Code refactoring, modifications and new features

1. Decode features outside of auto-encoder
2. Turn off HPM 1x1 conv by default
3. Change canonical feature map size from `feature_channels * 8 x 4 x 2` to `feature_channels * 2 x 16 x 8`
4. Use mean of canonical embeddings instead of mean of static features
5. Calculate static and dynamic loss separately
6. Calculate mean of parts in triplet loss instead of sum of parts
7. Add switch to log disentangled images
8. Change default configuration

1 files changed, 5 insertions, 21 deletions

diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index 35cb629..f04ffdb 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -95,15 +95,14 @@ class Decoder(nn.Module):
         self.trans_conv4 = DCGANConvTranspose2d(feature_channels, out_channels,
                                                 is_last_layer=True)
 
-    def forward(self, f_appearance, f_canonical, f_pose, no_trans_conv=False):
+    def forward(self, f_appearance, f_canonical, f_pose, cano_only=False):
         x = torch.cat((f_appearance, f_canonical, f_pose), dim=1)
         x = self.fc(x)
         x = F.relu(x.view(-1, self.feature_channels * 8, 4, 2), inplace=True)
-        # Decode canonical features without transpose convolutions
-        if no_trans_conv:
-            return x
         x = self.trans_conv1(x)
         x = self.trans_conv2(x)
+        if cano_only:
+            return x
         x = self.trans_conv3(x)
         x = torch.sigmoid(self.trans_conv4(x))
 
@@ -125,21 +124,6 @@ class AutoEncoder(nn.Module):
         # x_c1_t2 is the frame for later module
         (f_a_c1_t2, f_c_c1_t2, f_p_c1_t2) = self.encoder(x_c1_t2)
 
-        with torch.no_grad():
-            # Decode canonical features for HPM
-            x_c_c1_t2 = self.decoder(
-                torch.zeros_like(f_a_c1_t2),
-                f_c_c1_t2,
-                torch.zeros_like(f_p_c1_t2),
-                no_trans_conv=True
-            )
-            # Decode pose features for Part Net
-            x_p_c1_t2 = self.decoder(
-                torch.zeros_like(f_a_c1_t2),
-                torch.zeros_like(f_c_c1_t2),
-                f_p_c1_t2
-            )
-
         if self.training:
             # t1 is random time step, c2 is another condition
             (f_a_c1_t1, f_c_c1_t1, _) = self.encoder(x_c1_t1)
@@ -151,9 +135,9 @@ class AutoEncoder(nn.Module):
                                  + F.mse_loss(f_c_c1_t2, f_c_c2_t2))
 
             return (
-                (x_c_c1_t2, x_p_c1_t2),
+                (f_a_c1_t2, f_c_c1_t2, f_p_c1_t2),
                 (f_p_c1_t2, f_p_c2_t2),
                 (xrecon_loss_t2, cano_cons_loss_t2)
             )
         else:  # evaluating
-            return x_c_c1_t2, x_p_c1_t2
+            return f_c_c1_t2, f_p_c1_t2