| from fastai.vision import * | |
| class FeatureLoss(nn.Module): | |
| def __init__(self, m_feat, layer_ids, layer_wgts): | |
| super().__init__() | |
| self.m_feat = m_feat | |
| 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)) | |
| ] + [f'gram_{i}' for i in range(len(layer_ids))] | |
| 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 = [base_loss(input, target)] | |
| self.feat_losses += [base_loss(f_in, f_out) * w | |
| for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)] | |
| self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out)) * w ** 2 * 5e3 | |
| 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() | |