[Feature]: Support MoCa

configs/_base_/models/mvx-fpn_second.py

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+voxel_size = [0.05, 0.05, 0.1]
+point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+
+model = dict(
+ type='DynamicMVXFasterRCNN',
+ img_backbone=dict(
+ type='ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=1,
+ norm_cfg=dict(type='BN', requires_grad=False),
+ norm_eval=True,
+ style='caffe'),
+ img_neck=dict(
+ type='FPN',
+ in_channels=[256, 512, 1024, 2048],
+ out_channels=256,
+ num_outs=5),
+ pts_voxel_layer=dict(
+ max_num_points=-1,
+ point_cloud_range=point_cloud_range,
+ voxel_size=voxel_size,
+ max_voxels=(-1, -1)),
+ pts_voxel_encoder=dict(
+ type='DynamicVFE',
+ in_channels=4,
+ feat_channels=[64, 64],
+ with_distance=False,
+ voxel_size=voxel_size,
+ with_cluster_center=True,
+ with_voxel_center=True,
+ point_cloud_range=point_cloud_range,
+ fusion_layer=dict(
+ type='PointFusion',
+ img_channels=256,
+ pts_channels=64,
+ mid_channels=128,
+ out_channels=128,
+ img_levels=[0, 1, 2, 3, 4],
+ align_corners=False,
+ activate_out=True,
+ fuse_out=False)),
+ pts_middle_encoder=dict(
+ type='SparseEncoder',
+ in_channels=128,
+ sparse_shape=[41, 1600, 1408],
+ order=('conv', 'norm', 'act')),
+ pts_backbone=dict(
+ type='SECOND',
+ in_channels=256,
+ layer_nums=[5, 5],
+ layer_strides=[1, 2],
+ out_channels=[128, 256]),
+ pts_neck=dict(
+ type='SECONDFPN',
+ in_channels=[128, 256],
+ upsample_strides=[1, 2],
+ out_channels=[256, 256]),
+ pts_bbox_head=dict(
+ type='Anchor3DHead',
+ num_classes=3,
+ in_channels=512,
+ feat_channels=512,
+ use_direction_classifier=True,
+ anchor_generator=dict(
+ type='Anchor3DRangeGenerator',
+ ranges=[
+ [0, -40.0, -0.6, 70.4, 40.0, -0.6],
+ [0, -40.0, -0.6, 70.4, 40.0, -0.6],
+ [0, -40.0, -1.78, 70.4, 40.0, -1.78],
+ ],
+ sizes=[[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]],
+ rotations=[0, 1.57],
+ reshape_out=False),
+ assigner_per_size=True,
+ diff_rad_by_sin=True,
+ assign_per_class=True,
+ 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=2.0),
+ 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 Pedestrian
+ type='MaxIoUAssigner',
+ iou_calculator=dict(type='BboxOverlapsNearest3D'),
+ pos_iou_thr=0.35,
+ neg_iou_thr=0.2,
+ min_pos_iou=0.2,
+ ignore_iof_thr=-1),
+ dict( # for Cyclist
+ type='MaxIoUAssigner',
+ iou_calculator=dict(type='BboxOverlapsNearest3D'),
+ pos_iou_thr=0.35,
+ neg_iou_thr=0.2,
+ min_pos_iou=0.2,
+ ignore_iof_thr=-1),
+ dict( # for Car
+ type='MaxIoUAssigner',
+ iou_calculator=dict(type='BboxOverlapsNearest3D'),
+ pos_iou_thr=0.6,
+ neg_iou_thr=0.45,
+ min_pos_iou=0.45,
+ ignore_iof_thr=-1),
+ ],
+ allowed_border=0,
+ pos_weight=-1,
+ debug=False)),
+ test_cfg=dict(
+ pts=dict(
+ use_rotate_nms=True,
+ nms_across_levels=False,
+ nms_thr=0.01,
+ score_thr=0.1,
+ min_bbox_size=0,
+ nms_pre=100,
+ max_num=50)))

configs/moca/dv_mvx-fpn_second_secfpn_hybrid_moca_2x8_80e_kitti-3d-3class.py

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+_base_ = '../_base_/models/mvx-fpn_second.py'
+# model settings
+voxel_size = [0.05, 0.05, 0.1]
+point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+
+# dataset settings
+dataset_type = 'KittiDataset'
+data_root = 'data/kitti/'
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+img_norm_cfg = dict(
+ mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+
+db_sampler = dict(
+ type='MMDataBaseSampler',
+ data_root=data_root,
+ info_path=data_root + 'kitti_mm_dbinfos_train.pkl',
+ rate=1.0,
+ blending_type=['box', 'gaussian', 'poisson'],
+ depth_consistent=True,
+ check_2D_collision=True,
+ collision_thr=[0, 0.3, 0.5, 0.7],
+ prepare=dict(
+ filter_by_difficulty=[-1],
+ filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
+ classes=class_names,
+ sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6))
+
+input_modality = dict(use_lidar=True, use_camera=True)
+train_pipeline = [
+ dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='LoadAnnotations3D',
+ with_bbox_3d=True,
+ with_label_3d=True,
+ with_bbox=True,
+ with_label=True),
+ dict(type='ObjectSample', db_sampler=db_sampler, sample_2d=True),
+ dict(
+ type='Resize',
+ img_scale=[(640, 192), (2560, 768)],
+ multiscale_mode='range',
+ keep_ratio=True),
+ dict(
+ type='GlobalRotScaleTrans',
+ rot_range=[-0.78539816, 0.78539816],
+ scale_ratio_range=[0.95, 1.05],
+ translation_std=[0.2, 0.2, 0.2]),
+ 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='Normalize', **img_norm_cfg),
+ dict(type='Pad', size_divisor=32),
+ dict(type='DefaultFormatBundle3D', class_names=class_names),
+ dict(
+ type='Collect3D',
+ keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d']),
+]
+test_pipeline = [
+ dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='MultiScaleFlipAug3D',
+ img_scale=(1280, 384),
+ pts_scale_ratio=1,
+ flip=False,
+ transforms=[
+ dict(type='Resize', multiscale_mode='value', keep_ratio=True),
+ dict(
+ type='GlobalRotScaleTrans',
+ rot_range=[0, 0],
+ scale_ratio_range=[1., 1.],
+ translation_std=[0, 0, 0]),
+ dict(type='RandomFlip3D'),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(type='Pad', size_divisor=32),
+ dict(
+ type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+ dict(
+ type='DefaultFormatBundle3D',
+ class_names=class_names,
+ with_label=False),
+ dict(type='Collect3D', keys=['points', 'img'])
+ ])
+]
+
+data = dict(
+ samples_per_gpu=2,
+ workers_per_gpu=2,
+ train=dict(
+ type='RepeatDataset',
+ times=2,
+ dataset=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'kitti_infos_train.pkl',
+ split='training',
+ pts_prefix='velodyne_reduced',
+ pipeline=train_pipeline,
+ modality=input_modality,
+ classes=class_names,
+ test_mode=False)),
+ val=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'kitti_infos_val.pkl',
+ split='training',
+ pts_prefix='velodyne_reduced',
+ pipeline=test_pipeline,
+ modality=input_modality,
+ classes=class_names,
+ test_mode=True),
+ test=dict(
+ type=dataset_type,
+ data_root=data_root,
+ ann_file=data_root + 'kitti_infos_val.pkl',
+ split='training',
+ pts_prefix='velodyne_reduced',
+ pipeline=test_pipeline,
+ modality=input_modality,
+ classes=class_names,
+ test_mode=True))
+# Training settings
+optimizer = dict(
+ constructor='HybridOptimizerConstructor',
+ pts=dict(
+ type='AdamW',
+ lr=0.003,
+ betas=(0.95, 0.99),
+ weight_decay=0.01,
+ step_interval=1),
+ img=dict(
+ type='SGD',
+ lr=0.005,
+ momentum=0.9,
+ weight_decay=0.0001,
+ step_interval=1))
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+ policy='CosineAnnealing',
+ warmup='linear',
+ warmup_iters=1000,
+ warmup_ratio=1.0 / 10,
+ min_lr_ratio=1e-5)
+momentum_config = None
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+ interval=50,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ dict(type='TensorboardLoggerHook')
+ ])
+# yapf:enable
+evaluation = dict(interval=1)
+# runtime settings
+total_epochs = 40
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = None
+# You may need to download the model first is the network is unstable
+load_from = 'https://download.openmmlab.com/mmdetection3d/pretrain_models/mvx_faster_rcnn_detectron2-caffe_20e_coco-pretrain_gt-sample_kitti-3-class_moderate-79.3_20200207-a4a6a3c7.pth' # noqa
+resume_from = None
+workflow = [('train', 1)]