diff options
Diffstat (limited to 'config.py')
-rw-r--r-- | config.py | 71 |
1 files changed, 42 insertions, 29 deletions
@@ -41,35 +41,48 @@ config: Configuration = { }, # Hyperparameter tuning 'hyperparameter': { - # Auto-encoder feature channels coefficient - 'ae_feature_channels': 64, - # Appearance, canonical and pose feature dimensions - 'f_a_c_p_dims': (128, 128, 64), - # 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': True, - # 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 margin - 'triplet_margin': 0.2, - # Initial learning rate of Adam Optimizer - 'lr': 1e-4, - # Betas of Adam Optimizer - 'betas': (0.9, 0.999), + 'model': { + # Auto-encoder feature channels coefficient + 'ae_feature_channels': 64, + # Appearance, canonical and pose feature dimensions + 'f_a_c_p_dims': (128, 128, 64), + # 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': True, + # 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 margin + 'triplet_margin': 0.2, + }, + 'optimizer': { + # Initial learning rate of Adam Optimizer + 'lr': 1e-4, + # Coefficients used for computing running averages of + # gradient and its square + 'betas': (0.9, 0.999), + # Weight decay (L2 penalty) + 'weight_decay': 0.001, + }, + 'scheduler': { + # Period of learning rate decay + 'step_size': 500, + # Multiplicative factor of decay + 'gamma': 0.9, + } }, # Model metadata 'model': { |