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| # If point cloud range is changed, the models should also change their point | |
| # cloud range accordingly | |
| point_cloud_range = [-100, -100, -5, 100, 100, 3] | |
| # For Lyft we usually do 9-class detection | |
| class_names = [ | |
| 'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle', | |
| 'bicycle', 'pedestrian', 'animal' | |
| ] | |
| dataset_type = 'LyftDataset' | |
| data_root = 'data/lyft/' | |
| # Input modality for Lyft dataset, this is consistent with the submission | |
| # format which requires the information in input_modality. | |
| input_modality = dict( | |
| use_lidar=True, | |
| use_camera=False, | |
| use_radar=False, | |
| use_map=False, | |
| use_external=False) | |
| file_client_args = dict(backend='disk') | |
| # Uncomment the following if use ceph or other file clients. | |
| # See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient | |
| # for more details. | |
| # file_client_args = dict( | |
| # backend='petrel', | |
| # path_mapping=dict({ | |
| # './data/lyft/': 's3://lyft/lyft/', | |
| # 'data/lyft/': 's3://lyft/lyft/' | |
| # })) | |
| train_pipeline = [ | |
| dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=5, | |
| use_dim=5, | |
| file_client_args=file_client_args), | |
| dict( | |
| type='LoadPointsFromMultiSweeps', | |
| sweeps_num=10, | |
| file_client_args=file_client_args), | |
| dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True), | |
| dict( | |
| type='GlobalRotScaleTrans', | |
| rot_range=[-0.3925, 0.3925], | |
| scale_ratio_range=[0.95, 1.05], | |
| translation_std=[0, 0, 0]), | |
| 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='DefaultFormatBundle3D', class_names=class_names), | |
| dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d']) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=5, | |
| use_dim=5, | |
| file_client_args=file_client_args), | |
| dict( | |
| type='LoadPointsFromMultiSweeps', | |
| sweeps_num=10, | |
| file_client_args=file_client_args), | |
| dict( | |
| type='MultiScaleFlipAug3D', | |
| img_scale=(1333, 800), | |
| pts_scale_ratio=1, | |
| flip=False, | |
| transforms=[ | |
| dict( | |
| type='GlobalRotScaleTrans', | |
| rot_range=[0, 0], | |
| scale_ratio_range=[1., 1.], | |
| translation_std=[0, 0, 0]), | |
| dict(type='RandomFlip3D'), | |
| dict( | |
| type='PointsRangeFilter', point_cloud_range=point_cloud_range), | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=class_names, | |
| with_label=False), | |
| dict(type='Collect3D', keys=['points']) | |
| ]) | |
| ] | |
| # construct a pipeline for data and gt loading in show function | |
| # please keep its loading function consistent with test_pipeline (e.g. client) | |
| eval_pipeline = [ | |
| dict( | |
| type='LoadPointsFromFile', | |
| coord_type='LIDAR', | |
| load_dim=5, | |
| use_dim=5, | |
| file_client_args=file_client_args), | |
| dict( | |
| type='LoadPointsFromMultiSweeps', | |
| sweeps_num=10, | |
| file_client_args=file_client_args), | |
| dict( | |
| type='DefaultFormatBundle3D', | |
| class_names=class_names, | |
| with_label=False), | |
| dict(type='Collect3D', keys=['points']) | |
| ] | |
| data = dict( | |
| samples_per_gpu=2, | |
| workers_per_gpu=2, | |
| train=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'lyft_infos_train.pkl', | |
| pipeline=train_pipeline, | |
| classes=class_names, | |
| modality=input_modality, | |
| test_mode=False), | |
| val=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'lyft_infos_val.pkl', | |
| pipeline=test_pipeline, | |
| classes=class_names, | |
| modality=input_modality, | |
| test_mode=True), | |
| test=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file=data_root + 'lyft_infos_test.pkl', | |
| pipeline=test_pipeline, | |
| classes=class_names, | |
| modality=input_modality, | |
| test_mode=True)) | |
| # For Lyft dataset, we usually evaluate the model at the end of training. | |
| # Since the models are trained by 24 epochs by default, we set evaluation | |
| # interval to be 24. Please change the interval accordingly if you do not | |
| # use a default schedule. | |
| evaluation = dict(interval=24, pipeline=eval_pipeline) | |