File size: 19,534 Bytes
9c6594c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 |
# Copyright The Lightning team.
#
# 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.
from collections.abc import Sequence
from typing import Any, List, Optional, Union
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.image.ssim import _multiscale_ssim_update, _ssim_check_inputs, _ssim_update
from torchmetrics.metric import Metric
from torchmetrics.utilities.data import dim_zero_cat
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["StructuralSimilarityIndexMeasure.plot", "MultiScaleStructuralSimilarityIndexMeasure.plot"]
class StructuralSimilarityIndexMeasure(Metric):
"""Compute Structural Similarity Index Measure (SSIM_).
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): Predictions from model
- ``target`` (:class:`~torch.Tensor`): Ground truth values
As output of `forward` and `compute` the metric returns the following output
- ``ssim`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average SSIM value
over sample else returns tensor of shape ``(N,)`` with SSIM values per sample
Args:
preds: estimated image
target: ground truth image
gaussian_kernel: If ``True`` (default), a gaussian kernel is used, if ``False`` a uniform kernel is used
sigma: Standard deviation of the gaussian kernel, anisotropic kernels are possible.
Ignored if a uniform kernel is used
kernel_size: the size of the uniform kernel, anisotropic kernels are possible.
Ignored if a Gaussian kernel is used
reduction: a method to reduce metric score over individual batch scores
- ``'elementwise_mean'``: takes the mean
- ``'sum'``: takes the sum
- ``'none'`` or ``None``: no reduction will be applied
data_range:
the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
the range is calculated as the difference and input is clamped between the values.
k1: Parameter of SSIM.
k2: Parameter of SSIM.
return_full_image: If true, the full ``ssim`` image is returned as a second argument.
Mutually exclusive with ``return_contrast_sensitivity``
return_contrast_sensitivity: If true, the constant term is returned as a second argument.
The luminance term can be obtained with luminance=ssim/contrast
Mutually exclusive with ``return_full_image``
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example:
>>> import torch
>>> from torchmetrics.image import StructuralSimilarityIndexMeasure
>>> preds = torch.rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> ssim = StructuralSimilarityIndexMeasure(data_range=1.0)
>>> ssim(preds, target)
tensor(0.9219)
"""
higher_is_better: bool = True
is_differentiable: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
preds: List[Tensor]
target: List[Tensor]
def __init__(
self,
gaussian_kernel: bool = True,
sigma: Union[float, Sequence[float]] = 1.5,
kernel_size: Union[int, Sequence[int]] = 11,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
data_range: Optional[Union[float, tuple[float, float]]] = None,
k1: float = 0.01,
k2: float = 0.03,
return_full_image: bool = False,
return_contrast_sensitivity: bool = False,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
valid_reduction = ("elementwise_mean", "sum", "none", None)
if reduction not in valid_reduction:
raise ValueError(f"Argument `reduction` must be one of {valid_reduction}, but got {reduction}")
if reduction in ("elementwise_mean", "sum"):
self.add_state("similarity", default=torch.tensor(0.0), dist_reduce_fx="sum")
else:
self.add_state("similarity", default=[], dist_reduce_fx="cat")
self.add_state("total", default=torch.tensor(0.0), dist_reduce_fx="sum")
if return_contrast_sensitivity or return_full_image:
self.add_state("image_return", default=[], dist_reduce_fx="cat")
self.gaussian_kernel = gaussian_kernel
self.sigma = sigma
self.kernel_size = kernel_size
self.reduction = reduction
self.data_range = data_range
self.k1 = k1
self.k2 = k2
self.return_full_image = return_full_image
self.return_contrast_sensitivity = return_contrast_sensitivity
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
preds, target = _ssim_check_inputs(preds, target)
similarity_pack = _ssim_update(
preds,
target,
self.gaussian_kernel,
self.sigma,
self.kernel_size,
self.data_range,
self.k1,
self.k2,
self.return_full_image,
self.return_contrast_sensitivity,
)
if isinstance(similarity_pack, tuple):
similarity, image = similarity_pack
else:
similarity = similarity_pack
if self.return_contrast_sensitivity or self.return_full_image:
if not isinstance(self.image_return, list):
raise TypeError("Expected `self.image_return` to be a list when returning images.")
self.image_return.append(image)
if self.reduction in ("elementwise_mean", "sum"):
if not isinstance(self.similarity, torch.Tensor): # Ensure it's a Tensor
raise TypeError("Expected `self.similarity` to be a Tensor for reductions.")
self.similarity += similarity.sum()
if not isinstance(self.total, torch.Tensor):
raise TypeError("Expected `self.total` to be a Tensor.")
self.total += preds.shape[0]
else:
if not isinstance(self.similarity, list):
raise TypeError("Expected `self.similarity` to be a list when reduction='none'.")
self.similarity.append(similarity)
def compute(self) -> Union[Tensor, tuple[Tensor, Tensor]]:
"""Compute SSIM over state."""
if self.reduction == "elementwise_mean":
if isinstance(self.similarity, Tensor) and isinstance(self.total, Tensor):
similarity = self.similarity / self.total
else:
raise TypeError(
"Expected `self.similarity`and `self.total` to be of type Tensor for elementwise_mean reduction."
)
elif self.reduction == "sum":
if not isinstance(self.similarity, Tensor):
raise TypeError("Expected `self.similarity` to be a Tensor for sum reduction.")
similarity = self.similarity
else:
if isinstance(self.similarity, list):
similarity = dim_zero_cat(self.similarity) # Concatenate list of Tensors
else:
raise TypeError("Expected `self.similarity` to be a list for reduction='none'.")
if self.return_contrast_sensitivity or self.return_full_image:
if isinstance(self.image_return, list):
image_return = dim_zero_cat(self.image_return) # Concatenate list of Tensors
else:
raise TypeError("Expected `self.image_return` to be a list when returning images.")
return similarity, image_return
return similarity
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.image import StructuralSimilarityIndexMeasure
>>> preds = torch.rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> metric = StructuralSimilarityIndexMeasure(data_range=1.0)
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.image import StructuralSimilarityIndexMeasure
>>> preds = torch.rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> metric = StructuralSimilarityIndexMeasure(data_range=1.0)
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class MultiScaleStructuralSimilarityIndexMeasure(Metric):
"""Compute `MultiScaleSSIM`_, Multi-scale Structural Similarity Index Measure.
This metric is is a generalization of Structural Similarity Index Measure by incorporating image details at
different resolution scores.
As input to ``forward`` and ``update`` the metric accepts the following input
- ``preds`` (:class:`~torch.Tensor`): Predictions from model
- ``target`` (:class:`~torch.Tensor`): Ground truth values
As output of `forward` and `compute` the metric returns the following output
- ``msssim`` (:class:`~torch.Tensor`): if ``reduction!='none'`` returns float scalar tensor with average MSSSIM
value over sample else returns tensor of shape ``(N,)`` with SSIM values per sample
Args:
gaussian_kernel: If ``True`` (default), a gaussian kernel is used, if false a uniform kernel is used
kernel_size: size of the gaussian kernel
sigma: Standard deviation of the gaussian kernel
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean
- ``'sum'``: takes the sum
- ``'none'`` or ``None``: no reduction will be applied
data_range:
the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then
the range is calculated as the difference and input is clamped between the values.
The ``data_range`` must be given when ``dim`` is not None.
k1: Parameter of structural similarity index measure.
k2: Parameter of structural similarity index measure.
betas: Exponent parameters for individual similarities and contrastive sensitivities returned by different image
resolutions.
normalize: When MultiScaleStructuralSimilarityIndexMeasure loss is used for training, it is desirable to use
normalizes to improve the training stability. This `normalize` argument is out of scope of the original
implementation [1], and it is adapted from https://github.com/jorge-pessoa/pytorch-msssim instead.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Return:
Tensor with Multi-Scale SSIM score
Raises:
ValueError:
If ``kernel_size`` is not an int or a Sequence of ints with size 2 or 3.
ValueError:
If ``betas`` is not a tuple of floats with length 2.
ValueError:
If ``normalize`` is neither `None`, `ReLU` nor `simple`.
Example:
>>> from torch import rand
>>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> preds = torch.rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> ms_ssim = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> ms_ssim(preds, target)
tensor(0.9628)
"""
higher_is_better: bool = True
is_differentiable: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
preds: List[Tensor]
target: List[Tensor]
def __init__(
self,
gaussian_kernel: bool = True,
kernel_size: Union[int, Sequence[int]] = 11,
sigma: Union[float, Sequence[float]] = 1.5,
reduction: Literal["elementwise_mean", "sum", "none", None] = "elementwise_mean",
data_range: Optional[Union[float, tuple[float, float]]] = None,
k1: float = 0.01,
k2: float = 0.03,
betas: tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
normalize: Literal["relu", "simple", None] = "relu",
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
valid_reduction = ("elementwise_mean", "sum", "none", None)
if reduction not in valid_reduction:
raise ValueError(f"Argument `reduction` must be one of {valid_reduction}, but got {reduction}")
if reduction in ("elementwise_mean", "sum"):
self.add_state("similarity", default=torch.tensor(0.0), dist_reduce_fx="sum")
else:
self.add_state("similarity", default=[], dist_reduce_fx="cat")
self.add_state("total", default=torch.tensor(0.0), dist_reduce_fx="sum")
if not (isinstance(kernel_size, (Sequence, int))):
raise ValueError(
f"Argument `kernel_size` expected to be an sequence or an int, or a single int. Got {kernel_size}"
)
if isinstance(kernel_size, Sequence) and (
len(kernel_size) not in (2, 3) or not all(isinstance(ks, int) for ks in kernel_size)
):
raise ValueError(
"Argument `kernel_size` expected to be an sequence of size 2 or 3 where each element is an int, "
f"or a single int. Got {kernel_size}"
)
self.gaussian_kernel = gaussian_kernel
self.sigma = sigma
self.kernel_size = kernel_size
self.reduction = reduction
self.data_range = data_range
self.k1 = k1
self.k2 = k2
if not isinstance(betas, tuple):
raise ValueError("Argument `betas` is expected to be of a type tuple.")
if isinstance(betas, tuple) and not all(isinstance(beta, float) for beta in betas):
raise ValueError("Argument `betas` is expected to be a tuple of floats.")
self.betas = betas
if normalize and normalize not in ("relu", "simple"):
raise ValueError("Argument `normalize` to be expected either `None` or one of 'relu' or 'simple'")
self.normalize = normalize
def update(self, preds: Tensor, target: Tensor) -> None:
"""Update state with predictions and targets."""
preds, target = _ssim_check_inputs(preds, target)
similarity = _multiscale_ssim_update(
preds,
target,
self.gaussian_kernel,
self.sigma,
self.kernel_size,
self.data_range,
self.k1,
self.k2,
self.betas,
self.normalize,
)
if self.reduction in ("none", None):
if not isinstance(self.similarity, list):
raise TypeError("Expected `self.similarity` to be a list for reduction='none'.")
self.similarity.append(similarity)
else:
if not isinstance(self.similarity, Tensor):
raise TypeError("Expected `self.similarity` to be a Tensor for elementwise_mean or sum reduction.")
self.similarity += similarity.sum()
if not isinstance(self.total, Tensor):
raise TypeError("Expected `self.total` to be a Tensor.")
self.total += torch.tensor(preds.shape[0], dtype=self.total.dtype, device=self.total.device)
def compute(self) -> Tensor:
"""Compute MS-SSIM over state."""
if self.reduction in ("none", None):
if isinstance(self.similarity, list):
return dim_zero_cat(self.similarity)
raise TypeError("Expected `self.similarity` to be a list for reduction='none'.")
if self.reduction == "sum":
if isinstance(self.similarity, Tensor):
return self.similarity
raise TypeError("Expected `self.similarity` to be a Tensor for sum reduction.")
if isinstance(self.similarity, Tensor) and isinstance(self.total, Tensor):
return self.similarity / self.total
raise TypeError("Expected `self.similarity` and `self.total` to be Tensors for elementwise_mean reduction.")
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> from torch import rand
>>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> preds = rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> from torch import rand
>>> from torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> preds = rand([3, 3, 256, 256])
>>> target = preds * 0.75
>>> metric = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
|