散装代码：

import matplotlib.pyplot as plt
import torch
import torchvision
from torch.utils import data
from torchvision import transforms
from d2l import torch as d2ld2l.use_svg_display()# 通过ToTensor实例将图像数据从PIL类型变换成32位浮点数格式，
# 并除以255使得所有像素的数值均在0～1之间
trans = transforms.ToTensor()
mnist_train = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)
mnist_test = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)def get_fashion_mnist_labels(labels):  #@save"""返回Fashion-MNIST数据集的文本标签"""text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat','sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']return [text_labels[int(i)] for i in labels]def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  #@save"""绘制图像列表"""figsize = (num_cols * scale, num_rows * scale)_, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)axes = axes.flatten()for i, (ax, img) in enumerate(zip(axes, imgs)):if torch.is_tensor(img):# 图片张量ax.imshow(img.numpy())else:# PIL图片ax.imshow(img)ax.axes.get_xaxis().set_visible(False)ax.axes.get_yaxis().set_visible(False)if titles:ax.set_title(titles[i])return axesX, y = next(iter(data.DataLoader(mnist_train, batch_size=20)))
show_images(X.reshape(20, 28, 28), 2, 10, titles=get_fashion_mnist_labels(y));
plt.show()

下载数据集是为了训练模型的时候用

Fashion-MNIST是一个服装分类数据集，由10个类别的图像组成。我们将在后续章节中使用此数据集来评估各种分类算法。

由于图片处理不是重点，主要介绍函数功能：

输出标号对应字符串函数

def get_fashion_minist_labels(labels):

输入：

[0, 2]

输出：

['t-shirt', 'pullover']

图片打印函数

def show_images(imgs, num_rows, num_cos, titles = None, scale = 1.5):

trans = transforms.ToTensor()
mnist_train = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)
mnist_test = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)

从网上下载数据，下载在上一级的data文件夹内

X, y = next(iter(data.DataLoader(mnist_train, batch_size=20)))
show_images(X.reshape(20, 28, 28), 2, 10, titles=get_fashion_mnist_labels(y));
plt.show()

批量获取图片并显示

返还批量训练数据集与测试数据集函数：

def load_data_fashion_mnist(batch_size, resize=None):  #@save"""下载Fashion-MNIST数据集，然后将其加载到内存中"""trans = [transforms.ToTensor()]if resize:trans.insert(0, transforms.Resize(resize))trans = transforms.Compose(trans)mnist_train = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)mnist_test = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)return (data.DataLoader(mnist_train, batch_size, shuffle=True,num_workers=get_dataloader_workers()),data.DataLoader(mnist_test, batch_size, shuffle=False,num_workers=get_dataloader_workers()))

总结：

这些代码的作用是获取网络图片(Fashion-MNIST数据集)，将其下载至电脑，做好处理，以便后续训练模型以及检测模型