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import torch
import math
import numbers
import torch.nn.functional as F
import torchvision.transforms.functional as f
import torchvision.transforms as T
import torchvision.transforms.v2 as v2
from torchvision import transforms as _transforms
from typing import List, Optional, Tuple, Union
from scipy import ndimage
from torch import Tensor
from sn_calibration.src.evaluate_extremities import mirror_labels
class ToTensor(torch.nn.Module):
def __call__(self, sample):
image, target, mask = sample['image'], sample['target'], sample['mask']
return {'image': f.to_tensor(image).float(),
'target': torch.from_numpy(target).float(),
'mask': torch.from_numpy(mask).float()}
def __repr__(self) -> str:
return f"{self.__class__.__name__}()"
class RandomHorizontalFlip(torch.nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.p = p
self.swap_dict = {1:3, 2:2, 3:1, 4:7, 5:6, 6:5, 7:4, 8:11, 9:10, 10:9, 11:8, 12:15, 13:14, 14:13, 15:12,
16:19, 17:18, 18:17, 19:16, 20:23, 21:22, 22:21, 23:20, 24:27, 25:26, 26:25, 27:24, 28:30,
29:29, 30:28, 31:33, 32:32, 33:31, 34:36, 35:35, 36:34, 37:40, 38:39, 39:38, 40:37, 41:44,
42:43, 43:42, 44:41, 45:57, 46:56, 47:55, 48:49, 49:48, 50:52, 51:51, 52:50, 53:54, 54:53,
55:47, 56:46, 57:45, 58:58}
def forward(self, sample):
if torch.rand(1) < self.p:
image, target, mask = sample['image'], sample['target'], sample['mask']
image = f.hflip(image)
target = f.hflip(target)
target_swap, mask_swap = self.swap_layers(target, mask)
return {'image': image,
'target': target_swap,
'mask': mask_swap}
else:
return {'image': sample['image'],
'target': sample['target'],
'mask': sample['mask']}
def swap_layers(self, target, mask):
target_swap = torch.zeros_like(target)
mask_swap = torch.zeros_like(mask)
for kp in self.swap_dict.keys():
kp_swap = self.swap_dict[kp]
target_swap[kp_swap-1, :, :] = target[kp-1, :, :].clone()
mask_swap[kp_swap-1] = mask[kp-1].clone()
return target_swap, mask_swap
def __repr__(self) -> str:
return f"{self.__class__.__name__}(p={self.p})"
class AddGaussianNoise(torch.nn.Module):
def __init__(self, mean=0., std=2.):
self.std = std
self.mean = mean
def __call__(self, sample):
image = sample['image']
image += torch.randn(image.size()) * self.std + self.mean
image = torch.clip(image, 0, 1)
return {'image': image,
'target': sample['target'],
'mask': sample['mask']}
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
class ColorJitter(torch.nn.Module):
def __init__(
self,
brightness: Union[float, Tuple[float, float]] = 0,
contrast: Union[float, Tuple[float, float]] = 0,
saturation: Union[float, Tuple[float, float]] = 0,
hue: Union[float, Tuple[float, float]] = 0,
) -> None:
super().__init__()
self.brightness = self._check_input(brightness, "brightness")
self.contrast = self._check_input(contrast, "contrast")
self.saturation = self._check_input(saturation, "saturation")
self.hue = self._check_input(hue, "hue", center=0, bound=(-0.5, 0.5), clip_first_on_zero=False)
@torch.jit.unused
def _check_input(self, value, name, center=1, bound=(0, float("inf")), clip_first_on_zero=True):
if isinstance(value, numbers.Number):
if value < 0:
raise ValueError(f"If {name} is a single number, it must be non negative.")
value = [center - float(value), center + float(value)]
if clip_first_on_zero:
value[0] = max(value[0], 0.0)
elif isinstance(value, (tuple, list)) and len(value) == 2:
value = [float(value[0]), float(value[1])]
else:
raise TypeError(f"{name} should be a single number or a list/tuple with length 2.")
if not bound[0] <= value[0] <= value[1] <= bound[1]:
raise ValueError(f"{name} values should be between {bound}, but got {value}.")
# if value is 0 or (1., 1.) for brightness/contrast/saturation
# or (0., 0.) for hue, do nothing
if value[0] == value[1] == center:
return None
else:
return tuple(value)
@staticmethod
def get_params(
brightness: Optional[List[float]],
contrast: Optional[List[float]],
saturation: Optional[List[float]],
hue: Optional[List[float]],
) -> Tuple[Tensor, Optional[float], Optional[float], Optional[float], Optional[float]]:
"""Get the parameters for the randomized transform to be applied on image.
Args:
brightness (tuple of float (min, max), optional): The range from which the brightness_factor is chosen
uniformly. Pass None to turn off the transformation.
contrast (tuple of float (min, max), optional): The range from which the contrast_factor is chosen
uniformly. Pass None to turn off the transformation.
saturation (tuple of float (min, max), optional): The range from which the saturation_factor is chosen
uniformly. Pass None to turn off the transformation.
hue (tuple of float (min, max), optional): The range from which the hue_factor is chosen uniformly.
Pass None to turn off the transformation.
Returns:
tuple: The parameters used to apply the randomized transform
along with their random order.
"""
fn_idx = torch.randperm(4)
b = None if brightness is None else float(torch.empty(1).uniform_(brightness[0], brightness[1]))
c = None if contrast is None else float(torch.empty(1).uniform_(contrast[0], contrast[1]))
s = None if saturation is None else float(torch.empty(1).uniform_(saturation[0], saturation[1]))
h = None if hue is None else float(torch.empty(1).uniform_(hue[0], hue[1]))
return fn_idx, b, c, s, h
def forward(self, sample):
"""
Args:
img (PIL Image or Tensor): Input image.
Returns:
PIL Image or Tensor: Color jittered image.
"""
fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = self.get_params(
self.brightness, self.contrast, self.saturation, self.hue
)
image = sample['image']
for fn_id in fn_idx:
if fn_id == 0 and brightness_factor is not None:
image = f.adjust_brightness(image, brightness_factor)
elif fn_id == 1 and contrast_factor is not None:
image = f.adjust_contrast(image, contrast_factor)
elif fn_id == 2 and saturation_factor is not None:
image = f.adjust_saturation(image, saturation_factor)
elif fn_id == 3 and hue_factor is not None:
image = f.adjust_hue(image, hue_factor)
return {'image': image,
'target': sample['target'],
'mask': sample['mask']}
def __repr__(self) -> str:
s = (
f"{self.__class__.__name__}("
f"brightness={self.brightness}"
f", contrast={self.contrast}"
f", saturation={self.saturation}"
f", hue={self.hue})"
)
return s
transforms = v2.Compose([
ToTensor(),
RandomHorizontalFlip(p=.5),
ColorJitter(brightness=(0.05), contrast=(0.05), saturation=(0.05), hue=(0.05)),
AddGaussianNoise(0, .1)
])
no_transforms = v2.Compose([
ToTensor(),
])
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