数据并不总是满足机器学习算法所需的格式。我们使用transform对数据进行一些操作，使得其能适用于训练。
所有的TorchVision数据集都有两个参数，用以接受包含transform逻辑的可调用项-transform 修改features，targe_transform 修改标签。torchvision.transforms提供了几种现成的常用转换操作。
FashionMNIST features是PIL Image格式，标签是整型。为了训练，我们需要将其转换为标准的tensors，并且标签是one-hot编码的tensor。为了完成这些转换，使用 ToTensor 和 Lambda。
import torch
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda
ds = datasets.FashionMNIST(
root='data',
train=True,
download=True,
transform=ToTensor(),
# 在创建的具有10个0值数组中，单独取第一个维度的y位置（原始标签），赋为1，即为one-hot编码
target_tansform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0,
torch.tensor(y), value=1))
)
输出：
点击查看代码
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
ToTensor()
ToTensor将PIL图像或NumPy ndarray 转换为 FloatTensor。并且将图片像素值缩放到范围[0., 1.]
Lambda Transforms
Lambda转换可使用任何用户定义的lambda函数。这里，我们定义了一个函数，可以将整型转换成one-hot编码的tensor，首先创建一个大小为10的0值tensor，根据给定标签 y得到索引位置，调用scatter_将其赋为1。
target_transform = Lambda(lambda y: torch.zeros(
10,dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))