diff options
Diffstat (limited to 'models/rgb_part_net.py')
| -rw-r--r-- | models/rgb_part_net.py | 141 |
1 files changed, 101 insertions, 40 deletions
diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py index 755d5dc..0e7d8b3 100644 --- a/models/rgb_part_net.py +++ b/models/rgb_part_net.py @@ -16,6 +16,7 @@ class RGBPartNet(nn.Module): ae_in_channels: int = 3, ae_feature_channels: int = 64, f_a_c_p_dims: tuple[int, int, int] = (128, 128, 64), + hpm_use_1x1conv: bool = False, hpm_scales: tuple[int, ...] = (1, 2, 4), hpm_use_avg_pool: bool = True, hpm_use_max_pool: bool = True, @@ -26,9 +27,14 @@ class RGBPartNet(nn.Module): tfa_squeeze_ratio: int = 4, tfa_num_parts: int = 16, embedding_dims: int = 256, - triplet_margin: float = 0.2 + triplet_margins: tuple[float, float] = (0.2, 0.2), + image_log_on: bool = False ): super().__init__() + (self.f_a_dim, self.f_c_dim, self.f_p_dim) = f_a_c_p_dims + self.hpm_num_parts = sum(hpm_scales) + self.image_log_on = image_log_on + self.ae = AutoEncoder( ae_in_channels, ae_feature_channels, f_a_c_p_dims ) @@ -38,14 +44,16 @@ class RGBPartNet(nn.Module): ) out_channels = self.pn.tfa_in_channels self.hpm = HorizontalPyramidMatching( - ae_feature_channels * 8, out_channels, hpm_scales, - hpm_use_avg_pool, hpm_use_max_pool + ae_feature_channels * 2, out_channels, hpm_use_1x1conv, + hpm_scales, hpm_use_avg_pool, hpm_use_max_pool ) - total_parts = sum(hpm_scales) + tfa_num_parts - empty_fc = torch.empty(total_parts, out_channels, embedding_dims) + empty_fc = torch.empty(self.hpm_num_parts + tfa_num_parts, + out_channels, embedding_dims) self.fc_mat = nn.Parameter(empty_fc) - self.ba_triplet_loss = BatchAllTripletLoss(triplet_margin) + (hpm_margin, pn_margin) = triplet_margins + self.hpm_ba_trip = BatchAllTripletLoss(hpm_margin) + self.pn_ba_trip = BatchAllTripletLoss(pn_margin) def fc(self, x): return x @ self.fc_mat @@ -59,13 +67,11 @@ class RGBPartNet(nn.Module): # Step 1: Disentanglement # t, n, c, h, w - ((x_c_c1, x_p_c1), losses) = self._disentangle(x_c1, x_c2) + ((x_c_c1, x_p_c1), images, losses) = self._disentangle(x_c1, x_c2) - # Step 2.a: HPM & Static Gait Feature Aggregation - # t, n, c, h, w + # Step 2.a: Static Gait Feature Aggregation & HPM + # n, c, h, w x_c = self.hpm(x_c_c1) - # p, t, n, c - x_c = x_c.mean(dim=1) # p, n, c # Step 2.b: FPFE & TFA (Dynamic Gait Feature Aggregation) @@ -78,44 +84,83 @@ class RGBPartNet(nn.Module): x = self.fc(x) if self.training: - batch_all_triplet_loss = self.ba_triplet_loss(x, y) - losses = torch.stack((*losses, batch_all_triplet_loss)) - return losses + hpm_ba_trip = self.hpm_ba_trip(x[:self.hpm_num_parts], y) + pn_ba_trip = self.pn_ba_trip(x[self.hpm_num_parts:], y) + losses = torch.stack((*losses, hpm_ba_trip, pn_ba_trip)) + return losses, images 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 if self.training: - # Decoded canonical features and Pose images - x_c_c1, x_p_c1 = [], [] + # Encoded appearance, canonical and pose features + f_a_c1, f_c_c1, f_p_c1 = [], [], [] # Features required to calculate losses - f_p_c1, f_p_c2 = [], [] + 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]) - (x_c1_t2, f_p_t2, losses) = output + (f_c1_t2, f_p_t2, losses) = output - # Decoded features or image - (x_c_c1_t2, x_p_c1_t2) = x_c1_t2 - # Canonical Features for HPM - x_c_c1.append(x_c_c1_t2) - # Pose image for Part Net - x_p_c1.append(x_p_c1_t2) + (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_c1_t2, f_p_c2_t2) = f_p_t2 - f_p_c1.append(f_p_c1_t2) + (_, 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) - - x_c_c1 = torch.stack(x_c_c1) - x_p_c1 = torch.stack(x_p_c1) + 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) + + # 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) + + 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) + # 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)) @@ -123,20 +168,36 @@ class RGBPartNet(nn.Module): cano_cons_loss = torch.mean(torch.stack(cano_cons_loss)) return ((x_c_c1, x_p_c1), + (appearance_image, canonical_image, pose_image), (xrecon_loss, pose_sim_loss, cano_cons_loss)) else: # evaluating - x_c1 = x_c1.view(-1, c, h, w) - x_c_c1, x_p_c1 = self.ae(x_c1) - _, c_c, h_c, w_c = x_c_c1.size() - x_c_c1 = x_c_c1.view(t, n, c_c, h_c, w_c) - x_p_c1 = x_p_c1.view(t, n, c, h, w) - - return (x_c_c1, x_p_c1), None + 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: list[torch.Tensor], - f_p_c2: list[torch.Tensor]) -> torch.Tensor: - f_p_c1_mean = torch.stack(f_p_c1).mean(dim=0) - f_p_c2_mean = torch.stack(f_p_c2).mean(dim=0) + 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) |