Python深度学习pytorch实现图像分类数据集

import torch
import torchvision
from torch.utils import data
from torchvision import transforms
from d2l import torch as d2l
d2l.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到1之间
trans = transforms.ToTensor()
mnist_train = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)
mnist_test = torchvisino.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)

len(mnist_train), len(mnist_test)

(60000, 10000)

mnist_train[0][0].shape

torch.size([1, 28, 28])

def get_fashion_mnist_labels(labels):
	"""返回Fashion-MNIST数据集的本文标签。"""
	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):
	"""Plot a list of images."""
	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 axes

X, y = next(iter(data.DataLoader(mnist_train, batch_size=18)))
show_images(X.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y))

batch_size = 256
def get_dataloader_workers():
	"""使用4个进程来读取数据。"""
	return 4
train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers())

def load_data_fashion_mnist(batch_size, resize=None):
	"""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, transforms=trans, download=True)
	mnist_test = torchvision.datasets.FashionMNIST(root="../data", train=False, transforms=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()))

train_iter, test_iter = load_data_fashion_mnist(32, resize=64)
for X, y in train_iter:
	print(X.shape, X.dtype, y.shape, y.dtype)
	break

torch.Size([32, 1, 64, 64]) torch.float32 torch.Size([32]) torch.int64