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A10G
# 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 | |