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

-# model settings
-voxel_size = [0.25, 0.25, 8]
-point_cloud_range = [-50, -50, -5, 50, 50, 3]
-class_names = [
-    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
-    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
-]
-model = dict(
-    type='MVXFasterRCNNV2',
-    pts_voxel_layer=dict(
-        max_num_points=64,  # max_points_per_voxel
-        point_cloud_range=point_cloud_range,  # velodyne coordinates, x, y, z
-        voxel_size=voxel_size,
-        max_voxels=(30000, 40000),  # (training, testing) max_coxels
-    ),
-    pts_voxel_encoder=dict(
-        type='HardVFE',
-        num_input_features=4,
-        num_filters=[64, 64],
-        with_distance=False,
-        voxel_size=voxel_size,
-        with_cluster_center=True,
-        with_voxel_center=True,
-        point_cloud_range=point_cloud_range,
-        norm_cfg=dict(type='naiveSyncBN1d', eps=1e-3, momentum=0.01)),
-    pts_middle_encoder=dict(
-        type='PointPillarsScatter',
-        in_channels=64,
-        output_shape=[400, 400],  # checked from PointCloud3D
-    ),
-    pts_backbone=dict(
-        type='SECOND',
-        in_channels=64,
-        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
-        layer_nums=[3, 5, 5],
-        layer_strides=[2, 2, 2],
-        num_filters=[64, 128, 256],
-    ),
-    pts_neck=dict(
-        type='FPN',
-        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
-        act_cfg=dict(type='ReLU'),
-        in_channels=[64, 128, 256],
-        out_channels=256,
-        start_level=0,
-        num_outs=3,
-    ),
-    pts_bbox_head=dict(
-        type='Anchor3DVeloHead',
-        class_names=class_names,
-        num_classes=10,
-        in_channels=256,
-        feat_channels=256,
-        use_direction_classifier=True,
-        encode_bg_as_zeros=True,
-        anchor_generator=dict(
-            type='Anchor3DRangeGenerator',
-            ranges=[[-50, -50, -1.80, 50, 50, -1.80]],
-            strides=[1, 2, 4],
-            sizes=[
-                [0.577, 1.732, 1.],
-                [1., 1., 1.],
-                [0.4, 0.4, 1],
-            ],
-            custom_values=[0, 0],
-            rotations=[0, 1.57],
-            reshape_out=True),
-        assigner_per_size=False,
-        diff_rad_by_sin=True,
-        dir_offset=0.7854,  # pi/4
-        dir_limit_offset=0,
-        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', ),
-        loss_cls=dict(
-            type='FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
-        loss_dir=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2),
-    ),
-)
-# model training and testing settings
-train_cfg = dict(
-    pts=dict(
-        assigner=dict(  # for Car
-            type='MaxIoUAssigner',
-            iou_calculator=dict(type='BboxOverlapsNearest3D'),
-            pos_iou_thr=0.6,
-            neg_iou_thr=0.3,
-            min_pos_iou=0.3,
-            ignore_iof_thr=-1),
-        allowed_border=0,
-        code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
-        pos_weight=-1,
-        debug=False))
-test_cfg = dict(
-    pts=dict(
-        use_rotate_nms=True,
-        nms_across_levels=False,
-        nms_pre=1000,
-        nms_thr=0.2,
-        score_thr=0.05,
-        min_bbox_size=0,
-        max_per_img=500,
-        post_center_limit_range=point_cloud_range,
-        # TODO: check whether need to change this
-        # post_center_limit_range=[-59.6, -59.6, -6, 59.6, 59.6, 4],
-        # soft-nms is also supported for rcnn testing
-        # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
-    ))
-
-# dataset settings
-dataset_type = 'NuScenesDataset'
-data_root = 'data/nuscenes/'
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-input_modality = dict(
-    use_lidar=True,
-    use_depth=False,
-    use_lidar_intensity=True,
-    use_camera=False,
-)
-db_sampler = dict(
-    root_path=data_root,
-    info_path=data_root + 'nuscenes_dbinfos_train.pkl',
-    rate=1.0,
-    use_road_plane=False,
-    object_rot_range=[0.0, 0.0],
-    prepare=dict(),
-    sample_groups=dict(
-        bus=4,
-        trailer=4,
-        truck=4,
-    ),
-)
-
-train_pipeline = [
-    dict(
-        type='GlobalRotScale',
-        rot_uniform_noise=[-0.3925, 0.3925],
-        scaling_uniform_noise=[0.95, 1.05],
-        trans_normal_noise=[0, 0, 0]),
-    dict(type='RandomFlip3D', flip_ratio=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='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='RandomFlip3D', flip_ratio=0),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points']),
-]
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        root_path=data_root,
-        ann_file=data_root + 'nuscenes_infos_train.pkl',
-        pipeline=train_pipeline,
-        modality=input_modality,
-        class_names=class_names,
-        with_label=True),
-    val=dict(
-        type=dataset_type,
-        root_path=data_root,
-        ann_file=data_root + 'nuscenes_infos_val.pkl',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        class_names=class_names,
-        with_label=True),
-    test=dict(
-        type=dataset_type,
-        root_path=data_root,
-        ann_file=data_root + 'nuscenes_infos_val.pkl',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        class_names=class_names,
-        with_label=False))
-# optimizer
-optimizer = dict(type='AdamW', lr=0.001, weight_decay=0.01)
-# max_norm=10 is better for SECOND
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=1.0 / 1000,
-    step=[20, 23])
-momentum_config = None
-checkpoint_config = dict(interval=1)
-# yapf:disable
-evaluation = dict(interval=20)
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
-# runtime settings
-total_epochs = 24
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-work_dir = './work_dirs/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]

mmdet3d/models/necks/second_fpn.py

@@ -9,7 +9,7 @@ from .. import builder
 
 @NECKS.register_module()
 class SECONDFPN(nn.Module):
-    """FPN used in SECOND/PointPillars
+    """FPN used in SECOND/PointPillars/PartA2/MVXNet
 
     Args:
         in_channels (list[int]): Input channels of multi-scale feature maps