图像分类数据集
MNIST数据集是图像分类中广泛使用的数据集之一,但作为基准数据集过于简单。我们将使用类似但更复杂的Fashion-MNIST数据集
%matplotlib inline
import torch
import torchvision
from torch.utils import data
from torchvision import transforms
from d2l import torch as d2l
d2l.use_svg_display()
通过框架中的内置函数将 Fashion-MNIST 数据集下载并读取到内存中
数据集下载
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)
len(mnist_train), len(mnist_test)
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
100.0%
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
100.6%
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
71.3%IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.
Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)
mnist_train[0][0].shape
torch.Size([1, 28, 28])
两个可视化数据集的函数
def get_fashion_mnist_labels(labels):
"""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):
"""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):
ax.imshow(img.numpy())
else:
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
几个样本的图像及其相应的标签
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));
读取一小批量数据,大小为batch_size
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())
timer = d2l.Timer()
for X, y in train_iter:
continue
f'{timer.stop():.2f} sec'
'1.40 sec'
定义 load_data_fashion_mnist
函数
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,
transform=trans,
download=False)
mnist_test = torchvision.datasets.FashionMNIST(root="../data",
train=False,
transform=trans,
download=False)
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