Centerpoint config detector

configs/_base_/models/centerpoint_01voxel_second_secfpn_nus.py

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+voxel_size = [0.1, 0.1, 0.2]
+model = dict(
+    type='CenterPoint',
+    pts_voxel_layer=dict(
+        max_num_points=10, voxel_size=voxel_size, max_voxels=(90000, 120000)),
+    pts_voxel_encoder=dict(type='HardSimpleVFE', num_features=5),
+    pts_middle_encoder=dict(
+        type='SparseEncoder',
+        in_channels=5,
+        sparse_shape=[41, 1024, 1024],
+        output_channels=128,
+        order=('conv', 'norm', 'act'),
+        encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128,
+                                                                      128)),
+        encoder_paddings=((0, 0, 1), (0, 0, 1), (0, 0, [0, 1, 1]), (0, 0)),
+        block_type='basicblock'),
+    pts_backbone=dict(
+        type='SECOND',
+        in_channels=256,
+        out_channels=[128, 256],
+        layer_nums=[5, 5],
+        layer_strides=[1, 2],
+        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+        conv_cfg=dict(type='Conv2d', bias=False)),
+    pts_neck=dict(
+        type='SECONDFPN',
+        in_channels=[128, 256],
+        out_channels=[256, 256],
+        upsample_strides=[1, 2],
+        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+        upsample_cfg=dict(type='deconv', bias=False),
+        use_conv_for_no_stride=True),
+    pts_bbox_head=dict(
+        type='CenterHead',
+        mode='3d',
+        in_channels=sum([256, 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',
+            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),
+        seperate_head=dict(
+            type='SeparateHead', init_bias=-2.19, final_kernel=3),
+        loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
+        loss_bbox=dict(type='L1Loss', reduction='none', loss_weight=0.25),
+        norm_bbox=True))
+# model training and testing settings
+train_cfg = dict(
+    pts=dict(
+        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(
+        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],
+        post_max_size=83,
+        score_threshold=0.1,
+        out_size_factor=8,
+        voxel_size=voxel_size[:2],
+        nms_type='rotate',
+        nms_pre_max_size=1000,
+        nms_post_max_size=83,
+        nms_iou_threshold=0.2))

configs/_base_/models/centerpoint_02pillar_second_secfpn_nus.py

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+voxel_size = [0.2, 0.2, 8]
+model = dict(
+    type='CenterPoint',
+    pts_voxel_layer=dict(
+        max_num_points=20, voxel_size=voxel_size, max_voxels=(30000, 40000)),
+    pts_voxel_encoder=dict(
+        type='PillarFeatureNet',
+        in_channels=5,
+        feat_channels=[64],
+        with_distance=False,
+        voxel_size=(0.2, 0.2, 8),
+        norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
+        legacy=False),
+    pts_middle_encoder=dict(
+        type='PointPillarsScatter', in_channels=64, output_shape=(512, 512)),
+    pts_backbone=dict(
+        type='SECOND',
+        in_channels=64,
+        out_channels=[64, 128, 256],
+        layer_nums=[3, 5, 5],
+        layer_strides=[2, 2, 2],
+        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+        conv_cfg=dict(type='Conv2d', bias=False)),
+    pts_neck=dict(
+        type='SECONDFPN',
+        in_channels=[64, 128, 256],
+        out_channels=[128, 128, 128],
+        upsample_strides=[0.5, 1, 2],
+        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+        upsample_cfg=dict(type='deconv', bias=False),
+        use_conv_for_no_stride=True),
+    pts_bbox_head=dict(
+        type='CenterHead',
+        mode='3d',
+        in_channels=sum([128, 128, 128]),
+        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',
+            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=4,
+            voxel_size=voxel_size[:2],
+            code_size=9),
+        seperate_head=dict(
+            type='SeparateHead', init_bias=-2.19, final_kernel=3),
+        loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
+        loss_bbox=dict(type='L1Loss', reduction='none', loss_weight=0.25),
+        norm_bbox=True))
+# model training and testing settings
+train_cfg = dict(
+    pts=dict(
+        grid_size=[512, 512, 1],
+        voxel_size=voxel_size,
+        out_size_factor=4,
+        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(
+        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],
+        post_max_size=83,
+        score_threshold=0.1,
+        pc_range=[-51.2, -51.2],
+        out_size_factor=4,
+        voxel_size=voxel_size[:2],
+        nms_type='rotate',
+        nms_pre_max_size=1000,
+        nms_post_max_size=83,
+        nms_iou_threshold=0.2))

configs/centerpoint/README.md

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+# Center-based 3D Object Detection and Tracking
+
+## Introduction
+
+We implement CenterPoint and provide the result and checkpoints on nuScenes dataset.
+
+We follow the below style to name config files. Contributors are advised to follow the same style.
+`{xxx}` is required field and `[yyy]` is optional.
+
+`{model}`: model type like `centerpoint`.
+
+`{model setting}`: voxel size and voxel type like `01voxel`, `02pillar`.
+
+`{backbone}`: backbone type like `second`.
+
+`{neck}`: neck type like `secfpn`.
+
+`[dcn]`: Whether to use deformable convolution.
+
+`[circle]`: Whether to use circular nms.
+
+`[batch_per_gpu x gpu]`: GPUs and samples per GPU, 4x8 is used by default.
+
+`{schedule}`: training schedule, options are 1x, 2x, 20e, etc. 1x and 2x means 12 epochs and 24 epochs respectively. 20e is adopted in cascade models, which denotes 20 epochs. For 1x/2x, initial learning rate decays by a factor of 10 at the 8/16th and 11/22th epochs. For 20e, initial learning rate decays by a factor of 10 at the 16th and 19th epochs.
+
+`{dataset}`: dataset like nus-3d, kitti-3d, lyft-3d, scannet-3d, sunrgbd-3d. We also indicate the number of classes we are using if there exist multiple settings, e.g., kitti-3d-3class and kitti-3d-car means training on KITTI dataset with 3 classes and single class, respectively.
+```
+@article{yin2020center,
+  title={Center-based 3d object detection and tracking},
+  author={Yin, Tianwei and Zhou, Xingyi and Kr{\"a}henb{\"u}hl, Philipp},
+  journal={arXiv preprint arXiv:2006.11275},
+  year={2020}
+}
+```
+
+## Results
+
+### CenterPoint
+
+|Backbone|  Voxel type (voxel size)   |Dcn|Circular nms| Mem (GB) | Inf time (fps) | mAP |NDS| Download |
+| :---------: |:-----: |:-----: | :------: | :------------: | :----: |:----: | :------: |:------: |
+|[SECFPN](./centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py)|voxel (0.1)|✗|✗||||||
+|[SECFPN](./centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.1)|✗|✓||||||
+|[SECFPN](./centerpoint_01voxel_second_secfpn_dcn_4x8_cyclic_20e_nus.py)|voxel (0.1)|✓|✗||||||
+|[SECFPN](./centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.1)|✓|✓||||||
+|[SECFPN](./centerpoint_0075voxel_second_secfpn_4x8_cyclic_20e_nus.py)|voxel (0.075)|✗|✗||||||
+|[SECFPN](./centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.075)|✗|✓||||||
+|[SECFPN](./centerpoint_0075voxel_second_secfpn_dcn_4x8_cyclic_20e_nus.py)|voxel (0.075)|✓|✗||||||
+|[SECFPN](./centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)|voxel (0.075)|✓|✓||||||
+|[SECFPN](./centerpoint_02pillar_second_secfpn_4x8_cyclic_20e_nus.py)|pillar (0.2)|✗|✗||||||
+|[SECFPN](./centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py)|pillar (0.2)|✗|✓||||||
+|[SECFPN](./centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus.py)|pillar (0.2)|✓|✗||||||
+|[SECFPN](./centerpoint_02pillar_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py)|pillar (0.2)|✓|✓||||||

configs/centerpoint/centerpoint_0075voxel_second_secfpn_4x8_cyclic_20e_nus.py

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+_base_ = ['./centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py']
+
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+voxel_size = [0.075, 0.075, 0.2]
+point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
+# For nuScenes we usually do 10-class detection
+class_names = [
+    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
+    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
+]
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+file_client_args = dict(backend='disk')
+
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'nuscenes_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(
+        filter_by_difficulty=[-1],
+        filter_by_min_points=dict(
+            car=5,
+            truck=5,
+            bus=5,
+            trailer=5,
+            construction_vehicle=5,
+            traffic_cone=5,
+            barrier=5,
+            motorcycle=5,
+            bicycle=5,
+            pedestrian=5,
+        )),
+    classes=class_names,
+    sample_groups=dict(
+        car=2,
+        truck=3,
+        construction_vehicle=7,
+        bus=4,
+        trailer=6,
+        barrier=2,
+        motorcycle=6,
+        bicycle=6,
+        pedestrian=2,
+        traffic_cone=2,
+    ),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        load_dim=5,
+        use_dim=[0, 1, 2, 3, 4],
+        file_client_args=file_client_args))
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=9,
+        use_dim=[0, 1, 2, 3, 4],
+        file_client_args=file_client_args,
+        pad_empty_sweeps=True,
+        remove_close=True),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectNameFilter', classes=class_names),
+    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',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=9,
+        use_dim=[0, 1, 2, 3, 4],
+        file_client_args=file_client_args,
+        pad_empty_sweeps=True,
+        remove_close=True),
+    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'])
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+model = dict(
+    pts_voxel_layer=dict(
+        voxel_size=voxel_size, point_cloud_range=point_cloud_range),
+    pts_middle_encoder=dict(sparse_shape=[41, 1440, 1440]),
+    pts_bbox_head=dict(
+        bbox_coder=dict(
+            voxel_size=voxel_size[:2], pc_range=point_cloud_range[:2])))
+
+train_cfg = dict(
+    pts=dict(
+        grid_size=[1440, 1440, 40],
+        voxel_size=voxel_size,
+        point_cloud_range=point_cloud_range))
+
+test_cfg = dict(
+    pts=dict(voxel_size=voxel_size[:2], pc_range=point_cloud_range[:2]))