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"""
Configuration settings for the trajectory interpolation project.
This file defines a function `load_config()` which returns a dictionary
containing various parameters grouped by their purpose (e.g., data, model,
diffusion, training, sampling).
"""
from types import SimpleNamespace
import torch
def load_config():
config_args = {
'data': {
'traj_length': 256, # 修正：增加长度以容纳两个1小时(120点)的遮蔽和上下文
'dataset': 'TKY_temporal',
'traj_path1': './data/',
'num_workers': 16, # 增加数据加载线程，减少GPU等待
},
'train': {
'batch_size': 512, # 显著降低batch_size以适应增长的traj_length，避免显存溢出
'n_epochs': 50,
'n_iters': 5000000,
'snapshot_freq': 5000,
'validation_freq': 5,
'dis_gpu': False,
},
'trans': {
'input_dim': 3,
'embed_dim': 512,
'num_layers': 4,
'num_heads': 8,
'forward_dim': 256,
'dropout': 0.1,
'N_CLUSTER': 20,
},
'test': {
'batch_size': 256, # 同样降低测试batch size以适应增长的traj_length
'last_only': True,
},
'diffusion': {
'beta_schedule': 'linear',
'beta_start': 0.0001,
'beta_end': 0.05,
'num_diffusion_timesteps': 500,
},
'model': {
'type': "simple",
'attr_dim': 8,
'guidance_scale': 2,
'in_channels': 3,
'out_ch': 3,
'ch': 128,
'ch_mult': [1, 2, 2, 2],
'num_res_blocks': 2,
'attn_resolutions': [16],
'dropout': 0.1,
'var_type': 'fixedlarge',
'resamp_with_conv': True,
},
'data_source': 'TKY',
'data_dir': './data/TKY/manually_split/',
'normalization_params_file': './data/TKY/normalization_params.json',
}
# Create nested config structure to maintain compatibility
config = SimpleNamespace()
config.training = SimpleNamespace(**config_args['train'])
config.test = SimpleNamespace(**config_args['test'])
config.diffusion = SimpleNamespace(**config_args['diffusion'])
config.model = SimpleNamespace(**config_args['model'])
config.sampling = SimpleNamespace(**config_args['test']) # Use test config for sampling
# Add DDIM sampling configuration for faster testing
config.sampling.type = 'ddim' # Use DDIM instead of DDPM for 10x faster testing
config.sampling.ddim_steps = 50 # 50 steps instead of 500, 10x speedup
config.sampling.ddim_eta = 0.0 # Deterministic sampling
config.data = SimpleNamespace(**config_args['data'])
config.trans = SimpleNamespace(**config_args['trans'])
config.device = 'cuda' if torch.cuda.is_available() else 'cpu'
config.masking_strategy = 'multi_segment' # 新增：'multi_segment'
config.mask_segments = [60, 60] # 修正：基于“一分钟一点”，每段遮蔽60个点
config.mask_ratio = 0.2
config.mask_points_per_hour = 60 # 修正：基于“一分钟一点”的先验知识
config.z_score_normalization = False
config.dis_gpu = False # Distributed GPU training
# Add missing top-level config fields
config.learning_rate = 1.5e-4 # 降低学习率，提高训练稳定性
config.batch_size = config_args['train']['batch_size']
config.n_epochs = config_args['train']['n_epochs']
config.validation_freq = config_args['train']['validation_freq']
config.warmup_epochs = 5 # 减少warmup epochs，加速训练
config.contrastive_margin = 1.0
config.kmeans_memory_size = 15 # 增加K-means缓存，提高聚类效率
config.contrastive_loss_weight = 0.1
config.ce_loss_weight = 0.1
config.diffusion_loss_weight = 1.0
config.device_id = 0
config.use_amp = True # 启用混合精度训练
config.normalization_params_file = config_args['normalization_params_file']
config.data_source = config_args['data_source']
config.data_dir = config_args['data_dir']
config.traj_length = config_args['data']['traj_length']
return config |