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config = {
'system': {
# Disable accelerator
'disable_acc': False,
# 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/dis_only',
# Recorde disentangled image or not
'image_log_on': True
},
# Dataset settings
'dataset': {
# Name of dataset (CASIA-B or FVG)
'name': 'CASIA-B',
# Path to dataset root (required)
'root_dir': 'data/CASIA-B-MRCNN-V2/SEG',
# The number of subjects for training
'train_size': 74,
# Number of sampled frames per sequence (Training only)
'num_sampled_frames': 30,
# Truncate clips longer than `truncate_threshold`
'truncate_threshold': 40,
# Discard clips shorter than `discard_threshold`
'discard_threshold': 15,
# Number of input channels of model
'num_input_channels': 3,
# Resolution after resize, can be divided 16
'frame_size': (64, 48),
# Cache dataset or not
'cache_on': False,
},
# Dataloader settings
'dataloader': {
# Batch size (pr, k)
# `pr` denotes number of persons
# `k` denotes number of sequences per person
'batch_size': (2, 2),
# Number of workers of Dataloader
'num_workers': 4,
# Faster data transfer from RAM to GPU if enabled
'pin_memory': True,
},
# Hyperparameter tuning
'hyperparameter': {
'model': {
# Auto-encoder feature channels coefficient
'ae_feature_channels': 64,
# Appearance, canonical and pose feature dimensions
'f_a_c_p_dims': (192, 192, 96),
},
'optimizer': {
# Global parameters
# 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),
# Term added to the denominator
# 'eps': 1e-8,
# Weight decay (L2 penalty)
# 'weight_decay': 0,
# Use AMSGrad or not
# 'amsgrad': False,
},
'scheduler': {
# Period of learning rate decay
'step_size': 500,
# Multiplicative factor of decay
'gamma': 0.9,
}
},
# Model metadata
'model': {
# Model name, used for naming checkpoint
'name': 'RGB-GaitPart',
# Restoration iteration from checkpoint (single model)
# 'restore_iter': 0,
# Total iteration for training (single model)
# 'total_iter': 80000,
# 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),
},
}
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