当事先不知道训练样本的顺序和总数时，如何创建自定义 PyTorch 数据集? [英] How to create a custom PyTorch dataset when the order and the total number of training samples is not known in advance?

问题描述

我有一个 42 GB 的 jsonl 文件.这个文件的每个元素都是一个 json 对象.我从每个 json 对象创建训练样本.但是我从每个 json 对象中提取的训练样本数量可能在 0 到 5 个样本之间变化.在不读取内存中的整个 jsonl 文件的情况下创建自定义 PyTorch 数据集的最佳方法是什么?

I have a 42 GB jsonl file. Every element of this file is a json object. I create training samples from every json object. But the number of training samples from every json object that I extract can vary between 0 to 5 samples. What is the best way to create a custom PyTorch dataset without reading the entire jsonl file in memory?

这是我正在谈论的数据集 - Google Natural Questions.>

This is the dataset I am talking about - Google Natural Questions.

推荐答案

您有几个选择.

1. 如果有很多小文件不是问题，最简单的选择是将每个 json 对象预处理为单个文件.然后您可以根据请求的索引读取每一个.例如

    class SingleFileDataset(Dataset):
        def __init__(self, list_of_file_paths):
            self.list_of_file_paths = list_of_file_paths

        def __getitem__(self, index):
            return np.load(self.list_of_file_paths[index]) # Or equivalent reading code for single file

1. 您还可以将数据拆分为固定数量的文件，然后根据索引计算样本所在的文件.然后您需要将该文件打开到内存中并读取相应的索引.这给出了磁盘访问和内存使用之间的权衡.假设您有 n 个样本，我们在预处理期间将样本均匀地拆分为 c 个文件.现在，要读取索引 i 处的示例，我们将执行

1. You can also split the data into a constant number of files, and then calculate, given the index, which file the sample resides in. Then you need to open that file into memory and read the appropriate index. This gives a trade-off between disk access and memory usage. Assume you have n samples, and we split the samples into c files evenly during preprocessing. Now, to read the sample at index i we would do

    class SplitIntoFilesDataset(Dataset):
        def __init__(self, list_of_file_paths, n_splits):
            self.list_of_file_paths = list_of_file_paths
            self.n_splits = n_splits

        def __getitem__(self, index):
            # index // n_splits is the relevant file, and 
            # index % len(self) is the index in in that file
            file_to_load = self.list_of_file_paths[index // self.n_splits]
            # Load file
            file = np.load(file)
            datapoint = file[index % len(self)]

1. 最后，您可以使用允许访问磁盘行的 HDF5 文件.如果您有大量数据，这可能是最好的解决方案，因为数据将在磁盘上关闭.有一个实现这里，我复制粘贴在下面:

1. Finally, you could use a HDF5 file that allows access to rows on disk. This is possibly the best solution if you have a lot of data, since the data will be close on disk. There's an implementation here which I have copy pasted below:

import h5py
import torch
import torch.utils.data as data
class H5Dataset(data.Dataset):

    def __init__(self, file_path):
        super(H5Dataset, self).__init__()
        h5_file = h5py.File(file_path)
        self.data = h5_file.get('data')
        self.target = h5_file.get('label')

    def __getitem__(self, index):            
        return (torch.from_numpy(self.data[index,:,:,:]).float(),
                torch.from_numpy(self.target[index,:,:,:]).float())

    def __len__(self):
        return self.data.shape[0]

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