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July 12, 2018 19:44
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import torchvision.datasets as datasets | |
import torchvision.transforms as transforms | |
import torch | |
import torchvision | |
import matplotlib.pyplot as plt | |
import numpy as np | |
from custom_transforms import NRandomCrop | |
def imshow(img): | |
img = img / 2 + 0.5 | |
npimg = img.numpy() | |
plt.imshow(np.transpose(npimg, (1, 2, 0))) | |
plt.show() | |
mean, sd = (0.5, 0.5, 0.5), (0.5, 0.5, 0.5) | |
transform = transforms.Compose( | |
[NRandomCrop(size=32, n=5, padding=4), | |
transforms.Lambda( | |
lambda crops: torch.stack([transforms.Normalize(mean, sd)(transforms.ToTensor()(crop)) for crop in crops])), | |
] | |
) | |
train_data = datasets.CIFAR10(root='./data', | |
train=True, | |
download=True, | |
transform=transform) | |
train_loader = torch.utils.data.DataLoader(train_data, | |
batch_size=1, | |
shuffle=True, | |
num_workers=4) | |
classes = ('plane', 'car', 'bird', 'cat', | |
'deer', 'dog', 'frog', 'horse', 'ship', 'truck') | |
dataiter = iter(train_loader) | |
images, labels = dataiter.next() | |
# show images | |
imshow(torchvision.utils.make_grid(images.squeeze(0))) | |
# print labels | |
print(' '.join('%5s' % classes[labels[j]] for j in range(0))) |
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You can do something this in you training code. It only works with
batch_size=1
.