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test.py
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test.py
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# import torchvision.datasets as dset
# import torchvision.transforms as transforms
# import torch.utils.data
# dataroot = "data/celebA"
# image_size = 128
# batch_size = 128
# workers = 2
# dataset = dset.ImageFolder(root=dataroot,
# transform=transforms.Compose([
# transforms.Resize(image_size),
# transforms.CenterCrop(image_size),
# transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
# ]))
# # Create the dataloader
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
# shuffle=True, num_workers=workers)
# # Plot some training images
# img, c = next(next(iter(dataloader)))
# print(img.shape)
# print(c)
print("Hello")