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- import torch
- # from torch.utils.data import DataLoader, Dataset
- import paddle
- from paddle.io import Dataset, DataLoader
- paddle.device.set_device("cpu")
- class NormalDataset(Dataset):
- def __init__(self, num_of_data=1000):
- self.num_of_data = num_of_data
- self._data = list(range(num_of_data))
-
- def __len__(self):
- return self.num_of_data
-
- def __getitem__(self, item):
- return self._data[item]
- dataset = NormalDataset(20)
- dataloader = DataLoader(dataset, batch_size=2, use_buffer_reader=False)
- for i, b in enumerate(dataloader):
- print(b)
- if i >= 2:
- break
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