def cifar10(tnum=2):
dataset = mt_dataset.cifar10()
channel_stats = dict(mean=[0.4914, 0.4822, 0.4465],
std=[0.2470, 0.2435, 0.2616])
dataset['train_transformation'] = data.TransformNTimes(transforms.Compose([
data.RandomTranslateWithReflect(4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**channel_stats)
]),
n=tnum)
dataset['datadir'] = 'third_party/' + dataset['datadir']
return dataset
def cifar100():
channel_stats = dict(mean=[0.4914, 0.4822, 0.4465],
std=[0.2470, 0.2435, 0.2616
]) # should we use different stats - do this
train_transformation = data.TransformTwice(
transforms.Compose([
data.RandomTranslateWithReflect(4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**channel_stats)
]))
eval_transformation = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(**channel_stats)])
return {
'train_transformation': train_transformation,
'eval_transformation': eval_transformation,
'datadir': 'data-local/images/cifar/cifar100/by-image',
'num_classes': 100
}