def __init__(self, configer):
self.configer = configer
self.aug_train_transform = aug_trans.AugCompose(self.configer, split='train')
self.aug_val_transform = aug_trans.AugCompose(self.configer, split='val')
self.img_transform = trans.Compose([
trans.ToTensor(),
trans.Normalize(mean=self.configer.get('trans_params', 'mean'),
std=self.configer.get('trans_params', 'std')), ])
self.label_transform = trans.Compose([trans.ToTensor(), ])
def __init__(self, configer):
self.configer = configer
if self.configer.get('data', 'image_tool') == 'pil':
self.aug_train_transform = pil_aug_trans.PILAugCompose(
self.configer, split='train')
elif self.configer.get('data', 'image_tool') == 'cv2':
self.aug_train_transform = cv2_aug_trans.CV2AugCompose(
self.configer, split='train')
else:
Log.error('Not support {} image tool.'.format(
self.configer.get('data', 'image_tool')))
exit(1)
if self.configer.get('data', 'image_tool') == 'pil':
self.aug_val_transform = pil_aug_trans.PILAugCompose(self.configer,
split='val')
elif self.configer.get('data', 'image_tool') == 'cv2':
self.aug_val_transform = cv2_aug_trans.CV2AugCompose(self.configer,
split='val')
else:
Log.error('Not support {} image tool.'.format(
self.configer.get('data', 'image_tool')))
exit(1)
self.img_transform = trans.Compose([
trans.ToTensor(),
trans.Normalize(div_value=self.configer.get(
'normalize', 'div_value'),
mean=self.configer.get('normalize', 'mean'),
std=self.configer.get('normalize', 'std')),
])
def __init__(self, configer):
self.configer = configer
if self.configer.get('data', 'image_tool') == 'pil':
self.aug_train_transform = pil_aug_trans.PILAugCompose(
self.configer, split='train')
elif self.configer.get('data', 'image_tool') == 'cv2':
self.aug_train_transform = cv2_aug_trans.CV2AugCompose(
self.configer, split='train')
else:
Log.error('Not support {} image tool.'.format(
self.configer.get('data', 'image_tool')))
exit(1)
if self.configer.get('data', 'image_tool') == 'pil':
self.aug_val_transform = pil_aug_trans.PILAugCompose(self.configer,
split='val')
elif self.configer.get('data', 'image_tool') == 'cv2':
self.aug_val_transform = cv2_aug_trans.CV2AugCompose(self.configer,
split='val')
else:
Log.error('Not support {} image tool.'.format(
self.configer.get('data', 'image_tool')))
exit(1)
self.img_transform = trans.Compose([
trans.ToTensor(),
trans.Normalize(**self.configer.get('data', 'normalize')),
])
self.label_transform = trans.Compose([
trans.ToLabel(),
trans.ReLabel(255, -1),
])
def __init__(self, configer):
self.configer = configer
self.base_train_transform = trans.BaseCompose([
trans.RandomResize(),
trans.RandomRotate(self.configer.get('data', 'rotate_degree')),
trans.RandomCrop(self.configer.get('data', 'input_size')),
trans.RandomResize(size=self.configer.get('data', 'input_size')),
])
self.base_val_transform = trans.BaseCompose([
trans.RandomCrop(self.configer.get('data', 'input_size')),
trans.RandomResize(size=self.configer.get('data', 'input_size')),
])
self.input_transform = trans.Compose([
trans.ToTensor(),
trans.Normalize(mean=[128.0, 128.0, 128.0],
std=[256.0, 256.0, 256.0]),
])
self.label_transform = trans.Compose([
trans.ToTensor(),
])
