def get_train_val_loaders(self):
if self.args.dataset == 'cifar10':
train_transform, valid_transform = utils._data_transforms_cifar10(self.args)
train_data = dset.CIFAR10(root=self.args.data, train=True, download=True, transform=train_transform)
elif self.args.dataset == 'cifar100':
train_transform, valid_transform = utils._data_transforms_cifar100(self.args)
train_data = dset.CIFAR100(root=self.args.data, train=True, download=True, transform=train_transform)
elif self.args.dataset == 'svhn':
train_transform, valid_transform = utils._data_transforms_svhn(self.args)
train_data = dset.SVHN(root=self.args.data, split='train', download=True, transform=train_transform)
num_train = len(train_data)
indices = list(range(num_train))
split = int(np.floor(self.args.train_portion * num_train))
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=self.args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
pin_memory=True, num_workers=2)
valid_queue = torch.utils.data.DataLoader(
train_data, batch_size=self.args.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[split:num_train]),
pin_memory=True, num_workers=2)
return train_queue, valid_queue, train_transform, valid_transform
def get_train_val_loaders(self):
if self.args.dataset == 'cifar10':
train_transform, valid_transform = utils._data_transforms_cifar10(
self.args)
train_data = dset.CIFAR10(root=self.args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR10(root=self.args.data,
train=False,
download=True,
transform=valid_transform)
elif self.args.dataset == 'cifar100':
train_transform, valid_transform = utils._data_transforms_cifar100(
self.args)
train_data = dset.CIFAR100(root=self.args.data,
train=True,
download=True,
transform=train_transform)
valid_data = dset.CIFAR100(root=self.args.data,
train=False,
download=True,
transform=valid_transform)
elif self.args.dataset == 'svhn':
train_transform, valid_transform = utils._data_transforms_svhn(
self.args)
train_data = dset.SVHN(root=self.args.data,
split='train',
download=True,
transform=train_transform)
valid_data = dset.SVHN(root=self.args.data,
split='test',
download=True,
transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data,
batch_size=self.args.batch_size,
shuffle=True,
pin_memory=True,
num_workers=2)
valid_queue = torch.utils.data.DataLoader(
valid_data,
batch_size=self.args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=2)
return train_queue, valid_queue, train_transform, valid_transform