def is_loading_function(transform):
"""Judge whether a transform function is a loading function.
Note: `MultiScaleFlipAug3D` is a wrapper for multiple pipeline functions,
so we need to search if its inner transforms contain any loading function.
Args:
transform (dict | :obj:`Pipeline`): A transform config or a function.
Returns:
bool | None: Whether it is a loading function. None means can't judge.
When transform is `MultiScaleFlipAug3D`, we return None.
"""
# TODO: use more elegant way to distinguish loading modules
loading_functions = (LoadImageFromFile, LoadPointsFromFile,
LoadAnnotations3D, LoadMultiViewImageFromFiles,
LoadPointsFromMultiSweeps, DefaultFormatBundle3D,
Collect3D, LoadImageFromFileMono3D,
PointSegClassMapping)
if isinstance(transform, dict):
obj_cls = PIPELINES.get(transform['type'])
if obj_cls is None:
return False
if obj_cls in loading_functions:
return True
if obj_cls in (MultiScaleFlipAug3D, ):
return None
elif callable(transform):
if isinstance(transform, loading_functions):
return True
if isinstance(transform, MultiScaleFlipAug3D):
return None
return False
def get_loading_pipeline(pipeline):
"""Only keep loading image, points and annotations related configuration.
Args:
pipeline (list[dict]): Data pipeline configs.
Returns:
list[dict]: The new pipeline list with only keep
loading image, points and annotations related configuration.
Examples:
>>> pipelines = [
... dict(type='LoadPointsFromFile',
... coord_type='LIDAR', load_dim=4, use_dim=4),
... dict(type='LoadImageFromFile'),
... dict(type='LoadAnnotations3D',
... with_bbox=True, with_label_3d=True),
... dict(type='Resize',
... img_scale=[(640, 192), (2560, 768)], keep_ratio=True),
... dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
... dict(type='PointsRangeFilter',
... point_cloud_range=point_cloud_range),
... dict(type='ObjectRangeFilter',
... point_cloud_range=point_cloud_range),
... dict(type='PointShuffle'),
... dict(type='Normalize', **img_norm_cfg),
... dict(type='Pad', size_divisor=32),
... dict(type='DefaultFormatBundle3D', class_names=class_names),
... dict(type='Collect3D',
... keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
... ]
>>> expected_pipelines = [
... dict(type='LoadPointsFromFile',
... coord_type='LIDAR', load_dim=4, use_dim=4),
... dict(type='LoadImageFromFile'),
... dict(type='LoadAnnotations3D',
... with_bbox=True, with_label_3d=True),
... dict(type='DefaultFormatBundle3D', class_names=class_names),
... dict(type='Collect3D',
... keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
... ]
>>> assert expected_pipelines ==\
... get_loading_pipeline(pipelines)
"""
loading_pipeline_cfg = []
for cfg in pipeline:
obj_cls = PIPELINES.get(cfg['type'])
# TODO: use more elegant way to distinguish loading modules
if obj_cls is not None and obj_cls in (
LoadImageFromFile, LoadPointsFromFile, LoadAnnotations3D,
LoadMultiViewImageFromFiles, LoadPointsFromMultiSweeps,
DefaultFormatBundle3D, Collect3D):
loading_pipeline_cfg.append(cfg)
assert len(loading_pipeline_cfg) > 0, \
'The data pipeline in your config file must include ' \
'loading step.'
return loading_pipeline_cfg
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 = PIPELINES.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