Correct cross reconstruction loss calculated in DataParallel

2 files changed, 3 insertions, 7 deletions

diff --git a/models/model.py b/models/model.py
index 4f24ac5..cac165e 100644
--- a/models/model.py
+++ b/models/model.py
@@ -198,12 +198,8 @@ class Model:
             # Duplicate labels for each part
             y = y.unsqueeze(1).repeat(1, self.rgb_pn.module.num_total_parts)
             losses, images = self.rgb_pn(x_c1, x_c2, y)
-            losses = torch.stack((
-                # xrecon           cano_cons         pose_sim
-                losses[0].sum(), losses[1].mean(), losses[2].mean(),
-                # hpm_ba_trip       pn_ba_trip
-                losses[3].mean(), losses[4].mean()
-            ))
+            # Combine losses from different data splits
+            losses = losses.mean()
             loss = losses.sum()
             loss.backward()
             self.optimizer.step()
diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py
index bd3a31c..67acac3 100644
--- a/models/rgb_part_net.py
+++ b/models/rgb_part_net.py
@@ -84,7 +84,7 @@ class RGBPartNet(nn.Module):
             pn_ba_trip = self.pn_ba_trip(
                 x[self.hpm_num_parts:], y[self.hpm_num_parts:]
             )
-            losses = (*losses, hpm_ba_trip, pn_ba_trip)
+            losses = torch.stack((*losses, hpm_ba_trip, pn_ba_trip))
             return losses, images
         else:
             return x.unsqueeze(1).view(-1)