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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
import torch
from torch import Tensor
from torchmetrics.functional.image.ssim import _ssim_compute, _ssim_update
from torchmetrics.metric import Metric
from torchmetrics.utilities import rank_zero_warn
from torchmetrics.utilities.data import dim_zero_cat
class SSIM(Metric):
"""Computes Structual Similarity Index Measure (SSIM_).
Args:
kernel_size: size of the gaussian kernel (default: (11, 11))
sigma: Standard deviation of the gaussian kernel (default: (1.5, 1.5))
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. Default: 0.01
k2: Parameter of SSIM. Default: 0.03
Return:
Tensor with SSIM score
Example:
>>> from torchmetrics import SSIM
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> ssim = SSIM()
>>> ssim(preds, target)
tensor(0.9219)
"""
preds: List[Tensor]
target: List[Tensor]
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
)