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Running
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Zero
| """This file contains perceptual loss module using ConvNeXt-S. | |
| Copyright (2024) Bytedance Ltd. and/or its affiliates | |
| 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 | |
| http://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.functional as F | |
| from torchvision import models | |
| _IMAGENET_MEAN = [0.485, 0.456, 0.406] | |
| _IMAGENET_STD = [0.229, 0.224, 0.225] | |
| class PerceptualLoss(torch.nn.Module): | |
| def __init__(self, model_name: str = "convnext_s"): | |
| """Initializes the PerceptualLoss class. | |
| Args: | |
| model_name: A string, the name of the perceptual loss model to use. | |
| Raise: | |
| ValueError: If the model_name does not contain "convnext_s". | |
| """ | |
| super().__init__() | |
| if "convnext_s" not in model_name: | |
| raise ValueError(f"Unsupported Perceptual Loss model name {model_name}") | |
| self.convnext = models.convnext_small(weights=models.ConvNeXt_Small_Weights.IMAGENET1K_V1).eval() | |
| self.register_buffer("imagenet_mean", torch.Tensor(_IMAGENET_MEAN)[None, :, None, None]) | |
| self.register_buffer("imagenet_std", torch.Tensor(_IMAGENET_STD)[None, :, None, None]) | |
| for param in self.parameters(): | |
| param.requires_grad = False | |
| def forward(self, input: torch.Tensor, target: torch.Tensor): | |
| """Computes the perceptual loss. | |
| Args: | |
| input: A tensor of shape (B, C, H, W), the input image. Normalized to [0, 1]. | |
| target: A tensor of shape (B, C, H, W), the target image. Normalized to [0, 1]. | |
| Returns: | |
| A scalar tensor, the perceptual loss. | |
| """ | |
| # Always in eval mode. | |
| self.eval() | |
| input = torch.nn.functional.interpolate(input, size=224, mode="bilinear", align_corners=False, antialias=True) | |
| target = torch.nn.functional.interpolate(target, size=224, mode="bilinear", align_corners=False, antialias=True) | |
| pred_input = self.convnext((input - self.imagenet_mean) / self.imagenet_std) | |
| pred_target = self.convnext((target - self.imagenet_mean) / self.imagenet_std) | |
| loss = torch.nn.functional.mse_loss( | |
| pred_input, | |
| pred_target, | |
| reduction="mean") | |
| return loss |