == "htdemucs":
- inst_path = "output/htdemucs/youtube_audio/no_vocals.wav"
- else:
- inst_path = "output/mdx_extra_q/youtube_audio/no_vocals.wav"
- with wave.open(vocal_path, "w") as wave_file:
- wave_file.setnchannels(1)
- wave_file.setsampwidth(2)
- wave_file.setframerate(audio_data[0])
- wave_file.writeframes(audio_data[1].tobytes())
- command = f'ffmpeg -y -i {inst_path} -i {vocal_path} -filter_complex [1:a]volume={audio_volume}dB[v];[0:a][v]amix=inputs=2:duration=longest -b:a 320k -c:a libmp3lame {output_path}'
- result = subprocess.run(command.split(), stdout=subprocess.PIPE)
- print(result.stdout.decode())
- return output_path
-
-def load_hubert():
- global hubert_model
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(config.device)
- if config.is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- hubert_model.eval()
-
-def change_audio_mode(vc_audio_mode):
- if vc_audio_mode == "Input path":
- return (
- # Input & Upload
- gr.Textbox.update(visible=True),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "Upload audio":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=True),
- gr.Audio.update(visible=True),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "Youtube":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=True),
- gr.Textbox.update(visible=True),
- gr.Dropdown.update(visible=True),
- gr.Button.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Slider.update(visible=True),
- gr.Audio.update(visible=True),
- gr.Button.update(visible=True),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
- elif vc_audio_mode == "TTS Audio":
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=False),
- gr.Audio.update(visible=False),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=True),
- gr.Dropdown.update(visible=True)
- )
- else:
- return (
- # Input & Upload
- gr.Textbox.update(visible=False),
- gr.Checkbox.update(visible=True),
- gr.Audio.update(visible=True),
- # Youtube
- gr.Dropdown.update(visible=False),
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False),
- gr.Button.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Slider.update(visible=False),
- gr.Audio.update(visible=False),
- gr.Button.update(visible=False),
- # TTS
- gr.Textbox.update(visible=False),
- gr.Dropdown.update(visible=False)
- )
-
-def use_microphone(microphone):
- if microphone == True:
- return gr.Audio.update(source="microphone")
- else:
- return gr.Audio.update(source="upload")
-
-if __name__ == '__main__':
- load_hubert()
- categories = load_model()
- tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
- voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
- with gr.Blocks() as app:
- gr.Markdown(
- "\n\n"+
- "# RVC V2 MODELS GENSHIN IMPACT\n\n"+
- "### Recommended to use Google Colab to use other character and feature.\n\n"+
- "#### All of this voice samples are taken from the game Genshin Impact, and all voice credits belong to hoyoverse.\n\n"+
- "[](https://colab.research.google.com/drive/1EGHCk7wluqMX2krZhPI13Vhs21e07kOv)\n\n"+
- "
\n\n"+
- "[](https://github.com/ArkanDash/Multi-Model-RVC-Inference)"
- )
- for (folder_title, folder, description, models) in categories:
- with gr.TabItem(folder_title):
- if description:
- gr.Markdown(f"### {description}")
- with gr.Tabs():
- if not models:
- gr.Markdown("# No Model Loaded.")
- gr.Markdown("## Please add model or fix your model path.")
- continue
- for (name, title, author, cover, model_version, vc_fn) in models:
- with gr.TabItem(name):
- with gr.Row():
- gr.Markdown(
- ''
- f'
{title}
\n'+
- f'
RVC {model_version} Model
\n'+
- (f'
Model author: {author}
' if author else "")+
- (f'
' if cover else "")+
- '
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Do You Want to fine-tune a SD model?
-Strategy Talk
-Certains Certains Certains
-
-
-
-
{}
-
-const Input = React.forwardRef(
- ({ className, type, ...props }, ref) => {
- return (
- GAN Prior Embedded Network for Blind Face Restoration in the Wild | Github Repo
"
-
-
-gr.Interface(
- inference,
- [gr.inputs.Image(type="filepath", label="Input"),gr.inputs.Radio(["enhance", "colorize", "inpainting", "selfie", "enhanced+background"], type="value", default="enhance", label="Type")],
- gr.outputs.Image(type="filepath", label="Output"),
- title=title,
- description=description,
- article=article,
- examples=[
- ['enhance.png', 'enhance'],
- ['color.png', 'colorize'],
- ['inpainting.png', 'inpainting'],
- ['selfie.png', 'selfie']
- ],
- enable_queue=True
- ).launch()
\ No newline at end of file
diff --git a/spaces/Kluuking/google-vit-base/README.md b/spaces/Kluuking/google-vit-base/README.md
deleted file mode 100644
index b40f288fe390d4618ddf8c3cb339049973c1f851..0000000000000000000000000000000000000000
--- a/spaces/Kluuking/google-vit-base/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Google Vit Base
-emoji: 👁
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/stores/oandastore.py b/spaces/Lianjd/stock_dashboard/backtrader/stores/oandastore.py
deleted file mode 100644
index a549462f28c0d0faec9ae2ba31d70eabf518b575..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/stores/oandastore.py
+++ /dev/null
@@ -1,659 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see
-#include
-
-#include "libipc/shm.h"
-
-#include "libipc/utility/pimpl.h"
-#include "libipc/memory/resource.h"
-
-namespace ipc {
-namespace shm {
-
-class handle::handle_ : public pimpl {
-public:
- shm::id_t id_ = nullptr;
- void* m_ = nullptr;
-
- ipc::string n_;
- std::size_t s_ = 0;
-};
-
-handle::handle()
- : p_(p_->make()) {
-}
-
-handle::handle(char const * name, std::size_t size, unsigned mode)
- : handle() {
- acquire(name, size, mode);
-}
-
-handle::handle(handle&& rhs)
- : handle() {
- swap(rhs);
-}
-
-handle::~handle() {
- release();
- p_->clear();
-}
-
-void handle::swap(handle& rhs) {
- std::swap(p_, rhs.p_);
-}
-
-handle& handle::operator=(handle rhs) {
- swap(rhs);
- return *this;
-}
-
-bool handle::valid() const noexcept {
- return impl(p_)->m_ != nullptr;
-}
-
-std::size_t handle::size() const noexcept {
- return impl(p_)->s_;
-}
-
-char const * handle::name() const noexcept {
- return impl(p_)->n_.c_str();
-}
-
-std::int32_t handle::ref() const noexcept {
- return shm::get_ref(impl(p_)->id_);
-}
-
-void handle::sub_ref() noexcept {
- shm::sub_ref(impl(p_)->id_);
-}
-
-bool handle::acquire(char const * name, std::size_t size, unsigned mode) {
- release();
- impl(p_)->id_ = shm::acquire((impl(p_)->n_ = name).c_str(), size, mode);
- impl(p_)->m_ = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
- return valid();
-}
-
-std::int32_t handle::release() {
- if (impl(p_)->id_ == nullptr) return -1;
- return shm::release(detach());
-}
-
-void* handle::get() const {
- return impl(p_)->m_;
-}
-
-void handle::attach(id_t id) {
- if (id == nullptr) return;
- release();
- impl(p_)->id_ = id;
- impl(p_)->m_ = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
-}
-
-id_t handle::detach() {
- auto old = impl(p_)->id_;
- impl(p_)->id_ = nullptr;
- impl(p_)->m_ = nullptr;
- impl(p_)->s_ = 0;
- impl(p_)->n_.clear();
- return old;
-}
-
-} // namespace shm
-} // namespace ipc
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_sgd_1500e.py b/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_sgd_1500e.py
deleted file mode 100644
index 3368175eceafdd019087461c51643a08e2e06d95..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/_base_/schedules/schedule_sgd_1500e.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=1e-3, momentum=0.90, weight_decay=5e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='poly', power=0.9, min_lr=1e-7, by_epoch=True)
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=1500)
-checkpoint_config = dict(interval=100)
diff --git a/spaces/LuxOAI/ChatGpt-Web/app/store/config.ts b/spaces/LuxOAI/ChatGpt-Web/app/store/config.ts
deleted file mode 100644
index 6d81c10072fdd7aeac981655edf53a79ad0222b7..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/app/store/config.ts
+++ /dev/null
@@ -1,206 +0,0 @@
-import { create } from "zustand";
-import { persist } from "zustand/middleware";
-import { StoreKey } from "../constant";
-
-export enum SubmitKey {
- Enter = "Enter",
- CtrlEnter = "Ctrl + Enter",
- ShiftEnter = "Shift + Enter",
- AltEnter = "Alt + Enter",
- MetaEnter = "Meta + Enter",
-}
-
-export enum Theme {
- Auto = "auto",
- Dark = "dark",
- Light = "light",
-}
-
-export const DEFAULT_CONFIG = {
- bot: "Lemur" as BotType,
- submitKey: SubmitKey.Enter as SubmitKey,
- avatar: "1f603",
- fontSize: 14,
- theme: Theme.Auto as Theme,
- tightBorder: false,
- sendPreviewBubble: true,
- sidebarWidth: 300,
-
- disablePromptHint: false,
-
- dontShowMaskSplashScreen: false, // dont show splash screen when create chat
-
- modelConfig: {
- model: "gpt-3.5-turbo" as ModelType,
- temperature: 1,
- max_tokens: 2000,
- presence_penalty: 0,
- sendMemory: true,
- historyMessageCount: 8,
- compressMessageLengthThreshold: 1000,
- },
-};
-
-export type ChatConfig = typeof DEFAULT_CONFIG;
-
-export type ChatConfigStore = ChatConfig & {
- reset: () => void;
- update: (updater: (config: ChatConfig) => void) => void;
-};
-
-export type ModelConfig = ChatConfig["modelConfig"];
-
-const ENABLE_GPT4 = true;
-
-export const ALL_MODELS = [
- {
- name: "gpt-4",
- available: ENABLE_GPT4,
- },
- {
- name: "gpt-4-0314",
- available: ENABLE_GPT4,
- },
- {
- name: "gpt-4-32k",
- available: ENABLE_GPT4,
- },
- {
- name: "gpt-4-32k-0314",
- available: ENABLE_GPT4,
- },
- {
- name: "gpt-3.5-turbo",
- available: true,
- },
- {
- name: "gpt-3.5-turbo-0301",
- available: true,
- },
- {
- name: "qwen-v1", // 通义千问
- available: false,
- },
- {
- name: "ernie", // 文心一言
- available: false,
- },
- {
- name: "spark", // 讯飞星火
- available: false,
- },
- {
- name: "llama", // llama
- available: false,
- },
- {
- name: "chatglm", // chatglm-6b
- available: false,
- },
-] as const;
-
-export const ALL_BOT = [
- {
- name: "OpenAI",
- available: true,
- },
- {
- name: "OpenAI绘画",
- available: true,
- },
- {
- name: "必应",
- available: true,
- },
- // {
- // name: "必应绘画",
- // available: true,
- // },
- {
- name: "万卷",
- available: true,
- },
- {
- name: "Lemur",
- available: true,
- },
-];
-
-export type BotType = (typeof ALL_BOT)[number]["name"];
-export type ModelType = (typeof ALL_MODELS)[number]["name"];
-
-export function limitNumber(
- x: number,
- min: number,
- max: number,
- defaultValue: number,
-) {
- if (typeof x !== "number" || isNaN(x)) {
- return defaultValue;
- }
-
- return Math.min(max, Math.max(min, x));
-}
-
-export function limitModel(name: string) {
- return ALL_MODELS.some((m) => m.name === name && m.available)
- ? name
- : ALL_MODELS[4].name;
-}
-
-export function limitBot(name: string) {
- return ALL_BOT.some((m) => m.name === name && m.available)
- ? name
- : ALL_BOT[4].name;
-}
-
-export const ModalConfigValidator = {
- bot(x: string) {
- return limitBot(x) as BotType;
- },
- model(x: string) {
- return limitModel(x) as ModelType;
- },
- max_tokens(x: number) {
- return limitNumber(x, 0, 32000, 2000);
- },
- presence_penalty(x: number) {
- return limitNumber(x, -2, 2, 0);
- },
- temperature(x: number) {
- return limitNumber(x, 0, 1, 1);
- },
-};
-
-export const useAppConfig = create()(
- persist(
- (set, get) => ({
- ...DEFAULT_CONFIG,
-
- reset() {
- set(() => ({ ...DEFAULT_CONFIG }));
- },
-
- update(updater) {
- const config = { ...get() };
- updater(config);
- set(() => config);
- },
- }),
- {
- name: StoreKey.Config,
- version: 2,
- migrate(persistedState, version) {
- if (version === 2) return persistedState as any;
-
- const state = persistedState as ChatConfig;
- state.modelConfig.sendMemory = true;
- state.modelConfig.historyMessageCount = 4;
- state.modelConfig.compressMessageLengthThreshold = 1000;
- state.dontShowMaskSplashScreen = false;
-
- return state;
- },
- },
- ),
-);
diff --git a/spaces/Lynx1221/rvc-test1/infer_pack/attentions.py b/spaces/Lynx1221/rvc-test1/infer_pack/attentions.py
deleted file mode 100644
index 77cb63ffccf3e33badf22d50862a64ba517b487f..0000000000000000000000000000000000000000
--- a/spaces/Lynx1221/rvc-test1/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer_pack import commons
-from infer_pack import modules
-from infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- window_size=10,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- window_size=window_size,
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- proximal_bias=False,
- proximal_init=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- proximal_bias=proximal_bias,
- proximal_init=proximal_init,
- )
- )
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(
- MultiHeadAttention(
- hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- causal=True,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
- device=x.device, dtype=x.dtype
- )
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(
- self,
- channels,
- out_channels,
- n_heads,
- p_dropout=0.0,
- window_size=None,
- heads_share=True,
- block_length=None,
- proximal_bias=False,
- proximal_init=False,
- ):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
- self.emb_rel_v = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert (
- t_s == t_t
- ), "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(
- query / math.sqrt(self.k_channels), key_relative_embeddings
- )
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(
- device=scores.device, dtype=scores.dtype
- )
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert (
- t_s == t_t
- ), "Local attention is only available for self-attention."
- block_mask = (
- torch.ones_like(scores)
- .triu(-self.block_length)
- .tril(self.block_length)
- )
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(
- self.emb_rel_v, t_s
- )
- output = output + self._matmul_with_relative_values(
- relative_weights, value_relative_embeddings
- )
- output = (
- output.transpose(2, 3).contiguous().view(b, d, t_t)
- ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
- )
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[
- :, slice_start_position:slice_end_position
- ]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(
- x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
- )
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
- :, :, :length, length - 1 :
- ]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(
- x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
- )
- x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- filter_channels,
- kernel_size,
- p_dropout=0.0,
- activation=None,
- causal=False,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/Mahiruoshi/BangDream-Bert-VITS2/utils.py b/spaces/Mahiruoshi/BangDream-Bert-VITS2/utils.py
deleted file mode 100644
index 5f98aafadb83a9f341d6d9d3401c6c3101485b4e..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/BangDream-Bert-VITS2/utils.py
+++ /dev/null
@@ -1,356 +0,0 @@
-import os
-import glob
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logger = logging.getLogger(__name__)
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
- iteration = checkpoint_dict["iteration"]
- learning_rate = checkpoint_dict["learning_rate"]
- if (
- optimizer is not None
- and not skip_optimizer
- and checkpoint_dict["optimizer"] is not None
- ):
- optimizer.load_state_dict(checkpoint_dict["optimizer"])
- elif optimizer is None and not skip_optimizer:
- # else: Disable this line if Infer and resume checkpoint,then enable the line upper
- new_opt_dict = optimizer.state_dict()
- new_opt_dict_params = new_opt_dict["param_groups"][0]["params"]
- new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"]
- new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params
- optimizer.load_state_dict(new_opt_dict)
-
- saved_state_dict = checkpoint_dict["model"]
- if hasattr(model, "module"):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
-
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- # assert "emb_g" not in k
- new_state_dict[k] = saved_state_dict[k]
- assert saved_state_dict[k].shape == v.shape, (
- saved_state_dict[k].shape,
- v.shape,
- )
- except:
- # For upgrading from the old version
- if "ja_bert_proj" in k:
- v = torch.zeros_like(v)
- logger.warn(
- f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility"
- )
- else:
- logger.error(f"{k} is not in the checkpoint")
-
- new_state_dict[k] = v
-
- if hasattr(model, "module"):
- model.module.load_state_dict(new_state_dict, strict=False)
- else:
- model.load_state_dict(new_state_dict, strict=False)
-
- logger.info(
- "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration)
- )
-
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info(
- "Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path
- )
- )
- if hasattr(model, "module"):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save(
- {
- "model": state_dict,
- "iteration": iteration,
- "optimizer": optimizer.state_dict(),
- "learning_rate": learning_rate,
- },
- checkpoint_path,
- )
-
-
-def summarize(
- writer,
- global_step,
- scalars={},
- histograms={},
- images={},
- audios={},
- audio_sampling_rate=22050,
-):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats="HWC")
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
-
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger("matplotlib")
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
-
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger("matplotlib")
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(
- alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
- )
- fig.colorbar(im, ax=ax)
- xlabel = "Decoder timestep"
- if info is not None:
- xlabel += "\n\n" + info
- plt.xlabel(xlabel)
- plt.ylabel("Encoder timestep")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding="utf-8") as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "-c",
- "--config",
- type=str,
- default="./configs/base.json",
- help="JSON file for configuration",
- )
- parser.add_argument("-m", "--model", type=str, required=True, help="Model name")
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- with open(config_save_path, "w", encoding="utf-8") as f:
- f.write(data)
- else:
- with open(config_save_path, "r", vencoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True):
- """Freeing up space by deleting saved ckpts
-
- Arguments:
- path_to_models -- Path to the model directory
- n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
- sort_by_time -- True -> chronologically delete ckpts
- False -> lexicographically delete ckpts
- """
- import re
-
- ckpts_files = [
- f
- for f in os.listdir(path_to_models)
- if os.path.isfile(os.path.join(path_to_models, f))
- ]
-
- def name_key(_f):
- return int(re.compile("._(\\d+)\\.pth").match(_f).group(1))
-
- def time_key(_f):
- return os.path.getmtime(os.path.join(path_to_models, _f))
-
- sort_key = time_key if sort_by_time else name_key
-
- def x_sorted(_x):
- return sorted(
- [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")],
- key=sort_key,
- )
-
- to_del = [
- os.path.join(path_to_models, fn)
- for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep])
- ]
-
- def del_info(fn):
- return logger.info(f".. Free up space by deleting ckpt {fn}")
-
- def del_routine(x):
- return [os.remove(x), del_info(x)]
-
- [del_routine(fn) for fn in to_del]
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn(
- "{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- )
- )
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn(
- "git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]
- )
- )
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams:
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/modeling/__init__.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/model/modeling/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/interaction.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/interaction.py
deleted file mode 100644
index 19f83f9d58a00cac079a7ba5c239196378603b64..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/interaction.py
+++ /dev/null
@@ -1,252 +0,0 @@
-"""
-Contains all the types of interaction related to the GUI
-Not related to automatic evaluation in the DAVIS dataset
-
-You can inherit the Interaction class to create new interaction types
-undo is (sometimes partially) supported
-"""
-
-
-import torch
-import torch.nn.functional as F
-import numpy as np
-import cv2
-import time
-from .interactive_utils import color_map, index_numpy_to_one_hot_torch
-
-
-def aggregate_sbg(prob, keep_bg=False, hard=False):
- device = prob.device
- k, h, w = prob.shape
- ex_prob = torch.zeros((k+1, h, w), device=device)
- ex_prob[0] = 0.5
- ex_prob[1:] = prob
- ex_prob = torch.clamp(ex_prob, 1e-7, 1-1e-7)
- logits = torch.log((ex_prob /(1-ex_prob)))
-
- if hard:
- # Very low temperature o((⊙﹏⊙))o 🥶
- logits *= 1000
-
- if keep_bg:
- return F.softmax(logits, dim=0)
- else:
- return F.softmax(logits, dim=0)[1:]
-
-def aggregate_wbg(prob, keep_bg=False, hard=False):
- k, h, w = prob.shape
- new_prob = torch.cat([
- torch.prod(1-prob, dim=0, keepdim=True),
- prob
- ], 0).clamp(1e-7, 1-1e-7)
- logits = torch.log((new_prob /(1-new_prob)))
-
- if hard:
- # Very low temperature o((⊙﹏⊙))o 🥶
- logits *= 1000
-
- if keep_bg:
- return F.softmax(logits, dim=0)
- else:
- return F.softmax(logits, dim=0)[1:]
-
-class Interaction:
- def __init__(self, image, prev_mask, true_size, controller):
- self.image = image
- self.prev_mask = prev_mask
- self.controller = controller
- self.start_time = time.time()
-
- self.h, self.w = true_size
-
- self.out_prob = None
- self.out_mask = None
-
- def predict(self):
- pass
-
-
-class FreeInteraction(Interaction):
- def __init__(self, image, prev_mask, true_size, num_objects):
- """
- prev_mask should be index format numpy array
- """
- super().__init__(image, prev_mask, true_size, None)
-
- self.K = num_objects
-
- self.drawn_map = self.prev_mask.copy()
- self.curr_path = [[] for _ in range(self.K + 1)]
-
- self.size = None
-
- def set_size(self, size):
- self.size = size
-
- """
- k - object id
- vis - a tuple (visualization map, pass through alpha). None if not needed.
- """
- def push_point(self, x, y, k, vis=None):
- if vis is not None:
- vis_map, vis_alpha = vis
- selected = self.curr_path[k]
- selected.append((x, y))
- if len(selected) >= 2:
- cv2.line(self.drawn_map,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- k, thickness=self.size)
-
- # Plot visualization
- if vis is not None:
- # Visualization for drawing
- if k == 0:
- vis_map = cv2.line(vis_map,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- color_map[k], thickness=self.size)
- else:
- vis_map = cv2.line(vis_map,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- color_map[k], thickness=self.size)
- # Visualization on/off boolean filter
- vis_alpha = cv2.line(vis_alpha,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- 0.75, thickness=self.size)
-
- if vis is not None:
- return vis_map, vis_alpha
-
- def end_path(self):
- # Complete the drawing
- self.curr_path = [[] for _ in range(self.K + 1)]
-
- def predict(self):
- self.out_prob = index_numpy_to_one_hot_torch(self.drawn_map, self.K+1).cuda()
- # self.out_prob = torch.from_numpy(self.drawn_map).float().cuda()
- # self.out_prob, _ = pad_divide_by(self.out_prob, 16, self.out_prob.shape[-2:])
- # self.out_prob = aggregate_sbg(self.out_prob, keep_bg=True)
- return self.out_prob
-
-class ScribbleInteraction(Interaction):
- def __init__(self, image, prev_mask, true_size, controller, num_objects):
- """
- prev_mask should be in an indexed form
- """
- super().__init__(image, prev_mask, true_size, controller)
-
- self.K = num_objects
-
- self.drawn_map = np.empty((self.h, self.w), dtype=np.uint8)
- self.drawn_map.fill(255)
- # background + k
- self.curr_path = [[] for _ in range(self.K + 1)]
- self.size = 3
-
- """
- k - object id
- vis - a tuple (visualization map, pass through alpha). None if not needed.
- """
- def push_point(self, x, y, k, vis=None):
- if vis is not None:
- vis_map, vis_alpha = vis
- selected = self.curr_path[k]
- selected.append((x, y))
- if len(selected) >= 2:
- self.drawn_map = cv2.line(self.drawn_map,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- k, thickness=self.size)
-
- # Plot visualization
- if vis is not None:
- # Visualization for drawing
- if k == 0:
- vis_map = cv2.line(vis_map,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- color_map[k], thickness=self.size)
- else:
- vis_map = cv2.line(vis_map,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- color_map[k], thickness=self.size)
- # Visualization on/off boolean filter
- vis_alpha = cv2.line(vis_alpha,
- (int(round(selected[-2][0])), int(round(selected[-2][1]))),
- (int(round(selected[-1][0])), int(round(selected[-1][1]))),
- 0.75, thickness=self.size)
-
- # Optional vis return
- if vis is not None:
- return vis_map, vis_alpha
-
- def end_path(self):
- # Complete the drawing
- self.curr_path = [[] for _ in range(self.K + 1)]
-
- def predict(self):
- self.out_prob = self.controller.interact(self.image.unsqueeze(0), self.prev_mask, self.drawn_map)
- self.out_prob = aggregate_wbg(self.out_prob, keep_bg=True, hard=True)
- return self.out_prob
-
-
-class ClickInteraction(Interaction):
- def __init__(self, image, prev_mask, true_size, controller, tar_obj):
- """
- prev_mask in a prob. form
- """
- super().__init__(image, prev_mask, true_size, controller)
- self.tar_obj = tar_obj
-
- # negative/positive for each object
- self.pos_clicks = []
- self.neg_clicks = []
-
- self.out_prob = self.prev_mask.clone()
-
- """
- neg - Negative interaction or not
- vis - a tuple (visualization map, pass through alpha). None if not needed.
- """
- def push_point(self, x, y, neg, vis=None):
- # Clicks
- if neg:
- self.neg_clicks.append((x, y))
- else:
- self.pos_clicks.append((x, y))
-
- # Do the prediction
- self.obj_mask = self.controller.interact(self.image.unsqueeze(0), x, y, not neg)
-
- # Plot visualization
- if vis is not None:
- vis_map, vis_alpha = vis
- # Visualization for clicks
- if neg:
- vis_map = cv2.circle(vis_map,
- (int(round(x)), int(round(y))),
- 2, color_map[0], thickness=-1)
- else:
- vis_map = cv2.circle(vis_map,
- (int(round(x)), int(round(y))),
- 2, color_map[self.tar_obj], thickness=-1)
-
- vis_alpha = cv2.circle(vis_alpha,
- (int(round(x)), int(round(y))),
- 2, 1, thickness=-1)
-
- # Optional vis return
- return vis_map, vis_alpha
-
- def predict(self):
- self.out_prob = self.prev_mask.clone()
- # a small hack to allow the interacting object to overwrite existing masks
- # without remembering all the object probabilities
- self.out_prob = torch.clamp(self.out_prob, max=0.9)
- self.out_prob[self.tar_obj] = self.obj_mask
- self.out_prob = aggregate_wbg(self.out_prob[1:], keep_bg=True, hard=True)
- return self.out_prob
diff --git a/spaces/Makiing/coolb-in-gtest/src/lib/isomorphic/browser.ts b/spaces/Makiing/coolb-in-gtest/src/lib/isomorphic/browser.ts
deleted file mode 100644
index de125b1f1786d1618cb1ff47f403d76c6784f4ce..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/src/lib/isomorphic/browser.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-'use client'
-
-const debug = console.info.bind(console)
-
-class WebSocketAlias extends WebSocket {
- constructor(address: string | URL, ...args: any) {
- super(address)
- }
-}
-
-export default { fetch, WebSocket: WebSocketAlias, debug }
diff --git a/spaces/MarcCote/ScienceWorld/app.py b/spaces/MarcCote/ScienceWorld/app.py
deleted file mode 100644
index 2de72d337cd0dd545a7c2dbf0cd5581c575e508c..0000000000000000000000000000000000000000
--- a/spaces/MarcCote/ScienceWorld/app.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import streamlit as st
-import streamlit.components.v1 as components
-
-from scienceworld import ScienceWorldEnv
-
-description = """
-[Project Page](https://sciworld.apps.allenai.org) | [ArXiv Paper](https://arxiv.org/abs/2203.07540) | [Github Repo](https://github.com/allenai/ScienceWorld)
-"""
-st.title("ScienceWorld Demo")
-st.markdown(description)
-
-# Apply custom CSS.
-with open('style.css')as f:
- st.markdown(f"", unsafe_allow_html=True)
-
-env = st.session_state.get("env")
-if env is None:
- env = ScienceWorldEnv("")
- st.session_state["env"] = env
-
-seed = st.session_state.get("seed")
-obs = st.session_state.get("obs")
-infos = st.session_state.get("infos")
-history = st.session_state.get("history")
-if history is None:
- history = []
- st.session_state["history"] = history
-
-def clear_history():
- history.clear()
-
-
-with st.sidebar:
- st.title("ScienceWorld Demo")
- st.markdown(description)
- task = st.selectbox("Task:", env.getTaskNames(), on_change=clear_history)
-
-if len(history) == 0:
- env.load(task, 0, "")
- obs, infos = env.reset()
- st.session_state["obs"] = obs
- st.session_state["infos"] = infos
- history.append(("", env.getTaskDescription()))
- history.append(("look around", obs))
-
-def step():
- act = st.session_state.action
- if act:
- obs, reward, done, infos = env.step(act)
- history.append((act, obs))
- st.session_state["obs"] = obs
- st.session_state["infos"] = infos
-
- if act == "reset":
- clear_history()
-
-
-with st.sidebar:
- st.warning(env.getTaskDescription())
- st.success(f"Score: {infos['score']}")
-
- valid_actions = [""] + sorted(infos["valid"])
- if infos['score'] == 100:
- valid_actions = ["", "reset"]
-
- # act = st.selectbox('Action:', options=valid_actions, index=0, on_change=step, key="action")
-
-for act, obs in history:
- if act:
- st.write("> " + act)
-
- if obs:
- st.info(obs.replace('\n\t', '\n- '))
-
-act = st.selectbox('Action:', options=valid_actions, index=0, on_change=step, key="action")
-
-st.warning(f"Current score: {infos['score']} out of 100")
-
-if infos['score'] == 100:
- with st.sidebar:
- st.balloons()
-
- st.success("Congratulations! You have completed the task.")
-
-
-# Auto scroll at the bottom of the page.
-components.html(
-f"""
- {st.session_state.obs}
-
-
-# """,
-height=0
-)
diff --git a/spaces/Marshalls/testmtd/models/__init__.py b/spaces/Marshalls/testmtd/models/__init__.py
deleted file mode 100644
index d119db37faa327a3bd57a60e28ac1e7ec90a507d..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/models/__init__.py
+++ /dev/null
@@ -1,45 +0,0 @@
-print("HIIIIIIOOO")
-import importlib
-from .base_model import BaseModel
-print("HIIIIIIOOO")
-
-def find_model_using_name(model_name):
- # Given the option --model [modelname],
- # the file "models/modelname_model.py"
- # will be imported.
- # task_module = importlib.import_module(task_name)
- # model_filename = task_name + ".models." + model_name.lower() + "_model"
- model_filename = "models." + model_name.lower() + "_model"
- # modellib = importlib.import_module(model_filename, package=task_module)
- modellib = importlib.import_module(model_filename)
-
- # In the file, the class called ModelNameModel() will
- # be instantiated. It has to be a subclass of BaseModel,
- # and it is case-insensitive.
- model = None
- target_model_name = model_name.replace('_', '') + 'model'
- for name, cls in modellib.__dict__.items():
- if name.lower() == target_model_name.lower() \
- and next(iter(cls.__bases__)).__module__.endswith(BaseModel.__module__): # check that base class is BaseModel
- model = cls
-
- if model is None:
- raise NotImplementedError("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
-
- return model
-
-
-def get_option_setter(model_name):
- model_class = find_model_using_name(model_name)
- return model_class.modify_commandline_options
-
-
-def create_model(opt):
- instance = create_model_by_name(opt.model, opt)
- return instance
-
-def create_model_by_name(name, opt):
- model = find_model_using_name(name)
- instance = model(opt)
- print("model [%s] was created" % (instance.name()))
- return instance
diff --git a/spaces/Marshalls/testmtd/models/flowplusplus/__init__.py b/spaces/Marshalls/testmtd/models/flowplusplus/__init__.py
deleted file mode 100644
index cc21fd8d0d6301b267c3640a7ca34957cb1d909f..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/models/flowplusplus/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from models.flowplusplus.flowplusplus import FlowPlusPlus
diff --git a/spaces/MathysL/AutoGPT4/benchmark/__init__.py b/spaces/MathysL/AutoGPT4/benchmark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Matthijs/image2reverb/image2reverb/networks.py b/spaces/Matthijs/image2reverb/image2reverb/networks.py
deleted file mode 100644
index d31ae74588b021f2ef201fcb80a79249a79963fc..0000000000000000000000000000000000000000
--- a/spaces/Matthijs/image2reverb/image2reverb/networks.py
+++ /dev/null
@@ -1,344 +0,0 @@
-import os
-import numpy
-import torch
-import torch.nn as nn
-import torchvision.models as models
-import torch.utils.model_zoo as model_zoo
-from collections import OrderedDict
-from .layers import PixelWiseNormLayer, MiniBatchAverageLayer, EqualizedLearningRateLayer, Conv3x3, ConvBlock, upsample
-
-
-class Encoder(nn.Module):
- """Load encoder from pre-trained ResNet50 (places365 CNNs) model. Link: http://places2.csail.mit.edu/models_places365/resnet50_places365.pth.tar"""
- def __init__(self, model_weights, depth_model, constant_depth=None, device="cuda", train_enc=True):
- super().__init__()
- self.device = device
- self._constant_depth = constant_depth
- self.model = models.resnet50(num_classes=365)
-
- if model_weights:
- c = torch.load(model_weights, map_location=self.device)
- state_dict = {k.replace("module.", ""): v for k, v in c["state_dict"].items()}
- self.model.load_state_dict(state_dict)
-
- self._has_depth = False
- if depth_model:
- f = self.model.conv1.weight
- self.model.conv1.weight = torch.nn.Parameter(torch.cat((f, torch.randn(64, 1, 7, 7)), 1))
- self.model.to(self.device)
-
- encoder_path = os.path.join(depth_model, "encoder.pth")
- depth_decoder_path = os.path.join(depth_model, "depth.pth")
- self.depth_encoder = ResnetEncoder(18, False)
- loaded_dict_enc = torch.load(encoder_path, map_location=self.device)
-
- self.feed_height = loaded_dict_enc["height"]
- self.feed_width = loaded_dict_enc["width"]
- filtered_dict_enc = {k: v for k, v in loaded_dict_enc.items() if k in self.depth_encoder.state_dict()}
- self.depth_encoder.load_state_dict(filtered_dict_enc)
- self.depth_encoder.to(self.device)
- self.depth_encoder.eval()
-
- self.depth_decoder = DepthDecoder(num_ch_enc=self.depth_encoder.num_ch_enc, scales=range(4))
- loaded_dict = torch.load(depth_decoder_path, map_location=self.device)
- self.depth_decoder.load_state_dict(loaded_dict, strict=False)
- self.depth_decoder.to(self.device)
- self.depth_decoder.eval()
-
- self._has_depth = True
-
- if train_enc:
- self.model.train()
-
- def forward(self, x):
- if self._has_depth:
- d = torch.full((x.shape[0], 1, x.shape[2], x.shape[3]), self._constant_depth, device=x.device) if self._constant_depth is not None else list(self.depth_decoder(self.depth_encoder(x)).values())[-1]
- x = torch.cat((x, d), 1)
- return self.model.forward(x).unsqueeze(-1).unsqueeze(-1), x
-
-
-class Generator(nn.Module):
- """Build non-progressive variant of GANSynth generator."""
- def __init__(self, latent_size=512, mel_spec=False): # Encoder output should contain 2048 values
- super().__init__()
- self.latent_size = latent_size
- self._mel_spec = mel_spec
- self.build_model()
-
- def forward(self, x):
- return self.model(x)
-
- def build_model(self):
- model = []
- # Input block
- if self._mel_spec:
- model.append(nn.Conv2d(self.latent_size, 256, kernel_size=(4, 2), stride=1, padding=2, bias=False))
- else:
- model.append(nn.Conv2d(self.latent_size, 256, kernel_size=8, stride=1, padding=7, bias=False)) # Modified to k=8, p=7 for our image dimensions (i.e. 512x512)
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Upsample(scale_factor=2, mode="nearest"))
-
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Upsample(scale_factor=2, mode="nearest"))
-
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Upsample(scale_factor=2, mode="nearest"))
-
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Upsample(scale_factor=2, mode="nearest"))
-
- model.append(nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Upsample(scale_factor=2, mode="nearest"))
-
- model.append(nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Upsample(scale_factor=2, mode="nearest"))
-
- model.append(nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
- model.append(nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(PixelWiseNormLayer())
-
- model.append(nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.Tanh())
- self.model = nn.Sequential(*model)
-
-
-class Discriminator(nn.Module):
- def __init__(self, label_size=365, mel_spec=False):
- super().__init__()
- self._label_size = 365
- self._mel_spec = mel_spec
- self.build_model()
-
- def forward(self, x, l):
- d = self.model(x)
- if self._mel_spec:
- s = list(l.squeeze().shape)
- s[-1] = 19
- z = torch.cat((l.squeeze(), torch.zeros(s).type_as(x)), -1).reshape(d.shape[0], -1, 2, 4)
- else:
- s = list(l.squeeze().shape)
- s[-1] = 512 - s[-1]
- z = torch.cat((l.squeeze(), torch.zeros(s).type_as(x)), -1).reshape(d.shape[0], -1, 8, 8)
- k = torch.cat((d, z), 1)
- return self.output(k)
-
- def build_model(self):
- model = []
- model.append(nn.Conv2d(1, 32, kernel_size=1, stride=1, padding=0, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=False, count_include_pad=False))
-
- model.append(nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=False, count_include_pad=False))
-
- model.append(nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=False, count_include_pad=False))
-
- model.append(nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=False, count_include_pad=False))
-
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=False, count_include_pad=False))
-
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=False, count_include_pad=False))
-
- model.append(MiniBatchAverageLayer())
- model.append(nn.Conv2d(257, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
- model.append(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False))
- model.append(EqualizedLearningRateLayer(model[-1]))
- model.append(nn.LeakyReLU(negative_slope=0.2))
-
- output = [] # After the label concatenation
- if self._mel_spec:
- output.append(nn.Conv2d(304, 256, kernel_size=1, stride=1, padding=0, bias=False))
- else:
- output.append(nn.Conv2d(264, 256, kernel_size=1, stride=1, padding=0, bias=False))
-
- output.append(nn.Conv2d(256, 1, kernel_size=1, stride=1, padding=0, bias=False))
-
- # model.append(nn.Sigmoid()) # Output probability (in [0, 1])
- self.model = nn.Sequential(*model)
- self.output = nn.Sequential(*output)
-
-
-class ResnetEncoder(nn.Module):
- """Pytorch module for a resnet encoder
- """
- def __init__(self, num_layers, pretrained, num_input_images=1):
- super(ResnetEncoder, self).__init__()
-
- self.num_ch_enc = numpy.array([64, 64, 128, 256, 512])
-
- resnets = {18: models.resnet18,
- 34: models.resnet34,
- 50: models.resnet50,
- 101: models.resnet101,
- 152: models.resnet152}
-
- if num_layers not in resnets:
- raise ValueError("{} is not a valid number of resnet layers".format(num_layers))
-
- if num_input_images > 1:
- self.encoder = resnet_multiimage_input(num_layers, pretrained, num_input_images)
- else:
- self.encoder = resnets[num_layers](pretrained)
-
- if num_layers > 34:
- self.num_ch_enc[1:] *= 4
-
- def forward(self, input_image):
- self.features = []
- x = (input_image - 0.45) / 0.225
- x = self.encoder.conv1(x)
- x = self.encoder.bn1(x)
- self.features.append(self.encoder.relu(x))
- self.features.append(self.encoder.layer1(self.encoder.maxpool(self.features[-1])))
- self.features.append(self.encoder.layer2(self.features[-1]))
- self.features.append(self.encoder.layer3(self.features[-1]))
- self.features.append(self.encoder.layer4(self.features[-1]))
-
- return self.features
-
-
-
-class DepthDecoder(nn.Module):
- def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True):
- super(DepthDecoder, self).__init__()
-
- self.num_output_channels = num_output_channels
- self.use_skips = use_skips
- self.upsample_mode = "nearest"
- self.scales = scales
-
- self.num_ch_enc = num_ch_enc
- self.num_ch_dec = numpy.array([16, 32, 64, 128, 256])
-
- # decoder
- self.convs = OrderedDict()
- for i in range(4, -1, -1):
- # upconv_0
- num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1]
- num_ch_out = self.num_ch_dec[i]
- # self.convs[("upconv", i, 0)] = ConvBlock(num_ch_in, num_ch_out)
- setattr(self, "upconv_{}_0".format(i), ConvBlock(num_ch_in, num_ch_out))
-
- # upconv_1
- num_ch_in = self.num_ch_dec[i]
- if self.use_skips and i > 0:
- num_ch_in += self.num_ch_enc[i - 1]
- num_ch_out = self.num_ch_dec[i]
- # self.convs[("upconv", i, 1)] = ConvBlock(num_ch_in, num_ch_out)
- setattr(self, "upconv_{}_1".format(i), ConvBlock(num_ch_in, num_ch_out))
-
- for s in self.scales:
- # self.convs[("dispconv", s)] = Conv3x3(self.num_ch_dec[s], self.num_output_channels)
- setattr(self, "disp_{}".format(s), Conv3x3(self.num_ch_dec[s], self.num_output_channels))
-
- self.decoder = nn.ModuleList(
- [x for y in [[getattr(self, "upconv_{}_0".format(i)), getattr(self, "upconv_{}_1".format(i))] for i in range(4, -1, -1)] for x in y] +
- [getattr(self, "disp_{}".format(s)) for s in self.scales]
- )
- self.sigmoid = nn.Sigmoid()
-
- def forward(self, input_features):
- outputs = {}
-
- # decoder
- x = input_features[-1]
- for i in range(4, -1, -1):
- # x = self.convs[("upconv", i, 0)](x)
- x = getattr(self, "upconv_{}_0".format(i))(x)
- x = [upsample(x)]
- if self.use_skips and i > 0:
- x += [input_features[i - 1]]
- x = torch.cat(x, 1)
- # x = self.convs[("upconv", i, 1)](x)
- x = getattr(self, "upconv_{}_1".format(i))(x)
- if i in self.scales:
- outputs[("disp", i)] = self.sigmoid(getattr(self, "disp_{}".format(i))(x))
- # setattr(self, "outputs_disp_{}".format(i), self.sigmoid(getattr(self, "disp_{}".format(i))(x)))
-
- return outputs
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/canny/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/canny/__init__.py
deleted file mode 100644
index cb0da951dc838ec9dec2131007e036113281800b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/canny/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import cv2
-
-
-class CannyDetector:
- def __call__(self, img, low_threshold, high_threshold):
- return cv2.Canny(img, low_threshold, high_threshold)
diff --git a/spaces/Menna2211/TxTimg/Home.py b/spaces/Menna2211/TxTimg/Home.py
deleted file mode 100644
index 7e8c87d52c304daee0570935b8bf4e135d3b2abd..0000000000000000000000000000000000000000
--- a/spaces/Menna2211/TxTimg/Home.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import streamlit as st
-
-st.set_page_config(
- page_title="Home",
- page_icon="👋",
-)
-
-st.title("Welcome to My TxTStable! 👋")
-
-st.markdown(
- """
- ### TxTStable using Stable Diffusion:
- The application allows users to input a piece of text and generate an image that is related to the input text.
- - Hugging Face Model: [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
- - Github model: [github](https://github.com)
-"""
-)
diff --git a/spaces/Miam97/Test02/Dockerfile b/spaces/Miam97/Test02/Dockerfile
deleted file mode 100644
index 6c01c09373883afcb4ea34ae2d316cd596e1737b..0000000000000000000000000000000000000000
--- a/spaces/Miam97/Test02/Dockerfile
+++ /dev/null
@@ -1,21 +0,0 @@
-FROM node:18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md* .env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/model/SurfaceClassifier.py b/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/model/SurfaceClassifier.py
deleted file mode 100644
index af5afe4fdd4767f72549df258e5b67dea6ac671d..0000000000000000000000000000000000000000
--- a/spaces/Mileena/PIFu-Clothed-Human-Digitization/PIFu/lib/model/SurfaceClassifier.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class SurfaceClassifier(nn.Module):
- def __init__(self, filter_channels, num_views=1, no_residual=True, last_op=None):
- super(SurfaceClassifier, self).__init__()
-
- self.filters = []
- self.num_views = num_views
- self.no_residual = no_residual
- filter_channels = filter_channels
- self.last_op = last_op
-
- if self.no_residual:
- for l in range(0, len(filter_channels) - 1):
- self.filters.append(nn.Conv1d(
- filter_channels[l],
- filter_channels[l + 1],
- 1))
- self.add_module("conv%d" % l, self.filters[l])
- else:
- for l in range(0, len(filter_channels) - 1):
- if 0 != l:
- self.filters.append(
- nn.Conv1d(
- filter_channels[l] + filter_channels[0],
- filter_channels[l + 1],
- 1))
- else:
- self.filters.append(nn.Conv1d(
- filter_channels[l],
- filter_channels[l + 1],
- 1))
-
- self.add_module("conv%d" % l, self.filters[l])
-
- def forward(self, feature):
- '''
-
- :param feature: list of [BxC_inxHxW] tensors of image features
- :param xy: [Bx3xN] tensor of (x,y) coodinates in the image plane
- :return: [BxC_outxN] tensor of features extracted at the coordinates
- '''
-
- y = feature
- tmpy = feature
- for i, f in enumerate(self.filters):
- if self.no_residual:
- y = self._modules['conv' + str(i)](y)
- else:
- y = self._modules['conv' + str(i)](
- y if i == 0
- else torch.cat([y, tmpy], 1)
- )
- if i != len(self.filters) - 1:
- y = F.leaky_relu(y)
-
- if self.num_views > 1 and i == len(self.filters) // 2:
- y = y.view(
- -1, self.num_views, y.shape[1], y.shape[2]
- ).mean(dim=1)
- tmpy = feature.view(
- -1, self.num_views, feature.shape[1], feature.shape[2]
- ).mean(dim=1)
-
- if self.last_op:
- y = self.last_op(y)
-
- return y
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/sar/sar_resnet31_sequential-decoder_5e_union14m.py b/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/sar/sar_resnet31_sequential-decoder_5e_union14m.py
deleted file mode 100644
index 0739a86dc499e403a287c6b34d65498daa52c598..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textrecog/sar/sar_resnet31_sequential-decoder_5e_union14m.py
+++ /dev/null
@@ -1,111 +0,0 @@
-_base_ = [
- '../_base_/datasets/union14m_train.py',
- '../_base_/datasets/union14m_benchmark.py',
- '../_base_/datasets/cute80.py',
- '../_base_/datasets/iiit5k.py',
- '../_base_/datasets/svt.py',
- '../_base_/datasets/svtp.py',
- '../_base_/datasets/icdar2013.py',
- '../_base_/datasets/icdar2015.py',
- '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_adam_step_5e.py',
- '_base_sar_resnet31_parallel-decoder.py',
-]
-
-_base_.pop('model')
-dictionary = dict(
- type='Dictionary',
- dict_file= # noqa
- '{{ fileDirname }}/../../../dicts/english_digits_symbols_space.txt',
- with_padding=True,
- with_unknown=True,
- same_start_end=True,
- with_start=True,
- with_end=True)
-
-model = dict(
- type='SARNet',
- data_preprocessor=dict(
- type='TextRecogDataPreprocessor',
- mean=[127, 127, 127],
- std=[127, 127, 127]),
- backbone=dict(type='ResNet31OCR'),
- encoder=dict(
- type='SAREncoder',
- enc_bi_rnn=False,
- enc_do_rnn=0.1,
- enc_gru=False,
- ),
- decoder=dict(
- type='SequentialSARDecoder',
- enc_bi_rnn=False,
- dec_bi_rnn=False,
- dec_do_rnn=0,
- dec_gru=False,
- pred_dropout=0.1,
- d_k=512,
- pred_concat=True,
- postprocessor=dict(type='AttentionPostprocessor'),
- module_loss=dict(
- type='CEModuleLoss', ignore_first_char=True, reduction='mean'),
- dictionary=dictionary,
- max_seq_len=30))
-# dataset settings
-train_list = [
- _base_.union14m_challenging, _base_.union14m_hard, _base_.union14m_medium,
- _base_.union14m_normal, _base_.union14m_easy
-]
-val_list = [
- _base_.cute80_textrecog_test, _base_.iiit5k_textrecog_test,
- _base_.svt_textrecog_test, _base_.svtp_textrecog_test,
- _base_.icdar2013_textrecog_test, _base_.icdar2015_textrecog_test
-]
-test_list = [
- _base_.union14m_benchmark_artistic,
- _base_.union14m_benchmark_multi_oriented,
- _base_.union14m_benchmark_contextless,
- _base_.union14m_benchmark_curve,
- _base_.union14m_benchmark_incomplete,
- _base_.union14m_benchmark_incomplete_ori,
- _base_.union14m_benchmark_multi_words,
- _base_.union14m_benchmark_salient,
- _base_.union14m_benchmark_general,
-]
-
-train_dataset = dict(
- type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline)
-test_dataset = dict(
- type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline)
-val_dataset = dict(
- type='ConcatDataset', datasets=val_list, pipeline=_base_.test_pipeline)
-
-train_dataloader = dict(
- batch_size=128,
- num_workers=24,
- persistent_workers=True,
- sampler=dict(type='DefaultSampler', shuffle=True),
- dataset=train_dataset)
-
-test_dataloader = dict(
- batch_size=128,
- num_workers=4,
- persistent_workers=True,
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=test_dataset)
-
-val_dataloader = dict(
- batch_size=128,
- num_workers=4,
- persistent_workers=True,
- pin_memory=True,
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=val_dataset)
-
-val_evaluator = dict(
- dataset_prefixes=['CUTE80', 'IIIT5K', 'SVT', 'SVTP', 'IC13', 'IC15'])
-test_evaluator = dict(dataset_prefixes=[
- 'artistic', 'multi-oriented', 'contextless', 'curve', 'incomplete',
- 'incomplete-ori', 'multi-words', 'salient', 'general'
-])
diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/gatherers/naf_gatherer.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/gatherers/naf_gatherer.py
deleted file mode 100644
index 3251bde40ddd01885ee45c4ad21911156a3ecf07..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/gatherers/naf_gatherer.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import json
-import os
-import os.path as osp
-import shutil
-from typing import List, Tuple
-
-from mmocr.registry import DATA_GATHERERS
-from .base import BaseGatherer
-
-
-@DATA_GATHERERS.register_module()
-class NAFGatherer(BaseGatherer):
- """Gather the dataset file from NAF dataset. Specifically for the case that
- there is a split file that contains the names of different splits. For
- example,
-
- img_001.jpg train: img_001.jpg
- img_002.jpg ---> split_file ---> test: img_002.jpg
- img_003.jpg val: img_003.jpg
-
- Args:
- split_file (str, optional): The name of the split file. Defaults to
- "data_split.json".
- temp_dir (str, optional): The directory of the temporary images.
- Defaults to "temp_images".
- """
-
- def __init__(self,
- split_file='data_split.json',
- temp_dir: str = 'temp_images',
- **kwargs) -> None:
- super().__init__(**kwargs)
- self.temp_dir = temp_dir
- self.split_file = split_file
-
- def __call__(self) -> Tuple[List[str], List[str]]:
- """
- Returns:
- tuple(list[str], list[str]): The list of image paths and the list
- of annotation paths.
- """
-
- split_file = osp.join(self.data_root, self.split_file)
- with open(split_file, 'r') as f:
- split_data = json.load(f)
- img_list = list()
- ann_list = list()
- # Rename the key
- split_data['val'] = split_data.pop('valid')
- if not osp.exists(self.img_dir):
- os.makedirs(self.img_dir)
- current_split_data = split_data[self.split]
- for groups in current_split_data:
- for img_name in current_split_data[groups]:
- src_img = osp.join(self.data_root, self.temp_dir, img_name)
- dst_img = osp.join(self.img_dir, img_name)
- if not osp.exists(src_img):
- Warning(f'{src_img} does not exist!')
- continue
- # move the image to the new path
- shutil.move(src_img, dst_img)
- ann = osp.join(self.ann_dir, img_name.replace('.jpg', '.json'))
- img_list.append(dst_img)
- ann_list.append(ann)
- return img_list, ann_list
diff --git a/spaces/MrVicente/RA-BART/custom_bart/bart_onnx.py b/spaces/MrVicente/RA-BART/custom_bart/bart_onnx.py
deleted file mode 100644
index 533f6e962c86ce3049fe121a9c2eb8a4813a28d7..0000000000000000000000000000000000000000
--- a/spaces/MrVicente/RA-BART/custom_bart/bart_onnx.py
+++ /dev/null
@@ -1,240 +0,0 @@
-
-from collections import OrderedDict
-from typing import Any, Mapping, Optional
-
-import torch
-from transformers import PreTrainedTokenizer
-from transformers.onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
-from transformers.onnx.utils import compute_effective_axis_dimension
-from transformers.utils.generic import TensorType
-from transformers.utils.import_utils import is_torch_available
-
-class BartCustumOnnxConfig(OnnxSeq2SeqConfigWithPast):
- @property
- def inputs(self) -> Mapping[str, Mapping[int, str]]:
- if self.task in ["default", "seq2seq-lm"]:
- common_inputs = OrderedDict(
- [
- ("input_ids", {0: "batch", 1: "encoder_sequence"}),
- ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
- ("input_commonsense_relations", {0: "batch", 1: "encoder_sequence", 2: "encoder_sequence"}),
- ]
- )
-
- if self.use_past:
- common_inputs["decoder_input_ids"] = {0: "batch"}
- common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
- else:
- common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
- common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
-
- if self.use_past:
- self.fill_with_past_key_values_(common_inputs, direction="inputs")
- elif self.task == "causal-lm":
- # TODO: figure this case out.
- common_inputs = OrderedDict(
- [
- ("input_ids", {0: "batch", 1: "encoder_sequence"}),
- ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
- ]
- )
- if self.use_past:
- num_encoder_layers, _ = self.num_layers
- for i in range(num_encoder_layers):
- common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
- common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
- else:
- common_inputs = OrderedDict(
- [
- ("input_ids", {0: "batch", 1: "encoder_sequence"}),
- ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
- ("input_commonsense_relations", {0: "batch", 2: "encoder_sequence", 3: "encoder_sequence"}),
- ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
- ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
- ]
- )
-
- return common_inputs
-
- @property
- def outputs(self) -> Mapping[str, Mapping[int, str]]:
- if self.task in ["default", "seq2seq-lm"]:
- common_outputs = super().outputs
- else:
- common_outputs = super(OnnxConfigWithPast, self).outputs
- if self.use_past:
- num_encoder_layers, _ = self.num_layers
- for i in range(num_encoder_layers):
- common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
- common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
- return common_outputs
-
- def _generate_dummy_inputs_for_default_and_seq2seq_lm(
- self,
- tokenizer: PreTrainedTokenizer,
- batch_size: int = -1,
- seq_length: int = -1,
- is_pair: bool = False,
- framework: Optional[TensorType] = None,
- ) -> Mapping[str, Any]:
- encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
- tokenizer, batch_size, seq_length, is_pair, framework
- )
-
- # Generate decoder inputs
- decoder_seq_length = seq_length if not self.use_past else 1
- decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
- tokenizer, batch_size, decoder_seq_length, is_pair, framework
- )
- decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
- common_inputs = dict(**encoder_inputs, **decoder_inputs)
-
- if self.use_past:
- if not is_torch_available():
- raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
- else:
- import torch
- batch, encoder_seq_length = common_inputs["input_ids"].shape
- decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
- num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
- encoder_shape = (
- batch,
- num_encoder_attention_heads,
- encoder_seq_length,
- self._config.hidden_size // num_encoder_attention_heads,
- )
- decoder_past_length = decoder_seq_length + 3
- decoder_shape = (
- batch,
- num_decoder_attention_heads,
- decoder_past_length,
- self._config.hidden_size // num_decoder_attention_heads,
- )
-
- common_inputs["decoder_attention_mask"] = torch.cat(
- [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
- )
-
- common_inputs["past_key_values"] = []
- # If the number of encoder and decoder layers are present in the model configuration, both are considered
- num_encoder_layers, num_decoder_layers = self.num_layers
- min_num_layers = min(num_encoder_layers, num_decoder_layers)
- max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
- remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
-
- for _ in range(min_num_layers):
- common_inputs["past_key_values"].append(
- (
- torch.zeros(decoder_shape),
- torch.zeros(decoder_shape),
- torch.zeros(encoder_shape),
- torch.zeros(encoder_shape),
- )
- )
- # TODO: test this.
- shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
- for _ in range(min_num_layers, max_num_layers):
- common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
- return common_inputs
-
- def _generate_dummy_inputs_for_causal_lm(
- self,
- tokenizer: PreTrainedTokenizer,
- batch_size: int = -1,
- seq_length: int = -1,
- is_pair: bool = False,
- framework: Optional[TensorType] = None,
- ) -> Mapping[str, Any]:
- common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
- tokenizer, batch_size, seq_length, is_pair, framework
- )
-
- if self.use_past:
- if not is_torch_available():
- raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
- else:
- import torch
- batch, seqlen = common_inputs["input_ids"].shape
- # Not using the same length for past_key_values
- past_key_values_length = seqlen + 2
- num_encoder_layers, _ = self.num_layers
- num_encoder_attention_heads, _ = self.num_attention_heads
- past_shape = (
- batch,
- num_encoder_attention_heads,
- past_key_values_length,
- self._config.hidden_size // num_encoder_attention_heads,
- )
-
- mask_dtype = common_inputs["attention_mask"].dtype
- common_inputs["attention_mask"] = torch.cat(
- [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
- )
- common_inputs["past_key_values"] = [
- (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)
- ]
- return common_inputs
-
- def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
- self,
- tokenizer: PreTrainedTokenizer,
- batch_size: int = -1,
- seq_length: int = -1,
- is_pair: bool = False,
- framework: Optional[TensorType] = None,
- ) -> Mapping[str, Any]:
- # Copied from OnnxConfig.generate_dummy_inputs
- # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
- # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
- batch_size = compute_effective_axis_dimension(
- batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
- )
-
- # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
- token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
- seq_length = compute_effective_axis_dimension(
- seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
- )
-
- # Generate dummy inputs according to compute batch and sequence
- dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
- tmp_seq_length = seq_length + 2
- commonsense_relation= torch.IntTensor([[[0] * tmp_seq_length] * tmp_seq_length]* batch_size)
- common_inputs = dict(tokenizer(dummy_input,
- return_tensors=framework))
- common_inputs['input_commonsense_relations'] = commonsense_relation
- print('here:', common_inputs)
- return common_inputs
-
- def generate_dummy_inputs(
- self,
- tokenizer: PreTrainedTokenizer,
- batch_size: int = -1,
- seq_length: int = -1,
- is_pair: bool = False,
- framework: Optional[TensorType] = None,
- ) -> Mapping[str, Any]:
- if self.task in ["default", "seq2seq-lm"]:
- common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
- tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
- )
-
- elif self.task == "causal-lm":
- common_inputs = self._generate_dummy_inputs_for_causal_lm(
- tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
- )
- else:
- common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
- tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
- )
- if 'decoder_input_commonsense_relations' in common_inputs:
- del common_inputs['decoder_input_commonsense_relations']
- return common_inputs
-
- def _flatten_past_key_values_(self, flattened_output, name, idx, t):
- if self.task in ["default", "seq2seq-lm"]:
- flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
- else:
- flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
- flattened_output, name, idx, t
- )
\ No newline at end of file
diff --git a/spaces/MrZak/Learn-Up/app.py b/spaces/MrZak/Learn-Up/app.py
deleted file mode 100644
index 3381bce2d274de5d887745325249b5151a3e53d1..0000000000000000000000000000000000000000
--- a/spaces/MrZak/Learn-Up/app.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import openai
-import gradio
-openai.api_key = "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"
-
-messages = [{"role": "system", "content": "You are a teacher."}]
-
-def CustomChatGPT(user_input):
- messages.append({"role": "user", "content": user_input})
- response = openai.ChatCompletion.create(
- model = "gpt-3.5-turbo",
- messages = messages
- )
- ChatGPT_reply = ChatGPT_reply = response['choices'][0]['message']['content']
- messages.append({"role": "assistant", "content": ChatGPT_reply})
- return ChatGPT_reply
-
-demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text",)
-
-demo.launch()
diff --git a/spaces/NbAiLab/maken-clip-sketch/README.md b/spaces/NbAiLab/maken-clip-sketch/README.md
deleted file mode 100644
index f2b22f5d72c2b54db225f08585e25a813e4e83b3..0000000000000000000000000000000000000000
--- a/spaces/NbAiLab/maken-clip-sketch/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Maken Clip Sketch
-emoji: ✏️
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Nigomaster/Analizador_CVs/app.py b/spaces/Nigomaster/Analizador_CVs/app.py
deleted file mode 100644
index e989930ae6a14ae54c6450705ffa47aca1a740b3..0000000000000000000000000000000000000000
--- a/spaces/Nigomaster/Analizador_CVs/app.py
+++ /dev/null
@@ -1,174 +0,0 @@
-#Hello! It seems like you want to import the Streamlit library in Python. Streamlit is a powerful open-source framework used for building web applications with interactive data visualizations and machine learning models. To import Streamlit, you'll need to ensure that you have it installed in your Python environment.
-#Once you have Streamlit installed, you can import it into your Python script using the import statement,
-
-import langchain
-import streamlit as st
-from langchain.document_loaders import PyPDFLoader
-from langchain.document_loaders import PyMuPDFLoader
-from PyPDF2 import PdfReader
-from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
-from langchain.chat_models import ChatOpenAI
-import random
-
-
-
-from langchain.llms import OpenAI
-
-
-
-
-
-st.set_page_config(page_title="¡Analizador de CVs Inteligente! ")
-st.header("¡Sube tu CV y descubre tu potencial laboral!", divider='red')
-
-
-
-
-
-
-st.write("""
-
-
-¡Bienvenido al Analizador de CVs Inteligente! 🤖
-
-Para comenzar tu experiencia, sigue estos simples pasos:
-
-1.- Carga tu CV 📂: Haz clic en el botón "Browse Files" y selecciona el archivo de tu currículum en formato PDF (máximo 2 hojas). El sistema está listo para recibir tu información laboral.
-
-2.- ¡Analiza tu Trayectoria! 🔍: Una vez que hayas cargado tu CV, presiona el botón " ¡Analiza tu Trayectoria! 🔍". Esto desatará el análisis y revelará las oportunidades que se esconden en tu trayectoria.
-
-3.- Espera el Análisis: El Analizador de CVs Inteligente procesará tu currículum y, en breve, te presentará una visión de tu potencial laboral.
-
-
-
-¡Carga tu CV y comencemos a desbloquear nuevas oportunidades! 🚀💼
-
-
-""")
-
-
-st.divider()
-
-col1, col2 = st.columns(2)
-
-with col1:
- cv_file = st.file_uploader("Sube tu CV (máximo 2 MB)", type=["pdf"], key="cv_file", accept_multiple_files=False)
-
-text = ""
-
-with col2:
- st.write(" ")
- st.write(" ")
- st.write(" ")
- submit = st.button('¡Analiza tu Trayectoria! 🔍')
-
-if cv_file is not None:
- if submit:
-
- reader = PdfReader(cv_file)
- number_of_pages = len(reader.pages)
-
-
-
- number_of_pages = len(reader.pages)
- page = reader.pages[0]
- text = page.extract_text()
-
- text = text.replace('\n', " ")
- text = text.strip()
-
-
-
-
-
-
-
-
-
-
-
-
-data_manual = text
-human_template = """Eres un experto en reclutamiento y selección. Analiza el curriculum, entrega consejos de mejora, puntos fuertes y debiles y recomienda palabras
-claves para reclutadores. En caso de que no sea un curriculum, indicalo pero sin analizarlo. Solo debes analizar los documentos que son CV
-
-Despídete siempre con el nombre Rodrigo Pasten. Todo en 200 palabras o menos {query}"""
-human_template_prompt = HumanMessagePromptTemplate.from_template(human_template)
-
-chat_prompt = ChatPromptTemplate.from_messages([human_template_prompt])
-
-model = ChatOpenAI
-request = chat_prompt.format_prompt(query = data_manual).to_messages()
-chat = ChatOpenAI()
-
-saludos= ["¡Tu paciencia es muy valiosa!",
-"Agradezco tu paciencia.",
-"Gracias por esperar conmigo.",
-"Valoro tu paciencia.",
-"¡Gracias por ser tan paciente!",
-"Tu espera no pasa desapercibida, gracias.",
-"Estoy agradecido por tu paciencia.",
-"Tu paciencia es una virtud, gracias.",
-"Me alegra que seas paciente durante el proceso.",
-"Tu paciencia es fundamental para mi trabajo, gracias."]
-
-if submit:
- with st.spinner(f'Analizando... ⏳ Esto puede tomar entre 30 segundos y 1 minuto aproximadamente.\n \n \n{saludos[random.randint(0,9)]}'):
-
- st.subheader("Análisis:")
- result = chat(request)
- st.write(result.content)
-
-
-
-
-
-with st.sidebar:
- tab1, tab2 = st.tabs(["Contáctame", "Sobre Mi"])
-
-with tab1:
- st.write("""¡Hola! Estoy en la búsqueda activa de nuevas oportunidades laborales y estoy preparado para ser la pieza clave que impulsará tu equipo. ¿Estás listo para una transformación en tu departamento de Recursos Humanos? ¡Contáctame ahora mismo y descubre cómo puedo aportar un valor diferencial a tu empresa!""")
- st.subheader("Echa un vistazo a mi perfil:")
- st.link_button("Explora mi CV web", "https://rodrigopasten.github.io/Personal_web/")
- st.link_button("Conéctate en Linkedin", "https://www.linkedin.com/in/rodrigopastenc/")
- st.link_button("Mi portafolio en Github", "https://github.com/RodrigoPasten")
- st.link_button("Mi Blog", "https://nigomaster.pythonanywhere.com/")
-
- st.subheader("Contáctame por correo electrónico:")
- email_address = "Rodrigo.pasten.c@gmail.com"
- subject = "Te contacto por tu analizador de CVs"
- body = "Hola Rodrigo,"
-
- email_link = f'Enviar correo '
-
- st.markdown(email_link, unsafe_allow_html=True)
-
-
-
-with tab2:
- st.header("Sobre Mi")
- st.write("""
- ¡Hola!, soy un ingeniero en administración con una sólida experiencia en Recursos Humanos, especializado en el sector minero. Mi amplia trayectoria abarca desde el reclutamiento hasta la gestión de remuneraciones, pasando por la inclusión laboral de personas con discapacidad según la normativa chilena. Mi capacidad para calcular finiquitos y llevar a cabo desvinculaciones cumple con las regulaciones vigentes.
-
-Mi verdadera pasión radica en la innovación en RR.HH., y creo firmemente en la implementación de tecnologías avanzadas para optimizar procesos. He explorado áreas como People Analytics, Data Science y programación en Python, y poseo habilidades tanto en el desarrollo de aplicaciones frontend como backend. Esto me permite aportar soluciones tecnológicas integrales a diversos desafíos.
-
-Además, soy competente en herramientas como Power BI, Adobe InDesign y Streamlit, y mi dominio de Microsoft Excel es de nivel experto.
-
-En mi formación continua, me encuentro en la etapa final de mi carrera de Ingeniería Civil Industrial y persigo dos másteres: uno en Dirección de Personas y otro en Big Data. Estas especializaciones han fortalecido mi conocimiento en la gestión de equipos y el uso estratégico de los datos en la toma de decisiones.
-
-Mi objetivo es fusionar mi pasión por la tecnología con mi compromiso con el desarrollo humano, creyendo firmemente en que la tecnología puede potenciar el talento y generar un impacto positivo en las organizaciones.
-
- """)
-
-
- with open("CV interactivo Rodrigo Pasten.pdf", "rb") as file:
- btn = st.download_button(
- label="Descarga mi CV interactivo",
- data=file,
- file_name="CV interactivo Rodrigo Pasten.pdf",
- mime="pdf"
- )
-
-
-
-
diff --git a/spaces/Nikhatu/stable-diffusion-webui-cpu-the-best/README.md b/spaces/Nikhatu/stable-diffusion-webui-cpu-the-best/README.md
deleted file mode 100644
index 404430459e1b516be671298c7de21de7a039e30d..0000000000000000000000000000000000000000
--- a/spaces/Nikhatu/stable-diffusion-webui-cpu-the-best/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Stable Diffusion Webui on Cpu
-emoji: 🏃
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-python_version: 3.10.6
-duplicated_from: IoMa/stable-diffusion-webui-cpu-the-best
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py
deleted file mode 100644
index d6ee9c4a3602be9db8ddfe67d41ce8a96a98ad1e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/data/extracted_features_dataset.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import logging
-import os
-import contextlib
-
-import numpy as np
-import torch
-
-from fairseq.data import FairseqDataset, data_utils
-
-
-logger = logging.getLogger(__name__)
-
-
-class ExtractedFeaturesDataset(FairseqDataset):
- def __init__(
- self,
- path,
- split,
- min_length=3,
- max_length=None,
- labels=None,
- label_dict=None,
- shuffle=True,
- sort_by_length=True,
- ):
- super().__init__()
-
- self.min_length = min_length
- self.max_length = max_length
- self.shuffle = shuffle
- self.sort_by_length = sort_by_length
- self.label_dict = label_dict
-
- if labels is not None:
- assert label_dict is not None
-
- self.sizes = []
- self.offsets = []
- self.labels = []
-
- path = os.path.join(path, split)
- data_path = path
- self.data = np.load(data_path + ".npy", mmap_mode="r")
-
- offset = 0
- skipped = 0
-
- if not os.path.exists(path + f".{labels}"):
- labels = None
-
- with open(data_path + ".lengths", "r") as len_f, open(
- path + f".{labels}", "r"
- ) if labels is not None else contextlib.ExitStack() as lbl_f:
- for line in len_f:
- length = int(line.rstrip())
- lbl = None if labels is None else next(lbl_f).rstrip().split()
- if length >= min_length and (
- max_length is None or length <= max_length
- ):
- self.sizes.append(length)
- self.offsets.append(offset)
- if lbl is not None:
- self.labels.append(lbl)
- offset += length
-
- self.sizes = np.asarray(self.sizes)
- self.offsets = np.asarray(self.offsets)
-
- logger.info(f"loaded {len(self.offsets)}, skipped {skipped} samples")
-
- def __getitem__(self, index):
- offset = self.offsets[index]
- end = self.sizes[index] + offset
- feats = torch.from_numpy(self.data[offset:end].copy()).float()
-
- res = {"id": index, "features": feats}
- if len(self.labels) > 0:
- res["target"] = self.label_dict.encode_line(
- self.labels[index],
- line_tokenizer=lambda x: x,
- append_eos=False,
- )
-
- return res
-
- def __len__(self):
- return len(self.sizes)
-
- def collater(self, samples):
- if len(samples) == 0:
- return {}
-
- features = [s["features"] for s in samples]
- sizes = [len(s) for s in features]
-
- target_size = max(sizes)
-
- collated_features = features[0].new_zeros(
- len(features), target_size, features[0].size(-1)
- )
- padding_mask = torch.BoolTensor(collated_features.shape[:-1]).fill_(False)
- for i, (f, size) in enumerate(zip(features, sizes)):
- collated_features[i, :size] = f
- padding_mask[i, size:] = True
-
- res = {
- "id": torch.LongTensor([s["id"] for s in samples]),
- "net_input": {"features": collated_features, "padding_mask": padding_mask},
- }
-
- if len(self.labels) > 0:
- target = data_utils.collate_tokens(
- [s["target"] for s in samples],
- pad_idx=self.label_dict.pad(),
- left_pad=False,
- )
- res["target"] = target
- return res
-
- def num_tokens(self, index):
- return self.size(index)
-
- def size(self, index):
- return self.sizes[index]
-
- def ordered_indices(self):
- """Return an ordered list of indices. Batches will be constructed based
- on this order."""
- if self.shuffle:
- order = [np.random.permutation(len(self))]
- else:
- order = [np.arange(len(self))]
-
- if self.sort_by_length:
- order.append(self.sizes)
- return np.lexsort(order)[::-1]
- else:
- return order[0]
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/backtranslation/extract_bt_data.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/backtranslation/extract_bt_data.py
deleted file mode 100644
index e766391e873d0d9a9561d67d5864934b2fad0681..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/backtranslation/extract_bt_data.py
+++ /dev/null
@@ -1,72 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import fileinput
-
-from tqdm import tqdm
-
-
-def main():
- parser = argparse.ArgumentParser(
- description=(
- "Extract back-translations from the stdout of fairseq-generate. "
- "If there are multiply hypotheses for a source, we only keep the first one. "
- )
- )
- parser.add_argument("--output", required=True, help="output prefix")
- parser.add_argument(
- "--srclang", required=True, help="source language (extracted from H-* lines)"
- )
- parser.add_argument(
- "--tgtlang", required=True, help="target language (extracted from S-* lines)"
- )
- parser.add_argument("--minlen", type=int, help="min length filter")
- parser.add_argument("--maxlen", type=int, help="max length filter")
- parser.add_argument("--ratio", type=float, help="ratio filter")
- parser.add_argument("files", nargs="*", help="input files")
- args = parser.parse_args()
-
- def validate(src, tgt):
- srclen = len(src.split(" ")) if src != "" else 0
- tgtlen = len(tgt.split(" ")) if tgt != "" else 0
- if (
- (args.minlen is not None and (srclen < args.minlen or tgtlen < args.minlen))
- or (
- args.maxlen is not None
- and (srclen > args.maxlen or tgtlen > args.maxlen)
- )
- or (
- args.ratio is not None
- and (max(srclen, tgtlen) / float(min(srclen, tgtlen)) > args.ratio)
- )
- ):
- return False
- return True
-
- def safe_index(toks, index, default):
- try:
- return toks[index]
- except IndexError:
- return default
-
- with open(args.output + "." + args.srclang, "w") as src_h, open(
- args.output + "." + args.tgtlang, "w"
- ) as tgt_h:
- for line in tqdm(fileinput.input(args.files)):
- if line.startswith("S-"):
- tgt = safe_index(line.rstrip().split("\t"), 1, "")
- elif line.startswith("H-"):
- if tgt is not None:
- src = safe_index(line.rstrip().split("\t"), 2, "")
- if validate(src, tgt):
- print(src, file=src_h)
- print(tgt, file=tgt_h)
- tgt = None
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/layerdrop/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/layerdrop/README.md
deleted file mode 100644
index 4d48ee9615e1458e1e889635dc9938e427a7f64a..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/layerdrop/README.md
+++ /dev/null
@@ -1,154 +0,0 @@
-# Reducing Transformer Depth on Demand with Structured Dropout (Fan et al., 2019)
-This page contains information for how to train models with LayerDrop, based on this [paper](https://arxiv.org/abs/1909.11556).
-
-## Citation:
-If you found this technique useful, please cite our paper:
-```bibtex
-@article{fan2019reducing,
- title={Reducing Transformer Depth on Demand with Structured Dropout},
- author={Fan, Angela and Grave, Edouard and Joulin, Armand},
- journal={arXiv preprint arXiv:1909.11556},
- year={2019}
-}
-```
-
-## Pre-trained models
-
-Model | Description | Download
----|---|---
-`layerdrop_wmt_en_de_12_6` | Transformer + LayerDrop 0.2 trained on WMT16 en-de with 12 encoder and 6 decoder layers | [layerdrop_wmt_en_de_12_6.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/layerdrop_wmt_en_de_12_6.tar.gz)
-`roberta_layerdrop.base` | RoBERTa Base + LayerDrop 0.2 | [roberta_layerdrop.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.base.qnli.tar.gz)
-`roberta_layerdrop.large` | RoBERTa Large + LayerDrop 0.2 | [roberta_layerdrop.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.tar.gz)
-`roberta_layerdrop.large.mnli` | `roberta_layerdrop.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.mnli.tar.gz)
-`roberta_layerdrop.large.qnli` | `roberta_layerdrop.large` finetuned on [QNLI](https://arxiv.org/abs/1804.07461) | [roberta_layerdrop.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta_layerdrop.large.qnli.tar.gz)
-
-
-Evaluate performance of these pre-trained models:
-```bash
-# Example for Machine Translation
-fairseq-generate /path/to/bped/wmt/data --path nmt_checkpoint.pt \
- --beam 8 --lenpen 0.4 \
- --batch-size 64 \
- --remove-bpe \
- --gen-subset test > wmt16_gen.txt
-bash scripts/compound_split_bleu.sh wmt16_gen.txt
-# prints BLEU4 = 30.17
-```
-
-```python
-# Example for RoBERTa + LayerDrop finetuned on MNLI:
-from fairseq.models.roberta import RobertaModel
-
-roberta_layerdrop = RobertaModel.from_pretrained(
- '/path/to/MNLI/model',
- checkpoint_file='mnli_checkpoint.pt',
- data_name_or_path='/path/to/MNLI/data/MNLI-bin'
-)
-label_map = {0: 'contradiction', 2: 'neutral', 1: 'entailment'}
-ncorrect, nsamples = 0, 0
-roberta_layerdrop.cuda()
-roberta_layerdrop.eval()
-with open('/path/to/MNLI/data/dev_matched.tsv') as fin:
- fin.readline()
- for index, line in enumerate(fin):
- tokens = line.strip().split('\t')
- sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
- tokens = roberta_layerdrop.encode(sent1, sent2)
- prediction = roberta_layerdrop.predict('sentence_classification_head', tokens).argmax().item()
- prediction_label = label_map[prediction]
- ncorrect += int(prediction_label == target)
- nsamples += 1
-print('| Accuracy: ', float(ncorrect)/float(nsamples))
-# prints | Accuracy: 0.9026999490575649
-
-
-# Example for RoBERTa + LayerDrop finetuned on QNLI:
-roberta = RobertaModel.from_pretrained(
- '/path/to/QNLI/model',
- checkpoint_file='qnli_checkpoint.pt',
- data_name_or_path='/path/to/QNLI/data/QNLI-bin'
-)
-
-label_fn = lambda label: roberta.task.label_dictionary.string(
- [label + roberta.task.target_dictionary.nspecial]
-)
-ncorrect, nsamples = 0, 0
-roberta.cuda()
-roberta.eval()
-with open('/path/to/QNLI/data/dev.tsv') as fin:
- fin.readline()
- for index, line in enumerate(fin):
- tokens = line.strip().split('\t')
- sent1, sent2, target = tokens[1], tokens[2], tokens[3]
- tokens = roberta.encode(sent1, sent2)
- prediction = roberta.predict('sentence_classification_head', tokens).argmax().item()
- prediction_label = label_fn(prediction)
- ncorrect += int(prediction_label == target)
- nsamples += 1
-print('| Accuracy: ', float(ncorrect)/float(nsamples))
-# prints | Accuracy: 0.9480139117700896
-```
-
-
-## Example usage
-
-To train a model with LayerDrop, add the following flags. We recommend 0.2, a value that worked well in our experiments. For Language Models that are decoder-only, you need only the decoder flag. For RoBERTa, an encoder, you need only the encoder flag. The encoder and decoder LayerDrop values can be set differently.
-```
---encoder-layerdrop 0.2 --decoder-layerdrop 0.2
-```
-
-To prune a model that has been trained with LayerDrop, add the following flags followed by a comma separated list of which layers you would like to keep.
-```
---encoder-layers-to-keep 0,2,4,6,8,10,12,14 --decoder-layers-to-keep 0,2,4,6,8,10,12,14
-```
-Setting these flags should print a message such as:
-```
-| Pruning model to specified layer configuration
-```
-You should also see a smaller number of parameters in the model, for example the 16-Layer Transformer Language Model prints:
-```
-num. model params: 246933504
-```
-while a model pruned to 8 Layers prints:
-```
-num. model params: 146163712
-```
-
-If you would like to pick up training with a model that has been pruned, simply adding these flags is sufficient. If you would like to use a script that only does evaluation (no training), you may need to pass an override command. A specific example would be for language modeling:
-```bash
-fairseq-eval-lm /path/to/wikitext-103 \
- --path /path/to/model/checkpoint.pt \
- --model-overrides "{'decoder_layers_to_keep':'0,2,4,6,8,10,12,14'}"
-```
-This model override command overrides the training parameters and updates the model arguments so that the pruned model is run instead of the full model.
-
-## Reproduce Paper Results
-
-Looking to reproduce the results in the paper?
-
-1. For Translation on WMT16 en-de, we followed this setting [here](https://github.com/pytorch/fairseq/blob/main/examples/scaling_nmt/README.md)
-2. To train RoBERTa, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/roberta)
-3. To train Language Models on Wikitext-103, we followed this setting [here](https://github.com/pytorch/fairseq/tree/main/examples/language_model)
-
-
-## Tips
-
-1. If you would like to train large models with better performance, LayerDrop should be set to a smaller value such as 0.1 or 0.2. Too much LayerDrop will mean the model has too much regularization, so may not reach the best performance. Since LayerDrop adds regularization, you may achieve the best performance by slightly reducing the amount of standard dropout (for example, reduce by 0.1).
-
-2. If you would like to train large models to be pruned and made smaller, LayerDrop should be set to a larger value such as 0.5 if you want to prune very aggressively (such as removing half the network or more). If you would like to prune fewer layers away, LayerDrop can be set to a smaller value such as 0.2. Our experiments were conducted with low values of LayerDrop (such as 0.1 and 0.2), for reference.
-
-3. When pruning layers at inference time, it is best to spread out the layers remaining so they are evenly spaced throughout the network. For example, if you want to remove 50% of the network, keeping every other layer is good.
-
-
-## FAQ
-
-1. How did the sharing layers experiment work? In an appendix (https://openreview.net/pdf?id=SylO2yStDr) we added an experiment on Wikitext-103 language modeling that combined LayerDrop with Weight Sharing. We shared chunks of 2 layers such that every other layer had shared weights. For example, if our network has layers 1 through 6, then layer 1 and 2 are shared, layer 3 and 4 are shared, and layer 5 and 6 are shared.
-
-2. LayerDrop hasn't been helping in my setting? During training time, LayerDrop can help regularize your network. This is most important if your network is already overfitting - if your network is underfitting, it is possible LayerDrop is adding too much regularization. We recommend using smaller values (such as 0.1 or 0.2) and also decreasing the quantity of standard dropout (for example, reduce by 0.1).
-
-3. Can you train a model without LayerDrop and finetune with LayerDrop (e.g. for BERT)? In our experiments, we did not see great performance. Models such as RoBERTa have trained for a long time in the pre-training setting, so only finetuning with LayerDrop for a few epochs on a downstream task such as MNLI does not achieve the robustness required for successful pruning.
-
-
-## Having an issue or have a question?
-
-Please open an issue in this repository with the details of your question. Thanks!
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/modules/emformer.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/modules/emformer.py
deleted file mode 100644
index 6ef76bd012ba40b0395fec2ca9ae9e9c136ffe40..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/modules/emformer.py
+++ /dev/null
@@ -1,1837 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-
-import math
-import re
-from functools import partial
-from typing import List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-from fairseq.models import (
- FairseqEncoder,
-)
-from fairseq.models.speech_to_text.utils import (
- NoOp,
- lengths_to_padding_mask,
- segments_to_sequence,
-)
-from fairseq.models.speech_to_text.utils import (
- attention_suppression,
- layer_norm_backward_hook,
-)
-from torch import Tensor, device as Device
-from torch.quantization.qconfig import (
- default_dynamic_qconfig,
- per_channel_dynamic_qconfig,
-)
-
-
-class RelativePositionEmbedding(nn.Module):
- """
- Implementation according to https://arxiv.org/abs/1803.02155
- """
-
- def __init__(self, head_dim, max_position, norm_init=True):
- super().__init__()
- self.head_dim = head_dim
- self.max_position = max_position
- self.embeddings = nn.Parameter(torch.Tensor(max_position * 2 + 1, head_dim))
- if norm_init:
- nn.init.xavier_normal_(self.embeddings)
- else:
- nn.init.xavier_uniform_(self.embeddings)
-
- def forward(self, input: Tensor):
- output = nn.functional.embedding(input.long(), self.embeddings)
- return output
-
-
-class Fp32LayerNorm(nn.Module):
- def __init__(
- self,
- input_dim,
- clamp_grad=True,
- max_grad_value=256,
- eps=1e-5,
- elementwise_affine=True,
- ):
- super().__init__()
- self.torch_module = torch.nn.LayerNorm(
- input_dim, eps=eps, elementwise_affine=elementwise_affine
- )
- if clamp_grad:
- hook = partial(layer_norm_backward_hook, clamp_value=max_grad_value)
- self.torch_module.register_backward_hook(hook)
-
- def forward(self, input):
- output = torch.nn.functional.layer_norm(
- input.float(),
- self.torch_module.normalized_shape,
- self.torch_module.weight.float()
- if self.torch_module.weight is not None
- else None,
- self.torch_module.bias.float()
- if self.torch_module.bias is not None
- else None,
- self.torch_module.eps,
- ).type_as(input)
- return output
-
-
-# ------------------------------------------------------------------------------
-# PositionwiseFF
-# ------------------------------------------------------------------------------
-
-
-class PositionwiseFF(nn.Module):
- """
- FFN layer in transformer.
-
- Args:
- input_dim: input embedding dimension
- ffn_dim: FFN layer inner dimension
- dropout_on_fc1: dropout for first linear layer
- dropout_on_fc2: dropout fr second linear layer
- activation_fn: activation function used after first linear layer. \
- Only relu or gelu is supported.
-
- """
-
- def __init__(
- self, input_dim, ffn_dim, dropout_on_fc1, dropout_on_fc2, activation_fn
- ):
- super(PositionwiseFF, self).__init__()
-
- self.input_dim = input_dim
- self.ffn_dim = ffn_dim
- if activation_fn == "relu":
- ac = nn.ReLU()
- elif activation_fn == "gelu":
- ac = nn.GELU()
- else:
- raise ValueError("Unsupported activation_fn = ({})".format(activation_fn))
-
- # fc1 -> ac -> dropout -> fc2 -> dropout
- self.module = nn.Sequential(
- nn.Linear(input_dim, ffn_dim),
- ac,
- nn.Dropout(dropout_on_fc1),
- nn.Linear(ffn_dim, input_dim),
- nn.Dropout(dropout_on_fc2),
- )
-
- self.layer_norm = Fp32LayerNorm(input_dim)
-
- def forward(self, input):
- module_out = self.module(self.layer_norm(input))
- output = module_out + input
-
- return output
-
- def quantize_(self, params=None):
- if params and "per_channel" in params and params["per_channel"]:
- qconfig = per_channel_dynamic_qconfig
- else:
- qconfig = default_dynamic_qconfig
- torch.quantization.quantize_dynamic(
- self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
- )
- return self
-
-
-# ------------------------------------------------------------------------------
-# SummarizationLayer
-# ------------------------------------------------------------------------------
-
-
-class SummarizationLayer(nn.Module):
- def __init__(self, method, segment_size, embedding_dim):
- super(SummarizationLayer, self).__init__()
- self.segment_size = segment_size
- self.embedding_dim = embedding_dim
- nonlin_match = re.match(r"nonlinear\((?P[a-z]+),(?P[0-9]+)\)", method)
- self.method = method
- if method == "mean":
- self.module = nn.AvgPool1d(
- kernel_size=segment_size,
- stride=segment_size,
- ceil_mode=True,
- )
- elif method == "max":
- self.module = nn.MaxPool1d(
- kernel_size=segment_size,
- stride=segment_size,
- ceil_mode=True,
- )
- elif method == "linear":
- self.module = nn.Linear(segment_size, 1)
- elif nonlin_match:
- nonlin_args = nonlin_match.groupdict()
- act_type = nonlin_args["act"]
- hid_dim = int(nonlin_args["dim"])
- if act_type == "relu":
- act = nn.ReLU()
- elif act_type == "gelu":
- act = nn.GELU()
- else:
- raise ValueError("Unsupported activation_fn = ({})".format(act_type))
- self.module = nn.Sequential(
- nn.Linear(segment_size, hid_dim),
- act,
- nn.Linear(hid_dim, 1),
- )
- else:
- raise ValueError("Unsupported summarization method = ({})".format(method))
-
- def forward(self, input):
- # T, B, D -> B, D, T
- input = input.permute(1, 2, 0)
-
- if self.method == "mean" or self.method == "max":
- output = self.module(input)
- output = output.permute(2, 0, 1)
- return output
-
- full_seg_length = input.size(2) // self.segment_size * self.segment_size
- if full_seg_length > 0:
- # at least one seg is full
- B = input.size(0)
- D = input.size(1)
- input_todo = (
- input[:, :, :full_seg_length]
- .contiguous()
- .view(B, -1, self.segment_size)
- )
- output = self.module(input_todo)
- output = output.view(B, D, -1)
- else:
- output = input.new_zeros(input.size(0), input.size(1), 0)
- left = input.size(2) - full_seg_length
- if left > 0:
- # when last seg is not full, use zeros as last memory placeholder
- zeros = input.new_zeros(input.size(0), input.size(1), 1)
- output = torch.cat([output, zeros], dim=2)
- output = output.permute(2, 0, 1)
- return output
-
-
-# ------------------------------------------------------------------------------
-# NoSegAugmentedMemoryMultiheadAttentionBmm
-# ------------------------------------------------------------------------------
-
-
-class NoSegAugmentedMemoryMultiheadAttentionBmm(nn.Module):
- """
- Whole utterance augmented memory multihead attention using BMM.
-
- Different with previous augmented memory multihead attention where
- the utterance is chunked into segments. Here we use attention mask
- achieve so. The input embedding [right_context, utterance, summary]
- is a concatenation of right context, utterance and summary.
-
- Right context block is the concatenation of all the right context for
- each segments. [right_context_0, right_context_1, ..., right_context_n]
- For example, if we have utterance = [v0, v1, v2, ...., v20]. segment
- size 8, right_context size 4. Then the right context blocks =
- [v8, v9, v10, v11, v16, v17, v18, v19, 0, 0, 0, 0], where v8, v9, v10,
- and v11 are the right context for first segment. v16, v17, v18 and v19
- are the right context for second segment. 0, 0, 0 and 0 are right context
- for the last segment.
-
- utterance is corresponding to input embedding sequence
-
- summary is concatenation of average of each segments. [summary_0,
- summary_1, ..., ].
-
- In augmented memory multihead attention, the query is [right_context,
- utterance, summary], key is [memory, right_context, utterance]. Different
- with AugmentedMemoryMultiheadAttentionBmm, memory here is passed from
- previous attention layer. For the first attention layer, memory is average
- of each segment.
-
- Memory is a concatenation of memory from each segments in previous attention
- layer. For example, current layer is i, then memory is [m_0, m_1, ..., m_n].
- Each m_k is the output from seg_k in layer i-1.
-
- args:
- input_dim: input embedding dimension
- num_heads: number of heads in multihead self-attention
- dropout: attention dropout
- std_scale: if std_scale is not None. The weak attention suppression is
- turned on. For std_scale = 0.5, all the attention smaller than
- mean + 0.5 * std will be suppressed.
- scaled_init: whether to use scaled init for linear weight
- tanh_on_mem: whether to use tanh on memory output
- use_mem: whether to use memory or not. When max_memory_size is 0, then
- we don't have memory anymore.
- layer_index: current self-attention layer index that is used in depth
- initialization
- max_relative_position: max relative position used in relative position
- embedding
- rpe_old_option: To be compatible with previous model. The previous model
- was trained with attention += attention + rpe. The correct equation
- should be attention = attention + rpe
-
- """
-
- def __init__(
- self,
- input_dim,
- num_heads,
- dropout=0.0,
- std_scale=None,
- scaled_init=False,
- tanh_on_mem=False,
- use_mem=True,
- mini_batches=False,
- negative_inf="-inf",
- layer_index=-1,
- max_relative_position=0,
- rpe_old_option=True,
- ):
- if input_dim % num_heads:
- raise ValueError(
- "input_dim ({}) must be divisible by num_heads ({})".format(
- input_dim, num_heads
- )
- )
-
- super().__init__()
-
- embed_dim = input_dim
- self.e2h_kv = torch.nn.Linear(input_dim, 2 * input_dim, bias=True)
- self.e2h_q = torch.nn.Linear(input_dim, input_dim, bias=True)
- self.rpe_old_option = rpe_old_option
- if max_relative_position > 0:
- self.use_rpe = True
- self.rpe_k = RelativePositionEmbedding(
- head_dim=input_dim // num_heads,
- max_position=max_relative_position,
- )
- self.rpe_v = RelativePositionEmbedding(
- head_dim=input_dim // num_heads,
- max_position=max_relative_position,
- )
- else:
- self.use_rpe = False
- self.rpe_k = None
- self.rpe_v = None
- if scaled_init:
- if layer_index == -1:
- gain = 1.0 / math.sqrt(2)
- else:
- # https://arxiv.org/abs/2005.09684 depthwise initialization
- # stablize the training greatly. Use depthwise initialization to
- # replace incremental loss.
- gain = 1.0 / math.sqrt(layer_index + 1)
- torch.nn.init.xavier_uniform_(self.e2h_kv.weight, gain=gain)
- torch.nn.init.xavier_uniform_(self.e2h_q.weight, gain=gain)
-
- self.out_proj = torch.nn.Linear(embed_dim, embed_dim, bias=True)
-
- self.embed_dim = embed_dim
- self.num_heads = num_heads
- self.dropout = dropout
-
- self.head_dim = embed_dim // num_heads
- self.scaling = self.head_dim ** -0.5
-
- self.std_scale = std_scale
- self.use_mem = use_mem
- self.mini_batches = mini_batches
- self.negative_inf = negative_inf
-
- if tanh_on_mem:
- self.squash_mem = torch.tanh
- self.nonlinear_squash_mem = True
- else:
- self.squash_mem = NoOp()
- self.nonlinear_squash_mem = False
-
- def prepare_qkv(
- self,
- input: Tensor,
- mems: Tensor,
- lengths: Tensor,
- summary_length: int,
- lc_length: int,
- ):
- # T: right_context length + utterance_length + summary_length
- T, B, D = input.shape
- mem_length = mems.size(0)
- utterance_length = torch.max(lengths)
-
- right_context_blocks_length = T - utterance_length - summary_length
- rc_block = input[:right_context_blocks_length, :, :]
- utterance_block = input[right_context_blocks_length : T - summary_length, :, :]
-
- if B == 1:
- padding_mask = None
- else:
- klengths = lengths + mem_length + right_context_blocks_length + lc_length
- padding_mask = lengths_to_padding_mask(lengths=klengths)
-
- mem_rc_input = torch.cat([mems, rc_block, utterance_block], dim=0)
-
- # In training lc_length = 0
- key_length = mem_rc_input.size(0) + lc_length
- rc_input_sum = input
- q = self.e2h_q(rc_input_sum)
- kv = self.e2h_kv(mem_rc_input)
- k, v = kv.chunk(chunks=2, dim=2)
- result_qkv = (q, k, v)
- input_shape = (T, B, D)
- result_lengths_info = (
- mem_length,
- utterance_length,
- right_context_blocks_length,
- key_length,
- )
- if padding_mask is not None:
- assert padding_mask.size(0) == B
- assert padding_mask.size(1) == key_length
-
- return result_qkv, input_shape, result_lengths_info, padding_mask
-
- def prepare_attention_weights(
- self,
- q: Tensor,
- new_k: Tensor,
- new_v: Tensor,
- input_shape: Tuple[int, int, int],
- rpe: Optional[Tensor],
- ) -> Tuple[Tensor, Tensor, Tensor]:
- T, B, D = input_shape
- q = (
- q.contiguous().view(-1, B * self.num_heads, self.head_dim).transpose(0, 1)
- * self.scaling
- )
-
- k = (
- new_k.contiguous()
- .view(-1, B * self.num_heads, self.head_dim)
- .transpose(0, 1)
- )
-
- v = (
- new_v.contiguous()
- .view(-1, B * self.num_heads, self.head_dim)
- .transpose(0, 1)
- )
-
- attention_weights = torch.bmm(q, k.transpose(1, 2))
- if self.use_rpe and rpe is not None and self.rpe_v is not None:
- r_k = self.rpe_k(rpe)
- # [q, B*h, d] * [q, k, d] -> [B*h, q, k]
- attention_weights_rpe = torch.matmul(
- q.transpose(0, 1), r_k.transpose(1, 2)
- ).transpose(0, 1)
- attention_weights = attention_weights + attention_weights_rpe
- attention_weights_float = attention_weights.float()
-
- return attention_weights, attention_weights_float, v
-
- def prepare_attention_output(
- self,
- attention_weights: Tensor,
- attention_weights_float: Tensor,
- v: Tensor,
- input_shape: Tuple[int, int, int],
- key_length: int,
- padding_mask: Optional[Tensor],
- rpe: Optional[Tensor],
- ) -> Tensor:
- T, B, D = input_shape
- if padding_mask is not None:
- attention_weights_float = attention_weights_float.view(
- B, self.num_heads, T, key_length
- )
- attention_weights_float = attention_weights_float.masked_fill(
- padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), float("-inf")
- )
- attention_weights_float = attention_weights_float.view(
- B * self.num_heads, T, key_length
- )
-
- if self.std_scale is not None:
- attention_weights_float = attention_suppression(
- attention_weights_float, self.std_scale
- )
-
- attention_weights_float = torch.nn.functional.softmax(
- attention_weights_float, dim=-1
- )
- attention_weights = attention_weights_float.type_as(attention_weights)
-
- attention_probs = torch.nn.functional.dropout(
- attention_weights, p=self.dropout, training=self.training
- )
-
- # [T, key_length, B, n_head]+ [key_length, B, n_head, d_head]
- # -> [T, B, n_head, d_head]
- attention = torch.bmm(attention_probs, v)
- if self.use_rpe and rpe is not None and self.rpe_v is not None:
- r_v = self.rpe_v(rpe)
- attention_rpe = torch.matmul(
- attention_probs.transpose(0, 1), r_v
- ).transpose(0, 1)
-
- if self.rpe_old_option:
- attention += attention + attention_rpe
- else:
- attention = attention + attention_rpe
-
- assert list(attention.shape) == [B * self.num_heads, T, self.head_dim]
-
- attention = attention.transpose(0, 1).contiguous().view(T, B, self.embed_dim)
-
- rc_output_memory = self.out_proj(attention)
- return rc_output_memory
-
- @torch.jit.unused
- def forward(
- self,
- input: Tensor,
- lengths: Tensor,
- mems: Tensor,
- attention_mask: Tensor,
- pre_mems: Optional[Tensor] = None,
- left_context_key: Optional[Tensor] = None,
- left_context_val: Optional[Tensor] = None,
- rpe: Optional[Tensor] = None,
- ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
- """
- forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in training.
-
- args:
- input: formed in the following way
- [right_context_0, right_contex_1, ..., seg_0, seg_1,
- ..., summary_0, summary_1,..]
- lengths: the length of query which is [seg_0, seg_1, ....]
- mems: [mem_0, mem_1, ...].
- attention_mask: attention mask for query = [right_context, query, summary]
- key = [mem, right_context, query]. This is only used for traing.
-
- """
- if self.use_mem:
- mem_length = mems.size(0)
- summary_length = mem_length + 1
- if pre_mems is not None:
- mems = torch.cat([pre_mems, mems], dim=0)
- else:
- mem_length = 0
- summary_length = 0
-
- # In training, lc_length = 0
- if left_context_key is not None:
- lc_length = left_context_key.size(0)
- else:
- lc_length = 0
- results = self.prepare_qkv(
- input=input,
- mems=mems,
- lengths=lengths,
- summary_length=summary_length,
- lc_length=lc_length,
- )
- result_qkv, input_shape, result_lengths_info, padding_mask = results
- q, k, v = result_qkv
- (
- mem_length,
- utterance_length,
- right_context_blocks_length,
- key_length,
- ) = result_lengths_info
-
- if left_context_key is not None:
- # add the cache key and value
- new_k = torch.cat(
- [
- k[: mem_length + right_context_blocks_length, :, :],
- left_context_key,
- k[-utterance_length:, :, :],
- ],
- dim=0,
- )
- new_v = torch.cat(
- [
- v[: mem_length + right_context_blocks_length, :, :],
- left_context_val,
- v[-utterance_length:, :, :],
- ],
- dim=0,
- )
- next_k = new_k[mem_length + right_context_blocks_length :, :, :]
- next_v = new_v[mem_length + right_context_blocks_length :, :, :]
- else:
- new_k = k
- new_v = v
- next_k = None
- next_v = None
-
- attention_weights, attention_weights_float, v = self.prepare_attention_weights(
- q=q,
- new_k=new_k,
- new_v=new_v,
- input_shape=input_shape,
- rpe=rpe,
- )
-
- # mask attention
- attention_mask = attention_mask.unsqueeze(0)
- attention_weights_float = attention_weights_float.masked_fill(
- attention_mask, float(self.negative_inf)
- )
-
- rc_output_memory = self.prepare_attention_output(
- attention_weights=attention_weights,
- attention_weights_float=attention_weights_float,
- v=v,
- input_shape=input_shape,
- key_length=key_length,
- padding_mask=padding_mask,
- rpe=rpe,
- )
-
- if self.use_mem:
- # next_m length equals to summary length - 1
- # last memory is ignored
- if self.mini_batches:
- next_m = rc_output_memory[-summary_length:]
- else:
- next_m = rc_output_memory[-summary_length:-1]
-
- next_m = self.squash_mem(next_m)
- # rc and output
- rc_output = rc_output_memory[:-summary_length]
- if not self.nonlinear_squash_mem:
- next_m = torch.clamp(next_m, min=-10, max=10)
- else:
- next_m = mems
- rc_output = rc_output_memory
-
- return rc_output, next_m, next_k, next_v
-
- @torch.jit.export
- def forward_jit(
- self,
- input: Tensor,
- lengths: Tensor,
- mems: Tensor,
- left_context_key: Tensor,
- left_context_val: Tensor,
- rpe: Optional[Tensor],
- ) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
- """
- forward function for NoSegAugmentedMemoryMultiheadAttentionBmm in decoding.
-
- args:
- input: formed in the following way
- [right_context_0, right_contex_1, ..., seg_0, seg_1,
- ..., summary_0, summary_1,..]
- lengths: the length of query which is [seg_0, seg_1, ....]
- mems: [mem_0, mem_1, ...].
- left_context_key: left_context for key part. This is only used for online
- decoding. In training, this is empty tensor
- left_context_val: left_context for value part. This is only used for online
- decoding. In training, this is empty tensor
-
- """
- lc_length = left_context_key.size(0)
-
- # In decoding, summary_length = 1 or 0
- if self.use_mem:
- summary_length = 1
- else:
- summary_length = 0
-
- results = self.prepare_qkv(
- input=input,
- mems=mems,
- lengths=lengths,
- summary_length=summary_length,
- lc_length=lc_length,
- )
- result_qkv, input_shape, result_lengths_info, padding_mask = results
- q, k, v = result_qkv
- (
- mem_length,
- utterance_length,
- right_context_blocks_length,
- key_length,
- ) = result_lengths_info
-
- # add the cache key and value
- new_k = torch.cat(
- [
- k[: mem_length + right_context_blocks_length, :, :],
- left_context_key,
- k[-utterance_length:, :, :],
- ],
- dim=0,
- )
- new_v = torch.cat(
- [
- v[: mem_length + right_context_blocks_length, :, :],
- left_context_val,
- v[-utterance_length:, :, :],
- ],
- dim=0,
- )
- next_k = new_k[mem_length + right_context_blocks_length :, :, :]
- next_v = new_v[mem_length + right_context_blocks_length :, :, :]
-
- attention_weights, attention_weights_float, v = self.prepare_attention_weights(
- q=q,
- new_k=new_k,
- new_v=new_v,
- input_shape=input_shape,
- rpe=rpe,
- )
- # In online decoding, we don't have attention mask. But we still need
- # to disable the attention from summary query to memory
- attention_weights_float[:, -1, :mem_length] = float(self.negative_inf)
- rc_output_memory = self.prepare_attention_output(
- attention_weights=attention_weights,
- attention_weights_float=attention_weights_float,
- v=v,
- input_shape=input_shape,
- key_length=key_length,
- padding_mask=padding_mask,
- rpe=rpe,
- )
-
- # In decoding, summary length is 1
- if self.use_mem:
- next_m = rc_output_memory[-1:]
- next_m = self.squash_mem(next_m)
- # rc and output
- rc_output = rc_output_memory[:-1]
- if not self.nonlinear_squash_mem:
- next_m = torch.clamp(next_m, min=-10, max=10)
- else:
- rc_output = rc_output_memory
- # empty tensor as input mems
- next_m = mems
-
- return rc_output, next_m, next_k, next_v
-
- def quantize_(self, params=None):
- if params and "per_channel" in params and params["per_channel"]:
- qconfig = per_channel_dynamic_qconfig
- else:
- qconfig = default_dynamic_qconfig
- torch.quantization.quantize_dynamic(
- self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
- )
- return self
-
-
-class NoSegAugmentedMemoryTransformer(nn.Module):
- """
- Whole utterance augmented memory transformer.
-
- This is not pyspeech nn layer. It is used as a module in a master layer where
- multiple transformers is used.
- """
-
- def __init__(
- self,
- input_dim,
- num_heads,
- ffn_dim,
- dropout_in_attn=0.0,
- dropout_on_attn=None,
- dropout_on_fc1=None,
- dropout_on_fc2=None,
- activation_fn="relu",
- tanh_on_mem=False,
- std_scale=None,
- scaled_init=False,
- segment_size=128,
- use_mem=True,
- mini_batches=False,
- negative_inf="-inf",
- layer_index=-1,
- summarization_method="mean",
- max_relative_position=0,
- rpe_old_option=True,
- ):
- super(NoSegAugmentedMemoryTransformer, self).__init__()
-
- self.attention = NoSegAugmentedMemoryMultiheadAttentionBmm(
- input_dim=input_dim,
- num_heads=num_heads,
- dropout=dropout_in_attn,
- scaled_init=scaled_init,
- tanh_on_mem=tanh_on_mem,
- std_scale=std_scale,
- use_mem=use_mem,
- mini_batches=mini_batches,
- negative_inf=negative_inf,
- layer_index=layer_index,
- max_relative_position=max_relative_position,
- )
- self.dropout = nn.Dropout(dropout_on_attn)
- self.pos_ff = PositionwiseFF(
- input_dim=input_dim,
- ffn_dim=ffn_dim,
- dropout_on_fc1=dropout_on_fc1,
- dropout_on_fc2=dropout_on_fc2,
- activation_fn=activation_fn,
- )
- self.layer_norm_pre = Fp32LayerNorm(input_dim)
- self.layer_norm = Fp32LayerNorm(input_dim)
- self.segment_size = segment_size
- self.use_mem = use_mem
-
- self.memory_op = SummarizationLayer(
- summarization_method, segment_size, input_dim
- )
-
- def set_mini_batches(self, mini_batches):
- self.attention.mini_batches = mini_batches
-
- def gen_summary_queries(self, input):
- sum_input = self.memory_op(input)
- return sum_input
-
- def pre_attention_ops(self, input, right_context_blocks):
- rc_length = right_context_blocks.size(0)
- input_length = input.size(0)
-
- rc_and_input = torch.cat([right_context_blocks, input], dim=0)
- residual_input = rc_and_input
- rc_and_input = self.layer_norm_pre(rc_and_input)
-
- query_input = rc_and_input[-input_length:, :, :]
- return rc_length, input_length, residual_input, query_input, rc_and_input
-
- def after_attention_ops(self, attention_output, residual_input):
- output = self.dropout(attention_output)
- output = output + residual_input
- output = self.pos_ff(output)
- output = self.layer_norm(output)
- return output
-
- @torch.jit.export
- def forward_jit(
- self,
- input: Tensor,
- lengths: Tensor,
- mems: Tensor,
- left_context_key: Tensor,
- left_context_val: Tensor,
- right_context_blocks: Tensor,
- rpe: Optional[Tensor],
- ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
-
- results = self.pre_attention_ops(input, right_context_blocks)
- rc_length, input_length, residual_input, query_input, rc_and_input = results
-
- # In online decoding, the summary query size is always 1 or 0
- if self.use_mem:
- summary_query = self.gen_summary_queries(query_input)
- summary_query = summary_query[0:1, :, :]
- rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
- else:
- rc_qu_su = rc_and_input
-
- rc_output, next_m, next_k, next_v = self.attention.forward_jit(
- input=rc_qu_su,
- lengths=lengths,
- mems=mems,
- left_context_key=left_context_key,
- left_context_val=left_context_val,
- rpe=rpe,
- )
- rc_output = self.after_attention_ops(rc_output, residual_input)
- results = (
- rc_output[-input_length:, :, :],
- next_m,
- rc_output[0:rc_length, :, :],
- next_k,
- next_v,
- )
- return results
-
- @torch.jit.unused
- def forward(
- self,
- input,
- lengths,
- mems,
- right_context_blocks,
- attention_mask,
- pre_mems,
- left_context_key,
- left_context_val,
- rpe,
- ):
-
- results = self.pre_attention_ops(input, right_context_blocks)
- rc_length, input_length, residual_input, query_input, rc_and_input = results
- if self.use_mem:
- summary_query = self.gen_summary_queries(query_input)
- rc_qu_su = torch.cat([rc_and_input, summary_query], dim=0)
- else:
- rc_qu_su = rc_and_input
-
- rc_output, next_m, next_k, next_v = self.attention(
- input=rc_qu_su,
- lengths=lengths,
- mems=mems,
- attention_mask=attention_mask,
- pre_mems=pre_mems,
- left_context_key=left_context_key,
- left_context_val=left_context_val,
- rpe=rpe,
- )
-
- # [TODO] Note memory did not go through pos_ff. What happen if we pass
- # memory through the pos_ff as well?
- rc_output = self.after_attention_ops(rc_output, residual_input)
- results = (
- rc_output[-input_length:, :, :],
- next_m,
- rc_output[0:rc_length, :, :],
- next_k,
- next_v,
- )
-
- return results
-
-
-class NoSegAugmentedMemoryTransformerEncoderLayer(FairseqEncoder):
- """
- Whole utterance augmented memory transformer encoder layer. This is a master layer
- where we can define multiple augmented memory transformers. There are two reasons
- to setup the master layer.
- 1. We only need to define once about the attention mask. All the layers in the master
- layer share the same mask.
- 2. pyspeech nn layer has special input and output format. Defining one master layer is
- easier to passing memory between different layes inside the master layer
-
- args:
- input_dim: input embedding dimension
- num_heads: number of heads in multihead self-attention
- ffn_dim: ffn dimension in FFN layer
- num_layers: number of augmented memory transformer layers
- dropout_in_attn: dropout used in multi-head self-attention
- dropout_on_attn: dropout used for output from te multihead self-attention
- dropout_on_fc1: dropout used in FFN layer for the first linear layer
- dropout_on_fc2: dropout used in FFN layer for the second linear layer
- segment_size: segment size for each segment
- context_config: (left_context_size, right_context_size) defines the surround context size
- for each segment
- max_memory_size: maximum memory size used for each segment
- scaled_init: whether use scaled init for weight initialization in attention layer
- std_scale: if std_scale is not None. The weak attention suppression is
- turned on. For std_scale = 0.5, all the attention smaller than
- mean + 0.5 * std will be suppressed.
- activation_fn: activation function used in FFN layer. [ReLU, GELU] supported
- tanh_on_mem: whether use tanh on memory
- mini_batches: use mini-btach training
- negative_inf: the negative infinity value used in attention masking. default is "-inf".
- For some situation, e.g. LM. it is better to use "-1e8" to avoid nan issue.
- summarization_method: method to generate segment summrization embedding
- max_relative_position: max relatie position for relative position embedding
- rpe_old_option: To be compatible with previous model. The previous model
- was trained with attention += attention + rpe. The correct equation
- should be attention = attention + rpe
- [TODO]: remove the rpe_old_option by the end of 2021 Q1.
-
- """
-
- def __init__(
- self,
- input_dim,
- num_heads,
- ffn_dim,
- num_layers=1,
- dropout_in_attn=0.0,
- dropout_on_attn=0.0,
- dropout_on_fc1=0.0,
- dropout_on_fc2=0.0,
- segment_size=128,
- context_config=(0, 0),
- max_memory_size=0,
- scaled_init=True,
- std_scale=None,
- activation_fn="relu",
- tanh_on_mem=False,
- mini_batches=False,
- negative_inf="-inf",
- deep_init=True,
- summarization_method="mean",
- max_relative_position=0,
- rpe_old_option=True,
- ):
- super().__init__(None)
- if input_dim % num_heads:
- raise ValueError(
- "input_dim ({}) must be divisible by num_heads ({})".format(
- input_dim, num_heads
- )
- )
-
- # we used to support growing memory size. However, it will cause
- # cross stream batching failure. Now we need to have exact max memory size
- if max_memory_size < 0:
- raise ValueError("max_memory_size must be >= 0")
-
- # Only assign right_context. In decoding, left context will be cached.
- # No need to let the online decoder to re-assign the left context
- self.left_context, self.right_context = context_config
- self.segment_size = segment_size
- self.memory_dim = input_dim
- self.max_memory_size = max_memory_size
- self.mini_batches = mini_batches
- if self.max_memory_size != 0:
- self.use_mem = True
- else:
- self.use_mem = False
-
- self.memory_op = SummarizationLayer(
- summarization_method, segment_size, input_dim
- )
-
- self.layers = torch.nn.ModuleList()
- self.num_layers = num_layers
- self.max_relative_position = max_relative_position
- if self.max_relative_position > 0:
- self.use_rpe = True
- else:
- self.use_rpe = False
- for i in range(self.num_layers):
- if deep_init:
- layer_index = i
- else:
- layer_index = -1
-
- self.layers.append(
- NoSegAugmentedMemoryTransformer(
- num_heads=num_heads,
- input_dim=input_dim,
- ffn_dim=ffn_dim,
- dropout_in_attn=dropout_in_attn,
- dropout_on_attn=dropout_on_attn,
- dropout_on_fc1=dropout_on_fc1,
- dropout_on_fc2=dropout_on_fc2,
- segment_size=segment_size,
- std_scale=std_scale,
- activation_fn=activation_fn,
- tanh_on_mem=tanh_on_mem,
- scaled_init=scaled_init,
- use_mem=self.use_mem,
- mini_batches=mini_batches,
- negative_inf=negative_inf,
- layer_index=layer_index,
- summarization_method=summarization_method,
- max_relative_position=max_relative_position,
- rpe_old_option=rpe_old_option,
- )
- )
-
- def set_mini_batches(self, mini_batches):
- # handy function only used for unit test
- self.mini_batches = mini_batches
- for layer in self.layers:
- layer.set_mini_batches(mini_batches)
-
- def _get_relative_position(
- self,
- input: Tensor,
- max_relative_position: int,
- left_context_length: int,
- past_length: int,
- is_decoding: bool,
- ):
- # For training, we copy the right context to the start of the utterance
- # First dimension in distance is corresponding to query.
- # [right context, utterance, summary vector]
- # Second dimension in distance is corresponding to key.
- # [Memory bank, right context, utterance]
- # For summary vector in query part, the distance with
- # all other position is 2*max_position. For memory bank in key,
- # the distance with all other positions is 0.
-
- T, B, D = input.shape
- num_segs = math.ceil((T - self.right_context) / self.segment_size)
-
- # utterance
- u_st = past_length * self.segment_size
- u_ed = u_st + T
- utterance_ranges = torch.arange(u_st, u_ed - self.right_context)
-
- # left context. Only in minibatch or decoding
- left_context_ranges = torch.arange(u_st - left_context_length, u_st)
-
- # Right context block
- # right context + utterance
- right_context_blocks = []
- for i in range(0, num_segs - 1):
- st = (i + 1) * self.segment_size + u_st
- ed = st + self.right_context
- assert ed < u_ed
- temp = torch.arange(st, ed)
- right_context_blocks.append(temp)
- right_context_blocks.append(torch.arange(u_ed - self.right_context, u_ed))
- right_context_ranges = torch.cat(right_context_blocks)
-
- if self.use_mem:
- # Memory bank
- # The position for memory -n, .., -1
- if is_decoding:
- memory_size = min(past_length, self.max_memory_size)
- else:
- memory_size = num_segs + past_length - 1
- memory_bank_ranges = torch.arange(
- -max_relative_position - 1, -max_relative_position - 1 - memory_size, -1
- )
-
- # summary vector
- # The position for summary vector as the T+max_relative_position+1.
- # After the clamping, the relative position is max_relative_position
- summary_pos_st = u_ed + max_relative_position + 1
- summary_vector_ranges = torch.arange(
- summary_pos_st, summary_pos_st + num_segs
- )
-
- key_ranges = torch.cat(
- [
- memory_bank_ranges,
- right_context_ranges,
- left_context_ranges,
- utterance_ranges,
- ]
- )
-
- query_ranges = torch.cat(
- [right_context_ranges, utterance_ranges, summary_vector_ranges]
- )
- else:
- key_ranges = torch.cat(
- [right_context_ranges, left_context_ranges, utterance_ranges]
- )
-
- query_ranges = torch.cat([right_context_ranges, utterance_ranges])
-
- distance = key_ranges[None, :] - query_ranges[:, None]
- distance_clamp = (
- torch.clamp(distance, -max_relative_position, max_relative_position)
- + max_relative_position
- )
- distance_clamp = distance_clamp.to(input.device).long().detach()
- return distance_clamp
-
- def _get_attention_mask(self, input, past_length=0, left_context_cache=0):
- # attention mask for each query contains three parts:
- # 1. memory part
- # 2. left_context + segment
- # 3. right_context_block
- # so for each segment and its correspoinding right context block,
- # the attention matrix is formed by 9 parts:
- # [0, m, 0, 0, right_context, 0, 0, seg, 0]
- # [before memory, memory, after memory, before right context, right_context,
- # after right context, before seg, seg, after seg]
- #
- # Query is formed in the way as [right_context_blocks, utterance, summary]
- #
- # Note: put m and right_context before segment is convenient
- # for padding_mask operation.
- # Key lengths = m_length + right_context_block_length + lengths
- utterance_length, batch_size, _ = input.shape
- summary_length = math.ceil(utterance_length / self.segment_size)
- num_segs = summary_length
- rc_length = self.right_context * num_segs
- rc = self.right_context
- lc = self.left_context
-
- # using mini-batches, there is left context cache available for current
- # sequence.
- lcc = left_context_cache
-
- # max_memory_size is 0 then we don't have memory and summary
- # past_length is the memory carry from previous sequence
- if self.use_mem:
- mem_length = num_segs - 1 + past_length
- else:
- mem_length = 0
- rc_mask = []
- query_mask = []
- summary_mask = []
- for j in range(0, num_segs):
- ssize = min(self.segment_size, utterance_length - j * self.segment_size)
-
- rc_size = rc
- rc_mat = []
- q_mat = []
- s_mat = []
- m_start = max(j + past_length - self.max_memory_size, 0)
-
- # max_memory_size is 0, then we don't use memory
- if self.use_mem:
- # part 0: before memory
- rc_mat.append(input.new_zeros(rc_size, m_start))
- q_mat.append(input.new_zeros(ssize, m_start))
- s_mat.append(input.new_zeros(1, m_start))
-
- # part 1: memory
- col_1 = j + past_length - m_start
- rc_mat.append(torch.ones(rc_size, col_1, device=input.device))
- q_mat.append(torch.ones(ssize, col_1, device=input.device))
- # based on D22875746, disable summary query attention
- # on memeory is better for long form utterance
- s_mat.append(input.new_zeros(1, col_1))
-
- # part 2: after memory
- col_2 = mem_length - (j + past_length)
- rc_mat.append(input.new_zeros(rc_size, col_2))
- q_mat.append(input.new_zeros(ssize, col_2))
- s_mat.append(input.new_zeros(1, col_2))
-
- # part 3: before right context
- rc_start = j * rc
- rc_mat.append(input.new_zeros(rc_size, rc_start))
- q_mat.append(input.new_zeros(ssize, rc_start))
- s_mat.append(input.new_zeros(1, rc_start))
-
- # part 4: right context
- rc_end = rc_start + rc
- col_4 = rc
- rc_mat.append(torch.ones(rc_size, col_4, device=input.device))
- q_mat.append(torch.ones(ssize, col_4, device=input.device))
- s_mat.append(torch.ones(1, col_4, device=input.device))
-
- # part 5: after right context
- col_5 = rc_length - rc_end
- rc_mat.append(input.new_zeros(rc_size, col_5))
- q_mat.append(input.new_zeros(ssize, col_5))
- s_mat.append(input.new_zeros(1, col_5))
-
- # part 6: before query segment
- seg_start = max(j * self.segment_size + lcc - lc, 0)
- rc_mat.append(input.new_zeros(rc_size, seg_start))
- q_mat.append(input.new_zeros(ssize, seg_start))
- s_mat.append(input.new_zeros(1, seg_start))
-
- # part 7: query segment
- # note: right context is put in right context block
- # here we only need to consider about left context
- seg_end = min((j + 1) * self.segment_size + lcc, utterance_length + lcc)
- col_7 = seg_end - seg_start
- rc_mat.append(torch.ones(rc_size, col_7, device=input.device))
- q_mat.append(torch.ones(ssize, col_7, device=input.device))
- s_mat.append(torch.ones(1, col_7, device=input.device))
-
- # part 8: after query segment
- col_8 = utterance_length + lcc - seg_end
- rc_mat.append(input.new_zeros(rc_size, col_8))
- q_mat.append(input.new_zeros(ssize, col_8))
- s_mat.append(input.new_zeros(1, col_8))
-
- rc_mask.append(torch.cat(rc_mat, dim=1))
- query_mask.append(torch.cat(q_mat, dim=1))
- summary_mask.append(torch.cat(s_mat, dim=1))
-
- # no memory, then we don't need summary either
- if self.use_mem:
- attention_mask = (
- 1
- - torch.cat(
- [
- torch.cat(rc_mask, dim=0),
- torch.cat(query_mask, dim=0),
- torch.cat(summary_mask, dim=0),
- ],
- dim=0,
- )
- ).to(torch.bool)
- else:
- attention_mask = (
- 1
- - torch.cat(
- [torch.cat(rc_mask, dim=0), torch.cat(query_mask, dim=0)], dim=0
- )
- ).to(torch.bool)
-
- return attention_mask
-
- @torch.jit.export
- def init_state(
- self, batch_size: int, device: Optional[Device] = None
- ) -> List[Tensor]:
- empty_memory = torch.zeros(
- self.num_layers,
- self.max_memory_size,
- batch_size,
- self.memory_dim,
- device=device,
- )
- left_context_key = torch.zeros(
- self.num_layers,
- self.left_context,
- batch_size,
- self.memory_dim,
- device=device,
- )
- left_context_val = torch.zeros(
- self.num_layers,
- self.left_context,
- batch_size,
- self.memory_dim,
- device=device,
- )
- past_length = torch.zeros(1, batch_size, dtype=torch.int32, device=device)
-
- return [empty_memory, left_context_key, left_context_val, past_length]
-
- @torch.jit.export
- def batch_state(self, states: List[List[Tensor]]) -> List[Tensor]:
- if len(states) == 0:
- return []
- batched_m = []
- batched_lc_key = []
- batched_lc_val = []
- batched_past_length = []
- for state in states:
- if len(state) == 0:
- continue
- m, lc_key, lc_val, past_length = state
- batched_m.append(m)
- batched_lc_key.append(lc_key)
- batched_lc_val.append(lc_val)
- batched_past_length.append(past_length)
-
- if (
- (len(batched_m) == 0)
- or (len(batched_lc_key) == 0)
- or (len(batched_lc_val) == 0)
- or (len(batched_past_length) == 0)
- ):
- return [
- torch.tensor([]),
- torch.tensor([]),
- torch.tensor([]),
- torch.tensor([]),
- ]
-
- batched_m = torch.cat(batched_m, dim=2)
- batched_lc_key = torch.cat(batched_lc_key, dim=2)
- batched_lc_val = torch.cat(batched_lc_val, dim=2)
- batched_past_length = torch.cat(batched_past_length, dim=1)
- return [batched_m, batched_lc_key, batched_lc_val, batched_past_length]
-
- @torch.jit.export
- def reorder_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
- if len(state) == 0:
- return []
- m, lc_key, lc_val, past_length = state
- indices = indices.to(device=m.device)
- reord_m = torch.index_select(m, 2, indices)
- reord_lc_key = torch.index_select(lc_key, 2, indices)
- reord_lc_val = torch.index_select(lc_val, 2, indices)
- reord_past_length = torch.index_select(past_length, 1, indices)
- return [reord_m, reord_lc_key, reord_lc_val, reord_past_length]
-
- @torch.jit.export
- def reset_state(self, state: List[Tensor], indices: Tensor) -> List[Tensor]:
- m, lc_key, lc_val, past_length = state
- m = m.index_fill(dim=2, index=indices, value=0.0)
- lc_key = lc_key.index_fill(dim=2, index=indices, value=0.0)
- lc_val = lc_val.index_fill(dim=2, index=indices, value=0.0)
- past_length = past_length.index_fill(dim=1, index=indices, value=0)
-
- return [m, lc_key, lc_val, past_length]
-
- @torch.jit.export
- def state_size(self) -> int:
- return 4
-
- @torch.jit.export
- def batch_size_in_state(
- self, state: Optional[List[Tensor]], sloppy: bool = True
- ) -> Optional[int]:
- if state is None:
- return None
- return state[0].size(2)
-
- def gen_summary_queries(self, input):
- sum_input = self.memory_op(input)
- return sum_input
-
- def _gen_right_context_padded_input(self, input):
- # This function deals with input that is already
- # padded with right context (e.g. minibatch training)
- right_context_blocks = []
- T, B, D = input.shape
- num_segs = math.ceil((T - self.right_context) / self.segment_size)
- for i in range(0, num_segs - 1):
- st = (i + 1) * self.segment_size
- ed = st + self.right_context
- assert ed < T
- temp = input[st:ed, :, :]
- right_context_blocks.append(temp)
-
- # last segment right context is already available
- right_context_blocks.append(input[T - self.right_context :, :, :])
- return torch.cat(right_context_blocks, dim=0)
-
- def _gen_segs_right_context(self, input, lengths):
- segments = []
- T, B, D = input.size()
- nT = T - self.right_context
-
- # assume input is right context padded
- num_segs = math.ceil(nT / self.segment_size)
- # pad zeros to the utterance to make sure each
- # segment has the same right context. For the
- for i in range(0, num_segs - 1):
- st = i * self.segment_size
- ed = min(T, st + self.segment_size + self.right_context)
- temp = input[st:ed, :, :]
- rest_lengths = torch.clamp(
- lengths - self.segment_size, min=0, max=nT - (i + 1) * self.segment_size
- )
- segments.append((temp, lengths - rest_lengths + self.right_context))
- lengths = rest_lengths
-
- last_seg = input[st + self.segment_size :, :, :]
- segments.append((last_seg, rest_lengths + self.right_context))
-
- return segments
-
- @torch.jit.unused
- def forward(
- self, input: Tensor, padding_masks: Tensor, state: Optional[List[Tensor]] = None
- ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
- # Xutai: originally the second argument is lengths.
- lengths = (~padding_masks).sum(dim=1).long()
- # mini batch training.
- if self.mini_batches:
- return self.forward_mini_batches(input, lengths, state)
-
- # regular full sequence training. Note, assume the right context in provided
- # in the input.
- T, B, D = input.size()
- right_context_blocks = self._gen_right_context_padded_input(input)
-
- # generate the relative positional embedding
- if self.use_rpe:
- rpe = self._get_relative_position(
- input=input,
- max_relative_position=self.max_relative_position,
- left_context_length=0,
- past_length=0,
- is_decoding=False,
- )
- else:
- rpe = None
- input = input[: T - self.right_context, :, :]
-
- attention_mask = self._get_attention_mask(input)
-
- # firt layer use each segment mean as memory
- # ignore the last one seg average
- if self.use_mem:
- mems = self.gen_summary_queries(input)[:-1, :, :]
- else:
- mems = torch.zeros(0, input.size(1), input.size(2), device=input.device)
- mems = mems.type_as(input)
-
- output = input
- all_outputs = []
-
- for layer in self.layers:
- output, mems, right_context_blocks, _, _ = layer(
- input=output,
- lengths=lengths,
- attention_mask=attention_mask,
- mems=mems,
- right_context_blocks=right_context_blocks,
- pre_mems=None,
- left_context_key=None,
- left_context_val=None,
- rpe=rpe,
- )
- all_outputs.append(output)
- return output, padding_masks, [], all_outputs
-
- def forward_jit_mini_batch_init(
- self,
- seg: Tensor,
- state: Optional[List[Tensor]] = None,
- is_decoding: bool = False,
- ):
- # Prepare state. In whole sequence training, state is ignored.
- # For minibatch training, we need to prepare state
- if state is None:
- state = self.init_state(batch_size=seg.size(1), device=seg.device)
- if seg.dtype == torch.half:
- state = [state[0].half(), state[1].half(), state[2].half(), state[3]]
-
- if self.use_mem:
- # note input average only on seg, not on right context
- # first layer use each segmetn mean as memory. the last
- # one segment average is used in state
- full_mems = self.gen_summary_queries(seg)
- if is_decoding:
- mems = full_mems[0:1, :, :]
- state_mems = torch.cat([state[0][0], mems], dim=0)
- else:
- mems = full_mems[:-1, :, :]
- state_mems = torch.cat([state[0][0], full_mems], dim=0)
- else:
- mems = state[0][0]
- state_mems = mems
-
- # track processed segment number or memory number
- # the same batch as the same bumber of past length
- past_length = state[3][0][0].item()
- past_left_context = min(past_length * self.segment_size, self.left_context)
- past_length = min(self.max_memory_size, past_length)
-
- return state, mems, state_mems, past_length, past_left_context
-
- def state_update_before(
- self, layer: int, state: List[Tensor], past_length: int, past_left_context: int
- ):
- pre_mems = state[0][layer][self.max_memory_size - past_length :, :, :]
- lc_key = state[1][layer][self.left_context - past_left_context :, :, :]
- lc_val = state[2][layer][self.left_context - past_left_context :, :, :]
- return pre_mems, lc_key, lc_val
-
- def state_update_after(
- self,
- layer: int,
- state: List[Tensor],
- mems: Tensor,
- next_key: Tensor,
- next_val: Tensor,
- mems_list: List[Tensor],
- lc_key_list: List[Tensor],
- lc_val_list: List[Tensor],
- ):
- # mems is used for next layer
- if layer < self.num_layers - 1:
- state_mems = torch.cat([state[0][layer + 1], mems], dim=0)
- mems_list.append(state_mems[-self.max_memory_size :, :, :])
-
- # when mems pass to next sequence, we need the last memory. when mems
- # use for the next layer, we can ignore the last memory
- mems = mems[:-1, :, :]
-
- # note state[1][i] and state[2][i] original length equals to self.left_context
- new_k = torch.cat([state[1][layer], next_key], dim=0)
- new_v = torch.cat([state[2][layer], next_val], dim=0)
- lc_key_list.append(new_k[-self.left_context :, :, :])
- lc_val_list.append(new_v[-self.left_context :, :, :])
- return mems_list, lc_key_list, lc_val_list, mems
-
- def state_update_after_loop(
- self,
- state: List[Tensor],
- mems_list: List[Tensor],
- lc_key_list: List[Tensor],
- lc_val_list: List[Tensor],
- update_length: int,
- ):
- state[0] = torch.stack(mems_list, dim=0)
- state[1] = torch.stack(lc_key_list, dim=0)
- state[2] = torch.stack(lc_val_list, dim=0)
- state[3] = state[3] + update_length
- return state
-
- @torch.jit.unused
- def forward_mini_batches(
- self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
- ) -> Tuple[Tensor, Tensor, List[Tensor], List[Tensor]]:
- T, B, D = input.size()
-
- # input without right context
- seg = input[: T - self.right_context, :, :]
-
- # get right context blocks
- right_context_blocks = self._gen_right_context_padded_input(input)
-
- mems_list = []
- lc_key_list = []
- lc_val_list = []
- results = self.forward_jit_mini_batch_init(seg, state, False)
- state, mems, state_mems, past_length, past_left_context = results
-
- # relative position embedding
- if self.use_rpe:
- rpe = self._get_relative_position(
- input=input,
- max_relative_position=self.max_relative_position,
- left_context_length=past_left_context,
- past_length=past_length,
- is_decoding=False,
- )
- else:
- rpe = None
-
- # get attention mask based on seg (not include right context) and available
- # left context
- attention_mask = self._get_attention_mask(seg, past_length, past_left_context)
- mems_list.append(state_mems[-self.max_memory_size :, :, :])
- output = seg
- i = 0
- all_outputs = []
- for layer in self.layers:
- # In order to make cross stream batching work, mem, left context key
- # and left context value in the state should always be the same shape.
- # We use the past length to track the processed segment number. In this
- # way, we take out the essential memory, left context key and left
- # context val from the state. After finish the forward for current segment
- # we add the new memory, left context key and left context value into the
- # staate and trim out the oldest part to keep the shape consistent.
- pre_mems, lc_key, lc_val = self.state_update_before(
- i, state, past_length, past_left_context
- )
-
- output, mems, right_context_blocks, next_key, next_val = layer.forward(
- input=output,
- lengths=lengths,
- attention_mask=attention_mask,
- mems=mems,
- right_context_blocks=right_context_blocks,
- pre_mems=pre_mems,
- left_context_key=lc_key,
- left_context_val=lc_val,
- rpe=rpe,
- )
- all_outputs.append(output)
- mems_list, lc_key_list, lc_val_list, mems = self.state_update_after(
- layer=i,
- state=state,
- mems=mems,
- next_key=next_key,
- next_val=next_val,
- mems_list=mems_list,
- lc_key_list=lc_key_list,
- lc_val_list=lc_val_list,
- )
-
- i += 1
-
- # update state
- update_length = math.ceil((T - self.right_context) / self.segment_size)
- state = self.state_update_after_loop(
- state=state,
- mems_list=mems_list,
- lc_key_list=lc_key_list,
- lc_val_list=lc_val_list,
- update_length=update_length,
- )
-
- return output, lengths, state, all_outputs
-
- def forward_jit_test(
- self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
- ) -> Tuple[Tensor, Tensor, List[Tensor]]:
- """
- This one simulate sequence encoder forward jit. This is for unit test purpose.
- It is not used in training or decoding. Note, extra_right_context is set in
- the model. In unit test, input = [utterance, right_context], lengths =
- [utterance_length].
- args:
- input: input utterance
- lengths: utterance input length
- state: None here. input is whole utterance
- """
- # [TODO] sequence_to_segment has bug in lengths.
- seg_src_tokens_lengths = self._gen_segs_right_context(input, lengths)
-
- seg_enc_tokens_lengths: List[Tuple[Tensor, Tensor]] = []
- state: Optional[List[Tensor]] = None
- for seg_src_tokens, seg_src_lengths in seg_src_tokens_lengths:
- seg_enc_tokens, seg_enc_lengths, state = self.forward_jit(
- input=seg_src_tokens, lengths=seg_src_lengths, state=state
- )
- seg_enc_tokens_lengths.append((seg_enc_tokens, seg_enc_lengths))
-
- enc_tokens, enc_lengths = segments_to_sequence(
- segments=seg_enc_tokens_lengths, time_axis=0
- )
-
- state = [] # returns trivial state
-
- return enc_tokens, enc_lengths, state
-
- @torch.jit.export
- def forward_jit(
- self, input: Tensor, lengths: Tensor, state: Optional[List[Tensor]] = None
- ) -> Tuple[Tensor, Tensor, List[Tensor]]:
- """
- Forward helper for online decoding.
-
- args:
- input: [seg, right_context]. We assume in online we
- always padding the right context to the preset right context size.
- For the last segment, we may have short segment size, but right
- context size is the same as other segments
- lengths: utterance input length is the utterance segment length and
- right context size
- state: [memory, left_context_key, left_context_val]. To improve throughput,
- in addition to memory, we also cache key and value for left_context in
- multihead self-attention
- """
- # In online decoding, input = [segment, right_context]
- # Lengths = [segment_length, right_context_length]
- # so we need strip right context in output
- T, B, D = input.size()
- rc_str = T - self.right_context
- rc_end = T
- right_context_blocks = input[rc_str:rc_end, :, :]
- seg = input[:rc_str, :, :]
- lengths = torch.clamp(lengths - self.right_context, min=0)
- mems_list = []
- lc_key_list = []
- lc_val_list = []
-
- results = self.forward_jit_mini_batch_init(seg, state, True)
- state, mems, state_mems, past_length, past_left_context = results
-
- # relative position embedding
- if self.use_rpe:
- rpe = self._get_relative_position(
- input=input,
- max_relative_position=self.max_relative_position,
- left_context_length=past_left_context,
- past_length=past_length,
- is_decoding=True,
- )
- else:
- rpe = None
-
- # memory for first layer.
- mems_list.append(state_mems[-self.max_memory_size :, :, :])
- output = seg
- i = 0
- for layer in self.layers:
- # In order to make cross stream batching work, mem, left context key
- # and left context value in the state should always be the same shape.
- # We use the past length to track the processed segment number. In this
- # way, we take out the essential memory, left context key and left
- # context val from the state. After finish the forward for current segment
- # we add the new memory, left context key and left context value into the
- # staate and trim out the oldest part to keep the shape consistent.
- true_mems, lc_key, lc_val = self.state_update_before(
- layer=i,
- state=state,
- past_length=past_length,
- past_left_context=past_left_context,
- )
-
- output, mems, right_context_blocks, next_key, next_val = layer.forward_jit(
- input=output,
- lengths=lengths,
- mems=true_mems,
- right_context_blocks=right_context_blocks,
- left_context_key=lc_key,
- left_context_val=lc_val,
- rpe=rpe,
- )
- # mems is used for next layer
- mems_list, lc_key_list, lc_val_list, _ = self.state_update_after(
- layer=i,
- state=state,
- mems_list=mems_list,
- mems=mems,
- next_key=next_key,
- next_val=next_val,
- lc_key_list=lc_key_list,
- lc_val_list=lc_val_list,
- )
- i += 1
-
- # update state
- state = self.state_update_after_loop(
- state=state,
- mems_list=mems_list,
- lc_key_list=lc_key_list,
- lc_val_list=lc_val_list,
- update_length=1,
- )
-
- return output, lengths, state
-
- def quantize_(self, params=None):
- if params and "per_channel" in params and params["per_channel"]:
- qconfig = per_channel_dynamic_qconfig
- else:
- qconfig = default_dynamic_qconfig
- torch.quantization.quantize_dynamic(
- self, {torch.nn.Linear: qconfig}, dtype=torch.qint8, inplace=True
- )
- return self
-
-
-# ------------------------------------------------------------------------------
-# Emformer encoder for seq2seq model
-# This is a wrapper over the original emformer
-# ------------------------------------------------------------------------------
-def emformer_encoder(klass):
- class SpeechEncoder(klass):
- def __init__(self, args):
- super().__init__(args)
- stride = SpeechEncoder.conv_layer_stride(args)
- trf_left_context = args.segment_left_context // stride
- trf_right_context = args.segment_right_context // stride
- context_config = [trf_left_context, trf_right_context]
- self.transformer_layers = nn.ModuleList(
- [
- NoSegAugmentedMemoryTransformerEncoderLayer(
- input_dim=args.encoder_embed_dim,
- num_heads=args.encoder_attention_heads,
- ffn_dim=args.encoder_ffn_embed_dim,
- num_layers=args.encoder_layers,
- dropout_in_attn=args.dropout,
- dropout_on_attn=args.dropout,
- dropout_on_fc1=args.dropout,
- dropout_on_fc2=args.dropout,
- activation_fn=args.activation_fn,
- context_config=context_config,
- segment_size=args.segment_length,
- max_memory_size=args.max_memory_size,
- scaled_init=True, # TODO: use constant for now.
- tanh_on_mem=args.amtrf_tanh_on_mem,
- )
- ]
- )
-
- def forward(self, src_tokens, src_lengths):
- encoder_out = super().forward(src_tokens, src_lengths)
- output = encoder_out["encoder_out"][0]
- encoder_padding_masks = encoder_out["encoder_padding_mask"][0]
-
- # This is because that in the original implementation
- # the output didn't consider the last segment as right context.
- encoder_padding_masks = encoder_padding_masks[:, : output.size(0)]
-
- return {
- "encoder_out": [output],
- "encoder_padding_mask": [encoder_padding_masks],
- "encoder_embedding": [],
- "encoder_states": [],
- "src_tokens": [],
- "src_lengths": [],
- }
-
- @staticmethod
- def conv_layer_stride(args):
- # TODO: make it configurable from the args
- return 4
-
- SpeechEncoder.__name__ = klass.__name__
- return SpeechEncoder
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/adam.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/adam.py
deleted file mode 100644
index d3ae9e64a74774310adcd9968d2eae23368890f9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/optim/adam.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import math
-from collections.abc import Collection
-from dataclasses import dataclass, field
-from typing import Any, List
-
-import torch
-import torch.distributed as dist
-import torch.optim
-from fairseq.dataclass import FairseqDataclass
-from fairseq.optim import FairseqOptimizer, register_optimizer
-from fairseq.optim.fused_adam import get_fused_adam_class
-from omegaconf import II, OmegaConf
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class FairseqAdamConfig(FairseqDataclass):
- adam_betas: Any = field(
- default=(0.9, 0.999), metadata={"help": "betas for Adam optimizer"}
- )
- adam_eps: float = field(
- default=1e-8, metadata={"help": "epsilon for Adam optimizer"}
- )
- weight_decay: float = field(default=0.0, metadata={"help": "weight decay"})
- use_old_adam: bool = field(
- default=False, metadata={"help": "Use fairseq.optim.adam.Adam"}
- )
- fp16_adam_stats: bool = field(
- default=False, metadata={"help": "use FP16 stats (with automatic scaling)"}
- )
- # TODO common vars below in parent
- tpu: bool = II("common.tpu")
- lr: List[float] = II("optimization.lr")
-
-
-@register_optimizer("adam", dataclass=FairseqAdamConfig)
-class FairseqAdam(FairseqOptimizer):
- """Adam optimizer for fairseq.
-
- Important note: this optimizer corresponds to the "AdamW" variant of
- Adam in its weight decay behavior. As such, it is most closely
- analogous to torch.optim.AdamW from PyTorch.
- """
-
- def __init__(self, cfg: FairseqAdamConfig, params):
- super().__init__(cfg)
- fused_adam_cls = get_fused_adam_class()
- use_fused_adam = (
- not getattr(cfg, "use_old_adam", False)
- and fused_adam_cls is not None
- and torch.cuda.is_available()
- )
- if getattr(cfg, "tpu", False):
- if self.cfg.fp16_adam_stats:
- raise NotImplementedError("--fp16-adam-stats is only supported on GPU")
- # on TPUs we use the Adam defined here, since it
- # automatically casts gradients to FP32
- self._optimizer = Adam(params, **self.optimizer_config)
- elif use_fused_adam:
- logger.info("using FusedAdam")
- self._optimizer = fused_adam_cls(
- params,
- use_fp16_stats=self.cfg.fp16_adam_stats,
- **self.optimizer_config
- )
- else:
- if self.cfg.fp16_adam_stats:
- raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1")
- self._optimizer = Adam(params, **self.optimizer_config)
-
- @property
- def optimizer_config(self):
- """
- Return a kwarg dictionary that will be used to override optimizer
- args stored in checkpoints. This allows us to load a checkpoint and
- resume training using a different set of optimizer args, e.g., with a
- different learning rate.
- """
- return {
- "lr": self.cfg.lr[0]
- if isinstance(self.cfg.lr, Collection)
- else self.cfg.lr,
- "betas": eval(self.cfg.adam_betas)
- if isinstance(self.cfg.adam_betas, str)
- else OmegaConf.to_container(self.cfg.adam_betas),
- "eps": self.cfg.adam_eps,
- "weight_decay": self.cfg.weight_decay,
- }
-
- def average_params(self):
- """Reduce Params is only used during BMUF distributed training."""
- state_dict = self.optimizer.state_dict()
- total_gpus = float(dist.get_world_size())
-
- for _, value in state_dict["state"].items():
- value["exp_avg"] /= total_gpus
- value["exp_avg_sq"] /= total_gpus
- dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM)
- dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM)
-
-
-class Adam(torch.optim.Optimizer):
- r"""Implements Adam algorithm.
-
- This implementation is modified from torch.optim.Adam based on:
- `Fixed Weight Decay Regularization in Adam`
- (see https://arxiv.org/abs/1711.05101)
-
- It has been proposed in `Adam: A Method for Stochastic Optimization`_.
-
- Args:
- params (iterable): iterable of parameters to optimize or dicts defining
- parameter groups
- lr (float, optional): learning rate (default: 1e-3)
- betas (Tuple[float, float], optional): coefficients used for computing
- running averages of gradient and its square (default: (0.9, 0.999))
- eps (float, optional): term added to the denominator to improve
- numerical stability (default: 1e-8)
- weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
- amsgrad (boolean, optional): whether to use the AMSGrad variant of this
- algorithm from the paper `On the Convergence of Adam and Beyond`_
-
- .. _Adam\: A Method for Stochastic Optimization:
- https://arxiv.org/abs/1412.6980
- .. _On the Convergence of Adam and Beyond:
- https://openreview.net/forum?id=ryQu7f-RZ
- """
-
- def __init__(
- self,
- params,
- lr=1e-3,
- betas=(0.9, 0.999),
- eps=1e-8,
- weight_decay=0,
- amsgrad=False,
- ):
- defaults = dict(
- lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
- )
- super(Adam, self).__init__(params, defaults)
-
- @property
- def supports_memory_efficient_fp16(self):
- return True
-
- @property
- def supports_flat_params(self):
- return True
-
- def step(self, closure=None):
- """Performs a single optimization step.
-
- Args:
- closure (callable, optional): A closure that reevaluates the model
- and returns the loss.
- """
- loss = None
- if closure is not None:
- loss = closure()
-
- for group in self.param_groups:
- for p in group["params"]:
- if p.grad is None:
- continue
- grad = p.grad.data
- if grad.dtype in {torch.float16, torch.bfloat16}:
- grad = grad.float()
- if grad.is_sparse:
- raise RuntimeError(
- "Adam does not support sparse gradients, please consider SparseAdam instead"
- )
- amsgrad = group.get("amsgrad", False)
-
- p_data_fp32 = p.data
- if p.data.dtype in {torch.float16, torch.bfloat16}:
- p_data_fp32 = p_data_fp32.float()
-
- state = self.state[p]
-
- # State initialization
- if len(state) == 0:
- state["step"] = 0
- # Exponential moving average of gradient values
- state["exp_avg"] = torch.zeros_like(p_data_fp32)
- # Exponential moving average of squared gradient values
- state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
- if amsgrad:
- # Maintains max of all exp. moving avg. of sq. grad. values
- state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
- else:
- state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
- state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
- if amsgrad:
- state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(
- p_data_fp32
- )
-
- exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
- if amsgrad:
- max_exp_avg_sq = state["max_exp_avg_sq"]
- beta1, beta2 = group["betas"]
-
- state["step"] += 1
-
- # Decay the first and second moment running average coefficient
- exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
- exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
- if amsgrad:
- # Maintains the maximum of all 2nd moment running avg. till now
- torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
- # Use the max. for normalizing running avg. of gradient
- denom = max_exp_avg_sq.sqrt().add_(group["eps"])
- else:
- denom = exp_avg_sq.sqrt().add_(group["eps"])
-
- bias_correction1 = 1 - beta1 ** state["step"]
- bias_correction2 = 1 - beta2 ** state["step"]
- step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
-
- if group["weight_decay"] != 0:
- p_data_fp32.add_(
- p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
- )
-
- p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
-
- if p.data.dtype in {torch.float16, torch.bfloat16}:
- p.data.copy_(p_data_fp32)
-
- return loss
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docs/Makefile b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docs/Makefile
deleted file mode 100644
index 718eddce170fe13b67216baf9d4d25b20e860506..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/docs/Makefile
+++ /dev/null
@@ -1,19 +0,0 @@
-# Minimal makefile for Sphinx documentation
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-# You can set these variables from the command line.
-SPHINXOPTS =
-SPHINXBUILD = sphinx-build
-SOURCEDIR = .
-BUILDDIR = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
- @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
- @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/predict.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/predict.py
deleted file mode 100644
index 0f704d4043a28b1bc6d80a6862be4830fe4c51ef..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/predict.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/usr/bin/env python3
-
-# Example command:
-# ./bin/predict.py \
-# model.path= \
-# indir= \
-# outdir=
-
-import logging
-import os
-import sys
-import traceback
-
-from saicinpainting.evaluation.utils import move_to_device
-from saicinpainting.evaluation.refinement import refine_predict
-os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['OPENBLAS_NUM_THREADS'] = '1'
-os.environ['MKL_NUM_THREADS'] = '1'
-os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
-os.environ['NUMEXPR_NUM_THREADS'] = '1'
-
-import cv2
-import hydra
-import numpy as np
-import torch
-import tqdm
-import yaml
-from omegaconf import OmegaConf
-from torch.utils.data._utils.collate import default_collate
-
-from saicinpainting.training.data.datasets import make_default_val_dataset
-from saicinpainting.training.trainers import load_checkpoint
-from saicinpainting.utils import register_debug_signal_handlers
-
-LOGGER = logging.getLogger(__name__)
-
-
-@hydra.main(config_path='../configs/prediction', config_name='default.yaml')
-def main(predict_config: OmegaConf):
- try:
- register_debug_signal_handlers() # kill -10 will result in traceback dumped into log
-
- device = torch.device(predict_config.device)
-
- train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
- with open(train_config_path, 'r') as f:
- train_config = OmegaConf.create(yaml.safe_load(f))
-
- train_config.training_model.predict_only = True
- train_config.visualizer.kind = 'noop'
-
- out_ext = predict_config.get('out_ext', '.png')
-
- checkpoint_path = os.path.join(predict_config.model.path,
- 'models',
- predict_config.model.checkpoint)
- model = load_checkpoint(train_config, checkpoint_path, strict=False, map_location='cpu')
- model.freeze()
- if not predict_config.get('refine', False):
- model.to(device)
-
- if not predict_config.indir.endswith('/'):
- predict_config.indir += '/'
-
- dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
- for img_i in tqdm.trange(len(dataset)):
- mask_fname = dataset.mask_filenames[img_i]
- cur_out_fname = os.path.join(
- predict_config.outdir,
- os.path.splitext(mask_fname[len(predict_config.indir):])[0] + out_ext
- )
- os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
- batch = default_collate([dataset[img_i]])
- if predict_config.get('refine', False):
- assert 'unpad_to_size' in batch, "Unpadded size is required for the refinement"
- # image unpadding is taken care of in the refiner, so that output image
- # is same size as the input image
- cur_res = refine_predict(batch, model, **predict_config.refiner)
- cur_res = cur_res[0].permute(1,2,0).detach().cpu().numpy()
- else:
- with torch.no_grad():
- batch = move_to_device(batch, device)
- batch['mask'] = (batch['mask'] > 0) * 1
- batch = model(batch)
- cur_res = batch[predict_config.out_key][0].permute(1, 2, 0).detach().cpu().numpy()
- unpad_to_size = batch.get('unpad_to_size', None)
- if unpad_to_size is not None:
- orig_height, orig_width = unpad_to_size
- cur_res = cur_res[:orig_height, :orig_width]
-
- cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
- cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
- cv2.imwrite(cur_out_fname, cur_res)
-
- except KeyboardInterrupt:
- LOGGER.warning('Interrupted by user')
- except Exception as ex:
- LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
- sys.exit(1)
-
-
-if __name__ == '__main__':
- main()
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/split_tar.py b/spaces/OpenGVLab/InternGPT/third-party/lama/bin/split_tar.py
deleted file mode 100644
index ac1692addbb4191200c8c871fe356bb80d534c44..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/bin/split_tar.py
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/usr/bin/env python3
-
-
-import tqdm
-import webdataset as wds
-
-
-def main(args):
- input_dataset = wds.Dataset(args.infile)
- output_dataset = wds.ShardWriter(args.outpattern)
- for rec in tqdm.tqdm(input_dataset):
- output_dataset.write(rec)
-
-
-if __name__ == '__main__':
- import argparse
-
- aparser = argparse.ArgumentParser()
- aparser.add_argument('infile', type=str)
- aparser.add_argument('outpattern', type=str)
-
- main(aparser.parse_args())
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/losses/adversarial.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/losses/adversarial.py
deleted file mode 100644
index d6db2967ce5074d94ed3b4c51fc743ff2f7831b1..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/losses/adversarial.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import Tuple, Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BaseAdversarialLoss:
- def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- generator: nn.Module, discriminator: nn.Module):
- """
- Prepare for generator step
- :param real_batch: Tensor, a batch of real samples
- :param fake_batch: Tensor, a batch of samples produced by generator
- :param generator:
- :param discriminator:
- :return: None
- """
-
- def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- generator: nn.Module, discriminator: nn.Module):
- """
- Prepare for discriminator step
- :param real_batch: Tensor, a batch of real samples
- :param fake_batch: Tensor, a batch of samples produced by generator
- :param generator:
- :param discriminator:
- :return: None
- """
-
- def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
- mask: Optional[torch.Tensor] = None) \
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
- """
- Calculate generator loss
- :param real_batch: Tensor, a batch of real samples
- :param fake_batch: Tensor, a batch of samples produced by generator
- :param discr_real_pred: Tensor, discriminator output for real_batch
- :param discr_fake_pred: Tensor, discriminator output for fake_batch
- :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
- :return: total generator loss along with some values that might be interesting to log
- """
- raise NotImplemented()
-
- def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
- mask: Optional[torch.Tensor] = None) \
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
- """
- Calculate discriminator loss and call .backward() on it
- :param real_batch: Tensor, a batch of real samples
- :param fake_batch: Tensor, a batch of samples produced by generator
- :param discr_real_pred: Tensor, discriminator output for real_batch
- :param discr_fake_pred: Tensor, discriminator output for fake_batch
- :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
- :return: total discriminator loss along with some values that might be interesting to log
- """
- raise NotImplemented()
-
- def interpolate_mask(self, mask, shape):
- assert mask is not None
- assert self.allow_scale_mask or shape == mask.shape[-2:]
- if shape != mask.shape[-2:] and self.allow_scale_mask:
- if self.mask_scale_mode == 'maxpool':
- mask = F.adaptive_max_pool2d(mask, shape)
- else:
- mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)
- return mask
-
-def make_r1_gp(discr_real_pred, real_batch):
- if torch.is_grad_enabled():
- grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]
- grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()
- else:
- grad_penalty = 0
- real_batch.requires_grad = False
-
- return grad_penalty
-
-class NonSaturatingWithR1(BaseAdversarialLoss):
- def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,
- mask_scale_mode='nearest', extra_mask_weight_for_gen=0,
- use_unmasked_for_gen=True, use_unmasked_for_discr=True):
- self.gp_coef = gp_coef
- self.weight = weight
- # use for discr => use for gen;
- # otherwise we teach only the discr to pay attention to very small difference
- assert use_unmasked_for_gen or (not use_unmasked_for_discr)
- # mask as target => use unmasked for discr:
- # if we don't care about unmasked regions at all
- # then it doesn't matter if the value of mask_as_fake_target is true or false
- assert use_unmasked_for_discr or (not mask_as_fake_target)
- self.use_unmasked_for_gen = use_unmasked_for_gen
- self.use_unmasked_for_discr = use_unmasked_for_discr
- self.mask_as_fake_target = mask_as_fake_target
- self.allow_scale_mask = allow_scale_mask
- self.mask_scale_mode = mask_scale_mode
- self.extra_mask_weight_for_gen = extra_mask_weight_for_gen
-
- def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
- mask=None) \
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
- fake_loss = F.softplus(-discr_fake_pred)
- if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \
- not self.use_unmasked_for_gen: # == if masked region should be treated differently
- mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
- if not self.use_unmasked_for_gen:
- fake_loss = fake_loss * mask
- else:
- pixel_weights = 1 + mask * self.extra_mask_weight_for_gen
- fake_loss = fake_loss * pixel_weights
-
- return fake_loss.mean() * self.weight, dict()
-
- def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- generator: nn.Module, discriminator: nn.Module):
- real_batch.requires_grad = True
-
- def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
- mask=None) \
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
- real_loss = F.softplus(-discr_real_pred)
- grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef
- fake_loss = F.softplus(discr_fake_pred)
-
- if not self.use_unmasked_for_discr or self.mask_as_fake_target:
- # == if masked region should be treated differently
- mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
- # use_unmasked_for_discr=False only makes sense for fakes;
- # for reals there is no difference beetween two regions
- fake_loss = fake_loss * mask
- if self.mask_as_fake_target:
- fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)
-
- sum_discr_loss = real_loss + grad_penalty + fake_loss
- metrics = dict(discr_real_out=discr_real_pred.mean(),
- discr_fake_out=discr_fake_pred.mean(),
- discr_real_gp=grad_penalty)
- return sum_discr_loss.mean(), metrics
-
-class BCELoss(BaseAdversarialLoss):
- def __init__(self, weight):
- self.weight = weight
- self.bce_loss = nn.BCEWithLogitsLoss()
-
- def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
- real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)
- fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight
- return fake_loss, dict()
-
- def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
- generator: nn.Module, discriminator: nn.Module):
- real_batch.requires_grad = True
-
- def discriminator_loss(self,
- mask: torch.Tensor,
- discr_real_pred: torch.Tensor,
- discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
- real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)
- sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) + self.bce_loss(discr_fake_pred, mask)) / 2
- metrics = dict(discr_real_out=discr_real_pred.mean(),
- discr_fake_out=discr_fake_pred.mean(),
- discr_real_gp=0)
- return sum_discr_loss, metrics
-
-
-def make_discrim_loss(kind, **kwargs):
- if kind == 'r1':
- return NonSaturatingWithR1(**kwargs)
- elif kind == 'bce':
- return BCELoss(**kwargs)
- raise ValueError(f'Unknown adversarial loss kind {kind}')
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/losses/perceptual.py b/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/losses/perceptual.py
deleted file mode 100644
index 8c055c2b327ce7943682af5c5f9394b9fcbec506..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/saicinpainting/training/losses/perceptual.py
+++ /dev/null
@@ -1,113 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from models.ade20k import ModelBuilder
-from saicinpainting.utils import check_and_warn_input_range
-
-
-IMAGENET_MEAN = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None]
-IMAGENET_STD = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None]
-
-
-class PerceptualLoss(nn.Module):
- def __init__(self, normalize_inputs=True):
- super(PerceptualLoss, self).__init__()
-
- self.normalize_inputs = normalize_inputs
- self.mean_ = IMAGENET_MEAN
- self.std_ = IMAGENET_STD
-
- vgg = torchvision.models.vgg19(pretrained=True).features
- vgg_avg_pooling = []
-
- for weights in vgg.parameters():
- weights.requires_grad = False
-
- for module in vgg.modules():
- if module.__class__.__name__ == 'Sequential':
- continue
- elif module.__class__.__name__ == 'MaxPool2d':
- vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
- else:
- vgg_avg_pooling.append(module)
-
- self.vgg = nn.Sequential(*vgg_avg_pooling)
-
- def do_normalize_inputs(self, x):
- return (x - self.mean_.to(x.device)) / self.std_.to(x.device)
-
- def partial_losses(self, input, target, mask=None):
- check_and_warn_input_range(target, 0, 1, 'PerceptualLoss target in partial_losses')
-
- # we expect input and target to be in [0, 1] range
- losses = []
-
- if self.normalize_inputs:
- features_input = self.do_normalize_inputs(input)
- features_target = self.do_normalize_inputs(target)
- else:
- features_input = input
- features_target = target
-
- for layer in self.vgg[:30]:
-
- features_input = layer(features_input)
- features_target = layer(features_target)
-
- if layer.__class__.__name__ == 'ReLU':
- loss = F.mse_loss(features_input, features_target, reduction='none')
-
- if mask is not None:
- cur_mask = F.interpolate(mask, size=features_input.shape[-2:],
- mode='bilinear', align_corners=False)
- loss = loss * (1 - cur_mask)
-
- loss = loss.mean(dim=tuple(range(1, len(loss.shape))))
- losses.append(loss)
-
- return losses
-
- def forward(self, input, target, mask=None):
- losses = self.partial_losses(input, target, mask=mask)
- return torch.stack(losses).sum(dim=0)
-
- def get_global_features(self, input):
- check_and_warn_input_range(input, 0, 1, 'PerceptualLoss input in get_global_features')
-
- if self.normalize_inputs:
- features_input = self.do_normalize_inputs(input)
- else:
- features_input = input
-
- features_input = self.vgg(features_input)
- return features_input
-
-
-class ResNetPL(nn.Module):
- def __init__(self, weight=1,
- weights_path=None, arch_encoder='resnet50dilated', segmentation=True):
- super().__init__()
- self.impl = ModelBuilder.get_encoder(weights_path=weights_path,
- arch_encoder=arch_encoder,
- arch_decoder='ppm_deepsup',
- fc_dim=2048,
- segmentation=segmentation)
- self.impl.eval()
- for w in self.impl.parameters():
- w.requires_grad_(False)
-
- self.weight = weight
-
- def forward(self, pred, target):
- pred = (pred - IMAGENET_MEAN.to(pred)) / IMAGENET_STD.to(pred)
- target = (target - IMAGENET_MEAN.to(target)) / IMAGENET_STD.to(target)
-
- pred_feats = self.impl(pred, return_feature_maps=True)
- target_feats = self.impl(target, return_feature_maps=True)
-
- result = torch.stack([F.mse_loss(cur_pred, cur_target)
- for cur_pred, cur_target
- in zip(pred_feats, target_feats)]).sum() * self.weight
- return result
diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/data/transforms/joints2rots/config.py b/spaces/OpenMotionLab/MotionGPT/mGPT/data/transforms/joints2rots/config.py
deleted file mode 100644
index 9014befa889a0c132a05df36d6aa5a6cad4d9e08..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/mGPT/data/transforms/joints2rots/config.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import numpy as np
-from mGPT.utils.joints import mmm_joints, smplh2mmm_indexes
-
-# Map joints Name to SMPL joints idx
-JOINT_MAP = {
- 'MidHip': 0,
- 'LHip': 1,
- 'LKnee': 4,
- 'LAnkle': 7,
- 'LFoot': 10,
- 'RHip': 2,
- 'RKnee': 5,
- 'RAnkle': 8,
- 'RFoot': 11,
- 'LShoulder': 16,
- 'LElbow': 18,
- 'LWrist': 20,
- 'LHand': 22,
- 'RShoulder': 17,
- 'RElbow': 19,
- 'RWrist': 21,
- 'RHand': 23,
- 'spine1': 3,
- 'spine2': 6,
- 'spine3': 9,
- 'Neck': 12,
- 'Head': 15,
- 'LCollar': 13,
- 'Rcollar': 14,
- 'Nose': 24,
- 'REye': 26,
- 'LEye': 26,
- 'REar': 27,
- 'LEar': 28,
- 'LHeel': 31,
- 'RHeel': 34,
- 'OP RShoulder': 17,
- 'OP LShoulder': 16,
- 'OP RHip': 2,
- 'OP LHip': 1,
- 'OP Neck': 12,
-}
-
-mmm2smpl_correspondence = {
- "root": "MidHip",
- "BP": "spine1",
- "BT": "spine3",
- "BLN": "Neck",
- "BUN": "Head",
- "LS": "LShoulder",
- "LE": "LElbow",
- "LW": "LWrist",
- "RS": "RShoulder",
- "RE": "RElbow",
- "RW": "RWrist",
- "LH": "LHip",
- "LK": "LKnee",
- "LA": "LAnkle",
- "LMrot": "LHeel",
- "LF": "LFoot",
- "RH": "RHip",
- "RK": "RKnee",
- "RA": "RAnkle",
- "RMrot": "RHeel",
- "RF": "RFoot"
-}
-
-full_smpl_idx = range(24)
-key_smpl_idx = [0, 1, 4, 7, 2, 5, 8, 17, 19, 21, 16, 18, 20]
-
-AMASS_JOINT_MAP = {
- 'MidHip': 0,
- 'LHip': 1,
- 'LKnee': 4,
- 'LAnkle': 7,
- 'LFoot': 10,
- 'RHip': 2,
- 'RKnee': 5,
- 'RAnkle': 8,
- 'RFoot': 11,
- 'LShoulder': 16,
- 'LElbow': 18,
- 'LWrist': 20,
- 'RShoulder': 17,
- 'RElbow': 19,
- 'RWrist': 21,
- 'spine1': 3,
- 'spine2': 6,
- 'spine3': 9,
- 'Neck': 12,
- 'Head': 15,
- 'LCollar': 13,
- 'Rcollar': 14,
-}
-amass_idx = range(22)
-amass_smpl_idx = range(22)
-
-# cal mmm in smpl index
-smpl2mmm_correspondence = {
- val: key
- for key, val in mmm2smpl_correspondence.items()
-}
-smpl2mmm_indexes = [JOINT_MAP[mmm2smpl_correspondence[x]] for x in mmm_joints]
-
-# cal mmm joints map
-MMM_JOINT_MAP = {
- val: JOINT_MAP[val]
- for key, val in mmm2smpl_correspondence.items()
-}
-
-# mmm_idx = range(21)
-# mmm_smpl_dix = smpl2mmm_indexes
-# mmm_smpl_dix = smplh2mmm_indexes
-# todo - configable
-SMPL_MODEL_DIR = "/apdcephfs/share_1227775/shingxchen/AIMotion/TMOSTData/deps/smpl_models/"
-GMM_MODEL_DIR = "/apdcephfs/share_1227775/shingxchen/AIMotion/TMOSTData/deps/smpl_models/"
-SMPL_MEAN_FILE = "/apdcephfs/share_1227775/shingxchen/AIMotion/TMOSTData/deps/smpl_models/neutral_smpl_mean_params.h5"
-# for collsion
-Part_Seg_DIR = "/apdcephfs/share_1227775/shingxchen/AIMotion/TMOSTData/deps/smpl_models/smplx_parts_segm.pkl"
diff --git a/spaces/OpenShape/openshape-demo/README.md b/spaces/OpenShape/openshape-demo/README.md
deleted file mode 100644
index cfd1614cdbe9be82f33367c50a8a09b52b9695af..0000000000000000000000000000000000000000
--- a/spaces/OpenShape/openshape-demo/README.md
+++ /dev/null
@@ -1,17 +0,0 @@
----
-title: OpenShape Demo
-emoji: 🌖
-colorFrom: red
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-Demo of Paper [OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding](https://arxiv.org/abs/2305.10764)
-
-[Paper page on HF](https://huggingface.co/papers/2305.10764)
\ No newline at end of file
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/sampler_seed.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/sampler_seed.py
deleted file mode 100644
index ee0dc6bdd8df5775857028aaed5444c0f59caf80..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/hooks/sampler_seed.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class DistSamplerSeedHook(Hook):
- """Data-loading sampler for distributed training.
-
- When distributed training, it is only useful in conjunction with
- :obj:`EpochBasedRunner`, while :obj:`IterBasedRunner` achieves the same
- purpose with :obj:`IterLoader`.
- """
-
- def before_epoch(self, runner):
- if hasattr(runner.data_loader.sampler, 'set_epoch'):
- # in case the data loader uses `SequentialSampler` in Pytorch
- runner.data_loader.sampler.set_epoch(runner.epoch)
- elif hasattr(runner.data_loader.batch_sampler.sampler, 'set_epoch'):
- # batch sampler in pytorch warps the sampler as its attributes.
- runner.data_loader.batch_sampler.sampler.set_epoch(runner.epoch)
diff --git a/spaces/PROJECTAIGPT/AIAvatarSPEECH/README.md b/spaces/PROJECTAIGPT/AIAvatarSPEECH/README.md
deleted file mode 100644
index 6a60f7408a2c8844161961923bbc7564c061660d..0000000000000000000000000000000000000000
--- a/spaces/PROJECTAIGPT/AIAvatarSPEECH/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AIAvatarSPEECH
-emoji: 🏢
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/rnrs/arithmetic/flonums.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/rnrs/arithmetic/flonums.go
deleted file mode 100644
index b0d12746c49bfc960fb1e712f88b138f6aa0adf5..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/rnrs/arithmetic/flonums.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/De-limiter/models/base_models.py b/spaces/PeepDaSlan9/De-limiter/models/base_models.py
deleted file mode 100644
index 64b543b49a92e05a4b36177e00e9103fd082d579..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/models/base_models.py
+++ /dev/null
@@ -1,239 +0,0 @@
-import torch
-import torch.nn as nn
-from asteroid.models.base_models import (
- BaseEncoderMaskerDecoder,
- _unsqueeze_to_3d,
- _shape_reconstructed,
-)
-from asteroid.utils.torch_utils import pad_x_to_y, jitable_shape
-from einops import rearrange
-
-
-class BaseEncoderMaskerDecoderWithConfigs(BaseEncoderMaskerDecoder):
- def __init__(self, encoder, masker, decoder, encoder_activation=None, **kwargs):
- super().__init__(encoder, masker, decoder, encoder_activation)
- self.use_encoder = kwargs.get("use_encoder", True)
- self.apply_mask = kwargs.get("apply_mask", True)
- self.use_decoder = kwargs.get("use_decoder", True)
-
- def forward(self, wav):
- """
- Enc/Mask/Dec model forward with some additional options.
- Some of the models we use, like TFC-TDF-UNet, have no masker.
- In UMX or X-UMX, they already use masking in their model implementation.
- Since we do not want to manipulate the model codes, we use this wrapper.
-
- Args:
- wav (torch.Tensor): waveform tensor. 1D, 2D or 3D tensor, time last.
-
- Returns:
- torch.Tensor, of shape (batch, n_src, time) or (n_src, time).
- """
- # Remember shape to shape reconstruction, cast to Tensor for torchscript
- shape = jitable_shape(wav)
- # Reshape to (batch, n_mix, time)
- wav = _unsqueeze_to_3d(wav)
-
- # Real forward
- if self.use_encoder:
- tf_rep = self.forward_encoder(wav)
- else:
- tf_rep = wav
-
- est_masks = self.forward_masker(tf_rep)
-
- if self.apply_mask:
- masked_tf_rep = self.apply_masks(tf_rep, est_masks)
- else: # model already used masking
- masked_tf_rep = est_masks
-
- if self.use_decoder:
- decoded = self.forward_decoder(masked_tf_rep)
- reconstructed = pad_x_to_y(decoded, wav)
-
- return masked_tf_rep, _shape_reconstructed(reconstructed, shape)
-
- else: # In UMX or X-UMX, decoder is not used
- decoded = masked_tf_rep
-
- return decoded
-
-
-class BaseEncoderMaskerDecoder_mixture_consistency(BaseEncoderMaskerDecoder):
- def __init__(self, encoder, masker, decoder, encoder_activation=None):
- super().__init__(encoder, masker, decoder, encoder_activation)
-
- def forward(self, wav):
- """Enc/Mask/Dec model forward with mixture consistent output
-
- References:
- [1] : Wisdom, Scott, et al. "Differentiable consistency constraints for improved deep speech enhancement." ICASSP 2019.
- [2] : Wisdom, Scott, et al. "Unsupervised sound separation using mixture invariant training." NeurIPS 2020.
-
- Args:
- wav (torch.Tensor): waveform tensor. 1D, 2D or 3D tensor, time last.
-
- Returns:
- torch.Tensor, of shape (batch, n_src, time) or (n_src, time).
- """
- # Remember shape to shape reconstruction, cast to Tensor for torchscript
- shape = jitable_shape(wav)
- # Reshape to (batch, n_mix, time)
- wav = _unsqueeze_to_3d(wav)
-
- # Real forward
- tf_rep = self.forward_encoder(wav)
- est_masks = self.forward_masker(tf_rep)
- masked_tf_rep = self.apply_masks(tf_rep, est_masks)
- decoded = self.forward_decoder(masked_tf_rep)
-
- reconstructed = _shape_reconstructed(pad_x_to_y(decoded, wav), shape)
-
- reconstructed = reconstructed + 1 / reconstructed.shape[1] * (
- wav - reconstructed.sum(dim=1, keepdim=True)
- )
-
- return reconstructed
-
-
-class BaseEncoderMaskerDecoderWithConfigsMaskOnOutput(BaseEncoderMaskerDecoder):
- def __init__(self, encoder, masker, decoder, encoder_activation=None, **kwargs):
- super().__init__(encoder, masker, decoder, encoder_activation)
- self.use_encoder = kwargs.get("use_encoder", True)
- self.apply_mask = kwargs.get("apply_mask", True)
- self.use_decoder = kwargs.get("use_decoder", True)
- self.nb_channels = kwargs.get("nb_channels", 2)
- self.decoder_activation = kwargs.get("decoder_activation", "sigmoid")
- if self.decoder_activation == "sigmoid":
- self.act_after_dec = nn.Sigmoid()
- elif self.decoder_activation == "relu":
- self.act_after_dec = nn.ReLU()
- elif self.decoder_activation == "relu6":
- self.act_after_dec = nn.ReLU6()
- elif self.decoder_activation == "tanh":
- self.act_after_dec = nn.Tanh()
- elif self.decoder_activation == "none":
- self.act_after_dec = nn.Identity()
- else:
- self.act_after_dec = nn.Sigmoid()
-
- def forward(self, wav):
- """
- For the De-limit task, we will apply the mask on the output of the decoder.
- We want decoder to learn the sample-wise ratio of the sources.
-
- Args:
- wav (torch.Tensor): waveform tensor. 1D, 2D or 3D tensor, time last.
-
- Returns:
- torch.Tensor, of shape (batch, n_src, time) or (n_src, time).
- """
- # Remember shape to shape reconstruction, cast to Tensor for torchscript
- shape = jitable_shape(wav)
- # Reshape to (batch, n_mix, time)
- wav = _unsqueeze_to_3d(wav) # (batch, n_channels, time)
-
- # Real forward
- if self.use_encoder:
- tf_rep = self.forward_encoder(wav) # (batch, n_channels, freq, time)
- else:
- tf_rep = wav
-
- if self.nb_channels == 2:
- tf_rep = rearrange(
- tf_rep, "b c f t -> b (c f) t"
- ) # c == 2 when stereo input.
- est_masks = self.forward_masker(tf_rep) # (batch, 1, freq, time)
-
- # we are going to apply the mask on the output of the decoder
- if self.use_decoder:
- if self.nb_channels == 2:
- est_masks = rearrange(est_masks, "b 1 f t -> b f t")
- est_masks_decoded = self.forward_decoder(est_masks)
- est_masks_decoded = pad_x_to_y(est_masks_decoded, wav) # (batch, 1, time)
- est_masks_decoded = self.act_after_dec(
- est_masks_decoded
- ) # (batch, 1, time)
- decoded = wav * est_masks_decoded # (batch, n_channels, time)
-
- return (
- est_masks_decoded,
- decoded,
- )
-
- else:
- decoded = est_masks
-
- return (decoded,)
-
-
-class BaseEncoderMaskerDecoderWithConfigsMultiChannelAsteroid(BaseEncoderMaskerDecoder):
- def __init__(self, encoder, masker, decoder, encoder_activation=None, **kwargs):
- super().__init__(encoder, masker, decoder, encoder_activation)
- self.use_encoder = kwargs.get("use_encoder", True)
- self.apply_mask = kwargs.get("apply_mask", True)
- self.use_decoder = kwargs.get("use_decoder", True)
- self.nb_channels = kwargs.get("nb_channels", 2)
- self.decoder_activation = kwargs.get("decoder_activation", "none")
- if self.decoder_activation == "sigmoid":
- self.act_after_dec = nn.Sigmoid()
- elif self.decoder_activation == "relu":
- self.act_after_dec = nn.ReLU()
- elif self.decoder_activation == "relu6":
- self.act_after_dec = nn.ReLU6()
- elif self.decoder_activation == "tanh":
- self.act_after_dec = nn.Tanh()
- elif self.decoder_activation == "none":
- self.act_after_dec = nn.Identity()
- else:
- self.act_after_dec = nn.Sigmoid()
-
- def forward(self, wav):
- """
- Enc/Mask/Dec model forward with some additional options.
- For MultiChannel usage of asteroid-based models. (e.g. ConvTasNet)
-
-
- Args:
- wav (torch.Tensor): waveform tensor. 1D, 2D or 3D tensor, time last.
-
- Returns:
- torch.Tensor, of shape (batch, n_src, time) or (n_src, time).
- """
- # Remember shape to shape reconstruction, cast to Tensor for torchscript
- shape = jitable_shape(wav)
- # Reshape to (batch, n_mix, time)
- wav = _unsqueeze_to_3d(wav)
-
- # Real forward
- if self.use_encoder:
- tf_rep = self.forward_encoder(wav)
- else:
- tf_rep = wav
-
- if self.nb_channels == 2:
- tf_rep = rearrange(
- tf_rep, "b c f t -> b (c f) t"
- ) # c == 2 when stereo input.
- est_masks = self.forward_masker(tf_rep)
-
- if self.nb_channels == 2:
- tf_rep = rearrange(tf_rep, "b (c f) t -> b c f t", c=self.nb_channels)
-
- if self.apply_mask:
- # Since original asteroid implementation of masking includes unnecessary unsqueeze operation, we will do it manually.
- masked_tf_rep = est_masks * tf_rep
- else:
- masked_tf_rep = est_masks
-
- if self.use_decoder:
- decoded = self.forward_decoder(masked_tf_rep)
- reconstructed = pad_x_to_y(decoded, wav)
- reconstructed = self.act_after_dec(reconstructed)
-
- return masked_tf_rep, _shape_reconstructed(reconstructed, shape)
-
- else:
- decoded = masked_tf_rep
-
- return decoded
diff --git a/spaces/Pengyey/bingo-chuchu/src/components/tailwind-indicator.tsx b/spaces/Pengyey/bingo-chuchu/src/components/tailwind-indicator.tsx
deleted file mode 100644
index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000
--- a/spaces/Pengyey/bingo-chuchu/src/components/tailwind-indicator.tsx
+++ /dev/null
@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
- if (process.env.NODE_ENV === 'production') return null
-
- return (
-
-
xs
-
sm
-
md
-
lg
-
xl
-
2xl
-
, added 2000-03-18
-
-import os
-from distutils.core import Command
-from distutils.dir_util import remove_tree
-from distutils import log
-
-
-class clean(Command):
-
- description = "clean up temporary files from 'build' command"
- user_options = [
- ('build-base=', 'b', "base build directory (default: 'build.build-base')"),
- (
- 'build-lib=',
- None,
- "build directory for all modules (default: 'build.build-lib')",
- ),
- ('build-temp=', 't', "temporary build directory (default: 'build.build-temp')"),
- (
- 'build-scripts=',
- None,
- "build directory for scripts (default: 'build.build-scripts')",
- ),
- ('bdist-base=', None, "temporary directory for built distributions"),
- ('all', 'a', "remove all build output, not just temporary by-products"),
- ]
-
- boolean_options = ['all']
-
- def initialize_options(self):
- self.build_base = None
- self.build_lib = None
- self.build_temp = None
- self.build_scripts = None
- self.bdist_base = None
- self.all = None
-
- def finalize_options(self):
- self.set_undefined_options(
- 'build',
- ('build_base', 'build_base'),
- ('build_lib', 'build_lib'),
- ('build_scripts', 'build_scripts'),
- ('build_temp', 'build_temp'),
- )
- self.set_undefined_options('bdist', ('bdist_base', 'bdist_base'))
-
- def run(self):
- # remove the build/temp. directory (unless it's already
- # gone)
- if os.path.exists(self.build_temp):
- remove_tree(self.build_temp, dry_run=self.dry_run)
- else:
- log.debug("'%s' does not exist -- can't clean it", self.build_temp)
-
- if self.all:
- # remove build directories
- for directory in (self.build_lib, self.bdist_base, self.build_scripts):
- if os.path.exists(directory):
- remove_tree(directory, dry_run=self.dry_run)
- else:
- log.warn("'%s' does not exist -- can't clean it", directory)
-
- # just for the heck of it, try to remove the base build directory:
- # we might have emptied it right now, but if not we don't care
- if not self.dry_run:
- try:
- os.rmdir(self.build_base)
- log.info("removing '%s'", self.build_base)
- except OSError:
- pass
diff --git a/spaces/Realcat/image-matching-webui/third_party/SGMNet/utils/metrics.py b/spaces/Realcat/image-matching-webui/third_party/SGMNet/utils/metrics.py
deleted file mode 100644
index 0c4ddf4f0b9c5d045b627dea1c266b863246e1fd..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/SGMNet/utils/metrics.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from .transformations import quaternion_from_matrix
-import numpy as np
-import os
-import sys
-
-
-def evaluate_R_t(R_gt, t_gt, R, t):
- t = t.flatten()
- t_gt = t_gt.flatten()
-
- eps = 1e-15
-
- q_gt = quaternion_from_matrix(R_gt)
- q = quaternion_from_matrix(R)
- q = q / (np.linalg.norm(q) + eps)
- q_gt = q_gt / (np.linalg.norm(q_gt) + eps)
- loss_q = np.maximum(eps, (1.0 - np.sum(q * q_gt) ** 2))
- err_q = np.arccos(1 - 2 * loss_q)
-
- t = t / (np.linalg.norm(t) + eps)
- t_gt = t_gt / (np.linalg.norm(t_gt) + eps)
- loss_t = np.maximum(eps, (1.0 - np.sum(t * t_gt) ** 2))
- err_t = np.arccos(np.sqrt(1 - loss_t))
- return np.rad2deg(err_q), np.rad2deg(err_t)
-
-
-def pose_auc(errors, thresholds):
- sort_idx = np.argsort(errors)
- errors = np.array(errors.copy())[sort_idx]
- recall = (np.arange(len(errors)) + 1) / len(errors)
- errors = np.r_[0.0, errors]
- recall = np.r_[0.0, recall]
- aucs = []
- for t in thresholds[1:]:
- last_index = np.searchsorted(errors, t)
- r = np.r_[recall[:last_index], recall[last_index - 1]]
- e = np.r_[errors[:last_index], t]
- aucs.append(np.trapz(r, x=e) / t)
- return aucs
-
-
-def approx_pose_auc(errors, thresholds):
- qt_acc_hist, _ = np.histogram(errors, thresholds)
- num_pair = float(len(errors))
- qt_acc_hist = qt_acc_hist.astype(float) / num_pair
- qt_acc = np.cumsum(qt_acc_hist)
- approx_aucs = [np.mean(qt_acc[:i]) for i in range(1, len(thresholds))]
- return approx_aucs
-
-
-def compute_epi_inlier(x1, x2, E, inlier_th):
- num_pts1, num_pts2 = x1.shape[0], x2.shape[0]
- x1_h = np.concatenate([x1, np.ones([num_pts1, 1])], -1)
- x2_h = np.concatenate([x2, np.ones([num_pts2, 1])], -1)
- ep_line1 = x1_h @ E.T
- ep_line2 = x2_h @ E
- norm_factor = (
- 1 / np.sqrt((ep_line1[:, :2] ** 2).sum(1))
- + 1 / np.sqrt((ep_line2[:, :2] ** 2).sum(1))
- ) / 2
- dis = abs((ep_line1 * x2_h).sum(-1)) * norm_factor
- inlier_mask = dis < inlier_th
- return inlier_mask
diff --git a/spaces/Realcat/image-matching-webui/third_party/d2net/lib/exceptions.py b/spaces/Realcat/image-matching-webui/third_party/d2net/lib/exceptions.py
deleted file mode 100644
index e0ea28797ee37248afb2585461751925f98123e6..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/d2net/lib/exceptions.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class EmptyTensorError(Exception):
- pass
-
-
-class NoGradientError(Exception):
- pass
diff --git a/spaces/Reha2704/VToonify/vtoonify/model/stylegan/op_gpu/fused_act.py b/spaces/Reha2704/VToonify/vtoonify/model/stylegan/op_gpu/fused_act.py
deleted file mode 100644
index 815eca1905b7962a2314f6af3b3ab5daeb74a009..0000000000000000000000000000000000000000
--- a/spaces/Reha2704/VToonify/vtoonify/model/stylegan/op_gpu/fused_act.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-
-module_path = os.path.dirname(__file__)
-fused = load(
- "fused",
- sources=[
- os.path.join(module_path, "fused_bias_act.cpp"),
- os.path.join(module_path, "fused_bias_act_kernel.cu"),
- ],
-)
-
-
-class FusedLeakyReLUFunctionBackward(Function):
- @staticmethod
- def forward(ctx, grad_output, out, bias, negative_slope, scale):
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- empty = grad_output.new_empty(0)
-
- grad_input = fused.fused_bias_act(
- grad_output.contiguous(), empty, out, 3, 1, negative_slope, scale
- )
-
- dim = [0]
-
- if grad_input.ndim > 2:
- dim += list(range(2, grad_input.ndim))
-
- if bias:
- grad_bias = grad_input.sum(dim).detach()
-
- else:
- grad_bias = empty
-
- return grad_input, grad_bias
-
- @staticmethod
- def backward(ctx, gradgrad_input, gradgrad_bias):
- out, = ctx.saved_tensors
- gradgrad_out = fused.fused_bias_act(
- gradgrad_input.contiguous(), gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
- )
-
- return gradgrad_out, None, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
- @staticmethod
- def forward(ctx, input, bias, negative_slope, scale):
- empty = input.new_empty(0)
-
- ctx.bias = bias is not None
-
- if bias is None:
- bias = empty
-
- out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale)
- ctx.save_for_backward(out)
- ctx.negative_slope = negative_slope
- ctx.scale = scale
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- out, = ctx.saved_tensors
-
- grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
- grad_output, out, ctx.bias, ctx.negative_slope, ctx.scale
- )
-
- if not ctx.bias:
- grad_bias = None
-
- return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
- def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
- super().__init__()
-
- if bias:
- self.bias = nn.Parameter(torch.zeros(channel))
-
- else:
- self.bias = None
-
- self.negative_slope = negative_slope
- self.scale = scale
-
- def forward(self, input):
- return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if input.device.type == "cpu":
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
- )
- * scale
- )
-
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
- else:
- return FusedLeakyReLUFunction.apply(input.contiguous(), bias, negative_slope, scale)
diff --git a/spaces/Riksarkivet/htr_demo/helper/text/overview/changelog_roadmap/roadmap.md b/spaces/Riksarkivet/htr_demo/helper/text/overview/changelog_roadmap/roadmap.md
deleted file mode 100644
index ae72432fde4b08456b599757bd1cbe64fcf6383e..0000000000000000000000000000000000000000
--- a/spaces/Riksarkivet/htr_demo/helper/text/overview/changelog_roadmap/roadmap.md
+++ /dev/null
@@ -1,27 +0,0 @@
-## Roadmap
-
-Our roadmap is where you can learn about what features we're working on. Have any questions or comments about items on the roadmap? See **Overview** > **FAQ & Discussion** for feedback or collaboration.
-
-### Working on
-
-- Release Training and Eval data on HuggingFace
-
-- Add support for TrOCR models as Text recognition model:
-
- - Train a TrOCR model specialized on Swedish historical handwritten text.
-
-- Optimize the backend of the application:
- - Package the code
- - Add support for batch inference
- - Start a collaborative open source project
-
-### Backlog
-
-- Initial document classifier
-
-- Add support for Different segmentation strategies:
-
- - Table detection
- - Different text layouts with multiple columns
-
-- Add more endpoints for rest api and add a more extensive documentation
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/kd_one_stage.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/kd_one_stage.py
deleted file mode 100644
index 671ec19015c87fefd065b84ae887147f90cc892b..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/kd_one_stage.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import mmcv
-import torch
-from mmcv.runner import load_checkpoint
-
-from .. import build_detector
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class KnowledgeDistillationSingleStageDetector(SingleStageDetector):
- r"""Implementation of `Distilling the Knowledge in a Neural Network.
- `_.
-
- Args:
- teacher_config (str | dict): Config file path
- or the config object of teacher model.
- teacher_ckpt (str, optional): Checkpoint path of teacher model.
- If left as None, the model will not load any weights.
- """
-
- def __init__(self,
- backbone,
- neck,
- bbox_head,
- teacher_config,
- teacher_ckpt=None,
- eval_teacher=True,
- train_cfg=None,
- test_cfg=None,
- pretrained=None):
- super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
- pretrained)
- self.eval_teacher = eval_teacher
- # Build teacher model
- if isinstance(teacher_config, str):
- teacher_config = mmcv.Config.fromfile(teacher_config)
- self.teacher_model = build_detector(teacher_config['model'])
- if teacher_ckpt is not None:
- load_checkpoint(
- self.teacher_model, teacher_ckpt, map_location='cpu')
-
- def forward_train(self,
- img,
- img_metas,
- gt_bboxes,
- gt_labels,
- gt_bboxes_ignore=None):
- """
- Args:
- img (Tensor): Input images of shape (N, C, H, W).
- Typically these should be mean centered and std scaled.
- img_metas (list[dict]): A List of image info dict where each dict
- has: 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- :class:`mmdet.datasets.pipelines.Collect`.
- gt_bboxes (list[Tensor]): Each item are the truth boxes for each
- image in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[Tensor]): Class indices corresponding to each box
- gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
- boxes can be ignored when computing the loss.
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- x = self.extract_feat(img)
- with torch.no_grad():
- teacher_x = self.teacher_model.extract_feat(img)
- out_teacher = self.teacher_model.bbox_head(teacher_x)
- losses = self.bbox_head.forward_train(x, out_teacher, img_metas,
- gt_bboxes, gt_labels,
- gt_bboxes_ignore)
- return losses
-
- def cuda(self, device=None):
- """Since teacher_model is registered as a plain object, it is necessary
- to put the teacher model to cuda when calling cuda function."""
- self.teacher_model.cuda(device=device)
- return super().cuda(device=device)
-
- def train(self, mode=True):
- """Set the same train mode for teacher and student model."""
- if self.eval_teacher:
- self.teacher_model.train(False)
- else:
- self.teacher_model.train(mode)
- super().train(mode)
-
- def __setattr__(self, name, value):
- """Set attribute, i.e. self.name = value
-
- This reloading prevent the teacher model from being registered as a
- nn.Module. The teacher module is registered as a plain object, so that
- the teacher parameters will not show up when calling
- ``self.parameters``, ``self.modules``, ``self.children`` methods.
- """
- if name == 'teacher_model':
- object.__setattr__(self, name, value)
- else:
- super().__setattr__(name, value)
diff --git a/spaces/Saturdays/FER/app.py b/spaces/Saturdays/FER/app.py
deleted file mode 100644
index c87a46c0912466af2eb87a28616835e7cd792757..0000000000000000000000000000000000000000
--- a/spaces/Saturdays/FER/app.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import gradio as gr
-import pandas as pd
-import numpy as np
-import os
-from tqdm import tqdm
-import tensorflow as tf
-from tensorflow import keras
-from keras.utils import np_utils
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-import matplotlib.pyplot as plt
-
-new_model = tf.keras.models.load_model('modelo_entrenado.h5')
-objects = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
-y_pos = np.arange(len(objects))
-
-
-def predict_image(pic):
- img = image.load_img(pic, grayscale=True, target_size=(48, 48))
- x = image.img_to_array(img)
-
- x = np.expand_dims(x, axis = 0)
-
- x /= 255
-
-
- custom = new_model.predict(x)
-
- m=0.000000000000000000001
- a=custom[0]
- for i in range(0,len(a)):
- if a[i]>m:
- m=a[i]
- ind=i
-
- return ('Expression Prediction:',objects[ind])
-
-iface = gr.Interface(
- predict_image,
- [
-
- gr.inputs.Image(source="upload",type="filepath", label="Imagen")
- ],
-
- "text",
-
-
- interpretation="default",
- title = 'FER - Facial Expression Recognition',
- description = 'Probablemente nos daremos cuenta de que muchas veces se miente cuando se tratan las emociones, ¿pero nuestra cara también miente? https://saturdays.ai/2022/03/16/detectando-emociones-mediante-imagenes-con-inteligencia-artificial/ ',
- examples=[["28860.png"], ["28790.png"], ["28953.png"], ["30369.png"], ["28722.png"], ["29026.png"], ["28857.png"], ["28795.png"], ["28880.png"], ["28735.png"], ["28757.png"], ["28727.png"], ["28874.png"], ["28723.png"]],
- theme = 'grass'
- )
-
-
-
-iface.launch()
diff --git a/spaces/SerdarHelli/SDF-StyleGan-3D/README.md b/spaces/SerdarHelli/SDF-StyleGan-3D/README.md
deleted file mode 100644
index b0fa4963f194b1904478a90c2596809ae36bd400..0000000000000000000000000000000000000000
--- a/spaces/SerdarHelli/SDF-StyleGan-3D/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: SDF StyleGan 3D
-emoji: 📚
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
-license: mit
-tags:
-- making-demos
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Shakeb100/GroomingGenie_AI/clipseg/models/clipseg.py b/spaces/Shakeb100/GroomingGenie_AI/clipseg/models/clipseg.py
deleted file mode 100644
index a4640b34bbd1ca68a32114471d5585734c4af2fc..0000000000000000000000000000000000000000
--- a/spaces/Shakeb100/GroomingGenie_AI/clipseg/models/clipseg.py
+++ /dev/null
@@ -1,552 +0,0 @@
-import math
-from os.path import basename, dirname, join, isfile
-import torch
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules.activation import ReLU
-
-
-def precompute_clip_vectors():
-
- from trails.initialization import init_dataset
- lvis = init_dataset('LVIS_OneShot3', split='train', mask='text_label', image_size=224, aug=1, normalize=True,
- reduce_factor=None, add_bar=False, negative_prob=0.5)
-
- all_names = list(lvis.category_names.values())
-
- import clip
- from models.clip_prompts import imagenet_templates
- clip_model = clip.load("ViT-B/32", device='cuda', jit=False)[0]
- prompt_vectors = {}
- for name in all_names[:100]:
- with torch.no_grad():
- conditionals = [t.format(name).replace('_', ' ') for t in imagenet_templates]
- text_tokens = clip.tokenize(conditionals).cuda()
- cond = clip_model.encode_text(text_tokens).cpu()
-
- for cond, vec in zip(conditionals, cond):
- prompt_vectors[cond] = vec.cpu()
-
- import pickle
-
- pickle.dump(prompt_vectors, open('precomputed_prompt_vectors.pickle', 'wb'))
-
-
-def get_prompt_list(prompt):
- if prompt == 'plain':
- return ['{}']
- elif prompt == 'fixed':
- return ['a photo of a {}.']
- elif prompt == 'shuffle':
- return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
- elif prompt == 'shuffle+':
- return ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
- 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
- 'a bad photo of a {}.', 'a photo of the {}.']
- elif prompt == 'shuffle_clip':
- from models.clip_prompts import imagenet_templates
- return imagenet_templates
- else:
- raise ValueError('Invalid value for prompt')
-
-
-def forward_multihead_attention(x, b, with_aff=False, attn_mask=None):
- """
- Simplified version of multihead attention (taken from torch source code but without tons of if clauses).
- The mlp and layer norm come from CLIP.
- x: input.
- b: multihead attention module.
- """
-
- x_ = b.ln_1(x)
- q, k, v = nnf.linear(x_, b.attn.in_proj_weight, b.attn.in_proj_bias).chunk(3, dim=-1)
- tgt_len, bsz, embed_dim = q.size()
-
- head_dim = embed_dim // b.attn.num_heads
- scaling = float(head_dim) ** -0.5
-
- q = q.contiguous().view(tgt_len, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
- k = k.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
- v = v.contiguous().view(-1, bsz * b.attn.num_heads, b.attn.head_dim).transpose(0, 1)
-
- q = q * scaling
-
- attn_output_weights = torch.bmm(q, k.transpose(1, 2)) # n_heads * batch_size, tokens^2, tokens^2
- if attn_mask is not None:
-
-
- attn_mask_type, attn_mask = attn_mask
- n_heads = attn_output_weights.size(0) // attn_mask.size(0)
- attn_mask = attn_mask.repeat(n_heads, 1)
-
- if attn_mask_type == 'cls_token':
- # the mask only affects similarities compared to the readout-token.
- attn_output_weights[:, 0, 1:] = attn_output_weights[:, 0, 1:] * attn_mask[None,...]
- # attn_output_weights[:, 0, 0] = 0*attn_output_weights[:, 0, 0]
-
- if attn_mask_type == 'all':
- # print(attn_output_weights.shape, attn_mask[:, None].shape)
- attn_output_weights[:, 1:, 1:] = attn_output_weights[:, 1:, 1:] * attn_mask[:, None]
-
-
- attn_output_weights = torch.softmax(attn_output_weights, dim=-1)
-
- attn_output = torch.bmm(attn_output_weights, v)
- attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
- attn_output = b.attn.out_proj(attn_output)
-
- x = x + attn_output
- x = x + b.mlp(b.ln_2(x))
-
- if with_aff:
- return x, attn_output_weights
- else:
- return x
-
-
-class CLIPDenseBase(nn.Module):
-
- def __init__(self, version, reduce_cond, reduce_dim, prompt, n_tokens):
- super().__init__()
-
- import clip
-
- # prec = torch.FloatTensor
- self.clip_model, _ = clip.load(version, device='cpu', jit=False)
- self.model = self.clip_model.visual
-
- # if not None, scale conv weights such that we obtain n_tokens.
- self.n_tokens = n_tokens
-
- for p in self.clip_model.parameters():
- p.requires_grad_(False)
-
- # conditional
- if reduce_cond is not None:
- self.reduce_cond = nn.Linear(512, reduce_cond)
- for p in self.reduce_cond.parameters():
- p.requires_grad_(False)
- else:
- self.reduce_cond = None
-
- self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
- self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-
- self.reduce = nn.Linear(768, reduce_dim)
-
- self.prompt_list = get_prompt_list(prompt)
-
- # precomputed prompts
- import pickle
- if isfile('precomputed_prompt_vectors.pickle'):
- precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
- self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
- else:
- self.precomputed_prompts = dict()
-
- def rescaled_pos_emb(self, new_size):
- assert len(new_size) == 2
-
- a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
- b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
- return torch.cat([self.model.positional_embedding[:1], b])
-
- def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-
-
- with torch.no_grad():
-
- inp_size = x_inp.shape[2:]
-
- if self.n_tokens is not None:
- stride2 = x_inp.shape[2] // self.n_tokens
- conv_weight2 = nnf.interpolate(self.model.conv1.weight, (stride2, stride2), mode='bilinear', align_corners=True)
- x = nnf.conv2d(x_inp, conv_weight2, bias=self.model.conv1.bias, stride=stride2, dilation=self.model.conv1.dilation)
- else:
- x = self.model.conv1(x_inp) # shape = [*, width, grid, grid]
-
- x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
- x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
-
- x = torch.cat([self.model.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width]
-
- standard_n_tokens = 50 if self.model.conv1.kernel_size[0] == 32 else 197
-
- if x.shape[1] != standard_n_tokens:
- new_shape = int(math.sqrt(x.shape[1]-1))
- x = x + self.rescaled_pos_emb((new_shape, new_shape)).to(x.dtype)[None,:,:]
- else:
- x = x + self.model.positional_embedding.to(x.dtype)
-
- x = self.model.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
-
- activations, affinities = [], []
- for i, res_block in enumerate(self.model.transformer.resblocks):
-
- if mask is not None:
- mask_layer, mask_type, mask_tensor = mask
- if mask_layer == i or mask_layer == 'all':
- # import ipdb; ipdb.set_trace()
- size = int(math.sqrt(x.shape[0] - 1))
-
- attn_mask = (mask_type, nnf.interpolate(mask_tensor.unsqueeze(1).float(), (size, size)).view(mask_tensor.shape[0], size * size))
-
- else:
- attn_mask = None
- else:
- attn_mask = None
-
- x, aff_per_head = forward_multihead_attention(x, res_block, with_aff=True, attn_mask=attn_mask)
-
- if i in extract_layers:
- affinities += [aff_per_head]
-
- #if self.n_tokens is not None:
- # activations += [nnf.interpolate(x, inp_size, mode='bilinear', align_corners=True)]
- #else:
- activations += [x]
-
- if len(extract_layers) > 0 and i == max(extract_layers) and skip:
- print('early skip')
- break
-
- x = x.permute(1, 0, 2) # LND -> NLD
- x = self.model.ln_post(x[:, 0, :])
-
- if self.model.proj is not None:
- x = x @ self.model.proj
-
- return x, activations, affinities
-
- def sample_prompts(self, words, prompt_list=None):
-
- prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
- def get_cond_vec(self, conditional, batch_size):
- # compute conditional from a single string
- if conditional is not None and type(conditional) == str:
- cond = self.compute_conditional(conditional)
- cond = cond.repeat(batch_size, 1)
-
- # compute conditional from string list/tuple
- elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
- assert len(conditional) == batch_size
- cond = self.compute_conditional(conditional)
-
- # use conditional directly
- elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
- cond = conditional
-
- # compute conditional from image
- elif conditional is not None and type(conditional) == torch.Tensor:
- with torch.no_grad():
- cond, _, _ = self.visual_forward(conditional)
- else:
- raise ValueError('invalid conditional')
- return cond
-
- def compute_conditional(self, conditional):
- import clip
-
- dev = next(self.parameters()).device
-
- if type(conditional) in {list, tuple}:
- text_tokens = clip.tokenize(conditional).to(dev)
- cond = self.clip_model.encode_text(text_tokens)
- else:
- if conditional in self.precomputed_prompts:
- cond = self.precomputed_prompts[conditional].float().to(dev)
- else:
- text_tokens = clip.tokenize([conditional]).to(dev)
- cond = self.clip_model.encode_text(text_tokens)[0]
-
- if self.shift_vector is not None:
- return cond + self.shift_vector
- else:
- return cond
-
-
-def clip_load_untrained(version):
- assert version == 'ViT-B/16'
- from clip.model import CLIP
- from clip.clip import _MODELS, _download
- model = torch.jit.load(_download(_MODELS['ViT-B/16'])).eval()
- state_dict = model.state_dict()
-
- vision_width = state_dict["visual.conv1.weight"].shape[0]
- vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
- vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
- grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
- image_resolution = vision_patch_size * grid_size
- embed_dim = state_dict["text_projection"].shape[1]
- context_length = state_dict["positional_embedding"].shape[0]
- vocab_size = state_dict["token_embedding.weight"].shape[0]
- transformer_width = state_dict["ln_final.weight"].shape[0]
- transformer_heads = transformer_width // 64
- transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
- return CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size,
- context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)
-
-
-class CLIPDensePredT(CLIPDenseBase):
-
- def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
- extra_blocks=0, reduce_cond=None, fix_shift=False,
- learn_trans_conv_only=False, limit_to_clip_only=False, upsample=False,
- add_calibration=False, rev_activations=False, trans_conv=None, n_tokens=None):
-
- super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
- # device = 'cpu'
-
- self.extract_layers = extract_layers
- self.cond_layer = cond_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.process_cond = None
- self.rev_activations = rev_activations
-
- depth = len(extract_layers)
-
- if add_calibration:
- self.calibration_conds = 1
-
- self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
- self.add_activation1 = True
-
- self.version = version
-
- self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-
- if fix_shift:
- # self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'clip_text_shift_vector.pth')), requires_grad=False)
- self.shift_vector = nn.Parameter(torch.load(join(dirname(basename(__file__)), 'shift_text_to_vis.pth')), requires_grad=False)
- # self.shift_vector = nn.Parameter(-1*torch.load(join(dirname(basename(__file__)), 'shift2.pth')), requires_grad=False)
- else:
- self.shift_vector = None
-
- if trans_conv is None:
- trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
- else:
- # explicitly define transposed conv kernel size
- trans_conv_ks = (trans_conv, trans_conv)
-
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
- assert len(self.extract_layers) == depth
-
- self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
- self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
- self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
- # refinement and trans conv
-
- if learn_trans_conv_only:
- for p in self.parameters():
- p.requires_grad_(False)
-
- for p in self.trans_conv.parameters():
- p.requires_grad_(True)
-
- self.prompt_list = get_prompt_list(prompt)
-
-
- def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
- assert type(return_features) == bool
-
- inp_image = inp_image.to(self.model.positional_embedding.device)
-
- if mask is not None:
- raise ValueError('mask not supported')
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
- activation1 = activations[0]
- activations = activations[1:]
-
- _activations = activations[::-1] if not self.rev_activations else activations
-
- a = None
- for i, (activation, block, reduce) in enumerate(zip(_activations, self.blocks, self.reduces)):
-
- if a is not None:
- a = reduce(activation) + a
- else:
- a = reduce(activation)
-
- if i == self.cond_layer:
- if self.reduce_cond is not None:
- cond = self.reduce_cond(cond)
-
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- a = block(a)
-
- for block in self.extra_blocks:
- a = a + block(a)
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
-
- a = self.trans_conv(a)
-
- if self.n_tokens is not None:
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear', align_corners=True)
-
- if self.upsample_proj is not None:
- a = self.upsample_proj(a)
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
- if return_features:
- return a, visual_q, cond, [activation1] + activations
- else:
- return a,
-
-
-
-class CLIPDensePredTMasked(CLIPDensePredT):
-
- def __init__(self, version='ViT-B/32', extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4,
- prompt='fixed', extra_blocks=0, reduce_cond=None, fix_shift=False, learn_trans_conv_only=False,
- refine=None, limit_to_clip_only=False, upsample=False, add_calibration=False, n_tokens=None):
-
- super().__init__(version=version, extract_layers=extract_layers, cond_layer=cond_layer, reduce_dim=reduce_dim,
- n_heads=n_heads, prompt=prompt, extra_blocks=extra_blocks, reduce_cond=reduce_cond,
- fix_shift=fix_shift, learn_trans_conv_only=learn_trans_conv_only,
- limit_to_clip_only=limit_to_clip_only, upsample=upsample, add_calibration=add_calibration,
- n_tokens=n_tokens)
-
- def visual_forward_masked(self, img_s, seg_s):
- return super().visual_forward(img_s, mask=('all', 'cls_token', seg_s))
-
- def forward(self, img_q, cond_or_img_s, seg_s=None, return_features=False):
-
- if seg_s is None:
- cond = cond_or_img_s
- else:
- img_s = cond_or_img_s
-
- with torch.no_grad():
- cond, _, _ = self.visual_forward_masked(img_s, seg_s)
-
- return super().forward(img_q, cond, return_features=return_features)
-
-
-
-class CLIPDenseBaseline(CLIPDenseBase):
-
- def __init__(self, version='ViT-B/32', cond_layer=0,
- extract_layer=9, reduce_dim=128, reduce2_dim=None, prompt='fixed',
- reduce_cond=None, limit_to_clip_only=False, n_tokens=None):
-
- super().__init__(version, reduce_cond, reduce_dim, prompt, n_tokens)
- device = 'cpu'
-
- # self.cond_layer = cond_layer
- self.extract_layer = extract_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.shift_vector = None
-
- self.token_shape = {'ViT-B/32': (7, 7), 'ViT-B/16': (14, 14)}[version]
-
- assert reduce2_dim is not None
-
- self.reduce2 = nn.Sequential(
- nn.Linear(reduce_dim, reduce2_dim),
- nn.ReLU(),
- nn.Linear(reduce2_dim, reduce_dim)
- )
-
- trans_conv_ks = {'ViT-B/32': (32, 32), 'ViT-B/16': (16, 16)}[version]
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
-
- def forward(self, inp_image, conditional=None, return_features=False):
-
- inp_image = inp_image.to(self.model.positional_embedding.device)
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, affinities = self.visual_forward(x_inp, extract_layers=[self.extract_layer])
-
- a = activations[0]
- a = self.reduce(a)
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- if self.reduce2 is not None:
- a = self.reduce2(a)
-
- # the original model would execute a transformer block here
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
- a = self.trans_conv(a)
-
- if return_features:
- return a, visual_q, cond, activations
- else:
- return a,
-
-
-class CLIPSegMultiLabel(nn.Module):
-
- def __init__(self, model) -> None:
- super().__init__()
-
- from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC
-
- self.pascal_classes = VOC
-
- from models.clipseg import CLIPDensePredT
- from general_utils import load_model
- # self.clipseg = load_model('rd64-vit16-neg0.2-phrasecut', strict=False)
- self.clipseg = load_model(model, strict=False)
-
- self.clipseg.eval()
-
- def forward(self, x):
-
- bs = x.shape[0]
- out = torch.ones(21, bs, 352, 352).to(x.device) * -10
-
- for class_id, class_name in enumerate(self.pascal_classes):
-
- fac = 3 if class_name == 'background' else 1
-
- with torch.no_grad():
- pred = torch.sigmoid(self.clipseg(x, class_name)[0][:,0]) * fac
-
- out[class_id] += pred
-
-
- out = out.permute(1, 0, 2, 3)
-
- return out
-
- # construct output tensor
-
\ No newline at end of file
diff --git a/spaces/Shredder/CONBERT-3/predict.py b/spaces/Shredder/CONBERT-3/predict.py
deleted file mode 100644
index 8cbcb13a58a7515d7b33e1bc30be53ff92ec5acd..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-3/predict.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import time
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
-from multiprocessing import cpu_count
-
-from transformers import (
- AutoConfig,
- AutoModelForQuestionAnswering,
- AutoTokenizer,
- squad_convert_examples_to_features
-)
-
-from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
-from transformers.data.metrics.squad_metrics import compute_predictions_logits
-
-
-def run_prediction(question_texts, context_text, model_path, n_best_size=1):
- max_seq_length = 512
- doc_stride = 256
- n_best_size = n_best_size
- max_query_length = 64
- max_answer_length = 512
- do_lower_case = False
- null_score_diff_threshold = 0.0
-
- def to_list(tensor):
- return tensor.detach().cpu().tolist()
-
- config_class, model_class, tokenizer_class = (AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer)
- config = config_class.from_pretrained(model_path)
- tokenizer = tokenizer_class.from_pretrained(model_path, do_lower_case=True, use_fast=False)
- model = model_class.from_pretrained(model_path, config=config)
-
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
- model.to(device)
-
- processor = SquadV2Processor()
- examples = []
-
- timer = time.time()
- for i, question_text in enumerate(question_texts):
-
- example = SquadExample(
- qas_id=str(i),
- question_text=question_text,
- context_text=context_text,
- answer_text=None,
- start_position_character=None,
- title="Predict",
- answers=None,
- )
-
- examples.append(example)
- print(f'Created Squad Examples in {time.time()-timer} seconds')
-
- print(f'Number of CPUs: {cpu_count()}')
- timer = time.time()
- features, dataset = squad_convert_examples_to_features(
- examples=examples,
- tokenizer=tokenizer,
- max_seq_length=max_seq_length,
- doc_stride=doc_stride,
- max_query_length=max_query_length,
- is_training=False,
- return_dataset="pt",
- threads=cpu_count(),
- )
- print(f'Converted Examples to Features in {time.time()-timer} seconds')
-
- eval_sampler = SequentialSampler(dataset)
- eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
-
- all_results = []
-
- timer = time.time()
- for batch in eval_dataloader:
- model.eval()
- batch = tuple(t.to(device) for t in batch)
-
- with torch.no_grad():
- inputs = {
- "input_ids": batch[0],
- "attention_mask": batch[1],
- "token_type_ids": batch[2],
- }
-
- example_indices = batch[3]
-
- outputs = model(**inputs)
-
- for i, example_index in enumerate(example_indices):
- eval_feature = features[example_index.item()]
- unique_id = int(eval_feature.unique_id)
-
- output = [to_list(output[i]) for output in outputs.to_tuple()]
-
- start_logits, end_logits = output
- result = SquadResult(unique_id, start_logits, end_logits)
- all_results.append(result)
- print(f'Model predictions completed in {time.time()-timer} seconds')
-
- print(all_results)
-
- output_nbest_file = None
- if n_best_size > 1:
- output_nbest_file = "nbest.json"
-
- timer = time.time()
- final_predictions = compute_predictions_logits(
- all_examples=examples,
- all_features=features,
- all_results=all_results,
- n_best_size=n_best_size,
- max_answer_length=max_answer_length,
- do_lower_case=do_lower_case,
- output_prediction_file=None,
- output_nbest_file=output_nbest_file,
- output_null_log_odds_file=None,
- verbose_logging=False,
- version_2_with_negative=True,
- null_score_diff_threshold=null_score_diff_threshold,
- tokenizer=tokenizer
- )
- print(f'Logits converted to predictions in {time.time()-timer} seconds')
-
- return final_predictions
diff --git a/spaces/Sjmin/cosmos/Dockerfile b/spaces/Sjmin/cosmos/Dockerfile
deleted file mode 100644
index 4cb0ce42128d9a2ad33a395883f5e5455a38c707..0000000000000000000000000000000000000000
--- a/spaces/Sjmin/cosmos/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
- apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/cppipc/ipc.cpp b/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/cppipc/ipc.cpp
deleted file mode 100644
index c713b852ea5a51fbeb4729b64561da482caaf351..0000000000000000000000000000000000000000
--- a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/cppipc/ipc.cpp
+++ /dev/null
@@ -1,701 +0,0 @@
-
-#include
-#include
-#include
-#include // std::pair, std::move, std::forward
-#include
-#include // aligned_storage_t
-#include
-#include
-#include
-#include
-
-#include "libipc/ipc.h"
-#include "libipc/def.h"
-#include "libipc/shm.h"
-#include "libipc/pool_alloc.h"
-#include "libipc/queue.h"
-#include "libipc/policy.h"
-#include "libipc/rw_lock.h"
-#include "libipc/waiter.h"
-
-#include "libipc/utility/log.h"
-#include "libipc/utility/id_pool.h"
-#include "libipc/utility/scope_guard.h"
-#include "libipc/utility/utility.h"
-
-#include "libipc/memory/resource.h"
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_array.h"
-
-namespace {
-
-using msg_id_t = std::uint32_t;
-using acc_t = std::atomic;
-
-template
-struct msg_t;
-
-template
-struct msg_t<0, AlignSize> {
- msg_id_t cc_id_;
- msg_id_t id_;
- std::int32_t remain_;
- bool storage_;
-};
-
-template
-struct msg_t : msg_t<0, AlignSize> {
- std::aligned_storage_t data_ {};
-
- msg_t() = default;
- msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)
- : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {
- if (this->storage_) {
- if (data != nullptr) {
- // copy storage-id
- *reinterpret_cast(&data_) =
- *static_cast(data);
- }
- }
- else std::memcpy(&data_, data, size);
- }
-};
-
-template
-ipc::buff_t make_cache(T& data, std::size_t size) {
- auto ptr = ipc::mem::alloc(size);
- std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));
- return { ptr, size, ipc::mem::free };
-}
-
-struct cache_t {
- std::size_t fill_;
- ipc::buff_t buff_;
-
- cache_t(std::size_t f, ipc::buff_t && b)
- : fill_(f), buff_(std::move(b))
- {}
-
- void append(void const * data, std::size_t size) {
- if (fill_ >= buff_.size() || data == nullptr || size == 0) return;
- auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());
- std::memcpy(static_cast(buff_.data()) + fill_, data, new_fill - fill_);
- fill_ = new_fill;
- }
-};
-
-auto cc_acc() {
- static ipc::shm::handle acc_h("__CA_CONN__", sizeof(acc_t));
- return static_cast(acc_h.get());
-}
-
-IPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {
- return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;
-}
-
-IPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {
- return ipc::make_align(alignof(std::max_align_t), align_chunk_size(
- ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic)) + size));
-}
-
-struct chunk_t {
- std::atomic &conns() noexcept {
- return *reinterpret_cast *>(this);
- }
-
- void *data() noexcept {
- return reinterpret_cast(this)
- + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic));
- }
-};
-
-struct chunk_info_t {
- ipc::id_pool<> pool_;
- ipc::spin_lock lock_;
-
- IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {
- return ipc::id_pool<>::max_count * chunk_size;
- }
-
- ipc::byte_t *chunks_mem() noexcept {
- return reinterpret_cast(this + 1);
- }
-
- chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {
- if (id < 0) return nullptr;
- return reinterpret_cast(chunks_mem() + (chunk_size * id));
- }
-};
-
-auto& chunk_storages() {
- class chunk_handle_t {
- ipc::shm::handle handle_;
-
- public:
- chunk_info_t *get_info(std::size_t chunk_size) {
- if (!handle_.valid() &&
- !handle_.acquire( ("__CHUNK_INFO__" + ipc::to_string(chunk_size)).c_str(),
- sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {
- ipc::error("[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\n", chunk_size);
- return nullptr;
- }
- auto info = static_cast(handle_.get());
- if (info == nullptr) {
- ipc::error("[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\n", chunk_size);
- return nullptr;
- }
- return info;
- }
- };
- static ipc::map chunk_hs;
- return chunk_hs;
-}
-
-chunk_info_t *chunk_storage_info(std::size_t chunk_size) {
- auto &storages = chunk_storages();
- std::decay_t::iterator it;
- {
- static ipc::rw_lock lock;
- IPC_UNUSED_ std::shared_lock guard {lock};
- if ((it = storages.find(chunk_size)) == storages.end()) {
- using chunk_handle_t = std::decay_t::value_type::second_type;
- guard.unlock();
- IPC_UNUSED_ std::lock_guard guard {lock};
- it = storages.emplace(chunk_size, chunk_handle_t{}).first;
- }
- }
- return it->second.get_info(chunk_size);
-}
-
-std::pair acquire_storage(std::size_t size, ipc::circ::cc_t conns) {
- std::size_t chunk_size = calc_chunk_size(size);
- auto info = chunk_storage_info(chunk_size);
- if (info == nullptr) return {};
-
- info->lock_.lock();
- info->pool_.prepare();
- // got an unique id
- auto id = info->pool_.acquire();
- info->lock_.unlock();
-
- auto chunk = info->at(chunk_size, id);
- if (chunk == nullptr) return {};
- chunk->conns().store(conns, std::memory_order_relaxed);
- return { id, chunk->data() };
-}
-
-void *find_storage(ipc::storage_id_t id, std::size_t size) {
- if (id < 0) {
- ipc::error("[find_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
- return nullptr;
- }
- std::size_t chunk_size = calc_chunk_size(size);
- auto info = chunk_storage_info(chunk_size);
- if (info == nullptr) return nullptr;
- return info->at(chunk_size, id)->data();
-}
-
-void release_storage(ipc::storage_id_t id, std::size_t size) {
- if (id < 0) {
- ipc::error("[release_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
- return;
- }
- std::size_t chunk_size = calc_chunk_size(size);
- auto info = chunk_storage_info(chunk_size);
- if (info == nullptr) return;
- info->lock_.lock();
- info->pool_.release(id);
- info->lock_.unlock();
-}
-
-template
-bool sub_rc(ipc::wr,
- std::atomic &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {
- return true;
-}
-
-template
-bool sub_rc(ipc::wr,
- std::atomic &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {
- auto last_conns = curr_conns & ~conn_id;
- for (unsigned k = 0;;) {
- auto chunk_conns = conns.load(std::memory_order_acquire);
- if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {
- return (chunk_conns & last_conns) == 0;
- }
- ipc::yield(k);
- }
-}
-
-template
-void recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {
- if (id < 0) {
- ipc::error("[recycle_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
- return;
- }
- std::size_t chunk_size = calc_chunk_size(size);
- auto info = chunk_storage_info(chunk_size);
- if (info == nullptr) return;
-
- auto chunk = info->at(chunk_size, id);
- if (chunk == nullptr) return;
-
- if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {
- return;
- }
- info->lock_.lock();
- info->pool_.release(id);
- info->lock_.unlock();
-}
-
-template
-bool clear_message(void* p) {
- auto msg = static_cast(p);
- if (msg->storage_) {
- std::int32_t r_size = static_cast(ipc::data_length) + msg->remain_;
- if (r_size <= 0) {
- ipc::error("[clear_message] invalid msg size: %d\n", (int)r_size);
- return true;
- }
- release_storage(
- *reinterpret_cast(&msg->data_),
- static_cast(r_size));
- }
- return true;
-}
-
-struct conn_info_head {
-
- ipc::string name_;
- msg_id_t cc_id_; // connection-info id
- ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;
- ipc::shm::handle acc_h_;
-
- conn_info_head(char const * name)
- : name_ {name}
- , cc_id_ {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}
- , cc_waiter_{("__CC_CONN__" + name_).c_str()}
- , wt_waiter_{("__WT_CONN__" + name_).c_str()}
- , rd_waiter_{("__RD_CONN__" + name_).c_str()}
- , acc_h_ {("__AC_CONN__" + name_).c_str(), sizeof(acc_t)} {
- }
-
- void quit_waiting() {
- cc_waiter_.quit_waiting();
- wt_waiter_.quit_waiting();
- rd_waiter_.quit_waiting();
- }
-
- auto acc() {
- return static_cast(acc_h_.get());
- }
-
- auto& recv_cache() {
- thread_local ipc::unordered_map tls;
- return tls;
- }
-};
-
-template
-bool wait_for(W& waiter, F&& pred, std::uint64_t tm) {
- if (tm == 0) return !pred();
- for (unsigned k = 0; pred();) {
- bool ret = true;
- ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {
- ret = waiter.wait_if(std::forward(pred), tm);
- k = 0;
- });
- if (!ret) return false; // timeout or fail
- if (k == 0) break; // k has been reset
- }
- return true;
-}
-
-template
-struct queue_generator {
-
- using queue_t = ipc::queue, Policy>;
-
- struct conn_info_t : conn_info_head {
- queue_t que_;
-
- conn_info_t(char const * name)
- : conn_info_head{name}
- , que_{("__QU_CONN__" +
- ipc::to_string(DataSize) + "__" +
- ipc::to_string(AlignSize) + "__" + name).c_str()} {
- }
-
- void disconnect_receiver() {
- bool dis = que_.disconnect();
- this->quit_waiting();
- if (dis) {
- this->recv_cache().clear();
- }
- }
- };
-};
-
-template
-struct detail_impl {
-
-using policy_t = Policy;
-using flag_t = typename policy_t::flag_t;
-using queue_t = typename queue_generator::queue_t;
-using conn_info_t = typename queue_generator::conn_info_t;
-
-constexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {
- return static_cast(h);
-}
-
-constexpr static queue_t* queue_of(ipc::handle_t h) noexcept {
- return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);
-}
-
-/* API implementations */
-
-static void disconnect(ipc::handle_t h) {
- auto que = queue_of(h);
- if (que == nullptr) {
- return;
- }
- que->shut_sending();
- assert(info_of(h) != nullptr);
- info_of(h)->disconnect_receiver();
-}
-
-static bool reconnect(ipc::handle_t * ph, bool start_to_recv) {
- assert(ph != nullptr);
- assert(*ph != nullptr);
- auto que = queue_of(*ph);
- if (que == nullptr) {
- return false;
- }
- if (start_to_recv) {
- que->shut_sending();
- if (que->connect()) { // wouldn't connect twice
- info_of(*ph)->cc_waiter_.broadcast();
- return true;
- }
- return false;
- }
- // start_to_recv == false
- if (que->connected()) {
- info_of(*ph)->disconnect_receiver();
- }
- return que->ready_sending();
-}
-
-static bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {
- assert(ph != nullptr);
- if (*ph == nullptr) {
- *ph = ipc::mem::alloc(name);
- }
- return reconnect(ph, start_to_recv);
-}
-
-static void destroy(ipc::handle_t h) {
- disconnect(h);
- ipc::mem::free(info_of(h));
-}
-
-static std::size_t recv_count(ipc::handle_t h) noexcept {
- auto que = queue_of(h);
- if (que == nullptr) {
- return ipc::invalid_value;
- }
- return que->conn_count();
-}
-
-static bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
- auto que = queue_of(h);
- if (que == nullptr) {
- return false;
- }
- return wait_for(info_of(h)->cc_waiter_, [que, r_count] {
- return que->conn_count() < r_count;
- }, tm);
-}
-
-template
-static bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {
- if (data == nullptr || size == 0) {
- ipc::error("fail: send(%p, %zd)\n", data, size);
- return false;
- }
- auto que = queue_of(h);
- if (que == nullptr) {
- ipc::error("fail: send, queue_of(h) == nullptr\n");
- return false;
- }
- if (que->elems() == nullptr) {
- ipc::error("fail: send, queue_of(h)->elems() == nullptr\n");
- return false;
- }
- if (!que->ready_sending()) {
- ipc::error("fail: send, que->ready_sending() == false\n");
- return false;
- }
- ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);
- if (conns == 0) {
- ipc::error("fail: send, there is no receiver on this connection.\n");
- return false;
- }
- // calc a new message id
- auto acc = info_of(h)->acc();
- if (acc == nullptr) {
- ipc::error("fail: send, info_of(h)->acc() == nullptr\n");
- return false;
- }
- auto msg_id = acc->fetch_add(1, std::memory_order_relaxed);
- auto try_push = std::forward(gen_push)(info_of(h), que, msg_id);
- if (size > ipc::large_msg_limit) {
- auto dat = acquire_storage(size, conns);
- void * buf = dat.second;
- if (buf != nullptr) {
- std::memcpy(buf, data, size);
- return try_push(static_cast(size) -
- static_cast(ipc::data_length), &(dat.first), 0);
- }
- // try using message fragment
- //ipc::log("fail: shm::handle for big message. msg_id: %zd, size: %zd\n", msg_id, size);
- }
- // push message fragment
- std::int32_t offset = 0;
- for (std::int32_t i = 0; i < static_cast(size / ipc::data_length); ++i, offset += ipc::data_length) {
- if (!try_push(static_cast(size) - offset - static_cast(ipc::data_length),
- static_cast(data) + offset, ipc::data_length)) {
- return false;
- }
- }
- // if remain > 0, this is the last message fragment
- std::int32_t remain = static_cast(size) - offset;
- if (remain > 0) {
- if (!try_push(remain - static_cast(ipc::data_length),
- static_cast(data) + offset,
- static_cast(remain))) {
- return false;
- }
- }
- return true;
-}
-
-static bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
- return send([tm](auto info, auto que, auto msg_id) {
- return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
- if (!wait_for(info->wt_waiter_, [&] {
- return !que->push(
- [](void*) { return true; },
- info->cc_id_, msg_id, remain, data, size);
- }, tm)) {
- ipc::log("force_push: msg_id = %zd, remain = %d, size = %zd\n", msg_id, remain, size);
- if (!que->force_push(
- clear_message,
- info->cc_id_, msg_id, remain, data, size)) {
- return false;
- }
- }
- info->rd_waiter_.broadcast();
- return true;
- };
- }, h, data, size);
-}
-
-static bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
- return send([tm](auto info, auto que, auto msg_id) {
- return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
- if (!wait_for(info->wt_waiter_, [&] {
- return !que->push(
- [](void*) { return true; },
- info->cc_id_, msg_id, remain, data, size);
- }, tm)) {
- return false;
- }
- info->rd_waiter_.broadcast();
- return true;
- };
- }, h, data, size);
-}
-
-static ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {
- auto que = queue_of(h);
- if (que == nullptr) {
- ipc::error("fail: recv, queue_of(h) == nullptr\n");
- return {};
- }
- if (!que->connected()) {
- // hasn't connected yet, just return.
- return {};
- }
- auto& rc = info_of(h)->recv_cache();
- for (;;) {
- // pop a new message
- typename queue_t::value_t msg;
- if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {
- return !que->pop(msg);
- }, tm)) {
- // pop failed, just return.
- return {};
- }
- info_of(h)->wt_waiter_.broadcast();
- if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {
- continue; // ignore message to self
- }
- // msg.remain_ may minus & abs(msg.remain_) < data_length
- std::int32_t r_size = static_cast(ipc::data_length) + msg.remain_;
- if (r_size <= 0) {
- ipc::error("fail: recv, r_size = %d\n", (int)r_size);
- return {};
- }
- std::size_t msg_size = static_cast(r_size);
- // large message
- if (msg.storage_) {
- ipc::storage_id_t buf_id = *reinterpret_cast(&msg.data_);
- void* buf = find_storage(buf_id, msg_size);
- if (buf != nullptr) {
- struct recycle_t {
- ipc::storage_id_t storage_id;
- ipc::circ::cc_t curr_conns;
- ipc::circ::cc_t conn_id;
- } *r_info = ipc::mem::alloc(recycle_t{
- buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()
- });
- if (r_info == nullptr) {
- ipc::log("fail: ipc::mem::alloc.\n");
- return ipc::buff_t{buf, msg_size}; // no recycle
- } else {
- return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {
- auto r_info = static_cast(p_info);
- IPC_UNUSED_ auto finally = ipc::guard([r_info] {
- ipc::mem::free(r_info);
- });
- recycle_storage(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);
- }, r_info};
- }
- } else {
- ipc::log("fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\n", msg.id_, buf_id, msg_size);
- continue;
- }
- }
- // find cache with msg.id_
- auto cac_it = rc.find(msg.id_);
- if (cac_it == rc.end()) {
- if (msg_size <= ipc::data_length) {
- return make_cache(msg.data_, msg_size);
- }
- // gc
- if (rc.size() > 1024) {
- std::vector need_del;
- for (auto const & pair : rc) {
- auto cmp = std::minmax(msg.id_, pair.first);
- if (cmp.second - cmp.first > 8192) {
- need_del.push_back(pair.first);
- }
- }
- for (auto id : need_del) rc.erase(id);
- }
- // cache the first message fragment
- rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });
- }
- // has cached before this message
- else {
- auto& cac = cac_it->second;
- // this is the last message fragment
- if (msg.remain_ <= 0) {
- cac.append(&(msg.data_), msg_size);
- // finish this message, erase it from cache
- auto buff = std::move(cac.buff_);
- rc.erase(cac_it);
- return buff;
- }
- // there are remain datas after this message
- cac.append(&(msg.data_), ipc::data_length);
- }
- }
-}
-
-static ipc::buff_t try_recv(ipc::handle_t h) {
- return recv(h, 0);
-}
-
-}; // detail_impl
-
-template
-using policy_t = ipc::policy::choose;
-
-} // internal-linkage
-
-namespace ipc {
-
-template
-ipc::handle_t chan_impl::inited() {
- ipc::detail::waiter::init();
- return nullptr;
-}
-
-template
-bool chan_impl::connect(ipc::handle_t * ph, char const * name, unsigned mode) {
- return detail_impl>::connect(ph, name, mode & receiver);
-}
-
-template
-bool chan_impl::reconnect(ipc::handle_t * ph, unsigned mode) {
- return detail_impl>::reconnect(ph, mode & receiver);
-}
-
-template
-void chan_impl::disconnect(ipc::handle_t h) {
- detail_impl>::disconnect(h);
-}
-
-template
-void chan_impl::destroy(ipc::handle_t h) {
- detail_impl>::destroy(h);
-}
-
-template
-char const * chan_impl::name(ipc::handle_t h) {
- auto info = detail_impl>::info_of(h);
- return (info == nullptr) ? nullptr : info->name_.c_str();
-}
-
-template
-std::size_t chan_impl::recv_count(ipc::handle_t h) {
- return detail_impl>::recv_count(h);
-}
-
-template
-bool chan_impl::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
- return detail_impl>::wait_for_recv(h, r_count, tm);
-}
-
-template
-bool chan_impl::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
- return detail_impl>::send(h, data, size, tm);
-}
-
-template
-buff_t chan_impl::recv(ipc::handle_t h, std::uint64_t tm) {
- return detail_impl>::recv(h, tm);
-}
-
-template
-bool chan_impl::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
- return detail_impl>::try_send(h, data, size, tm);
-}
-
-template
-buff_t chan_impl::try_recv(ipc::handle_t h) {
- return detail_impl>::try_recv(h);
-}
-
-template struct chan_impl>;
-// template struct chan_impl>; // TBD
-// template struct chan_impl>; // TBD
-template struct chan_impl>;
-template struct chan_impl>;
-
-} // namespace ipc
diff --git a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/latex/attention/parameter_attention.tex b/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/latex/attention/parameter_attention.tex
deleted file mode 100644
index 7bc4fe452dbdbfe44ff72f0cdbd37acd5c786ce6..0000000000000000000000000000000000000000
--- a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/latex/attention/parameter_attention.tex
+++ /dev/null
@@ -1,45 +0,0 @@
-\pagebreak
-\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
-
-In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between. In fact, these networks too can be seen as a form of attention. Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
-
-\begin{align*}
- FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
- A(q, K, V) = Softmax(qK^T)V
-\end{align*}
-
-Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
-
-%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
-
-Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer. These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
-
-In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head. The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection. This matches the number of parameters in the position-wise feed-forward network that we replaced. While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
-
-In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
-
-Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
-
-\begin{table}[h]
-\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model. All metrics are on the English-to-German translation development set, newstest2013.}
-\label{tab:parameter_attention}
-\begin{center}
-\vspace{-2mm}
-%\scalebox{1.0}{
-\begin{tabular}{c|cccccc|cccc}
-\hline\rule{0pt}{2.0ex}
- & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
-\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
- \multirow{2}{*}{$n_p$} &
- PPL & BLEU & params & training\\
- & & & & & & & (dev) & (dev) & $\times10^6$ & time \\
-\hline\rule{0pt}{2.0ex}
-base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
-\hline\rule{0pt}{2.0ex}
-AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5 & 65 & 16 hours\\
-AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9} & 65 & 16 hours \\
-\hline
-\end{tabular}
-%}
-\end{center}
-\end{table}
diff --git a/spaces/SuYuanS/AudioCraft_Plus/CHANGELOG.md b/spaces/SuYuanS/AudioCraft_Plus/CHANGELOG.md
deleted file mode 100644
index aabf9130b0a67aca9beaac9f2cb1a40237a4468d..0000000000000000000000000000000000000000
--- a/spaces/SuYuanS/AudioCraft_Plus/CHANGELOG.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# Changelog
-
-All notable changes to this project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
-
-## [1.0.0] - 2023-08-02
-
-Major revision, added training code for EnCodec, AudioGen, MusicGen, and MultiBandDiffusion.
-Added pretrained model for AudioGen and MultiBandDiffusion.
-
-## [0.0.2] - 2023-08-01
-
-Improved demo, fixed top p (thanks @jnordberg).
-
-Compressor tanh on output to avoid clipping with some style (especially piano).
-Now repeating the conditioning periodically if it is too short.
-
-More options when launching Gradio app locally (thanks @ashleykleynhans).
-
-Testing out PyTorch 2.0 memory efficient attention.
-
-Added extended generation (infinite length) by slowly moving the windows.
-Note that other implementations exist: https://github.com/camenduru/MusicGen-colab.
-
-## [0.0.1] - 2023-06-09
-
-Initial release, with model evaluation only.
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/vegalite/data.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/vegalite/data.py
deleted file mode 100644
index 30289160beb260a896f04812d1eb586eb7c306ea..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/vegalite/data.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from toolz import curried
-from ..utils.core import sanitize_dataframe
-from ..utils.data import (
- MaxRowsError,
- curry,
- limit_rows,
- pipe,
- sample,
- to_csv,
- to_json,
- to_values,
- check_data_type,
-)
-from ..utils.data import DataTransformerRegistry as _DataTransformerRegistry
-
-
-@curried.curry
-def default_data_transformer(data, max_rows=5000):
- return curried.pipe(data, limit_rows(max_rows=max_rows), to_values)
-
-
-class DataTransformerRegistry(_DataTransformerRegistry):
- def disable_max_rows(self):
- """Disable the MaxRowsError."""
- options = self.options
- if self.active == "default":
- options = options.copy()
- options["max_rows"] = None
- return self.enable(**options)
-
-
-__all__ = (
- "DataTransformerRegistry",
- "MaxRowsError",
- "curry",
- "sanitize_dataframe",
- "default_data_transformer",
- "limit_rows",
- "pipe",
- "sample",
- "to_csv",
- "to_json",
- "to_values",
- "check_data_type",
-)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/attr/validators.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/attr/validators.py
deleted file mode 100644
index 1488554f789526d8d85eb467250a64a64489362d..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/attr/validators.py
+++ /dev/null
@@ -1,720 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-"""
-Commonly useful validators.
-"""
-
-
-import operator
-import re
-
-from contextlib import contextmanager
-from re import Pattern
-
-from ._config import get_run_validators, set_run_validators
-from ._make import _AndValidator, and_, attrib, attrs
-from .converters import default_if_none
-from .exceptions import NotCallableError
-
-
-__all__ = [
- "and_",
- "deep_iterable",
- "deep_mapping",
- "disabled",
- "ge",
- "get_disabled",
- "gt",
- "in_",
- "instance_of",
- "is_callable",
- "le",
- "lt",
- "matches_re",
- "max_len",
- "min_len",
- "not_",
- "optional",
- "provides",
- "set_disabled",
-]
-
-
-def set_disabled(disabled):
- """
- Globally disable or enable running validators.
-
- By default, they are run.
-
- :param disabled: If ``True``, disable running all validators.
- :type disabled: bool
-
- .. warning::
-
- This function is not thread-safe!
-
- .. versionadded:: 21.3.0
- """
- set_run_validators(not disabled)
-
-
-def get_disabled():
- """
- Return a bool indicating whether validators are currently disabled or not.
-
- :return: ``True`` if validators are currently disabled.
- :rtype: bool
-
- .. versionadded:: 21.3.0
- """
- return not get_run_validators()
-
-
-@contextmanager
-def disabled():
- """
- Context manager that disables running validators within its context.
-
- .. warning::
-
- This context manager is not thread-safe!
-
- .. versionadded:: 21.3.0
- """
- set_run_validators(False)
- try:
- yield
- finally:
- set_run_validators(True)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _InstanceOfValidator:
- type = attrib()
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if not isinstance(value, self.type):
- raise TypeError(
- "'{name}' must be {type!r} (got {value!r} that is a "
- "{actual!r}).".format(
- name=attr.name,
- type=self.type,
- actual=value.__class__,
- value=value,
- ),
- attr,
- self.type,
- value,
- )
-
- def __repr__(self):
- return "".format(
- type=self.type
- )
-
-
-def instance_of(type):
- """
- A validator that raises a `TypeError` if the initializer is called
- with a wrong type for this particular attribute (checks are performed using
- `isinstance` therefore it's also valid to pass a tuple of types).
-
- :param type: The type to check for.
- :type type: type or tuple of type
-
- :raises TypeError: With a human readable error message, the attribute
- (of type `attrs.Attribute`), the expected type, and the value it
- got.
- """
- return _InstanceOfValidator(type)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _MatchesReValidator:
- pattern = attrib()
- match_func = attrib()
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if not self.match_func(value):
- raise ValueError(
- "'{name}' must match regex {pattern!r}"
- " ({value!r} doesn't)".format(
- name=attr.name, pattern=self.pattern.pattern, value=value
- ),
- attr,
- self.pattern,
- value,
- )
-
- def __repr__(self):
- return "".format(
- pattern=self.pattern
- )
-
-
-def matches_re(regex, flags=0, func=None):
- r"""
- A validator that raises `ValueError` if the initializer is called
- with a string that doesn't match *regex*.
-
- :param regex: a regex string or precompiled pattern to match against
- :param int flags: flags that will be passed to the underlying re function
- (default 0)
- :param callable func: which underlying `re` function to call. Valid options
- are `re.fullmatch`, `re.search`, and `re.match`; the default ``None``
- means `re.fullmatch`. For performance reasons, the pattern is always
- precompiled using `re.compile`.
-
- .. versionadded:: 19.2.0
- .. versionchanged:: 21.3.0 *regex* can be a pre-compiled pattern.
- """
- valid_funcs = (re.fullmatch, None, re.search, re.match)
- if func not in valid_funcs:
- raise ValueError(
- "'func' must be one of {}.".format(
- ", ".join(
- sorted(
- e and e.__name__ or "None" for e in set(valid_funcs)
- )
- )
- )
- )
-
- if isinstance(regex, Pattern):
- if flags:
- raise TypeError(
- "'flags' can only be used with a string pattern; "
- "pass flags to re.compile() instead"
- )
- pattern = regex
- else:
- pattern = re.compile(regex, flags)
-
- if func is re.match:
- match_func = pattern.match
- elif func is re.search:
- match_func = pattern.search
- else:
- match_func = pattern.fullmatch
-
- return _MatchesReValidator(pattern, match_func)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _ProvidesValidator:
- interface = attrib()
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if not self.interface.providedBy(value):
- raise TypeError(
- "'{name}' must provide {interface!r} which {value!r} "
- "doesn't.".format(
- name=attr.name, interface=self.interface, value=value
- ),
- attr,
- self.interface,
- value,
- )
-
- def __repr__(self):
- return "".format(
- interface=self.interface
- )
-
-
-def provides(interface):
- """
- A validator that raises a `TypeError` if the initializer is called
- with an object that does not provide the requested *interface* (checks are
- performed using ``interface.providedBy(value)`` (see `zope.interface
- ".format(
- what=repr(self.validator)
- )
-
-
-def optional(validator):
- """
- A validator that makes an attribute optional. An optional attribute is one
- which can be set to ``None`` in addition to satisfying the requirements of
- the sub-validator.
-
- :param Callable | tuple[Callable] | list[Callable] validator: A validator
- (or validators) that is used for non-``None`` values.
-
- .. versionadded:: 15.1.0
- .. versionchanged:: 17.1.0 *validator* can be a list of validators.
- .. versionchanged:: 23.1.0 *validator* can also be a tuple of validators.
- """
- if isinstance(validator, (list, tuple)):
- return _OptionalValidator(_AndValidator(validator))
-
- return _OptionalValidator(validator)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _InValidator:
- options = attrib()
-
- def __call__(self, inst, attr, value):
- try:
- in_options = value in self.options
- except TypeError: # e.g. `1 in "abc"`
- in_options = False
-
- if not in_options:
- raise ValueError(
- "'{name}' must be in {options!r} (got {value!r})".format(
- name=attr.name, options=self.options, value=value
- ),
- attr,
- self.options,
- value,
- )
-
- def __repr__(self):
- return "".format(
- options=self.options
- )
-
-
-def in_(options):
- """
- A validator that raises a `ValueError` if the initializer is called
- with a value that does not belong in the options provided. The check is
- performed using ``value in options``.
-
- :param options: Allowed options.
- :type options: list, tuple, `enum.Enum`, ...
-
- :raises ValueError: With a human readable error message, the attribute (of
- type `attrs.Attribute`), the expected options, and the value it
- got.
-
- .. versionadded:: 17.1.0
- .. versionchanged:: 22.1.0
- The ValueError was incomplete until now and only contained the human
- readable error message. Now it contains all the information that has
- been promised since 17.1.0.
- """
- return _InValidator(options)
-
-
-@attrs(repr=False, slots=False, hash=True)
-class _IsCallableValidator:
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if not callable(value):
- message = (
- "'{name}' must be callable "
- "(got {value!r} that is a {actual!r})."
- )
- raise NotCallableError(
- msg=message.format(
- name=attr.name, value=value, actual=value.__class__
- ),
- value=value,
- )
-
- def __repr__(self):
- return ""
-
-
-def is_callable():
- """
- A validator that raises a `attrs.exceptions.NotCallableError` if the
- initializer is called with a value for this particular attribute
- that is not callable.
-
- .. versionadded:: 19.1.0
-
- :raises attrs.exceptions.NotCallableError: With a human readable error
- message containing the attribute (`attrs.Attribute`) name,
- and the value it got.
- """
- return _IsCallableValidator()
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _DeepIterable:
- member_validator = attrib(validator=is_callable())
- iterable_validator = attrib(
- default=None, validator=optional(is_callable())
- )
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if self.iterable_validator is not None:
- self.iterable_validator(inst, attr, value)
-
- for member in value:
- self.member_validator(inst, attr, member)
-
- def __repr__(self):
- iterable_identifier = (
- ""
- if self.iterable_validator is None
- else f" {self.iterable_validator!r}"
- )
- return (
- ""
- ).format(
- iterable_identifier=iterable_identifier,
- member=self.member_validator,
- )
-
-
-def deep_iterable(member_validator, iterable_validator=None):
- """
- A validator that performs deep validation of an iterable.
-
- :param member_validator: Validator(s) to apply to iterable members
- :param iterable_validator: Validator to apply to iterable itself
- (optional)
-
- .. versionadded:: 19.1.0
-
- :raises TypeError: if any sub-validators fail
- """
- if isinstance(member_validator, (list, tuple)):
- member_validator = and_(*member_validator)
- return _DeepIterable(member_validator, iterable_validator)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _DeepMapping:
- key_validator = attrib(validator=is_callable())
- value_validator = attrib(validator=is_callable())
- mapping_validator = attrib(default=None, validator=optional(is_callable()))
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if self.mapping_validator is not None:
- self.mapping_validator(inst, attr, value)
-
- for key in value:
- self.key_validator(inst, attr, key)
- self.value_validator(inst, attr, value[key])
-
- def __repr__(self):
- return (
- ""
- ).format(key=self.key_validator, value=self.value_validator)
-
-
-def deep_mapping(key_validator, value_validator, mapping_validator=None):
- """
- A validator that performs deep validation of a dictionary.
-
- :param key_validator: Validator to apply to dictionary keys
- :param value_validator: Validator to apply to dictionary values
- :param mapping_validator: Validator to apply to top-level mapping
- attribute (optional)
-
- .. versionadded:: 19.1.0
-
- :raises TypeError: if any sub-validators fail
- """
- return _DeepMapping(key_validator, value_validator, mapping_validator)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _NumberValidator:
- bound = attrib()
- compare_op = attrib()
- compare_func = attrib()
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if not self.compare_func(value, self.bound):
- raise ValueError(
- "'{name}' must be {op} {bound}: {value}".format(
- name=attr.name,
- op=self.compare_op,
- bound=self.bound,
- value=value,
- )
- )
-
- def __repr__(self):
- return "".format(
- op=self.compare_op, bound=self.bound
- )
-
-
-def lt(val):
- """
- A validator that raises `ValueError` if the initializer is called
- with a number larger or equal to *val*.
-
- :param val: Exclusive upper bound for values
-
- .. versionadded:: 21.3.0
- """
- return _NumberValidator(val, "<", operator.lt)
-
-
-def le(val):
- """
- A validator that raises `ValueError` if the initializer is called
- with a number greater than *val*.
-
- :param val: Inclusive upper bound for values
-
- .. versionadded:: 21.3.0
- """
- return _NumberValidator(val, "<=", operator.le)
-
-
-def ge(val):
- """
- A validator that raises `ValueError` if the initializer is called
- with a number smaller than *val*.
-
- :param val: Inclusive lower bound for values
-
- .. versionadded:: 21.3.0
- """
- return _NumberValidator(val, ">=", operator.ge)
-
-
-def gt(val):
- """
- A validator that raises `ValueError` if the initializer is called
- with a number smaller or equal to *val*.
-
- :param val: Exclusive lower bound for values
-
- .. versionadded:: 21.3.0
- """
- return _NumberValidator(val, ">", operator.gt)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _MaxLengthValidator:
- max_length = attrib()
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if len(value) > self.max_length:
- raise ValueError(
- "Length of '{name}' must be <= {max}: {len}".format(
- name=attr.name, max=self.max_length, len=len(value)
- )
- )
-
- def __repr__(self):
- return f""
-
-
-def max_len(length):
- """
- A validator that raises `ValueError` if the initializer is called
- with a string or iterable that is longer than *length*.
-
- :param int length: Maximum length of the string or iterable
-
- .. versionadded:: 21.3.0
- """
- return _MaxLengthValidator(length)
-
-
-@attrs(repr=False, frozen=True, slots=True)
-class _MinLengthValidator:
- min_length = attrib()
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if len(value) < self.min_length:
- raise ValueError(
- "Length of '{name}' must be => {min}: {len}".format(
- name=attr.name, min=self.min_length, len=len(value)
- )
- )
-
- def __repr__(self):
- return f""
-
-
-def min_len(length):
- """
- A validator that raises `ValueError` if the initializer is called
- with a string or iterable that is shorter than *length*.
-
- :param int length: Minimum length of the string or iterable
-
- .. versionadded:: 22.1.0
- """
- return _MinLengthValidator(length)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _SubclassOfValidator:
- type = attrib()
-
- def __call__(self, inst, attr, value):
- """
- We use a callable class to be able to change the ``__repr__``.
- """
- if not issubclass(value, self.type):
- raise TypeError(
- "'{name}' must be a subclass of {type!r} "
- "(got {value!r}).".format(
- name=attr.name,
- type=self.type,
- value=value,
- ),
- attr,
- self.type,
- value,
- )
-
- def __repr__(self):
- return "".format(
- type=self.type
- )
-
-
-def _subclass_of(type):
- """
- A validator that raises a `TypeError` if the initializer is called
- with a wrong type for this particular attribute (checks are performed using
- `issubclass` therefore it's also valid to pass a tuple of types).
-
- :param type: The type to check for.
- :type type: type or tuple of types
-
- :raises TypeError: With a human readable error message, the attribute
- (of type `attrs.Attribute`), the expected type, and the value it
- got.
- """
- return _SubclassOfValidator(type)
-
-
-@attrs(repr=False, slots=True, hash=True)
-class _NotValidator:
- validator = attrib()
- msg = attrib(
- converter=default_if_none(
- "not_ validator child '{validator!r}' "
- "did not raise a captured error"
- )
- )
- exc_types = attrib(
- validator=deep_iterable(
- member_validator=_subclass_of(Exception),
- iterable_validator=instance_of(tuple),
- ),
- )
-
- def __call__(self, inst, attr, value):
- try:
- self.validator(inst, attr, value)
- except self.exc_types:
- pass # suppress error to invert validity
- else:
- raise ValueError(
- self.msg.format(
- validator=self.validator,
- exc_types=self.exc_types,
- ),
- attr,
- self.validator,
- value,
- self.exc_types,
- )
-
- def __repr__(self):
- return (
- ""
- ).format(
- what=self.validator,
- exc_types=self.exc_types,
- )
-
-
-def not_(validator, *, msg=None, exc_types=(ValueError, TypeError)):
- """
- A validator that wraps and logically 'inverts' the validator passed to it.
- It will raise a `ValueError` if the provided validator *doesn't* raise a
- `ValueError` or `TypeError` (by default), and will suppress the exception
- if the provided validator *does*.
-
- Intended to be used with existing validators to compose logic without
- needing to create inverted variants, for example, ``not_(in_(...))``.
-
- :param validator: A validator to be logically inverted.
- :param msg: Message to raise if validator fails.
- Formatted with keys ``exc_types`` and ``validator``.
- :type msg: str
- :param exc_types: Exception type(s) to capture.
- Other types raised by child validators will not be intercepted and
- pass through.
-
- :raises ValueError: With a human readable error message,
- the attribute (of type `attrs.Attribute`),
- the validator that failed to raise an exception,
- the value it got,
- and the expected exception types.
-
- .. versionadded:: 22.2.0
- """
- try:
- exc_types = tuple(exc_types)
- except TypeError:
- exc_types = (exc_types,)
- return _NotValidator(validator, msg, exc_types)
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_import_class.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_import_class.py
deleted file mode 100644
index ee3527c50d34e274d59127f99778e76db2889a92..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_import_class.py
+++ /dev/null
@@ -1,68 +0,0 @@
-#Note: code gotten from _pydev_imports_tipper.
-
-import sys
-
-def _imp(name, log=None):
- try:
- return __import__(name)
- except:
- if '.' in name:
- sub = name[0:name.rfind('.')]
-
- if log is not None:
- log.add_content('Unable to import', name, 'trying with', sub)
- log.add_exception()
-
- return _imp(sub, log)
- else:
- s = 'Unable to import module: %s - sys.path: %s' % (str(name), sys.path)
- if log is not None:
- log.add_content(s)
- log.add_exception()
-
- raise ImportError(s)
-
-
-IS_IPY = False
-if sys.platform == 'cli':
- IS_IPY = True
- _old_imp = _imp
- def _imp(name, log=None):
- #We must add a reference in clr for .Net
- import clr #@UnresolvedImport
- initial_name = name
- while '.' in name:
- try:
- clr.AddReference(name)
- break #If it worked, that's OK.
- except:
- name = name[0:name.rfind('.')]
- else:
- try:
- clr.AddReference(name)
- except:
- pass #That's OK (not dot net module).
-
- return _old_imp(initial_name, log)
-
-
-def import_name(name, log=None):
- mod = _imp(name, log)
-
- components = name.split('.')
-
- old_comp = None
- for comp in components[1:]:
- try:
- #this happens in the following case:
- #we have mx.DateTime.mxDateTime.mxDateTime.pyd
- #but after importing it, mx.DateTime.mxDateTime shadows access to mxDateTime.pyd
- mod = getattr(mod, comp)
- except AttributeError:
- if old_comp != comp:
- raise
-
- old_comp = comp
-
- return mod
-
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/test_time_augmentation.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/test_time_augmentation.py
deleted file mode 100644
index 625f8ba9a01275df64967c097912538337ec91dc..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/modeling/test_time_augmentation.py
+++ /dev/null
@@ -1,307 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import numpy as np
-from contextlib import contextmanager
-from itertools import count
-from typing import List
-import torch
-from fvcore.transforms import HFlipTransform, NoOpTransform
-from torch import nn
-from torch.nn.parallel import DistributedDataParallel
-
-from annotator.oneformer.detectron2.config import configurable
-from annotator.oneformer.detectron2.data.detection_utils import read_image
-from annotator.oneformer.detectron2.data.transforms import (
- RandomFlip,
- ResizeShortestEdge,
- ResizeTransform,
- apply_augmentations,
-)
-from annotator.oneformer.detectron2.structures import Boxes, Instances
-
-from .meta_arch import GeneralizedRCNN
-from .postprocessing import detector_postprocess
-from .roi_heads.fast_rcnn import fast_rcnn_inference_single_image
-
-__all__ = ["DatasetMapperTTA", "GeneralizedRCNNWithTTA"]
-
-
-class DatasetMapperTTA:
- """
- Implement test-time augmentation for detection data.
- It is a callable which takes a dataset dict from a detection dataset,
- and returns a list of dataset dicts where the images
- are augmented from the input image by the transformations defined in the config.
- This is used for test-time augmentation.
- """
-
- @configurable
- def __init__(self, min_sizes: List[int], max_size: int, flip: bool):
- """
- Args:
- min_sizes: list of short-edge size to resize the image to
- max_size: maximum height or width of resized images
- flip: whether to apply flipping augmentation
- """
- self.min_sizes = min_sizes
- self.max_size = max_size
- self.flip = flip
-
- @classmethod
- def from_config(cls, cfg):
- return {
- "min_sizes": cfg.TEST.AUG.MIN_SIZES,
- "max_size": cfg.TEST.AUG.MAX_SIZE,
- "flip": cfg.TEST.AUG.FLIP,
- }
-
- def __call__(self, dataset_dict):
- """
- Args:
- dict: a dict in standard model input format. See tutorials for details.
-
- Returns:
- list[dict]:
- a list of dicts, which contain augmented version of the input image.
- The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.
- Each dict has field "transforms" which is a TransformList,
- containing the transforms that are used to generate this image.
- """
- numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy()
- shape = numpy_image.shape
- orig_shape = (dataset_dict["height"], dataset_dict["width"])
- if shape[:2] != orig_shape:
- # It transforms the "original" image in the dataset to the input image
- pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1])
- else:
- pre_tfm = NoOpTransform()
-
- # Create all combinations of augmentations to use
- aug_candidates = [] # each element is a list[Augmentation]
- for min_size in self.min_sizes:
- resize = ResizeShortestEdge(min_size, self.max_size)
- aug_candidates.append([resize]) # resize only
- if self.flip:
- flip = RandomFlip(prob=1.0)
- aug_candidates.append([resize, flip]) # resize + flip
-
- # Apply all the augmentations
- ret = []
- for aug in aug_candidates:
- new_image, tfms = apply_augmentations(aug, np.copy(numpy_image))
- torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1)))
-
- dic = copy.deepcopy(dataset_dict)
- dic["transforms"] = pre_tfm + tfms
- dic["image"] = torch_image
- ret.append(dic)
- return ret
-
-
-class GeneralizedRCNNWithTTA(nn.Module):
- """
- A GeneralizedRCNN with test-time augmentation enabled.
- Its :meth:`__call__` method has the same interface as :meth:`GeneralizedRCNN.forward`.
- """
-
- def __init__(self, cfg, model, tta_mapper=None, batch_size=3):
- """
- Args:
- cfg (CfgNode):
- model (GeneralizedRCNN): a GeneralizedRCNN to apply TTA on.
- tta_mapper (callable): takes a dataset dict and returns a list of
- augmented versions of the dataset dict. Defaults to
- `DatasetMapperTTA(cfg)`.
- batch_size (int): batch the augmented images into this batch size for inference.
- """
- super().__init__()
- if isinstance(model, DistributedDataParallel):
- model = model.module
- assert isinstance(
- model, GeneralizedRCNN
- ), "TTA is only supported on GeneralizedRCNN. Got a model of type {}".format(type(model))
- self.cfg = cfg.clone()
- assert not self.cfg.MODEL.KEYPOINT_ON, "TTA for keypoint is not supported yet"
- assert (
- not self.cfg.MODEL.LOAD_PROPOSALS
- ), "TTA for pre-computed proposals is not supported yet"
-
- self.model = model
-
- if tta_mapper is None:
- tta_mapper = DatasetMapperTTA(cfg)
- self.tta_mapper = tta_mapper
- self.batch_size = batch_size
-
- @contextmanager
- def _turn_off_roi_heads(self, attrs):
- """
- Open a context where some heads in `model.roi_heads` are temporarily turned off.
- Args:
- attr (list[str]): the attribute in `model.roi_heads` which can be used
- to turn off a specific head, e.g., "mask_on", "keypoint_on".
- """
- roi_heads = self.model.roi_heads
- old = {}
- for attr in attrs:
- try:
- old[attr] = getattr(roi_heads, attr)
- except AttributeError:
- # The head may not be implemented in certain ROIHeads
- pass
-
- if len(old.keys()) == 0:
- yield
- else:
- for attr in old.keys():
- setattr(roi_heads, attr, False)
- yield
- for attr in old.keys():
- setattr(roi_heads, attr, old[attr])
-
- def _batch_inference(self, batched_inputs, detected_instances=None):
- """
- Execute inference on a list of inputs,
- using batch size = self.batch_size, instead of the length of the list.
-
- Inputs & outputs have the same format as :meth:`GeneralizedRCNN.inference`
- """
- if detected_instances is None:
- detected_instances = [None] * len(batched_inputs)
-
- outputs = []
- inputs, instances = [], []
- for idx, input, instance in zip(count(), batched_inputs, detected_instances):
- inputs.append(input)
- instances.append(instance)
- if len(inputs) == self.batch_size or idx == len(batched_inputs) - 1:
- outputs.extend(
- self.model.inference(
- inputs,
- instances if instances[0] is not None else None,
- do_postprocess=False,
- )
- )
- inputs, instances = [], []
- return outputs
-
- def __call__(self, batched_inputs):
- """
- Same input/output format as :meth:`GeneralizedRCNN.forward`
- """
-
- def _maybe_read_image(dataset_dict):
- ret = copy.copy(dataset_dict)
- if "image" not in ret:
- image = read_image(ret.pop("file_name"), self.model.input_format)
- image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW
- ret["image"] = image
- if "height" not in ret and "width" not in ret:
- ret["height"] = image.shape[1]
- ret["width"] = image.shape[2]
- return ret
-
- return [self._inference_one_image(_maybe_read_image(x)) for x in batched_inputs]
-
- def _inference_one_image(self, input):
- """
- Args:
- input (dict): one dataset dict with "image" field being a CHW tensor
-
- Returns:
- dict: one output dict
- """
- orig_shape = (input["height"], input["width"])
- augmented_inputs, tfms = self._get_augmented_inputs(input)
- # Detect boxes from all augmented versions
- with self._turn_off_roi_heads(["mask_on", "keypoint_on"]):
- # temporarily disable roi heads
- all_boxes, all_scores, all_classes = self._get_augmented_boxes(augmented_inputs, tfms)
- # merge all detected boxes to obtain final predictions for boxes
- merged_instances = self._merge_detections(all_boxes, all_scores, all_classes, orig_shape)
-
- if self.cfg.MODEL.MASK_ON:
- # Use the detected boxes to obtain masks
- augmented_instances = self._rescale_detected_boxes(
- augmented_inputs, merged_instances, tfms
- )
- # run forward on the detected boxes
- outputs = self._batch_inference(augmented_inputs, augmented_instances)
- # Delete now useless variables to avoid being out of memory
- del augmented_inputs, augmented_instances
- # average the predictions
- merged_instances.pred_masks = self._reduce_pred_masks(outputs, tfms)
- merged_instances = detector_postprocess(merged_instances, *orig_shape)
- return {"instances": merged_instances}
- else:
- return {"instances": merged_instances}
-
- def _get_augmented_inputs(self, input):
- augmented_inputs = self.tta_mapper(input)
- tfms = [x.pop("transforms") for x in augmented_inputs]
- return augmented_inputs, tfms
-
- def _get_augmented_boxes(self, augmented_inputs, tfms):
- # 1: forward with all augmented images
- outputs = self._batch_inference(augmented_inputs)
- # 2: union the results
- all_boxes = []
- all_scores = []
- all_classes = []
- for output, tfm in zip(outputs, tfms):
- # Need to inverse the transforms on boxes, to obtain results on original image
- pred_boxes = output.pred_boxes.tensor
- original_pred_boxes = tfm.inverse().apply_box(pred_boxes.cpu().numpy())
- all_boxes.append(torch.from_numpy(original_pred_boxes).to(pred_boxes.device))
-
- all_scores.extend(output.scores)
- all_classes.extend(output.pred_classes)
- all_boxes = torch.cat(all_boxes, dim=0)
- return all_boxes, all_scores, all_classes
-
- def _merge_detections(self, all_boxes, all_scores, all_classes, shape_hw):
- # select from the union of all results
- num_boxes = len(all_boxes)
- num_classes = self.cfg.MODEL.ROI_HEADS.NUM_CLASSES
- # +1 because fast_rcnn_inference expects background scores as well
- all_scores_2d = torch.zeros(num_boxes, num_classes + 1, device=all_boxes.device)
- for idx, cls, score in zip(count(), all_classes, all_scores):
- all_scores_2d[idx, cls] = score
-
- merged_instances, _ = fast_rcnn_inference_single_image(
- all_boxes,
- all_scores_2d,
- shape_hw,
- 1e-8,
- self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
- self.cfg.TEST.DETECTIONS_PER_IMAGE,
- )
-
- return merged_instances
-
- def _rescale_detected_boxes(self, augmented_inputs, merged_instances, tfms):
- augmented_instances = []
- for input, tfm in zip(augmented_inputs, tfms):
- # Transform the target box to the augmented image's coordinate space
- pred_boxes = merged_instances.pred_boxes.tensor.cpu().numpy()
- pred_boxes = torch.from_numpy(tfm.apply_box(pred_boxes))
-
- aug_instances = Instances(
- image_size=input["image"].shape[1:3],
- pred_boxes=Boxes(pred_boxes),
- pred_classes=merged_instances.pred_classes,
- scores=merged_instances.scores,
- )
- augmented_instances.append(aug_instances)
- return augmented_instances
-
- def _reduce_pred_masks(self, outputs, tfms):
- # Should apply inverse transforms on masks.
- # We assume only resize & flip are used. pred_masks is a scale-invariant
- # representation, so we handle flip specially
- for output, tfm in zip(outputs, tfms):
- if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
- output.pred_masks = output.pred_masks.flip(dims=[3])
- all_pred_masks = torch.stack([o.pred_masks for o in outputs], dim=0)
- avg_pred_masks = torch.mean(all_pred_masks, dim=0)
- return avg_pred_masks
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py
deleted file mode 100644
index eecfe2f31e65147aec47704b9e775e82d9f5fa9a..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/tracking/vanilla_hungarian_bbox_iou_tracker.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2004-present Facebook. All Rights Reserved.
-
-import numpy as np
-from typing import List
-
-from annotator.oneformer.detectron2.config import CfgNode as CfgNode_
-from annotator.oneformer.detectron2.config import configurable
-from annotator.oneformer.detectron2.structures import Instances
-from annotator.oneformer.detectron2.structures.boxes import pairwise_iou
-from annotator.oneformer.detectron2.tracking.utils import LARGE_COST_VALUE, create_prediction_pairs
-
-from .base_tracker import TRACKER_HEADS_REGISTRY
-from .hungarian_tracker import BaseHungarianTracker
-
-
-@TRACKER_HEADS_REGISTRY.register()
-class VanillaHungarianBBoxIOUTracker(BaseHungarianTracker):
- """
- Hungarian algo based tracker using bbox iou as metric
- """
-
- @configurable
- def __init__(
- self,
- *,
- video_height: int,
- video_width: int,
- max_num_instances: int = 200,
- max_lost_frame_count: int = 0,
- min_box_rel_dim: float = 0.02,
- min_instance_period: int = 1,
- track_iou_threshold: float = 0.5,
- **kwargs,
- ):
- """
- Args:
- video_height: height the video frame
- video_width: width of the video frame
- max_num_instances: maximum number of id allowed to be tracked
- max_lost_frame_count: maximum number of frame an id can lost tracking
- exceed this number, an id is considered as lost
- forever
- min_box_rel_dim: a percentage, smaller than this dimension, a bbox is
- removed from tracking
- min_instance_period: an instance will be shown after this number of period
- since its first showing up in the video
- track_iou_threshold: iou threshold, below this number a bbox pair is removed
- from tracking
- """
- super().__init__(
- video_height=video_height,
- video_width=video_width,
- max_num_instances=max_num_instances,
- max_lost_frame_count=max_lost_frame_count,
- min_box_rel_dim=min_box_rel_dim,
- min_instance_period=min_instance_period,
- )
- self._track_iou_threshold = track_iou_threshold
-
- @classmethod
- def from_config(cls, cfg: CfgNode_):
- """
- Old style initialization using CfgNode
-
- Args:
- cfg: D2 CfgNode, config file
- Return:
- dictionary storing arguments for __init__ method
- """
- assert "VIDEO_HEIGHT" in cfg.TRACKER_HEADS
- assert "VIDEO_WIDTH" in cfg.TRACKER_HEADS
- video_height = cfg.TRACKER_HEADS.get("VIDEO_HEIGHT")
- video_width = cfg.TRACKER_HEADS.get("VIDEO_WIDTH")
- max_num_instances = cfg.TRACKER_HEADS.get("MAX_NUM_INSTANCES", 200)
- max_lost_frame_count = cfg.TRACKER_HEADS.get("MAX_LOST_FRAME_COUNT", 0)
- min_box_rel_dim = cfg.TRACKER_HEADS.get("MIN_BOX_REL_DIM", 0.02)
- min_instance_period = cfg.TRACKER_HEADS.get("MIN_INSTANCE_PERIOD", 1)
- track_iou_threshold = cfg.TRACKER_HEADS.get("TRACK_IOU_THRESHOLD", 0.5)
- return {
- "_target_": "detectron2.tracking.vanilla_hungarian_bbox_iou_tracker.VanillaHungarianBBoxIOUTracker", # noqa
- "video_height": video_height,
- "video_width": video_width,
- "max_num_instances": max_num_instances,
- "max_lost_frame_count": max_lost_frame_count,
- "min_box_rel_dim": min_box_rel_dim,
- "min_instance_period": min_instance_period,
- "track_iou_threshold": track_iou_threshold,
- }
-
- def build_cost_matrix(self, instances: Instances, prev_instances: Instances) -> np.ndarray:
- """
- Build the cost matrix for assignment problem
- (https://en.wikipedia.org/wiki/Assignment_problem)
-
- Args:
- instances: D2 Instances, for current frame predictions
- prev_instances: D2 Instances, for previous frame predictions
-
- Return:
- the cost matrix in numpy array
- """
- assert instances is not None and prev_instances is not None
- # calculate IoU of all bbox pairs
- iou_all = pairwise_iou(
- boxes1=instances.pred_boxes,
- boxes2=self._prev_instances.pred_boxes,
- )
- bbox_pairs = create_prediction_pairs(
- instances, self._prev_instances, iou_all, threshold=self._track_iou_threshold
- )
- # assign large cost value to make sure pair below IoU threshold won't be matched
- cost_matrix = np.full((len(instances), len(prev_instances)), LARGE_COST_VALUE)
- return self.assign_cost_matrix_values(cost_matrix, bbox_pairs)
-
- def assign_cost_matrix_values(self, cost_matrix: np.ndarray, bbox_pairs: List) -> np.ndarray:
- """
- Based on IoU for each pair of bbox, assign the associated value in cost matrix
-
- Args:
- cost_matrix: np.ndarray, initialized 2D array with target dimensions
- bbox_pairs: list of bbox pair, in each pair, iou value is stored
- Return:
- np.ndarray, cost_matrix with assigned values
- """
- for pair in bbox_pairs:
- # assign -1 for IoU above threshold pairs, algorithms will minimize cost
- cost_matrix[pair["idx"]][pair["prev_idx"]] = -1
- return cost_matrix
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/git.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/git.py
deleted file mode 100644
index 8d1d499376744954308bdf96f80e5b5a39a24195..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/vcs/git.py
+++ /dev/null
@@ -1,526 +0,0 @@
-import logging
-import os.path
-import pathlib
-import re
-import urllib.parse
-import urllib.request
-from typing import List, Optional, Tuple
-
-from pip._internal.exceptions import BadCommand, InstallationError
-from pip._internal.utils.misc import HiddenText, display_path, hide_url
-from pip._internal.utils.subprocess import make_command
-from pip._internal.vcs.versioncontrol import (
- AuthInfo,
- RemoteNotFoundError,
- RemoteNotValidError,
- RevOptions,
- VersionControl,
- find_path_to_project_root_from_repo_root,
- vcs,
-)
-
-urlsplit = urllib.parse.urlsplit
-urlunsplit = urllib.parse.urlunsplit
-
-
-logger = logging.getLogger(__name__)
-
-
-GIT_VERSION_REGEX = re.compile(
- r"^git version " # Prefix.
- r"(\d+)" # Major.
- r"\.(\d+)" # Dot, minor.
- r"(?:\.(\d+))?" # Optional dot, patch.
- r".*$" # Suffix, including any pre- and post-release segments we don't care about.
-)
-
-HASH_REGEX = re.compile("^[a-fA-F0-9]{40}$")
-
-# SCP (Secure copy protocol) shorthand. e.g. 'git@example.com:foo/bar.git'
-SCP_REGEX = re.compile(
- r"""^
- # Optional user, e.g. 'git@'
- (\w+@)?
- # Server, e.g. 'github.com'.
- ([^/:]+):
- # The server-side path. e.g. 'user/project.git'. Must start with an
- # alphanumeric character so as not to be confusable with a Windows paths
- # like 'C:/foo/bar' or 'C:\foo\bar'.
- (\w[^:]*)
- $""",
- re.VERBOSE,
-)
-
-
-def looks_like_hash(sha: str) -> bool:
- return bool(HASH_REGEX.match(sha))
-
-
-class Git(VersionControl):
- name = "git"
- dirname = ".git"
- repo_name = "clone"
- schemes = (
- "git+http",
- "git+https",
- "git+ssh",
- "git+git",
- "git+file",
- )
- # Prevent the user's environment variables from interfering with pip:
- # https://github.com/pypa/pip/issues/1130
- unset_environ = ("GIT_DIR", "GIT_WORK_TREE")
- default_arg_rev = "HEAD"
-
- @staticmethod
- def get_base_rev_args(rev: str) -> List[str]:
- return [rev]
-
- def is_immutable_rev_checkout(self, url: str, dest: str) -> bool:
- _, rev_options = self.get_url_rev_options(hide_url(url))
- if not rev_options.rev:
- return False
- if not self.is_commit_id_equal(dest, rev_options.rev):
- # the current commit is different from rev,
- # which means rev was something else than a commit hash
- return False
- # return False in the rare case rev is both a commit hash
- # and a tag or a branch; we don't want to cache in that case
- # because that branch/tag could point to something else in the future
- is_tag_or_branch = bool(self.get_revision_sha(dest, rev_options.rev)[0])
- return not is_tag_or_branch
-
- def get_git_version(self) -> Tuple[int, ...]:
- version = self.run_command(
- ["version"],
- command_desc="git version",
- show_stdout=False,
- stdout_only=True,
- )
- match = GIT_VERSION_REGEX.match(version)
- if not match:
- logger.warning("Can't parse git version: %s", version)
- return ()
- return tuple(int(c) for c in match.groups())
-
- @classmethod
- def get_current_branch(cls, location: str) -> Optional[str]:
- """
- Return the current branch, or None if HEAD isn't at a branch
- (e.g. detached HEAD).
- """
- # git-symbolic-ref exits with empty stdout if "HEAD" is a detached
- # HEAD rather than a symbolic ref. In addition, the -q causes the
- # command to exit with status code 1 instead of 128 in this case
- # and to suppress the message to stderr.
- args = ["symbolic-ref", "-q", "HEAD"]
- output = cls.run_command(
- args,
- extra_ok_returncodes=(1,),
- show_stdout=False,
- stdout_only=True,
- cwd=location,
- )
- ref = output.strip()
-
- if ref.startswith("refs/heads/"):
- return ref[len("refs/heads/") :]
-
- return None
-
- @classmethod
- def get_revision_sha(cls, dest: str, rev: str) -> Tuple[Optional[str], bool]:
- """
- Return (sha_or_none, is_branch), where sha_or_none is a commit hash
- if the revision names a remote branch or tag, otherwise None.
-
- Args:
- dest: the repository directory.
- rev: the revision name.
- """
- # Pass rev to pre-filter the list.
- output = cls.run_command(
- ["show-ref", rev],
- cwd=dest,
- show_stdout=False,
- stdout_only=True,
- on_returncode="ignore",
- )
- refs = {}
- # NOTE: We do not use splitlines here since that would split on other
- # unicode separators, which can be maliciously used to install a
- # different revision.
- for line in output.strip().split("\n"):
- line = line.rstrip("\r")
- if not line:
- continue
- try:
- ref_sha, ref_name = line.split(" ", maxsplit=2)
- except ValueError:
- # Include the offending line to simplify troubleshooting if
- # this error ever occurs.
- raise ValueError(f"unexpected show-ref line: {line!r}")
-
- refs[ref_name] = ref_sha
-
- branch_ref = f"refs/remotes/origin/{rev}"
- tag_ref = f"refs/tags/{rev}"
-
- sha = refs.get(branch_ref)
- if sha is not None:
- return (sha, True)
-
- sha = refs.get(tag_ref)
-
- return (sha, False)
-
- @classmethod
- def _should_fetch(cls, dest: str, rev: str) -> bool:
- """
- Return true if rev is a ref or is a commit that we don't have locally.
-
- Branches and tags are not considered in this method because they are
- assumed to be always available locally (which is a normal outcome of
- ``git clone`` and ``git fetch --tags``).
- """
- if rev.startswith("refs/"):
- # Always fetch remote refs.
- return True
-
- if not looks_like_hash(rev):
- # Git fetch would fail with abbreviated commits.
- return False
-
- if cls.has_commit(dest, rev):
- # Don't fetch if we have the commit locally.
- return False
-
- return True
-
- @classmethod
- def resolve_revision(
- cls, dest: str, url: HiddenText, rev_options: RevOptions
- ) -> RevOptions:
- """
- Resolve a revision to a new RevOptions object with the SHA1 of the
- branch, tag, or ref if found.
-
- Args:
- rev_options: a RevOptions object.
- """
- rev = rev_options.arg_rev
- # The arg_rev property's implementation for Git ensures that the
- # rev return value is always non-None.
- assert rev is not None
-
- sha, is_branch = cls.get_revision_sha(dest, rev)
-
- if sha is not None:
- rev_options = rev_options.make_new(sha)
- rev_options.branch_name = rev if is_branch else None
-
- return rev_options
-
- # Do not show a warning for the common case of something that has
- # the form of a Git commit hash.
- if not looks_like_hash(rev):
- logger.warning(
- "Did not find branch or tag '%s', assuming revision or ref.",
- rev,
- )
-
- if not cls._should_fetch(dest, rev):
- return rev_options
-
- # fetch the requested revision
- cls.run_command(
- make_command("fetch", "-q", url, rev_options.to_args()),
- cwd=dest,
- )
- # Change the revision to the SHA of the ref we fetched
- sha = cls.get_revision(dest, rev="FETCH_HEAD")
- rev_options = rev_options.make_new(sha)
-
- return rev_options
-
- @classmethod
- def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
- """
- Return whether the current commit hash equals the given name.
-
- Args:
- dest: the repository directory.
- name: a string name.
- """
- if not name:
- # Then avoid an unnecessary subprocess call.
- return False
-
- return cls.get_revision(dest) == name
-
- def fetch_new(
- self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
- ) -> None:
- rev_display = rev_options.to_display()
- logger.info("Cloning %s%s to %s", url, rev_display, display_path(dest))
- if verbosity <= 0:
- flags: Tuple[str, ...] = ("--quiet",)
- elif verbosity == 1:
- flags = ()
- else:
- flags = ("--verbose", "--progress")
- if self.get_git_version() >= (2, 17):
- # Git added support for partial clone in 2.17
- # https://git-scm.com/docs/partial-clone
- # Speeds up cloning by functioning without a complete copy of repository
- self.run_command(
- make_command(
- "clone",
- "--filter=blob:none",
- *flags,
- url,
- dest,
- )
- )
- else:
- self.run_command(make_command("clone", *flags, url, dest))
-
- if rev_options.rev:
- # Then a specific revision was requested.
- rev_options = self.resolve_revision(dest, url, rev_options)
- branch_name = getattr(rev_options, "branch_name", None)
- logger.debug("Rev options %s, branch_name %s", rev_options, branch_name)
- if branch_name is None:
- # Only do a checkout if the current commit id doesn't match
- # the requested revision.
- if not self.is_commit_id_equal(dest, rev_options.rev):
- cmd_args = make_command(
- "checkout",
- "-q",
- rev_options.to_args(),
- )
- self.run_command(cmd_args, cwd=dest)
- elif self.get_current_branch(dest) != branch_name:
- # Then a specific branch was requested, and that branch
- # is not yet checked out.
- track_branch = f"origin/{branch_name}"
- cmd_args = [
- "checkout",
- "-b",
- branch_name,
- "--track",
- track_branch,
- ]
- self.run_command(cmd_args, cwd=dest)
- else:
- sha = self.get_revision(dest)
- rev_options = rev_options.make_new(sha)
-
- logger.info("Resolved %s to commit %s", url, rev_options.rev)
-
- #: repo may contain submodules
- self.update_submodules(dest)
-
- def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
- self.run_command(
- make_command("config", "remote.origin.url", url),
- cwd=dest,
- )
- cmd_args = make_command("checkout", "-q", rev_options.to_args())
- self.run_command(cmd_args, cwd=dest)
-
- self.update_submodules(dest)
-
- def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
- # First fetch changes from the default remote
- if self.get_git_version() >= (1, 9):
- # fetch tags in addition to everything else
- self.run_command(["fetch", "-q", "--tags"], cwd=dest)
- else:
- self.run_command(["fetch", "-q"], cwd=dest)
- # Then reset to wanted revision (maybe even origin/master)
- rev_options = self.resolve_revision(dest, url, rev_options)
- cmd_args = make_command("reset", "--hard", "-q", rev_options.to_args())
- self.run_command(cmd_args, cwd=dest)
- #: update submodules
- self.update_submodules(dest)
-
- @classmethod
- def get_remote_url(cls, location: str) -> str:
- """
- Return URL of the first remote encountered.
-
- Raises RemoteNotFoundError if the repository does not have a remote
- url configured.
- """
- # We need to pass 1 for extra_ok_returncodes since the command
- # exits with return code 1 if there are no matching lines.
- stdout = cls.run_command(
- ["config", "--get-regexp", r"remote\..*\.url"],
- extra_ok_returncodes=(1,),
- show_stdout=False,
- stdout_only=True,
- cwd=location,
- )
- remotes = stdout.splitlines()
- try:
- found_remote = remotes[0]
- except IndexError:
- raise RemoteNotFoundError
-
- for remote in remotes:
- if remote.startswith("remote.origin.url "):
- found_remote = remote
- break
- url = found_remote.split(" ")[1]
- return cls._git_remote_to_pip_url(url.strip())
-
- @staticmethod
- def _git_remote_to_pip_url(url: str) -> str:
- """
- Convert a remote url from what git uses to what pip accepts.
-
- There are 3 legal forms **url** may take:
-
- 1. A fully qualified url: ssh://git@example.com/foo/bar.git
- 2. A local project.git folder: /path/to/bare/repository.git
- 3. SCP shorthand for form 1: git@example.com:foo/bar.git
-
- Form 1 is output as-is. Form 2 must be converted to URI and form 3 must
- be converted to form 1.
-
- See the corresponding test test_git_remote_url_to_pip() for examples of
- sample inputs/outputs.
- """
- if re.match(r"\w+://", url):
- # This is already valid. Pass it though as-is.
- return url
- if os.path.exists(url):
- # A local bare remote (git clone --mirror).
- # Needs a file:// prefix.
- return pathlib.PurePath(url).as_uri()
- scp_match = SCP_REGEX.match(url)
- if scp_match:
- # Add an ssh:// prefix and replace the ':' with a '/'.
- return scp_match.expand(r"ssh://\1\2/\3")
- # Otherwise, bail out.
- raise RemoteNotValidError(url)
-
- @classmethod
- def has_commit(cls, location: str, rev: str) -> bool:
- """
- Check if rev is a commit that is available in the local repository.
- """
- try:
- cls.run_command(
- ["rev-parse", "-q", "--verify", "sha^" + rev],
- cwd=location,
- log_failed_cmd=False,
- )
- except InstallationError:
- return False
- else:
- return True
-
- @classmethod
- def get_revision(cls, location: str, rev: Optional[str] = None) -> str:
- if rev is None:
- rev = "HEAD"
- current_rev = cls.run_command(
- ["rev-parse", rev],
- show_stdout=False,
- stdout_only=True,
- cwd=location,
- )
- return current_rev.strip()
-
- @classmethod
- def get_subdirectory(cls, location: str) -> Optional[str]:
- """
- Return the path to Python project root, relative to the repo root.
- Return None if the project root is in the repo root.
- """
- # find the repo root
- git_dir = cls.run_command(
- ["rev-parse", "--git-dir"],
- show_stdout=False,
- stdout_only=True,
- cwd=location,
- ).strip()
- if not os.path.isabs(git_dir):
- git_dir = os.path.join(location, git_dir)
- repo_root = os.path.abspath(os.path.join(git_dir, ".."))
- return find_path_to_project_root_from_repo_root(location, repo_root)
-
- @classmethod
- def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
- """
- Prefixes stub URLs like 'user@hostname:user/repo.git' with 'ssh://'.
- That's required because although they use SSH they sometimes don't
- work with a ssh:// scheme (e.g. GitHub). But we need a scheme for
- parsing. Hence we remove it again afterwards and return it as a stub.
- """
- # Works around an apparent Git bug
- # (see https://article.gmane.org/gmane.comp.version-control.git/146500)
- scheme, netloc, path, query, fragment = urlsplit(url)
- if scheme.endswith("file"):
- initial_slashes = path[: -len(path.lstrip("/"))]
- newpath = initial_slashes + urllib.request.url2pathname(path).replace(
- "\\", "/"
- ).lstrip("/")
- after_plus = scheme.find("+") + 1
- url = scheme[:after_plus] + urlunsplit(
- (scheme[after_plus:], netloc, newpath, query, fragment),
- )
-
- if "://" not in url:
- assert "file:" not in url
- url = url.replace("git+", "git+ssh://")
- url, rev, user_pass = super().get_url_rev_and_auth(url)
- url = url.replace("ssh://", "")
- else:
- url, rev, user_pass = super().get_url_rev_and_auth(url)
-
- return url, rev, user_pass
-
- @classmethod
- def update_submodules(cls, location: str) -> None:
- if not os.path.exists(os.path.join(location, ".gitmodules")):
- return
- cls.run_command(
- ["submodule", "update", "--init", "--recursive", "-q"],
- cwd=location,
- )
-
- @classmethod
- def get_repository_root(cls, location: str) -> Optional[str]:
- loc = super().get_repository_root(location)
- if loc:
- return loc
- try:
- r = cls.run_command(
- ["rev-parse", "--show-toplevel"],
- cwd=location,
- show_stdout=False,
- stdout_only=True,
- on_returncode="raise",
- log_failed_cmd=False,
- )
- except BadCommand:
- logger.debug(
- "could not determine if %s is under git control "
- "because git is not available",
- location,
- )
- return None
- except InstallationError:
- return None
- return os.path.normpath(r.rstrip("\r\n"))
-
- @staticmethod
- def should_add_vcs_url_prefix(repo_url: str) -> bool:
- """In either https or ssh form, requirements must be prefixed with git+."""
- return True
-
-
-vcs.register(Git)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/progress.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/progress.py
deleted file mode 100644
index 8b0a315f32466ac03a205898394f958f221818a7..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/progress.py
+++ /dev/null
@@ -1,1702 +0,0 @@
-import io
-import sys
-import typing
-import warnings
-from abc import ABC, abstractmethod
-from collections import deque
-from dataclasses import dataclass, field
-from datetime import timedelta
-from io import RawIOBase, UnsupportedOperation
-from math import ceil
-from mmap import mmap
-from operator import length_hint
-from os import PathLike, stat
-from threading import Event, RLock, Thread
-from types import TracebackType
-from typing import (
- Any,
- BinaryIO,
- Callable,
- ContextManager,
- Deque,
- Dict,
- Generic,
- Iterable,
- List,
- NamedTuple,
- NewType,
- Optional,
- Sequence,
- TextIO,
- Tuple,
- Type,
- TypeVar,
- Union,
-)
-
-if sys.version_info >= (3, 8):
- from typing import Literal
-else:
- from pip._vendor.typing_extensions import Literal # pragma: no cover
-
-from . import filesize, get_console
-from .console import Console, Group, JustifyMethod, RenderableType
-from .highlighter import Highlighter
-from .jupyter import JupyterMixin
-from .live import Live
-from .progress_bar import ProgressBar
-from .spinner import Spinner
-from .style import StyleType
-from .table import Column, Table
-from .text import Text, TextType
-
-TaskID = NewType("TaskID", int)
-
-ProgressType = TypeVar("ProgressType")
-
-GetTimeCallable = Callable[[], float]
-
-
-_I = typing.TypeVar("_I", TextIO, BinaryIO)
-
-
-class _TrackThread(Thread):
- """A thread to periodically update progress."""
-
- def __init__(self, progress: "Progress", task_id: "TaskID", update_period: float):
- self.progress = progress
- self.task_id = task_id
- self.update_period = update_period
- self.done = Event()
-
- self.completed = 0
- super().__init__()
-
- def run(self) -> None:
- task_id = self.task_id
- advance = self.progress.advance
- update_period = self.update_period
- last_completed = 0
- wait = self.done.wait
- while not wait(update_period):
- completed = self.completed
- if last_completed != completed:
- advance(task_id, completed - last_completed)
- last_completed = completed
-
- self.progress.update(self.task_id, completed=self.completed, refresh=True)
-
- def __enter__(self) -> "_TrackThread":
- self.start()
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- self.done.set()
- self.join()
-
-
-def track(
- sequence: Union[Sequence[ProgressType], Iterable[ProgressType]],
- description: str = "Working...",
- total: Optional[float] = None,
- auto_refresh: bool = True,
- console: Optional[Console] = None,
- transient: bool = False,
- get_time: Optional[Callable[[], float]] = None,
- refresh_per_second: float = 10,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- update_period: float = 0.1,
- disable: bool = False,
- show_speed: bool = True,
-) -> Iterable[ProgressType]:
- """Track progress by iterating over a sequence.
-
- Args:
- sequence (Iterable[ProgressType]): A sequence (must support "len") you wish to iterate over.
- description (str, optional): Description of task show next to progress bar. Defaults to "Working".
- total: (float, optional): Total number of steps. Default is len(sequence).
- auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
- transient: (bool, optional): Clear the progress on exit. Defaults to False.
- console (Console, optional): Console to write to. Default creates internal Console instance.
- refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
- style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
- complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
- finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
- pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
- update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
- disable (bool, optional): Disable display of progress.
- show_speed (bool, optional): Show speed if total isn't known. Defaults to True.
- Returns:
- Iterable[ProgressType]: An iterable of the values in the sequence.
-
- """
-
- columns: List["ProgressColumn"] = (
- [TextColumn("[progress.description]{task.description}")] if description else []
- )
- columns.extend(
- (
- BarColumn(
- style=style,
- complete_style=complete_style,
- finished_style=finished_style,
- pulse_style=pulse_style,
- ),
- TaskProgressColumn(show_speed=show_speed),
- TimeRemainingColumn(elapsed_when_finished=True),
- )
- )
- progress = Progress(
- *columns,
- auto_refresh=auto_refresh,
- console=console,
- transient=transient,
- get_time=get_time,
- refresh_per_second=refresh_per_second or 10,
- disable=disable,
- )
-
- with progress:
- yield from progress.track(
- sequence, total=total, description=description, update_period=update_period
- )
-
-
-class _Reader(RawIOBase, BinaryIO):
- """A reader that tracks progress while it's being read from."""
-
- def __init__(
- self,
- handle: BinaryIO,
- progress: "Progress",
- task: TaskID,
- close_handle: bool = True,
- ) -> None:
- self.handle = handle
- self.progress = progress
- self.task = task
- self.close_handle = close_handle
- self._closed = False
-
- def __enter__(self) -> "_Reader":
- self.handle.__enter__()
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- self.close()
-
- def __iter__(self) -> BinaryIO:
- return self
-
- def __next__(self) -> bytes:
- line = next(self.handle)
- self.progress.advance(self.task, advance=len(line))
- return line
-
- @property
- def closed(self) -> bool:
- return self._closed
-
- def fileno(self) -> int:
- return self.handle.fileno()
-
- def isatty(self) -> bool:
- return self.handle.isatty()
-
- @property
- def mode(self) -> str:
- return self.handle.mode
-
- @property
- def name(self) -> str:
- return self.handle.name
-
- def readable(self) -> bool:
- return self.handle.readable()
-
- def seekable(self) -> bool:
- return self.handle.seekable()
-
- def writable(self) -> bool:
- return False
-
- def read(self, size: int = -1) -> bytes:
- block = self.handle.read(size)
- self.progress.advance(self.task, advance=len(block))
- return block
-
- def readinto(self, b: Union[bytearray, memoryview, mmap]): # type: ignore[no-untyped-def, override]
- n = self.handle.readinto(b) # type: ignore[attr-defined]
- self.progress.advance(self.task, advance=n)
- return n
-
- def readline(self, size: int = -1) -> bytes: # type: ignore[override]
- line = self.handle.readline(size)
- self.progress.advance(self.task, advance=len(line))
- return line
-
- def readlines(self, hint: int = -1) -> List[bytes]:
- lines = self.handle.readlines(hint)
- self.progress.advance(self.task, advance=sum(map(len, lines)))
- return lines
-
- def close(self) -> None:
- if self.close_handle:
- self.handle.close()
- self._closed = True
-
- def seek(self, offset: int, whence: int = 0) -> int:
- pos = self.handle.seek(offset, whence)
- self.progress.update(self.task, completed=pos)
- return pos
-
- def tell(self) -> int:
- return self.handle.tell()
-
- def write(self, s: Any) -> int:
- raise UnsupportedOperation("write")
-
-
-class _ReadContext(ContextManager[_I], Generic[_I]):
- """A utility class to handle a context for both a reader and a progress."""
-
- def __init__(self, progress: "Progress", reader: _I) -> None:
- self.progress = progress
- self.reader: _I = reader
-
- def __enter__(self) -> _I:
- self.progress.start()
- return self.reader.__enter__()
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- self.progress.stop()
- self.reader.__exit__(exc_type, exc_val, exc_tb)
-
-
-def wrap_file(
- file: BinaryIO,
- total: int,
- *,
- description: str = "Reading...",
- auto_refresh: bool = True,
- console: Optional[Console] = None,
- transient: bool = False,
- get_time: Optional[Callable[[], float]] = None,
- refresh_per_second: float = 10,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- disable: bool = False,
-) -> ContextManager[BinaryIO]:
- """Read bytes from a file while tracking progress.
-
- Args:
- file (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode.
- total (int): Total number of bytes to read.
- description (str, optional): Description of task show next to progress bar. Defaults to "Reading".
- auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
- transient: (bool, optional): Clear the progress on exit. Defaults to False.
- console (Console, optional): Console to write to. Default creates internal Console instance.
- refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
- style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
- complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
- finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
- pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
- disable (bool, optional): Disable display of progress.
- Returns:
- ContextManager[BinaryIO]: A context manager yielding a progress reader.
-
- """
-
- columns: List["ProgressColumn"] = (
- [TextColumn("[progress.description]{task.description}")] if description else []
- )
- columns.extend(
- (
- BarColumn(
- style=style,
- complete_style=complete_style,
- finished_style=finished_style,
- pulse_style=pulse_style,
- ),
- DownloadColumn(),
- TimeRemainingColumn(),
- )
- )
- progress = Progress(
- *columns,
- auto_refresh=auto_refresh,
- console=console,
- transient=transient,
- get_time=get_time,
- refresh_per_second=refresh_per_second or 10,
- disable=disable,
- )
-
- reader = progress.wrap_file(file, total=total, description=description)
- return _ReadContext(progress, reader)
-
-
-@typing.overload
-def open(
- file: Union[str, "PathLike[str]", bytes],
- mode: Union[Literal["rt"], Literal["r"]],
- buffering: int = -1,
- encoding: Optional[str] = None,
- errors: Optional[str] = None,
- newline: Optional[str] = None,
- *,
- total: Optional[int] = None,
- description: str = "Reading...",
- auto_refresh: bool = True,
- console: Optional[Console] = None,
- transient: bool = False,
- get_time: Optional[Callable[[], float]] = None,
- refresh_per_second: float = 10,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- disable: bool = False,
-) -> ContextManager[TextIO]:
- pass
-
-
-@typing.overload
-def open(
- file: Union[str, "PathLike[str]", bytes],
- mode: Literal["rb"],
- buffering: int = -1,
- encoding: Optional[str] = None,
- errors: Optional[str] = None,
- newline: Optional[str] = None,
- *,
- total: Optional[int] = None,
- description: str = "Reading...",
- auto_refresh: bool = True,
- console: Optional[Console] = None,
- transient: bool = False,
- get_time: Optional[Callable[[], float]] = None,
- refresh_per_second: float = 10,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- disable: bool = False,
-) -> ContextManager[BinaryIO]:
- pass
-
-
-def open(
- file: Union[str, "PathLike[str]", bytes],
- mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r",
- buffering: int = -1,
- encoding: Optional[str] = None,
- errors: Optional[str] = None,
- newline: Optional[str] = None,
- *,
- total: Optional[int] = None,
- description: str = "Reading...",
- auto_refresh: bool = True,
- console: Optional[Console] = None,
- transient: bool = False,
- get_time: Optional[Callable[[], float]] = None,
- refresh_per_second: float = 10,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- disable: bool = False,
-) -> Union[ContextManager[BinaryIO], ContextManager[TextIO]]:
- """Read bytes from a file while tracking progress.
-
- Args:
- path (Union[str, PathLike[str], BinaryIO]): The path to the file to read, or a file-like object in binary mode.
- mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt".
- buffering (int): The buffering strategy to use, see :func:`io.open`.
- encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`.
- errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`.
- newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open`
- total: (int, optional): Total number of bytes to read. Must be provided if reading from a file handle. Default for a path is os.stat(file).st_size.
- description (str, optional): Description of task show next to progress bar. Defaults to "Reading".
- auto_refresh (bool, optional): Automatic refresh, disable to force a refresh after each iteration. Default is True.
- transient: (bool, optional): Clear the progress on exit. Defaults to False.
- console (Console, optional): Console to write to. Default creates internal Console instance.
- refresh_per_second (float): Number of times per second to refresh the progress information. Defaults to 10.
- style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
- complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
- finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
- pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
- disable (bool, optional): Disable display of progress.
- encoding (str, optional): The encoding to use when reading in text mode.
-
- Returns:
- ContextManager[BinaryIO]: A context manager yielding a progress reader.
-
- """
-
- columns: List["ProgressColumn"] = (
- [TextColumn("[progress.description]{task.description}")] if description else []
- )
- columns.extend(
- (
- BarColumn(
- style=style,
- complete_style=complete_style,
- finished_style=finished_style,
- pulse_style=pulse_style,
- ),
- DownloadColumn(),
- TimeRemainingColumn(),
- )
- )
- progress = Progress(
- *columns,
- auto_refresh=auto_refresh,
- console=console,
- transient=transient,
- get_time=get_time,
- refresh_per_second=refresh_per_second or 10,
- disable=disable,
- )
-
- reader = progress.open(
- file,
- mode=mode,
- buffering=buffering,
- encoding=encoding,
- errors=errors,
- newline=newline,
- total=total,
- description=description,
- )
- return _ReadContext(progress, reader) # type: ignore[return-value, type-var]
-
-
-class ProgressColumn(ABC):
- """Base class for a widget to use in progress display."""
-
- max_refresh: Optional[float] = None
-
- def __init__(self, table_column: Optional[Column] = None) -> None:
- self._table_column = table_column
- self._renderable_cache: Dict[TaskID, Tuple[float, RenderableType]] = {}
- self._update_time: Optional[float] = None
-
- def get_table_column(self) -> Column:
- """Get a table column, used to build tasks table."""
- return self._table_column or Column()
-
- def __call__(self, task: "Task") -> RenderableType:
- """Called by the Progress object to return a renderable for the given task.
-
- Args:
- task (Task): An object containing information regarding the task.
-
- Returns:
- RenderableType: Anything renderable (including str).
- """
- current_time = task.get_time()
- if self.max_refresh is not None and not task.completed:
- try:
- timestamp, renderable = self._renderable_cache[task.id]
- except KeyError:
- pass
- else:
- if timestamp + self.max_refresh > current_time:
- return renderable
-
- renderable = self.render(task)
- self._renderable_cache[task.id] = (current_time, renderable)
- return renderable
-
- @abstractmethod
- def render(self, task: "Task") -> RenderableType:
- """Should return a renderable object."""
-
-
-class RenderableColumn(ProgressColumn):
- """A column to insert an arbitrary column.
-
- Args:
- renderable (RenderableType, optional): Any renderable. Defaults to empty string.
- """
-
- def __init__(
- self, renderable: RenderableType = "", *, table_column: Optional[Column] = None
- ):
- self.renderable = renderable
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> RenderableType:
- return self.renderable
-
-
-class SpinnerColumn(ProgressColumn):
- """A column with a 'spinner' animation.
-
- Args:
- spinner_name (str, optional): Name of spinner animation. Defaults to "dots".
- style (StyleType, optional): Style of spinner. Defaults to "progress.spinner".
- speed (float, optional): Speed factor of spinner. Defaults to 1.0.
- finished_text (TextType, optional): Text used when task is finished. Defaults to " ".
- """
-
- def __init__(
- self,
- spinner_name: str = "dots",
- style: Optional[StyleType] = "progress.spinner",
- speed: float = 1.0,
- finished_text: TextType = " ",
- table_column: Optional[Column] = None,
- ):
- self.spinner = Spinner(spinner_name, style=style, speed=speed)
- self.finished_text = (
- Text.from_markup(finished_text)
- if isinstance(finished_text, str)
- else finished_text
- )
- super().__init__(table_column=table_column)
-
- def set_spinner(
- self,
- spinner_name: str,
- spinner_style: Optional[StyleType] = "progress.spinner",
- speed: float = 1.0,
- ) -> None:
- """Set a new spinner.
-
- Args:
- spinner_name (str): Spinner name, see python -m rich.spinner.
- spinner_style (Optional[StyleType], optional): Spinner style. Defaults to "progress.spinner".
- speed (float, optional): Speed factor of spinner. Defaults to 1.0.
- """
- self.spinner = Spinner(spinner_name, style=spinner_style, speed=speed)
-
- def render(self, task: "Task") -> RenderableType:
- text = (
- self.finished_text
- if task.finished
- else self.spinner.render(task.get_time())
- )
- return text
-
-
-class TextColumn(ProgressColumn):
- """A column containing text."""
-
- def __init__(
- self,
- text_format: str,
- style: StyleType = "none",
- justify: JustifyMethod = "left",
- markup: bool = True,
- highlighter: Optional[Highlighter] = None,
- table_column: Optional[Column] = None,
- ) -> None:
- self.text_format = text_format
- self.justify: JustifyMethod = justify
- self.style = style
- self.markup = markup
- self.highlighter = highlighter
- super().__init__(table_column=table_column or Column(no_wrap=True))
-
- def render(self, task: "Task") -> Text:
- _text = self.text_format.format(task=task)
- if self.markup:
- text = Text.from_markup(_text, style=self.style, justify=self.justify)
- else:
- text = Text(_text, style=self.style, justify=self.justify)
- if self.highlighter:
- self.highlighter.highlight(text)
- return text
-
-
-class BarColumn(ProgressColumn):
- """Renders a visual progress bar.
-
- Args:
- bar_width (Optional[int], optional): Width of bar or None for full width. Defaults to 40.
- style (StyleType, optional): Style for the bar background. Defaults to "bar.back".
- complete_style (StyleType, optional): Style for the completed bar. Defaults to "bar.complete".
- finished_style (StyleType, optional): Style for a finished bar. Defaults to "bar.finished".
- pulse_style (StyleType, optional): Style for pulsing bars. Defaults to "bar.pulse".
- """
-
- def __init__(
- self,
- bar_width: Optional[int] = 40,
- style: StyleType = "bar.back",
- complete_style: StyleType = "bar.complete",
- finished_style: StyleType = "bar.finished",
- pulse_style: StyleType = "bar.pulse",
- table_column: Optional[Column] = None,
- ) -> None:
- self.bar_width = bar_width
- self.style = style
- self.complete_style = complete_style
- self.finished_style = finished_style
- self.pulse_style = pulse_style
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> ProgressBar:
- """Gets a progress bar widget for a task."""
- return ProgressBar(
- total=max(0, task.total) if task.total is not None else None,
- completed=max(0, task.completed),
- width=None if self.bar_width is None else max(1, self.bar_width),
- pulse=not task.started,
- animation_time=task.get_time(),
- style=self.style,
- complete_style=self.complete_style,
- finished_style=self.finished_style,
- pulse_style=self.pulse_style,
- )
-
-
-class TimeElapsedColumn(ProgressColumn):
- """Renders time elapsed."""
-
- def render(self, task: "Task") -> Text:
- """Show time elapsed."""
- elapsed = task.finished_time if task.finished else task.elapsed
- if elapsed is None:
- return Text("-:--:--", style="progress.elapsed")
- delta = timedelta(seconds=int(elapsed))
- return Text(str(delta), style="progress.elapsed")
-
-
-class TaskProgressColumn(TextColumn):
- """Show task progress as a percentage.
-
- Args:
- text_format (str, optional): Format for percentage display. Defaults to "[progress.percentage]{task.percentage:>3.0f}%".
- text_format_no_percentage (str, optional): Format if percentage is unknown. Defaults to "".
- style (StyleType, optional): Style of output. Defaults to "none".
- justify (JustifyMethod, optional): Text justification. Defaults to "left".
- markup (bool, optional): Enable markup. Defaults to True.
- highlighter (Optional[Highlighter], optional): Highlighter to apply to output. Defaults to None.
- table_column (Optional[Column], optional): Table Column to use. Defaults to None.
- show_speed (bool, optional): Show speed if total is unknown. Defaults to False.
- """
-
- def __init__(
- self,
- text_format: str = "[progress.percentage]{task.percentage:>3.0f}%",
- text_format_no_percentage: str = "",
- style: StyleType = "none",
- justify: JustifyMethod = "left",
- markup: bool = True,
- highlighter: Optional[Highlighter] = None,
- table_column: Optional[Column] = None,
- show_speed: bool = False,
- ) -> None:
-
- self.text_format_no_percentage = text_format_no_percentage
- self.show_speed = show_speed
- super().__init__(
- text_format=text_format,
- style=style,
- justify=justify,
- markup=markup,
- highlighter=highlighter,
- table_column=table_column,
- )
-
- @classmethod
- def render_speed(cls, speed: Optional[float]) -> Text:
- """Render the speed in iterations per second.
-
- Args:
- task (Task): A Task object.
-
- Returns:
- Text: Text object containing the task speed.
- """
- if speed is None:
- return Text("", style="progress.percentage")
- unit, suffix = filesize.pick_unit_and_suffix(
- int(speed),
- ["", "×10³", "×10⁶", "×10⁹", "×10¹²"],
- 1000,
- )
- data_speed = speed / unit
- return Text(f"{data_speed:.1f}{suffix} it/s", style="progress.percentage")
-
- def render(self, task: "Task") -> Text:
- if task.total is None and self.show_speed:
- return self.render_speed(task.finished_speed or task.speed)
- text_format = (
- self.text_format_no_percentage if task.total is None else self.text_format
- )
- _text = text_format.format(task=task)
- if self.markup:
- text = Text.from_markup(_text, style=self.style, justify=self.justify)
- else:
- text = Text(_text, style=self.style, justify=self.justify)
- if self.highlighter:
- self.highlighter.highlight(text)
- return text
-
-
-class TimeRemainingColumn(ProgressColumn):
- """Renders estimated time remaining.
-
- Args:
- compact (bool, optional): Render MM:SS when time remaining is less than an hour. Defaults to False.
- elapsed_when_finished (bool, optional): Render time elapsed when the task is finished. Defaults to False.
- """
-
- # Only refresh twice a second to prevent jitter
- max_refresh = 0.5
-
- def __init__(
- self,
- compact: bool = False,
- elapsed_when_finished: bool = False,
- table_column: Optional[Column] = None,
- ):
- self.compact = compact
- self.elapsed_when_finished = elapsed_when_finished
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> Text:
- """Show time remaining."""
- if self.elapsed_when_finished and task.finished:
- task_time = task.finished_time
- style = "progress.elapsed"
- else:
- task_time = task.time_remaining
- style = "progress.remaining"
-
- if task.total is None:
- return Text("", style=style)
-
- if task_time is None:
- return Text("--:--" if self.compact else "-:--:--", style=style)
-
- # Based on https://github.com/tqdm/tqdm/blob/master/tqdm/std.py
- minutes, seconds = divmod(int(task_time), 60)
- hours, minutes = divmod(minutes, 60)
-
- if self.compact and not hours:
- formatted = f"{minutes:02d}:{seconds:02d}"
- else:
- formatted = f"{hours:d}:{minutes:02d}:{seconds:02d}"
-
- return Text(formatted, style=style)
-
-
-class FileSizeColumn(ProgressColumn):
- """Renders completed filesize."""
-
- def render(self, task: "Task") -> Text:
- """Show data completed."""
- data_size = filesize.decimal(int(task.completed))
- return Text(data_size, style="progress.filesize")
-
-
-class TotalFileSizeColumn(ProgressColumn):
- """Renders total filesize."""
-
- def render(self, task: "Task") -> Text:
- """Show data completed."""
- data_size = filesize.decimal(int(task.total)) if task.total is not None else ""
- return Text(data_size, style="progress.filesize.total")
-
-
-class MofNCompleteColumn(ProgressColumn):
- """Renders completed count/total, e.g. ' 10/1000'.
-
- Best for bounded tasks with int quantities.
-
- Space pads the completed count so that progress length does not change as task progresses
- past powers of 10.
-
- Args:
- separator (str, optional): Text to separate completed and total values. Defaults to "/".
- """
-
- def __init__(self, separator: str = "/", table_column: Optional[Column] = None):
- self.separator = separator
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> Text:
- """Show completed/total."""
- completed = int(task.completed)
- total = int(task.total) if task.total is not None else "?"
- total_width = len(str(total))
- return Text(
- f"{completed:{total_width}d}{self.separator}{total}",
- style="progress.download",
- )
-
-
-class DownloadColumn(ProgressColumn):
- """Renders file size downloaded and total, e.g. '0.5/2.3 GB'.
-
- Args:
- binary_units (bool, optional): Use binary units, KiB, MiB etc. Defaults to False.
- """
-
- def __init__(
- self, binary_units: bool = False, table_column: Optional[Column] = None
- ) -> None:
- self.binary_units = binary_units
- super().__init__(table_column=table_column)
-
- def render(self, task: "Task") -> Text:
- """Calculate common unit for completed and total."""
- completed = int(task.completed)
-
- unit_and_suffix_calculation_base = (
- int(task.total) if task.total is not None else completed
- )
- if self.binary_units:
- unit, suffix = filesize.pick_unit_and_suffix(
- unit_and_suffix_calculation_base,
- ["bytes", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB", "ZiB", "YiB"],
- 1024,
- )
- else:
- unit, suffix = filesize.pick_unit_and_suffix(
- unit_and_suffix_calculation_base,
- ["bytes", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"],
- 1000,
- )
- precision = 0 if unit == 1 else 1
-
- completed_ratio = completed / unit
- completed_str = f"{completed_ratio:,.{precision}f}"
-
- if task.total is not None:
- total = int(task.total)
- total_ratio = total / unit
- total_str = f"{total_ratio:,.{precision}f}"
- else:
- total_str = "?"
-
- download_status = f"{completed_str}/{total_str} {suffix}"
- download_text = Text(download_status, style="progress.download")
- return download_text
-
-
-class TransferSpeedColumn(ProgressColumn):
- """Renders human readable transfer speed."""
-
- def render(self, task: "Task") -> Text:
- """Show data transfer speed."""
- speed = task.finished_speed or task.speed
- if speed is None:
- return Text("?", style="progress.data.speed")
- data_speed = filesize.decimal(int(speed))
- return Text(f"{data_speed}/s", style="progress.data.speed")
-
-
-class ProgressSample(NamedTuple):
- """Sample of progress for a given time."""
-
- timestamp: float
- """Timestamp of sample."""
- completed: float
- """Number of steps completed."""
-
-
-@dataclass
-class Task:
- """Information regarding a progress task.
-
- This object should be considered read-only outside of the :class:`~Progress` class.
-
- """
-
- id: TaskID
- """Task ID associated with this task (used in Progress methods)."""
-
- description: str
- """str: Description of the task."""
-
- total: Optional[float]
- """Optional[float]: Total number of steps in this task."""
-
- completed: float
- """float: Number of steps completed"""
-
- _get_time: GetTimeCallable
- """Callable to get the current time."""
-
- finished_time: Optional[float] = None
- """float: Time task was finished."""
-
- visible: bool = True
- """bool: Indicates if this task is visible in the progress display."""
-
- fields: Dict[str, Any] = field(default_factory=dict)
- """dict: Arbitrary fields passed in via Progress.update."""
-
- start_time: Optional[float] = field(default=None, init=False, repr=False)
- """Optional[float]: Time this task was started, or None if not started."""
-
- stop_time: Optional[float] = field(default=None, init=False, repr=False)
- """Optional[float]: Time this task was stopped, or None if not stopped."""
-
- finished_speed: Optional[float] = None
- """Optional[float]: The last speed for a finished task."""
-
- _progress: Deque[ProgressSample] = field(
- default_factory=lambda: deque(maxlen=1000), init=False, repr=False
- )
-
- _lock: RLock = field(repr=False, default_factory=RLock)
- """Thread lock."""
-
- def get_time(self) -> float:
- """float: Get the current time, in seconds."""
- return self._get_time()
-
- @property
- def started(self) -> bool:
- """bool: Check if the task as started."""
- return self.start_time is not None
-
- @property
- def remaining(self) -> Optional[float]:
- """Optional[float]: Get the number of steps remaining, if a non-None total was set."""
- if self.total is None:
- return None
- return self.total - self.completed
-
- @property
- def elapsed(self) -> Optional[float]:
- """Optional[float]: Time elapsed since task was started, or ``None`` if the task hasn't started."""
- if self.start_time is None:
- return None
- if self.stop_time is not None:
- return self.stop_time - self.start_time
- return self.get_time() - self.start_time
-
- @property
- def finished(self) -> bool:
- """Check if the task has finished."""
- return self.finished_time is not None
-
- @property
- def percentage(self) -> float:
- """float: Get progress of task as a percentage. If a None total was set, returns 0"""
- if not self.total:
- return 0.0
- completed = (self.completed / self.total) * 100.0
- completed = min(100.0, max(0.0, completed))
- return completed
-
- @property
- def speed(self) -> Optional[float]:
- """Optional[float]: Get the estimated speed in steps per second."""
- if self.start_time is None:
- return None
- with self._lock:
- progress = self._progress
- if not progress:
- return None
- total_time = progress[-1].timestamp - progress[0].timestamp
- if total_time == 0:
- return None
- iter_progress = iter(progress)
- next(iter_progress)
- total_completed = sum(sample.completed for sample in iter_progress)
- speed = total_completed / total_time
- return speed
-
- @property
- def time_remaining(self) -> Optional[float]:
- """Optional[float]: Get estimated time to completion, or ``None`` if no data."""
- if self.finished:
- return 0.0
- speed = self.speed
- if not speed:
- return None
- remaining = self.remaining
- if remaining is None:
- return None
- estimate = ceil(remaining / speed)
- return estimate
-
- def _reset(self) -> None:
- """Reset progress."""
- self._progress.clear()
- self.finished_time = None
- self.finished_speed = None
-
-
-class Progress(JupyterMixin):
- """Renders an auto-updating progress bar(s).
-
- Args:
- console (Console, optional): Optional Console instance. Default will an internal Console instance writing to stdout.
- auto_refresh (bool, optional): Enable auto refresh. If disabled, you will need to call `refresh()`.
- refresh_per_second (Optional[float], optional): Number of times per second to refresh the progress information or None to use default (10). Defaults to None.
- speed_estimate_period: (float, optional): Period (in seconds) used to calculate the speed estimate. Defaults to 30.
- transient: (bool, optional): Clear the progress on exit. Defaults to False.
- redirect_stdout: (bool, optional): Enable redirection of stdout, so ``print`` may be used. Defaults to True.
- redirect_stderr: (bool, optional): Enable redirection of stderr. Defaults to True.
- get_time: (Callable, optional): A callable that gets the current time, or None to use Console.get_time. Defaults to None.
- disable (bool, optional): Disable progress display. Defaults to False
- expand (bool, optional): Expand tasks table to fit width. Defaults to False.
- """
-
- def __init__(
- self,
- *columns: Union[str, ProgressColumn],
- console: Optional[Console] = None,
- auto_refresh: bool = True,
- refresh_per_second: float = 10,
- speed_estimate_period: float = 30.0,
- transient: bool = False,
- redirect_stdout: bool = True,
- redirect_stderr: bool = True,
- get_time: Optional[GetTimeCallable] = None,
- disable: bool = False,
- expand: bool = False,
- ) -> None:
- assert refresh_per_second > 0, "refresh_per_second must be > 0"
- self._lock = RLock()
- self.columns = columns or self.get_default_columns()
- self.speed_estimate_period = speed_estimate_period
-
- self.disable = disable
- self.expand = expand
- self._tasks: Dict[TaskID, Task] = {}
- self._task_index: TaskID = TaskID(0)
- self.live = Live(
- console=console or get_console(),
- auto_refresh=auto_refresh,
- refresh_per_second=refresh_per_second,
- transient=transient,
- redirect_stdout=redirect_stdout,
- redirect_stderr=redirect_stderr,
- get_renderable=self.get_renderable,
- )
- self.get_time = get_time or self.console.get_time
- self.print = self.console.print
- self.log = self.console.log
-
- @classmethod
- def get_default_columns(cls) -> Tuple[ProgressColumn, ...]:
- """Get the default columns used for a new Progress instance:
- - a text column for the description (TextColumn)
- - the bar itself (BarColumn)
- - a text column showing completion percentage (TextColumn)
- - an estimated-time-remaining column (TimeRemainingColumn)
- If the Progress instance is created without passing a columns argument,
- the default columns defined here will be used.
-
- You can also create a Progress instance using custom columns before
- and/or after the defaults, as in this example:
-
- progress = Progress(
- SpinnerColumn(),
- *Progress.default_columns(),
- "Elapsed:",
- TimeElapsedColumn(),
- )
-
- This code shows the creation of a Progress display, containing
- a spinner to the left, the default columns, and a labeled elapsed
- time column.
- """
- return (
- TextColumn("[progress.description]{task.description}"),
- BarColumn(),
- TaskProgressColumn(),
- TimeRemainingColumn(),
- )
-
- @property
- def console(self) -> Console:
- return self.live.console
-
- @property
- def tasks(self) -> List[Task]:
- """Get a list of Task instances."""
- with self._lock:
- return list(self._tasks.values())
-
- @property
- def task_ids(self) -> List[TaskID]:
- """A list of task IDs."""
- with self._lock:
- return list(self._tasks.keys())
-
- @property
- def finished(self) -> bool:
- """Check if all tasks have been completed."""
- with self._lock:
- if not self._tasks:
- return True
- return all(task.finished for task in self._tasks.values())
-
- def start(self) -> None:
- """Start the progress display."""
- if not self.disable:
- self.live.start(refresh=True)
-
- def stop(self) -> None:
- """Stop the progress display."""
- self.live.stop()
- if not self.console.is_interactive:
- self.console.print()
-
- def __enter__(self) -> "Progress":
- self.start()
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]],
- exc_val: Optional[BaseException],
- exc_tb: Optional[TracebackType],
- ) -> None:
- self.stop()
-
- def track(
- self,
- sequence: Union[Iterable[ProgressType], Sequence[ProgressType]],
- total: Optional[float] = None,
- task_id: Optional[TaskID] = None,
- description: str = "Working...",
- update_period: float = 0.1,
- ) -> Iterable[ProgressType]:
- """Track progress by iterating over a sequence.
-
- Args:
- sequence (Sequence[ProgressType]): A sequence of values you want to iterate over and track progress.
- total: (float, optional): Total number of steps. Default is len(sequence).
- task_id: (TaskID): Task to track. Default is new task.
- description: (str, optional): Description of task, if new task is created.
- update_period (float, optional): Minimum time (in seconds) between calls to update(). Defaults to 0.1.
-
- Returns:
- Iterable[ProgressType]: An iterable of values taken from the provided sequence.
- """
- if total is None:
- total = float(length_hint(sequence)) or None
-
- if task_id is None:
- task_id = self.add_task(description, total=total)
- else:
- self.update(task_id, total=total)
-
- if self.live.auto_refresh:
- with _TrackThread(self, task_id, update_period) as track_thread:
- for value in sequence:
- yield value
- track_thread.completed += 1
- else:
- advance = self.advance
- refresh = self.refresh
- for value in sequence:
- yield value
- advance(task_id, 1)
- refresh()
-
- def wrap_file(
- self,
- file: BinaryIO,
- total: Optional[int] = None,
- *,
- task_id: Optional[TaskID] = None,
- description: str = "Reading...",
- ) -> BinaryIO:
- """Track progress file reading from a binary file.
-
- Args:
- file (BinaryIO): A file-like object opened in binary mode.
- total (int, optional): Total number of bytes to read. This must be provided unless a task with a total is also given.
- task_id (TaskID): Task to track. Default is new task.
- description (str, optional): Description of task, if new task is created.
-
- Returns:
- BinaryIO: A readable file-like object in binary mode.
-
- Raises:
- ValueError: When no total value can be extracted from the arguments or the task.
- """
- # attempt to recover the total from the task
- total_bytes: Optional[float] = None
- if total is not None:
- total_bytes = total
- elif task_id is not None:
- with self._lock:
- total_bytes = self._tasks[task_id].total
- if total_bytes is None:
- raise ValueError(
- f"unable to get the total number of bytes, please specify 'total'"
- )
-
- # update total of task or create new task
- if task_id is None:
- task_id = self.add_task(description, total=total_bytes)
- else:
- self.update(task_id, total=total_bytes)
-
- return _Reader(file, self, task_id, close_handle=False)
-
- @typing.overload
- def open(
- self,
- file: Union[str, "PathLike[str]", bytes],
- mode: Literal["rb"],
- buffering: int = -1,
- encoding: Optional[str] = None,
- errors: Optional[str] = None,
- newline: Optional[str] = None,
- *,
- total: Optional[int] = None,
- task_id: Optional[TaskID] = None,
- description: str = "Reading...",
- ) -> BinaryIO:
- pass
-
- @typing.overload
- def open(
- self,
- file: Union[str, "PathLike[str]", bytes],
- mode: Union[Literal["r"], Literal["rt"]],
- buffering: int = -1,
- encoding: Optional[str] = None,
- errors: Optional[str] = None,
- newline: Optional[str] = None,
- *,
- total: Optional[int] = None,
- task_id: Optional[TaskID] = None,
- description: str = "Reading...",
- ) -> TextIO:
- pass
-
- def open(
- self,
- file: Union[str, "PathLike[str]", bytes],
- mode: Union[Literal["rb"], Literal["rt"], Literal["r"]] = "r",
- buffering: int = -1,
- encoding: Optional[str] = None,
- errors: Optional[str] = None,
- newline: Optional[str] = None,
- *,
- total: Optional[int] = None,
- task_id: Optional[TaskID] = None,
- description: str = "Reading...",
- ) -> Union[BinaryIO, TextIO]:
- """Track progress while reading from a binary file.
-
- Args:
- path (Union[str, PathLike[str]]): The path to the file to read.
- mode (str): The mode to use to open the file. Only supports "r", "rb" or "rt".
- buffering (int): The buffering strategy to use, see :func:`io.open`.
- encoding (str, optional): The encoding to use when reading in text mode, see :func:`io.open`.
- errors (str, optional): The error handling strategy for decoding errors, see :func:`io.open`.
- newline (str, optional): The strategy for handling newlines in text mode, see :func:`io.open`.
- total (int, optional): Total number of bytes to read. If none given, os.stat(path).st_size is used.
- task_id (TaskID): Task to track. Default is new task.
- description (str, optional): Description of task, if new task is created.
-
- Returns:
- BinaryIO: A readable file-like object in binary mode.
-
- Raises:
- ValueError: When an invalid mode is given.
- """
- # normalize the mode (always rb, rt)
- _mode = "".join(sorted(mode, reverse=False))
- if _mode not in ("br", "rt", "r"):
- raise ValueError("invalid mode {!r}".format(mode))
-
- # patch buffering to provide the same behaviour as the builtin `open`
- line_buffering = buffering == 1
- if _mode == "br" and buffering == 1:
- warnings.warn(
- "line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used",
- RuntimeWarning,
- )
- buffering = -1
- elif _mode in ("rt", "r"):
- if buffering == 0:
- raise ValueError("can't have unbuffered text I/O")
- elif buffering == 1:
- buffering = -1
-
- # attempt to get the total with `os.stat`
- if total is None:
- total = stat(file).st_size
-
- # update total of task or create new task
- if task_id is None:
- task_id = self.add_task(description, total=total)
- else:
- self.update(task_id, total=total)
-
- # open the file in binary mode,
- handle = io.open(file, "rb", buffering=buffering)
- reader = _Reader(handle, self, task_id, close_handle=True)
-
- # wrap the reader in a `TextIOWrapper` if text mode
- if mode in ("r", "rt"):
- return io.TextIOWrapper(
- reader,
- encoding=encoding,
- errors=errors,
- newline=newline,
- line_buffering=line_buffering,
- )
-
- return reader
-
- def start_task(self, task_id: TaskID) -> None:
- """Start a task.
-
- Starts a task (used when calculating elapsed time). You may need to call this manually,
- if you called ``add_task`` with ``start=False``.
-
- Args:
- task_id (TaskID): ID of task.
- """
- with self._lock:
- task = self._tasks[task_id]
- if task.start_time is None:
- task.start_time = self.get_time()
-
- def stop_task(self, task_id: TaskID) -> None:
- """Stop a task.
-
- This will freeze the elapsed time on the task.
-
- Args:
- task_id (TaskID): ID of task.
- """
- with self._lock:
- task = self._tasks[task_id]
- current_time = self.get_time()
- if task.start_time is None:
- task.start_time = current_time
- task.stop_time = current_time
-
- def update(
- self,
- task_id: TaskID,
- *,
- total: Optional[float] = None,
- completed: Optional[float] = None,
- advance: Optional[float] = None,
- description: Optional[str] = None,
- visible: Optional[bool] = None,
- refresh: bool = False,
- **fields: Any,
- ) -> None:
- """Update information associated with a task.
-
- Args:
- task_id (TaskID): Task id (returned by add_task).
- total (float, optional): Updates task.total if not None.
- completed (float, optional): Updates task.completed if not None.
- advance (float, optional): Add a value to task.completed if not None.
- description (str, optional): Change task description if not None.
- visible (bool, optional): Set visible flag if not None.
- refresh (bool): Force a refresh of progress information. Default is False.
- **fields (Any): Additional data fields required for rendering.
- """
- with self._lock:
- task = self._tasks[task_id]
- completed_start = task.completed
-
- if total is not None and total != task.total:
- task.total = total
- task._reset()
- if advance is not None:
- task.completed += advance
- if completed is not None:
- task.completed = completed
- if description is not None:
- task.description = description
- if visible is not None:
- task.visible = visible
- task.fields.update(fields)
- update_completed = task.completed - completed_start
-
- current_time = self.get_time()
- old_sample_time = current_time - self.speed_estimate_period
- _progress = task._progress
-
- popleft = _progress.popleft
- while _progress and _progress[0].timestamp < old_sample_time:
- popleft()
- if update_completed > 0:
- _progress.append(ProgressSample(current_time, update_completed))
- if (
- task.total is not None
- and task.completed >= task.total
- and task.finished_time is None
- ):
- task.finished_time = task.elapsed
-
- if refresh:
- self.refresh()
-
- def reset(
- self,
- task_id: TaskID,
- *,
- start: bool = True,
- total: Optional[float] = None,
- completed: int = 0,
- visible: Optional[bool] = None,
- description: Optional[str] = None,
- **fields: Any,
- ) -> None:
- """Reset a task so completed is 0 and the clock is reset.
-
- Args:
- task_id (TaskID): ID of task.
- start (bool, optional): Start the task after reset. Defaults to True.
- total (float, optional): New total steps in task, or None to use current total. Defaults to None.
- completed (int, optional): Number of steps completed. Defaults to 0.
- visible (bool, optional): Enable display of the task. Defaults to True.
- description (str, optional): Change task description if not None. Defaults to None.
- **fields (str): Additional data fields required for rendering.
- """
- current_time = self.get_time()
- with self._lock:
- task = self._tasks[task_id]
- task._reset()
- task.start_time = current_time if start else None
- if total is not None:
- task.total = total
- task.completed = completed
- if visible is not None:
- task.visible = visible
- if fields:
- task.fields = fields
- if description is not None:
- task.description = description
- task.finished_time = None
- self.refresh()
-
- def advance(self, task_id: TaskID, advance: float = 1) -> None:
- """Advance task by a number of steps.
-
- Args:
- task_id (TaskID): ID of task.
- advance (float): Number of steps to advance. Default is 1.
- """
- current_time = self.get_time()
- with self._lock:
- task = self._tasks[task_id]
- completed_start = task.completed
- task.completed += advance
- update_completed = task.completed - completed_start
- old_sample_time = current_time - self.speed_estimate_period
- _progress = task._progress
-
- popleft = _progress.popleft
- while _progress and _progress[0].timestamp < old_sample_time:
- popleft()
- while len(_progress) > 1000:
- popleft()
- _progress.append(ProgressSample(current_time, update_completed))
- if (
- task.total is not None
- and task.completed >= task.total
- and task.finished_time is None
- ):
- task.finished_time = task.elapsed
- task.finished_speed = task.speed
-
- def refresh(self) -> None:
- """Refresh (render) the progress information."""
- if not self.disable and self.live.is_started:
- self.live.refresh()
-
- def get_renderable(self) -> RenderableType:
- """Get a renderable for the progress display."""
- renderable = Group(*self.get_renderables())
- return renderable
-
- def get_renderables(self) -> Iterable[RenderableType]:
- """Get a number of renderables for the progress display."""
- table = self.make_tasks_table(self.tasks)
- yield table
-
- def make_tasks_table(self, tasks: Iterable[Task]) -> Table:
- """Get a table to render the Progress display.
-
- Args:
- tasks (Iterable[Task]): An iterable of Task instances, one per row of the table.
-
- Returns:
- Table: A table instance.
- """
- table_columns = (
- (
- Column(no_wrap=True)
- if isinstance(_column, str)
- else _column.get_table_column().copy()
- )
- for _column in self.columns
- )
- table = Table.grid(*table_columns, padding=(0, 1), expand=self.expand)
-
- for task in tasks:
- if task.visible:
- table.add_row(
- *(
- (
- column.format(task=task)
- if isinstance(column, str)
- else column(task)
- )
- for column in self.columns
- )
- )
- return table
-
- def __rich__(self) -> RenderableType:
- """Makes the Progress class itself renderable."""
- with self._lock:
- return self.get_renderable()
-
- def add_task(
- self,
- description: str,
- start: bool = True,
- total: Optional[float] = 100.0,
- completed: int = 0,
- visible: bool = True,
- **fields: Any,
- ) -> TaskID:
- """Add a new 'task' to the Progress display.
-
- Args:
- description (str): A description of the task.
- start (bool, optional): Start the task immediately (to calculate elapsed time). If set to False,
- you will need to call `start` manually. Defaults to True.
- total (float, optional): Number of total steps in the progress if known.
- Set to None to render a pulsing animation. Defaults to 100.
- completed (int, optional): Number of steps completed so far. Defaults to 0.
- visible (bool, optional): Enable display of the task. Defaults to True.
- **fields (str): Additional data fields required for rendering.
-
- Returns:
- TaskID: An ID you can use when calling `update`.
- """
- with self._lock:
- task = Task(
- self._task_index,
- description,
- total,
- completed,
- visible=visible,
- fields=fields,
- _get_time=self.get_time,
- _lock=self._lock,
- )
- self._tasks[self._task_index] = task
- if start:
- self.start_task(self._task_index)
- new_task_index = self._task_index
- self._task_index = TaskID(int(self._task_index) + 1)
- self.refresh()
- return new_task_index
-
- def remove_task(self, task_id: TaskID) -> None:
- """Delete a task if it exists.
-
- Args:
- task_id (TaskID): A task ID.
-
- """
- with self._lock:
- del self._tasks[task_id]
-
-
-if __name__ == "__main__": # pragma: no coverage
-
- import random
- import time
-
- from .panel import Panel
- from .rule import Rule
- from .syntax import Syntax
- from .table import Table
-
- syntax = Syntax(
- '''def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]:
- """Iterate and generate a tuple with a flag for last value."""
- iter_values = iter(values)
- try:
- previous_value = next(iter_values)
- except StopIteration:
- return
- for value in iter_values:
- yield False, previous_value
- previous_value = value
- yield True, previous_value''',
- "python",
- line_numbers=True,
- )
-
- table = Table("foo", "bar", "baz")
- table.add_row("1", "2", "3")
-
- progress_renderables = [
- "Text may be printed while the progress bars are rendering.",
- Panel("In fact, [i]any[/i] renderable will work"),
- "Such as [magenta]tables[/]...",
- table,
- "Pretty printed structures...",
- {"type": "example", "text": "Pretty printed"},
- "Syntax...",
- syntax,
- Rule("Give it a try!"),
- ]
-
- from itertools import cycle
-
- examples = cycle(progress_renderables)
-
- console = Console(record=True)
-
- with Progress(
- SpinnerColumn(),
- *Progress.get_default_columns(),
- TimeElapsedColumn(),
- console=console,
- transient=False,
- ) as progress:
-
- task1 = progress.add_task("[red]Downloading", total=1000)
- task2 = progress.add_task("[green]Processing", total=1000)
- task3 = progress.add_task("[yellow]Thinking", total=None)
-
- while not progress.finished:
- progress.update(task1, advance=0.5)
- progress.update(task2, advance=0.3)
- time.sleep(0.01)
- if random.randint(0, 100) < 1:
- progress.log(next(examples))
diff --git a/spaces/Tayaba171/CALText-TextRecognizer/README.md b/spaces/Tayaba171/CALText-TextRecognizer/README.md
deleted file mode 100644
index e9d27309f80cf1225afd52aee235ca0219957b5d..0000000000000000000000000000000000000000
--- a/spaces/Tayaba171/CALText-TextRecognizer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CALText TextRecognizer
-emoji: 🌍
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.43.2
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/README.md b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/README.md
deleted file mode 100644
index d3e1d5cf533555e19c6326777f792ac82a560a84..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Probabilistic two-stage detection
-Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network.
-
-
-
-
-> [**Probabilistic two-stage detection**](http://arxiv.org/abs/2103.07461),
-> Xingyi Zhou, Vladlen Koltun, Philipp Krähenbühl,
-> *arXiv technical report ([arXiv 2103.07461](http://arxiv.org/abs/2103.07461))*
-
-Contact: [zhouxy@cs.utexas.edu](mailto:zhouxy@cs.utexas.edu). Any questions or discussions are welcomed!
-
-## Abstract
-
-We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score of the detector. A standard region proposal network (RPN) cannot infer this likelihood sufficiently well, but many one-stage detectors can. We show how to build a probabilistic two-stage detector from any state-of-the-art one-stage detector. The resulting detectors are faster and more accurate than both their one- and two-stage precursors. Our detector achieves 56.4 mAP on COCO test-dev with single-scale testing, outperforming all published results. Using a lightweight backbone, our detector achieves 49.2 mAP on COCO at 33 fps on a Titan Xp.
-
-## Summary
-
-- Two-stage CenterNet: First stage estimates object probabilities, second stage conditionally classifies objects.
-
-- Resulting detector is faster and more accurate than both traditional two-stage detectors (fewer proposals required), and one-stage detectors (lighter first stage head).
-
-- Our best model achieves 56.4 mAP on COCO test-dev.
-
-- This repo also includes a detectron2-based CenterNet implementation with better accuracy (42.5 mAP at 70FPS) and a new FPN version of CenterNet (40.2 mAP with Res50_1x).
-
-## Main results
-
-All models are trained with multi-scale training, and tested with a single scale. The FPS is tested on a Titan RTX GPU.
-More models and details can be found in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md).
-
-#### COCO
-
-| Model | COCO val mAP | FPS |
-|-------------------------------------------|---------------|-------|
-| CenterNet-S4_DLA_8x | 42.5 | 71 |
-| CenterNet2_R50_1x | 42.9 | 24 |
-| CenterNet2_X101-DCN_2x | 49.9 | 8 |
-| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST | 56.1 | 5 |
-| CenterNet2_DLA-BiFPN-P5_24x_ST | 49.2 | 38 |
-
-
-#### LVIS
-
-| Model | val mAP box |
-| ------------------------- | ----------- |
-| CenterNet2_R50_1x | 26.5 |
-| CenterNet2_FedLoss_R50_1x | 28.3 |
-
-
-#### Objects365
-
-| Model | val mAP |
-|-------------------------------------------|----------|
-| CenterNet2_R50_1x | 22.6 |
-
-## Installation
-
-Our project is developed on [detectron2](https://github.com/facebookresearch/detectron2). Please follow the official detectron2 [installation](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). All our code is under `projects/CenterNet2/`. In theory, you should be able to copy-paste `projects/CenterNet2/` to the latest detectron2 release or your own detectron2 repo to run our project. There might be API changes in future detectron2 releases that make it incompatible.
-
-We use the default detectron2 demo script. To run inference on an image folder using our pre-trained model, run
-
-~~~
-python projects/CenterNet2/demo/demo.py --config-file projects/CenterNet2/configs/CenterNet2_R50_1x.yaml --input path/to/image/ --opts MODEL.WEIGHTS models/CenterNet2_R50_1x.pth
-~~~
-
-## Benchmark evaluation and training
-
-Please check detectron2 [GETTING_STARTED.md](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for running evaluation and training. Our config files are under `projects/CenterNet2/configs` and the pre-trained models are in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md).
-
-
-## License
-
-Our code under `projects/CenterNet2/` is under [Apache 2.0 license](projects/CenterNet2/LICENSE). `projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py` are from [AdelaiDet](https://github.com/aim-uofa/AdelaiDet), which follows the original [non-commercial license](https://github.com/aim-uofa/AdelaiDet/blob/master/LICENSE). The code from detectron2 follows the original [Apache 2.0 license](LICENSE).
-
-## Citation
-
-If you find this project useful for your research, please use the following BibTeX entry.
-
- @inproceedings{zhou2021probablistic,
- title={Probabilistic two-stage detection},
- author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
- booktitle={arXiv preprint arXiv:2103.07461},
- year={2021}
- }
diff --git a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/diffusionmodules/model.py b/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index b089eebbe1676d8249005bb9def002ff5180715b..0000000000000000000000000000000000000000
--- a/spaces/Thaweewat/ControlNet-Architecture/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,852 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-from typing import Optional, Any
-
-from ldm.modules.attention import MemoryEfficientCrossAttention
-
-try:
- import xformers
- import xformers.ops
- XFORMERS_IS_AVAILBLE = True
-except:
- XFORMERS_IS_AVAILBLE = False
- print("No module 'xformers'. Proceeding without it.")
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
- """
- This matches the implementation in Denoising Diffusion Probabilistic Models:
- From Fairseq.
- Build sinusoidal embeddings.
- This matches the implementation in tensor2tensor, but differs slightly
- from the description in Section 3.5 of "Attention Is All You Need".
- """
- assert len(timesteps.shape) == 1
-
- half_dim = embedding_dim // 2
- emb = math.log(10000) / (half_dim - 1)
- emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
- emb = emb.to(device=timesteps.device)
- emb = timesteps.float()[:, None] * emb[None, :]
- emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
- if embedding_dim % 2 == 1: # zero pad
- emb = torch.nn.functional.pad(emb, (0,1,0,0))
- return emb
-
-
-def nonlinearity(x):
- # swish
- return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
- return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
- if self.with_conv:
- x = self.conv(x)
- return x
-
-
-class Downsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=2,
- padding=0)
-
- def forward(self, x):
- if self.with_conv:
- pad = (0,1,0,1)
- x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
- x = self.conv(x)
- else:
- x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
- return x
-
-
-class ResnetBlock(nn.Module):
- def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
- dropout, temb_channels=512):
- super().__init__()
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
-
- self.norm1 = Normalize(in_channels)
- self.conv1 = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if temb_channels > 0:
- self.temb_proj = torch.nn.Linear(temb_channels,
- out_channels)
- self.norm2 = Normalize(out_channels)
- self.dropout = torch.nn.Dropout(dropout)
- self.conv2 = torch.nn.Conv2d(out_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- self.conv_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- else:
- self.nin_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x, temb):
- h = x
- h = self.norm1(h)
- h = nonlinearity(h)
- h = self.conv1(h)
-
- if temb is not None:
- h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
- h = self.norm2(h)
- h = nonlinearity(h)
- h = self.dropout(h)
- h = self.conv2(h)
-
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- x = self.conv_shortcut(x)
- else:
- x = self.nin_shortcut(x)
-
- return x+h
-
-
-class AttnBlock(nn.Module):
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b,c,h,w = q.shape
- q = q.reshape(b,c,h*w)
- q = q.permute(0,2,1) # b,hw,c
- k = k.reshape(b,c,h*w) # b,c,hw
- w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
- w_ = w_ * (int(c)**(-0.5))
- w_ = torch.nn.functional.softmax(w_, dim=2)
-
- # attend to values
- v = v.reshape(b,c,h*w)
- w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
- h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
- h_ = h_.reshape(b,c,h,w)
-
- h_ = self.proj_out(h_)
-
- return x+h_
-
-class MemoryEfficientAttnBlock(nn.Module):
- """
- Uses xformers efficient implementation,
- see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
- Note: this is a single-head self-attention operation
- """
- #
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.attention_op: Optional[Any] = None
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- B, C, H, W = q.shape
- q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
-
- q, k, v = map(
- lambda t: t.unsqueeze(3)
- .reshape(B, t.shape[1], 1, C)
- .permute(0, 2, 1, 3)
- .reshape(B * 1, t.shape[1], C)
- .contiguous(),
- (q, k, v),
- )
- out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
- out = (
- out.unsqueeze(0)
- .reshape(B, 1, out.shape[1], C)
- .permute(0, 2, 1, 3)
- .reshape(B, out.shape[1], C)
- )
- out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
- out = self.proj_out(out)
- return x+out
-
-
-class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
- def forward(self, x, context=None, mask=None):
- b, c, h, w = x.shape
- x = rearrange(x, 'b c h w -> b (h w) c')
- out = super().forward(x, context=context, mask=mask)
- out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
- return x + out
-
-
-def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
- assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
- if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
- attn_type = "vanilla-xformers"
- print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
- if attn_type == "vanilla":
- assert attn_kwargs is None
- return AttnBlock(in_channels)
- elif attn_type == "vanilla-xformers":
- print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
- return MemoryEfficientAttnBlock(in_channels)
- elif type == "memory-efficient-cross-attn":
- attn_kwargs["query_dim"] = in_channels
- return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
- elif attn_type == "none":
- return nn.Identity(in_channels)
- else:
- raise NotImplementedError()
-
-
-class Model(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = self.ch*4
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- self.use_timestep = use_timestep
- if self.use_timestep:
- # timestep embedding
- self.temb = nn.Module()
- self.temb.dense = nn.ModuleList([
- torch.nn.Linear(self.ch,
- self.temb_ch),
- torch.nn.Linear(self.temb_ch,
- self.temb_ch),
- ])
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- skip_in = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- if i_block == self.num_res_blocks:
- skip_in = ch*in_ch_mult[i_level]
- block.append(ResnetBlock(in_channels=block_in+skip_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x, t=None, context=None):
- #assert x.shape[2] == x.shape[3] == self.resolution
- if context is not None:
- # assume aligned context, cat along channel axis
- x = torch.cat((x, context), dim=1)
- if self.use_timestep:
- # timestep embedding
- assert t is not None
- temb = get_timestep_embedding(t, self.ch)
- temb = self.temb.dense[0](temb)
- temb = nonlinearity(temb)
- temb = self.temb.dense[1](temb)
- else:
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](
- torch.cat([h, hs.pop()], dim=1), temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
- def get_last_layer(self):
- return self.conv_out.weight
-
-
-class Encoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
- **ignore_kwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.in_ch_mult = in_ch_mult
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- 2*z_channels if double_z else z_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # timestep embedding
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class Decoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
- attn_type="vanilla", **ignorekwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
- self.give_pre_end = give_pre_end
- self.tanh_out = tanh_out
-
- # compute in_ch_mult, block_in and curr_res at lowest res
- in_ch_mult = (1,)+tuple(ch_mult)
- block_in = ch*ch_mult[self.num_resolutions-1]
- curr_res = resolution // 2**(self.num_resolutions-1)
- self.z_shape = (1,z_channels,curr_res,curr_res)
- print("Working with z of shape {} = {} dimensions.".format(
- self.z_shape, np.prod(self.z_shape)))
-
- # z to block_in
- self.conv_in = torch.nn.Conv2d(z_channels,
- block_in,
- kernel_size=3,
- stride=1,
- padding=1)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, z):
- #assert z.shape[1:] == self.z_shape[1:]
- self.last_z_shape = z.shape
-
- # timestep embedding
- temb = None
-
- # z to block_in
- h = self.conv_in(z)
-
- # middle
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](h, temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- if self.give_pre_end:
- return h
-
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- if self.tanh_out:
- h = torch.tanh(h)
- return h
-
-
-class SimpleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, *args, **kwargs):
- super().__init__()
- self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
- ResnetBlock(in_channels=in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=2 * in_channels,
- out_channels=4 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=4 * in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- nn.Conv2d(2*in_channels, in_channels, 1),
- Upsample(in_channels, with_conv=True)])
- # end
- self.norm_out = Normalize(in_channels)
- self.conv_out = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- for i, layer in enumerate(self.model):
- if i in [1,2,3]:
- x = layer(x, None)
- else:
- x = layer(x)
-
- h = self.norm_out(x)
- h = nonlinearity(h)
- x = self.conv_out(h)
- return x
-
-
-class UpsampleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
- ch_mult=(2,2), dropout=0.0):
- super().__init__()
- # upsampling
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- block_in = in_channels
- curr_res = resolution // 2 ** (self.num_resolutions - 1)
- self.res_blocks = nn.ModuleList()
- self.upsample_blocks = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- res_block = []
- block_out = ch * ch_mult[i_level]
- for i_block in range(self.num_res_blocks + 1):
- res_block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- self.res_blocks.append(nn.ModuleList(res_block))
- if i_level != self.num_resolutions - 1:
- self.upsample_blocks.append(Upsample(block_in, True))
- curr_res = curr_res * 2
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # upsampling
- h = x
- for k, i_level in enumerate(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks + 1):
- h = self.res_blocks[i_level][i_block](h, None)
- if i_level != self.num_resolutions - 1:
- h = self.upsample_blocks[k](h)
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class LatentRescaler(nn.Module):
- def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
- super().__init__()
- # residual block, interpolate, residual block
- self.factor = factor
- self.conv_in = nn.Conv2d(in_channels,
- mid_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
- self.attn = AttnBlock(mid_channels)
- self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
-
- self.conv_out = nn.Conv2d(mid_channels,
- out_channels,
- kernel_size=1,
- )
-
- def forward(self, x):
- x = self.conv_in(x)
- for block in self.res_block1:
- x = block(x, None)
- x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
- x = self.attn(x)
- for block in self.res_block2:
- x = block(x, None)
- x = self.conv_out(x)
- return x
-
-
-class MergedRescaleEncoder(nn.Module):
- def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True,
- ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- intermediate_chn = ch * ch_mult[-1]
- self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
- z_channels=intermediate_chn, double_z=False, resolution=resolution,
- attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
- out_ch=None)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
- mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.encoder(x)
- x = self.rescaler(x)
- return x
-
-
-class MergedRescaleDecoder(nn.Module):
- def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
- dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- tmp_chn = z_channels*ch_mult[-1]
- self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
- resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
- ch_mult=ch_mult, resolution=resolution, ch=ch)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
- out_channels=tmp_chn, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Upsampler(nn.Module):
- def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
- super().__init__()
- assert out_size >= in_size
- num_blocks = int(np.log2(out_size//in_size))+1
- factor_up = 1.+ (out_size % in_size)
- print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
- self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
- out_channels=in_channels)
- self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
- attn_resolutions=[], in_channels=None, ch=in_channels,
- ch_mult=[ch_mult for _ in range(num_blocks)])
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Resize(nn.Module):
- def __init__(self, in_channels=None, learned=False, mode="bilinear"):
- super().__init__()
- self.with_conv = learned
- self.mode = mode
- if self.with_conv:
- print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
- raise NotImplementedError()
- assert in_channels is not None
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=4,
- stride=2,
- padding=1)
-
- def forward(self, x, scale_factor=1.0):
- if scale_factor==1.0:
- return x
- else:
- x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
- return x
diff --git a/spaces/Toritto/Genshin-impact-IA-project-v1/vc_infer_pipeline.py b/spaces/Toritto/Genshin-impact-IA-project-v1/vc_infer_pipeline.py
deleted file mode 100644
index 82c15f59a8072e1b317fa1d750ccc1b814a6989d..0000000000000000000000000000000000000000
--- a/spaces/Toritto/Genshin-impact-IA-project-v1/vc_infer_pipeline.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import numpy as np, parselmouth, torch, pdb, sys, os
-from time import time as ttime
-import torch.nn.functional as F
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
-from scipy import signal
-from functools import lru_cache
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
- audio = input_audio_path2wav[input_audio_path]
- f0, t = pyworld.harvest(
- audio,
- fs=fs,
- f0_ceil=f0max,
- f0_floor=f0min,
- frame_period=frame_period,
- )
- f0 = pyworld.stonemask(audio, f0, t, fs)
- return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比
- # print(data1.max(),data2.max())
- rms1 = librosa.feature.rms(
- y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
- ) # 每半秒一个点
- rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
- rms1 = torch.from_numpy(rms1)
- rms1 = F.interpolate(
- rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.from_numpy(rms2)
- rms2 = F.interpolate(
- rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
- data2 *= (
- torch.pow(rms1, torch.tensor(1 - rate))
- * torch.pow(rms2, torch.tensor(rate - 1))
- ).numpy()
- return data2
-
-
-class VC(object):
- def __init__(self, tgt_sr, config):
- self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
- config.x_pad,
- config.x_query,
- config.x_center,
- config.x_max,
- config.is_half,
- )
- self.sr = 16000 # hubert输入采样率
- self.window = 160 # 每帧点数
- self.t_pad = self.sr * self.x_pad # 每条前后pad时间
- self.t_pad_tgt = tgt_sr * self.x_pad
- self.t_pad2 = self.t_pad * 2
- self.t_query = self.sr * self.x_query # 查询切点前后查询时间
- self.t_center = self.sr * self.x_center # 查询切点位置
- self.t_max = self.sr * self.x_max # 免查询时长阈值
- self.device = config.device
-
- def get_f0(
- self,
- input_audio_path,
- x,
- p_len,
- f0_up_key,
- f0_method,
- filter_radius,
- inp_f0=None,
- ):
- global input_audio_path2wav
- time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- if f0_method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif f0_method == "harvest":
- input_audio_path2wav[input_audio_path] = x.astype(np.double)
- f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
- if filter_radius > 2:
- f0 = signal.medfilt(f0, 3)
- elif f0_method == "crepe":
- model = "full"
- # Pick a batch size that doesn't cause memory errors on your gpu
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- elif f0_method == "rmvpe":
- if hasattr(self, "model_rmvpe") == False:
- from rmvpe import RMVPE
-
- print("loading rmvpe model")
- self.model_rmvpe = RMVPE(
- "rmvpe.pt", is_half=self.is_half, device=self.device
- )
- f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
- f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
- :shape
- ]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9 if version == "v1" else 12,
- }
- t0 = ttime()
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
- if protect < 0.5 and pitch != None and pitchf != None:
- feats0 = feats.clone()
- if (
- isinstance(index, type(None)) == False
- and isinstance(big_npy, type(None)) == False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half:
- npy = npy.astype("float32")
-
- # _, I = index.search(npy, 1)
- # npy = big_npy[I.squeeze()]
-
- score, ix = index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
- if self.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
-
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- if protect < 0.5 and pitch != None and pitchf != None:
- feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
- 0, 2, 1
- )
- t1 = ttime()
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch != None and pitchf != None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
-
- if protect < 0.5 and pitch != None and pitchf != None:
- pitchff = pitchf.clone()
- pitchff[pitchf > 0] = 1
- pitchff[pitchf < 1] = protect
- pitchff = pitchff.unsqueeze(-1)
- feats = feats * pitchff + feats0 * (1 - pitchff)
- feats = feats.to(feats0.dtype)
- p_len = torch.tensor([p_len], device=self.device).long()
- with torch.no_grad():
- if pitch != None and pitchf != None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
- .data.cpu()
- .float()
- .numpy()
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
- )
- del feats, p_len, padding_mask
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- t2 = ttime()
- times[0] += t1 - t0
- times[2] += t2 - t1
- return audio1
-
- def pipeline(
- self,
- model,
- net_g,
- sid,
- audio,
- input_audio_path,
- times,
- f0_up_key,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- f0_file=None,
- ):
- if (
- file_index != ""
- # and file_big_npy != ""
- # and os.path.exists(file_big_npy) == True
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
- try:
- index = faiss.read_index(file_index)
- # big_npy = np.load(file_big_npy)
- big_npy = index.reconstruct_n(0, index.ntotal)
- except:
- traceback.print_exc()
- index = big_npy = None
- else:
- index = big_npy = None
- audio = signal.filtfilt(bh, ah, audio)
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
- opt_ts = []
- if audio_pad.shape[0] > self.t_max:
- audio_sum = np.zeros_like(audio)
- for i in range(self.window):
- audio_sum += audio_pad[i : i - self.window]
- for t in range(self.t_center, audio.shape[0], self.t_center):
- opt_ts.append(
- t
- - self.t_query
- + np.where(
- np.abs(audio_sum[t - self.t_query : t + self.t_query])
- == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
- )[0][0]
- )
- s = 0
- audio_opt = []
- t = None
- t1 = ttime()
- audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
- p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
- if hasattr(f0_file, "name") == True:
- try:
- with open(f0_file.name, "r") as f:
- lines = f.read().strip("\n").split("\n")
- inp_f0 = []
- for line in lines:
- inp_f0.append([float(i) for i in line.split(",")])
- inp_f0 = np.array(inp_f0, dtype="float32")
- except:
- traceback.print_exc()
- sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
- pitch, pitchf = None, None
- if if_f0 == 1:
- pitch, pitchf = self.get_f0(
- input_audio_path,
- audio_pad,
- p_len,
- f0_up_key,
- f0_method,
- filter_radius,
- inp_f0,
- )
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- if self.device == "mps":
- pitchf = pitchf.astype(np.float32)
- pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
- pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
- t2 = ttime()
- times[1] += t2 - t1
- for t in opt_ts:
- t = t // self.window * self.window
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- pitch[:, s // self.window : (t + self.t_pad2) // self.window],
- pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- s = t
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- pitch[:, t // self.window :] if t is not None else pitch,
- pitchf[:, t // self.window :] if t is not None else pitchf,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- audio_opt = np.concatenate(audio_opt)
- if rms_mix_rate != 1:
- audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
- if resample_sr >= 16000 and tgt_sr != resample_sr:
- audio_opt = librosa.resample(
- audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
- )
- audio_max = np.abs(audio_opt).max() / 0.99
- max_int16 = 32768
- if audio_max > 1:
- max_int16 /= audio_max
- audio_opt = (audio_opt * max_int16).astype(np.int16)
- del pitch, pitchf, sid
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return audio_opt
diff --git a/spaces/VISION23/V23ChatBot/README.md b/spaces/VISION23/V23ChatBot/README.md
deleted file mode 100644
index ab64b3a1f8b2d6b9a6bab7a14ee339e076b29327..0000000000000000000000000000000000000000
--- a/spaces/VISION23/V23ChatBot/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: V23ChatBot
-emoji: 🏢
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Theb.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Theb.py
deleted file mode 100644
index aa43ebc55d74ffaa722fe008424fce97c622a323..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Theb.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://theb.ai'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
- path = os.path.dirname(os.path.realpath(__file__))
- config = json.dumps({
- 'messages': messages,
- 'model': model}, separators=(',', ':'))
-
- cmd = ['python3', f'{path}/helpers/theb.py', config]
-
- p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
- for line in iter(p.stdout.readline, b''):
- yield line.decode('utf-8')
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Vision-CAIR/minigpt4/minigpt4/models/mini_gpt4.py b/spaces/Vision-CAIR/minigpt4/minigpt4/models/mini_gpt4.py
deleted file mode 100644
index b9f9c269690703a24a258a184e349862cb87ca49..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/minigpt4/minigpt4/models/mini_gpt4.py
+++ /dev/null
@@ -1,263 +0,0 @@
-"""
- Copyright (c) 2023, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-import logging
-import random
-import os
-import torch
-from torch.cuda.amp import autocast as autocast
-import torch.nn as nn
-
-from minigpt4.common.registry import registry
-from minigpt4.models.blip2 import Blip2Base, disabled_train
-from minigpt4.models.modeling_llama import LlamaForCausalLM
-from transformers import LlamaTokenizer
-
-
-@registry.register_model("mini_gpt4")
-class MiniGPT4(Blip2Base):
- """
- BLIP2 GPT-LLAMA model.
- """
-
- PRETRAINED_MODEL_CONFIG_DICT = {
- "pretrain_vicuna": "configs/models/minigpt4.yaml",
- }
-
- def __init__(
- self,
- vit_model="eva_clip_g",
- q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
- img_size=224,
- drop_path_rate=0,
- use_grad_checkpoint=False,
- vit_precision="fp16",
- freeze_vit=True,
- freeze_qformer=True,
- num_query_token=32,
- llama_model="",
- llama_cache_dir='',
- prompt_path="",
- prompt_template="",
- max_txt_len=32,
- end_sym='\n',
- ):
- super().__init__()
-
- self.tokenizer = self.init_tokenizer()
-
- print('Loading VIT')
- self.visual_encoder, self.ln_vision = self.init_vision_encoder(
- vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
- )
- if freeze_vit:
- for name, param in self.visual_encoder.named_parameters():
- param.requires_grad = False
- self.visual_encoder = self.visual_encoder.eval()
- self.visual_encoder.train = disabled_train
- for name, param in self.ln_vision.named_parameters():
- param.requires_grad = False
- self.ln_vision = self.ln_vision.eval()
- self.ln_vision.train = disabled_train
- logging.info("freeze vision encoder")
- print('Loading VIT Done')
-
- print('Loading Q-Former')
- self.Qformer, self.query_tokens = self.init_Qformer(
- num_query_token, self.visual_encoder.num_features
- )
- self.Qformer.cls = None
- self.Qformer.bert.embeddings.word_embeddings = None
- self.Qformer.bert.embeddings.position_embeddings = None
- for layer in self.Qformer.bert.encoder.layer:
- layer.output = None
- layer.intermediate = None
- self.load_from_pretrained(url_or_filename=q_former_model)
-
- if freeze_qformer:
- for name, param in self.Qformer.named_parameters():
- param.requires_grad = False
- self.Qformer = self.Qformer.eval()
- self.Qformer.train = disabled_train
- self.query_tokens.requires_grad = False
- logging.info("freeze Qformer")
- print('Loading Q-Former Done')
-
- print('Loading LLAMA')
- self.llama_tokenizer = LlamaTokenizer.from_pretrained('Vision-CAIR/vicuna-7b', use_fast=False, use_auth_token=True)
- self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
-
- if llama_cache_dir:
- self.llama_model = LlamaForCausalLM.from_pretrained(
- 'Vision-CAIR/vicuna-7b', load_in_8bit=True, torch_dtype=torch.float16, device_map="auto", use_auth_token=True
- )
- else:
- self.llama_model = LlamaForCausalLM.from_pretrained(
- 'Vision-CAIR/vicuna-7b', load_in_8bit=True, torch_dtype=torch.float16, device_map="auto", use_auth_token=True
- )
- for name, param in self.llama_model.named_parameters():
- param.requires_grad = False
- print('Loading LLAMA Done')
-
- self.llama_proj = nn.Linear(
- self.Qformer.config.hidden_size, self.llama_model.config.hidden_size
- )
- self.max_txt_len = max_txt_len
- self.end_sym = end_sym
-
- if prompt_path:
- with open(prompt_path, 'r') as f:
- raw_prompts = f.read().splitlines()
- filted_prompts = [raw_prompt for raw_prompt in raw_prompts if "" in raw_prompt]
- self.prompt_list = [prompt_template.format(p) for p in filted_prompts]
- print('Load {} training prompts'.format(len(self.prompt_list)))
- print('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
- else:
- self.prompt_list = []
-
- def vit_to_cpu(self):
- self.ln_vision.to("cpu")
- self.ln_vision.float()
- self.visual_encoder.to("cpu")
- self.visual_encoder.float()
-
- def encode_img(self, image):
- device = image.device
- self.vit_to_cpu()
- image = image.to("cpu")
- with self.maybe_autocast():
- image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
- image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
-
- query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
- query_output = self.Qformer.bert(
- query_embeds=query_tokens,
- encoder_hidden_states=image_embeds,
- encoder_attention_mask=image_atts,
- return_dict=True,
- )
-
- inputs_llama = self.llama_proj(query_output.last_hidden_state)
- atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
- return inputs_llama, atts_llama
-
- def prompt_wrap(self, img_embeds, atts_img, prompt):
- if prompt:
- batch_size = img_embeds.shape[0]
- p_before, p_after = prompt.split('')
- p_before_tokens = self.llama_tokenizer(
- p_before, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
- p_after_tokens = self.llama_tokenizer(
- p_after, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
- p_before_embeds = self.llama_model.model.embed_tokens(p_before_tokens.input_ids).expand(batch_size, -1, -1)
- p_after_embeds = self.llama_model.model.embed_tokens(p_after_tokens.input_ids).expand(batch_size, -1, -1)
- wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds, p_after_embeds], dim=1)
- wrapped_atts_img = atts_img[:, :1].expand(-1, wrapped_img_embeds.shape[1])
- return wrapped_img_embeds, wrapped_atts_img
- else:
- return img_embeds, atts_img
-
- def forward(self, samples):
- image = samples["image"]
- img_embeds, atts_img = self.encode_img(image)
- if hasattr(samples, 'question_split'): # VQA dataset
- print('VQA Batch')
- vqa_prompt = '###Human: '
- img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, vqa_prompt)
- elif self.prompt_list:
- prompt = random.choice(self.prompt_list)
- img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompt)
-
- self.llama_tokenizer.padding_side = "right"
-
- text = [t + self.end_sym for t in samples["text_input"]]
-
- to_regress_tokens = self.llama_tokenizer(
- text,
- return_tensors="pt",
- padding="longest",
- truncation=True,
- max_length=self.max_txt_len,
- add_special_tokens=False
- ).to(image.device)
-
- targets = to_regress_tokens.input_ids.masked_fill(
- to_regress_tokens.input_ids == self.llama_tokenizer.pad_token_id, -100
- )
-
- empty_targets = (
- torch.ones([atts_img.shape[0], atts_img.shape[1]+1],
- dtype=torch.long).to(image.device).fill_(-100) # plus one for bos
- )
- targets = torch.cat([empty_targets, targets], dim=1)
-
- batch_size = img_embeds.shape[0]
- bos = torch.ones([batch_size, 1],
- dtype=to_regress_tokens.input_ids.dtype,
- device=to_regress_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id
- bos_embeds = self.llama_model.model.embed_tokens(bos)
- atts_bos = atts_img[:, :1]
-
- to_regress_embeds = self.llama_model.model.embed_tokens(to_regress_tokens.input_ids)
- inputs_embeds = torch.cat([bos_embeds, img_embeds, to_regress_embeds], dim=1)
- attention_mask = torch.cat([atts_bos, atts_img, to_regress_tokens.attention_mask], dim=1)
-
- with self.maybe_autocast():
- outputs = self.llama_model(
- inputs_embeds=inputs_embeds,
- attention_mask=attention_mask,
- return_dict=True,
- labels=targets,
- )
- loss = outputs.loss
-
- return {"loss": loss}
-
- @classmethod
- def from_config(cls, cfg):
- vit_model = cfg.get("vit_model", "eva_clip_g")
- q_former_model = cfg.get("q_former_model", "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth")
- img_size = cfg.get("image_size")
- num_query_token = cfg.get("num_query_token")
- llama_model = cfg.get("llama_model")
-
- drop_path_rate = cfg.get("drop_path_rate", 0)
- use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)
- vit_precision = cfg.get("vit_precision", "fp16")
- freeze_vit = cfg.get("freeze_vit", True)
- freeze_qformer = cfg.get("freeze_qformer", True)
- llama_cache_dir = cfg.get("llama_cache_dir", "")
-
- prompt_path = cfg.get("prompt_path", "")
- prompt_template = cfg.get("prompt_template", "")
- max_txt_len = cfg.get("max_txt_len", 32)
- end_sym = cfg.get("end_sym", '\n')
-
- model = cls(
- vit_model=vit_model,
- q_former_model=q_former_model,
- img_size=img_size,
- drop_path_rate=drop_path_rate,
- use_grad_checkpoint=use_grad_checkpoint,
- vit_precision=vit_precision,
- freeze_vit=freeze_vit,
- freeze_qformer=freeze_qformer,
- llama_cache_dir=llama_cache_dir,
- num_query_token=num_query_token,
- llama_model=llama_model,
- prompt_path=prompt_path,
- prompt_template=prompt_template,
- max_txt_len=max_txt_len,
- end_sym=end_sym
- )
-
- ckpt_path = cfg.get("ckpt", "") # load weights of MiniGPT-4
- if ckpt_path:
- print("Load BLIP2-LLM Checkpoint: {}".format(ckpt_path))
- ckpt = torch.load(ckpt_path, map_location="cpu")
- msg = model.load_state_dict(ckpt['model'], strict=False)
-
- return model
diff --git a/spaces/Volkopat/SegmentAnythingxGroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py b/spaces/Volkopat/SegmentAnythingxGroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py
deleted file mode 100644
index eac7e896bbe85a670824bfe8ef487d0535d5bd99..0000000000000000000000000000000000000000
--- a/spaces/Volkopat/SegmentAnythingxGroundingDINO/groundingdino/models/GroundingDINO/backbone/position_encoding.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# DINO
-# Copyright (c) 2022 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-"""
-Various positional encodings for the transformer.
-"""
-import math
-
-import torch
-from torch import nn
-
-from groundingdino.util.misc import NestedTensor
-
-
-class PositionEmbeddingSine(nn.Module):
- """
- This is a more standard version of the position embedding, very similar to the one
- used by the Attention is all you need paper, generalized to work on images.
- """
-
- def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
- super().__init__()
- self.num_pos_feats = num_pos_feats
- self.temperature = temperature
- self.normalize = normalize
- if scale is not None and normalize is False:
- raise ValueError("normalize should be True if scale is passed")
- if scale is None:
- scale = 2 * math.pi
- self.scale = scale
-
- def forward(self, tensor_list: NestedTensor):
- x = tensor_list.tensors
- mask = tensor_list.mask
- assert mask is not None
- not_mask = ~mask
- y_embed = not_mask.cumsum(1, dtype=torch.float32)
- x_embed = not_mask.cumsum(2, dtype=torch.float32)
- if self.normalize:
- eps = 1e-6
- # if os.environ.get("SHILONG_AMP", None) == '1':
- # eps = 1e-4
- # else:
- # eps = 1e-6
- y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
- x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
- dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
- dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
- pos_x = x_embed[:, :, :, None] / dim_t
- pos_y = y_embed[:, :, :, None] / dim_t
- pos_x = torch.stack(
- (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
- ).flatten(3)
- pos_y = torch.stack(
- (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
- ).flatten(3)
- pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
- return pos
-
-
-class PositionEmbeddingSineHW(nn.Module):
- """
- This is a more standard version of the position embedding, very similar to the one
- used by the Attention is all you need paper, generalized to work on images.
- """
-
- def __init__(
- self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
- ):
- super().__init__()
- self.num_pos_feats = num_pos_feats
- self.temperatureH = temperatureH
- self.temperatureW = temperatureW
- self.normalize = normalize
- if scale is not None and normalize is False:
- raise ValueError("normalize should be True if scale is passed")
- if scale is None:
- scale = 2 * math.pi
- self.scale = scale
-
- def forward(self, tensor_list: NestedTensor):
- x = tensor_list.tensors
- mask = tensor_list.mask
- assert mask is not None
- not_mask = ~mask
- y_embed = not_mask.cumsum(1, dtype=torch.float32)
- x_embed = not_mask.cumsum(2, dtype=torch.float32)
-
- # import ipdb; ipdb.set_trace()
-
- if self.normalize:
- eps = 1e-6
- y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
- x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
- dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
- dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
- pos_x = x_embed[:, :, :, None] / dim_tx
-
- dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
- dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
- pos_y = y_embed[:, :, :, None] / dim_ty
-
- pos_x = torch.stack(
- (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
- ).flatten(3)
- pos_y = torch.stack(
- (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
- ).flatten(3)
- pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-
- # import ipdb; ipdb.set_trace()
-
- return pos
-
-
-class PositionEmbeddingLearned(nn.Module):
- """
- Absolute pos embedding, learned.
- """
-
- def __init__(self, num_pos_feats=256):
- super().__init__()
- self.row_embed = nn.Embedding(50, num_pos_feats)
- self.col_embed = nn.Embedding(50, num_pos_feats)
- self.reset_parameters()
-
- def reset_parameters(self):
- nn.init.uniform_(self.row_embed.weight)
- nn.init.uniform_(self.col_embed.weight)
-
- def forward(self, tensor_list: NestedTensor):
- x = tensor_list.tensors
- h, w = x.shape[-2:]
- i = torch.arange(w, device=x.device)
- j = torch.arange(h, device=x.device)
- x_emb = self.col_embed(i)
- y_emb = self.row_embed(j)
- pos = (
- torch.cat(
- [
- x_emb.unsqueeze(0).repeat(h, 1, 1),
- y_emb.unsqueeze(1).repeat(1, w, 1),
- ],
- dim=-1,
- )
- .permute(2, 0, 1)
- .unsqueeze(0)
- .repeat(x.shape[0], 1, 1, 1)
- )
- return pos
-
-
-def build_position_encoding(args):
- N_steps = args.hidden_dim // 2
- if args.position_embedding in ("v2", "sine"):
- # TODO find a better way of exposing other arguments
- position_embedding = PositionEmbeddingSineHW(
- N_steps,
- temperatureH=args.pe_temperatureH,
- temperatureW=args.pe_temperatureW,
- normalize=True,
- )
- elif args.position_embedding in ("v3", "learned"):
- position_embedding = PositionEmbeddingLearned(N_steps)
- else:
- raise ValueError(f"not supported {args.position_embedding}")
-
- return position_embedding
diff --git a/spaces/Wauplin/pynecone-on-spaces-template/default_app/__init__.py b/spaces/Wauplin/pynecone-on-spaces-template/default_app/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Widium/Style-Recreation/functions/processing.py b/spaces/Widium/Style-Recreation/functions/processing.py
deleted file mode 100644
index c1deff81de3c243cbde86a4018f7b8b46ccacdd6..0000000000000000000000000000000000000000
--- a/spaces/Widium/Style-Recreation/functions/processing.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# *************************************************************************** #
-# #
-# processing.py #
-# #
-# By: Widium #
-# Github : https://github.com/widium #
-# #
-# Created: 2022/11/10 09:10:04 by ebennace #
-# Updated: 2023/05/04 11:37:55 by Widium #
-# #
-# **************************************************************************** ## =============== Import =================== #
-import tensorflow as tf
-import numpy as np
-
-from numpy import ndarray
-from tensorflow import Tensor
-from keras.applications.vgg19 import preprocess_input
-
-# ======================================== #
-
-def create_batch_image(img : Tensor):
- """
- Create a batch of images with a single image by expanding its dimensions.
-
- Args:
- img: The input image as a tensor.
-
- Returns:
- Tensor: The batched image tensor.
- """
- img = tf.expand_dims(tf.constant(img),axis=0)
- return (img)
-
-# ======================================== #
-
-def remove_batch_dimension(array : ndarray):
- """Remove the batch dimension from a NumPy array.
-
- Args:
- array: The input NumPy array with a batch dimension.
-
- Returns:
- np.ndarray: The reshaped array without the batch dimension.
- """
- array = np.reshape(array, (array.shape[1], array.shape[2], array.shape[3]))
- return (array)
-
-# ======================================== #
-
-def preprocessing_img(img : Tensor):
- """
- Preprocess an image for input into a VGG network.
-
- Args:
- img: The input image as a tensor.
-
- Returns:
- Tensor: The preprocessed image tensor.
- """
- img = inverse_normalize_image(img)
- preprocessed_img = preprocess_input(img)
- return preprocessed_img
-
-# ======================================== #
-
-def Normalize_image(img : Tensor):
- """
- Normalize an image by dividing its pixel values by 255.
-
- Args:
- img: The input image as a tensor.
-
- Returns:
- Tensor: The normalized image tensor.
- """
- img = img / 255.
- return (img)
-
-# ======================================== #
-
-def inverse_normalize_image(img : Tensor):
- """
- Inverse the normalization of an image by multiplying its pixel values by 255.
-
- Args:
- img: The input image as a tensor.
-
- Returns:
- Tensor: The denormalized image tensor.
- """
- img = img * 255
- return (img)
-
-# ======================================== #
\ No newline at end of file
diff --git a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/text/korean.py b/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/text/korean.py
deleted file mode 100644
index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000
--- a/spaces/XlalalaX/VITS-Umamusume-voice-synthesizer/text/korean.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ㄳ', 'ㄱㅅ'),
- ('ㄵ', 'ㄴㅈ'),
- ('ㄶ', 'ㄴㅎ'),
- ('ㄺ', 'ㄹㄱ'),
- ('ㄻ', 'ㄹㅁ'),
- ('ㄼ', 'ㄹㅂ'),
- ('ㄽ', 'ㄹㅅ'),
- ('ㄾ', 'ㄹㅌ'),
- ('ㄿ', 'ㄹㅍ'),
- ('ㅀ', 'ㄹㅎ'),
- ('ㅄ', 'ㅂㅅ'),
- ('ㅘ', 'ㅗㅏ'),
- ('ㅙ', 'ㅗㅐ'),
- ('ㅚ', 'ㅗㅣ'),
- ('ㅝ', 'ㅜㅓ'),
- ('ㅞ', 'ㅜㅔ'),
- ('ㅟ', 'ㅜㅣ'),
- ('ㅢ', 'ㅡㅣ'),
- ('ㅑ', 'ㅣㅏ'),
- ('ㅒ', 'ㅣㅐ'),
- ('ㅕ', 'ㅣㅓ'),
- ('ㅖ', 'ㅣㅔ'),
- ('ㅛ', 'ㅣㅗ'),
- ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', '에이'),
- ('b', '비'),
- ('c', '시'),
- ('d', '디'),
- ('e', '이'),
- ('f', '에프'),
- ('g', '지'),
- ('h', '에이치'),
- ('i', '아이'),
- ('j', '제이'),
- ('k', '케이'),
- ('l', '엘'),
- ('m', '엠'),
- ('n', '엔'),
- ('o', '오'),
- ('p', '피'),
- ('q', '큐'),
- ('r', '아르'),
- ('s', '에스'),
- ('t', '티'),
- ('u', '유'),
- ('v', '브이'),
- ('w', '더블유'),
- ('x', '엑스'),
- ('y', '와이'),
- ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('t͡ɕ','ʧ'),
- ('d͡ʑ','ʥ'),
- ('ɲ','n^'),
- ('ɕ','ʃ'),
- ('ʷ','w'),
- ('ɭ','l`'),
- ('ʎ','ɾ'),
- ('ɣ','ŋ'),
- ('ɰ','ɯ'),
- ('ʝ','j'),
- ('ʌ','ə'),
- ('ɡ','g'),
- ('\u031a','#'),
- ('\u0348','='),
- ('\u031e',''),
- ('\u0320',''),
- ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
- for regex, replacement in _latin_to_hangul:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def divide_hangul(text):
- text = j2hcj(h2j(text))
- for regex, replacement in _hangul_divided:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def hangul_number(num, sino=True):
- '''Reference https://github.com/Kyubyong/g2pK'''
- num = re.sub(',', '', num)
-
- if num == '0':
- return '영'
- if not sino and num == '20':
- return '스무'
-
- digits = '123456789'
- names = '일이삼사오육칠팔구'
- digit2name = {d: n for d, n in zip(digits, names)}
-
- modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
- decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
- digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
- digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
- spelledout = []
- for i, digit in enumerate(num):
- i = len(num) - i - 1
- if sino:
- if i == 0:
- name = digit2name.get(digit, '')
- elif i == 1:
- name = digit2name.get(digit, '') + '십'
- name = name.replace('일십', '십')
- else:
- if i == 0:
- name = digit2mod.get(digit, '')
- elif i == 1:
- name = digit2dec.get(digit, '')
- if digit == '0':
- if i % 4 == 0:
- last_three = spelledout[-min(3, len(spelledout)):]
- if ''.join(last_three) == '':
- spelledout.append('')
- continue
- else:
- spelledout.append('')
- continue
- if i == 2:
- name = digit2name.get(digit, '') + '백'
- name = name.replace('일백', '백')
- elif i == 3:
- name = digit2name.get(digit, '') + '천'
- name = name.replace('일천', '천')
- elif i == 4:
- name = digit2name.get(digit, '') + '만'
- name = name.replace('일만', '만')
- elif i == 5:
- name = digit2name.get(digit, '') + '십'
- name = name.replace('일십', '십')
- elif i == 6:
- name = digit2name.get(digit, '') + '백'
- name = name.replace('일백', '백')
- elif i == 7:
- name = digit2name.get(digit, '') + '천'
- name = name.replace('일천', '천')
- elif i == 8:
- name = digit2name.get(digit, '') + '억'
- elif i == 9:
- name = digit2name.get(digit, '') + '십'
- elif i == 10:
- name = digit2name.get(digit, '') + '백'
- elif i == 11:
- name = digit2name.get(digit, '') + '천'
- elif i == 12:
- name = digit2name.get(digit, '') + '조'
- elif i == 13:
- name = digit2name.get(digit, '') + '십'
- elif i == 14:
- name = digit2name.get(digit, '') + '백'
- elif i == 15:
- name = digit2name.get(digit, '') + '천'
- spelledout.append(name)
- return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
- '''Reference https://github.com/Kyubyong/g2pK'''
- tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
- for token in tokens:
- num, classifier = token
- if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
- spelledout = hangul_number(num, sino=False)
- else:
- spelledout = hangul_number(num, sino=True)
- text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
- # digit by digit for remaining digits
- digits = '0123456789'
- names = '영일이삼사오육칠팔구'
- for d, n in zip(digits, names):
- text = text.replace(d, n)
- return text
-
-
-def korean_to_lazy_ipa(text):
- text = latin_to_hangul(text)
- text = number_to_hangul(text)
- text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text)
- for regex, replacement in _ipa_to_lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def korean_to_ipa(text):
- text = korean_to_lazy_ipa(text)
- return text.replace('ʧ','tʃ').replace('ʥ','dʑ')
diff --git a/spaces/XzJosh/Ava2-Bert-VITS2/commons.py b/spaces/XzJosh/Ava2-Bert-VITS2/commons.py
deleted file mode 100644
index 9ad0444b61cbadaa388619986c2889c707d873ce..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Ava2-Bert-VITS2/commons.py
+++ /dev/null
@@ -1,161 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(
- length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = (
- math.log(float(max_timescale) / float(min_timescale)) /
- (num_timescales - 1))
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2,3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1. / norm_type)
- return total_norm
diff --git a/spaces/XzJosh/Spade-Bert-VITS2/text/cleaner.py b/spaces/XzJosh/Spade-Bert-VITS2/text/cleaner.py
deleted file mode 100644
index 64bd5f7296f66c94f3a335666c53706bb5fe5b39..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Spade-Bert-VITS2/text/cleaner.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from text import chinese, cleaned_text_to_sequence
-
-
-language_module_map = {
- 'ZH': chinese
-}
-
-
-def clean_text(text, language):
- language_module = language_module_map[language]
- norm_text = language_module.text_normalize(text)
- phones, tones, word2ph = language_module.g2p(norm_text)
- return norm_text, phones, tones, word2ph
-
-def clean_text_bert(text, language):
- language_module = language_module_map[language]
- norm_text = language_module.text_normalize(text)
- phones, tones, word2ph = language_module.g2p(norm_text)
- bert = language_module.get_bert_feature(norm_text, word2ph)
- return phones, tones, bert
-
-def text_to_sequence(text, language):
- norm_text, phones, tones, word2ph = clean_text(text, language)
- return cleaned_text_to_sequence(phones, tones, language)
-
-if __name__ == '__main__':
- pass
diff --git a/spaces/YuAnthony/Audio-Caption/tools/argument_parsing.py b/spaces/YuAnthony/Audio-Caption/tools/argument_parsing.py
deleted file mode 100644
index fab3bc4c78a5994b1047f7b5de2c8df18ae38bd3..0000000000000000000000000000000000000000
--- a/spaces/YuAnthony/Audio-Caption/tools/argument_parsing.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from argparse import ArgumentParser
-
-# __author__ = 'Konstantinos Drossos -- Tampere University'
-# __docformat__ = 'reStructuredText'
-# __all__ = ['get_argument_parser']
-
-
-def get_argument_parser():
- """Creates and returns the ArgumentParser for this project.
-
- :return: The argument parser.
- :rtype: argparse.ArgumentParser
- """
- arg_parser = ArgumentParser()
- the_args = [
- # ---------------------------------
- [['--config-file', '-c'],
- {'type': str,
- 'default': 'main_settings',
- 'help': 'The settings file (without extension).'}],
- # ---------------------------------
- [['--file-dir', '-d'],
- {'type': str,
- 'default': 'settings',
- 'help': 'Directory that holds the settings file (default: `settings`).'}],
- # ---------------------------------
- [['--file-ext', '-e'],
- {'type': str,
- 'default': 'yaml',
- 'help': 'Extension of the settings file (default: `yaml`).'}],
- # ---------------------------------
- [['--verbose', '-v'],
- {'default': True,
- 'action': 'store_true',
- 'help': 'Be verbose flag (default True).'}]]
-
- [arg_parser.add_argument(*i[0], **i[1]) for i in the_args]
-
- return arg_parser
-
-# EOF
diff --git a/spaces/Yuliang/ECON/lib/common/imutils.py b/spaces/Yuliang/ECON/lib/common/imutils.py
deleted file mode 100644
index 287ab057ee20ac6d28ae8f7a83a77f681af040db..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/imutils.py
+++ /dev/null
@@ -1,364 +0,0 @@
-import os
-
-os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
-import cv2
-import mediapipe as mp
-import numpy as np
-import torch
-import torch.nn.functional as F
-from kornia.geometry.transform import get_affine_matrix2d, warp_affine
-from PIL import Image
-from rembg import remove
-from rembg.session_factory import new_session
-from torchvision import transforms
-
-from lib.pymafx.core import constants
-
-
-def transform_to_tensor(res, mean=None, std=None, is_tensor=False):
- all_ops = []
- if res is not None:
- all_ops.append(transforms.Resize(size=res))
- if not is_tensor:
- all_ops.append(transforms.ToTensor())
- if mean is not None and std is not None:
- all_ops.append(transforms.Normalize(mean=mean, std=std))
- return transforms.Compose(all_ops)
-
-
-def get_affine_matrix_wh(w1, h1, w2, h2):
-
- transl = torch.tensor([(w2 - w1) / 2.0, (h2 - h1) / 2.0]).unsqueeze(0)
- center = torch.tensor([w1 / 2.0, h1 / 2.0]).unsqueeze(0)
- scale = torch.min(torch.tensor([w2 / w1, h2 / h1])).repeat(2).unsqueeze(0)
- M = get_affine_matrix2d(transl, center, scale, angle=torch.tensor([0.]))
-
- return M
-
-
-def get_affine_matrix_box(boxes, w2, h2):
-
- # boxes [left, top, right, bottom]
- width = boxes[:, 2] - boxes[:, 0] #(N,)
- height = boxes[:, 3] - boxes[:, 1] #(N,)
- center = torch.tensor([(boxes[:, 0] + boxes[:, 2]) / 2.0,
- (boxes[:, 1] + boxes[:, 3]) / 2.0]).T #(N,2)
- scale = torch.min(torch.tensor([w2 / width, h2 / height]),
- dim=0)[0].unsqueeze(1).repeat(1, 2) * 0.9 #(N,2)
- transl = torch.cat([w2 / 2.0 - center[:, 0:1], h2 / 2.0 - center[:, 1:2]], dim=1) #(N,2)
- M = get_affine_matrix2d(transl, center, scale, angle=torch.tensor([
- 0.,
- ] * transl.shape[0]))
-
- return M
-
-
-def load_img(img_file):
-
- if img_file.endswith("exr"):
- img = cv2.imread(img_file, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
- else:
- img = cv2.imread(img_file, cv2.IMREAD_UNCHANGED)
-
- # considering non 8-bit image
- if img.dtype != np.uint8:
- img = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)
-
- if len(img.shape) == 2:
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-
- if not img_file.endswith("png"):
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- else:
- img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)
-
- return torch.tensor(img).permute(2, 0, 1).unsqueeze(0).float(), img.shape[:2]
-
-
-def get_keypoints(image):
- def collect_xyv(x, body=True):
- lmk = x.landmark
- all_lmks = []
- for i in range(len(lmk)):
- visibility = lmk[i].visibility if body else 1.0
- all_lmks.append(torch.Tensor([lmk[i].x, lmk[i].y, lmk[i].z, visibility]))
- return torch.stack(all_lmks).view(-1, 4)
-
- mp_holistic = mp.solutions.holistic
-
- with mp_holistic.Holistic(
- static_image_mode=True,
- model_complexity=2,
- ) as holistic:
- results = holistic.process(image)
-
- fake_kps = torch.zeros(33, 4)
-
- result = {}
- result["body"] = collect_xyv(results.pose_landmarks) if results.pose_landmarks else fake_kps
- result["lhand"] = collect_xyv(
- results.left_hand_landmarks, False
- ) if results.left_hand_landmarks else fake_kps
- result["rhand"] = collect_xyv(
- results.right_hand_landmarks, False
- ) if results.right_hand_landmarks else fake_kps
- result["face"] = collect_xyv(
- results.face_landmarks, False
- ) if results.face_landmarks else fake_kps
-
- return result
-
-
-def get_pymafx(image, landmarks):
-
- # image [3,512,512]
-
- item = {
- 'img_body': F.interpolate(image.unsqueeze(0), size=224, mode='bicubic',
- align_corners=True)[0]
- }
-
- for part in ['lhand', 'rhand', 'face']:
- kp2d = landmarks[part]
- kp2d_valid = kp2d[kp2d[:, 3] > 0.]
- if len(kp2d_valid) > 0:
- bbox = [
- min(kp2d_valid[:, 0]),
- min(kp2d_valid[:, 1]),
- max(kp2d_valid[:, 0]),
- max(kp2d_valid[:, 1])
- ]
- center_part = [(bbox[2] + bbox[0]) / 2., (bbox[3] + bbox[1]) / 2.]
- scale_part = 2. * max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 2
-
- # handle invalid part keypoints
- if len(kp2d_valid) < 1 or scale_part < 0.01:
- center_part = [0, 0]
- scale_part = 0.5
- kp2d[:, 3] = 0
-
- center_part = torch.tensor(center_part).float()
-
- theta_part = torch.zeros(1, 2, 3)
- theta_part[:, 0, 0] = scale_part
- theta_part[:, 1, 1] = scale_part
- theta_part[:, :, -1] = center_part
-
- grid = F.affine_grid(theta_part, torch.Size([1, 3, 224, 224]), align_corners=False)
- img_part = F.grid_sample(image.unsqueeze(0), grid, align_corners=False).squeeze(0).float()
-
- item[f'img_{part}'] = img_part
-
- theta_i_inv = torch.zeros_like(theta_part)
- theta_i_inv[:, 0, 0] = 1. / theta_part[:, 0, 0]
- theta_i_inv[:, 1, 1] = 1. / theta_part[:, 1, 1]
- theta_i_inv[:, :, -1] = -theta_part[:, :, -1] / theta_part[:, 0, 0].unsqueeze(-1)
- item[f'{part}_theta_inv'] = theta_i_inv[0]
-
- return item
-
-
-def remove_floats(mask):
-
- # 1. find all the contours
- # 2. fillPoly "True" for the largest one
- # 3. fillPoly "False" for its childrens
-
- new_mask = np.zeros(mask.shape)
- cnts, hier = cv2.findContours(mask.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
- cnt_index = sorted(range(len(cnts)), key=lambda k: cv2.contourArea(cnts[k]), reverse=True)
- body_cnt = cnts[cnt_index[0]]
- childs_cnt_idx = np.where(np.array(hier)[0, :, -1] == cnt_index[0])[0]
- childs_cnt = [cnts[idx] for idx in childs_cnt_idx]
- cv2.fillPoly(new_mask, [body_cnt], 1)
- cv2.fillPoly(new_mask, childs_cnt, 0)
-
- return new_mask
-
-
-def process_image(img_file, hps_type, single, input_res, detector):
-
- img_raw, (in_height, in_width) = load_img(img_file)
- tgt_res = input_res * 2
- M_square = get_affine_matrix_wh(in_width, in_height, tgt_res, tgt_res)
- img_square = warp_affine(
- img_raw,
- M_square[:, :2], (tgt_res, ) * 2,
- mode='bilinear',
- padding_mode='zeros',
- align_corners=True
- )
-
- # detection for bbox
- predictions = detector(img_square / 255.)[0]
-
- if single:
- top_score = predictions["scores"][predictions["labels"] == 1].max()
- human_ids = torch.where(predictions["scores"] == top_score)[0]
- else:
- human_ids = torch.logical_and(predictions["labels"] == 1,
- predictions["scores"] > 0.9).nonzero().squeeze(1)
-
- boxes = predictions["boxes"][human_ids, :].detach().cpu().numpy()
- masks = predictions["masks"][human_ids, :, :].permute(0, 2, 3, 1).detach().cpu().numpy()
-
- M_crop = get_affine_matrix_box(boxes, input_res, input_res)
-
- img_icon_lst = []
- img_crop_lst = []
- img_hps_lst = []
- img_mask_lst = []
- landmark_lst = []
- hands_visibility_lst = []
- img_pymafx_lst = []
-
- uncrop_param = {
- "ori_shape": [in_height, in_width], "box_shape": [input_res, input_res], "square_shape":
- [tgt_res, tgt_res], "M_square": M_square, "M_crop": M_crop
- }
-
- for idx in range(len(boxes)):
-
- # mask out the pixels of others
- if len(masks) > 1:
- mask_detection = (masks[np.arange(len(masks)) != idx]).max(axis=0)
- else:
- mask_detection = masks[0] * 0.
-
- img_square_rgba = torch.cat([
- img_square.squeeze(0).permute(1, 2, 0),
- torch.tensor(mask_detection < 0.4) * 255
- ],
- dim=2)
-
- img_crop = warp_affine(
- img_square_rgba.unsqueeze(0).permute(0, 3, 1, 2),
- M_crop[idx:idx + 1, :2], (input_res, ) * 2,
- mode='bilinear',
- padding_mode='zeros',
- align_corners=True
- ).squeeze(0).permute(1, 2, 0).numpy().astype(np.uint8)
-
- # get accurate person segmentation mask
- img_rembg = remove(img_crop, post_process_mask=True, session=new_session("u2net"))
- img_mask = remove_floats(img_rembg[:, :, [3]])
-
- mean_icon = std_icon = (0.5, 0.5, 0.5)
- img_np = (img_rembg[..., :3] * img_mask).astype(np.uint8)
- img_icon = transform_to_tensor(512, mean_icon, std_icon)(
- Image.fromarray(img_np)
- ) * torch.tensor(img_mask).permute(2, 0, 1)
- img_hps = transform_to_tensor(224, constants.IMG_NORM_MEAN,
- constants.IMG_NORM_STD)(Image.fromarray(img_np))
-
- landmarks = get_keypoints(img_np)
-
- # get hands visibility
- hands_visibility = [True, True]
- if landmarks['lhand'][:, -1].mean() == 0.:
- hands_visibility[0] = False
- if landmarks['rhand'][:, -1].mean() == 0.:
- hands_visibility[1] = False
- hands_visibility_lst.append(hands_visibility)
-
- if hps_type == 'pymafx':
- img_pymafx_lst.append(
- get_pymafx(
- transform_to_tensor(512, constants.IMG_NORM_MEAN,
- constants.IMG_NORM_STD)(Image.fromarray(img_np)), landmarks
- )
- )
-
- img_crop_lst.append(torch.tensor(img_crop).permute(2, 0, 1) / 255.0)
- img_icon_lst.append(img_icon)
- img_hps_lst.append(img_hps)
- img_mask_lst.append(torch.tensor(img_mask[..., 0]))
- landmark_lst.append(landmarks['body'])
-
- # required image tensors / arrays
-
- # img_icon (tensor): (-1, 1), [3,512,512]
- # img_hps (tensor): (-2.11, 2.44), [3,224,224]
-
- # img_np (array): (0, 255), [512,512,3]
- # img_rembg (array): (0, 255), [512,512,4]
- # img_mask (array): (0, 1), [512,512,1]
- # img_crop (array): (0, 255), [512,512,4]
-
- return_dict = {
- "img_icon": torch.stack(img_icon_lst).float(), #[N, 3, res, res]
- "img_crop": torch.stack(img_crop_lst).float(), #[N, 4, res, res]
- "img_hps": torch.stack(img_hps_lst).float(), #[N, 3, res, res]
- "img_raw": img_raw, #[1, 3, H, W]
- "img_mask": torch.stack(img_mask_lst).float(), #[N, res, res]
- "uncrop_param": uncrop_param,
- "landmark": torch.stack(landmark_lst), #[N, 33, 4]
- "hands_visibility": hands_visibility_lst,
- }
-
- img_pymafx = {}
-
- if len(img_pymafx_lst) > 0:
- for idx in range(len(img_pymafx_lst)):
- for key in img_pymafx_lst[idx].keys():
- if key not in img_pymafx.keys():
- img_pymafx[key] = [img_pymafx_lst[idx][key]]
- else:
- img_pymafx[key] += [img_pymafx_lst[idx][key]]
-
- for key in img_pymafx.keys():
- img_pymafx[key] = torch.stack(img_pymafx[key]).float()
-
- return_dict.update({"img_pymafx": img_pymafx})
-
- return return_dict
-
-
-def blend_rgb_norm(norms, data):
-
- # norms [N, 3, res, res]
- masks = (norms.sum(dim=1) != norms[0, :, 0, 0].sum()).float().unsqueeze(1)
- norm_mask = F.interpolate(
- torch.cat([norms, masks], dim=1).detach(),
- size=data["uncrop_param"]["box_shape"],
- mode="bilinear",
- align_corners=False
- )
- final = data["img_raw"].type_as(norm_mask)
-
- for idx in range(len(norms)):
-
- norm_pred = (norm_mask[idx:idx + 1, :3, :, :] + 1.0) * 255.0 / 2.0
- mask_pred = norm_mask[idx:idx + 1, 3:4, :, :].repeat(1, 3, 1, 1)
-
- norm_ori = unwrap(norm_pred, data["uncrop_param"], idx)
- mask_ori = unwrap(mask_pred, data["uncrop_param"], idx)
-
- final = final * (1.0 - mask_ori) + norm_ori * mask_ori
-
- return final.detach().cpu()
-
-
-def unwrap(image, uncrop_param, idx):
-
- device = image.device
-
- img_square = warp_affine(
- image,
- torch.inverse(uncrop_param["M_crop"])[idx:idx + 1, :2].to(device),
- uncrop_param["square_shape"],
- mode='bilinear',
- padding_mode='zeros',
- align_corners=True
- )
-
- img_ori = warp_affine(
- img_square,
- torch.inverse(uncrop_param["M_square"])[:, :2].to(device),
- uncrop_param["ori_shape"],
- mode='bilinear',
- padding_mode='zeros',
- align_corners=True
- )
-
- return img_ori
diff --git a/spaces/Zayn/Image_Captioning_Using_Vision_Transformer_and_GPT-2/app.py b/spaces/Zayn/Image_Captioning_Using_Vision_Transformer_and_GPT-2/app.py
deleted file mode 100644
index db5e776c1d3d29cad766ccdc7c13b052cc970230..0000000000000000000000000000000000000000
--- a/spaces/Zayn/Image_Captioning_Using_Vision_Transformer_and_GPT-2/app.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from PIL import Image
-from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, PreTrainedTokenizerFast
-import requests
-
-model = VisionEncoderDecoderModel.from_pretrained("Zayn/vit2distilgpt2")
-vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
-tokenizer = PreTrainedTokenizerFast.from_pretrained("distilgpt2")
-
-def vit2distilgpt2(img):
- pixel_values = vit_feature_extractor(images=img, return_tensors="pt").pixel_values
- encoder_outputs = model.generate(pixel_values.to('cpu'),num_beams=5)
- generated_sentences = tokenizer.batch_decode(encoder_outputs, skip_special_tokens =True)
-
- return(generated_sentences[0].split('.')[0])
-
-import gradio as gr
-
-inputs = [
- gr.inputs.Image(type="pil", label = "Original Image")
-]
-
-outputs = [
- gr.outputs.Textbox(label = 'Caption')
-]
-title = "Image Captioning using Vision Transformer and GPT-2"
-description = "Developed by Zayn"
-article = "< a href='https://huggingface.co/Zayn/vit2distilgpt2'>Hugging Face AI Community"
-examples = [
- ["car.jpg"]
-]
-gr.Interface(
- vit2distilgpt2,
- inputs,
- outputs,
- title = title,
- description = description,
- article = article,
- examples = examples,
- theme = "huggingface",
-).launch(debug=True,enable_queue=True)
\ No newline at end of file
diff --git a/spaces/akdeniz27/zero-shot-text-classification-with-multilingual-t5/README.md b/spaces/akdeniz27/zero-shot-text-classification-with-multilingual-t5/README.md
deleted file mode 100644
index 0c0555faf6a475576c54bc02983cf1f45b853ee9..0000000000000000000000000000000000000000
--- a/spaces/akdeniz27/zero-shot-text-classification-with-multilingual-t5/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Zero Shot Text Classification With Multilingual T5
-emoji: 🏢
-colorFrom: red
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/yesno/voc1/local/data_download.sh b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/yesno/voc1/local/data_download.sh
deleted file mode 100644
index 7805faacb54100647a373d11c69ba17ba53ced21..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/egs/yesno/voc1/local/data_download.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-
-# Copyright 2019 Tomoki Hayashi
-# MIT License (https://opensource.org/licenses/MIT)
-
-download_dir=$1
-
-# check arguments
-if [ $# != 1 ]; then
- echo "Usage: $0 "
- exit 1
-fi
-
-set -euo pipefail
-
-cwd=$(pwd)
-if [ ! -e "${download_dir}/LJSpeech-1.1" ]; then
- mkdir -p "${download_dir}"
- cd "${download_dir}"
- wget http://www.openslr.org/resources/1/waves_yesno.tar.gz
- tar -xvzf waves_yesno.tar.gz
- rm ./waves_yesno/README*
- rm waves_yesno.tar.gz
- cd "${cwd}"
- echo "Successfully downloaded data."
-else
- echo "Already exists. Skipped."
-fi
diff --git a/spaces/akhaliq/deeplab2/model/layers/axial_block_groups.py b/spaces/akhaliq/deeplab2/model/layers/axial_block_groups.py
deleted file mode 100644
index 594b26381fc99960f6dd5c656b0b63a71a4be6bb..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/model/layers/axial_block_groups.py
+++ /dev/null
@@ -1,443 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Implements convolutional and attentional residual block groups."""
-
-import math
-import tensorflow as tf
-
-from deeplab2.model import utils
-from deeplab2.model.layers import activations
-from deeplab2.model.layers import axial_blocks
-from deeplab2.model.layers import drop_path
-from deeplab2.model.layers import dual_path_transformer
-from deeplab2.model.layers import positional_encodings
-from deeplab2.model.layers import recompute_grad as recompute_grad_lib
-
-# We will apply 10x larger learning rates on transformer layers. This global
-# variable name will be accessed when we build the optimizers. This keyword is
-# reserved and should not be a part of the variable names in a classification
-# pretrained backbone.
-TRANSFORMER = 'transformer'
-
-
-def _get_current_names(index):
- current_name = '_block{}'.format(index + 1)
- transformer_current_name = '_block{}_{}'.format(index + 1, TRANSFORMER)
- return current_name, transformer_current_name
-
-
-class BlockGroup(tf.keras.layers.Layer):
- """Applies a group of residual blocks with dual path transformer layers [1].
-
- An optional dual-path transformer layer is inserted after each residual block.
- The transformer layer performs memory2pixel attention, pixel2memory attention,
- and memory2memory self-attention, while the standard residual block applies
- the pixel2pixel axial-attention, global-attention, or spatial convolution.
-
- Reference:
- [1] MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers,
- CVPR 2021. https://arxiv.org/abs/2012.00759
- Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen.
- """
-
- def __init__(self,
- filters,
- num_blocks,
- name,
- original_resnet_stride,
- original_resnet_input_stride,
- output_stride=16,
- backbone_type='resnet_beta',
- positional_encoding_type=None,
- use_global_beyond_stride=0,
- use_axial_beyond_stride=16,
- use_transformer_beyond_stride=32,
- use_sac_beyond_stride=0,
- use_squeeze_and_excite=False,
- conv_use_recompute_grad=False,
- axial_use_recompute_grad=True,
- recompute_within_stride=0,
- transformer_use_recompute_grad=False,
- transformer_expansion=1,
- drop_path_keep_prob=0.8,
- drop_path_beyond_stride=16,
- drop_path_schedule='constant',
- activation='relu',
- attention_bottleneck_expansion=2,
- axial_layer_config=None,
- dual_path_transformer_layer_config=None,
- bn_layer=tf.keras.layers.BatchNormalization,
- conv_kernel_weight_decay=0.0):
- """Initializes a BlockGroup layer.
-
- Args:
- filters: An integer, the base number of channels for this block group.
- num_blocks: An integer, the number of blocks for this block group.
- name: A string, the name of the block group.
- original_resnet_stride: An integer, the original resnet stride for this
- block, usually 1 or 2. The stride will be applied if
- original_resnet_input_stride is smaller than the desired output_stride.
- Otherwise, the stride will not be applied, and atrous convolution will
- be used after the first block.
- original_resnet_input_stride: An integer, the total input stride in the
- original resnet. For example, the total input stride for the last stage
- of the original resnet is 16, and the total output stride is 32. This
- stride differs from the true stride of the feature in that we might use
- atrous convolution to change both the input and output stride to, e.g.
- 8, but its original resnet input stride remains the same. In this case,
- we also use the original resnet input stride to compute the atrous rate.
- output_stride: An integer, the desired output_stride for the ResNet.
- backbone_type: A string, the type of the backbone. Supports 'resnet',
- 'resnet_beta', and 'wider_resnet'. The 'resnet' refers to the original
- resnet with a 7x7 convolutional stem. The 'resnet_beta' means a resnet
- but with an inception stem. The 'wider_resnet' is a wider variant of
- resnet with extensively used 3x3 convolutions.
- positional_encoding_type: A string, type of the positional encoding.
- Support '2D', '1D', and None.
- use_global_beyond_stride: An integer, the stride beyond which we use
- global attention. Set to 0 if no global attention is desired. Defaults
- to 0, i.e. we do not use global attention.
- use_axial_beyond_stride: An integer, the stride beyond which we use axial
- attention. Note that use_global_beyond_stride has a higher priority,
- i.e. we use global attention if the stride is also beyond
- use_global_beyond_stride. Set to 0 if no axial attention is desired.
- Defaults to 16 as in MaX-DeepLab.
- use_transformer_beyond_stride: An integer, the stride beyond which we use
- a transformer layer. Set to 0 if no transformer is desired. Defaults to
- 32 as in MaX-DeepLab-S.
- use_sac_beyond_stride: An integer. Use the Switchable Atrous Convolution
- (SAC) beyond the specified stride. For example, if
- `use_sac_beyond_stride` = 16, SAC will be applied to the network stage
- whose output stride >= 16 (i.e., 16 and 32). Set to 0 or -1 to disable
- it. Defaults to 0 as SAC is not used in MaX-DeepLab.
- use_squeeze_and_excite: A boolean, whether squeeze-and-excite (SE) is
- used. Defaults to False as SE is not used in MaX-DeepLab.
- conv_use_recompute_grad: A boolean, whether to use the gradient
- checkpointing trick for convolutional blocks. This trick reduces
- accelerator memory usage, but takes longer to compute gradients.
- Defaults to False since convolutional layers are memory efficient.
- axial_use_recompute_grad: A boolean, whether to use the gradient
- checkpointing trick for axial blocks. This trick reduces accelerator
- memory usage, but takes longer to compute gradients. Defaults to True
- since it saves memory for axial blocks.
- recompute_within_stride: An integer, the stride within which we use the
- gradient checkpointing trick. This trick reduces accelerator memory
- usage, but takes longer to compute gradients. Defaults to 0 (do not
- recompute any layer).
- transformer_use_recompute_grad: A boolean, whether to use the gradient
- checkpointing trick for dual-path transformer blocks. This trick reduces
- accelerator memory usage, but takes longer to compute gradients.
- Defaults to False.
- transformer_expansion: An integer, the expansion ratio for the transformer
- bottleneck.
- drop_path_keep_prob: A float, the keep probability for dropping path.
- Defaults to 0.8 as in MaX-DeepLab-S.
- drop_path_beyond_stride: An integer, the stride beyond which we apply drop
- path augmentation. Defaults to 16 as in MaX-DeepLab-S.
- drop_path_schedule: A string, the drop path schedule. Currently, we
- support 'constant': use the same drop path keep probability for all
- stages, and 'linear': linearly decrease the drop path keep probability
- from 1.0 at 0-th stage (or STEM) to `drop_path_keep_prob` at last stage.
- activation: A string, type of activation function to apply. Support
- 'relu', 'swish' (or 'silu'), 'gelu', 'approximated_gelu', and 'elu'.
- attention_bottleneck_expansion: An integer, the expansion ratio for
- axial attention blocks.
- axial_layer_config: A dict, an argument dictionary for the axial layer.
- dual_path_transformer_layer_config: A dict, an argument dictionary for the
- transformer.
- bn_layer: An optional tf.keras.layers.Layer that computes the
- normalization (default: tf.keras.layers.BatchNormalization).
- conv_kernel_weight_decay: A float, the weight decay for convolution
- kernels.
-
- Raises:
- ValueError: If backbone_type is not one of 'resnet', 'resnet_beta', or
- 'wider_resnet'.
- ValueError: original_resnet_input_stride is not power of 2.
- ValueError: output_stride is not power of 2.
- """
- if original_resnet_input_stride & (original_resnet_input_stride - 1):
- raise ValueError('original_resnet_input_stride is not power of 2.')
- if output_stride & (output_stride - 1):
- raise ValueError('output_stride is not power of 2.')
-
- super(BlockGroup, self).__init__(name=name)
- self._add_absolute_positional_encoding = None
- self._activation_fn = activations.get_activation(activation)
- self._num_blocks = num_blocks
- self._drop_path_keep_prob = []
- self._recompute_grad = []
- self._transformer_use_recompute_grad = transformer_use_recompute_grad
- if dual_path_transformer_layer_config is None:
- dual_path_transformer_layer_config = {}
- original_resnet_current_stride = original_resnet_input_stride
-
- use_sac = (original_resnet_input_stride * original_resnet_stride >=
- use_sac_beyond_stride > 0)
-
- recompute_grad = (original_resnet_input_stride * original_resnet_stride <=
- recompute_within_stride)
-
- for index in range(num_blocks):
- current_name, transformer_current_name = _get_current_names(index)
-
- # Compute the current strides. If there is a stride for this block group,
- # we do it in the first residual block.
- if index == 0 and original_resnet_input_stride < output_stride:
- current_strides = original_resnet_stride
- else:
- current_strides = 1
-
- # Compute the current atrous rate.
- if original_resnet_current_stride > output_stride:
- atrous_rate = original_resnet_current_stride // output_stride
- else:
- atrous_rate = 1
-
- # Compute the atrous rate for the second conv in the first basic block.
- if (index == 0 and original_resnet_input_stride * original_resnet_stride >
- output_stride):
- basic_block_second_conv_atrous_rate = (
- original_resnet_input_stride * original_resnet_stride //
- output_stride)
- else:
- basic_block_second_conv_atrous_rate = atrous_rate
-
- # Compute the current drop_path_keep_prob.
- current_stage = math.log2(original_resnet_current_stride) - 1
- if original_resnet_current_stride >= drop_path_beyond_stride:
- current_drop_path_keep_prob = drop_path.get_drop_path_keep_prob(
- drop_path_keep_prob, drop_path_schedule,
- current_stage=int(round(current_stage)),
- num_stages=4)
- else:
- current_drop_path_keep_prob = 1.0
-
- # Compute which block_fn to use for this residual block.
- if original_resnet_current_stride >= use_global_beyond_stride > 0:
- attention_type = 'global'
- recompute_grad = axial_use_recompute_grad or recompute_grad
- filters_list = [filters * attention_bottleneck_expansion,
- filters,
- filters * 4]
- elif original_resnet_current_stride >= use_axial_beyond_stride > 0:
- attention_type = 'axial'
- recompute_grad = axial_use_recompute_grad or recompute_grad
- filters_list = [filters * attention_bottleneck_expansion,
- filters,
- filters * 4]
- elif backbone_type == 'resnet' or backbone_type == 'resnet_beta':
- attention_type = None
- recompute_grad = conv_use_recompute_grad or recompute_grad
- filters_list = [filters,
- filters,
- filters * 4]
- elif backbone_type == 'wider_resnet':
- if original_resnet_input_stride * original_resnet_stride < 32:
- # Wider-ResNet uses conv basic blocks except the last stage.
- attention_type = None
- recompute_grad = conv_use_recompute_grad or recompute_grad
- filters_list = [filters * 4,
- filters * 4]
- else:
- # Wider-ResNet uses an expanded bottleneck block in the last stage.
- attention_type = None
- recompute_grad = conv_use_recompute_grad or recompute_grad
- filters_list = [filters,
- filters * 2,
- filters * 4]
- else:
- raise ValueError(backbone_type + ' is not supported.')
-
- self._drop_path_keep_prob.append(current_drop_path_keep_prob)
- # Apply the residual block.
- # The inputs to block_fn should be activated features.
- block_fn = axial_blocks.AxialBlock(
- filters_list,
- kernel_size=3,
- strides=current_strides,
- atrous_rate=atrous_rate,
- use_squeeze_and_excite=use_squeeze_and_excite,
- use_sac=use_sac,
- bn_layer=bn_layer,
- activation=activation,
- name=current_name[1:],
- conv_kernel_weight_decay=conv_kernel_weight_decay,
- basic_block_second_conv_atrous_rate=(
- basic_block_second_conv_atrous_rate),
- attention_type=attention_type,
- axial_layer_config=axial_layer_config)
- self._recompute_grad.append(recompute_grad)
- utils.safe_setattr(self, current_name, block_fn)
-
- # Modify the original_resnet_stride according to the strides.
- if index == 0 and original_resnet_stride > 1:
- original_resnet_current_stride *= original_resnet_stride
- # Add absolute positional encoding if we will apply global attention
- # beyond this stride.
- if original_resnet_current_stride == use_global_beyond_stride > 0:
- self._add_absolute_positional_encoding = (
- positional_encodings.AddAbsolutePositionalEncoding(
- 'add_absolute_positional_encoding',
- positional_encoding_type, bn_layer, conv_kernel_weight_decay))
- if original_resnet_current_stride >= use_transformer_beyond_stride > 0:
- # Apply a dual-path transformer.
- transformer_block_fn = dual_path_transformer.DualPathTransformerLayer(
- name=transformer_current_name[1:],
- filters=int(128 * transformer_expansion),
- activation=activation,
- bn_layer=bn_layer,
- conv_kernel_weight_decay=conv_kernel_weight_decay,
- **dual_path_transformer_layer_config)
- utils.safe_setattr(self, transformer_current_name, transformer_block_fn)
- else:
- utils.safe_setattr(self, transformer_current_name, None)
- # Avoid using recompute_grad for the first call that builds the sub-layers.
- # Otherwise, recompute_grad will not track newly built model parameters.
- self._first_building_call = True
-
- def call(self, inputs, training=False):
- """Performs a forward pass.
-
- Args:
- inputs: two tensors. The first tensor is a pixel_space_input with shape
- [batch, height, width, pixel_channels]. The second tensor is
- memory_space_input with shape [batch, length, memory_channels]. This
- input will be used only if a transformer is used. Otherwise, the input
- is returned unmodified.
- training: A boolean flag indicating whether training behavior should be
- used (default: False).
-
- Returns:
- output: An output [batch, height, width, filters * 4] tensor.
- activated_output: An activated output [batch, height, width, filters * 4]
- tensor.
- memory_space_output: A memory space output [batch, length,
- memory_channels] tensor.
- """
- # The pixel space inputs are activated features.
- activated_features, memory_space_output = inputs
-
- # Recompute_grad takes only float tensors as inputs. It does not allow
- # bools or boolean tensors. For this reason, we cast training to a float
- # tensor and cast it back after we go through the recompute_grad wrap.
- float_tensor_training = tf.cast(training, tf.float32)
-
- for index in range(self._num_blocks):
- current_name, transformer_current_name = _get_current_names(index)
- block_fn_no_recompute = getattr(
- self, current_name)
- transformer_block_fn_no_recompute = getattr(
- self, transformer_current_name)
- current_drop_path_keep_prob = self._drop_path_keep_prob[index]
-
- # Wrap the layer if we want to recompute it in the backward pass.
- if (self._recompute_grad[index] and training):
- # The seed is not actually used since we do not have any random
- # operation in the recomputed function. The purpose of the provided seed
- # is to prevent recompute_grad from generating a new seed variable which
- # is not compatible with model exporting.
- block_fn = recompute_grad_lib.recompute_grad(
- block_fn_no_recompute, seed=tf.constant(0, tf.int32))
- else:
- block_fn = block_fn_no_recompute
-
- # The inputs to block_fn should be activated features.
- block_fn_inputs = [activated_features, float_tensor_training]
- # We have to define drop_path_masks outside the layer call and pass it
- # into the layer, because tf.recompute_grad (gradient checkpointing) does
- # not allow any randomness within the function call. In addition,
- # recompute_grad functions can only take Tensors as inputs, so we do not
- # pass the drop_path_random_mask (when it is None) into block_fn.
- if current_drop_path_keep_prob < 1.0 and training:
- drop_path_random_mask = drop_path.generate_drop_path_random_mask(
- activated_features, current_drop_path_keep_prob)
-
- block_fn_inputs.append(drop_path_random_mask)
-
- # Build the sub-layers when the block_fn is called for the first time.
- # Otherwise, recompute_grad will not track newly built model parameters.
- if self._first_building_call:
- _ = block_fn_no_recompute(tuple(block_fn_inputs))
- # Apply the residual block.
- features, activated_features = block_fn(tuple(block_fn_inputs))
-
- if index == 0 and self._add_absolute_positional_encoding is not None:
- features = self._add_absolute_positional_encoding(features,
- training=training)
- activated_features = self._activation_fn(features)
-
- if transformer_block_fn_no_recompute is not None:
- # Reshape pixel space features from 4D to 3D.
- _, height, width, channels = features.get_shape().as_list()
- features = tf.reshape(
- features, [-1, height * width, channels])
-
- # Wrap the layer if we want to recompute it in the backward pass.
- if (self._transformer_use_recompute_grad and training):
- # The seed is not actually used since we do not have any random
- # operation in the recomputed function. The purpose of the provided
- # seed is to prevent recompute_grad from generating a new seed
- # variable which is not compatible with model exporting.
- transformer_block_fn = recompute_grad_lib.recompute_grad(
- transformer_block_fn_no_recompute, seed=tf.constant(0, tf.int32))
- else:
- transformer_block_fn = transformer_block_fn_no_recompute
-
- transformer_block_fn_input_list = [
- features, memory_space_output, float_tensor_training]
- # We have to define drop_path_masks outside the layer call and pass it
- # into the layer, because recompute_grad (gradient checkpointing) does
- # not allow any randomness within the function call. In addition,
- # recompute_grad functions can only take Tensors as inputs, so we do not
- # pass the drop_path_masks (when they are None) into
- # transformer_block_fn.
- if current_drop_path_keep_prob < 1.0 and training:
- # Drop path random mask for pixel space attention.
- pixel_space_drop_path_mask = drop_path.generate_drop_path_random_mask(
- memory_space_output, current_drop_path_keep_prob)
- # Drop path random mask for memory space attention.
- memory_space_attention_drop_path_mask = (
- drop_path.generate_drop_path_random_mask(
- memory_space_output, current_drop_path_keep_prob))
- # Drop path random mask for memory space feed-forward network.
- memory_space_feed_forward_network_drop_path_mask = (
- drop_path.generate_drop_path_random_mask(
- memory_space_output, current_drop_path_keep_prob))
- transformer_block_fn_input_list += [
- pixel_space_drop_path_mask,
- memory_space_attention_drop_path_mask,
- memory_space_feed_forward_network_drop_path_mask]
-
- # Build the sub-layers when the transformer_block_fn is called for the
- # first time. Otherwise, recompute_grad will not track newly built model
- # parameters.
- if self._first_building_call:
- _ = transformer_block_fn_no_recompute(
- tuple(transformer_block_fn_input_list))
- # Apply a dual-path transformer.
- features, activated_features, memory_space_output = (
- transformer_block_fn(tuple(transformer_block_fn_input_list)))
-
- # Reshape pixel space features back to 4D.
- features = tf.reshape(features, [-1, height, width, channels])
- activated_features = tf.reshape(activated_features,
- [-1, height, width, channels])
- # Now the first call has finished and the sub-layers have been built.
- self._first_building_call = False
- # We also return the non-activated output so that the function is compatible
- # with a decoder that takes a non-activated tensor as input.
- return features, activated_features, memory_space_output
diff --git a/spaces/alamin655/Personas/conversant/demo/ui.py b/spaces/alamin655/Personas/conversant/demo/ui.py
deleted file mode 100644
index f4cd08df6681032133d65cdf6c31e0ad5aa7e78e..0000000000000000000000000000000000000000
--- a/spaces/alamin655/Personas/conversant/demo/ui.py
+++ /dev/null
@@ -1,368 +0,0 @@
-# Copyright (c) 2022 Cohere Inc. and its affiliates.
-#
-# Licensed under the MIT License (the "License");
-# you may not use this file except in compliance with the License.
-#
-# You may obtain a copy of the License in the LICENSE file at the top
-# level of this repository.
-
-
-from collections import defaultdict
-
-import streamlit as st
-from streamlit_ace import st_ace
-from streamlit_talk import message as st_message
-
-from conversant.demo import utils
-
-
-def render_bot_partial_reply(utterance, idx):
- """Renders a partial reply message from the bot.
-
- Args:
- utterance (str): The utterance to be rendered.
- idx (int): The index of the turn.
- """
- st_message(
- value=utterance,
- animate_from=""
- if "prev_partial_chunk" not in st.session_state
- else st.session_state.prev_partial_chunk,
- use_typewriter=True,
- key=f"{idx}_bot",
- avatar_style=st.session_state.bot_avatar,
- generation_complete=(not st.session_state.partial_reply_in_progress),
- )
-
-
-def draw_chat_history() -> None:
- """Renders the chat history in Streamlit.
-
- The messages are rendered using streamlit-chat, a custom Streamlit component
- for a chatbot UI.
- Reference: https://github.com/AI-Yash/st-chat
- """
- for i, turn in enumerate(st.session_state.bot.chat_history):
-
- # If there is only one turn, then we should only show the
- # bot utterance (but using the typewriter and partial reply effect),
- # skipping over the first injected user utterance.
- if len(st.session_state.bot.chat_history) == 1:
- if "bot" in turn:
- render_bot_partial_reply(turn["bot"], i)
-
- # If we are at the last conversation turn, the bot utterance
- # will be rendered as a partial reply with the typewriter effect.
- elif i == len(st.session_state.bot.chat_history) - 1:
- if "user" in turn:
- st_message(
- value=turn["user"],
- is_user=True,
- key=f"{i}_user",
- avatar_style=st.session_state.user_avatar,
- )
- if "bot" in turn:
- render_bot_partial_reply(turn["bot"], i)
-
- else:
- # If there is more than one turn, the first turn should skip over
- # the first injected user utterance.
- if i != 0 and "user" in turn:
- st_message(
- value=turn["user"],
- is_user=True,
- key=f"{i}_user",
- avatar_style=st.session_state.user_avatar,
- )
- if "bot" in turn:
- st_message(
- value=turn["bot"],
- key=f"{i}_bot",
- avatar_style=st.session_state.bot_avatar,
- )
-
-
-def draw_disclaimer() -> None:
- """Adds a disclaimer about the personas in this demo."""
- if st.session_state.persona != "parrot":
- st.write(
- "_Each persona is powered by [Cohere](https://cohere.com)'s large language "
- "models, and these examples are meant purely for demonstrative purposes. "
- "These personas are works of fiction, are not factually grounded, and "
- "should not be taken too seriously!_"
- )
- else:
- st.write(
- "_The Parrot persona does not make use of [Cohere](https://cohere.com)'s "
- "large language models. Instead, it repeats back whatever message it "
- "receives._"
- )
-
-
-def draw_chatbot_config_form() -> None:
- """Adds widgets to edit the chatbot config."""
- config = st.session_state.snapshot_chatbot_config
- max_context_examples = st.slider(
- label="max_context_examples",
- min_value=0,
- max_value=20,
- value=config["max_context_examples"],
- help="The number of interactions to keep as context for the chatbot.",
- )
- st.session_state.bot.configure_chatbot(
- {"max_context_examples": max_context_examples}
- )
-
-
-def draw_client_config_form() -> None:
- """Adds widgets to edit the client config."""
- st.write(
- "For more information on these parameters, see "
- "https://docs.cohere.ai/generate-reference"
- )
- config = st.session_state.snapshot_client_config
- model_options = ["", "small", "medium", "large", "xlarge"]
- model = st.selectbox(
- label="model",
- options=model_options,
- index=model_options.index(config["model"])
- if config["model"] in model_options
- else 0,
- help="The size of the Cohere model used to generate with.",
- )
- model_id_override = st.text_input(
- label="model ID override",
- value=model if model else config["model"],
- help=(
- "The full ID of a custom model. See "
- "https://docs.cohere.ai/generate-reference#model-optional for more details."
- ),
- )
- if model != model_id_override:
- st.warning(
- "WARNING: This demo does not validate that the model ID used for override "
- "is valid.",
- )
- max_tokens = st.number_input(
- label="max_tokens",
- value=config["max_tokens"],
- help="The number of tokens to predict per response.",
- )
- temperature = st.slider(
- label="temperature",
- min_value=0.0,
- max_value=5.0,
- value=config["temperature"],
- help=(
- "The degree of randomness for the response. Large temperature values may "
- "yield overly random results!"
- ),
- )
- frequency_penalty = st.slider(
- label="frequency_penalty",
- min_value=0.0,
- max_value=1.0,
- value=config["frequency_penalty"],
- help=(
- "Penalty to reduce repetitiveness of generated tokens, weighted by their "
- "frequency. Large penalty values may yield strange results!"
- ),
- )
- presence_penalty = st.slider(
- label="presence_penalty",
- min_value=0.0,
- max_value=1.0,
- value=config["presence_penalty"],
- help=(
- "Penalty to reduce repetitiveness of generated tokens, weighted equally "
- "to all present tokens. Large penalty values may yield strange results!"
- ),
- )
- # This allows the user to add their own stop sequences to a multiselect form
- # below.
- if "current_stop_sequences" not in st.session_state:
- st.session_state.current_stop_sequences = [
- utils.escape_string(stop_seq) for stop_seq in config["stop_sequences"]
- ]
- new_stop_seq = st.text_input(
- label="add new stop sequence",
- help="Add a stop sequence to the selection below.",
- )
- if (
- new_stop_seq != ""
- and new_stop_seq not in st.session_state.current_stop_sequences
- ):
- st.session_state.current_stop_sequences.append(new_stop_seq)
- # Use the list of stop sequences in the session state, including any user added ones
- # as the defaults for a multiselect form.
- st.multiselect(
- label="stop_sequences",
- options=st.session_state.current_stop_sequences,
- default=st.session_state.current_stop_sequences,
- key="selected_stop_sequences",
- help=(
- "The generated response will be cut off at the first instance of any of "
- "these stop sequences."
- ),
- )
-
- st.session_state.bot.configure_client(
- {
- "model": model_id_override,
- "max_tokens": int(max_tokens),
- "temperature": temperature,
- "frequency_penalty": frequency_penalty,
- "presence_penalty": presence_penalty,
- "stop_sequences": [
- utils.unescape_string(stop_seq)
- for stop_seq in st.session_state.selected_stop_sequences
- ], # Stop sequences need to be unescaped e.g. from \\n to \n
- }
- )
-
-
-def draw_prompt_form(disabled: bool = False) -> None:
- """Adds a form for configuring the prompt through its fields.
-
- The form is rendered as disabled when we only need to show the non-editable values
- of a prompt. This is used when the JSON editor is active.
-
- Args:
- disabled (bool): Whether or not the form should be rendered as disabled.
- """
- # Batches elements together as a form with a common submit button.
- with st.form("prompt_form"):
- # When the form is disabled, each time it is rendered its values need to be
- # taken from the current prompt config. Otherwise, its values should be taken
- # from the snapshot of the prompt config whenever it is first rendered.
- config = (
- defaultdict(str, st.session_state.bot.prompt.to_dict())
- if disabled
- else defaultdict(str, st.session_state.snapshot_prompt_config)
- )
- # We need to be careful about indexing into the dictionaries here
- # because when editing the prompt JSON, keys can end up malformed.
- default_preamble = config["preamble"]
- default_example_separator = config["example_separator"]
- default_user_name = (
- config["headers"]["user"] if "user" in config["headers"] else ""
- )
- default_bot_name = (
- config["headers"]["bot"] if "bot" in config["headers"] else ""
- )
- # This is where we create the text areas for the form.
- preamble = st.text_area(
- label="preamble",
- disabled=disabled,
- value=utils.escape_string(
- default_preamble
- ), # Display chars like \n in the text area by escaping them to \\n
- help=(
- "A string that directs the chatbot to behae in certain ways by "
- "describing its function and characteristics (i.e. a description of "
- "a bot's persona). Accepts escape sequences like \\n."
- ),
- )
- example_separator = st.text_input(
- label="example_separator",
- disabled=disabled,
- value=utils.escape_string(
- default_example_separator
- ), # Display chars like \n in the text area by escaping them to \\n
- help="A separator for each example. Accepts escape sequences like \\n.",
- )
- user_name = st.text_input(
- label="user",
- disabled=disabled,
- value=utils.escape_string(
- default_user_name
- ), # Display chars like \n in the text area by escaping them to \\n
- help="The name of the user. Defaults to 'User'.",
- )
- bot_name = st.text_input(
- label="bot",
- disabled=disabled,
- value=utils.escape_string(
- default_bot_name
- ), # Display chars like \n in the text area by escaping them to \\n
- help="The name of the chatbot.",
- )
- # Because prompt examples have a more complex structure, it is not very user
- # friendly to render them as form input fields.
- st.text_input(
- label="examples",
- placeholder="Please edit examples with the JSON editor.",
- disabled=True,
- help=(
- "A list of examples to illustrate how the chatbot should respond to "
- "a user."
- ),
- )
- # Upon submitting the form, we will save the form values in to the current
- # prompt config, then update the bot. Any errors should be saved.
- submitted = st.form_submit_button("Update")
- if submitted:
- try:
- # Strings need to be unescaped e.g. from \\n to \n
- current_config = st.session_state.bot.prompt.to_dict()
- current_config["preamble"] = utils.unescape_string(preamble)
- current_config["example_separator"] = utils.unescape_string(
- example_separator
- )
- current_config["headers"]["user"] = utils.unescape_string(user_name)
- current_config["headers"]["bot"] = utils.unescape_string(bot_name)
- st.session_state.bot.prompt.update(current_config)
- st.session_state.error = ""
- except Exception as e:
- st.session_state.error = e
-
-
-def draw_prompt_json_editor(max_height: int) -> None:
- """Renders an streamlit-ace editor into the app.
-
- streamlit-ace is a custom Streamlitcomponent for an Ace editor.
- Reference: https://github.com/okld/streamlit-ace
-
- Args:
- max_height (int): Desired height of the UI element expressed in pixels.
- If set to None, height will auto adjust to editor's content.
- None by default.
- """
- st.write("**Prompt (JSON):**")
- st_ace(
- value=f"{st.session_state.bot.prompt.to_json_string()}",
- placeholder="Enter a JSON representation of a prompt.",
- height=max_height,
- language="json",
- wrap=True,
- auto_update=True,
- key="json_editor_input",
- theme="monokai",
- )
-
-
-def draw_prompt_view(json: bool = False) -> None:
- """Adds a representation of the prompt in JSON or as a string.
-
- Args:
- json (bool): Whether to render the prompt as a JSON object.
- """
- if json:
- st.write("**Prompt (JSON):**")
- st.json(st.session_state.bot.prompt.to_dict())
- else:
- st.write(
- f"**{st.session_state.bot.prompt.bot_name} responds to you using the "
- "prompt below:**"
- )
- # If the current JSON string is malformed, show the error to the user to help
- # with debugging.
- if "error" in st.session_state and st.session_state.error:
- st.exception(st.session_state.error)
- else:
- st.code(
- st.session_state.bot.get_current_prompt("{Your message here}"),
- language="markdown",
- )
- if st.session_state.bot.chat_history:
- st.write("_(includes the current chat history)_")
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/idna/package_data.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/idna/package_data.py
deleted file mode 100644
index f5ea87c12bd5bf459bab40a566f4bd3ebd01d9d3..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/idna/package_data.py
+++ /dev/null
@@ -1,2 +0,0 @@
-__version__ = '3.3'
-
diff --git a/spaces/aliabid94/AutoGPT/autogpt/json_utils/utilities.py b/spaces/aliabid94/AutoGPT/autogpt/json_utils/utilities.py
deleted file mode 100644
index eb9bb687750460fed2f4547b67e41f8e8c877a41..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/json_utils/utilities.py
+++ /dev/null
@@ -1,54 +0,0 @@
-"""Utilities for the json_fixes package."""
-import json
-import re
-
-from jsonschema import Draft7Validator
-
-from autogpt.config import Config
-from autogpt.logs import logger
-
-CFG = Config()
-
-
-def extract_char_position(error_message: str) -> int:
- """Extract the character position from the JSONDecodeError message.
-
- Args:
- error_message (str): The error message from the JSONDecodeError
- exception.
-
- Returns:
- int: The character position.
- """
-
- char_pattern = re.compile(r"\(char (\d+)\)")
- if match := char_pattern.search(error_message):
- return int(match[1])
- else:
- raise ValueError("Character position not found in the error message.")
-
-
-def validate_json(json_object: object, schema_name: object) -> object:
- """
- :type schema_name: object
- :param schema_name:
- :type json_object: object
- """
- with open(f"autogpt/json_utils/{schema_name}.json", "r") as f:
- schema = json.load(f)
- validator = Draft7Validator(schema)
-
- if errors := sorted(validator.iter_errors(json_object), key=lambda e: e.path):
- logger.error("The JSON object is invalid.")
- if CFG.debug_mode:
- logger.error(
- json.dumps(json_object, indent=4)
- ) # Replace 'json_object' with the variable containing the JSON data
- logger.error("The following issues were found:")
-
- for error in errors:
- logger.error(f"Error: {error.message}")
- elif CFG.debug_mode:
- print("The JSON object is valid.")
-
- return json_object
diff --git a/spaces/amanmibra/void-demo-aisf/pipelines/train.py b/spaces/amanmibra/void-demo-aisf/pipelines/train.py
deleted file mode 100644
index 5af7ab0f615b892db4940c85abb1bc782d0d5e0b..0000000000000000000000000000000000000000
--- a/spaces/amanmibra/void-demo-aisf/pipelines/train.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import sys
-sys.path.append('..')
-import time
-
-# torch
-import torch
-import torchaudio
-from torch import nn
-from torch.utils.data import DataLoader
-
-# modal
-from modal import Mount, Secret, Stub, gpu, create_package_mounts
-
-# internal
-from pipelines.images import training_image_pip
-
-# model
-from dataset import VoiceDataset
-from cnn import CNNetwork
-
-# script defaults
-BATCH_SIZE = 128
-EPOCHS = 100
-LEARNING_RATE = 0.001
-
-TRAIN_FILE="data/aisf/augmented/train"
-TEST_FILE="data/aisf/augmented/test"
-SAMPLE_RATE=48000
-
-stub = Stub(
- "void-training",
- image=training_image_pip,
-)
-
-@stub.function(
- gpu=gpu.A100(memory=20),
- mounts=[
- Mount.from_local_file(local_path='dataset.py'),
- Mount.from_local_file(local_path='cnn.py'),
- ],
- timeout=EPOCHS * 200,
- secret=Secret.from_name("wandb"),
-)
-def train(
- model,
- train_dataloader,
- loss_fn,
- optimizer,
- origin_device="cuda",
- epochs=10,
- test_dataloader=None,
- wandb_enabled=False,
- ):
- import os
-
- import time
- import torch
- import wandb
-
- print("Begin model training...")
- begin = time.time()
-
- modal_device = origin_device
-
- # set model to cuda
- if torch.cuda.is_available() and modal_device != "cuda":
- modal_device = "cuda"
- model = model.to(modal_device)
-
- # metrics
- training_acc = []
- training_loss = []
- testing_acc = []
- testing_loss = []
-
- if wandb_enabled:
- wandb.init(project="void-training")
-
- for i in range(epochs):
- print(f"Epoch {i + 1}/{epochs}")
- then = time.time()
-
- # train model
- model, train_epoch_loss, train_epoch_acc = train_epoch.call(model, train_dataloader, loss_fn, optimizer, modal_device)
-
- # training metrics
- training_loss.append(train_epoch_loss/len(train_dataloader))
- training_acc.append(train_epoch_acc/len(train_dataloader))
- if wandb_enabled:
- wandb.log({'training_loss': training_loss[i], 'training_acc': training_acc[i]})
-
- now = time.time()
- print("Training Loss: {:.2f}, Training Accuracy: {:.4f}, Time: {:.2f}s".format(training_loss[i], training_acc[i], now - then))
-
- if test_dataloader:
- # test model
- test_epoch_loss, test_epoch_acc = validate_epoch.call(model, test_dataloader, loss_fn, modal_device)
-
- # testing metrics
- testing_loss.append(test_epoch_loss/len(test_dataloader))
- testing_acc.append(test_epoch_acc/len(test_dataloader))
-
- print("Testing Loss: {:.2f}, Testing Accuracy {:.4f}".format(testing_loss[i], testing_acc[i]))
-
- if wandb_enabled:
- wandb.log({'testing_loss': testing_loss[i], 'testing_acc': testing_acc[i]})
-
- print ("-------------------------------------------------------- \n")
-
- end = time.time()
- wandb.finish()
- print("-------- Finished Training --------")
- print("-------- Total Time -- {:.2f}s --------".format(end - begin))
-
- return model.to(origin_device)
-
-@stub.function(
- gpu=gpu.A100(memory=20),
- mounts=[
- Mount.from_local_file(local_path='dataset.py'),
- Mount.from_local_file(local_path='cnn.py'),
- ],
- timeout=600,
-)
-def train_epoch(model, train_dataloader, loss_fn, optimizer, device):
- import torch
- from tqdm import tqdm
-
- train_loss = 0.0
- train_acc = 0.0
- total = 0.0
-
- model.train()
-
- for wav, target in tqdm(train_dataloader):
- wav, target = wav.to(device), target.to(device)
-
- # calculate loss
- output = model(wav)
- loss = loss_fn(output, target)
-
- # backprop and update weights
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
- # metrics
- train_loss += loss.item()
- prediction = torch.argmax(output, 1)
- train_acc += (prediction == target).sum().item()/len(prediction)
- total += 1
-
- return model, train_loss, train_acc
-
-@stub.function(
- gpu=gpu.A100(memory=20),
- mounts=[
- Mount.from_local_file(local_path='dataset.py'),
- Mount.from_local_file(local_path='cnn.py'),
- ],
-)
-def validate_epoch(model, test_dataloader, loss_fn, device):
- from tqdm import tqdm
-
- test_loss = 0.0
- test_acc = 0.0
- total = 0.0
-
- model.eval()
-
- with torch.no_grad():
- for wav, target in tqdm(test_dataloader, "Testing batch..."):
- wav, target = wav.to(device), target.to(device)
-
- output = model(wav)
- loss = loss_fn(output, target)
-
- test_loss += loss.item()
- prediciton = torch.argmax(output, 1)
- test_acc += (prediciton == target).sum().item()/len(prediciton)
- total += 1
-
- return test_loss, test_acc
-
-def save_model(model):
- now = time.strftime("%Y%m%d_%H%M%S")
- model_filename = f"models/void_{now}.pth"
- torch.save(model.state_dict(), model_filename)
- print(f"Trained void model saved at {model_filename}")
-
-def get_device():
- if torch.cuda.is_available():
- device = "cuda"
- else:
- device = "cpu"
-
- return device
-
-@stub.local_entrypoint()
-def main():
- print("Initiating model training...")
- device = get_device()
-
- # instantiating our dataset object and create data loader
- mel_spectrogram = torchaudio.transforms.MelSpectrogram(
- sample_rate=SAMPLE_RATE,
- n_fft=2048,
- hop_length=512,
- n_mels=128
- )
-
- # dataset/dataloader
- train_dataset = VoiceDataset(TRAIN_FILE, mel_spectrogram, device, time_limit_in_secs=3)
- train_dataloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
-
- test_dataset = VoiceDataset(TEST_FILE, mel_spectrogram, device, time_limit_in_secs=3)
- test_dataloader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)
-
- # construct model
- model = CNNetwork()
-
- # init loss function and optimizer
- loss_fn = nn.CrossEntropyLoss()
- optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
-
- # train model
- model = train.call(model, train_dataloader, loss_fn, optimizer, device, EPOCHS, test_dataloader, True)
-
- # save model
- save_model(model)
-
\ No newline at end of file
diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/src/os/win/pa_win_wdmks_utils.h b/spaces/amarchheda/ChordDuplicate/portaudio/src/os/win/pa_win_wdmks_utils.h
deleted file mode 100644
index f524cf7f3c7c0fb23318f0813c9f5345e0e9957c..0000000000000000000000000000000000000000
--- a/spaces/amarchheda/ChordDuplicate/portaudio/src/os/win/pa_win_wdmks_utils.h
+++ /dev/null
@@ -1,65 +0,0 @@
-#ifndef PA_WIN_WDMKS_UTILS_H
-#define PA_WIN_WDMKS_UTILS_H
-
-/*
- * PortAudio Portable Real-Time Audio Library
- * Windows WDM KS utilities
- *
- * Copyright (c) 1999 - 2007 Ross Bencina, Andrew Baldwin
- *
- * Permission is hereby granted, free of charge, to any person obtaining
- * a copy of this software and associated documentation files
- * (the "Software"), to deal in the Software without restriction,
- * including without limitation the rights to use, copy, modify, merge,
- * publish, distribute, sublicense, and/or sell copies of the Software,
- * and to permit persons to whom the Software is furnished to do so,
- * subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be
- * included in all copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
- * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
- * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
- * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR
- * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
- * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
- * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- */
-
-/*
- * The text above constitutes the entire PortAudio license; however,
- * the PortAudio community also makes the following non-binding requests:
- *
- * Any person wishing to distribute modifications to the Software is
- * requested to send the modifications to the original developer so that
- * they can be incorporated into the canonical version. It is also
- * requested that these non-binding requests be included along with the
- * license above.
- */
-
-/** @file
- @brief Utilities for working with the Windows WDM KS API
-*/
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-/**
- Query for the maximum number of channels supported by any pin of the
- specified device. Returns 0 if the query fails for any reason.
-
- @param wcharDevicePath A system level PnP interface path, supplied as a WCHAR unicode string.
- Declared as void* to avoid introducing a dependency on wchar_t here.
-
- @param isInput A flag specifying whether to query for input (non-zero) or output (zero) channels.
-*/
-int PaWin_WDMKS_QueryFilterMaximumChannelCount( void *wcharDevicePath, int isInput );
-
-
-#ifdef __cplusplus
-}
-#endif /* __cplusplus */
-
-#endif /* PA_WIN_WDMKS_UTILS_H */
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/loggers/comet/comet_utils.py b/spaces/anaclaudia13ct/insect_detection/utils/loggers/comet/comet_utils.py
deleted file mode 100644
index 3cbd45156b576d09024fd11ea9dce83d4a6e5143..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/loggers/comet/comet_utils.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import logging
-import os
-from urllib.parse import urlparse
-
-try:
- import comet_ml
-except (ModuleNotFoundError, ImportError):
- comet_ml = None
-
-import yaml
-
-logger = logging.getLogger(__name__)
-
-COMET_PREFIX = "comet://"
-COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
-COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt")
-
-
-def download_model_checkpoint(opt, experiment):
- model_dir = f"{opt.project}/{experiment.name}"
- os.makedirs(model_dir, exist_ok=True)
-
- model_name = COMET_MODEL_NAME
- model_asset_list = experiment.get_model_asset_list(model_name)
-
- if len(model_asset_list) == 0:
- logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}")
- return
-
- model_asset_list = sorted(
- model_asset_list,
- key=lambda x: x["step"],
- reverse=True,
- )
- logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list}
-
- resource_url = urlparse(opt.weights)
- checkpoint_filename = resource_url.query
-
- if checkpoint_filename:
- asset_id = logged_checkpoint_map.get(checkpoint_filename)
- else:
- asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME)
- checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME
-
- if asset_id is None:
- logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment")
- return
-
- try:
- logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}")
- asset_filename = checkpoint_filename
-
- model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
- model_download_path = f"{model_dir}/{asset_filename}"
- with open(model_download_path, "wb") as f:
- f.write(model_binary)
-
- opt.weights = model_download_path
-
- except Exception as e:
- logger.warning("COMET WARNING: Unable to download checkpoint from Comet")
- logger.exception(e)
-
-
-def set_opt_parameters(opt, experiment):
- """Update the opts Namespace with parameters
- from Comet's ExistingExperiment when resuming a run
-
- Args:
- opt (argparse.Namespace): Namespace of command line options
- experiment (comet_ml.APIExperiment): Comet API Experiment object
- """
- asset_list = experiment.get_asset_list()
- resume_string = opt.resume
-
- for asset in asset_list:
- if asset["fileName"] == "opt.yaml":
- asset_id = asset["assetId"]
- asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
- opt_dict = yaml.safe_load(asset_binary)
- for key, value in opt_dict.items():
- setattr(opt, key, value)
- opt.resume = resume_string
-
- # Save hyperparameters to YAML file
- # Necessary to pass checks in training script
- save_dir = f"{opt.project}/{experiment.name}"
- os.makedirs(save_dir, exist_ok=True)
-
- hyp_yaml_path = f"{save_dir}/hyp.yaml"
- with open(hyp_yaml_path, "w") as f:
- yaml.dump(opt.hyp, f)
- opt.hyp = hyp_yaml_path
-
-
-def check_comet_weights(opt):
- """Downloads model weights from Comet and updates the
- weights path to point to saved weights location
-
- Args:
- opt (argparse.Namespace): Command Line arguments passed
- to YOLOv5 training script
-
- Returns:
- None/bool: Return True if weights are successfully downloaded
- else return None
- """
- if comet_ml is None:
- return
-
- if isinstance(opt.weights, str):
- if opt.weights.startswith(COMET_PREFIX):
- api = comet_ml.API()
- resource = urlparse(opt.weights)
- experiment_path = f"{resource.netloc}{resource.path}"
- experiment = api.get(experiment_path)
- download_model_checkpoint(opt, experiment)
- return True
-
- return None
-
-
-def check_comet_resume(opt):
- """Restores run parameters to its original state based on the model checkpoint
- and logged Experiment parameters.
-
- Args:
- opt (argparse.Namespace): Command Line arguments passed
- to YOLOv5 training script
-
- Returns:
- None/bool: Return True if the run is restored successfully
- else return None
- """
- if comet_ml is None:
- return
-
- if isinstance(opt.resume, str):
- if opt.resume.startswith(COMET_PREFIX):
- api = comet_ml.API()
- resource = urlparse(opt.resume)
- experiment_path = f"{resource.netloc}{resource.path}"
- experiment = api.get(experiment_path)
- set_opt_parameters(opt, experiment)
- download_model_checkpoint(opt, experiment)
-
- return True
-
- return None
diff --git a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/util/box_ops.py b/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/util/box_ops.py
deleted file mode 100644
index 781068d294e576954edb4bd07b6e0f30e4e1bcd9..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/panoptic-segment-anything/GroundingDINO/groundingdino/util/box_ops.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-"""
-Utilities for bounding box manipulation and GIoU.
-"""
-import torch
-from torchvision.ops.boxes import box_area
-
-
-def box_cxcywh_to_xyxy(x):
- x_c, y_c, w, h = x.unbind(-1)
- b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
- return torch.stack(b, dim=-1)
-
-
-def box_xyxy_to_cxcywh(x):
- x0, y0, x1, y1 = x.unbind(-1)
- b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
- return torch.stack(b, dim=-1)
-
-
-# modified from torchvision to also return the union
-def box_iou(boxes1, boxes2):
- area1 = box_area(boxes1)
- area2 = box_area(boxes2)
-
- # import ipdb; ipdb.set_trace()
- lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
- rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
-
- wh = (rb - lt).clamp(min=0) # [N,M,2]
- inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
-
- union = area1[:, None] + area2 - inter
-
- iou = inter / (union + 1e-6)
- return iou, union
-
-
-def generalized_box_iou(boxes1, boxes2):
- """
- Generalized IoU from https://giou.stanford.edu/
-
- The boxes should be in [x0, y0, x1, y1] format
-
- Returns a [N, M] pairwise matrix, where N = len(boxes1)
- and M = len(boxes2)
- """
- # degenerate boxes gives inf / nan results
- # so do an early check
- assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
- assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
- # except:
- # import ipdb; ipdb.set_trace()
- iou, union = box_iou(boxes1, boxes2)
-
- lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
- rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
-
- wh = (rb - lt).clamp(min=0) # [N,M,2]
- area = wh[:, :, 0] * wh[:, :, 1]
-
- return iou - (area - union) / (area + 1e-6)
-
-
-# modified from torchvision to also return the union
-def box_iou_pairwise(boxes1, boxes2):
- area1 = box_area(boxes1)
- area2 = box_area(boxes2)
-
- lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]
- rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]
-
- wh = (rb - lt).clamp(min=0) # [N,2]
- inter = wh[:, 0] * wh[:, 1] # [N]
-
- union = area1 + area2 - inter
-
- iou = inter / union
- return iou, union
-
-
-def generalized_box_iou_pairwise(boxes1, boxes2):
- """
- Generalized IoU from https://giou.stanford.edu/
-
- Input:
- - boxes1, boxes2: N,4
- Output:
- - giou: N, 4
- """
- # degenerate boxes gives inf / nan results
- # so do an early check
- assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
- assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
- assert boxes1.shape == boxes2.shape
- iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4
-
- lt = torch.min(boxes1[:, :2], boxes2[:, :2])
- rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
-
- wh = (rb - lt).clamp(min=0) # [N,2]
- area = wh[:, 0] * wh[:, 1]
-
- return iou - (area - union) / area
-
-
-def masks_to_boxes(masks):
- """Compute the bounding boxes around the provided masks
-
- The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
-
- Returns a [N, 4] tensors, with the boxes in xyxy format
- """
- if masks.numel() == 0:
- return torch.zeros((0, 4), device=masks.device)
-
- h, w = masks.shape[-2:]
-
- y = torch.arange(0, h, dtype=torch.float)
- x = torch.arange(0, w, dtype=torch.float)
- y, x = torch.meshgrid(y, x)
-
- x_mask = masks * x.unsqueeze(0)
- x_max = x_mask.flatten(1).max(-1)[0]
- x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
- y_mask = masks * y.unsqueeze(0)
- y_max = y_mask.flatten(1).max(-1)[0]
- y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
-
- return torch.stack([x_min, y_min, x_max, y_max], 1)
-
-
-if __name__ == "__main__":
- x = torch.rand(5, 4)
- y = torch.rand(3, 4)
- iou, union = box_iou(x, y)
- import ipdb
-
- ipdb.set_trace()
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/README.md b/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/README.md
deleted file mode 100644
index b9fb17c8f09fa6e8c217087e31fb8c52d96da536..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/vocoder/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Mozilla TTS Vocoders (Experimental)
-
-Here there are vocoder model implementations which can be combined with the other TTS models.
-
-Currently, following models are implemented:
-
-- Melgan
-- MultiBand-Melgan
-- ParallelWaveGAN
-- GAN-TTS (Discriminator Only)
-
-It is also very easy to adapt different vocoder models as we provide a flexible and modular (but not too modular) framework.
-
-## Training a model
-
-You can see here an example (Soon)[Colab Notebook]() training MelGAN with LJSpeech dataset.
-
-In order to train a new model, you need to gather all wav files into a folder and give this folder to `data_path` in '''config.json'''
-
-You need to define other relevant parameters in your ```config.json``` and then start traning with the following command.
-
-```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --config_path path/to/config.json```
-
-Example config files can be found under `tts/vocoder/configs/` folder.
-
-You can continue a previous training run by the following command.
-
-```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --continue_path path/to/your/model/folder```
-
-You can fine-tune a pre-trained model by the following command.
-
-```CUDA_VISIBLE_DEVICES='0' python tts/bin/train_vocoder.py --restore_path path/to/your/model.pth```
-
-Restoring a model starts a new training in a different folder. It only restores model weights with the given checkpoint file. However, continuing a training starts from the same directory where the previous training run left off.
-
-You can also follow your training runs on Tensorboard as you do with our TTS models.
-
-## Acknowledgement
-Thanks to @kan-bayashi for his [repository](https://github.com/kan-bayashi/ParallelWaveGAN) being the start point of our work.
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/color_syncnet_train.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/color_syncnet_train.py
deleted file mode 100644
index afa00544386cb9627f0d899476abbc82b37958ed..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/color_syncnet_train.py
+++ /dev/null
@@ -1,279 +0,0 @@
-from os.path import dirname, join, basename, isfile
-from tqdm import tqdm
-
-from models import SyncNet_color as SyncNet
-import audio
-
-import torch
-from torch import nn
-from torch import optim
-import torch.backends.cudnn as cudnn
-from torch.utils import data as data_utils
-import numpy as np
-
-from glob import glob
-
-import os, random, cv2, argparse
-from hparams import hparams, get_image_list
-
-parser = argparse.ArgumentParser(description='Code to train the expert lip-sync discriminator')
-
-parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True)
-
-parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str)
-parser.add_argument('--checkpoint_path', help='Resumed from this checkpoint', default=None, type=str)
-
-args = parser.parse_args()
-
-
-global_step = 0
-global_epoch = 0
-use_cuda = torch.cuda.is_available()
-print('use_cuda: {}'.format(use_cuda))
-
-syncnet_T = 5
-syncnet_mel_step_size = 16
-
-class Dataset(object):
- def __init__(self, split):
- self.all_videos = get_image_list(args.data_root, split)
-
- def get_frame_id(self, frame):
- return int(basename(frame).split('.')[0])
-
- def get_window(self, start_frame):
- start_id = self.get_frame_id(start_frame)
- vidname = dirname(start_frame)
-
- window_fnames = []
- for frame_id in range(start_id, start_id + syncnet_T):
- frame = join(vidname, '{}.jpg'.format(frame_id))
- if not isfile(frame):
- return None
- window_fnames.append(frame)
- return window_fnames
-
- def crop_audio_window(self, spec, start_frame):
- # num_frames = (T x hop_size * fps) / sample_rate
- start_frame_num = self.get_frame_id(start_frame)
- start_idx = int(80. * (start_frame_num / float(hparams.fps)))
-
- end_idx = start_idx + syncnet_mel_step_size
-
- return spec[start_idx : end_idx, :]
-
-
- def __len__(self):
- return len(self.all_videos)
-
- def __getitem__(self, idx):
- while 1:
- idx = random.randint(0, len(self.all_videos) - 1)
- vidname = self.all_videos[idx]
-
- img_names = list(glob(join(vidname, '*.jpg')))
- if len(img_names) <= 3 * syncnet_T:
- continue
- img_name = random.choice(img_names)
- wrong_img_name = random.choice(img_names)
- while wrong_img_name == img_name:
- wrong_img_name = random.choice(img_names)
-
- if random.choice([True, False]):
- y = torch.ones(1).float()
- chosen = img_name
- else:
- y = torch.zeros(1).float()
- chosen = wrong_img_name
-
- window_fnames = self.get_window(chosen)
- if window_fnames is None:
- continue
-
- window = []
- all_read = True
- for fname in window_fnames:
- img = cv2.imread(fname)
- if img is None:
- all_read = False
- break
- try:
- img = cv2.resize(img, (hparams.img_size, hparams.img_size))
- except Exception as e:
- all_read = False
- break
-
- window.append(img)
-
- if not all_read: continue
-
- try:
- wavpath = join(vidname, "audio.wav")
- wav = audio.load_wav(wavpath, hparams.sample_rate)
-
- orig_mel = audio.melspectrogram(wav).T
- except Exception as e:
- continue
-
- mel = self.crop_audio_window(orig_mel.copy(), img_name)
-
- if (mel.shape[0] != syncnet_mel_step_size):
- continue
-
- # H x W x 3 * T
- x = np.concatenate(window, axis=2) / 255.
- x = x.transpose(2, 0, 1)
- x = x[:, x.shape[1]//2:]
-
- x = torch.FloatTensor(x)
- mel = torch.FloatTensor(mel.T).unsqueeze(0)
-
- return x, mel, y
-
-logloss = nn.BCELoss()
-def cosine_loss(a, v, y):
- d = nn.functional.cosine_similarity(a, v)
- loss = logloss(d.unsqueeze(1), y)
-
- return loss
-
-def train(device, model, train_data_loader, test_data_loader, optimizer,
- checkpoint_dir=None, checkpoint_interval=None, nepochs=None):
-
- global global_step, global_epoch
- resumed_step = global_step
-
- while global_epoch < nepochs:
- running_loss = 0.
- prog_bar = tqdm(enumerate(train_data_loader))
- for step, (x, mel, y) in prog_bar:
- model.train()
- optimizer.zero_grad()
-
- # Transform data to CUDA device
- x = x.to(device)
-
- mel = mel.to(device)
-
- a, v = model(mel, x)
- y = y.to(device)
-
- loss = cosine_loss(a, v, y)
- loss.backward()
- optimizer.step()
-
- global_step += 1
- cur_session_steps = global_step - resumed_step
- running_loss += loss.item()
-
- if global_step == 1 or global_step % checkpoint_interval == 0:
- save_checkpoint(
- model, optimizer, global_step, checkpoint_dir, global_epoch)
-
- if global_step % hparams.syncnet_eval_interval == 0:
- with torch.no_grad():
- eval_model(test_data_loader, global_step, device, model, checkpoint_dir)
-
- prog_bar.set_description('Loss: {}'.format(running_loss / (step + 1)))
-
- global_epoch += 1
-
-def eval_model(test_data_loader, global_step, device, model, checkpoint_dir):
- eval_steps = 1400
- print('Evaluating for {} steps'.format(eval_steps))
- losses = []
- while 1:
- for step, (x, mel, y) in enumerate(test_data_loader):
-
- model.eval()
-
- # Transform data to CUDA device
- x = x.to(device)
-
- mel = mel.to(device)
-
- a, v = model(mel, x)
- y = y.to(device)
-
- loss = cosine_loss(a, v, y)
- losses.append(loss.item())
-
- if step > eval_steps: break
-
- averaged_loss = sum(losses) / len(losses)
- print(averaged_loss)
-
- return
-
-def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch):
-
- checkpoint_path = join(
- checkpoint_dir, "checkpoint_step{:09d}.pth".format(global_step))
- optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None
- torch.save({
- "state_dict": model.state_dict(),
- "optimizer": optimizer_state,
- "global_step": step,
- "global_epoch": epoch,
- }, checkpoint_path)
- print("Saved checkpoint:", checkpoint_path)
-
-def _load(checkpoint_path):
- if use_cuda:
- checkpoint = torch.load(checkpoint_path)
- else:
- checkpoint = torch.load(checkpoint_path,
- map_location=lambda storage, loc: storage)
- return checkpoint
-
-def load_checkpoint(path, model, optimizer, reset_optimizer=False):
- global global_step
- global global_epoch
-
- print("Load checkpoint from: {}".format(path))
- checkpoint = _load(path)
- model.load_state_dict(checkpoint["state_dict"])
- if not reset_optimizer:
- optimizer_state = checkpoint["optimizer"]
- if optimizer_state is not None:
- print("Load optimizer state from {}".format(path))
- optimizer.load_state_dict(checkpoint["optimizer"])
- global_step = checkpoint["global_step"]
- global_epoch = checkpoint["global_epoch"]
-
- return model
-
-if __name__ == "__main__":
- checkpoint_dir = args.checkpoint_dir
- checkpoint_path = args.checkpoint_path
-
- if not os.path.exists(checkpoint_dir): os.mkdir(checkpoint_dir)
-
- # Dataset and Dataloader setup
- train_dataset = Dataset('train')
- test_dataset = Dataset('val')
-
- train_data_loader = data_utils.DataLoader(
- train_dataset, batch_size=hparams.syncnet_batch_size, shuffle=True,
- num_workers=hparams.num_workers)
-
- test_data_loader = data_utils.DataLoader(
- test_dataset, batch_size=hparams.syncnet_batch_size,
- num_workers=8)
-
- device = torch.device("cuda" if use_cuda else "cpu")
-
- # Model
- model = SyncNet().to(device)
- print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad)))
-
- optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
- lr=hparams.syncnet_lr)
-
- if checkpoint_path is not None:
- load_checkpoint(checkpoint_path, model, optimizer, reset_optimizer=False)
-
- train(device, model, train_data_loader, test_data_loader, optimizer,
- checkpoint_dir=checkpoint_dir,
- checkpoint_interval=hparams.syncnet_checkpoint_interval,
- nepochs=hparams.nepochs)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Util/number.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Util/number.py
deleted file mode 100644
index 279ffe0c6a9d90b592c91b1a74a40252982ec137..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Crypto/Util/number.py
+++ /dev/null
@@ -1,1500 +0,0 @@
-#
-# number.py : Number-theoretic functions
-#
-# Part of the Python Cryptography Toolkit
-#
-# Written by Andrew M. Kuchling, Barry A. Warsaw, and others
-#
-# ===================================================================
-# The contents of this file are dedicated to the public domain. To
-# the extent that dedication to the public domain is not available,
-# everyone is granted a worldwide, perpetual, royalty-free,
-# non-exclusive license to exercise all rights associated with the
-# contents of this file for any purpose whatsoever.
-# No rights are reserved.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
-# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
-# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
-# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-# ===================================================================
-#
-
-import math
-import sys
-import struct
-from Crypto import Random
-from Crypto.Util.py3compat import iter_range
-
-# Backward compatibility
-_fastmath = None
-
-
-def ceil_div(n, d):
- """Return ceil(n/d), that is, the smallest integer r such that r*d >= n"""
-
- if d == 0:
- raise ZeroDivisionError()
- if (n < 0) or (d < 0):
- raise ValueError("Non positive values")
- r, q = divmod(n, d)
- if (n != 0) and (q != 0):
- r += 1
- return r
-
-
-def size (N):
- """Returns the size of the number N in bits."""
-
- if N < 0:
- raise ValueError("Size in bits only avialable for non-negative numbers")
-
- bits = 0
- while N >> bits:
- bits += 1
- return bits
-
-
-def getRandomInteger(N, randfunc=None):
- """Return a random number at most N bits long.
-
- If :data:`randfunc` is omitted, then :meth:`Random.get_random_bytes` is used.
-
- .. deprecated:: 3.0
- This function is for internal use only and may be renamed or removed in
- the future. Use :func:`Crypto.Random.random.getrandbits` instead.
- """
-
- if randfunc is None:
- randfunc = Random.get_random_bytes
-
- S = randfunc(N>>3)
- odd_bits = N % 8
- if odd_bits != 0:
- rand_bits = ord(randfunc(1)) >> (8-odd_bits)
- S = struct.pack('B', rand_bits) + S
- value = bytes_to_long(S)
- return value
-
-def getRandomRange(a, b, randfunc=None):
- """Return a random number *n* so that *a <= n < b*.
-
- If :data:`randfunc` is omitted, then :meth:`Random.get_random_bytes` is used.
-
- .. deprecated:: 3.0
- This function is for internal use only and may be renamed or removed in
- the future. Use :func:`Crypto.Random.random.randrange` instead.
- """
-
- range_ = b - a - 1
- bits = size(range_)
- value = getRandomInteger(bits, randfunc)
- while value > range_:
- value = getRandomInteger(bits, randfunc)
- return a + value
-
-def getRandomNBitInteger(N, randfunc=None):
- """Return a random number with exactly N-bits,
- i.e. a random number between 2**(N-1) and (2**N)-1.
-
- If :data:`randfunc` is omitted, then :meth:`Random.get_random_bytes` is used.
-
- .. deprecated:: 3.0
- This function is for internal use only and may be renamed or removed in
- the future.
- """
-
- value = getRandomInteger (N-1, randfunc)
- value |= 2 ** (N-1) # Ensure high bit is set
- assert size(value) >= N
- return value
-
-def GCD(x,y):
- """Greatest Common Denominator of :data:`x` and :data:`y`.
- """
-
- x = abs(x) ; y = abs(y)
- while x > 0:
- x, y = y % x, x
- return y
-
-def inverse(u, v):
- """The inverse of :data:`u` *mod* :data:`v`."""
-
- u3, v3 = u, v
- u1, v1 = 1, 0
- while v3 > 0:
- q = u3 // v3
- u1, v1 = v1, u1 - v1*q
- u3, v3 = v3, u3 - v3*q
- while u1<0:
- u1 = u1 + v
- return u1
-
-# Given a number of bits to generate and a random generation function,
-# find a prime number of the appropriate size.
-
-def getPrime(N, randfunc=None):
- """Return a random N-bit prime number.
-
- N must be an integer larger than 1.
- If randfunc is omitted, then :meth:`Random.get_random_bytes` is used.
- """
- if randfunc is None:
- randfunc = Random.get_random_bytes
-
- if N < 2:
- raise ValueError("N must be larger than 1")
-
- while True:
- number = getRandomNBitInteger(N, randfunc) | 1
- if isPrime(number, randfunc=randfunc):
- break
- return number
-
-
-def _rabinMillerTest(n, rounds, randfunc=None):
- """_rabinMillerTest(n:long, rounds:int, randfunc:callable):int
- Tests if n is prime.
- Returns 0 when n is definitely composite.
- Returns 1 when n is probably prime.
- Returns 2 when n is definitely prime.
-
- If randfunc is omitted, then Random.new().read is used.
-
- This function is for internal use only and may be renamed or removed in
- the future.
- """
- # check special cases (n==2, n even, n < 2)
- if n < 3 or (n & 1) == 0:
- return n == 2
- # n might be very large so it might be beneficial to precalculate n-1
- n_1 = n - 1
- # determine m and b so that 2**b * m = n - 1 and b maximal
- b = 0
- m = n_1
- while (m & 1) == 0:
- b += 1
- m >>= 1
-
- tested = []
- # we need to do at most n-2 rounds.
- for i in iter_range (min (rounds, n-2)):
- # randomly choose a < n and make sure it hasn't been tested yet
- a = getRandomRange (2, n, randfunc)
- while a in tested:
- a = getRandomRange (2, n, randfunc)
- tested.append (a)
- # do the rabin-miller test
- z = pow (a, m, n) # (a**m) % n
- if z == 1 or z == n_1:
- continue
- composite = 1
- for r in iter_range(b):
- z = (z * z) % n
- if z == 1:
- return 0
- elif z == n_1:
- composite = 0
- break
- if composite:
- return 0
- return 1
-
-def getStrongPrime(N, e=0, false_positive_prob=1e-6, randfunc=None):
- r"""
- Return a random strong *N*-bit prime number.
- In this context, *p* is a strong prime if *p-1* and *p+1* have at
- least one large prime factor.
-
- Args:
- N (integer): the exact length of the strong prime.
- It must be a multiple of 128 and > 512.
- e (integer): if provided, the returned prime (minus 1)
- will be coprime to *e* and thus suitable for RSA where
- *e* is the public exponent.
- false_positive_prob (float):
- The statistical probability for the result not to be actually a
- prime. It defaults to 10\ :sup:`-6`.
- Note that the real probability of a false-positive is far less. This is
- just the mathematically provable limit.
- randfunc (callable):
- A function that takes a parameter *N* and that returns
- a random byte string of such length.
- If omitted, :func:`Crypto.Random.get_random_bytes` is used.
- Return:
- The new strong prime.
-
- .. deprecated:: 3.0
- This function is for internal use only and may be renamed or removed in
- the future.
- """
-
- # This function was implemented following the
- # instructions found in the paper:
- # "FAST GENERATION OF RANDOM, STRONG RSA PRIMES"
- # by Robert D. Silverman
- # RSA Laboratories
- # May 17, 1997
- # which by the time of writing could be freely downloaded here:
- # http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.2713&rep=rep1&type=pdf
-
- if randfunc is None:
- randfunc = Random.get_random_bytes
-
- # Use the accelerator if available
- if _fastmath is not None:
- return _fastmath.getStrongPrime(long(N), long(e), false_positive_prob,
- randfunc)
-
- if (N < 512) or ((N % 128) != 0):
- raise ValueError ("bits must be multiple of 128 and > 512")
-
- rabin_miller_rounds = int(math.ceil(-math.log(false_positive_prob)/math.log(4)))
-
- # calculate range for X
- # lower_bound = sqrt(2) * 2^{511 + 128*x}
- # upper_bound = 2^{512 + 128*x} - 1
- x = (N - 512) >> 7;
- # We need to approximate the sqrt(2) in the lower_bound by an integer
- # expression because floating point math overflows with these numbers
- lower_bound = (14142135623730950489 * (2 ** (511 + 128*x))) // 10000000000000000000
- upper_bound = (1 << (512 + 128*x)) - 1
- # Randomly choose X in calculated range
- X = getRandomRange (lower_bound, upper_bound, randfunc)
-
- # generate p1 and p2
- p = [0, 0]
- for i in (0, 1):
- # randomly choose 101-bit y
- y = getRandomNBitInteger (101, randfunc)
- # initialize the field for sieving
- field = [0] * 5 * len (sieve_base)
- # sieve the field
- for prime in sieve_base:
- offset = y % prime
- for j in iter_range((prime - offset) % prime, len (field), prime):
- field[j] = 1
-
- # look for suitable p[i] starting at y
- result = 0
- for j in range(len(field)):
- composite = field[j]
- # look for next canidate
- if composite:
- continue
- tmp = y + j
- result = _rabinMillerTest (tmp, rabin_miller_rounds)
- if result > 0:
- p[i] = tmp
- break
- if result == 0:
- raise RuntimeError ("Couln't find prime in field. "
- "Developer: Increase field_size")
-
- # Calculate R
- # R = (p2^{-1} mod p1) * p2 - (p1^{-1} mod p2) * p1
- tmp1 = inverse (p[1], p[0]) * p[1] # (p2^-1 mod p1)*p2
- tmp2 = inverse (p[0], p[1]) * p[0] # (p1^-1 mod p2)*p1
- R = tmp1 - tmp2 # (p2^-1 mod p1)*p2 - (p1^-1 mod p2)*p1
-
- # search for final prime number starting by Y0
- # Y0 = X + (R - X mod p1p2)
- increment = p[0] * p[1]
- X = X + (R - (X % increment))
- while 1:
- is_possible_prime = 1
- # first check candidate against sieve_base
- for prime in sieve_base:
- if (X % prime) == 0:
- is_possible_prime = 0
- break
- # if e is given make sure that e and X-1 are coprime
- # this is not necessarily a strong prime criterion but useful when
- # creating them for RSA where the p-1 and q-1 should be coprime to
- # the public exponent e
- if e and is_possible_prime:
- if e & 1:
- if GCD(e, X-1) != 1:
- is_possible_prime = 0
- else:
- if GCD(e, (X-1) // 2) != 1:
- is_possible_prime = 0
-
- # do some Rabin-Miller-Tests
- if is_possible_prime:
- result = _rabinMillerTest (X, rabin_miller_rounds)
- if result > 0:
- break
- X += increment
- # abort when X has more bits than requested
- # TODO: maybe we shouldn't abort but rather start over.
- if X >= 1 << N:
- raise RuntimeError ("Couln't find prime in field. "
- "Developer: Increase field_size")
- return X
-
-def isPrime(N, false_positive_prob=1e-6, randfunc=None):
- r"""Test if a number *N* is a prime.
-
- Args:
- false_positive_prob (float):
- The statistical probability for the result not to be actually a
- prime. It defaults to 10\ :sup:`-6`.
- Note that the real probability of a false-positive is far less.
- This is just the mathematically provable limit.
- randfunc (callable):
- A function that takes a parameter *N* and that returns
- a random byte string of such length.
- If omitted, :func:`Crypto.Random.get_random_bytes` is used.
-
- Return:
- `True` is the input is indeed prime.
- """
-
- if randfunc is None:
- randfunc = Random.get_random_bytes
-
- if _fastmath is not None:
- return _fastmath.isPrime(long(N), false_positive_prob, randfunc)
-
- if N < 3 or N & 1 == 0:
- return N == 2
- for p in sieve_base:
- if N == p:
- return 1
- if N % p == 0:
- return 0
-
- rounds = int(math.ceil(-math.log(false_positive_prob)/math.log(4)))
- return _rabinMillerTest(N, rounds, randfunc)
-
-
-# Improved conversion functions contributed by Barry Warsaw, after
-# careful benchmarking
-
-import struct
-
-def long_to_bytes(n, blocksize=0):
- """Convert a positive integer to a byte string using big endian encoding.
-
- If :data:`blocksize` is absent or zero, the byte string will
- be of minimal length.
-
- Otherwise, the length of the byte string is guaranteed to be a multiple
- of :data:`blocksize`. If necessary, zeroes (``\\x00``) are added at the left.
-
- .. note::
- In Python 3, if you are sure that :data:`n` can fit into
- :data:`blocksize` bytes, you can simply use the native method instead::
-
- >>> n.to_bytes(blocksize, 'big')
-
- For instance::
-
- >>> n = 80
- >>> n.to_bytes(2, 'big')
- b'\\x00P'
-
- However, and unlike this ``long_to_bytes()`` function,
- an ``OverflowError`` exception is raised if :data:`n` does not fit.
- """
-
- if n < 0 or blocksize < 0:
- raise ValueError("Values must be non-negative")
-
- result = []
- pack = struct.pack
-
- # Fill the first block independently from the value of n
- bsr = blocksize
- while bsr >= 8:
- result.insert(0, pack('>Q', n & 0xFFFFFFFFFFFFFFFF))
- n = n >> 64
- bsr -= 8
-
- while bsr >= 4:
- result.insert(0, pack('>I', n & 0xFFFFFFFF))
- n = n >> 32
- bsr -= 4
-
- while bsr > 0:
- result.insert(0, pack('>B', n & 0xFF))
- n = n >> 8
- bsr -= 1
-
- if n == 0:
- if len(result) == 0:
- bresult = b'\x00'
- else:
- bresult = b''.join(result)
- else:
- # The encoded number exceeds the block size
- while n > 0:
- result.insert(0, pack('>Q', n & 0xFFFFFFFFFFFFFFFF))
- n = n >> 64
- result[0] = result[0].lstrip(b'\x00')
- bresult = b''.join(result)
- # bresult has minimum length here
- if blocksize > 0:
- target_len = ((len(bresult) - 1) // blocksize + 1) * blocksize
- bresult = b'\x00' * (target_len - len(bresult)) + bresult
-
- return bresult
-
-
-def bytes_to_long(s):
- """Convert a byte string to a long integer (big endian).
-
- In Python 3.2+, use the native method instead::
-
- >>> int.from_bytes(s, 'big')
-
- For instance::
-
- >>> int.from_bytes(b'\x00P', 'big')
- 80
-
- This is (essentially) the inverse of :func:`long_to_bytes`.
- """
- acc = 0
-
- unpack = struct.unpack
-
- # Up to Python 2.7.4, struct.unpack can't work with bytearrays nor
- # memoryviews
- if sys.version_info[0:3] < (2, 7, 4):
- if isinstance(s, bytearray):
- s = bytes(s)
- elif isinstance(s, memoryview):
- s = s.tobytes()
-
- length = len(s)
- if length % 4:
- extra = (4 - length % 4)
- s = b'\x00' * extra + s
- length = length + extra
- for i in range(0, length, 4):
- acc = (acc << 32) + unpack('>I', s[i:i+4])[0]
- return acc
-
-
-# For backwards compatibility...
-import warnings
-def long2str(n, blocksize=0):
- warnings.warn("long2str() has been replaced by long_to_bytes()")
- return long_to_bytes(n, blocksize)
-def str2long(s):
- warnings.warn("str2long() has been replaced by bytes_to_long()")
- return bytes_to_long(s)
-
-
-# The first 10000 primes used for checking primality.
-# This should be enough to eliminate most of the odd
-# numbers before needing to do a Rabin-Miller test at all.
-sieve_base = (
- 2, 3, 5, 7, 11, 13, 17, 19, 23, 29,
- 31, 37, 41, 43, 47, 53, 59, 61, 67, 71,
- 73, 79, 83, 89, 97, 101, 103, 107, 109, 113,
- 127, 131, 137, 139, 149, 151, 157, 163, 167, 173,
- 179, 181, 191, 193, 197, 199, 211, 223, 227, 229,
- 233, 239, 241, 251, 257, 263, 269, 271, 277, 281,
- 283, 293, 307, 311, 313, 317, 331, 337, 347, 349,
- 353, 359, 367, 373, 379, 383, 389, 397, 401, 409,
- 419, 421, 431, 433, 439, 443, 449, 457, 461, 463,
- 467, 479, 487, 491, 499, 503, 509, 521, 523, 541,
- 547, 557, 563, 569, 571, 577, 587, 593, 599, 601,
- 607, 613, 617, 619, 631, 641, 643, 647, 653, 659,
- 661, 673, 677, 683, 691, 701, 709, 719, 727, 733,
- 739, 743, 751, 757, 761, 769, 773, 787, 797, 809,
- 811, 821, 823, 827, 829, 839, 853, 857, 859, 863,
- 877, 881, 883, 887, 907, 911, 919, 929, 937, 941,
- 947, 953, 967, 971, 977, 983, 991, 997, 1009, 1013,
- 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061, 1063, 1069,
- 1087, 1091, 1093, 1097, 1103, 1109, 1117, 1123, 1129, 1151,
- 1153, 1163, 1171, 1181, 1187, 1193, 1201, 1213, 1217, 1223,
- 1229, 1231, 1237, 1249, 1259, 1277, 1279, 1283, 1289, 1291,
- 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373,
- 1381, 1399, 1409, 1423, 1427, 1429, 1433, 1439, 1447, 1451,
- 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511,
- 1523, 1531, 1543, 1549, 1553, 1559, 1567, 1571, 1579, 1583,
- 1597, 1601, 1607, 1609, 1613, 1619, 1621, 1627, 1637, 1657,
- 1663, 1667, 1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733,
- 1741, 1747, 1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811,
- 1823, 1831, 1847, 1861, 1867, 1871, 1873, 1877, 1879, 1889,
- 1901, 1907, 1913, 1931, 1933, 1949, 1951, 1973, 1979, 1987,
- 1993, 1997, 1999, 2003, 2011, 2017, 2027, 2029, 2039, 2053,
- 2063, 2069, 2081, 2083, 2087, 2089, 2099, 2111, 2113, 2129,
- 2131, 2137, 2141, 2143, 2153, 2161, 2179, 2203, 2207, 2213,
- 2221, 2237, 2239, 2243, 2251, 2267, 2269, 2273, 2281, 2287,
- 2293, 2297, 2309, 2311, 2333, 2339, 2341, 2347, 2351, 2357,
- 2371, 2377, 2381, 2383, 2389, 2393, 2399, 2411, 2417, 2423,
- 2437, 2441, 2447, 2459, 2467, 2473, 2477, 2503, 2521, 2531,
- 2539, 2543, 2549, 2551, 2557, 2579, 2591, 2593, 2609, 2617,
- 2621, 2633, 2647, 2657, 2659, 2663, 2671, 2677, 2683, 2687,
- 2689, 2693, 2699, 2707, 2711, 2713, 2719, 2729, 2731, 2741,
- 2749, 2753, 2767, 2777, 2789, 2791, 2797, 2801, 2803, 2819,
- 2833, 2837, 2843, 2851, 2857, 2861, 2879, 2887, 2897, 2903,
- 2909, 2917, 2927, 2939, 2953, 2957, 2963, 2969, 2971, 2999,
- 3001, 3011, 3019, 3023, 3037, 3041, 3049, 3061, 3067, 3079,
- 3083, 3089, 3109, 3119, 3121, 3137, 3163, 3167, 3169, 3181,
- 3187, 3191, 3203, 3209, 3217, 3221, 3229, 3251, 3253, 3257,
- 3259, 3271, 3299, 3301, 3307, 3313, 3319, 3323, 3329, 3331,
- 3343, 3347, 3359, 3361, 3371, 3373, 3389, 3391, 3407, 3413,
- 3433, 3449, 3457, 3461, 3463, 3467, 3469, 3491, 3499, 3511,
- 3517, 3527, 3529, 3533, 3539, 3541, 3547, 3557, 3559, 3571,
- 3581, 3583, 3593, 3607, 3613, 3617, 3623, 3631, 3637, 3643,
- 3659, 3671, 3673, 3677, 3691, 3697, 3701, 3709, 3719, 3727,
- 3733, 3739, 3761, 3767, 3769, 3779, 3793, 3797, 3803, 3821,
- 3823, 3833, 3847, 3851, 3853, 3863, 3877, 3881, 3889, 3907,
- 3911, 3917, 3919, 3923, 3929, 3931, 3943, 3947, 3967, 3989,
- 4001, 4003, 4007, 4013, 4019, 4021, 4027, 4049, 4051, 4057,
- 4073, 4079, 4091, 4093, 4099, 4111, 4127, 4129, 4133, 4139,
- 4153, 4157, 4159, 4177, 4201, 4211, 4217, 4219, 4229, 4231,
- 4241, 4243, 4253, 4259, 4261, 4271, 4273, 4283, 4289, 4297,
- 4327, 4337, 4339, 4349, 4357, 4363, 4373, 4391, 4397, 4409,
- 4421, 4423, 4441, 4447, 4451, 4457, 4463, 4481, 4483, 4493,
- 4507, 4513, 4517, 4519, 4523, 4547, 4549, 4561, 4567, 4583,
- 4591, 4597, 4603, 4621, 4637, 4639, 4643, 4649, 4651, 4657,
- 4663, 4673, 4679, 4691, 4703, 4721, 4723, 4729, 4733, 4751,
- 4759, 4783, 4787, 4789, 4793, 4799, 4801, 4813, 4817, 4831,
- 4861, 4871, 4877, 4889, 4903, 4909, 4919, 4931, 4933, 4937,
- 4943, 4951, 4957, 4967, 4969, 4973, 4987, 4993, 4999, 5003,
- 5009, 5011, 5021, 5023, 5039, 5051, 5059, 5077, 5081, 5087,
- 5099, 5101, 5107, 5113, 5119, 5147, 5153, 5167, 5171, 5179,
- 5189, 5197, 5209, 5227, 5231, 5233, 5237, 5261, 5273, 5279,
- 5281, 5297, 5303, 5309, 5323, 5333, 5347, 5351, 5381, 5387,
- 5393, 5399, 5407, 5413, 5417, 5419, 5431, 5437, 5441, 5443,
- 5449, 5471, 5477, 5479, 5483, 5501, 5503, 5507, 5519, 5521,
- 5527, 5531, 5557, 5563, 5569, 5573, 5581, 5591, 5623, 5639,
- 5641, 5647, 5651, 5653, 5657, 5659, 5669, 5683, 5689, 5693,
- 5701, 5711, 5717, 5737, 5741, 5743, 5749, 5779, 5783, 5791,
- 5801, 5807, 5813, 5821, 5827, 5839, 5843, 5849, 5851, 5857,
- 5861, 5867, 5869, 5879, 5881, 5897, 5903, 5923, 5927, 5939,
- 5953, 5981, 5987, 6007, 6011, 6029, 6037, 6043, 6047, 6053,
- 6067, 6073, 6079, 6089, 6091, 6101, 6113, 6121, 6131, 6133,
- 6143, 6151, 6163, 6173, 6197, 6199, 6203, 6211, 6217, 6221,
- 6229, 6247, 6257, 6263, 6269, 6271, 6277, 6287, 6299, 6301,
- 6311, 6317, 6323, 6329, 6337, 6343, 6353, 6359, 6361, 6367,
- 6373, 6379, 6389, 6397, 6421, 6427, 6449, 6451, 6469, 6473,
- 6481, 6491, 6521, 6529, 6547, 6551, 6553, 6563, 6569, 6571,
- 6577, 6581, 6599, 6607, 6619, 6637, 6653, 6659, 6661, 6673,
- 6679, 6689, 6691, 6701, 6703, 6709, 6719, 6733, 6737, 6761,
- 6763, 6779, 6781, 6791, 6793, 6803, 6823, 6827, 6829, 6833,
- 6841, 6857, 6863, 6869, 6871, 6883, 6899, 6907, 6911, 6917,
- 6947, 6949, 6959, 6961, 6967, 6971, 6977, 6983, 6991, 6997,
- 7001, 7013, 7019, 7027, 7039, 7043, 7057, 7069, 7079, 7103,
- 7109, 7121, 7127, 7129, 7151, 7159, 7177, 7187, 7193, 7207,
- 7211, 7213, 7219, 7229, 7237, 7243, 7247, 7253, 7283, 7297,
- 7307, 7309, 7321, 7331, 7333, 7349, 7351, 7369, 7393, 7411,
- 7417, 7433, 7451, 7457, 7459, 7477, 7481, 7487, 7489, 7499,
- 7507, 7517, 7523, 7529, 7537, 7541, 7547, 7549, 7559, 7561,
- 7573, 7577, 7583, 7589, 7591, 7603, 7607, 7621, 7639, 7643,
- 7649, 7669, 7673, 7681, 7687, 7691, 7699, 7703, 7717, 7723,
- 7727, 7741, 7753, 7757, 7759, 7789, 7793, 7817, 7823, 7829,
- 7841, 7853, 7867, 7873, 7877, 7879, 7883, 7901, 7907, 7919,
- 7927, 7933, 7937, 7949, 7951, 7963, 7993, 8009, 8011, 8017,
- 8039, 8053, 8059, 8069, 8081, 8087, 8089, 8093, 8101, 8111,
- 8117, 8123, 8147, 8161, 8167, 8171, 8179, 8191, 8209, 8219,
- 8221, 8231, 8233, 8237, 8243, 8263, 8269, 8273, 8287, 8291,
- 8293, 8297, 8311, 8317, 8329, 8353, 8363, 8369, 8377, 8387,
- 8389, 8419, 8423, 8429, 8431, 8443, 8447, 8461, 8467, 8501,
- 8513, 8521, 8527, 8537, 8539, 8543, 8563, 8573, 8581, 8597,
- 8599, 8609, 8623, 8627, 8629, 8641, 8647, 8663, 8669, 8677,
- 8681, 8689, 8693, 8699, 8707, 8713, 8719, 8731, 8737, 8741,
- 8747, 8753, 8761, 8779, 8783, 8803, 8807, 8819, 8821, 8831,
- 8837, 8839, 8849, 8861, 8863, 8867, 8887, 8893, 8923, 8929,
- 8933, 8941, 8951, 8963, 8969, 8971, 8999, 9001, 9007, 9011,
- 9013, 9029, 9041, 9043, 9049, 9059, 9067, 9091, 9103, 9109,
- 9127, 9133, 9137, 9151, 9157, 9161, 9173, 9181, 9187, 9199,
- 9203, 9209, 9221, 9227, 9239, 9241, 9257, 9277, 9281, 9283,
- 9293, 9311, 9319, 9323, 9337, 9341, 9343, 9349, 9371, 9377,
- 9391, 9397, 9403, 9413, 9419, 9421, 9431, 9433, 9437, 9439,
- 9461, 9463, 9467, 9473, 9479, 9491, 9497, 9511, 9521, 9533,
- 9539, 9547, 9551, 9587, 9601, 9613, 9619, 9623, 9629, 9631,
- 9643, 9649, 9661, 9677, 9679, 9689, 9697, 9719, 9721, 9733,
- 9739, 9743, 9749, 9767, 9769, 9781, 9787, 9791, 9803, 9811,
- 9817, 9829, 9833, 9839, 9851, 9857, 9859, 9871, 9883, 9887,
- 9901, 9907, 9923, 9929, 9931, 9941, 9949, 9967, 9973, 10007,
- 10009, 10037, 10039, 10061, 10067, 10069, 10079, 10091, 10093, 10099,
- 10103, 10111, 10133, 10139, 10141, 10151, 10159, 10163, 10169, 10177,
- 10181, 10193, 10211, 10223, 10243, 10247, 10253, 10259, 10267, 10271,
- 10273, 10289, 10301, 10303, 10313, 10321, 10331, 10333, 10337, 10343,
- 10357, 10369, 10391, 10399, 10427, 10429, 10433, 10453, 10457, 10459,
- 10463, 10477, 10487, 10499, 10501, 10513, 10529, 10531, 10559, 10567,
- 10589, 10597, 10601, 10607, 10613, 10627, 10631, 10639, 10651, 10657,
- 10663, 10667, 10687, 10691, 10709, 10711, 10723, 10729, 10733, 10739,
- 10753, 10771, 10781, 10789, 10799, 10831, 10837, 10847, 10853, 10859,
- 10861, 10867, 10883, 10889, 10891, 10903, 10909, 10937, 10939, 10949,
- 10957, 10973, 10979, 10987, 10993, 11003, 11027, 11047, 11057, 11059,
- 11069, 11071, 11083, 11087, 11093, 11113, 11117, 11119, 11131, 11149,
- 11159, 11161, 11171, 11173, 11177, 11197, 11213, 11239, 11243, 11251,
- 11257, 11261, 11273, 11279, 11287, 11299, 11311, 11317, 11321, 11329,
- 11351, 11353, 11369, 11383, 11393, 11399, 11411, 11423, 11437, 11443,
- 11447, 11467, 11471, 11483, 11489, 11491, 11497, 11503, 11519, 11527,
- 11549, 11551, 11579, 11587, 11593, 11597, 11617, 11621, 11633, 11657,
- 11677, 11681, 11689, 11699, 11701, 11717, 11719, 11731, 11743, 11777,
- 11779, 11783, 11789, 11801, 11807, 11813, 11821, 11827, 11831, 11833,
- 11839, 11863, 11867, 11887, 11897, 11903, 11909, 11923, 11927, 11933,
- 11939, 11941, 11953, 11959, 11969, 11971, 11981, 11987, 12007, 12011,
- 12037, 12041, 12043, 12049, 12071, 12073, 12097, 12101, 12107, 12109,
- 12113, 12119, 12143, 12149, 12157, 12161, 12163, 12197, 12203, 12211,
- 12227, 12239, 12241, 12251, 12253, 12263, 12269, 12277, 12281, 12289,
- 12301, 12323, 12329, 12343, 12347, 12373, 12377, 12379, 12391, 12401,
- 12409, 12413, 12421, 12433, 12437, 12451, 12457, 12473, 12479, 12487,
- 12491, 12497, 12503, 12511, 12517, 12527, 12539, 12541, 12547, 12553,
- 12569, 12577, 12583, 12589, 12601, 12611, 12613, 12619, 12637, 12641,
- 12647, 12653, 12659, 12671, 12689, 12697, 12703, 12713, 12721, 12739,
- 12743, 12757, 12763, 12781, 12791, 12799, 12809, 12821, 12823, 12829,
- 12841, 12853, 12889, 12893, 12899, 12907, 12911, 12917, 12919, 12923,
- 12941, 12953, 12959, 12967, 12973, 12979, 12983, 13001, 13003, 13007,
- 13009, 13033, 13037, 13043, 13049, 13063, 13093, 13099, 13103, 13109,
- 13121, 13127, 13147, 13151, 13159, 13163, 13171, 13177, 13183, 13187,
- 13217, 13219, 13229, 13241, 13249, 13259, 13267, 13291, 13297, 13309,
- 13313, 13327, 13331, 13337, 13339, 13367, 13381, 13397, 13399, 13411,
- 13417, 13421, 13441, 13451, 13457, 13463, 13469, 13477, 13487, 13499,
- 13513, 13523, 13537, 13553, 13567, 13577, 13591, 13597, 13613, 13619,
- 13627, 13633, 13649, 13669, 13679, 13681, 13687, 13691, 13693, 13697,
- 13709, 13711, 13721, 13723, 13729, 13751, 13757, 13759, 13763, 13781,
- 13789, 13799, 13807, 13829, 13831, 13841, 13859, 13873, 13877, 13879,
- 13883, 13901, 13903, 13907, 13913, 13921, 13931, 13933, 13963, 13967,
- 13997, 13999, 14009, 14011, 14029, 14033, 14051, 14057, 14071, 14081,
- 14083, 14087, 14107, 14143, 14149, 14153, 14159, 14173, 14177, 14197,
- 14207, 14221, 14243, 14249, 14251, 14281, 14293, 14303, 14321, 14323,
- 14327, 14341, 14347, 14369, 14387, 14389, 14401, 14407, 14411, 14419,
- 14423, 14431, 14437, 14447, 14449, 14461, 14479, 14489, 14503, 14519,
- 14533, 14537, 14543, 14549, 14551, 14557, 14561, 14563, 14591, 14593,
- 14621, 14627, 14629, 14633, 14639, 14653, 14657, 14669, 14683, 14699,
- 14713, 14717, 14723, 14731, 14737, 14741, 14747, 14753, 14759, 14767,
- 14771, 14779, 14783, 14797, 14813, 14821, 14827, 14831, 14843, 14851,
- 14867, 14869, 14879, 14887, 14891, 14897, 14923, 14929, 14939, 14947,
- 14951, 14957, 14969, 14983, 15013, 15017, 15031, 15053, 15061, 15073,
- 15077, 15083, 15091, 15101, 15107, 15121, 15131, 15137, 15139, 15149,
- 15161, 15173, 15187, 15193, 15199, 15217, 15227, 15233, 15241, 15259,
- 15263, 15269, 15271, 15277, 15287, 15289, 15299, 15307, 15313, 15319,
- 15329, 15331, 15349, 15359, 15361, 15373, 15377, 15383, 15391, 15401,
- 15413, 15427, 15439, 15443, 15451, 15461, 15467, 15473, 15493, 15497,
- 15511, 15527, 15541, 15551, 15559, 15569, 15581, 15583, 15601, 15607,
- 15619, 15629, 15641, 15643, 15647, 15649, 15661, 15667, 15671, 15679,
- 15683, 15727, 15731, 15733, 15737, 15739, 15749, 15761, 15767, 15773,
- 15787, 15791, 15797, 15803, 15809, 15817, 15823, 15859, 15877, 15881,
- 15887, 15889, 15901, 15907, 15913, 15919, 15923, 15937, 15959, 15971,
- 15973, 15991, 16001, 16007, 16033, 16057, 16061, 16063, 16067, 16069,
- 16073, 16087, 16091, 16097, 16103, 16111, 16127, 16139, 16141, 16183,
- 16187, 16189, 16193, 16217, 16223, 16229, 16231, 16249, 16253, 16267,
- 16273, 16301, 16319, 16333, 16339, 16349, 16361, 16363, 16369, 16381,
- 16411, 16417, 16421, 16427, 16433, 16447, 16451, 16453, 16477, 16481,
- 16487, 16493, 16519, 16529, 16547, 16553, 16561, 16567, 16573, 16603,
- 16607, 16619, 16631, 16633, 16649, 16651, 16657, 16661, 16673, 16691,
- 16693, 16699, 16703, 16729, 16741, 16747, 16759, 16763, 16787, 16811,
- 16823, 16829, 16831, 16843, 16871, 16879, 16883, 16889, 16901, 16903,
- 16921, 16927, 16931, 16937, 16943, 16963, 16979, 16981, 16987, 16993,
- 17011, 17021, 17027, 17029, 17033, 17041, 17047, 17053, 17077, 17093,
- 17099, 17107, 17117, 17123, 17137, 17159, 17167, 17183, 17189, 17191,
- 17203, 17207, 17209, 17231, 17239, 17257, 17291, 17293, 17299, 17317,
- 17321, 17327, 17333, 17341, 17351, 17359, 17377, 17383, 17387, 17389,
- 17393, 17401, 17417, 17419, 17431, 17443, 17449, 17467, 17471, 17477,
- 17483, 17489, 17491, 17497, 17509, 17519, 17539, 17551, 17569, 17573,
- 17579, 17581, 17597, 17599, 17609, 17623, 17627, 17657, 17659, 17669,
- 17681, 17683, 17707, 17713, 17729, 17737, 17747, 17749, 17761, 17783,
- 17789, 17791, 17807, 17827, 17837, 17839, 17851, 17863, 17881, 17891,
- 17903, 17909, 17911, 17921, 17923, 17929, 17939, 17957, 17959, 17971,
- 17977, 17981, 17987, 17989, 18013, 18041, 18043, 18047, 18049, 18059,
- 18061, 18077, 18089, 18097, 18119, 18121, 18127, 18131, 18133, 18143,
- 18149, 18169, 18181, 18191, 18199, 18211, 18217, 18223, 18229, 18233,
- 18251, 18253, 18257, 18269, 18287, 18289, 18301, 18307, 18311, 18313,
- 18329, 18341, 18353, 18367, 18371, 18379, 18397, 18401, 18413, 18427,
- 18433, 18439, 18443, 18451, 18457, 18461, 18481, 18493, 18503, 18517,
- 18521, 18523, 18539, 18541, 18553, 18583, 18587, 18593, 18617, 18637,
- 18661, 18671, 18679, 18691, 18701, 18713, 18719, 18731, 18743, 18749,
- 18757, 18773, 18787, 18793, 18797, 18803, 18839, 18859, 18869, 18899,
- 18911, 18913, 18917, 18919, 18947, 18959, 18973, 18979, 19001, 19009,
- 19013, 19031, 19037, 19051, 19069, 19073, 19079, 19081, 19087, 19121,
- 19139, 19141, 19157, 19163, 19181, 19183, 19207, 19211, 19213, 19219,
- 19231, 19237, 19249, 19259, 19267, 19273, 19289, 19301, 19309, 19319,
- 19333, 19373, 19379, 19381, 19387, 19391, 19403, 19417, 19421, 19423,
- 19427, 19429, 19433, 19441, 19447, 19457, 19463, 19469, 19471, 19477,
- 19483, 19489, 19501, 19507, 19531, 19541, 19543, 19553, 19559, 19571,
- 19577, 19583, 19597, 19603, 19609, 19661, 19681, 19687, 19697, 19699,
- 19709, 19717, 19727, 19739, 19751, 19753, 19759, 19763, 19777, 19793,
- 19801, 19813, 19819, 19841, 19843, 19853, 19861, 19867, 19889, 19891,
- 19913, 19919, 19927, 19937, 19949, 19961, 19963, 19973, 19979, 19991,
- 19993, 19997, 20011, 20021, 20023, 20029, 20047, 20051, 20063, 20071,
- 20089, 20101, 20107, 20113, 20117, 20123, 20129, 20143, 20147, 20149,
- 20161, 20173, 20177, 20183, 20201, 20219, 20231, 20233, 20249, 20261,
- 20269, 20287, 20297, 20323, 20327, 20333, 20341, 20347, 20353, 20357,
- 20359, 20369, 20389, 20393, 20399, 20407, 20411, 20431, 20441, 20443,
- 20477, 20479, 20483, 20507, 20509, 20521, 20533, 20543, 20549, 20551,
- 20563, 20593, 20599, 20611, 20627, 20639, 20641, 20663, 20681, 20693,
- 20707, 20717, 20719, 20731, 20743, 20747, 20749, 20753, 20759, 20771,
- 20773, 20789, 20807, 20809, 20849, 20857, 20873, 20879, 20887, 20897,
- 20899, 20903, 20921, 20929, 20939, 20947, 20959, 20963, 20981, 20983,
- 21001, 21011, 21013, 21017, 21019, 21023, 21031, 21059, 21061, 21067,
- 21089, 21101, 21107, 21121, 21139, 21143, 21149, 21157, 21163, 21169,
- 21179, 21187, 21191, 21193, 21211, 21221, 21227, 21247, 21269, 21277,
- 21283, 21313, 21317, 21319, 21323, 21341, 21347, 21377, 21379, 21383,
- 21391, 21397, 21401, 21407, 21419, 21433, 21467, 21481, 21487, 21491,
- 21493, 21499, 21503, 21517, 21521, 21523, 21529, 21557, 21559, 21563,
- 21569, 21577, 21587, 21589, 21599, 21601, 21611, 21613, 21617, 21647,
- 21649, 21661, 21673, 21683, 21701, 21713, 21727, 21737, 21739, 21751,
- 21757, 21767, 21773, 21787, 21799, 21803, 21817, 21821, 21839, 21841,
- 21851, 21859, 21863, 21871, 21881, 21893, 21911, 21929, 21937, 21943,
- 21961, 21977, 21991, 21997, 22003, 22013, 22027, 22031, 22037, 22039,
- 22051, 22063, 22067, 22073, 22079, 22091, 22093, 22109, 22111, 22123,
- 22129, 22133, 22147, 22153, 22157, 22159, 22171, 22189, 22193, 22229,
- 22247, 22259, 22271, 22273, 22277, 22279, 22283, 22291, 22303, 22307,
- 22343, 22349, 22367, 22369, 22381, 22391, 22397, 22409, 22433, 22441,
- 22447, 22453, 22469, 22481, 22483, 22501, 22511, 22531, 22541, 22543,
- 22549, 22567, 22571, 22573, 22613, 22619, 22621, 22637, 22639, 22643,
- 22651, 22669, 22679, 22691, 22697, 22699, 22709, 22717, 22721, 22727,
- 22739, 22741, 22751, 22769, 22777, 22783, 22787, 22807, 22811, 22817,
- 22853, 22859, 22861, 22871, 22877, 22901, 22907, 22921, 22937, 22943,
- 22961, 22963, 22973, 22993, 23003, 23011, 23017, 23021, 23027, 23029,
- 23039, 23041, 23053, 23057, 23059, 23063, 23071, 23081, 23087, 23099,
- 23117, 23131, 23143, 23159, 23167, 23173, 23189, 23197, 23201, 23203,
- 23209, 23227, 23251, 23269, 23279, 23291, 23293, 23297, 23311, 23321,
- 23327, 23333, 23339, 23357, 23369, 23371, 23399, 23417, 23431, 23447,
- 23459, 23473, 23497, 23509, 23531, 23537, 23539, 23549, 23557, 23561,
- 23563, 23567, 23581, 23593, 23599, 23603, 23609, 23623, 23627, 23629,
- 23633, 23663, 23669, 23671, 23677, 23687, 23689, 23719, 23741, 23743,
- 23747, 23753, 23761, 23767, 23773, 23789, 23801, 23813, 23819, 23827,
- 23831, 23833, 23857, 23869, 23873, 23879, 23887, 23893, 23899, 23909,
- 23911, 23917, 23929, 23957, 23971, 23977, 23981, 23993, 24001, 24007,
- 24019, 24023, 24029, 24043, 24049, 24061, 24071, 24077, 24083, 24091,
- 24097, 24103, 24107, 24109, 24113, 24121, 24133, 24137, 24151, 24169,
- 24179, 24181, 24197, 24203, 24223, 24229, 24239, 24247, 24251, 24281,
- 24317, 24329, 24337, 24359, 24371, 24373, 24379, 24391, 24407, 24413,
- 24419, 24421, 24439, 24443, 24469, 24473, 24481, 24499, 24509, 24517,
- 24527, 24533, 24547, 24551, 24571, 24593, 24611, 24623, 24631, 24659,
- 24671, 24677, 24683, 24691, 24697, 24709, 24733, 24749, 24763, 24767,
- 24781, 24793, 24799, 24809, 24821, 24841, 24847, 24851, 24859, 24877,
- 24889, 24907, 24917, 24919, 24923, 24943, 24953, 24967, 24971, 24977,
- 24979, 24989, 25013, 25031, 25033, 25037, 25057, 25073, 25087, 25097,
- 25111, 25117, 25121, 25127, 25147, 25153, 25163, 25169, 25171, 25183,
- 25189, 25219, 25229, 25237, 25243, 25247, 25253, 25261, 25301, 25303,
- 25307, 25309, 25321, 25339, 25343, 25349, 25357, 25367, 25373, 25391,
- 25409, 25411, 25423, 25439, 25447, 25453, 25457, 25463, 25469, 25471,
- 25523, 25537, 25541, 25561, 25577, 25579, 25583, 25589, 25601, 25603,
- 25609, 25621, 25633, 25639, 25643, 25657, 25667, 25673, 25679, 25693,
- 25703, 25717, 25733, 25741, 25747, 25759, 25763, 25771, 25793, 25799,
- 25801, 25819, 25841, 25847, 25849, 25867, 25873, 25889, 25903, 25913,
- 25919, 25931, 25933, 25939, 25943, 25951, 25969, 25981, 25997, 25999,
- 26003, 26017, 26021, 26029, 26041, 26053, 26083, 26099, 26107, 26111,
- 26113, 26119, 26141, 26153, 26161, 26171, 26177, 26183, 26189, 26203,
- 26209, 26227, 26237, 26249, 26251, 26261, 26263, 26267, 26293, 26297,
- 26309, 26317, 26321, 26339, 26347, 26357, 26371, 26387, 26393, 26399,
- 26407, 26417, 26423, 26431, 26437, 26449, 26459, 26479, 26489, 26497,
- 26501, 26513, 26539, 26557, 26561, 26573, 26591, 26597, 26627, 26633,
- 26641, 26647, 26669, 26681, 26683, 26687, 26693, 26699, 26701, 26711,
- 26713, 26717, 26723, 26729, 26731, 26737, 26759, 26777, 26783, 26801,
- 26813, 26821, 26833, 26839, 26849, 26861, 26863, 26879, 26881, 26891,
- 26893, 26903, 26921, 26927, 26947, 26951, 26953, 26959, 26981, 26987,
- 26993, 27011, 27017, 27031, 27043, 27059, 27061, 27067, 27073, 27077,
- 27091, 27103, 27107, 27109, 27127, 27143, 27179, 27191, 27197, 27211,
- 27239, 27241, 27253, 27259, 27271, 27277, 27281, 27283, 27299, 27329,
- 27337, 27361, 27367, 27397, 27407, 27409, 27427, 27431, 27437, 27449,
- 27457, 27479, 27481, 27487, 27509, 27527, 27529, 27539, 27541, 27551,
- 27581, 27583, 27611, 27617, 27631, 27647, 27653, 27673, 27689, 27691,
- 27697, 27701, 27733, 27737, 27739, 27743, 27749, 27751, 27763, 27767,
- 27773, 27779, 27791, 27793, 27799, 27803, 27809, 27817, 27823, 27827,
- 27847, 27851, 27883, 27893, 27901, 27917, 27919, 27941, 27943, 27947,
- 27953, 27961, 27967, 27983, 27997, 28001, 28019, 28027, 28031, 28051,
- 28057, 28069, 28081, 28087, 28097, 28099, 28109, 28111, 28123, 28151,
- 28163, 28181, 28183, 28201, 28211, 28219, 28229, 28277, 28279, 28283,
- 28289, 28297, 28307, 28309, 28319, 28349, 28351, 28387, 28393, 28403,
- 28409, 28411, 28429, 28433, 28439, 28447, 28463, 28477, 28493, 28499,
- 28513, 28517, 28537, 28541, 28547, 28549, 28559, 28571, 28573, 28579,
- 28591, 28597, 28603, 28607, 28619, 28621, 28627, 28631, 28643, 28649,
- 28657, 28661, 28663, 28669, 28687, 28697, 28703, 28711, 28723, 28729,
- 28751, 28753, 28759, 28771, 28789, 28793, 28807, 28813, 28817, 28837,
- 28843, 28859, 28867, 28871, 28879, 28901, 28909, 28921, 28927, 28933,
- 28949, 28961, 28979, 29009, 29017, 29021, 29023, 29027, 29033, 29059,
- 29063, 29077, 29101, 29123, 29129, 29131, 29137, 29147, 29153, 29167,
- 29173, 29179, 29191, 29201, 29207, 29209, 29221, 29231, 29243, 29251,
- 29269, 29287, 29297, 29303, 29311, 29327, 29333, 29339, 29347, 29363,
- 29383, 29387, 29389, 29399, 29401, 29411, 29423, 29429, 29437, 29443,
- 29453, 29473, 29483, 29501, 29527, 29531, 29537, 29567, 29569, 29573,
- 29581, 29587, 29599, 29611, 29629, 29633, 29641, 29663, 29669, 29671,
- 29683, 29717, 29723, 29741, 29753, 29759, 29761, 29789, 29803, 29819,
- 29833, 29837, 29851, 29863, 29867, 29873, 29879, 29881, 29917, 29921,
- 29927, 29947, 29959, 29983, 29989, 30011, 30013, 30029, 30047, 30059,
- 30071, 30089, 30091, 30097, 30103, 30109, 30113, 30119, 30133, 30137,
- 30139, 30161, 30169, 30181, 30187, 30197, 30203, 30211, 30223, 30241,
- 30253, 30259, 30269, 30271, 30293, 30307, 30313, 30319, 30323, 30341,
- 30347, 30367, 30389, 30391, 30403, 30427, 30431, 30449, 30467, 30469,
- 30491, 30493, 30497, 30509, 30517, 30529, 30539, 30553, 30557, 30559,
- 30577, 30593, 30631, 30637, 30643, 30649, 30661, 30671, 30677, 30689,
- 30697, 30703, 30707, 30713, 30727, 30757, 30763, 30773, 30781, 30803,
- 30809, 30817, 30829, 30839, 30841, 30851, 30853, 30859, 30869, 30871,
- 30881, 30893, 30911, 30931, 30937, 30941, 30949, 30971, 30977, 30983,
- 31013, 31019, 31033, 31039, 31051, 31063, 31069, 31079, 31081, 31091,
- 31121, 31123, 31139, 31147, 31151, 31153, 31159, 31177, 31181, 31183,
- 31189, 31193, 31219, 31223, 31231, 31237, 31247, 31249, 31253, 31259,
- 31267, 31271, 31277, 31307, 31319, 31321, 31327, 31333, 31337, 31357,
- 31379, 31387, 31391, 31393, 31397, 31469, 31477, 31481, 31489, 31511,
- 31513, 31517, 31531, 31541, 31543, 31547, 31567, 31573, 31583, 31601,
- 31607, 31627, 31643, 31649, 31657, 31663, 31667, 31687, 31699, 31721,
- 31723, 31727, 31729, 31741, 31751, 31769, 31771, 31793, 31799, 31817,
- 31847, 31849, 31859, 31873, 31883, 31891, 31907, 31957, 31963, 31973,
- 31981, 31991, 32003, 32009, 32027, 32029, 32051, 32057, 32059, 32063,
- 32069, 32077, 32083, 32089, 32099, 32117, 32119, 32141, 32143, 32159,
- 32173, 32183, 32189, 32191, 32203, 32213, 32233, 32237, 32251, 32257,
- 32261, 32297, 32299, 32303, 32309, 32321, 32323, 32327, 32341, 32353,
- 32359, 32363, 32369, 32371, 32377, 32381, 32401, 32411, 32413, 32423,
- 32429, 32441, 32443, 32467, 32479, 32491, 32497, 32503, 32507, 32531,
- 32533, 32537, 32561, 32563, 32569, 32573, 32579, 32587, 32603, 32609,
- 32611, 32621, 32633, 32647, 32653, 32687, 32693, 32707, 32713, 32717,
- 32719, 32749, 32771, 32779, 32783, 32789, 32797, 32801, 32803, 32831,
- 32833, 32839, 32843, 32869, 32887, 32909, 32911, 32917, 32933, 32939,
- 32941, 32957, 32969, 32971, 32983, 32987, 32993, 32999, 33013, 33023,
- 33029, 33037, 33049, 33053, 33071, 33073, 33083, 33091, 33107, 33113,
- 33119, 33149, 33151, 33161, 33179, 33181, 33191, 33199, 33203, 33211,
- 33223, 33247, 33287, 33289, 33301, 33311, 33317, 33329, 33331, 33343,
- 33347, 33349, 33353, 33359, 33377, 33391, 33403, 33409, 33413, 33427,
- 33457, 33461, 33469, 33479, 33487, 33493, 33503, 33521, 33529, 33533,
- 33547, 33563, 33569, 33577, 33581, 33587, 33589, 33599, 33601, 33613,
- 33617, 33619, 33623, 33629, 33637, 33641, 33647, 33679, 33703, 33713,
- 33721, 33739, 33749, 33751, 33757, 33767, 33769, 33773, 33791, 33797,
- 33809, 33811, 33827, 33829, 33851, 33857, 33863, 33871, 33889, 33893,
- 33911, 33923, 33931, 33937, 33941, 33961, 33967, 33997, 34019, 34031,
- 34033, 34039, 34057, 34061, 34123, 34127, 34129, 34141, 34147, 34157,
- 34159, 34171, 34183, 34211, 34213, 34217, 34231, 34253, 34259, 34261,
- 34267, 34273, 34283, 34297, 34301, 34303, 34313, 34319, 34327, 34337,
- 34351, 34361, 34367, 34369, 34381, 34403, 34421, 34429, 34439, 34457,
- 34469, 34471, 34483, 34487, 34499, 34501, 34511, 34513, 34519, 34537,
- 34543, 34549, 34583, 34589, 34591, 34603, 34607, 34613, 34631, 34649,
- 34651, 34667, 34673, 34679, 34687, 34693, 34703, 34721, 34729, 34739,
- 34747, 34757, 34759, 34763, 34781, 34807, 34819, 34841, 34843, 34847,
- 34849, 34871, 34877, 34883, 34897, 34913, 34919, 34939, 34949, 34961,
- 34963, 34981, 35023, 35027, 35051, 35053, 35059, 35069, 35081, 35083,
- 35089, 35099, 35107, 35111, 35117, 35129, 35141, 35149, 35153, 35159,
- 35171, 35201, 35221, 35227, 35251, 35257, 35267, 35279, 35281, 35291,
- 35311, 35317, 35323, 35327, 35339, 35353, 35363, 35381, 35393, 35401,
- 35407, 35419, 35423, 35437, 35447, 35449, 35461, 35491, 35507, 35509,
- 35521, 35527, 35531, 35533, 35537, 35543, 35569, 35573, 35591, 35593,
- 35597, 35603, 35617, 35671, 35677, 35729, 35731, 35747, 35753, 35759,
- 35771, 35797, 35801, 35803, 35809, 35831, 35837, 35839, 35851, 35863,
- 35869, 35879, 35897, 35899, 35911, 35923, 35933, 35951, 35963, 35969,
- 35977, 35983, 35993, 35999, 36007, 36011, 36013, 36017, 36037, 36061,
- 36067, 36073, 36083, 36097, 36107, 36109, 36131, 36137, 36151, 36161,
- 36187, 36191, 36209, 36217, 36229, 36241, 36251, 36263, 36269, 36277,
- 36293, 36299, 36307, 36313, 36319, 36341, 36343, 36353, 36373, 36383,
- 36389, 36433, 36451, 36457, 36467, 36469, 36473, 36479, 36493, 36497,
- 36523, 36527, 36529, 36541, 36551, 36559, 36563, 36571, 36583, 36587,
- 36599, 36607, 36629, 36637, 36643, 36653, 36671, 36677, 36683, 36691,
- 36697, 36709, 36713, 36721, 36739, 36749, 36761, 36767, 36779, 36781,
- 36787, 36791, 36793, 36809, 36821, 36833, 36847, 36857, 36871, 36877,
- 36887, 36899, 36901, 36913, 36919, 36923, 36929, 36931, 36943, 36947,
- 36973, 36979, 36997, 37003, 37013, 37019, 37021, 37039, 37049, 37057,
- 37061, 37087, 37097, 37117, 37123, 37139, 37159, 37171, 37181, 37189,
- 37199, 37201, 37217, 37223, 37243, 37253, 37273, 37277, 37307, 37309,
- 37313, 37321, 37337, 37339, 37357, 37361, 37363, 37369, 37379, 37397,
- 37409, 37423, 37441, 37447, 37463, 37483, 37489, 37493, 37501, 37507,
- 37511, 37517, 37529, 37537, 37547, 37549, 37561, 37567, 37571, 37573,
- 37579, 37589, 37591, 37607, 37619, 37633, 37643, 37649, 37657, 37663,
- 37691, 37693, 37699, 37717, 37747, 37781, 37783, 37799, 37811, 37813,
- 37831, 37847, 37853, 37861, 37871, 37879, 37889, 37897, 37907, 37951,
- 37957, 37963, 37967, 37987, 37991, 37993, 37997, 38011, 38039, 38047,
- 38053, 38069, 38083, 38113, 38119, 38149, 38153, 38167, 38177, 38183,
- 38189, 38197, 38201, 38219, 38231, 38237, 38239, 38261, 38273, 38281,
- 38287, 38299, 38303, 38317, 38321, 38327, 38329, 38333, 38351, 38371,
- 38377, 38393, 38431, 38447, 38449, 38453, 38459, 38461, 38501, 38543,
- 38557, 38561, 38567, 38569, 38593, 38603, 38609, 38611, 38629, 38639,
- 38651, 38653, 38669, 38671, 38677, 38693, 38699, 38707, 38711, 38713,
- 38723, 38729, 38737, 38747, 38749, 38767, 38783, 38791, 38803, 38821,
- 38833, 38839, 38851, 38861, 38867, 38873, 38891, 38903, 38917, 38921,
- 38923, 38933, 38953, 38959, 38971, 38977, 38993, 39019, 39023, 39041,
- 39043, 39047, 39079, 39089, 39097, 39103, 39107, 39113, 39119, 39133,
- 39139, 39157, 39161, 39163, 39181, 39191, 39199, 39209, 39217, 39227,
- 39229, 39233, 39239, 39241, 39251, 39293, 39301, 39313, 39317, 39323,
- 39341, 39343, 39359, 39367, 39371, 39373, 39383, 39397, 39409, 39419,
- 39439, 39443, 39451, 39461, 39499, 39503, 39509, 39511, 39521, 39541,
- 39551, 39563, 39569, 39581, 39607, 39619, 39623, 39631, 39659, 39667,
- 39671, 39679, 39703, 39709, 39719, 39727, 39733, 39749, 39761, 39769,
- 39779, 39791, 39799, 39821, 39827, 39829, 39839, 39841, 39847, 39857,
- 39863, 39869, 39877, 39883, 39887, 39901, 39929, 39937, 39953, 39971,
- 39979, 39983, 39989, 40009, 40013, 40031, 40037, 40039, 40063, 40087,
- 40093, 40099, 40111, 40123, 40127, 40129, 40151, 40153, 40163, 40169,
- 40177, 40189, 40193, 40213, 40231, 40237, 40241, 40253, 40277, 40283,
- 40289, 40343, 40351, 40357, 40361, 40387, 40423, 40427, 40429, 40433,
- 40459, 40471, 40483, 40487, 40493, 40499, 40507, 40519, 40529, 40531,
- 40543, 40559, 40577, 40583, 40591, 40597, 40609, 40627, 40637, 40639,
- 40693, 40697, 40699, 40709, 40739, 40751, 40759, 40763, 40771, 40787,
- 40801, 40813, 40819, 40823, 40829, 40841, 40847, 40849, 40853, 40867,
- 40879, 40883, 40897, 40903, 40927, 40933, 40939, 40949, 40961, 40973,
- 40993, 41011, 41017, 41023, 41039, 41047, 41051, 41057, 41077, 41081,
- 41113, 41117, 41131, 41141, 41143, 41149, 41161, 41177, 41179, 41183,
- 41189, 41201, 41203, 41213, 41221, 41227, 41231, 41233, 41243, 41257,
- 41263, 41269, 41281, 41299, 41333, 41341, 41351, 41357, 41381, 41387,
- 41389, 41399, 41411, 41413, 41443, 41453, 41467, 41479, 41491, 41507,
- 41513, 41519, 41521, 41539, 41543, 41549, 41579, 41593, 41597, 41603,
- 41609, 41611, 41617, 41621, 41627, 41641, 41647, 41651, 41659, 41669,
- 41681, 41687, 41719, 41729, 41737, 41759, 41761, 41771, 41777, 41801,
- 41809, 41813, 41843, 41849, 41851, 41863, 41879, 41887, 41893, 41897,
- 41903, 41911, 41927, 41941, 41947, 41953, 41957, 41959, 41969, 41981,
- 41983, 41999, 42013, 42017, 42019, 42023, 42043, 42061, 42071, 42073,
- 42083, 42089, 42101, 42131, 42139, 42157, 42169, 42179, 42181, 42187,
- 42193, 42197, 42209, 42221, 42223, 42227, 42239, 42257, 42281, 42283,
- 42293, 42299, 42307, 42323, 42331, 42337, 42349, 42359, 42373, 42379,
- 42391, 42397, 42403, 42407, 42409, 42433, 42437, 42443, 42451, 42457,
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- 92251, 92269, 92297, 92311, 92317, 92333, 92347, 92353, 92357, 92363,
- 92369, 92377, 92381, 92383, 92387, 92399, 92401, 92413, 92419, 92431,
- 92459, 92461, 92467, 92479, 92489, 92503, 92507, 92551, 92557, 92567,
- 92569, 92581, 92593, 92623, 92627, 92639, 92641, 92647, 92657, 92669,
- 92671, 92681, 92683, 92693, 92699, 92707, 92717, 92723, 92737, 92753,
- 92761, 92767, 92779, 92789, 92791, 92801, 92809, 92821, 92831, 92849,
- 92857, 92861, 92863, 92867, 92893, 92899, 92921, 92927, 92941, 92951,
- 92957, 92959, 92987, 92993, 93001, 93047, 93053, 93059, 93077, 93083,
- 93089, 93097, 93103, 93113, 93131, 93133, 93139, 93151, 93169, 93179,
- 93187, 93199, 93229, 93239, 93241, 93251, 93253, 93257, 93263, 93281,
- 93283, 93287, 93307, 93319, 93323, 93329, 93337, 93371, 93377, 93383,
- 93407, 93419, 93427, 93463, 93479, 93481, 93487, 93491, 93493, 93497,
- 93503, 93523, 93529, 93553, 93557, 93559, 93563, 93581, 93601, 93607,
- 93629, 93637, 93683, 93701, 93703, 93719, 93739, 93761, 93763, 93787,
- 93809, 93811, 93827, 93851, 93871, 93887, 93889, 93893, 93901, 93911,
- 93913, 93923, 93937, 93941, 93949, 93967, 93971, 93979, 93983, 93997,
- 94007, 94009, 94033, 94049, 94057, 94063, 94079, 94099, 94109, 94111,
- 94117, 94121, 94151, 94153, 94169, 94201, 94207, 94219, 94229, 94253,
- 94261, 94273, 94291, 94307, 94309, 94321, 94327, 94331, 94343, 94349,
- 94351, 94379, 94397, 94399, 94421, 94427, 94433, 94439, 94441, 94447,
- 94463, 94477, 94483, 94513, 94529, 94531, 94541, 94543, 94547, 94559,
- 94561, 94573, 94583, 94597, 94603, 94613, 94621, 94649, 94651, 94687,
- 94693, 94709, 94723, 94727, 94747, 94771, 94777, 94781, 94789, 94793,
- 94811, 94819, 94823, 94837, 94841, 94847, 94849, 94873, 94889, 94903,
- 94907, 94933, 94949, 94951, 94961, 94993, 94999, 95003, 95009, 95021,
- 95027, 95063, 95071, 95083, 95087, 95089, 95093, 95101, 95107, 95111,
- 95131, 95143, 95153, 95177, 95189, 95191, 95203, 95213, 95219, 95231,
- 95233, 95239, 95257, 95261, 95267, 95273, 95279, 95287, 95311, 95317,
- 95327, 95339, 95369, 95383, 95393, 95401, 95413, 95419, 95429, 95441,
- 95443, 95461, 95467, 95471, 95479, 95483, 95507, 95527, 95531, 95539,
- 95549, 95561, 95569, 95581, 95597, 95603, 95617, 95621, 95629, 95633,
- 95651, 95701, 95707, 95713, 95717, 95723, 95731, 95737, 95747, 95773,
- 95783, 95789, 95791, 95801, 95803, 95813, 95819, 95857, 95869, 95873,
- 95881, 95891, 95911, 95917, 95923, 95929, 95947, 95957, 95959, 95971,
- 95987, 95989, 96001, 96013, 96017, 96043, 96053, 96059, 96079, 96097,
- 96137, 96149, 96157, 96167, 96179, 96181, 96199, 96211, 96221, 96223,
- 96233, 96259, 96263, 96269, 96281, 96289, 96293, 96323, 96329, 96331,
- 96337, 96353, 96377, 96401, 96419, 96431, 96443, 96451, 96457, 96461,
- 96469, 96479, 96487, 96493, 96497, 96517, 96527, 96553, 96557, 96581,
- 96587, 96589, 96601, 96643, 96661, 96667, 96671, 96697, 96703, 96731,
- 96737, 96739, 96749, 96757, 96763, 96769, 96779, 96787, 96797, 96799,
- 96821, 96823, 96827, 96847, 96851, 96857, 96893, 96907, 96911, 96931,
- 96953, 96959, 96973, 96979, 96989, 96997, 97001, 97003, 97007, 97021,
- 97039, 97073, 97081, 97103, 97117, 97127, 97151, 97157, 97159, 97169,
- 97171, 97177, 97187, 97213, 97231, 97241, 97259, 97283, 97301, 97303,
- 97327, 97367, 97369, 97373, 97379, 97381, 97387, 97397, 97423, 97429,
- 97441, 97453, 97459, 97463, 97499, 97501, 97511, 97523, 97547, 97549,
- 97553, 97561, 97571, 97577, 97579, 97583, 97607, 97609, 97613, 97649,
- 97651, 97673, 97687, 97711, 97729, 97771, 97777, 97787, 97789, 97813,
- 97829, 97841, 97843, 97847, 97849, 97859, 97861, 97871, 97879, 97883,
- 97919, 97927, 97931, 97943, 97961, 97967, 97973, 97987, 98009, 98011,
- 98017, 98041, 98047, 98057, 98081, 98101, 98123, 98129, 98143, 98179,
- 98207, 98213, 98221, 98227, 98251, 98257, 98269, 98297, 98299, 98317,
- 98321, 98323, 98327, 98347, 98369, 98377, 98387, 98389, 98407, 98411,
- 98419, 98429, 98443, 98453, 98459, 98467, 98473, 98479, 98491, 98507,
- 98519, 98533, 98543, 98561, 98563, 98573, 98597, 98621, 98627, 98639,
- 98641, 98663, 98669, 98689, 98711, 98713, 98717, 98729, 98731, 98737,
- 98773, 98779, 98801, 98807, 98809, 98837, 98849, 98867, 98869, 98873,
- 98887, 98893, 98897, 98899, 98909, 98911, 98927, 98929, 98939, 98947,
- 98953, 98963, 98981, 98993, 98999, 99013, 99017, 99023, 99041, 99053,
- 99079, 99083, 99089, 99103, 99109, 99119, 99131, 99133, 99137, 99139,
- 99149, 99173, 99181, 99191, 99223, 99233, 99241, 99251, 99257, 99259,
- 99277, 99289, 99317, 99347, 99349, 99367, 99371, 99377, 99391, 99397,
- 99401, 99409, 99431, 99439, 99469, 99487, 99497, 99523, 99527, 99529,
- 99551, 99559, 99563, 99571, 99577, 99581, 99607, 99611, 99623, 99643,
- 99661, 99667, 99679, 99689, 99707, 99709, 99713, 99719, 99721, 99733,
- 99761, 99767, 99787, 99793, 99809, 99817, 99823, 99829, 99833, 99839,
- 99859, 99871, 99877, 99881, 99901, 99907, 99923, 99929, 99961, 99971,
- 99989, 99991, 100003, 100019, 100043, 100049, 100057, 100069, 100103, 100109,
-100129, 100151, 100153, 100169, 100183, 100189, 100193, 100207, 100213, 100237,
-100267, 100271, 100279, 100291, 100297, 100313, 100333, 100343, 100357, 100361,
-100363, 100379, 100391, 100393, 100403, 100411, 100417, 100447, 100459, 100469,
-100483, 100493, 100501, 100511, 100517, 100519, 100523, 100537, 100547, 100549,
-100559, 100591, 100609, 100613, 100621, 100649, 100669, 100673, 100693, 100699,
-100703, 100733, 100741, 100747, 100769, 100787, 100799, 100801, 100811, 100823,
-100829, 100847, 100853, 100907, 100913, 100927, 100931, 100937, 100943, 100957,
-100981, 100987, 100999, 101009, 101021, 101027, 101051, 101063, 101081, 101089,
-101107, 101111, 101113, 101117, 101119, 101141, 101149, 101159, 101161, 101173,
-101183, 101197, 101203, 101207, 101209, 101221, 101267, 101273, 101279, 101281,
-101287, 101293, 101323, 101333, 101341, 101347, 101359, 101363, 101377, 101383,
-101399, 101411, 101419, 101429, 101449, 101467, 101477, 101483, 101489, 101501,
-101503, 101513, 101527, 101531, 101533, 101537, 101561, 101573, 101581, 101599,
-101603, 101611, 101627, 101641, 101653, 101663, 101681, 101693, 101701, 101719,
-101723, 101737, 101741, 101747, 101749, 101771, 101789, 101797, 101807, 101833,
-101837, 101839, 101863, 101869, 101873, 101879, 101891, 101917, 101921, 101929,
-101939, 101957, 101963, 101977, 101987, 101999, 102001, 102013, 102019, 102023,
-102031, 102043, 102059, 102061, 102071, 102077, 102079, 102101, 102103, 102107,
-102121, 102139, 102149, 102161, 102181, 102191, 102197, 102199, 102203, 102217,
-102229, 102233, 102241, 102251, 102253, 102259, 102293, 102299, 102301, 102317,
-102329, 102337, 102359, 102367, 102397, 102407, 102409, 102433, 102437, 102451,
-102461, 102481, 102497, 102499, 102503, 102523, 102533, 102539, 102547, 102551,
-102559, 102563, 102587, 102593, 102607, 102611, 102643, 102647, 102653, 102667,
-102673, 102677, 102679, 102701, 102761, 102763, 102769, 102793, 102797, 102811,
-102829, 102841, 102859, 102871, 102877, 102881, 102911, 102913, 102929, 102931,
-102953, 102967, 102983, 103001, 103007, 103043, 103049, 103067, 103069, 103079,
-103087, 103091, 103093, 103099, 103123, 103141, 103171, 103177, 103183, 103217,
-103231, 103237, 103289, 103291, 103307, 103319, 103333, 103349, 103357, 103387,
-103391, 103393, 103399, 103409, 103421, 103423, 103451, 103457, 103471, 103483,
-103511, 103529, 103549, 103553, 103561, 103567, 103573, 103577, 103583, 103591,
-103613, 103619, 103643, 103651, 103657, 103669, 103681, 103687, 103699, 103703,
-103723, 103769, 103787, 103801, 103811, 103813, 103837, 103841, 103843, 103867,
-103889, 103903, 103913, 103919, 103951, 103963, 103967, 103969, 103979, 103981,
-103991, 103993, 103997, 104003, 104009, 104021, 104033, 104047, 104053, 104059,
-104087, 104089, 104107, 104113, 104119, 104123, 104147, 104149, 104161, 104173,
-104179, 104183, 104207, 104231, 104233, 104239, 104243, 104281, 104287, 104297,
-104309, 104311, 104323, 104327, 104347, 104369, 104381, 104383, 104393, 104399,
-104417, 104459, 104471, 104473, 104479, 104491, 104513, 104527, 104537, 104543,
-104549, 104551, 104561, 104579, 104593, 104597, 104623, 104639, 104651, 104659,
-104677, 104681, 104683, 104693, 104701, 104707, 104711, 104717, 104723, 104729,
-)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/test/setuptestframework.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/test/setuptestframework.py
deleted file mode 100644
index 98029574a3abfabd85a7237780569ef649ce8bf6..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/test/setuptestframework.py
+++ /dev/null
@@ -1,134 +0,0 @@
-#!/usr/bin/python2
-# Configure this in order to run the testcases.
-"setuptestframework.py v 2.6.0.8"
-import os
-import sys
-import tempfile
-import shutil
-
-try:
- OSErrors = (WindowsError, OSError)
-except NameError: # not running on Windows
- OSErrors = OSError
-
-
-def maketemp():
- temphome = tempfile.gettempdir()
- tempdir = os.path.join(temphome, "adodbapi_test")
- try:
- os.mkdir(tempdir)
- except:
- pass
- return tempdir
-
-
-def _cleanup_function(testfolder, mdb_name):
- try:
- os.unlink(os.path.join(testfolder, mdb_name))
- except:
- pass # mdb database not present
- try:
- shutil.rmtree(testfolder)
- print(" cleaned up folder", testfolder)
- except:
- pass # test package not present
-
-
-def getcleanupfunction():
- return _cleanup_function
-
-
-def find_ado_path():
- adoName = os.path.normpath(os.getcwd() + "/../../adodbapi.py")
- adoPackage = os.path.dirname(adoName)
- return adoPackage
-
-
-# make a new package directory for the test copy of ado
-def makeadopackage(testfolder):
- adoName = os.path.normpath(os.getcwd() + "/../adodbapi.py")
- adoPath = os.path.dirname(adoName)
- if os.path.exists(adoName):
- newpackage = os.path.join(testfolder, "adodbapi")
- try:
- os.mkdir(newpackage)
- except OSErrors:
- print(
- "*Note: temporary adodbapi package already exists: may be two versions running?"
- )
- for f in os.listdir(adoPath):
- if f.endswith(".py"):
- shutil.copy(os.path.join(adoPath, f), newpackage)
- if sys.version_info >= (3, 0): # only when running Py3.n
- save = sys.stdout
- sys.stdout = None
- from lib2to3.main import main # use 2to3 to make test package
-
- main("lib2to3.fixes", args=["-n", "-w", newpackage])
- sys.stdout = save
- return testfolder
- else:
- raise EnvironmentError("Connot find source of adodbapi to test.")
-
-
-def makemdb(testfolder, mdb_name):
- # following setup code borrowed from pywin32 odbc test suite
- # kindly contributed by Frank Millman.
- import os
-
- _accessdatasource = os.path.join(testfolder, mdb_name)
- if os.path.isfile(_accessdatasource):
- print("using JET database=", _accessdatasource)
- else:
- try:
- from win32com.client.gencache import EnsureDispatch
- from win32com.client import constants
-
- win32 = True
- except ImportError: # perhaps we are running IronPython
- win32 = False # iron Python
- try:
- from System import Activator, Type
- except:
- pass
-
- # Create a brand-new database - what is the story with these?
- dbe = None
- for suffix in (".36", ".35", ".30"):
- try:
- if win32:
- dbe = EnsureDispatch("DAO.DBEngine" + suffix)
- else:
- type = Type.GetTypeFromProgID("DAO.DBEngine" + suffix)
- dbe = Activator.CreateInstance(type)
- break
- except:
- pass
- if dbe:
- print(" ...Creating ACCESS db at " + _accessdatasource)
- if win32:
- workspace = dbe.Workspaces(0)
- newdb = workspace.CreateDatabase(
- _accessdatasource, constants.dbLangGeneral, constants.dbVersion40
- )
- else:
- newdb = dbe.CreateDatabase(
- _accessdatasource, ";LANGID=0x0409;CP=1252;COUNTRY=0"
- )
- newdb.Close()
- else:
- print(" ...copying test ACCESS db to " + _accessdatasource)
- mdbName = os.path.abspath(
- os.path.join(os.path.dirname(__file__), "..", "examples", "test.mdb")
- )
- import shutil
-
- shutil.copy(mdbName, _accessdatasource)
-
- return _accessdatasource
-
-
-if __name__ == "__main__":
- print("Setting up a Jet database for server to use for remote testing...")
- temp = maketemp()
- makemdb(temp, "server_test.mdb")
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/test/test_adodbapi_dbapi20.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/test/test_adodbapi_dbapi20.py
deleted file mode 100644
index 12c9e0b377e7c479bca0027ba6bdb883a9466d8e..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/adodbapi/test/test_adodbapi_dbapi20.py
+++ /dev/null
@@ -1,200 +0,0 @@
-print("This module depends on the dbapi20 compliance tests created by Stuart Bishop")
-print("(see db-sig mailing list history for info)")
-import platform
-import unittest
-import sys
-
-import dbapi20
-import setuptestframework
-
-testfolder = setuptestframework.maketemp()
-if "--package" in sys.argv:
- pth = setuptestframework.makeadopackage(testfolder)
- sys.argv.remove("--package")
-else:
- pth = setuptestframework.find_ado_path()
-if pth not in sys.path:
- sys.path.insert(1, pth)
-# function to clean up the temporary folder -- calling program must run this function before exit.
-cleanup = setuptestframework.getcleanupfunction()
-
-import adodbapi
-import adodbapi.is64bit as is64bit
-
-db = adodbapi
-
-if "--verbose" in sys.argv:
- db.adodbapi.verbose = 3
-
-print(adodbapi.version)
-print("Tested with dbapi20 %s" % dbapi20.__version__)
-
-try:
- onWindows = bool(sys.getwindowsversion()) # seems to work on all versions of Python
-except:
- onWindows = False
-
-node = platform.node()
-
-conn_kws = {}
-host = "testsql.2txt.us,1430" # if None, will use macro to fill in node name
-instance = r"%s\SQLEXPRESS"
-conn_kws["name"] = "adotest"
-
-conn_kws["user"] = "adotestuser" # None implies Windows security
-conn_kws["password"] = "Sq1234567"
-# macro definition for keyword "security" using macro "auto_security"
-conn_kws["macro_auto_security"] = "security"
-
-if host is None:
- conn_kws["macro_getnode"] = ["host", instance]
-else:
- conn_kws["host"] = host
-
-conn_kws[
- "provider"
-] = "Provider=MSOLEDBSQL;DataTypeCompatibility=80;MARS Connection=True;"
-connStr = "%(provider)s; %(security)s; Initial Catalog=%(name)s;Data Source=%(host)s"
-
-if onWindows and node != "z-PC":
- pass # default should make a local SQL Server connection
-elif node == "xxx": # try Postgres database
- _computername = "25.223.161.222"
- _databasename = "adotest"
- _username = "adotestuser"
- _password = "12345678"
- _driver = "PostgreSQL Unicode"
- _provider = ""
- connStr = "%sDriver={%s};Server=%s;Database=%s;uid=%s;pwd=%s;" % (
- _provider,
- _driver,
- _computername,
- _databasename,
- _username,
- _password,
- )
-elif node == "yyy": # ACCESS data base is known to fail some tests.
- if is64bit.Python():
- driver = "Microsoft.ACE.OLEDB.12.0"
- else:
- driver = "Microsoft.Jet.OLEDB.4.0"
- testmdb = setuptestframework.makemdb(testfolder)
- connStr = r"Provider=%s;Data Source=%s" % (driver, testmdb)
-else: # try a remote connection to an SQL server
- conn_kws["proxy_host"] = "25.44.77.176"
- import adodbapi.remote
-
- db = adodbapi.remote
-
-print("Using Connection String like=%s" % connStr)
-print("Keywords=%s" % repr(conn_kws))
-
-
-class test_adodbapi(dbapi20.DatabaseAPI20Test):
- driver = db
- connect_args = (connStr,)
- connect_kw_args = conn_kws
-
- def __init__(self, arg):
- dbapi20.DatabaseAPI20Test.__init__(self, arg)
-
- def getTestMethodName(self):
- return self.id().split(".")[-1]
-
- def setUp(self):
- # Call superclass setUp In case this does something in the
- # future
- dbapi20.DatabaseAPI20Test.setUp(self)
- if self.getTestMethodName() == "test_callproc":
- con = self._connect()
- engine = con.dbms_name
- ## print('Using database Engine=%s' % engine) ##
- if engine != "MS Jet":
- sql = """
- create procedure templower
- @theData varchar(50)
- as
- select lower(@theData)
- """
- else: # Jet
- sql = """
- create procedure templower
- (theData varchar(50))
- as
- select lower(theData);
- """
- cur = con.cursor()
- try:
- cur.execute(sql)
- con.commit()
- except:
- pass
- cur.close()
- con.close()
- self.lower_func = "templower"
-
- def tearDown(self):
- if self.getTestMethodName() == "test_callproc":
- con = self._connect()
- cur = con.cursor()
- try:
- cur.execute("drop procedure templower")
- except:
- pass
- con.commit()
- dbapi20.DatabaseAPI20Test.tearDown(self)
-
- def help_nextset_setUp(self, cur):
- "Should create a procedure called deleteme"
- 'that returns two result sets, first the number of rows in booze then "name from booze"'
- sql = """
- create procedure deleteme as
- begin
- select count(*) from %sbooze
- select name from %sbooze
- end
- """ % (
- self.table_prefix,
- self.table_prefix,
- )
- cur.execute(sql)
-
- def help_nextset_tearDown(self, cur):
- "If cleaning up is needed after nextSetTest"
- try:
- cur.execute("drop procedure deleteme")
- except:
- pass
-
- def test_nextset(self):
- con = self._connect()
- try:
- cur = con.cursor()
-
- stmts = [self.ddl1] + self._populate()
- for sql in stmts:
- cur.execute(sql)
-
- self.help_nextset_setUp(cur)
-
- cur.callproc("deleteme")
- numberofrows = cur.fetchone()
- assert numberofrows[0] == 6
- assert cur.nextset()
- names = cur.fetchall()
- assert len(names) == len(self.samples)
- s = cur.nextset()
- assert s == None, "No more return sets, should return None"
- finally:
- try:
- self.help_nextset_tearDown(cur)
- finally:
- con.close()
-
- def test_setoutputsize(self):
- pass
-
-
-if __name__ == "__main__":
- unittest.main()
- cleanup(testfolder, None)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/log.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/log.py
deleted file mode 100644
index 3cecea2bac185df741bccd0a32a5fef9cfe23299..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/log.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import logging
-
-access_logger = logging.getLogger("aiohttp.access")
-client_logger = logging.getLogger("aiohttp.client")
-internal_logger = logging.getLogger("aiohttp.internal")
-server_logger = logging.getLogger("aiohttp.server")
-web_logger = logging.getLogger("aiohttp.web")
-ws_logger = logging.getLogger("aiohttp.websocket")
diff --git a/spaces/aseuteurideu/audio_deepfake_detector/data/generate_dataset_to_tfrecord.py b/spaces/aseuteurideu/audio_deepfake_detector/data/generate_dataset_to_tfrecord.py
deleted file mode 100644
index dfe07905cebfa505ac8e0a39bce810fd3d222ed8..0000000000000000000000000000000000000000
--- a/spaces/aseuteurideu/audio_deepfake_detector/data/generate_dataset_to_tfrecord.py
+++ /dev/null
@@ -1,178 +0,0 @@
-#Code outsourced from https://github.com/deepmind/dmvr/tree/master and later modified.
-
-"""Python script to generate TFRecords of SequenceExample from raw videos."""
-
-import contextlib
-import math
-import os
-import cv2
-from typing import Dict, Optional, Sequence
-import moviepy.editor
-from absl import app
-from absl import flags
-import ffmpeg
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-
-import warnings
-warnings.filterwarnings('ignore')
-
-flags.DEFINE_string("csv_path", "fakeavceleb_1k.csv", "Input csv")
-flags.DEFINE_string("output_path", "fakeavceleb_tfrec", "Tfrecords output path.")
-flags.DEFINE_string("video_root_path", "./",
- "Root directory containing the raw videos.")
-flags.DEFINE_integer(
- "num_shards", 4, "Number of shards to output, -1 means"
- "it will automatically adapt to the sqrt(num_examples).")
-flags.DEFINE_bool("decode_audio", False, "Whether or not to decode the audio")
-flags.DEFINE_bool("shuffle_csv", False, "Whether or not to shuffle the csv.")
-FLAGS = flags.FLAGS
-
-
-_JPEG_HEADER = b"\xff\xd8"
-
-
-@contextlib.contextmanager
-def _close_on_exit(writers):
- """Call close on all writers on exit."""
- try:
- yield writers
- finally:
- for writer in writers:
- writer.close()
-
-
-def add_float_list(key: str, values: Sequence[float],
- sequence: tf.train.SequenceExample):
- sequence.feature_lists.feature_list[key].feature.add(
- ).float_list.value[:] = values
-
-
-def add_bytes_list(key: str, values: Sequence[bytes],
- sequence: tf.train.SequenceExample):
- sequence.feature_lists.feature_list[key].feature.add().bytes_list.value[:] = values
-
-
-def add_int_list(key: str, values: Sequence[int],
- sequence: tf.train.SequenceExample):
- sequence.feature_lists.feature_list[key].feature.add().int64_list.value[:] = values
-
-
-def set_context_int_list(key: str, value: Sequence[int],
- sequence: tf.train.SequenceExample):
- sequence.context.feature[key].int64_list.value[:] = value
-
-
-def set_context_bytes(key: str, value: bytes,
- sequence: tf.train.SequenceExample):
- sequence.context.feature[key].bytes_list.value[:] = (value,)
-
-def set_context_bytes_list(key: str, value: Sequence[bytes],
- sequence: tf.train.SequenceExample):
- sequence.context.feature[key].bytes_list.value[:] = value
-
-
-def set_context_float(key: str, value: float,
- sequence: tf.train.SequenceExample):
- sequence.context.feature[key].float_list.value[:] = (value,)
-
-
-def set_context_int(key: str, value: int, sequence: tf.train.SequenceExample):
- sequence.context.feature[key].int64_list.value[:] = (value,)
-
-
-def extract_frames(video_path, fps = 10, min_resize = 256):
- '''Load n number of frames from a video'''
- v_cap = cv2.VideoCapture(video_path)
- v_len = int(v_cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
- if fps is None:
- sample = np.arange(0, v_len)
- else:
- sample = np.linspace(0, v_len - 1, fps).astype(int)
-
- frames = []
- for j in range(v_len):
- success = v_cap.grab()
- if j in sample:
- success, frame = v_cap.retrieve()
- if not success:
- continue
-
- frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- frame = cv2.resize(frame, (min_resize, min_resize))
- frames.append(frame)
-
- v_cap.release()
- frame_np = np.stack(frames)
- return frame_np.tobytes()
-
-def extract_audio(video_path: str,
- sampling_rate: int = 16_000):
- """Extract raw mono audio float list from video_path with ffmpeg."""
- video = moviepy.editor.VideoFileClip(video_path)
- audio = video.audio.to_soundarray()
- #Load first channel.
- audio = audio[:, 0]
-
- return np.array(audio)
-
-#Each of the features can be coerced into a tf.train.Example-compatible type using one of the _bytes_feature, _float_feature and the _int64_feature.
-#You can then create a tf.train.Example message from these encoded features.
-
-def serialize_example(video_path: str, label_name: str, label_map: Optional[Dict[str, int]] = None):
- # Initiate the sequence example.
- seq_example = tf.train.SequenceExample()
-
- imgs_encoded = extract_frames(video_path, fps = 10)
-
- audio = extract_audio(video_path)
-
- set_context_bytes(f'image/encoded', imgs_encoded, seq_example)
- set_context_bytes("video_path", video_path.encode(), seq_example)
- set_context_bytes("WAVEFORM/feature/floats", audio.tobytes(), seq_example)
- set_context_int("clip/label/index", label_map[label_name], seq_example)
- set_context_bytes("clip/label/text", label_name.encode(), seq_example)
- return seq_example
-
-
-def main(argv):
- del argv
- # reads the input csv.
- input_csv = pd.read_csv(FLAGS.csv_path)
- if FLAGS.num_shards == -1:
- num_shards = int(math.sqrt(len(input_csv)))
- else:
- num_shards = FLAGS.num_shards
- # Set up the TFRecordWriters.
- basename = os.path.splitext(os.path.basename(FLAGS.csv_path))[0]
- shard_names = [
- os.path.join(FLAGS.output_path, f"{basename}-{i:05d}-of-{num_shards:05d}")
- for i in range(num_shards)
- ]
- writers = [tf.io.TFRecordWriter(shard_name) for shard_name in shard_names]
-
- if "label" in input_csv:
- unique_labels = list(set(input_csv["label"].values))
- l_map = {unique_labels[i]: i for i in range(len(unique_labels))}
- else:
- l_map = None
-
- if FLAGS.shuffle_csv:
- input_csv = input_csv.sample(frac=1)
- with _close_on_exit(writers) as writers:
- row_count = 0
- for row in input_csv.itertuples():
- index = row[0]
- v = row[1]
- if os.name == 'posix':
- v = v.str.replace('\\', '/')
- l = row[2]
- row_count += 1
- print("Processing example %d of %d (%d%%) \r" %(row_count, len(input_csv), row_count * 100 / len(input_csv)), end="")
- seq_ex = serialize_example(video_path = v, label_name = l,label_map = l_map)
- writers[index % len(writers)].write(seq_ex.SerializeToString())
-
-if __name__ == "__main__":
- app.run(main)
diff --git a/spaces/ashercn97/AsherTesting/extensions/multimodal/README.md b/spaces/ashercn97/AsherTesting/extensions/multimodal/README.md
deleted file mode 100644
index 10bbc7f5db1afd082674d581221f73851b59aec3..0000000000000000000000000000000000000000
--- a/spaces/ashercn97/AsherTesting/extensions/multimodal/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Multimodal
-
-## Description
-
-Adds support for multimodality (text+images) to text-generation-webui.
-
-https://user-images.githubusercontent.com/3718215/233817203-69b57e77-0c55-4fd6-b742-3204bb13b8fc.mp4
-
-## Usage
-
-To run this extension, download a LLM that supports multimodality, and then start server.py with the appropriate `--multimodal-pipeline` argument. Examples:
-
-```
-python server.py --model wojtab_llava-7b-v0-4bit-128g --multimodal-pipeline llava-7b --chat
-python3 server.py --model wojtab_llava-13b-v0-4bit-128g --multimodal-pipeline llava-13b --chat
-python server.py --model anon8231489123_vicuna-13b-GPTQ-4bit-128g --multimodal-pipeline minigpt4-13b --chat
-python server.py --model llama-7b-4bit --multimodal-pipeline minigpt4-7b --chat
-```
-
-There is built-in support for LLaVA-v0-13B and LLaVA-v0-7b. To install `minigpt4`:
-
-- clone https://github.com/Wojtab/minigpt-4-pipeline into `extensions/multimodal/pipelines`
-- install the requirements.txt
-
-The same procedure should be used to install other pipelines, which can then be used with `--multimodal-pipeline [pipeline name]`. For additional multimodal pipelines refer to the compatibility section below.
-
-Do note, that each image takes up a considerable amount of tokens, so adjust `max_new_tokens` to be at most 1700 (recommended value is between 200 to 500), so the images don't get truncated.
-
-To send an image, just upload it to the extension field below chat, and send a prompt as always. The image will be added to the end of your message. If you wish to modify the placement, include a string ` ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "620o1BxdNbgq"
- },
- "source": [
- "# **Stable Diffusion 2.1**\n",
- "Gradio app for [Stable Diffusion 2](https://huggingface.co/stabilityai/stable-diffusion-2) by [Stability AI](https://stability.ai/) (v2-1_768-ema-pruned.ckpt).\n",
- "It uses [Hugging Face](https://huggingface.co/) Diffusers🧨 implementation.\n",
- "\n",
- "Currently supported pipelines are `text-to-image`, `image-to-image`, `inpainting`, `4x upscaling` and `depth-to-image`.\n",
- "\n",
- "![\"Buy](\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\")
](https://www.buymeacoffee.com/anzorq)\n",
- "![\"GitHub](\"https://img.shields.io/github/stars/qunash/stable-diffusion-2-gui?style=social\")
\n",
- " ![\"Twitter](\"https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social\")
