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# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.transforms import LoadImageFromFile
from mmdet.datasets.transforms import LoadAnnotations, LoadPanopticAnnotations
from mmdet.registry import TRANSFORMS
def get_loading_pipeline(pipeline):
"""Only keep loading image and annotations related configuration.
Args:
pipeline (list[dict]): Data pipeline configs.
Returns:
list[dict]: The new pipeline list with only keep
loading image and annotations related configuration.
Examples:
>>> pipelines = [
... dict(type='LoadImageFromFile'),
... dict(type='LoadAnnotations', with_bbox=True),
... dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
... dict(type='RandomFlip', flip_ratio=0.5),
... dict(type='Normalize', **img_norm_cfg),
... dict(type='Pad', size_divisor=32),
... dict(type='DefaultFormatBundle'),
... dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
... ]
>>> expected_pipelines = [
... dict(type='LoadImageFromFile'),
... dict(type='LoadAnnotations', with_bbox=True)
... ]
>>> assert expected_pipelines ==\
... get_loading_pipeline(pipelines)
"""
loading_pipeline_cfg = []
for cfg in pipeline:
obj_cls = TRANSFORMS.get(cfg['type'])
# TODO：use more elegant way to distinguish loading modules
if obj_cls is not None and obj_cls in (LoadImageFromFile,
LoadAnnotations,
LoadPanopticAnnotations):
loading_pipeline_cfg.append(cfg)
assert len(loading_pipeline_cfg) == 2, \
'The data pipeline in your config file must include ' \
'loading image and annotations related pipeline.'
return loading_pipeline_cfg
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