| | import collections |
| |
|
| | from mmcv.utils import build_from_cfg |
| |
|
| | from ..builder import PIPELINES |
| |
|
| |
|
| | @PIPELINES.register_module() |
| | class Compose(object): |
| | """Compose multiple transforms sequentially. |
| | |
| | Args: |
| | transforms (Sequence[dict | callable]): Sequence of transform object or |
| | config dict to be composed. |
| | """ |
| |
|
| | def __init__(self, transforms): |
| | assert isinstance(transforms, collections.abc.Sequence) |
| | self.transforms = [] |
| | for transform in transforms: |
| | if isinstance(transform, dict): |
| | transform = build_from_cfg(transform, PIPELINES) |
| | self.transforms.append(transform) |
| | elif callable(transform): |
| | self.transforms.append(transform) |
| | else: |
| | raise TypeError('transform must be callable or a dict') |
| |
|
| | def __call__(self, data): |
| | """Call function to apply transforms sequentially. |
| | |
| | Args: |
| | data (dict): A result dict contains the data to transform. |
| | |
| | Returns: |
| | dict: Transformed data. |
| | """ |
| |
|
| | for t in self.transforms: |
| | |
| | data = t(data) |
| | if data is None: |
| | return None |
| | return data |
| |
|
| | def __repr__(self): |
| | format_string = self.__class__.__name__ + '(' |
| | for t in self.transforms: |
| | format_string += '\n' |
| | format_string += f' {t}' |
| | format_string += '\n)' |
| | return format_string |
| |
|
