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331292a992
* feat(mmdet3d): test pointpillars and centerpoint on ort, openvino and trt passed * fix(centerpoint): mvx_two_stage input error * fix(review): remove mode decorator * fix(mmdet3d): review advices * fix(regression): update mmdet3d.yml and test ort/openvino passed * unittest(mmdet3d): fix * fix(unittest): fix * fix(mmdet3d): unittest * fix(mmdet3d): unittest * fix(CI): remove mmcv.Config * fix(mmdet3d): unittest * fix(mmdet3d): support torch1.12 * fix(CI): use bigger point cloud file * improvement(mmdet3d): align backend outputs with torch * fix(mmdet3d): remove useless * style(mmdet3d): format code * style(mmdet3d): remove useless * fix(mmdet3d): sync vis_task * unittest(mmdet3d): add test * docs(mmdet3d): add docstring * unittest(ci): add unittest data * fix(mmdet3d): review advices * feat(mmdet3d): convert fail * style(mmdet3d): docstring * style(mmdet3d): docstring
134 lines
4.7 KiB
Python
134 lines
4.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# If point cloud range is changed, the models should also change their point
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# cloud range accordingly
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point_cloud_range = [-50, -50, -5, 50, 50, 3]
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# For nuScenes we usually do 10-class detection
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class_names = [
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'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
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'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
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]
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metainfo = dict(CLASSES=class_names)
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dataset_type = 'NuScenesDataset'
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data_root = 'data/nuscenes/'
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# Input modality for nuScenes dataset, this is consistent with the submission
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# format which requires the information in input_modality.
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input_modality = dict(use_lidar=True, use_camera=False)
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data_prefix = dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP')
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file_client_args = dict(backend='disk')
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# Uncomment the following if use ceph or other file clients.
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# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
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# for more details.
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# file_client_args = dict(
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# backend='petrel',
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# path_mapping=dict({
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# './data/nuscenes/': 's3://nuscenes/nuscenes/',
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# 'data/nuscenes/': 's3://nuscenes/nuscenes/'
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# }))
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train_pipeline = [
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dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
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dict(type='LoadPointsFromMultiSweeps', sweeps_num=10),
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dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
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dict(
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type='GlobalRotScaleTrans',
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rot_range=[-0.3925, 0.3925],
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scale_ratio_range=[0.95, 1.05],
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translation_std=[0, 0, 0]),
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dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
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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='ObjectNameFilter', classes=class_names),
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dict(type='PointShuffle'),
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dict(
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type='Pack3DDetInputs',
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keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
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]
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test_pipeline = [
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dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
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dict(type='LoadPointsFromMultiSweeps', sweeps_num=10, test_mode=True),
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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(
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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(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
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dict(type='LoadPointsFromMultiSweeps', sweeps_num=10, test_mode=True),
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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=4,
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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=dataset_type,
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data_root=data_root,
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ann_file='nuscenes_infos_train.pkl',
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pipeline=train_pipeline,
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metainfo=metainfo,
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modality=input_modality,
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test_mode=False,
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data_prefix=data_prefix,
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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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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=dataset_type,
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data_root=data_root,
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ann_file='nuscenes_infos_val.pkl',
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pipeline=test_pipeline,
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metainfo=metainfo,
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modality=input_modality,
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data_prefix=data_prefix,
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test_mode=True,
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box_type_3d='LiDAR'))
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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=dataset_type,
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data_root=data_root,
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ann_file='nuscenes_infos_val.pkl',
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pipeline=test_pipeline,
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metainfo=metainfo,
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modality=input_modality,
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test_mode=True,
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data_prefix=data_prefix,
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box_type_3d='LiDAR'))
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val_evaluator = dict(
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type='NuScenesMetric',
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data_root=data_root,
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ann_file=data_root + 'nuscenes_infos_val.pkl',
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metric='bbox')
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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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