|
- _base_ = ['../_base_/schedules/cosine.py', '../_base_/default_runtime.py']
-
- # model settings
- 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)))
-
- # 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)
- 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),
- 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'])
- ])
- ]
- # 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='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
- dict(type='LoadImageFromFile'),
- 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,
- box_type_3d='LiDAR')),
- 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,
- box_type_3d='LiDAR'),
- 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,
- box_type_3d='LiDAR'))
-
- # Training settings
- optimizer = dict(weight_decay=0.01)
- # max_norm=10 is better for SECOND
- optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-
- evaluation = dict(interval=1, pipeline=eval_pipeline)
-
- # 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
|