def __init__(self, args):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.Resize(256),
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(0.4,0.4,0.4),
transforms.ToTensor(),
Lighting(0.1, _imagenet_pca['eigval'], _imagenet_pca['eigvec']),
normalize,
])
transform_test = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])
trainset = MINCDataloder(root=os.path.expanduser('~/data/minc-2500/'),
train=True, transform=transform_train)
testset = MINCDataloder(root=os.path.expanduser('~/data/minc-2500/'),
train=False, transform=transform_test)
kwargs = {'num_workers': 8, 'pin_memory': True} if args.cuda else {}
trainloader = torch.utils.data.DataLoader(trainset, batch_size=
args.batch_size, shuffle=True, **kwargs)
testloader = torch.utils.data.DataLoader(testset, batch_size=
args.test_batch_size, shuffle=False, **kwargs)
self.trainloader = trainloader
self.testloader = testloader
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