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[Feature] Support new config type (open-mmlab#2608)
* support new configs * support new configs * verify configs
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‎mmdet3d/configs/_base_/__init__.py

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# Copyright (c) OpenMMLab. All rights reserved.

‎mmdet3d/configs/_base_/datasets/__init__.py

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# Copyright (c) OpenMMLab. All rights reserved.

‎mmdet3d/configs/_base_/datasets/kitti_3d_3class.py

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# Copyright (c) OpenMMLab. All rights reserved.
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from mmengine.dataset.dataset_wrapper import RepeatDataset
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from mmengine.dataset.sampler import DefaultSampler
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from mmengine.visualization.vis_backend import LocalVisBackend
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from mmdet3d.datasets.kitti_dataset import KittiDataset
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from mmdet3d.datasets.transforms.formating import Pack3DDetInputs
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from mmdet3d.datasets.transforms.loading import (LoadAnnotations3D,
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LoadPointsFromFile)
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from mmdet3d.datasets.transforms.test_time_aug import MultiScaleFlipAug3D
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from mmdet3d.datasets.transforms.transforms_3d import ( # noqa
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GlobalRotScaleTrans, ObjectNoise, ObjectRangeFilter, ObjectSample,
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PointShuffle, PointsRangeFilter, RandomFlip3D)
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from mmdet3d.evaluation.metrics.kitti_metric import KittiMetric
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from mmdet3d.visualization.local_visualizer import Det3DLocalVisualizer
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# dataset settings
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dataset_type = 'KittiDataset'
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data_root = 'data/kitti/'
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class_names = ['Pedestrian', 'Cyclist', 'Car']
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point_cloud_range = [0, -40, -3, 70.4, 40, 1]
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input_modality = dict(use_lidar=True, use_camera=False)
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metainfo = dict(classes=class_names)
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# Example to use different file client
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# Method 1: simply set the data root and let the file I/O module
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# automatically infer from prefix (not support LMDB and Memcache yet)
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# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
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# Method 2: Use backend_args, file_client_args in versions before 1.1.0
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# backend_args = dict(
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# backend='petrel',
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# path_mapping=dict({
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# './data/': 's3://openmmlab/datasets/detection3d/',
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# 'data/': 's3://openmmlab/datasets/detection3d/'
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# }))
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backend_args = None
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db_sampler = dict(
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data_root=data_root,
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info_path=data_root + 'kitti_dbinfos_train.pkl',
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rate=1.0,
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prepare=dict(
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filter_by_difficulty=[-1],
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filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
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classes=class_names,
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sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6),
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points_loader=dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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backend_args=backend_args),
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backend_args=backend_args)
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train_pipeline = [
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4, # x, y, z, intensity
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use_dim=4,
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backend_args=backend_args),
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dict(type=LoadAnnotations3D, with_bbox_3d=True, with_label_3d=True),
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dict(type=ObjectSample, db_sampler=db_sampler),
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dict(
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type=ObjectNoise,
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num_try=100,
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translation_std=[1.0, 1.0, 0.5],
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global_rot_range=[0.0, 0.0],
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rot_range=[-0.78539816, 0.78539816]),
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dict(type=RandomFlip3D, flip_ratio_bev_horizontal=0.5),
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dict(
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type=GlobalRotScaleTrans,
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rot_range=[-0.78539816, 0.78539816],
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scale_ratio_range=[0.95, 1.05]),
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dict(type=PointsRangeFilter, point_cloud_range=point_cloud_range),
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dict(type=ObjectRangeFilter, point_cloud_range=point_cloud_range),
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dict(type=PointShuffle),
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dict(
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type=Pack3DDetInputs, keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
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]
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test_pipeline = [
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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backend_args=backend_args),
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dict(
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type=MultiScaleFlipAug3D,
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img_scale=(1333, 800),
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pts_scale_ratio=1,
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flip=False,
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transforms=[
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dict(
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type=GlobalRotScaleTrans,
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rot_range=[0, 0],
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scale_ratio_range=[1., 1.],
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translation_std=[0, 0, 0]),
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dict(type=RandomFlip3D),
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dict(type=PointsRangeFilter, point_cloud_range=point_cloud_range)
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]),
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dict(type=Pack3DDetInputs, keys=['points'])
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]
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# construct a pipeline for data and gt loading in show function
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# please keep its loading function consistent with test_pipeline (e.g. client)
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eval_pipeline = [
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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backend_args=backend_args),
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dict(type=Pack3DDetInputs, keys=['points'])
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]
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train_dataloader = dict(
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batch_size=6,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type=DefaultSampler, shuffle=True),
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dataset=dict(
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type=RepeatDataset,
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times=2,
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dataset=dict(
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type=KittiDataset,
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data_root=data_root,
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ann_file='kitti_infos_train.pkl',
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data_prefix=dict(pts='training/velodyne_reduced'),
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pipeline=train_pipeline,
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modality=input_modality,
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test_mode=False,
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metainfo=metainfo,
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# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
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# and box_type_3d='Depth' in sunrgbd and scannet dataset.
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box_type_3d='LiDAR',
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backend_args=backend_args)))
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val_dataloader = dict(
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type=DefaultSampler, shuffle=False),
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dataset=dict(
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type=KittiDataset,
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data_root=data_root,
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data_prefix=dict(pts='training/velodyne_reduced'),
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ann_file='kitti_infos_val.pkl',
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pipeline=test_pipeline,
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modality=input_modality,
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test_mode=True,
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metainfo=metainfo,
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box_type_3d='LiDAR',
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backend_args=backend_args))
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test_dataloader = dict(
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type=DefaultSampler, shuffle=False),
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dataset=dict(
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type=KittiDataset,
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data_root=data_root,
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data_prefix=dict(pts='training/velodyne_reduced'),
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ann_file='kitti_infos_val.pkl',
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pipeline=test_pipeline,
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modality=input_modality,
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test_mode=True,
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metainfo=metainfo,
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box_type_3d='LiDAR',
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backend_args=backend_args))
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val_evaluator = dict(
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type=KittiMetric,
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ann_file=data_root + 'kitti_infos_val.pkl',
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metric='bbox',
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backend_args=backend_args)
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test_evaluator = val_evaluator
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vis_backends = [dict(type=LocalVisBackend)]
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visualizer = dict(
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type=Det3DLocalVisualizer, vis_backends=vis_backends, name='visualizer')

‎mmdet3d/configs/_base_/datasets/kitti_3d_car.py

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# Copyright (c) OpenMMLab. All rights reserved.
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from mmengine.dataset.dataset_wrapper import RepeatDataset
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from mmengine.dataset.sampler import DefaultSampler
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from mmengine.visualization.vis_backend import LocalVisBackend
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from mmdet3d.datasets.kitti_dataset import KittiDataset
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from mmdet3d.datasets.transforms.formating import Pack3DDetInputs
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from mmdet3d.datasets.transforms.loading import (LoadAnnotations3D,
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LoadPointsFromFile)
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from mmdet3d.datasets.transforms.test_time_aug import MultiScaleFlipAug3D
11+
from mmdet3d.datasets.transforms.transforms_3d import ( # noqa
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GlobalRotScaleTrans, ObjectNoise, ObjectRangeFilter, ObjectSample,
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PointShuffle, PointsRangeFilter, RandomFlip3D)
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from mmdet3d.evaluation.metrics.kitti_metric import KittiMetric
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from mmdet3d.visualization.local_visualizer import Det3DLocalVisualizer
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# dataset settings
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dataset_type = 'KittiDataset'
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data_root = 'data/kitti/'
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class_names = ['Car']
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point_cloud_range = [0, -40, -3, 70.4, 40, 1]
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input_modality = dict(use_lidar=True, use_camera=False)
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metainfo = dict(classes=class_names)
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# Example to use different file client
26+
# Method 1: simply set the data root and let the file I/O module
27+
# automatically infer from prefix (not support LMDB and Memcache yet)
28+
29+
# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
30+
31+
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
32+
# backend_args = dict(
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# backend='petrel',
34+
# path_mapping=dict({
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# './data/': 's3://openmmlab/datasets/detection3d/',
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# 'data/': 's3://openmmlab/datasets/detection3d/'
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# }))
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backend_args = None
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db_sampler = dict(
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data_root=data_root,
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info_path=data_root + 'kitti_dbinfos_train.pkl',
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rate=1.0,
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prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
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classes=class_names,
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sample_groups=dict(Car=15),
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points_loader=dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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backend_args=backend_args),
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backend_args=backend_args)
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train_pipeline = [
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4, # x, y, z, intensity
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use_dim=4,
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backend_args=backend_args),
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dict(type=LoadAnnotations3D, with_bbox_3d=True, with_label_3d=True),
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dict(type=ObjectSample, db_sampler=db_sampler),
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dict(
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type=ObjectNoise,
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num_try=100,
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translation_std=[1.0, 1.0, 0.5],
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global_rot_range=[0.0, 0.0],
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rot_range=[-0.78539816, 0.78539816]),
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dict(type=RandomFlip3D, flip_ratio_bev_horizontal=0.5),
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dict(
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type=GlobalRotScaleTrans,
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rot_range=[-0.78539816, 0.78539816],
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scale_ratio_range=[0.95, 1.05]),
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dict(type=PointsRangeFilter, point_cloud_range=point_cloud_range),
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dict(type=ObjectRangeFilter, point_cloud_range=point_cloud_range),
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dict(type=PointShuffle),
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dict(
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type=Pack3DDetInputs, keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
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]
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test_pipeline = [
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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backend_args=backend_args),
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dict(
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type=MultiScaleFlipAug3D,
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img_scale=(1333, 800),
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pts_scale_ratio=1,
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flip=False,
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transforms=[
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dict(
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type=GlobalRotScaleTrans,
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rot_range=[0, 0],
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scale_ratio_range=[1., 1.],
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translation_std=[0, 0, 0]),
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dict(type=RandomFlip3D),
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dict(type=PointsRangeFilter, point_cloud_range=point_cloud_range)
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]),
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dict(type=Pack3DDetInputs, keys=['points'])
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]
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# construct a pipeline for data and gt loading in show function
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# please keep its loading function consistent with test_pipeline (e.g. client)
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eval_pipeline = [
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4,
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backend_args=backend_args),
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dict(type=Pack3DDetInputs, keys=['points'])
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]
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train_dataloader = dict(
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batch_size=6,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type=DefaultSampler, shuffle=True),
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dataset=dict(
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type=RepeatDataset,
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times=2,
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dataset=dict(
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type=KittiDataset,
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data_root=data_root,
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ann_file='kitti_infos_train.pkl',
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data_prefix=dict(pts='training/velodyne_reduced'),
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pipeline=train_pipeline,
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modality=input_modality,
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test_mode=False,
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metainfo=metainfo,
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# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
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# and box_type_3d='Depth' in sunrgbd and scannet dataset.
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box_type_3d='LiDAR',
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backend_args=backend_args)))
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val_dataloader = dict(
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type=DefaultSampler, shuffle=False),
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dataset=dict(
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type=KittiDataset,
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data_root=data_root,
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data_prefix=dict(pts='training/velodyne_reduced'),
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ann_file='kitti_infos_val.pkl',
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pipeline=test_pipeline,
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modality=input_modality,
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test_mode=True,
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metainfo=metainfo,
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box_type_3d='LiDAR',
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backend_args=backend_args))
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test_dataloader = dict(
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batch_size=1,
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num_workers=1,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type=DefaultSampler, shuffle=False),
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dataset=dict(
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type=KittiDataset,
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data_root=data_root,
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data_prefix=dict(pts='training/velodyne_reduced'),
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ann_file='kitti_infos_val.pkl',
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pipeline=test_pipeline,
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modality=input_modality,
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test_mode=True,
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metainfo=metainfo,
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box_type_3d='LiDAR',
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backend_args=backend_args))
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val_evaluator = dict(
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type=KittiMetric,
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ann_file=data_root + 'kitti_infos_val.pkl',
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metric='bbox',
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backend_args=backend_args)
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test_evaluator = val_evaluator
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vis_backends = [dict(type=LocalVisBackend)]
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visualizer = dict(
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type=Det3DLocalVisualizer, vis_backends=vis_backends, name='visualizer')

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