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authorJordan Gong <jordan.gong@protonmail.com>2021-03-12 13:56:17 +0800
committerJordan Gong <jordan.gong@protonmail.com>2021-03-12 13:56:17 +0800
commitc74df416b00f837ba051f3947be92f76e7afbd88 (patch)
tree02983df94008bbb427c2066c5f619e0ffdefe1c5 /models/auto_encoder.py
parent1b8d1614168ce6590c5e029c7f1007ac9b17048c (diff)
Code refactoring
1. Separate FCs and triplet losses for HPM and PartNet 2. Remove FC-equivalent 1x1 conv layers in HPM 3. Support adjustable learning rate schedulers
Diffstat (limited to 'models/auto_encoder.py')
-rw-r--r--models/auto_encoder.py2
1 files changed, 1 insertions, 1 deletions
diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index e6a3e60..4fece69 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -171,7 +171,7 @@ class AutoEncoder(nn.Module):
return (
(f_a_c1_t2_, f_c_c1_t2_, f_p_c1_t2_),
- torch.stack((xrecon_loss, cano_cons_loss, pose_sim_loss * 10))
+ (xrecon_loss, cano_cons_loss, pose_sim_loss * 10)
)
else: # evaluating
return f_c_c1_t2_, f_p_c1_t2_