diff options
Diffstat (limited to 'models/rgb_part_net.py')
| -rw-r--r-- | models/rgb_part_net.py | 168 |
1 files changed, 53 insertions, 115 deletions
diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py index 0e7d8b3..8ebcfd3 100644 --- a/models/rgb_part_net.py +++ b/models/rgb_part_net.py @@ -1,8 +1,5 @@ -import random - import torch import torch.nn as nn -import torch.nn.functional as F from models.auto_encoder import AutoEncoder from models.hpm import HorizontalPyramidMatching @@ -59,24 +56,18 @@ class RGBPartNet(nn.Module): return x @ self.fc_mat def forward(self, x_c1, x_c2=None, y=None): - # Step 0: Swap batch_size and time dimensions for next step - # n, t, c, h, w - x_c1 = x_c1.transpose(0, 1) - if self.training: - x_c2 = x_c2.transpose(0, 1) - # Step 1: Disentanglement - # t, n, c, h, w - ((x_c_c1, x_p_c1), images, losses) = self._disentangle(x_c1, x_c2) + # n, t, c, h, w + ((x_c, x_p), losses, images) = self._disentangle(x_c1, x_c2) # Step 2.a: Static Gait Feature Aggregation & HPM # n, c, h, w - x_c = self.hpm(x_c_c1) + x_c = self.hpm(x_c) # p, n, c # Step 2.b: FPFE & TFA (Dynamic Gait Feature Aggregation) - # t, n, c, h, w - x_p = self.pn(x_p_c1) + # n, t, c, h, w + x_p = self.pn(x_p) # p, n, c # Step 3: Cat feature map together and fc @@ -91,113 +82,60 @@ class RGBPartNet(nn.Module): else: return x.unsqueeze(1).view(-1) - def _disentangle(self, x_c1, x_c2=None): - t, n, c, h, w = x_c1.size() - device = x_c1.device + def _disentangle(self, x_c1_t2, x_c2_t2=None): + n, t, c, h, w = x_c1_t2.size() + device = x_c1_t2.device + x_c1_t1 = x_c1_t2[:, torch.randperm(t), :, :, :] if self.training: - # Encoded appearance, canonical and pose features - f_a_c1, f_c_c1, f_p_c1 = [], [], [] - # Features required to calculate losses - f_p_c2 = [] - xrecon_loss, cano_cons_loss = [], [] - for t2 in range(t): - t1 = random.randrange(t) - output = self.ae(x_c1[t2], x_c1[t1], x_c2[t2]) - (f_c1_t2, f_p_t2, losses) = output - - (f_a_c1_t2, f_c_c1_t2, f_p_c1_t2) = f_c1_t2 - if self.image_log_on: - f_a_c1.append(f_a_c1_t2) - # Save canonical features and pose features - f_c_c1.append(f_c_c1_t2) - f_p_c1.append(f_p_c1_t2) - - # Losses per time step - # Used in pose similarity loss - (_, f_p_c2_t2) = f_p_t2 - f_p_c2.append(f_p_c2_t2) - - # Cross reconstruction loss and canonical loss - (xrecon_loss_t2, cano_cons_loss_t2) = losses - xrecon_loss.append(xrecon_loss_t2) - cano_cons_loss.append(cano_cons_loss_t2) - if self.image_log_on: - f_a_c1 = torch.stack(f_a_c1) - f_c_c1_mean = torch.stack(f_c_c1).mean(0) - f_p_c1 = torch.stack(f_p_c1) - f_p_c2 = torch.stack(f_p_c2) - + ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2) # Decode features - appearance_image, canonical_image, pose_image = None, None, None with torch.no_grad(): - # Decode average canonical features to higher dimension - x_c_c1 = self.ae.decoder( - torch.zeros((n, self.f_a_dim), device=device), - f_c_c1_mean, - torch.zeros((n, self.f_p_dim), device=device), - cano_only=True - ) - # Decode pose features to images - f_p_c1_ = f_p_c1.view(t * n, -1) - x_p_c1_ = self.ae.decoder( - torch.zeros((t * n, self.f_a_dim), device=device), - torch.zeros((t * n, self.f_c_dim), device=device), - f_p_c1_ - ) - x_p_c1 = x_p_c1_.view(t, n, c, h, w) + x_c = self._decode_cano_feature(f_c_, n, t, device) + x_p = self._decode_pose_feature(f_p_, n, t, c, h, w, device) + i_a, i_c, i_p = None, None, None if self.image_log_on: - # Decode appearance features - f_a_c1_ = f_a_c1.view(t * n, -1) - appearance_image_ = self.ae.decoder( - f_a_c1_, - torch.zeros((t * n, self.f_c_dim), device=device), - torch.zeros((t * n, self.f_p_dim), device=device) - ) - appearance_image = appearance_image_.view(t, n, c, h, w) + i_a = self._decode_appr_feature(f_a_, n, t, c, h, w, device) # Continue decoding canonical features - canonical_image = self.ae.decoder.trans_conv3(x_c_c1) - canonical_image = torch.sigmoid( - self.ae.decoder.trans_conv4(canonical_image) - ) - pose_image = x_p_c1 - - # Losses - xrecon_loss = torch.sum(torch.stack(xrecon_loss)) - pose_sim_loss = self._pose_sim_loss(f_p_c1, f_p_c2) * 10 - cano_cons_loss = torch.mean(torch.stack(cano_cons_loss)) + i_c = self.ae.decoder.trans_conv3(x_c) + i_c = torch.sigmoid(self.ae.decoder.trans_conv4(i_c)) + i_p = x_p - return ((x_c_c1, x_p_c1), - (appearance_image, canonical_image, pose_image), - (xrecon_loss, pose_sim_loss, cano_cons_loss)) + return (x_c, x_p), losses, (i_a, i_c, i_p) else: # evaluating - x_c1_ = x_c1.view(t * n, c, h, w) - (f_c_c1_, f_p_c1_) = self.ae(x_c1_) - - # Canonical features - f_c_c1 = f_c_c1_.view(t, n, -1) - f_c_c1_mean = f_c_c1.mean(0) - x_c_c1 = self.ae.decoder( - torch.zeros((n, self.f_a_dim)), - f_c_c1_mean, - torch.zeros((n, self.f_p_dim)), - cano_only=True - ) - - # Pose features - x_p_c1_ = self.ae.decoder( - torch.zeros((t * n, self.f_a_dim)), - torch.zeros((t * n, self.f_c_dim)), - f_p_c1_ - ) - x_p_c1 = x_p_c1_.view(t, n, c, h, w) - - return (x_c_c1, x_p_c1), None, None - - @staticmethod - def _pose_sim_loss(f_p_c1: torch.Tensor, - f_p_c2: torch.Tensor) -> torch.Tensor: - f_p_c1_mean = f_p_c1.mean(dim=0) - f_p_c2_mean = f_p_c2.mean(dim=0) - return F.mse_loss(f_p_c1_mean, f_p_c2_mean) + f_c_, f_p_ = self.ae(x_c1_t2) + x_c = self._decode_cano_feature(f_c_, n, t, device) + x_p = self._decode_pose_feature(f_p_, n, t, c, h, w, device) + return (x_c, x_p), None, None + + def _decode_appr_feature(self, f_a_, n, t, c, h, w, device): + # Decode appearance features + x_a_ = self.ae.decoder( + f_a_, + torch.zeros((n * t, self.f_c_dim), device=device), + torch.zeros((n * t, self.f_p_dim), device=device) + ) + x_a = x_a_.view(n, t, c, h, w) + return x_a + + def _decode_cano_feature(self, f_c_, n, t, device): + # Decode average canonical features to higher dimension + f_c = f_c_.view(n, t, -1) + x_c = self.ae.decoder( + torch.zeros((n, self.f_a_dim), device=device), + f_c.mean(1), + torch.zeros((n, self.f_p_dim), device=device), + cano_only=True + ) + return x_c + + def _decode_pose_feature(self, f_p_, n, t, c, h, w, device): + # Decode pose features to images + x_p_ = self.ae.decoder( + torch.zeros((n * t, self.f_a_dim), device=device), + torch.zeros((n * t, self.f_c_dim), device=device), + f_p_ + ) + x_p = x_p_.view(n, t, c, h, w) + return x_p |