File size: 7,962 Bytes
3d1f2c9 |
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 |
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: 4, 2: 5, 3: 6, 4: 1, 5: 2, 6: 3, 7: 10, 8: 12, 9: 11, 10: 7, 11: 9, 12: 8, 13: 13,
14: 14, 15: 16, 16: 15, 17: 17, 18: 21, 19: 22, 20: 23, 21: 18, 22: 19, 23: 20, 24:24}
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(),
])
|