OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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55 lines
1.6 KiB
55 lines
1.6 KiB
# Copyright (c) OpenMMLab. All rights reserved. |
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import collections |
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from mmcv.utils import build_from_cfg |
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from ..builder import PIPELINES |
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@PIPELINES.register_module() |
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class Compose: |
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"""Compose multiple transforms sequentially. |
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Args: |
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transforms (Sequence[dict | callable]): Sequence of transform object or |
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config dict to be composed. |
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""" |
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def __init__(self, transforms): |
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assert isinstance(transforms, collections.abc.Sequence) |
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self.transforms = [] |
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for transform in transforms: |
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if isinstance(transform, dict): |
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transform = build_from_cfg(transform, PIPELINES) |
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self.transforms.append(transform) |
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elif callable(transform): |
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self.transforms.append(transform) |
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else: |
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raise TypeError('transform must be callable or a dict') |
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def __call__(self, data): |
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"""Call function to apply transforms sequentially. |
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Args: |
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data (dict): A result dict contains the data to transform. |
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Returns: |
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dict: Transformed data. |
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""" |
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for t in self.transforms: |
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data = t(data) |
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if data is None: |
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return None |
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return data |
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def __repr__(self): |
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format_string = self.__class__.__name__ + '(' |
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for t in self.transforms: |
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str_ = t.__repr__() |
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if 'Compose(' in str_: |
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str_ = str_.replace('\n', '\n ') |
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format_string += '\n' |
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format_string += f' {str_}' |
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format_string += '\n)' |
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return format_string
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