/
presets.py
48 lines (40 loc) · 1.55 KB
/
presets.py
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from torchvision.transforms import transforms
class ClassificationPresetTrain:
def __init__(self,
crop_size,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
hflip_prob=0.5,
auto_augment_policy=None,
random_erase_prob=0.0):
trans = [transforms.RandomResizedCrop(crop_size)]
# if hflip_prob > 0:
# trans.append(transforms.RandomHorizontalFlip(hflip_prob))
if auto_augment_policy is not None:
aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy)
trans.append(autoaugment.AutoAugment(policy=aa_policy))
trans.extend([
transforms.ToTensor(),
transforms.Normalize(
mean=mean, std=std),
])
# if random_erase_prob > 0:
# trans.append(transforms.RandomErasing(p=random_erase_prob))
self.transforms = transforms.Compose(trans)
def __call__(self, img):
return self.transforms(img)
class ClassificationPresetEval:
def __init__(self,
crop_size,
resize_size=256,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)):
self.transforms = transforms.Compose([
transforms.Resize(resize_size),
transforms.CenterCrop(crop_size),
transforms.ToTensor(),
transforms.Normalize(
mean=mean, std=std),
])
def __call__(self, img):
return self.transforms(img)