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ssim.py
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ssim.py
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# Copyright The PyTorch 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 typing import Any, List, Optional, Sequence, Tuple
import torch
from deprecate import deprecated, void
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.image.ssim import _multiscale_ssim_compute, _ssim_compute, _ssim_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import _future_warning, rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat
class StructuralSimilarityIndexMeasure(Metric):
"""Computes Structual Similarity Index Measure (SSIM_).
Args:
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 (default)
- ``'sum'``: takes the sum
- ``'none'``: no reduction will be applied
data_range: Range of the image. If ``None``, it is determined from the image (max - min)
k1: Parameter of SSIM.
k2: Parameter of SSIM.
Return:
Tensor with SSIM score
Example:
>>> from torchmetrics import StructuralSimilarityIndexMeasure
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> ssim = StructuralSimilarityIndexMeasure()
>>> ssim(preds, target)
tensor(0.9219)
"""
preds: List[Tensor]
target: List[Tensor]
higher_is_better = True
def __init__(
self,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: str = "elementwise_mean",
data_range: Optional[float] = None,
k1: float = 0.01,
k2: float = 0.03,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
)
rank_zero_warn(
"Metric `SSIM` will save all targets and"
" predictions in buffer. For large datasets this may lead"
" to large memory footprint."
)
self.add_state("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
self.kernel_size = kernel_size
self.sigma = sigma
self.data_range = data_range
self.k1 = k1
self.k2 = k2
self.reduction = reduction
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
preds, target = _ssim_update(preds, target)
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
"""Computes explained variance over state."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _ssim_compute(
preds, target, self.kernel_size, self.sigma, self.reduction, self.data_range, self.k1, self.k2
)
class SSIM(StructuralSimilarityIndexMeasure):
"""Computes Structual Similarity Index Measure (SSIM_).
.. deprecated:: v0.7
Use :class:`torchmetrics.StructuralSimilarityIndexMeasure`. Will be removed in v0.8.
Example:
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> ssim = SSIM()
>>> ssim(preds, target)
tensor(0.9219)
"""
@deprecated(target=StructuralSimilarityIndexMeasure, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
def __init__(
self,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: str = "elementwise_mean",
data_range: Optional[float] = None,
k1: float = 0.01,
k2: float = 0.03,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
) -> None:
void(kernel_size, sigma, reduction, data_range, k1, k2, compute_on_step, dist_sync_on_step, process_group)
class MultiScaleStructuralSimilarityIndexMeasure(Metric):
"""Computes `MultiScaleSSIM`_, Multi-scale Structural Similarity Index Measure, which is a generalization of
Structural Similarity Index Measure by incorporating image details at different resolution scores.
Args:
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 (default)
- ``'sum'``: takes the sum
- ``'none'``: no reduction will be applied
data_range: Range of the image. If ``None``, it is determined from the image (max - min)
k1: Parameter of structural similarity index measure.
k2: Parameter of structural similarity index measure.
betas: Exponent parameters for individual similarities and contrastive sensitivies 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.
Return:
Tensor with Multi-Scale SSIM score
Example:
>>> from torchmetrics import MultiScaleStructuralSimilarityIndexMeasure
>>> preds = torch.rand([1, 1, 256, 256], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> ms_ssim = MultiScaleStructuralSimilarityIndexMeasure()
>>> ms_ssim(preds, target)
tensor(0.9558)
References:
[1] Multi-Scale Structural Similarity For Image Quality Assessment by Zhou Wang, Eero P. Simoncelli and Alan C.
Bovik `MultiScaleSSIM`_
"""
preds: List[Tensor]
target: List[Tensor]
higher_is_better = True
is_differentiable = True
def __init__(
self,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: str = "elementwise_mean",
data_range: Optional[float] = None,
k1: float = 0.01,
k2: float = 0.03,
betas: Tuple[float, ...] = (0.0448, 0.2856, 0.3001, 0.2363, 0.1333),
normalize: Optional[Literal["relu", "simple"]] = None,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
)
rank_zero_warn(
"Metric `MS_SSIM` will save all targets and"
" predictions in buffer. For large datasets this may lead"
" to large memory footprint."
)
self.add_state("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
all_kernel_ints = all(isinstance(ks, int) for ks in kernel_size)
if not isinstance(kernel_size, Sequence) or len(kernel_size) != 2 or not all_kernel_ints:
raise ValueError(
"Argument `kernel_size` expected to be an sequence of size 2 where each element is an int"
f" but got {kernel_size}"
)
self.kernel_size = kernel_size
self.sigma = sigma
self.data_range = data_range
self.k1 = k1
self.k2 = k2
self.reduction = reduction
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: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model of shape `[N, C, H, W]`
target: Ground truth values of shape `[N, C, H, W]`
"""
preds, target = _ssim_update(preds, target)
self.preds.append(preds)
self.target.append(target)
def compute(self) -> Tensor:
"""Computes explained variance over state."""
preds = dim_zero_cat(self.preds)
target = dim_zero_cat(self.target)
return _multiscale_ssim_compute(
preds,
target,
self.kernel_size,
self.sigma,
self.reduction,
self.data_range,
self.k1,
self.k2,
self.betas,
self.normalize,
)