diff options
| -rw-r--r-- | models/auto_encoder.py | 15 | ||||
| -rw-r--r-- | models/model.py | 42 | ||||
| -rw-r--r-- | models/rgb_part_net.py | 8 | ||||
| -rw-r--r-- | requirements.txt | 2 |
4 files changed, 36 insertions, 31 deletions
diff --git a/models/auto_encoder.py b/models/auto_encoder.py index dbd1da0..023b462 100644 --- a/models/auto_encoder.py +++ b/models/auto_encoder.py @@ -153,27 +153,18 @@ class AutoEncoder(nn.Module): x_c1_t2_pred_ = self.decoder(f_a_c1_t1_, f_c_c1_t1_, f_p_c1_t2_) x_c1_t2_pred = x_c1_t2_pred_.view(n, t, c, h, w) - xrecon_loss = torch.stack([ - F.mse_loss(x_c1_t2[:, i, :, :, :], x_c1_t2_pred[:, i, :, :, :]) - for i in range(t) - ]).sum() - f_c_c1_t1 = f_c_c1_t1_.view(n, t, -1) f_c_c1_t2 = f_c_c1_t2_.view(n, t, -1) f_c_c2_t2 = f_c_c2_t2_.view(n, t, -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(t) - ]).mean() f_p_c1_t2 = f_p_c1_t2_.view(n, t, -1) f_p_c2_t2 = f_p_c2_t2_.view(n, t, -1) - pose_sim_loss = F.mse_loss(f_p_c1_t2.mean(1), f_p_c2_t2.mean(1)) return ( (f_a_c1_t2_, f_c_c1_t2_, f_p_c1_t2_), - torch.stack((xrecon_loss, cano_cons_loss, pose_sim_loss * 10)) + (x_c1_t2_pred, + (f_c_c1_t1, f_c_c1_t2, f_c_c2_t2), + (f_p_c1_t2, f_p_c2_t2)) ) else: # evaluating return f_c_c1_t2_, f_p_c1_t2_ diff --git a/models/model.py b/models/model.py index 22996fe..2a74c8c 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: diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py index 1cda91c..2853571 100644 --- a/models/rgb_part_net.py +++ b/models/rgb_part_net.py @@ -52,7 +52,7 @@ class RGBPartNet(nn.Module): def forward(self, x_c1, x_c2=None): # Step 1: Disentanglement # n, t, c, h, w - ((x_c, x_p), ae_losses, images) = self._disentangle(x_c1, x_c2) + ((x_c, x_p), images, f_loss) = self._disentangle(x_c1, x_c2) # Step 2.a: Static Gait Feature Aggregation & HPM # n, c, h, w @@ -69,7 +69,7 @@ class RGBPartNet(nn.Module): x = self.fc(x) if self.training: - return x.transpose(0, 1), ae_losses, images + return x.transpose(0, 1), images, f_loss else: return x.unsqueeze(1).view(-1) @@ -78,7 +78,7 @@ class RGBPartNet(nn.Module): device = x_c1_t2.device if self.training: x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :] - ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2) + (f_a_, f_c_, f_p_), f_loss = self.ae(x_c1_t2, x_c1_t1, x_c2_t2) # Decode features x_c = self._decode_cano_feature(f_c_, n, t, device) x_p_ = self._decode_pose_feature(f_p_, n, t, device) @@ -95,7 +95,7 @@ class RGBPartNet(nn.Module): i_p_ = torch.sigmoid(self.ae.decoder.trans_conv4(i_p_)) i_p = i_p_.view(n, t, c, h, w) - return (x_c, x_p), losses, (i_a, i_c, i_p) + return (x_c, x_p), (i_a, i_c, i_p), f_loss else: # evaluating f_c_, f_p_ = self.ae(x_c1_t2) diff --git a/requirements.txt b/requirements.txt index 926a587..de81280 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -torch~=1.7.1 +torch~=1.8.0 torchvision~=0.8.0a0+ecf4e9c numpy~=1.19.4 tqdm~=4.58.0 |