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ssim.py
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ssim.py
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# 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 typing import Any, List, Optional, Sequence, Tuple, 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:
self.image_return.append(image)
if self.reduction in ("elementwise_mean", "sum"):
self.similarity += similarity.sum()
self.total += preds.shape[0]
else:
self.similarity.append(similarity)
def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:
"""Compute SSIM over state."""
if self.reduction == "elementwise_mean":
similarity = self.similarity / self.total
elif self.reduction == "sum":
similarity = self.similarity
else:
similarity = dim_zero_cat(self.similarity)
if self.return_contrast_sensitivity or self.return_full_image:
image_return = dim_zero_cat(self.image_return)
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 torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> import torch
>>> gen = torch.manual_seed(42)
>>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> ms_ssim = MultiScaleStructuralSimilarityIndexMeasure(data_range=1.0)
>>> ms_ssim(preds, target)
tensor(0.9627)
"""
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):
self.similarity.append(similarity)
else:
self.similarity += similarity.sum()
self.total += preds.shape[0]
def compute(self) -> Tensor:
"""Compute MS-SSIM over state."""
if self.reduction in ("none", None):
return dim_zero_cat(self.similarity)
if self.reduction == "sum":
return self.similarity
return self.similarity / self.total
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 torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> import torch
>>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
>>> 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 torchmetrics.image import MultiScaleStructuralSimilarityIndexMeasure
>>> import torch
>>> preds = torch.rand([3, 3, 256, 256], generator=torch.manual_seed(42))
>>> 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)