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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 | |
| class FeatureLoss(nn.Module): | |
| def __init__(self, layer_wgts=[20, 70, 10]): | |
| 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.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() | |
| # Refactored code, originally from https://github.com/VinceMarron/style_transfer | |
| class WassFeatureLoss(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) | |
| # Prevents nasty bug that happens very occassionally- divide by zero. Why such things happen? | |
| if n == 0: | |
| return None, None | |
| cov = torch.mm(tensor, tensor.t()) / float(n) | |
| return mu, cov | |
| def _get_style_vals(self, tensor): | |
| mean, cov = self._calc_2_moments(tensor) | |
| if mean is None: | |
| return None, None, None | |
| 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] | |
| if styles[0][0] is not None: | |
| 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() | |