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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__ = ['LPIPSLoss'] | |
| class LPIPSLoss(nn.Module): | |
| """ | |
| Compute LPIPS loss between two images. | |
| """ | |
| def __init__(self, device, prefech: bool = False): | |
| super().__init__() | |
| self.device = device | |
| self.cached_models = {} | |
| if prefech: | |
| self.prefetch_models() | |
| def _get_model(self, model_name: str): | |
| if model_name not in self.cached_models: | |
| import warnings | |
| with warnings.catch_warnings(): | |
| warnings.filterwarnings('ignore', category=UserWarning) | |
| import lpips | |
| _model = lpips.LPIPS(net=model_name, eval_mode=True, verbose=False).to(self.device) | |
| _model = torch.compile(_model) | |
| self.cached_models[model_name] = _model | |
| return self.cached_models[model_name] | |
| def prefetch_models(self): | |
| _model_names = ['alex', 'vgg'] | |
| for model_name in _model_names: | |
| self._get_model(model_name) | |
| def forward(self, x, y, is_training: bool = True): | |
| """ | |
| Assume images are 0-1 scaled and channel first. | |
| Args: | |
| x: [N, M, C, H, W] | |
| y: [N, M, C, H, W] | |
| is_training: whether to use VGG or AlexNet. | |
| Returns: | |
| Mean-reduced LPIPS loss across batch. | |
| """ | |
| model_name = 'vgg' if is_training else 'alex' | |
| loss_fn = self._get_model(model_name) | |
| 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 = loss_fn(x, y, normalize=True).mean(dim=[1, 2, 3]) | |
| batch_loss = image_loss.reshape(N, M).mean(dim=1) | |
| all_loss = batch_loss.mean() | |
| return all_loss | |