Merge branch 'master' into disentangling_only

# Conflicts:
#	config.py
#	models/hpm.py
#	models/layers.py
#	models/model.py
#	models/part_net.py
#	models/rgb_part_net.py
#	test/part_net.py
#	utils/configuration.py
#	utils/triplet_loss.py

1 files changed, 15 insertions, 8 deletions

diff --git a/config.py b/config.py
index afd40d5..dc4e0ba 100644
--- a/config.py
+++ b/config.py
@@ -9,7 +9,9 @@ config: Configuration = {
         # Directory used in training or testing for temporary storage
         'save_dir': 'runs/dis_only',
         # Recorde disentangled image or not
-        'image_log_on': True
+        'image_log_on': True,
+        # The number of subjects for validating (Part of testing set)
+        'val_size': 10,
     },
     # Dataset settings
     'dataset': {
@@ -37,7 +39,7 @@ config: Configuration = {
         # Batch size (pr, k)
         # `pr` denotes number of persons
         # `k` denotes number of sequences per person
-        'batch_size': (2, 2),
+        'batch_size': (4, 6),
         # Number of workers of Dataloader
         'num_workers': 4,
         # Faster data transfer from RAM to GPU if enabled
@@ -61,15 +63,20 @@ config: Configuration = {
             # Term added to the denominator
             # 'eps': 1e-8,
             # Weight decay (L2 penalty)
-            # 'weight_decay': 0,
+            'weight_decay': 0.001,
             # Use AMSGrad or not
             # 'amsgrad': False,
         },
         'scheduler': {
-            # Period of learning rate decay
-            'step_size': 500,
-            # Multiplicative factor of decay
-            'gamma': 0.9,
+            # Step start to decay
+            'start_step': 500,
+            # Multiplicative factor of decay in the end
+            'final_gamma': 0.01,
+
+            # Local parameters (override global ones)
+            # 'hpm': {
+            #     'final_gamma': 0.001
+            # }
         }
     },
     # Model metadata
@@ -83,6 +90,6 @@ config: Configuration = {
         # Restoration iteration (multiple models, e.g. nm, bg and cl)
         'restore_iters': (0, 0, 0),
         # Total iteration for training (multiple models)
-        'total_iters': (80_000, 80_000, 80_000),
+        'total_iters': (30_000, 40_000, 60_000),
     },
 }