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- from fastai import *
- from fastai.core import *
- from fastai.torch_core import *
- from fastai.callbacks import hook_outputs
- import torchvision.models as models
- #"Before activations" in ESRGAN paper
- class FeatureLoss(nn.Module):
- def __init__(self, layer_wgts=[5,15,2]):
- super().__init__()
- self.m_feat = models.vgg16_bn(True).features.cuda().eval()
- requires_grad(self.m_feat, False)
- blocks = [i-2 for i,o in enumerate(children(self.m_feat)) if isinstance(o,nn.MaxPool2d)]
- layer_ids = blocks[2:5]
- self.loss_features = [self.m_feat[i] for i in layer_ids]
- self.hooks = hook_outputs(self.loss_features, detach=False)
- self.wgts = layer_wgts
- self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))]
- self.base_loss = F.l1_loss
- def _make_features(self, x, clone=False):
- self.m_feat(x)
- return [(o.clone() if clone else o) for o in self.hooks.stored]
- def forward(self, input, target):
- out_feat = self._make_features(target, clone=True)
- in_feat = self._make_features(input)
- self.feat_losses = [self.base_loss(input,target)]
- self.feat_losses += [self.base_loss(f_in, f_out)*w
- for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
-
- self.metrics = dict(zip(self.metric_names, self.feat_losses))
- return sum(self.feat_losses)
-
- def __del__(self): self.hooks.remove()
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