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from fastai import * |
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from fastai.core import * |
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from fastai.torch_core import * |
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from fastai.callbacks import hook_outputs |
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import torchvision.models as models |
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class FeatureLoss(nn.Module): |
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def __init__(self, layer_wgts=[20, 70, 10]): |
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super().__init__() |
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self.m_feat = models.vgg16_bn(True).features.cuda().eval() |
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requires_grad(self.m_feat, False) |
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blocks = [ |
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i - 1 |
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for i, o in enumerate(children(self.m_feat)) |
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if isinstance(o, nn.MaxPool2d) |
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] |
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layer_ids = blocks[2:5] |
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self.loss_features = [self.m_feat[i] for i in layer_ids] |
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self.hooks = hook_outputs(self.loss_features, detach=False) |
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self.wgts = layer_wgts |
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self.metric_names = ['pixel'] + [f'feat_{i}' for i in range(len(layer_ids))] |
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self.base_loss = F.l1_loss |
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def _make_features(self, x, clone=False): |
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self.m_feat(x) |
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return [(o.clone() if clone else o) for o in self.hooks.stored] |
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def forward(self, input, target): |
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out_feat = self._make_features(target, clone=True) |
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in_feat = self._make_features(input) |
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self.feat_losses = [self.base_loss(input, target)] |
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self.feat_losses += [ |
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self.base_loss(f_in, f_out) * w |
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for f_in, f_out, w in zip(in_feat, out_feat, self.wgts) |
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] |
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self.metrics = dict(zip(self.metric_names, self.feat_losses)) |
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return sum(self.feat_losses) |
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def __del__(self): |
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self.hooks.remove() |
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class WassFeatureLoss(nn.Module): |
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def __init__(self, layer_wgts=[5, 15, 2], wass_wgts=[3.0, 0.7, 0.01]): |
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super().__init__() |
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self.m_feat = models.vgg16_bn(True).features.cuda().eval() |
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requires_grad(self.m_feat, False) |
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blocks = [ |
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i - 1 |
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for i, o in enumerate(children(self.m_feat)) |
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if isinstance(o, nn.MaxPool2d) |
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] |
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layer_ids = blocks[2:5] |
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self.loss_features = [self.m_feat[i] for i in layer_ids] |
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self.hooks = hook_outputs(self.loss_features, detach=False) |
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self.wgts = layer_wgts |
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self.wass_wgts = wass_wgts |
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self.metric_names = ( |
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['pixel'] |
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+ [f'feat_{i}' for i in range(len(layer_ids))] |
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+ [f'wass_{i}' for i in range(len(layer_ids))] |
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) |
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self.base_loss = F.l1_loss |
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def _make_features(self, x, clone=False): |
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self.m_feat(x) |
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return [(o.clone() if clone else o) for o in self.hooks.stored] |
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def _calc_2_moments(self, tensor): |
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chans = tensor.shape[1] |
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tensor = tensor.view(1, chans, -1) |
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n = tensor.shape[2] |
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mu = tensor.mean(2) |
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tensor = (tensor - mu[:, :, None]).squeeze(0) |
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if n == 0: |
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return None, None |
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cov = torch.mm(tensor, tensor.t()) / float(n) |
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return mu, cov |
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def _get_style_vals(self, tensor): |
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mean, cov = self._calc_2_moments(tensor) |
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if mean is None: |
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return None, None, None |
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eigvals, eigvects = torch.symeig(cov, eigenvectors=True) |
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eigroot_mat = torch.diag(torch.sqrt(eigvals.clamp(min=0))) |
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root_cov = torch.mm(torch.mm(eigvects, eigroot_mat), eigvects.t()) |
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tr_cov = eigvals.clamp(min=0).sum() |
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return mean, tr_cov, root_cov |
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def _calc_l2wass_dist( |
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self, mean_stl, tr_cov_stl, root_cov_stl, mean_synth, cov_synth |
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): |
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tr_cov_synth = torch.symeig(cov_synth, eigenvectors=True)[0].clamp(min=0).sum() |
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mean_diff_squared = (mean_stl - mean_synth).pow(2).sum() |
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cov_prod = torch.mm(torch.mm(root_cov_stl, cov_synth), root_cov_stl) |
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var_overlap = torch.sqrt( |
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torch.symeig(cov_prod, eigenvectors=True)[0].clamp(min=0) + 1e-8 |
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).sum() |
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dist = mean_diff_squared + tr_cov_stl + tr_cov_synth - 2 * var_overlap |
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return dist |
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def _single_wass_loss(self, pred, targ): |
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mean_test, tr_cov_test, root_cov_test = targ |
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mean_synth, cov_synth = self._calc_2_moments(pred) |
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loss = self._calc_l2wass_dist( |
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mean_test, tr_cov_test, root_cov_test, mean_synth, cov_synth |
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) |
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return loss |
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def forward(self, input, target): |
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out_feat = self._make_features(target, clone=True) |
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in_feat = self._make_features(input) |
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self.feat_losses = [self.base_loss(input, target)] |
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self.feat_losses += [ |
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self.base_loss(f_in, f_out) * w |
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for f_in, f_out, w in zip(in_feat, out_feat, self.wgts) |
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] |
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styles = [self._get_style_vals(i) for i in out_feat] |
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if styles[0][0] is not None: |
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self.feat_losses += [ |
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self._single_wass_loss(f_pred, f_targ) * w |
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for f_pred, f_targ, w in zip(in_feat, styles, self.wass_wgts) |
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] |
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self.metrics = dict(zip(self.metric_names, self.feat_losses)) |
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return sum(self.feat_losses) |
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def __del__(self): |
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self.hooks.remove() |
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