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| import warnings | |
| import mmcv | |
| from ..builder import PIPELINES | |
| from .compose import Compose | |
| class MultiScaleFlipAug(object): | |
| """Test-time augmentation with multiple scales and flipping. | |
| An example configuration is as followed: | |
| .. code-block:: | |
| img_scale=[(1333, 400), (1333, 800)], | |
| flip=True, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size_divisor=32), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']), | |
| ] | |
| After MultiScaleFLipAug with above configuration, the results are wrapped | |
| into lists of the same length as followed: | |
| .. code-block:: | |
| dict( | |
| img=[...], | |
| img_shape=[...], | |
| scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)] | |
| flip=[False, True, False, True] | |
| ... | |
| ) | |
| Args: | |
| transforms (list[dict]): Transforms to apply in each augmentation. | |
| img_scale (tuple | list[tuple] | None): Images scales for resizing. | |
| scale_factor (float | list[float] | None): Scale factors for resizing. | |
| flip (bool): Whether apply flip augmentation. Default: False. | |
| flip_direction (str | list[str]): Flip augmentation directions, | |
| options are "horizontal" and "vertical". If flip_direction is list, | |
| multiple flip augmentations will be applied. | |
| It has no effect when flip == False. Default: "horizontal". | |
| """ | |
| def __init__(self, | |
| transforms, | |
| img_scale=None, | |
| scale_factor=None, | |
| flip=False, | |
| flip_direction='horizontal'): | |
| self.transforms = Compose(transforms) | |
| assert (img_scale is None) ^ (scale_factor is None), ( | |
| 'Must have but only one variable can be setted') | |
| if img_scale is not None: | |
| self.img_scale = img_scale if isinstance(img_scale, | |
| list) else [img_scale] | |
| self.scale_key = 'scale' | |
| assert mmcv.is_list_of(self.img_scale, tuple) | |
| else: | |
| self.img_scale = scale_factor if isinstance( | |
| scale_factor, list) else [scale_factor] | |
| self.scale_key = 'scale_factor' | |
| self.flip = flip | |
| self.flip_direction = flip_direction if isinstance( | |
| flip_direction, list) else [flip_direction] | |
| assert mmcv.is_list_of(self.flip_direction, str) | |
| if not self.flip and self.flip_direction != ['horizontal']: | |
| warnings.warn( | |
| 'flip_direction has no effect when flip is set to False') | |
| if (self.flip | |
| and not any([t['type'] == 'RandomFlip' for t in transforms])): | |
| warnings.warn( | |
| 'flip has no effect when RandomFlip is not in transforms') | |
| def __call__(self, results): | |
| """Call function to apply test time augment transforms on results. | |
| Args: | |
| results (dict): Result dict contains the data to transform. | |
| Returns: | |
| dict[str: list]: The augmented data, where each value is wrapped | |
| into a list. | |
| """ | |
| aug_data = [] | |
| flip_args = [(False, None)] | |
| if self.flip: | |
| flip_args += [(True, direction) | |
| for direction in self.flip_direction] | |
| for scale in self.img_scale: | |
| for flip, direction in flip_args: | |
| _results = results.copy() | |
| _results[self.scale_key] = scale | |
| _results['flip'] = flip | |
| _results['flip_direction'] = direction | |
| data = self.transforms(_results) | |
| aug_data.append(data) | |
| # list of dict to dict of list | |
| aug_data_dict = {key: [] for key in aug_data[0]} | |
| for data in aug_data: | |
| for key, val in data.items(): | |
| aug_data_dict[key].append(val) | |
| return aug_data_dict | |
| def __repr__(self): | |
| repr_str = self.__class__.__name__ | |
| repr_str += f'(transforms={self.transforms}, ' | |
| repr_str += f'img_scale={self.img_scale}, flip={self.flip}, ' | |
| repr_str += f'flip_direction={self.flip_direction})' | |
| return repr_str | |