def dataLoader(is_train=True, cuda=True, batch_size=64, shuffle=True):
if is_train:
trans = [transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize(mean=[n/255.
for n in [129.3, 124.1, 112.4]], std=[n/255. for n in [68.2, 65.4, 70.4]])]
trans = transforms.Compose(trans)
train_set = td.CIFAR100('data', train=True, transform=trans)
size = len(train_set.train_labels)
train_loader = torch.utils.data.DataLoader(
train_set, batch_size=batch_size, shuffle=shuffle)
else:
trans = [transforms.ToTensor(),
transforms.Normalize(mean=[n/255.
for n in [129.3, 124.1, 112.4]], std=[n/255. for n in [68.2, 65.4, 70.4]])]
trans = transforms.Compose(trans)
test_set = td.CIFAR100('data', train=False, transform=trans)
size = len(test_set.test_labels)
train_loader = torch.utils.data.DataLoader(
test_set, batch_size=batch_size, shuffle=shuffle)
return train_loader, size
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