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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__ = ['TVLoss'] | |
| class TVLoss(nn.Module): | |
| """ | |
| Total variance loss. | |
| """ | |
| def __init__(self): | |
| super().__init__() | |
| def numel_excluding_first_dim(self, x): | |
| return x.numel() // x.shape[0] | |
| def forward(self, x): | |
| """ | |
| Assume batched and channel first with inner sizes. | |
| Args: | |
| x: [N, M, C, H, W] | |
| Returns: | |
| Mean-reduced TV loss with element-level scaling. | |
| """ | |
| N, M, C, H, W = x.shape | |
| x = x.reshape(N*M, C, H, W) | |
| diff_i = x[..., 1:, :] - x[..., :-1, :] | |
| diff_j = x[..., :, 1:] - x[..., :, :-1] | |
| div_i = self.numel_excluding_first_dim(diff_i) | |
| div_j = self.numel_excluding_first_dim(diff_j) | |
| tv_i = diff_i.pow(2).sum(dim=[1,2,3]) / div_i | |
| tv_j = diff_j.pow(2).sum(dim=[1,2,3]) / div_j | |
| tv = tv_i + tv_j | |
| batch_tv = tv.reshape(N, M).mean(dim=1) | |
| all_tv = batch_tv.mean() | |
| return all_tv | |