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49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
from torch.utils.data import DataLoader
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from data_provider_pretrain.data_loader import Dataset_ETT_hour, Dataset_ETT_minute
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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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}
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def data_provider(args, data, data_path, pretrain=True, flag='train'):
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Data = data_dict[data]
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timeenc = 0 if args.embed != 'timeF' else 1
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percent = args.percent
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if flag == 'test':
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shuffle_flag = False
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drop_last = True
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batch_size = args.batch_size
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freq = args.freq
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else:
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shuffle_flag = True
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drop_last = True
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batch_size = args.batch_size
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freq = args.freq
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data_set = Data(
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root_path=args.root_path,
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data_path=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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percent=percent,
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seasonal_patterns=args.seasonal_patterns,
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pretrain=pretrain
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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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return data_set, data_loader
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