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36c95ba | 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 | """Module containing functionals for intensity normalisation."""
from typing import List, Tuple, Union
import torch
import torch.nn as nn
__all__ = ["normalize", "normalize_min_max", "denormalize", "Normalize", "Denormalize"]
class Normalize(nn.Module):
r"""Normalize a tensor image with mean and standard deviation.
.. math::
\text{input[channel] = (input[channel] - mean[channel]) / std[channel]}
Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels,
Args:
mean: Mean for each channel.
std: Standard deviations for each channel.
Shape:
- Input: Image tensor of size :math:`(*, C, ...)`.
- Output: Normalised tensor with same size as input :math:`(*, C, ...)`.
Examples:
>>> x = torch.rand(1, 4, 3, 3)
>>> out = Normalize(0.0, 255.)(x)
>>> out.shape
torch.Size([1, 4, 3, 3])
>>> x = torch.rand(1, 4, 3, 3)
>>> mean = torch.zeros(4)
>>> std = 255. * torch.ones(4)
>>> out = Normalize(mean, std)(x)
>>> out.shape
torch.Size([1, 4, 3, 3])
"""
def __init__(
self,
mean: Union[torch.Tensor, Tuple[float], List[float], float],
std: Union[torch.Tensor, Tuple[float], List[float], float],
) -> None:
super().__init__()
if isinstance(mean, float):
mean = torch.tensor([mean])
if isinstance(std, float):
std = torch.tensor([std])
if isinstance(mean, (tuple, list)):
mean = torch.tensor(mean)
if isinstance(std, (tuple, list)):
std = torch.tensor(std)
self.mean = mean
self.std = std
def forward(self, input: torch.Tensor) -> torch.Tensor:
return normalize(input, self.mean, self.std)
def __repr__(self):
repr = f"(mean={self.mean}, std={self.std})"
return self.__class__.__name__ + repr
def normalize(data: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
r"""Normalize an image/video tensor with mean and standard deviation.
.. math::
\text{input[channel] = (input[channel] - mean[channel]) / std[channel]}
Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels,
Args:
data: Image tensor of size :math:`(B, C, *)`.
mean: Mean for each channel.
std: Standard deviations for each channel.
Return:
Normalised tensor with same size as input :math:`(B, C, *)`.
Examples:
>>> x = torch.rand(1, 4, 3, 3)
>>> out = normalize(x, torch.tensor([0.0]), torch.tensor([255.]))
>>> out.shape
torch.Size([1, 4, 3, 3])
>>> x = torch.rand(1, 4, 3, 3)
>>> mean = torch.zeros(4)
>>> std = 255. * torch.ones(4)
>>> out = normalize(x, mean, std)
>>> out.shape
torch.Size([1, 4, 3, 3])
"""
shape = data.shape
if len(mean.shape) == 0 or mean.shape[0] == 1:
mean = mean.expand(shape[1])
if len(std.shape) == 0 or std.shape[0] == 1:
std = std.expand(shape[1])
# Allow broadcast on channel dimension
if mean.shape and mean.shape[0] != 1:
if mean.shape[0] != data.shape[1] and mean.shape[:2] != data.shape[:2]:
raise ValueError(f"mean length and number of channels do not match. Got {mean.shape} and {data.shape}.")
# Allow broadcast on channel dimension
if std.shape and std.shape[0] != 1:
if std.shape[0] != data.shape[1] and std.shape[:2] != data.shape[:2]:
raise ValueError(f"std length and number of channels do not match. Got {std.shape} and {data.shape}.")
mean = torch.as_tensor(mean, device=data.device, dtype=data.dtype)
std = torch.as_tensor(std, device=data.device, dtype=data.dtype)
if mean.shape:
mean = mean[..., :, None]
if std.shape:
std = std[..., :, None]
out: torch.Tensor = (data.view(shape[0], shape[1], -1) - mean) / std
return out.view(shape)
class Denormalize(nn.Module):
r"""Denormalize a tensor image with mean and standard deviation.
.. math::
\text{input[channel] = (input[channel] * std[channel]) + mean[channel]}
Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels,
Args:
mean: Mean for each channel.
std: Standard deviations for each channel.
Shape:
- Input: Image tensor of size :math:`(*, C, ...)`.
- Output: Denormalised tensor with same size as input :math:`(*, C, ...)`.
Examples:
>>> x = torch.rand(1, 4, 3, 3)
>>> out = Denormalize(0.0, 255.)(x)
>>> out.shape
torch.Size([1, 4, 3, 3])
>>> x = torch.rand(1, 4, 3, 3, 3)
>>> mean = torch.zeros(1, 4)
>>> std = 255. * torch.ones(1, 4)
>>> out = Denormalize(mean, std)(x)
>>> out.shape
torch.Size([1, 4, 3, 3, 3])
"""
def __init__(self, mean: Union[torch.Tensor, float], std: Union[torch.Tensor, float]) -> None:
super().__init__()
self.mean = mean
self.std = std
def forward(self, input: torch.Tensor) -> torch.Tensor:
return denormalize(input, self.mean, self.std)
def __repr__(self):
repr = f"(mean={self.mean}, std={self.std})"
return self.__class__.__name__ + repr
def denormalize(data: torch.Tensor, mean: Union[torch.Tensor, float], std: Union[torch.Tensor, float]) -> torch.Tensor:
r"""Denormalize an image/video tensor with mean and standard deviation.
.. math::
\text{input[channel] = (input[channel] * std[channel]) + mean[channel]}
Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels,
Args:
input: Image tensor of size :math:`(B, C, *)`.
mean: Mean for each channel.
std: Standard deviations for each channel.
Return:
Denormalised tensor with same size as input :math:`(B, C, *)`.
Examples:
>>> x = torch.rand(1, 4, 3, 3)
>>> out = denormalize(x, 0.0, 255.)
>>> out.shape
torch.Size([1, 4, 3, 3])
>>> x = torch.rand(1, 4, 3, 3, 3)
>>> mean = torch.zeros(1, 4)
>>> std = 255. * torch.ones(1, 4)
>>> out = denormalize(x, mean, std)
>>> out.shape
torch.Size([1, 4, 3, 3, 3])
"""
shape = data.shape
if isinstance(mean, float):
mean = torch.tensor([mean] * shape[1], device=data.device, dtype=data.dtype)
if isinstance(std, float):
std = torch.tensor([std] * shape[1], device=data.device, dtype=data.dtype)
if not isinstance(data, torch.Tensor):
raise TypeError(f"data should be a tensor. Got {type(data)}")
if not isinstance(mean, torch.Tensor):
raise TypeError(f"mean should be a tensor or a float. Got {type(mean)}")
if not isinstance(std, torch.Tensor):
raise TypeError(f"std should be a tensor or float. Got {type(std)}")
# Allow broadcast on channel dimension
if mean.shape and mean.shape[0] != 1:
if mean.shape[0] != data.shape[-3] and mean.shape[:2] != data.shape[:2]:
raise ValueError(f"mean length and number of channels do not match. Got {mean.shape} and {data.shape}.")
# Allow broadcast on channel dimension
if std.shape and std.shape[0] != 1:
if std.shape[0] != data.shape[-3] and std.shape[:2] != data.shape[:2]:
raise ValueError(f"std length and number of channels do not match. Got {std.shape} and {data.shape}.")
mean = torch.as_tensor(mean, device=data.device, dtype=data.dtype)
std = torch.as_tensor(std, device=data.device, dtype=data.dtype)
if mean.shape:
mean = mean[..., :, None]
if std.shape:
std = std[..., :, None]
out: torch.Tensor = (data.view(shape[0], shape[1], -1) * std) + mean
return out.view(shape)
def normalize_min_max(x: torch.Tensor, min_val: float = 0.0, max_val: float = 1.0, eps: float = 1e-6) -> torch.Tensor:
r"""Normalise an image/video tensor by MinMax and re-scales the value between a range.
The data is normalised using the following formulation:
.. math::
y_i = (b - a) * \frac{x_i - \text{min}(x)}{\text{max}(x) - \text{min}(x)} + a
where :math:`a` is :math:`\text{min_val}` and :math:`b` is :math:`\text{max_val}`.
Args:
x: The image tensor to be normalised with shape :math:`(B, C, *)`.
min_val: The minimum value for the new range.
max_val: The maximum value for the new range.
eps: Float number to avoid zero division.
Returns:
The normalised image tensor with same shape as input :math:`(B, C, *)`.
Example:
>>> x = torch.rand(1, 5, 3, 3)
>>> x_norm = normalize_min_max(x, min_val=-1., max_val=1.)
>>> x_norm.min()
tensor(-1.)
>>> x_norm.max()
tensor(1.0000)
"""
if not isinstance(x, torch.Tensor):
raise TypeError(f"data should be a tensor. Got: {type(x)}.")
if not isinstance(min_val, float):
raise TypeError(f"'min_val' should be a float. Got: {type(min_val)}.")
if not isinstance(max_val, float):
raise TypeError(f"'b' should be a float. Got: {type(max_val)}.")
if len(x.shape) < 3:
raise ValueError(f"Input shape must be at least a 3d tensor. Got: {x.shape}.")
shape = x.shape
B, C = shape[0], shape[1]
x_min: torch.Tensor = x.view(B, C, -1).min(-1)[0].view(B, C, 1)
x_max: torch.Tensor = x.view(B, C, -1).max(-1)[0].view(B, C, 1)
x_out: torch.Tensor = (max_val - min_val) * (x.view(B, C, -1) - x_min) / (x_max - x_min + eps) + min_val
return x_out.view(shape)
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