Update cfg

configs/kitti/hv_pointpillars_secfpn_6x8_80e_kitti-3d-car.py

-# model settings
-voxel_size = [0.16, 0.16, 4]
-point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
-model = dict(
-    type='VoxelNet',
-    voxel_layer=dict(
-        max_num_points=64,
-        point_cloud_range=point_cloud_range,
-        voxel_size=voxel_size,
-        max_voxels=(12000, 20000)  # (training, testing) max_coxels
-    ),
-    voxel_encoder=dict(
-        type='PillarFeatureNet',
-        in_channels=4,
-        feat_channels=[64],
-        with_distance=False,
-        voxel_size=voxel_size,
-        point_cloud_range=point_cloud_range,
-    ),
-    middle_encoder=dict(
-        type='PointPillarsScatter',
-        in_channels=64,
-        output_shape=[496, 432],
-    ),
-    backbone=dict(
-        type='SECOND',
-        in_channels=64,
-        layer_nums=[3, 5, 5],
-        layer_strides=[2, 2, 2],
-        out_channels=[64, 128, 256],
-    ),
-    neck=dict(
-        type='SECONDFPN',
-        in_channels=[64, 128, 256],
-        upsample_strides=[1, 2, 4],
-        out_channels=[128, 128, 128],
-    ),
-    bbox_head=dict(
-        type='Anchor3DHead',
-        num_classes=1,
-        in_channels=384,
-        feat_channels=384,
-        use_direction_classifier=True,
-        anchor_generator=dict(
-            type='Anchor3DRangeGenerator',
-            ranges=[[0, -39.68, -1.78, 69.12, 39.68, -1.78]],
-            sizes=[[1.6, 3.9, 1.56]],
-            rotations=[0, 1.57],
-            reshape_out=True),
-        diff_rad_by_sin=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(
-    assigner=dict(
-        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(
-    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 = ['Car']
-input_modality = dict(use_lidar=True, use_camera=False)
-db_sampler = dict(
-    data_root=data_root,
-    info_path=data_root + 'kitti_dbinfos_train.pkl',
-    rate=1.0,
-    object_rot_range=[0.0, 0.0],
-    prepare=dict(
-        filter_by_difficulty=[-1],
-        filter_by_min_points=dict(Car=5),
-    ),
-    sample_groups=dict(Car=15),
-    classes=class_names)
-
-train_pipeline = [
-    dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),
-    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
-    dict(type='ObjectSample', db_sampler=db_sampler),
-    dict(
-        type='ObjectNoise',
-        num_try=100,
-        loc_noise_std=[0.25, 0.25, 0.25],
-        global_rot_range=[0.0, 0.0],
-        rot_uniform_noise=[-0.15707963267, 0.15707963267]),
-    dict(type='RandomFlip3D', flip_ratio=0.5),
-    dict(
-        type='GlobalRotScale',
-        rot_uniform_noise=[-0.78539816, 0.78539816],
-        scaling_uniform_noise=[0.95, 1.05]),
-    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=4, use_dim=4),
-    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=6,
-    workers_per_gpu=4,
-    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))
-# optimizer
-lr = 0.001  # max learning rate
-optimizer = dict(
-    type='AdamW',
-    lr=lr,
-    betas=(0.95, 0.99),  # the momentum is change during training
-    weight_decay=0.01)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='cyclic',
-    target_ratio=(10, 1e-4),
-    cyclic_times=1,
-    step_ratio_up=0.4,
-)
-momentum_config = dict(
-    policy='cyclic',
-    target_ratio=(0.85 / 0.95, 1),
-    cyclic_times=1,
-    step_ratio_up=0.4,
-)
-checkpoint_config = dict(interval=1)
-evaluation = dict(interval=1)
-# yapf:disable
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
-# runtime settings
-total_epochs = 40
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-work_dir = './work_dirs/pp_secfpn_80e'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]

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

@@ -104,17 +104,9 @@ db_sampler = dict(
     object_rot_range=[0.0, 0.0],
     prepare=dict(
         filter_by_difficulty=[-1],
-        filter_by_min_points=dict(
-            Car=5,
-            Pedestrian=10,
-            Cyclist=10,
-        )),
+        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
     classes=class_names,
-    sample_groups=dict(
-        Car=12,
-        Pedestrian=6,
-        Cyclist=6,
-    ))
+    sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10))
 
 train_pipeline = [
     dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),

configs/kitti/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py → configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py

@@ -7,34 +7,27 @@ model = dict(
         max_num_points=64,
         point_cloud_range=point_cloud_range,
         voxel_size=voxel_size,
-        max_voxels=(12000, 20000)  # (training, testing) max_coxels
-    ),
+        max_voxels=(12000, 20000)),
     voxel_encoder=dict(
         type='PillarFeatureNet',
         in_channels=4,
         feat_channels=[64],
         with_distance=False,
         voxel_size=voxel_size,
-        point_cloud_range=point_cloud_range,
-    ),
+        point_cloud_range=point_cloud_range),
     middle_encoder=dict(
-        type='PointPillarsScatter',
-        in_channels=64,
-        output_shape=[496, 432],
-    ),
+        type='PointPillarsScatter', in_channels=64, output_shape=[496, 432]),
     backbone=dict(
         type='SECOND',
         in_channels=64,
         layer_nums=[3, 5, 5],
         layer_strides=[2, 2, 2],
-        out_channels=[64, 128, 256],
-    ),
+        out_channels=[64, 128, 256]),
     neck=dict(
         type='SECONDFPN',
         in_channels=[64, 128, 256],
         upsample_strides=[1, 2, 4],
-        out_channels=[128, 128, 128],
-    ),
+        out_channels=[128, 128, 128]),
     bbox_head=dict(
         type='Anchor3DHead',
         num_classes=1,
@@ -57,9 +50,7 @@ model = dict(
             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),
-    ),
-)
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)))
 # model training and testing settings
 train_cfg = dict(
     assigner=dict(
@@ -91,10 +82,7 @@ db_sampler = dict(
     info_path=data_root + 'kitti_dbinfos_train.pkl',
     rate=1.0,
     object_rot_range=[0.0, 0.0],
-    prepare=dict(
-        filter_by_difficulty=[-1],
-        filter_by_min_points=dict(Car=5),
-    ),
+    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
     sample_groups=dict(Car=15),
     classes=class_names)
 
@@ -117,7 +105,7 @@ train_pipeline = [
     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']),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
 test_pipeline = [
     dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),
@@ -126,7 +114,7 @@ test_pipeline = [
         type='DefaultFormatBundle3D',
         class_names=class_names,
         with_label=False),
-    dict(type='Collect3D', keys=['points']),
+    dict(type='Collect3D', keys=['points'])
 ]
 
 data = dict(
@@ -178,14 +166,12 @@ lr_config = dict(
     policy='cyclic',
     target_ratio=(10, 1e-4),
     cyclic_times=1,
-    step_ratio_up=0.4,
-)
+    step_ratio_up=0.4)
 momentum_config = dict(
     policy='cyclic',
     target_ratio=(0.85 / 0.95, 1),
     cyclic_times=1,
-    step_ratio_up=0.4,
-)
+    step_ratio_up=0.4)
 checkpoint_config = dict(interval=1)
 evaluation = dict(interval=1)
 # yapf:disable

tests/test_config.py

@@ -85,7 +85,7 @@ def test_config_build_pipeline():
 
     # Other configs needs database sampler.
     config_names = [
-        'nus/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py',
+        'pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py',
     ]
 
     print('Using {} config files'.format(len(config_names)))
@@ -117,9 +117,9 @@ def test_config_data_pipeline():
     # Only tests a representative subset of configurations
     # TODO: test pipelines using Albu, current Albu throw None given empty GT
     config_names = [
-        'nus/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py',
-        'nus/retinanet_r50_fpn_caffe_2x8_1x_nus.py',
-        'kitti/'
+        'mvxnet/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py',
+        'mvxnet/retinanet_r50_fpn_caffe_2x8_1x_nus.py',
+        'mvxnet/'
         'faster_rcnn_r50_fpn_caffe_1x_kitti-2d-3class_coco-3x-pretrain.py',
     ]
 

tests/test_forward.py

@@ -55,7 +55,7 @@ def _get_detector_cfg(fname):
 
 
 def test_faster_rcnn_forward():
-    _test_two_stage_forward('nus/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py')
+    _test_two_stage_forward('mvxnet/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py')
 
 
 def _test_two_stage_forward(cfg_file):

tests/test_heads.py

@@ -71,7 +71,7 @@ def test_anchor3d_head_loss():
     if not torch.cuda.is_available():
         pytest.skip('test requires GPU and torch+cuda')
     bbox_head_cfg = _get_head_cfg(
-        'kitti/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
+        'second/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
 
     from mmdet3d.models.builder import build_head
     self = build_head(bbox_head_cfg)
@@ -123,7 +123,7 @@ def test_anchor3d_head_getboxes():
     if not torch.cuda.is_available():
         pytest.skip('test requires GPU and torch+cuda')
     bbox_head_cfg = _get_head_cfg(
-        'kitti/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
+        'second/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
 
     from mmdet3d.models.builder import build_head
     self = build_head(bbox_head_cfg)
@@ -154,7 +154,7 @@ def test_parta2_rpnhead_getboxes():
     if not torch.cuda.is_available():
         pytest.skip('test requires GPU and torch+cuda')
     rpn_head_cfg, proposal_cfg = _get_rpn_head_cfg(
-        'kitti/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')
+        'parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')
 
     from mmdet3d.models.builder import build_head
     self = build_head(rpn_head_cfg)