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Source code for pytorch_lightning.metrics.regression.ssim

# Copyright The PyTorch Lightning team.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# 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
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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.add_state("y", default=[], dist_reduce_fx=None) self.add_state("y_pred", default=[], dist_reduce_fx=None) 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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