def _add_random_erasing(self):
transform = transforms.random_erasing(
self.config['random_erasing_prob'],
self.config['random_erasing_area_ratio_range'],
self.config['random_erasing_min_aspect_ratio'],
self.config['random_erasing_max_attempt'])
self._train_transforms.append(transform)
def _get_random_erasing_train_transform(self):
transform = torchvision.transforms.Compose([
transforms.normalize(self.mean, self.std),
transforms.random_erasing(
self.config['random_erasing_prob'],
self.config['random_erasing_area_ratio_range'],
self.config['random_erasing_min_aspect_ratio'],
self.config['random_erasing_max_attempt']),
transforms.to_tensor(),
])
return transform
def _get_random_erasing_train_transform(self):
transform = torchvision.transforms.Compose([
torchvision.transforms.Resize((32,32)),
torchvision.transforms.ColorJitter(0.1,0.1,0.1),
# torchvision.transforms.RandomRotation(15),
# RandomAffine(degrees=15,scale=(0.8,1.2),shear=15),
torchvision.transforms.RandomCrop(32, padding=4),
torchvision.transforms.RandomHorizontalFlip(),
transforms.normalize(self.mean, self.std),
transforms.random_erasing(
self.config['random_erasing_prob'],
self.config['random_erasing_area_ratio_range'],
self.config['random_erasing_min_aspect_ratio'],
self.config['random_erasing_max_attempt']),
transforms.to_tensor(),
])
return transform
