collate_fn for PyTorch DataLoader

collate_fn_example.py

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


class MyDataset(Dataset):
    def __init__(self):
        x = np.random.rand(1000, 3)  # 1000 3-dim samples
        self.x = [x[i].tolist() for i in range(1000)]
        y = np.random.randint(low=0, high=2, size=(1000,))
        self.y = [y[i] for i in range(1000)]

    def __len__(self):
        return len(self.x)

    def __getitem__(self, idx):
        return self.x[idx], self.y[idx]


def collate_fn(batch):
    data_list, label_list = [], []
    for _data, _label in batch:
        data_list.append(_data)
        label_list.append(_label)
    return torch.Tensor(data_list), torch.LongTensor(label_list)


if __name__ == "__main__":
    dataset = MyDataset()
    print(len(dataset))
    print(dataset[-1])

    dataloader = DataLoader(dataset, batch_size=3, shuffle=False, collate_fn=collate_fn)
    for data, label in dataloader:
        print(type(data))
        print(data)
        print(type(label))
        print(label)
        break

explicit definition of collate_fn is not required if self.x and self.y are numpy arrays already. collate_fn=None will create two tensors for each batch.

Thank you very much!
I am learning Pytorch and I don't know how to Dataloader 15002828 data input the model. Your example teache me how to do that. Thank you a thousand times.