def build_train_transform(self, image_size=None, print_log=True):
if image_size is None:
image_size = self.image_size
if print_log:
print('Color jitter: %s, resize_scale: %s, img_size: %s' %
(self.distort_color, self.resize_scale, image_size))
if isinstance(image_size, list):
resize_transform_class = MyRandomResizedCrop
print(
'Use MyRandomResizedCrop: %s, \t %s' %
MyRandomResizedCrop.get_candidate_image_size(),
'sync=%s, continuous=%s' %
(MyRandomResizedCrop.SYNC_DISTRIBUTED,
MyRandomResizedCrop.CONTINUOUS))
else:
resize_transform_class = transforms.RandomResizedCrop
if self.subsample == 1:
# random_resize_crop -> random_horizontal_flip
train_transforms = [
resize_transform_class(image_size,
scale=(self.resize_scale, 1.0)),
transforms.RandomHorizontalFlip(),
]
# color augmentation (optional)
color_transform = None
if self.distort_color == 'torch':
color_transform = transforms.ColorJitter(brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.1)
elif self.distort_color == 'tf':
color_transform = transforms.ColorJitter(brightness=32. / 255.,
saturation=0.5)
if color_transform is not None:
train_transforms.append(color_transform)
else:
train_transforms = [
transforms.Resize(int(math.ceil(image_size / 0.875))),
transforms.CenterCrop(image_size),
]
train_transforms += [
transforms.ToTensor(),
self.normalize,
]
train_transforms = transforms.Compose(train_transforms)
return train_transforms
def build_train_transform(self, image_size=None, print_log=True):
if image_size is None:
image_size = self.image_size
if print_log:
print("Color jitter: %s, resize_scale: %s, img_size: %s" %
(self.distort_color, self.resize_scale, image_size))
if isinstance(image_size, list):
resize_transform_class = MyRandomResizedCrop
print(
"Use MyRandomResizedCrop: %s, \t %s" %
MyRandomResizedCrop.get_candidate_image_size(),
"sync=%s, continuous=%s" % (
MyRandomResizedCrop.SYNC_DISTRIBUTED,
MyRandomResizedCrop.CONTINUOUS,
),
)
else:
resize_transform_class = transforms.RandomResizedCrop
# random_resize_crop -> random_horizontal_flip
train_transforms = [
resize_transform_class(image_size,
scale=(self.resize_scale, 1.0),
interpolation=Image.BICUBIC),
transforms.RandomHorizontalFlip(),
]
# color augmentation (optional)
color_transform = None
if self.distort_color == "torch":
color_transform = transforms.ColorJitter(brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.1)
elif self.distort_color == "tf":
color_transform = transforms.ColorJitter(brightness=32.0 / 255.0,
saturation=0.5)
if color_transform is not None:
train_transforms.append(color_transform)
train_transforms += [
transforms.ToTensor(),
self.normalize,
]
train_transforms = transforms.Compose(train_transforms)
return train_transforms