PyTorch 小功能之 TensorDataset

TensorDataset 可以用来对 tensor 进行打包，就好像 python 中的 zip 功能。该类通过每一个 tensor 的第一个维度进行索引。因此，该类中的 tensor 第一维度必须相等。

from torch.utils.data import TensorDataset
import torch
from torch.utils.data import DataLoader

a = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9], [1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = torch.tensor([44, 55, 66, 44, 55, 66, 44, 55, 66, 44, 55, 66])
train_ids = TensorDataset(a, b) 
# 切片输出
print(train_ids[0:2])
print(= * 80)
# 循环取数据
for x_train, y_label in train_ids:
    print(x_train, y_label)
# DataLoader进行数据封装
print(= * 80)
train_loader = DataLoader(dataset=train_ids, batch_size=4, shuffle=True)
for i, data in enumerate(train_loader, 1):  # 注意enumerate返回值有两个,一个是序号，一个是数据（包含训练数据和标签）
    x_data, label = data
    print( batch:{0} x_data:{1}  label: {2}.format(i, x_data, label))

运行结果：

(tensor([[1, 2, 3],
        [4, 5, 6]]), tensor([44, 55]))
================================================================================
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
tensor([1, 2, 3]) tensor(44)
tensor([4, 5, 6]) tensor(55)
tensor([7, 8, 9]) tensor(66)
================================================================================
 batch:1 x_data:tensor([[1, 2, 3],
        [1, 2, 3],
        [4, 5, 6],
        [4, 5, 6]])  label: tensor([44, 44, 55, 55])
 batch:2 x_data:tensor([[4, 5, 6],
        [7, 8, 9],
        [7, 8, 9],
        [7, 8, 9]])  label: tensor([55, 66, 66, 66])
 batch:3 x_data:tensor([[1, 2, 3],
        [1, 2, 3],
        [7, 8, 9],
        [4, 5, 6]])  label: tensor([44, 44, 66, 55])

注意：TensorDataset 中的参数必须是 tensor