diff options
Diffstat (limited to 'models/model.py')
| -rw-r--r-- | models/model.py | 42 |
1 files changed, 28 insertions, 14 deletions
diff --git a/models/model.py b/models/model.py index e8b16a9..c2d70db 100644 --- a/models/model.py +++ b/models/model.py @@ -172,6 +172,7 @@ class Model: triplet_is_hard, triplet_is_mean, None ) + num_sampled_frames = dataset_config.get('num_sampled_frames', 30) num_pairs = (self.pr*self.k-1) * (self.pr*self.k) // 2 num_pos_pairs = (self.k*(self.k-1)//2) * self.pr @@ -230,18 +231,31 @@ class Model: # forward + backward + optimize x_c1 = batch_c1['clip'].to(self.device) x_c2 = batch_c2['clip'].to(self.device) - embedding, ae_losses, images = self.rgb_pn(x_c1, x_c2) + embedding, images, feature_for_loss = self.rgb_pn(x_c1, x_c2) + x_c1_pred = feature_for_loss[0] + xrecon_loss = torch.stack([ + F.mse_loss(x_c1_pred[:, i, :, :, :], x_c1[:, i, :, :, :]) + for i in range(num_sampled_frames) + ]).sum() + f_c_c1_t1, f_c_c1_t2, f_c_c2_t2 = feature_for_loss[1] + cano_cons_loss = torch.stack([ + F.mse_loss(f_c_c1_t1[:, i, :], f_c_c1_t2[:, i, :]) + + F.mse_loss(f_c_c1_t2[:, i, :], f_c_c2_t2[:, i, :]) + for i in range(num_sampled_frames) + ]).mean() + f_p_c1_t2, f_p_c2_t2 = feature_for_loss[2] + pose_sim_loss = F.mse_loss( + f_p_c1_t2.mean(1), f_p_c2_t2.mean(1) + ) * 10 y = batch_c1['label'].to(self.device) # Duplicate labels for each part y = y.repeat(self.rgb_pn.module.num_total_parts, 1) embedding = embedding.transpose(0, 1) - trip_loss, dist, num_non_zero = self.triplet_loss(embedding, y) - losses = torch.cat(( - ae_losses.view(-1, 3).mean(0), - torch.stack(( - trip_loss[:self.rgb_pn.module.hpm_num_parts].mean(), - trip_loss[self.rgb_pn.module.hpm_num_parts:].mean() - )) + triplet_loss, dist, num_non_zero = self.triplet_loss(embedding, y) + hpm_loss = triplet_loss[:self.rgb_pn.module.hpm_num_parts].mean() + pn_loss = triplet_loss[self.rgb_pn.module.hpm_num_parts:].mean() + losses = torch.stack(( + xrecon_loss, cano_cons_loss, pose_sim_loss, hpm_loss, pn_loss )) loss = losses.sum() loss.backward() @@ -251,13 +265,13 @@ class Model: running_loss += losses.detach() # Write losses to TensorBoard self.writer.add_scalar('Loss/all', loss, self.curr_iter) - self.writer.add_scalars('Loss/disentanglement', dict(zip(( - 'Cross reconstruction loss', 'Canonical consistency loss', - 'Pose similarity loss' - ), ae_losses)), self.curr_iter) + self.writer.add_scalars('Loss/disentanglement', { + 'Cross reconstruction loss': xrecon_loss, + 'Canonical consistency loss': cano_cons_loss, + 'Pose similarity loss': pose_sim_loss + }, self.curr_iter) self.writer.add_scalars('Loss/triplet loss', { - 'HPM': losses[3], - 'PartNet': losses[4] + 'HPM': hpm_loss, 'PartNet': pn_loss }, self.curr_iter) # None-zero losses in batch if num_non_zero is not None: |