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.RandomResize(size=self.configer.get('data', 'input_size')), ]) self.img_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.ToLabel(), ])