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Merge pull request HuangJunJie2017#51 from HuangJunJie2017/bevdepth
Support BEVDepth
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| # Copyright (c) Phigent Robotics. All rights reserved. | ||
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| _base_ = ['../_base_/datasets/nus-3d.py', | ||
| '../_base_/default_runtime.py'] | ||
| # Global | ||
| # If point cloud range is changed, the models should also change their point | ||
| # cloud range accordingly | ||
| point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0] | ||
| # For nuScenes we usually do 10-class detection | ||
| class_names = [ | ||
| 'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier', | ||
| 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone' | ||
| ] | ||
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| data_config={ | ||
| 'cams': ['CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT', | ||
| 'CAM_BACK_LEFT', 'CAM_BACK', 'CAM_BACK_RIGHT'], | ||
| 'Ncams': 6, | ||
| 'input_size': (256, 704), | ||
| 'src_size': (900, 1600), | ||
|
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| # Augmentation | ||
| 'resize': (-0.06, 0.11), | ||
| 'rot': (-5.4, 5.4), | ||
| 'flip': True, | ||
| 'crop_h': (0.0, 0.0), | ||
| 'resize_test':0.04, | ||
| } | ||
|
|
||
| # Model | ||
| grid_config={ | ||
| 'xbound': [-51.2, 51.2, 0.8], | ||
| 'ybound': [-51.2, 51.2, 0.8], | ||
| 'zbound': [-10.0, 10.0, 20.0], | ||
| 'dbound': [1.0, 60.0, 1.0],} | ||
|
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| voxel_size = [0.1, 0.1, 0.2] | ||
|
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| numC_Trans=64 | ||
|
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| model = dict( | ||
| type='BEVDepth', | ||
| img_backbone=dict( | ||
| pretrained='torchvision://resnet50', | ||
| type='ResNet', | ||
| depth=50, | ||
| num_stages=4, | ||
| out_indices=(2, 3), | ||
| frozen_stages=-1, | ||
| norm_cfg=dict(type='BN', requires_grad=True), | ||
| norm_eval=False, | ||
| with_cp=True, | ||
| style='pytorch'), | ||
| img_neck=dict( | ||
| type='FPNForBEVDet', | ||
| in_channels=[1024, 2048], | ||
| out_channels=512, | ||
| num_outs=1, | ||
| start_level=0, | ||
| out_ids=[0]), | ||
| img_view_transformer=dict(type='ViewTransformerLSSBEVDepth', | ||
| loss_depth_weight=100.0, | ||
| grid_config=grid_config, | ||
| data_config=data_config, | ||
| numC_Trans=numC_Trans, | ||
| extra_depth_net=dict(type='ResNetForBEVDet', | ||
| numC_input=256, | ||
| num_layer=[3,], | ||
| num_channels=[256,], | ||
| stride=[1,])), | ||
| img_bev_encoder_backbone = dict(type='ResNetForBEVDet', numC_input=numC_Trans), | ||
| img_bev_encoder_neck = dict(type='FPN_LSS', | ||
| in_channels=numC_Trans*8+numC_Trans*2, | ||
| out_channels=256), | ||
| pts_bbox_head=dict( | ||
| type='CenterHead', | ||
| task_specific=True, | ||
| in_channels=256, | ||
| tasks=[ | ||
| dict(num_class=1, class_names=['car']), | ||
| dict(num_class=2, class_names=['truck', 'construction_vehicle']), | ||
| dict(num_class=2, class_names=['bus', 'trailer']), | ||
| dict(num_class=1, class_names=['barrier']), | ||
| dict(num_class=2, class_names=['motorcycle', 'bicycle']), | ||
| dict(num_class=2, class_names=['pedestrian', 'traffic_cone']), | ||
| ], | ||
| common_heads=dict( | ||
| reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)), | ||
| share_conv_channel=64, | ||
| bbox_coder=dict( | ||
| type='CenterPointBBoxCoder', | ||
| pc_range=point_cloud_range[:2], | ||
| post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0], | ||
| max_num=500, | ||
| score_threshold=0.1, | ||
| out_size_factor=8, | ||
| voxel_size=voxel_size[:2], | ||
| code_size=9), | ||
| separate_head=dict( | ||
| type='SeparateHead', init_bias=-2.19, final_kernel=3), | ||
| loss_cls=dict(type='GaussianFocalLoss', reduction='mean'), | ||
| loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25), | ||
| norm_bbox=True), | ||
| # model training and testing settings | ||
| train_cfg=dict( | ||
| pts=dict( | ||
| point_cloud_range=point_cloud_range, | ||
| grid_size=[1024, 1024, 40], | ||
| voxel_size=voxel_size, | ||
| out_size_factor=8, | ||
| dense_reg=1, | ||
| gaussian_overlap=0.1, | ||
| max_objs=500, | ||
| min_radius=2, | ||
| code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2])), | ||
| test_cfg=dict( | ||
| pts=dict( | ||
| pc_range=point_cloud_range[:2], | ||
| post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0], | ||
| max_per_img=500, | ||
| max_pool_nms=False, | ||
| min_radius=[4, 12, 10, 1, 0.85, 0.175], | ||
| score_threshold=0.1, | ||
| out_size_factor=8, | ||
| voxel_size=voxel_size[:2], | ||
| # nms_type='circle', | ||
| pre_max_size=1000, | ||
| post_max_size=83, | ||
| # nms_thr=0.2, | ||
|
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||
| # Scale-NMS | ||
| nms_type=['rotate', 'rotate', 'rotate', 'circle', 'rotate', 'rotate'], | ||
| nms_thr=[0.2, 0.2, 0.2, 0.2, 0.2, 0.5], | ||
| nms_rescale_factor=[1.0, [0.7, 0.7], [0.4, 0.55], 1.1, [1.0, 1.0], [4.5, 9.0]] | ||
| ))) | ||
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||
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| # Data | ||
| dataset_type = 'NuScenesDataset' | ||
| data_root = 'data/nuscenes/' | ||
| file_client_args = dict(backend='disk') | ||
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| train_pipeline = [ | ||
| dict(type='LoadMultiViewImageFromFiles_BEVDet', is_train=True, data_config=data_config), | ||
| dict( | ||
| type='LoadPointsFromFile', | ||
| coord_type='LIDAR', | ||
| load_dim=5, | ||
| use_dim=5, | ||
| 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], | ||
| update_img2lidar=True), | ||
| dict( | ||
| type='RandomFlip3D', | ||
| sync_2d=False, | ||
| flip_ratio_bev_horizontal=0.5, | ||
| flip_ratio_bev_vertical=0.5, | ||
| update_img2lidar=True), | ||
| dict(type='PointToMultiViewDepth',), | ||
| dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range), | ||
| dict(type='ObjectNameFilter', classes=class_names), | ||
| dict(type='DefaultFormatBundle3D', class_names=class_names), | ||
| dict(type='Collect3D', keys=['img_inputs', 'gt_bboxes_3d', 'gt_labels_3d'], | ||
| meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img', | ||
| 'depth2img', 'cam2img', 'pad_shape', | ||
| 'scale_factor', 'flip', 'pcd_horizontal_flip', | ||
| 'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d', | ||
| 'img_norm_cfg', 'pcd_trans', 'sample_idx', | ||
| 'pcd_scale_factor', 'pcd_rotation', 'pts_filename', | ||
| 'transformation_3d_flow', 'img_info')) | ||
| ] | ||
|
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| test_pipeline = [ | ||
| dict(type='LoadMultiViewImageFromFiles_BEVDet', data_config=data_config), | ||
| # load lidar points for --show in test.py only | ||
| dict( | ||
| type='LoadPointsFromFile', | ||
| coord_type='LIDAR', | ||
| load_dim=5, | ||
| use_dim=5, | ||
| file_client_args=file_client_args), | ||
| dict(type='PointToMultiViewDepth', ), | ||
| dict( | ||
| type='MultiScaleFlipAug3D', | ||
| img_scale=(1333, 800), | ||
| pts_scale_ratio=1, | ||
| flip=False, | ||
| transforms=[ | ||
| dict( | ||
| type='DefaultFormatBundle3D', | ||
| class_names=class_names, | ||
| with_label=False), | ||
| dict(type='Collect3D', keys=['points','img_inputs']) | ||
| ]) | ||
| ] | ||
| # 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='LoadMultiViewImageFromFiles_BEVDet', data_config=data_config), | ||
| dict( | ||
| type='LoadPointsFromFile', | ||
| coord_type='LIDAR', | ||
| load_dim=5, | ||
| use_dim=5, | ||
| file_client_args=file_client_args), | ||
| dict(type='PointToMultiViewDepth', ), | ||
| dict( | ||
| type='DefaultFormatBundle3D', | ||
| class_names=class_names, | ||
| with_label=False), | ||
| dict(type='Collect3D', keys=['img_inputs']) | ||
| ] | ||
|
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||
| input_modality = dict( | ||
| use_lidar=False, | ||
| use_camera=True, | ||
| use_radar=False, | ||
| use_map=False, | ||
| use_external=False) | ||
|
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| data = dict( | ||
| samples_per_gpu=8, | ||
| workers_per_gpu=4, | ||
| train=dict( | ||
| type='CBGSDataset', | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| ann_file=data_root + 'nuscenes_infos_train.pkl', | ||
| pipeline=train_pipeline, | ||
| classes=class_names, | ||
| test_mode=False, | ||
| use_valid_flag=True, | ||
| modality=input_modality, | ||
| # we use box_type_3d='LiDAR' in kitti and nuscenes dataset | ||
| # and box_type_3d='Depth' in sunrgbd and scannet dataset. | ||
| box_type_3d='LiDAR', | ||
| img_info_prototype='bevdet')), | ||
| val=dict(pipeline=test_pipeline, classes=class_names, | ||
| modality=input_modality, img_info_prototype='bevdet'), | ||
| test=dict(pipeline=test_pipeline, classes=class_names, | ||
| modality=input_modality, img_info_prototype='bevdet')) | ||
|
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| # Optimizer | ||
| optimizer = dict(type='AdamW', lr=2e-4, weight_decay=0.01) | ||
| optimizer_config = dict(grad_clip=None) | ||
| lr_config = dict( | ||
| policy='step', | ||
| warmup='linear', | ||
| warmup_iters=500, | ||
| warmup_ratio=0.001, | ||
| step=[16, 22]) | ||
| runner = dict(type='EpochBasedRunner', max_epochs=24) |
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