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from torchvision import transforms
import torchvision
import torch
# global, whole training dataset
# x' = (x-mean)/std
# x'*std + mean => x
# timm.data.IMAGENET_DEFAULT_MEAN: (0.485, 0.456, 0.406)
# timm.data.IMAGENET_DEFAULT_STD: (0.229, 0.224, 0.225)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.1307], std=[0.3081]),
transforms.Resize()
])
# MNIST dataset
mnist = torchvision.datasets.MNIST(root='./data/',
train=True,
transform=transform,
download=True)
batch_size = 32
# Data loader
data_loader = torch.utils.data.DataLoader(dataset=mnist,
batch_size=batch_size,
shuffle=True)
epochs = 10
for epoch in range(epochs):
for i, (images, t) in enumerate(data_loader):
print(images)
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