Merge branch 'data_parallel' into data_parallel_py3.8

1 files changed, 3 insertions, 12 deletions

diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index dbd1da0..023b462 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -153,27 +153,18 @@ class AutoEncoder(nn.Module):
             x_c1_t2_pred_ = self.decoder(f_a_c1_t1_, f_c_c1_t1_, f_p_c1_t2_)
             x_c1_t2_pred = x_c1_t2_pred_.view(n, t, c, h, w)
 
-            xrecon_loss = torch.stack([
-                F.mse_loss(x_c1_t2[:, i, :, :, :], x_c1_t2_pred[:, i, :, :, :])
-                for i in range(t)
-            ]).sum()
-
             f_c_c1_t1 = f_c_c1_t1_.view(n, t, -1)
             f_c_c1_t2 = f_c_c1_t2_.view(n, t, -1)
             f_c_c2_t2 = f_c_c2_t2_.view(n, t, -1)
-            cano_cons_loss = torch.stack([
-                F.mse_loss(f_c_c1_t1[:, i, :], f_c_c1_t2[:, i, :])
-                + F.mse_loss(f_c_c1_t2[:, i, :], f_c_c2_t2[:, i, :])
-                for i in range(t)
-            ]).mean()
 
             f_p_c1_t2 = f_p_c1_t2_.view(n, t, -1)
             f_p_c2_t2 = f_p_c2_t2_.view(n, t, -1)
-            pose_sim_loss = F.mse_loss(f_p_c1_t2.mean(1), f_p_c2_t2.mean(1))
 
             return (
                 (f_a_c1_t2_, f_c_c1_t2_, f_p_c1_t2_),
-                torch.stack((xrecon_loss, cano_cons_loss, pose_sim_loss * 10))
+                (x_c1_t2_pred,
+                 (f_c_c1_t1, f_c_c1_t2, f_c_c2_t2),
+                 (f_p_c1_t2, f_p_c2_t2))
             )
         else:  # evaluating
             return f_c_c1_t2_, f_p_c1_t2_