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| Original file line number | Diff line number | Diff line change |
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| @@ -1,10 +1,14 @@ | ||
| version: 2 | ||
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| build: | ||
| os: ubuntu-22.04 | ||
| tools: | ||
| python: "3.8" | ||
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| formats: | ||
| - epub | ||
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| python: | ||
| version: 3.7 | ||
| install: | ||
| - requirements: requirements/docs.txt | ||
| - requirements: requirements/readthedocs.txt |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,184 @@ | ||
| # dataset settings | ||
| # D3 in the config name means the whole dataset is divided into 3 folds | ||
| # We only use one fold for efficient experiments | ||
| dataset_type = 'WaymoDataset' | ||
| data_root = 'data/waymo/kitti_format/' | ||
| class_names = ['Pedestrian', 'Cyclist', 'Car'] | ||
| metainfo = dict(classes=class_names) | ||
| input_modality = dict(use_lidar=False, use_camera=True) | ||
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| # Example to use different file client | ||
| # Method 1: simply set the data root and let the file I/O module | ||
| # automatically infer from prefix (not support LMDB and Memcache yet) | ||
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| # data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/' | ||
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| # Method 2: Use backend_args, file_client_args in versions before 1.1.0 | ||
| # backend_args = dict( | ||
| # backend='petrel', | ||
| # path_mapping=dict({ | ||
| # './data/': 's3://openmmlab/datasets/detection3d/', | ||
| # 'data/': 's3://openmmlab/datasets/detection3d/' | ||
| # })) | ||
| backend_args = None | ||
|
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| train_pipeline = [ | ||
| dict(type='LoadImageFromFileMono3D', backend_args=backend_args), | ||
| dict( | ||
| type='LoadAnnotations3D', | ||
| with_bbox=True, | ||
| with_label=True, | ||
| with_attr_label=False, | ||
| with_bbox_3d=True, | ||
| with_label_3d=True, | ||
| with_bbox_depth=True), | ||
| # base shape (1248, 832), scale (0.95, 1.05) | ||
| dict( | ||
| type='RandomResize3D', | ||
| scale=(1248, 832), | ||
| ratio_range=(0.95, 1.05), | ||
| # ratio_range=(1., 1.), | ||
| interpolation='nearest', | ||
| keep_ratio=True, | ||
| ), | ||
| dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), | ||
| dict( | ||
| type='Pack3DDetInputs', | ||
| keys=[ | ||
| 'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d', | ||
| 'gt_labels_3d', 'centers_2d', 'depths' | ||
| ]), | ||
| ] | ||
|
|
||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFileMono3D', backend_args=backend_args), | ||
| dict( | ||
| type='RandomResize3D', | ||
| scale=(1248, 832), | ||
| ratio_range=(1., 1.), | ||
| interpolation='nearest', | ||
| keep_ratio=True), | ||
| dict( | ||
| type='Pack3DDetInputs', | ||
| keys=['img'], | ||
| meta_keys=[ | ||
| 'box_type_3d', 'img_shape', 'cam2img', 'scale_factor', | ||
| 'sample_idx', 'context_name', 'timestamp', 'lidar2cam' | ||
| ]), | ||
| ] | ||
| # 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='LoadImageFromFileMono3D', backend_args=backend_args), | ||
| dict( | ||
| type='RandomResize3D', | ||
| scale=(1248, 832), | ||
| ratio_range=(1., 1.), | ||
| interpolation='nearest', | ||
| keep_ratio=True), | ||
| dict( | ||
| type='Pack3DDetInputs', | ||
| keys=['img'], | ||
| meta_keys=[ | ||
| 'box_type_3d', 'img_shape', 'cam2img', 'scale_factor', | ||
| 'sample_idx', 'context_name', 'timestamp', 'lidar2cam' | ||
| ]), | ||
| ] | ||
|
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||
| train_dataloader = dict( | ||
| batch_size=3, | ||
| num_workers=3, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file='waymo_infos_train.pkl', | ||
| data_prefix=dict( | ||
| pts='training/velodyne', | ||
| CAM_FRONT='training/image_0', | ||
| CAM_FRONT_LEFT='training/image_1', | ||
| CAM_FRONT_RIGHT='training/image_2', | ||
| CAM_SIDE_LEFT='training/image_3', | ||
| CAM_SIDE_RIGHT='training/image_4'), | ||
| pipeline=train_pipeline, | ||
| modality=input_modality, | ||
| test_mode=False, | ||
| metainfo=metainfo, | ||
| cam_sync_instances=True, | ||
| # we use box_type_3d='LiDAR' in kitti and nuscenes dataset | ||
| # and box_type_3d='Depth' in sunrgbd and scannet dataset. | ||
| box_type_3d='Camera', | ||
| load_type='fov_image_based', | ||
| # load one frame every three frames | ||
| load_interval=3, | ||
| backend_args=backend_args)) | ||
|
|
||
| val_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=1, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| data_prefix=dict( | ||
| pts='training/velodyne', | ||
| CAM_FRONT='training/image_0', | ||
| CAM_FRONT_LEFT='training/image_1', | ||
| CAM_FRONT_RIGHT='training/image_2', | ||
| CAM_SIDE_LEFT='training/image_3', | ||
| CAM_SIDE_RIGHT='training/image_4'), | ||
| ann_file='waymo_infos_val.pkl', | ||
| pipeline=eval_pipeline, | ||
| modality=input_modality, | ||
| test_mode=True, | ||
| metainfo=metainfo, | ||
| cam_sync_instances=True, | ||
| # we use box_type_3d='LiDAR' in kitti and nuscenes dataset | ||
| # and box_type_3d='Depth' in sunrgbd and scannet dataset. | ||
| box_type_3d='Camera', | ||
| load_type='fov_image_based', | ||
| load_eval_anns=False, | ||
| backend_args=backend_args)) | ||
|
|
||
| test_dataloader = dict( | ||
| batch_size=1, | ||
| num_workers=1, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| data_prefix=dict( | ||
| pts='training/velodyne', | ||
| CAM_FRONT='training/image_0', | ||
| CAM_FRONT_LEFT='training/image_1', | ||
| CAM_FRONT_RIGHT='training/image_2', | ||
| CAM_SIDE_LEFT='training/image_3', | ||
| CAM_SIDE_RIGHT='training/image_4'), | ||
| ann_file='waymo_infos_val.pkl', | ||
| pipeline=eval_pipeline, | ||
| modality=input_modality, | ||
| test_mode=True, | ||
| metainfo=metainfo, | ||
| cam_sync_instances=True, | ||
| # we use box_type_3d='LiDAR' in kitti and nuscenes dataset | ||
| # and box_type_3d='Depth' in sunrgbd and scannet dataset. | ||
| box_type_3d='Camera', | ||
| load_type='fov_image_based', | ||
| backend_args=backend_args)) | ||
|
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| val_evaluator = dict( | ||
| type='WaymoMetric', | ||
| waymo_bin_file='./data/waymo/waymo_format/fov_gt.bin', | ||
| metric='LET_mAP', | ||
| load_type='fov_image_based', | ||
| result_prefix='./pgd_fov_pred') | ||
| test_evaluator = val_evaluator | ||
|
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| vis_backends = [dict(type='LocalVisBackend')] | ||
| visualizer = dict( | ||
| type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer') |
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