87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
from data_provider.data_loader import Dataset_ETT_hour, Dataset_ETT_minute, Dataset_Custom, Dataset_M4, PSMSegLoader, \
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MSLSegLoader, SMAPSegLoader, SMDSegLoader, SWATSegLoader, UEAloader
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from data_provider.uea import collate_fn
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from torch.utils.data import DataLoader
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data_dict = {
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'ETTh1': Dataset_ETT_hour,
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'ETTh2': Dataset_ETT_hour,
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'ETTm1': Dataset_ETT_minute,
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'ETTm2': Dataset_ETT_minute,
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'custom': Dataset_Custom,
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'm4': Dataset_M4,
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'PSM': PSMSegLoader,
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'MSL': MSLSegLoader,
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'SMAP': SMAPSegLoader,
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'SMD': SMDSegLoader,
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'SWAT': SWATSegLoader,
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'UEA': UEAloader
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}
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def data_provider(args, flag):
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Data = data_dict[args.data]
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timeenc = 0 if args.embed != 'timeF' else 1
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shuffle_flag = False if (flag == 'test' or flag == 'TEST') else True
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drop_last = False
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batch_size = args.batch_size
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freq = args.freq
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if args.task_name == 'anomaly_detection':
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drop_last = False
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data_set = Data(
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args = args,
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root_path=args.root_path,
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win_size=args.seq_len,
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flag=flag,
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)
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print(flag, len(data_set))
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data_loader = DataLoader(
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data_set,
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batch_size=batch_size,
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shuffle=shuffle_flag,
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num_workers=args.num_workers,
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drop_last=drop_last)
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return data_set, data_loader
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elif args.task_name == 'classification':
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drop_last = False
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data_set = Data(
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args = args,
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root_path=args.root_path,
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flag=flag,
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)
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data_loader = DataLoader(
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data_set,
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batch_size=batch_size,
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shuffle=shuffle_flag,
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num_workers=args.num_workers,
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drop_last=drop_last,
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collate_fn=lambda x: collate_fn(x, max_len=args.seq_len)
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)
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return data_set, data_loader
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else:
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if args.data == 'm4':
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drop_last = False
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data_set = Data(
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args = args,
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root_path=args.root_path,
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data_path=args.data_path,
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flag=flag,
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size=[args.seq_len, args.label_len, args.pred_len],
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features=args.features,
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target=args.target,
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timeenc=timeenc,
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freq=freq,
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seasonal_patterns=args.seasonal_patterns
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)
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print(flag, len(data_set))
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data_loader = DataLoader(
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data_set,
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batch_size=batch_size,
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shuffle=shuffle_flag,
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num_workers=args.num_workers,
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drop_last=drop_last)
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return data_set, data_loader
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