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-rw-r--r--config.py38
1 files changed, 4 insertions, 34 deletions
diff --git a/config.py b/config.py
index c47d2ba..00dc850 100644
--- a/config.py
+++ b/config.py
@@ -5,9 +5,9 @@ config = {
# GPU(s) used in training or testing if available
'CUDA_VISIBLE_DEVICES': '0',
# Directory used in training or testing for temporary storage
- 'save_dir': 'runs',
+ 'save_dir': 'runs/dis_only',
# Recorde disentangled image or not
- 'image_log_on': False
+ 'image_log_on': True
},
# Dataset settings
'dataset': {
@@ -35,7 +35,7 @@ config = {
# Batch size (pr, k)
# `pr` denotes number of persons
# `k` denotes number of sequences per person
- 'batch_size': (4, 8),
+ 'batch_size': (2, 2),
# Number of workers of Dataloader
'num_workers': 4,
# Faster data transfer from RAM to GPU if enabled
@@ -47,35 +47,10 @@ config = {
# Auto-encoder feature channels coefficient
'ae_feature_channels': 64,
# Appearance, canonical and pose feature dimensions
- 'f_a_c_p_dims': (128, 128, 64),
- # Use 1x1 convolution in dimensionality reduction
- 'hpm_use_1x1conv': False,
- # HPM pyramid scales, of which sum is number of parts
- 'hpm_scales': (1, 2, 4),
- # Global pooling method
- 'hpm_use_avg_pool': True,
- 'hpm_use_max_pool': False,
- # FConv feature channels coefficient
- 'fpfe_feature_channels': 32,
- # FConv blocks kernel sizes
- 'fpfe_kernel_sizes': ((5, 3), (3, 3), (3, 3)),
- # FConv blocks paddings
- 'fpfe_paddings': ((2, 1), (1, 1), (1, 1)),
- # FConv blocks halving
- 'fpfe_halving': (0, 2, 3),
- # Attention squeeze ratio
- 'tfa_squeeze_ratio': 4,
- # Number of parts after Part Net
- 'tfa_num_parts': 16,
- # Embedding dimension for each part
- 'embedding_dims': 256,
- # Triplet loss margins for HPM and PartNet
- 'triplet_margins': (0.2, 0.2),
+ 'f_a_c_p_dims': (192, 192, 96),
},
'optimizer': {
# Global parameters
- # Iteration start to optimize non-disentangling parts
- # 'start_iter': 0,
# Initial learning rate of Adam Optimizer
'lr': 1e-4,
# Coefficients used for computing running averages of
@@ -87,11 +62,6 @@ config = {
# 'weight_decay': 0,
# Use AMSGrad or not
# 'amsgrad': False,
-
- # Local parameters (override global ones)
- 'auto_encoder': {
- 'weight_decay': 0.001
- },
},
'scheduler': {
# Period of learning rate decay