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import torch
from utils.configuration import Configuration
config: Configuration = {
'system': {
# Device(s) used in training and testing (CPU or CUDA)
'device': torch.device('cuda'),
# GPU(s) used in training or testing, if CUDA enabled
'CUDA_VISIBLE_DEVICES': '0',
# Directory used in training or testing for temporary storage
'save_dir': 'runs',
},
# Dataset settings
'dataset': {
# Name of dataset (CASIA-B or FVG)
'name': 'CASIA-B',
# Path to dataset root
'root_dir': 'dataset/output/CASIA-B',
# The number of subjects for training
'train_size': 74,
# Number of sampled frames per sequence (Training only)
'num_sampled_frames': 30,
# Discard clips shorter than `discard_threshold`
'discard_threshold': 15,
# Number of input channels of model
'num_input_channels': 3,
# Resolution after resize, height : width should be 2 : 1
'frame_size': (64, 32),
# 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': (8, 16),
# Number of workers of Dataloader
'num_workers': 4,
# Faster data transfer from RAM to GPU if enabled
'pin_memory': True,
},
# Hyperparameter tuning
'hyperparameter': {
# Hidden dimension of FC
'hidden_dim': 256,
# Initial learning rate of Adam Optimizer
'lr': 1e-4,
# Betas of Adam Optimizer
'betas': (0.9, 0.999),
# Batch Hard or Batch Full Triplet loss
# `hard` for BH, `all` for BA
'hard_or_all': 'all',
# Triplet loss margin
'margin': 0.2,
},
# Model metadata
'model': {
# Model name, used for naming checkpoint
'name': 'RGB-GaitPart',
# Restoration iteration from checkpoint
'restore_iter': 0,
# Total iteration for training
'total_iter': 80000,
},
}
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