构造迭代读取的Dataloader，首先需要可迭代的DataSet，这一部分详细请参考：pytorch构造可迭代的Dataset——IterableDataset（pytorch Data学习二），下面直接开始封装到DataLoader中

封装IterableDataset到DataLoader

比如文件test_csv.csv：

1,2,3,4,1
1,2,3,4,2
1,2,3,4,3
1,2,3,4,4
1,2,3,4,5

1. 一般文本封装方法

由于DataLoader得到的迭代数据都是Tensor格式的数据，因此需要将文本转换为tensor格式，修改dataset的__iter__方法为：

import torch
from torch.utils.data import IterableDataset, DataLoader
import numpy as np


class MyIterableDataset(IterableDataset):
    def __init__(self, file_path):
        self.file_path = file_path

    def __iter__(self):
        with open(self.file_path, 'r') as file_obj:
            for line in file_obj:
                line_data = line.strip('\n').split(',')
                yield torch.from_numpy(np.array(line_data, dtype='int')) # 这里按照自己的代码看格式哈

然后封装即可：

if __name__ == '__main__':
    dataset = MyIterableDataset('test_csv.csv')
    dataloader = DataLoader(dataset, batch_size=3) # 这里batch_size=3，意味着每次读取dataloader都会循环三次dataset
    for data in dataloader:
        print(data)

完整代码：

import torch
from torch.utils.data import IterableDataset, DataLoader
import numpy as np


class MyIterableDataset(IterableDataset):
    def __init__(self, file_path):
        self.file_path = file_path

    def __iter__(self):
        with open(self.file_path, 'r') as file_obj:
            for line in file_obj:
                line_data = line.strip('\n').split(',')
                yield torch.from_numpy(np.array(line_data, dtype='int'))


if __name__ == '__main__':
    dataset = MyIterableDataset('test_csv.csv')
    dataloader = DataLoader(dataset, batch_size=3)
    for data in dataloader:
        print(data)

2. pandas read_xxx封装方法

思路同上，代码如下：

class PandasIterableDataset(IterableDataset):
    def __init__(self, file_path):
        import pandas as pd

        self.data_iter = pd.read_csv(file_path, iterator=True, header=None, chunksize=1)

    def __iter__(self):
        for data in self.data_iter:
            yield torch.from_numpy(np.array(data).flatten())


if __name__ == '__main__':
    dataset = PandasIterableDataset('test_csv.csv')
    dataloader = DataLoader(dataset, batch_size=3)
    for data in dataloader:
        print(data)