from fastai import * from fastai.core import * from fastai.torch_core import * from fastai.callbacks import hook_outputs import torchvision.models as models class FeatureLoss(nn.Module): def __init__(self, layer_wgts:[float]=[5.0,15.0,2.0], gram_wgt:float=5e3): super().__init__() self.gram_wgt = gram_wgt self.base_loss = F.l1_loss self.m_feat = models.vgg16_bn(True).features.cuda().eval() requires_grad(self.m_feat, False) blocks = [i-1 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)) ] + [f'gram_{i}' for i in range(len(layer_ids))] def _gram_matrix(self, x:torch.Tensor): n,c,h,w = x.size() x = x.view(n, c, -1) return (x @ x.transpose(1,2))/(c*h*w) def make_features(self, x:torch.Tensor, clone=False): self.m_feat(x) return [(o.clone() if clone else o) for o in self.hooks.stored] def forward(self, input:torch.Tensor, target:torch.Tensor): out_feat = self.make_features(target, clone=True) in_feat = self.make_features(input) 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.feat_losses += [self.base_loss(input,target)] self.feat_losses += [self.base_loss(self._gram_matrix(f_in), self._gram_matrix(f_out))*w**2 * self.gram_wgt 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() class FeatureLoss2(nn.Module): def __init__(self, layer_wgts:[float]=[20.0,70.0,10.0], gram_wgt:float=5e3): super().__init__() self.gram_wgt = gram_wgt self.base_loss = F.l1_loss self.m_feat = models.vgg16_bn(True).features.cuda().eval() requires_grad(self.m_feat, False) blocks = [i-1 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)) ] + [f'gram_{i}' for i in range(len(layer_ids))] def _gram_matrix(self, x:torch.Tensor): n,c,h,w = x.size() x = x.view(n, c, -1) return (x @ x.transpose(1,2))/(c*h*w) def make_features(self, x:torch.Tensor, clone=False): self.m_feat(x) return [(o.clone() if clone else o) for o in self.hooks.stored] def forward(self, input:torch.Tensor, target:torch.Tensor): out_feat = self.make_features(target, clone=True) in_feat = self.make_features(input) 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.feat_losses += [self.base_loss(input,target)*100] self.feat_losses += [self.base_loss(self._gram_matrix(f_in), self._gram_matrix(f_out))*w**2 * self.gram_wgt 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() #Includes wasserstein loss class FeatureLoss3(nn.Module): def __init__(self, layer_wgts=[5,15,2], wass_wgts=[3.0,0.7,0.01]): super().__init__() self.m_feat = models.vgg16_bn(True).features.cuda().eval() requires_grad(self.m_feat, False) blocks = [i-1 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.wass_wgts = wass_wgts self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids)) ] + [f'wass_{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 _calc_2_moments(self, tensor): chans = tensor.shape[1] tensor = tensor.view(1, chans, -1) n = tensor.shape[2] mu = tensor.mean(2) tensor = (tensor - mu[:,:,None]).squeeze(0) cov = torch.mm(tensor, tensor.t()) / float(n) return mu, cov def _get_style_vals(self, tensor): mean, cov = self._calc_2_moments(tensor) eigvals, eigvects = torch.symeig(cov, eigenvectors=True) eigroot_mat = torch.diag(torch.sqrt(eigvals.clamp(min=0))) root_cov = torch.mm(torch.mm(eigvects, eigroot_mat), eigvects.t()) tr_cov = eigvals.clamp(min=0).sum() return mean, tr_cov, root_cov def _calc_l2wass_dist(self, mean_stl, tr_cov_stl, root_cov_stl, mean_synth, cov_synth): tr_cov_synth = torch.symeig(cov_synth, eigenvectors=True)[0].clamp(min=0).sum() mean_diff_squared = (mean_stl - mean_synth).pow(2).sum() cov_prod = torch.mm(torch.mm(root_cov_stl, cov_synth), root_cov_stl) var_overlap = torch.sqrt(torch.symeig(cov_prod, eigenvectors=True)[0].clamp(min=0)+1e-8).sum() dist = mean_diff_squared + tr_cov_stl + tr_cov_synth - 2*var_overlap return dist def _single_wass_loss(self, pred, targ): mean_test, tr_cov_test, root_cov_test = targ mean_synth, cov_synth = self._calc_2_moments(pred) loss = self._calc_l2wass_dist(mean_test, tr_cov_test, root_cov_test, mean_synth, cov_synth) return loss 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)] styles = [self._get_style_vals(i) for i in out_feat] self.feat_losses += [self._single_wass_loss(f_pred, f_targ)*w for f_pred, f_targ, w in zip(in_feat, styles, self.wass_wgts)] self.metrics = dict(zip(self.metric_names, self.feat_losses)) return sum(self.feat_losses) def __del__(self): self.hooks.remove() #"Before activations" in ESRGAN paper class FeatureLoss4(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()