Bug fixes

1. Turn off autograd while decoding canonical and pose features
2. Change default batch size to (4, 8)

1 files changed, 10 insertions, 9 deletions

diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index 5e7558b..36be868 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -132,15 +132,16 @@ 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)
 
-        # 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
-        )
+        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