Merge branch 'lyft_baselines' into 'master'

Lyft baselines

See merge request open-mmlab/mmdet.3d!98

.pre-commit-config.yaml

@@ -24,5 +24,6 @@ repos:
       - id: end-of-file-fixer
       - id: requirements-txt-fixer
       - id: double-quote-string-fixer
+      - id: check-merge-conflict
       - id: fix-encoding-pragma
         args: ["--remove"]

configs/_base_/datasets/lyft-3d.py

+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-80, -80, -5, 80, 80, 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',
+        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',
+        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'])
+        ])
+]
+
+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)

configs/_base_/datasets/range100_lyft-3d.py

+# 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',
+        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',
+        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'])
+        ])
+]
+
+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)

configs/_base_/models/hv_pointpillars_fpn_lyft.py

+_base_ = './hv_pointpillars_fpn_nus.py'
+
+# model settings (based on nuScenes model settings)
+# Voxel size for voxel encoder
+# Usually voxel size is changed consistently with the point cloud range
+# If point cloud range is modified, do remember to change all related
+# keys in the config.
+model = dict(
+    pts_voxel_layer=dict(
+        max_num_points=20,
+        point_cloud_range=[-80, -80, -5, 80, 80, 3],
+        max_voxels=(60000, 60000)),
+    pts_voxel_encoder=dict(
+        feat_channels=[64], point_cloud_range=[-80, -80, -5, 80, 80, 3]),
+    pts_middle_encoder=dict(output_shape=[640, 640]),
+    pts_bbox_head=dict(
+        num_classes=9,
+        anchor_generator=dict(
+            ranges=[[-80, -80, -1.8, 80, 80, -1.8]], custom_values=[]),
+        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7)))
+# model training settings (based on nuScenes model settings)
+train_cfg = dict(pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))

configs/_base_/models/hv_pointpillars_fpn_range100_lyft.py

+_base_ = './hv_pointpillars_fpn_nus.py'
+
+# model settings (based on nuScenes model settings)
+# Voxel size for voxel encoder
+# Usually voxel size is changed consistently with the point cloud range
+# If point cloud range is modified, do remember to change all related
+# keys in the config.
+model = dict(
+    pts_voxel_layer=dict(
+        max_num_points=20,
+        point_cloud_range=[-100, -100, -5, 100, 100, 3],
+        max_voxels=(60000, 60000)),
+    pts_voxel_encoder=dict(
+        feat_channels=[64], point_cloud_range=[-100, -100, -5, 100, 100, 3]),
+    pts_middle_encoder=dict(output_shape=[800, 800]),
+    pts_bbox_head=dict(
+        num_classes=9,
+        anchor_generator=dict(
+            ranges=[[-100, -100, -1.8, 100, 100, -1.8]], custom_values=[]),
+        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7)))
+# model training settings (based on nuScenes model settings)
+train_cfg = dict(pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))

configs/free_anchor/README.md

@@ -25,7 +25,7 @@ The hyperparameters are specifically tuned according to the original paper.
 
 ```python
 _base_ = [
-    '../_base_/models/pointpillars_second_fpn.py',
+    '../_base_/models/hv_pointpillars_fpn_lyft.py',
     '../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py',
     '../_base_/default_runtime.py'
 ]

configs/free_anchor/hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py

 _base_ = [
-    '../_base_/models/pointpillars_second_fpn.py',
+    '../_base_/models/hv_pointpillars_fpn_lyft.py',
     '../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py',
     '../_base_/default_runtime.py'
 ]

configs/pointpillars/README.md

@@ -30,3 +30,10 @@ We implement PointPillars and provide the results and checkpoints on KITTI and n
 | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
 |[SECFPN](./hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||35.17|49.7|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725-0817d270.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d_20200620_230725.log.json)|
 |[FPN](./hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|16.4||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)|
+
+### Lyft
+
+|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
+| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
+|[SECFPN](./hv_pointpillars_secfpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||13.4|13.4||
+|[FPN](./hv_pointpillars_fpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||14.0|14.2||

configs/pointpillars/hv_pointpillars_fpn_sbn-all_1x16_2x_lyft-3d.py

+_base_ = [
+    '../_base_/models/hv_pointpillars_fpn_lyft.py',
+    '../_base_/datasets/lyft-3d.py', '../_base_/schedules/schedule_2x.py',
+    '../_base_/default_runtime.py'
+]

configs/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py

 _base_ = [
-    '../_base_/models/pointpillars_second_fpn.py',
+    '../_base_/models/hv_pointpillars_fpn_nus.py',
     '../_base_/datasets/nus-3d.py', '../_base_/schedules/schedule_2x.py',
     '../_base_/default_runtime.py'
 ]

configs/pointpillars/hv_pointpillars_fpn_sbn-all_range100_2x8_2x_lyft-3d.py

+_base_ = [
+    '../_base_/models/hv_pointpillars_fpn_range100_lyft.py',
+    '../_base_/datasets/range100_lyft-3d.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]

configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py

 _base_ = [
-    '../_base_/models/hv_pointpillars_secfpn.py',
+    '../_base_/models/hv_pointpillars_secfpn_kitti.py',
     '../_base_/datasets/kitti-3d-3class.py',
     '../_base_/schedules/cyclic_40e.py', '../_base_/default_runtime.py'
 ]

configs/pointpillars/hv_pointpillars_secfpn_sbn-all_1x16_2x_lyft-3d.py

+_base_ = [
+    '../_base_/models/hv_pointpillars_fpn_lyft.py',
+    '../_base_/datasets/lyft-3d.py',
+    '../_base_/schedules/schedule_2x.py',
+    '../_base_/default_runtime.py',
+]
+# model settings
+model = dict(
+    pts_neck=dict(
+        _delete_=True,
+        type='SECONDFPN',
+        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
+        in_channels=[64, 128, 256],
+        upsample_strides=[1, 2, 4],
+        out_channels=[128, 128, 128]),
+    pts_bbox_head=dict(
+        in_channels=384,
+        feat_channels=384,
+        anchor_generator=dict(
+            _delete_=True,
+            type='AlignedAnchor3DRangeGenerator',
+            ranges=[[-80, -80, -1.0715024, -80, -80, -1.0715024],
+                    [-80, -80, -0.3033737, -80, -80, -0.3033737],
+                    [-80, -80, -0.3519405, -80, -80, -0.3519405],
+                    [-80, -80, -0.8871424, -80, -80, -0.8871424],
+                    [-80, -80, -0.6276341, -80, -80, -0.6276341],
+                    [-80, -80, -1.3220503, -80, -80, -1.3220503],
+                    [-80, -80, -1.0709302, -80, -80, -1.0709302],
+                    [-80, -80, -0.9122268, -80, -80, -0.9122268],
+                    [-80, -80, -1.8012227, -80, -80, -1.8012227]],
+            sizes=[
+                [1.92, 4.75, 1.71],  # car
+                [2.84, 10.24, 3.44],  # truck
+                [2.92, 12.70, 3.42],  # bus
+                [2.42, 6.52, 2.34],  # emergency vehicle
+                [2.75, 8.17, 3.20],  # other vehicle
+                [0.96, 2.35, 1.59],  # motorcycle
+                [0.63, 1.76, 1.44],  # bicycle
+                [0.76, 0.80, 1.76],  # pedestrian
+                [0.35, 0.73, 0.50]  # animal
+            ],
+            rotations=[0, 1.57],
+            reshape_out=True)))

configs/pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py

 _base_ = [
-    '../_base_/models/pointpillars_second_fpn.py',
+    '../_base_/models/hv_pointpillars_fpn_nus.py',
     '../_base_/datasets/nus-3d.py',
     '../_base_/schedules/schedule_2x.py',
     '../_base_/default_runtime.py',

configs/pointpillars/hv_pointpillars_secfpn_sbn-all_range100_2x8_2x_lyft-3d.py

+_base_ = [
+    '../_base_/models/hv_pointpillars_fpn_range100_lyft.py',
+    '../_base_/datasets/range100_lyft-3d.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    pts_neck=dict(
+        _delete_=True,
+        type='SECONDFPN',
+        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
+        in_channels=[64, 128, 256],
+        upsample_strides=[1, 2, 4],
+        out_channels=[128, 128, 128]),
+    pts_bbox_head=dict(
+        in_channels=384,
+        feat_channels=384,
+        anchor_generator=dict(
+            _delete_=True,
+            type='AlignedAnchor3DRangeGenerator',
+            ranges=[[-100, -100, -1.0715024, -100, -100, -1.0715024],
+                    [-100, -100, -0.3033737, -100, -100, -0.3033737],
+                    [-100, -100, -0.3519405, -100, -100, -0.3519405],
+                    [-100, -100, -0.8871424, -100, -100, -0.8871424],
+                    [-100, -100, -0.6276341, -100, -100, -0.6276341],
+                    [-100, -100, -1.3220503, -100, -100, -1.3220503],
+                    [-100, -100, -1.0709302, -100, -100, -1.0709302],
+                    [-100, -100, -0.9122268, -100, -100, -0.9122268],
+                    [-100, -100, -1.8012227, -100, -100, -1.8012227]],
+            sizes=[
+                [1.92, 4.75, 1.71],  # car
+                [2.84, 10.24, 3.44],  # truck
+                [2.92, 12.70, 3.42],  # bus
+                [2.42, 6.52, 2.34],  # emergency vehicle
+                [2.75, 8.17, 3.20],  # other vehicle
+                [0.96, 2.35, 1.59],  # motorcycle
+                [0.63, 1.76, 1.44],  # bicycle
+                [0.76, 0.80, 1.76],  # pedestrian
+                [0.35, 0.73, 0.50]  # animal
+            ],
+            rotations=[0, 1.57],
+            reshape_out=True)))

configs/regnet/README.md

@@ -47,7 +47,7 @@ For other pre-trained models or self-implemented regnet models, the users are re
 
 ## Results
 
-### PointPillars
+### nuScenes
 
 |  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | mAP |NDS| Download |
 | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
@@ -56,3 +56,12 @@ For other pre-trained models or self-implemented regnet models, the users are re
 |[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.1||40.0|53.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405-2fa62f3d.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_fpn_sbn-all_4x8_2x_nus-3d_20200620_230405.log.json)|
 |[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|17.3||44.8|56.4|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239-c694dce7.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d_20200620_230239.log.json)|
 |[RegNetX-1.6gF-FPN](./hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py)|2x|24.0||48.2|59.3|[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311-dcd4e090.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection3d/v0.1.0_models/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d_20200629_050311.log.json)|
+
+### Lyft
+
+|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | Private Score | Public Score | Download |
+| :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
+|[SECFPN](../pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||13.4|13.4||
+|[RegNetX-400MF-SECFPN](./hv_pointpillars_regnet-400mf_secfpn_sbn-all_4x8_2x_lyft-3d.py)|  2x  ||||||
+|[FPN](../pointpillars/hv_pointpillars_fpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||14.0|14.2||
+|[RegNetX-400MF-FPN](./hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_lyft-3d.py)|2x|||15.5|15.6||

configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py

 _base_ = [
-    '../_base_/models/pointpillars_second_fpn.py',
+    '../_base_/models/hv_pointpillars_fpn_lyft.py',
     '../_base_/datasets/nus-3d.py',
     '../_base_/schedules/schedule_2x.py',
     '../_base_/default_runtime.py',