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# Source code for pytorch_lightning.metrics.regression.ssim

# Copyright The PyTorch Lightning team.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import torch
from typing import Any, Optional, Sequence

from pytorch_lightning.metrics.metric import Metric
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.metrics.functional.ssim import _ssim_update, _ssim_compute

[docs]class SSIM(Metric):
"""
Computes Structual Similarity Index Measure
<https://en.wikipedia.org/wiki/Structural_similarity>_ (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 pytorch_lightning.metrics import SSIM
>>> preds = torch.rand([16, 1, 16, 16])
>>> target = preds * 0.75
>>> ssim = SSIM()
>>> ssim(preds, target)
tensor(0.9219)
"""

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,
):
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.kernel_size = kernel_size
self.sigma = sigma
self.data_range = data_range
self.k1 = k1
self.k2 = k2
self.reduction = reduction

[docs]    def update(self, preds: torch.Tensor, target: torch.Tensor):
"""
Update state with predictions and targets.

Args:
preds: Predictions from model
target: Ground truth values
"""
preds, target = _ssim_update(preds, target)
self.y_pred.append(preds)
self.y.append(target)

[docs]    def compute(self):
"""
Computes explained variance over state.
"""
preds = torch.cat(self.y_pred, dim=0)
target = torch.cat(self.y, dim=0)
return _ssim_compute(
preds, target, self.kernel_size, self.sigma, self.reduction, self.data_range, self.k1, self.k2
)


© Copyright Copyright (c) 2018-2021, William Falcon et al... Revision c462b274.

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