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""" Pyramid Vision Transformer v2 |
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@misc{wang2021pvtv2, |
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title={PVTv2: Improved Baselines with Pyramid Vision Transformer}, |
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author={Wenhai Wang and Enze Xie and Xiang Li and Deng-Ping Fan and Kaitao Song and Ding Liang and |
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Tong Lu and Ping Luo and Ling Shao}, |
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year={2021}, |
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eprint={2106.13797}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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Based on Apache 2.0 licensed code at https://github.com/whai362/PVT |
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Modifications and timm support by / Copyright 2022, Ross Wightman |
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""" |
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import math |
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from functools import partial |
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from typing import Tuple, List, Callable, Union |
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import torch |
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import torch.nn as nn |
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import torch.utils.checkpoint as checkpoint |
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD |
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from .helpers import build_model_with_cfg |
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from .layers import DropPath, to_2tuple, to_ntuple, trunc_normal_ |
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from .registry import register_model |
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__all__ = ['PyramidVisionTransformerV2'] |
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def _cfg(url='', **kwargs): |
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return { |
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'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), |
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'crop_pct': 0.9, 'interpolation': 'bicubic', |
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, |
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'first_conv': 'patch_embed.proj', 'classifier': 'head', 'fixed_input_size': False, |
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**kwargs |
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} |
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default_cfgs = { |
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'pvt_v2_b0': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth'), |
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'pvt_v2_b1': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b1.pth'), |
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'pvt_v2_b2': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2.pth'), |
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'pvt_v2_b3': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b3.pth'), |
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'pvt_v2_b4': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b4.pth'), |
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'pvt_v2_b5': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b5.pth'), |
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'pvt_v2_b2_li': _cfg(url='https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b2_li.pth') |
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} |
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class MlpWithDepthwiseConv(nn.Module): |
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def __init__( |
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self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, |
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drop=0., extra_relu=False): |
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super().__init__() |
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out_features = out_features or in_features |
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hidden_features = hidden_features or in_features |
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self.fc1 = nn.Linear(in_features, hidden_features) |
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self.relu = nn.ReLU() if extra_relu else nn.Identity() |
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self.dwconv = nn.Conv2d(hidden_features, hidden_features, 3, 1, 1, bias=True, groups=hidden_features) |
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self.act = act_layer() |
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self.fc2 = nn.Linear(hidden_features, out_features) |
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self.drop = nn.Dropout(drop) |
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def forward(self, x, feat_size: List[int]): |
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x = self.fc1(x) |
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B, N, C = x.shape |
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x = x.transpose(1, 2).view(B, C, feat_size[0], feat_size[1]) |
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x = self.relu(x) |
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x = self.dwconv(x) |
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x = x.flatten(2).transpose(1, 2) |
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x = self.act(x) |
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x = self.drop(x) |
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x = self.fc2(x) |
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x = self.drop(x) |
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return x |
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class Attention(nn.Module): |
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def __init__( |
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self, |
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dim, |
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num_heads=8, |
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sr_ratio=1, |
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linear_attn=False, |
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qkv_bias=True, |
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attn_drop=0., |
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proj_drop=0. |
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): |
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super().__init__() |
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assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}." |
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self.dim = dim |
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self.num_heads = num_heads |
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self.head_dim = dim // num_heads |
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self.scale = self.head_dim ** -0.5 |
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self.q = nn.Linear(dim, dim, bias=qkv_bias) |
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self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) |
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self.attn_drop = nn.Dropout(attn_drop) |
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self.proj = nn.Linear(dim, dim) |
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self.proj_drop = nn.Dropout(proj_drop) |
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if not linear_attn: |
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self.pool = None |
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if sr_ratio > 1: |
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self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) |
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self.norm = nn.LayerNorm(dim) |
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else: |
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self.sr = None |
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self.norm = None |
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self.act = None |
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else: |
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self.pool = nn.AdaptiveAvgPool2d(7) |
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self.sr = nn.Conv2d(dim, dim, kernel_size=1, stride=1) |
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self.norm = nn.LayerNorm(dim) |
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self.act = nn.GELU() |
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def forward(self, x, feat_size: List[int]): |
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B, N, C = x.shape |
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H, W = feat_size |
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q = self.q(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) |
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if self.pool is not None: |
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x_ = x.permute(0, 2, 1).reshape(B, C, H, W) |
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x_ = self.sr(self.pool(x_)).reshape(B, C, -1).permute(0, 2, 1) |
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x_ = self.norm(x_) |
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x_ = self.act(x_) |
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kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) |
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else: |
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if self.sr is not None: |
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x_ = x.permute(0, 2, 1).reshape(B, C, H, W) |
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x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1) |
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x_ = self.norm(x_) |
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kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) |
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else: |
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kv = self.kv(x).reshape(B, -1, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) |
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k, v = kv.unbind(0) |
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attn = (q @ k.transpose(-2, -1)) * self.scale |
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attn = attn.softmax(dim=-1) |
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attn = self.attn_drop(attn) |
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x = (attn @ v).transpose(1, 2).reshape(B, N, C) |
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x = self.proj(x) |
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x = self.proj_drop(x) |
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return x |
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class Block(nn.Module): |
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def __init__( |
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self, dim, num_heads, mlp_ratio=4., sr_ratio=1, linear_attn=False, qkv_bias=False, |
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drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): |
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super().__init__() |
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self.norm1 = norm_layer(dim) |
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self.attn = Attention( |
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dim, |
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num_heads=num_heads, |
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sr_ratio=sr_ratio, |
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linear_attn=linear_attn, |
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qkv_bias=qkv_bias, |
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attn_drop=attn_drop, |
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proj_drop=drop, |
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) |
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
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self.norm2 = norm_layer(dim) |
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self.mlp = MlpWithDepthwiseConv( |
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in_features=dim, |
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hidden_features=int(dim * mlp_ratio), |
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act_layer=act_layer, |
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drop=drop, |
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extra_relu=linear_attn |
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) |
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def forward(self, x, feat_size: List[int]): |
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x = x + self.drop_path(self.attn(self.norm1(x), feat_size)) |
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x = x + self.drop_path(self.mlp(self.norm2(x), feat_size)) |
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return x |
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class OverlapPatchEmbed(nn.Module): |
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""" Image to Patch Embedding |
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""" |
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def __init__(self, patch_size=7, stride=4, in_chans=3, embed_dim=768): |
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super().__init__() |
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patch_size = to_2tuple(patch_size) |
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assert max(patch_size) > stride, "Set larger patch_size than stride" |
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self.patch_size = patch_size |
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self.proj = nn.Conv2d( |
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in_chans, embed_dim, kernel_size=patch_size, stride=stride, |
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padding=(patch_size[0] // 2, patch_size[1] // 2)) |
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self.norm = nn.LayerNorm(embed_dim) |
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def forward(self, x): |
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x = self.proj(x) |
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feat_size = x.shape[-2:] |
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x = x.flatten(2).transpose(1, 2) |
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x = self.norm(x) |
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return x, feat_size |
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class PyramidVisionTransformerStage(nn.Module): |
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def __init__( |
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self, |
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dim: int, |
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dim_out: int, |
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depth: int, |
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downsample: bool = True, |
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num_heads: int = 8, |
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sr_ratio: int = 1, |
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linear_attn: bool = False, |
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mlp_ratio: float = 4.0, |
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qkv_bias: bool = True, |
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drop: float = 0., |
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attn_drop: float = 0., |
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drop_path: Union[List[float], float] = 0.0, |
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norm_layer: Callable = nn.LayerNorm, |
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): |
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super().__init__() |
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self.grad_checkpointing = False |
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if downsample: |
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self.downsample = OverlapPatchEmbed( |
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patch_size=3, |
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stride=2, |
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in_chans=dim, |
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embed_dim=dim_out) |
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else: |
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assert dim == dim_out |
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self.downsample = None |
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self.blocks = nn.ModuleList([Block( |
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dim=dim_out, |
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num_heads=num_heads, |
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sr_ratio=sr_ratio, |
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linear_attn=linear_attn, |
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mlp_ratio=mlp_ratio, |
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qkv_bias=qkv_bias, |
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drop=drop, |
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attn_drop=attn_drop, |
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drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, |
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norm_layer=norm_layer, |
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) for i in range(depth)]) |
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self.norm = norm_layer(dim_out) |
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def forward(self, x, feat_size: List[int]) -> Tuple[torch.Tensor, List[int]]: |
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if self.downsample is not None: |
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x, feat_size = self.downsample(x) |
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for blk in self.blocks: |
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if self.grad_checkpointing and not torch.jit.is_scripting(): |
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x = checkpoint.checkpoint(blk, x, feat_size) |
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else: |
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x = blk(x, feat_size) |
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x = self.norm(x) |
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x = x.reshape(x.shape[0], feat_size[0], feat_size[1], -1).permute(0, 3, 1, 2).contiguous() |
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return x, feat_size |
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class PyramidVisionTransformerV2(nn.Module): |
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def __init__( |
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self, |
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img_size=None, |
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in_chans=3, |
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num_classes=1000, |
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global_pool='avg', |
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depths=(3, 4, 6, 3), |
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embed_dims=(64, 128, 256, 512), |
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num_heads=(1, 2, 4, 8), |
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sr_ratios=(8, 4, 2, 1), |
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mlp_ratios=(8., 8., 4., 4.), |
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qkv_bias=True, |
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linear=False, |
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drop_rate=0., |
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attn_drop_rate=0., |
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drop_path_rate=0., |
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norm_layer=nn.LayerNorm, |
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): |
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super().__init__() |
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self.num_classes = num_classes |
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assert global_pool in ('avg', '') |
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self.global_pool = global_pool |
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self.depths = depths |
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num_stages = len(depths) |
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mlp_ratios = to_ntuple(num_stages)(mlp_ratios) |
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num_heads = to_ntuple(num_stages)(num_heads) |
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sr_ratios = to_ntuple(num_stages)(sr_ratios) |
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assert(len(embed_dims)) == num_stages |
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self.patch_embed = OverlapPatchEmbed( |
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patch_size=7, |
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stride=4, |
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in_chans=in_chans, |
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embed_dim=embed_dims[0]) |
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dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)] |
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cur = 0 |
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prev_dim = embed_dims[0] |
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self.stages = nn.ModuleList() |
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for i in range(num_stages): |
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self.stages.append(PyramidVisionTransformerStage( |
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dim=prev_dim, |
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dim_out=embed_dims[i], |
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depth=depths[i], |
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downsample=i > 0, |
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num_heads=num_heads[i], |
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sr_ratio=sr_ratios[i], |
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mlp_ratio=mlp_ratios[i], |
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linear_attn=linear, |
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qkv_bias=qkv_bias, |
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drop=drop_rate, |
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attn_drop=attn_drop_rate, |
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drop_path=dpr[i], |
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norm_layer=norm_layer |
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)) |
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prev_dim = embed_dims[i] |
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cur += depths[i] |
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self.num_features = embed_dims[-1] |
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self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity() |
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self.apply(self._init_weights) |
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def _init_weights(self, m): |
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if isinstance(m, nn.Linear): |
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trunc_normal_(m.weight, std=.02) |
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if isinstance(m, nn.Linear) and m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.Conv2d): |
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fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels |
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fan_out //= m.groups |
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m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) |
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if m.bias is not None: |
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m.bias.data.zero_() |
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def freeze_patch_emb(self): |
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self.patch_embed.requires_grad = False |
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@torch.jit.ignore |
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def no_weight_decay(self): |
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return {} |
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@torch.jit.ignore |
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def group_matcher(self, coarse=False): |
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matcher = dict( |
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stem=r'^patch_embed', |
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blocks=r'^stages\.(\d+)' |
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) |
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return matcher |
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@torch.jit.ignore |
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def set_grad_checkpointing(self, enable=True): |
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for s in self.stages: |
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s.grad_checkpointing = enable |
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def get_classifier(self): |
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return self.head |
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def reset_classifier(self, num_classes, global_pool=None): |
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self.num_classes = num_classes |
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if global_pool is not None: |
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assert global_pool in ('avg', '') |
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self.global_pool = global_pool |
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self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() |
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def forward_features(self, x): |
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x, feat_size = self.patch_embed(x) |
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for stage in self.stages: |
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x, feat_size = stage(x, feat_size=feat_size) |
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return x |
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def forward_head(self, x, pre_logits: bool = False): |
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if self.global_pool: |
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x = x.mean(dim=(-1, -2)) |
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return x if pre_logits else self.head(x) |
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def forward(self, x): |
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x = self.forward_features(x) |
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x = self.forward_head(x) |
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return x |
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def _checkpoint_filter_fn(state_dict, model): |
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""" Remap original checkpoints -> timm """ |
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if 'patch_embed.proj.weight' in state_dict: |
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return state_dict |
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out_dict = {} |
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import re |
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for k, v in state_dict.items(): |
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if k.startswith('patch_embed'): |
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k = k.replace('patch_embed1', 'patch_embed') |
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k = k.replace('patch_embed2', 'stages.1.downsample') |
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k = k.replace('patch_embed3', 'stages.2.downsample') |
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k = k.replace('patch_embed4', 'stages.3.downsample') |
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k = k.replace('dwconv.dwconv', 'dwconv') |
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k = re.sub(r'block(\d+).(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.blocks.{x.group(2)}', k) |
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k = re.sub(r'^norm(\d+)', lambda x: f'stages.{int(x.group(1)) - 1}.norm', k) |
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out_dict[k] = v |
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return out_dict |
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def _create_pvt2(variant, pretrained=False, **kwargs): |
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if kwargs.get('features_only', None): |
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raise RuntimeError('features_only not implemented for Vision Transformer models.') |
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model = build_model_with_cfg( |
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PyramidVisionTransformerV2, variant, pretrained, |
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pretrained_filter_fn=_checkpoint_filter_fn, |
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**kwargs |
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) |
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return model |
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@register_model |
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def pvt_v2_b0(pretrained=False, **kwargs): |
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model_kwargs = dict( |
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depths=(2, 2, 2, 2), embed_dims=(32, 64, 160, 256), num_heads=(1, 2, 5, 8), |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
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return _create_pvt2('pvt_v2_b0', pretrained=pretrained, **model_kwargs) |
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@register_model |
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def pvt_v2_b1(pretrained=False, **kwargs): |
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model_kwargs = dict( |
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depths=(2, 2, 2, 2), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
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return _create_pvt2('pvt_v2_b1', pretrained=pretrained, **model_kwargs) |
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@register_model |
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def pvt_v2_b2(pretrained=False, **kwargs): |
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model_kwargs = dict( |
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depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
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return _create_pvt2('pvt_v2_b2', pretrained=pretrained, **model_kwargs) |
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@register_model |
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def pvt_v2_b3(pretrained=False, **kwargs): |
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model_kwargs = dict( |
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depths=(3, 4, 18, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
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return _create_pvt2('pvt_v2_b3', pretrained=pretrained, **model_kwargs) |
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@register_model |
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def pvt_v2_b4(pretrained=False, **kwargs): |
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model_kwargs = dict( |
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depths=(3, 8, 27, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) |
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return _create_pvt2('pvt_v2_b4', pretrained=pretrained, **model_kwargs) |
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@register_model |
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def pvt_v2_b5(pretrained=False, **kwargs): |
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model_kwargs = dict( |
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depths=(3, 6, 40, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), |
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mlp_ratios=(4, 4, 4, 4), norm_layer=partial(nn.LayerNorm, eps=1e-6), |
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**kwargs) |
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return _create_pvt2('pvt_v2_b5', pretrained=pretrained, **model_kwargs) |
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@register_model |
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def pvt_v2_b2_li(pretrained=False, **kwargs): |
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model_kwargs = dict( |
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depths=(3, 4, 6, 3), embed_dims=(64, 128, 320, 512), num_heads=(1, 2, 5, 8), |
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norm_layer=partial(nn.LayerNorm, eps=1e-6), linear=True, **kwargs) |
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return _create_pvt2('pvt_v2_b2_li', pretrained=pretrained, **model_kwargs) |
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