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| # Copyright (c) 2023-2024, Zexin He | |
| # | |
| # 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 | |
| # | |
| # https://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. | |
| import torch | |
| import torch.nn as nn | |
| __all__ = ['PixelLoss'] | |
| class PixelLoss(nn.Module): | |
| """ | |
| Pixel-wise loss between two images. | |
| """ | |
| def __init__(self, option: str = 'mse'): | |
| super().__init__() | |
| self.loss_fn = self._build_from_option(option) | |
| def _build_from_option(option: str, reduction: str = 'none'): | |
| if option == 'mse': | |
| return nn.MSELoss(reduction=reduction) | |
| elif option == 'l1': | |
| return nn.L1Loss(reduction=reduction) | |
| else: | |
| raise NotImplementedError(f'Unknown pixel loss option: {option}') | |
| def forward(self, x, y): | |
| """ | |
| Assume images are channel first. | |
| Args: | |
| x: [N, M, C, H, W] | |
| y: [N, M, C, H, W] | |
| Returns: | |
| Mean-reduced pixel loss across batch. | |
| """ | |
| N, M, C, H, W = x.shape | |
| x = x.reshape(N*M, C, H, W) | |
| y = y.reshape(N*M, C, H, W) | |
| image_loss = self.loss_fn(x, y).mean(dim=[1, 2, 3]) | |
| batch_loss = image_loss.reshape(N, M).mean(dim=1) | |
| all_loss = batch_loss.mean() | |
| return all_loss | |