[Enhance] Move train_cfg test_cfg to model (#307)

* Move train_cfg/test_cfg to model

* Move train_cfg/test_cfg to model

* Move train_cfg/test_cfg to model

* Move train_cfg/test_cfg to model

* Move train_cfg/test_cfg to model

* Move train_cfg/test_cfg to model

* Move train_cfg/test_cfg to model

* Move train_cfg and test_cfg into model

* modify centerpoint configs

* Modify docs

* modify build_detector

* modify test_config_build_detector

* modify build_detector parameters

* Adopt the same strategy in build_detector

configs/centerpoint/centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py

 _base_ = ['./centerpoint_0075voxel_second_secfpn_4x8_cyclic_20e_nus.py']
 
-test_cfg = dict(pts=dict(nms_type='circle'))
+model = dict(test_cfg=dict(pts=dict(nms_type='circle')))

configs/centerpoint/centerpoint_0075voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py

@@ -12,6 +12,5 @@ model = dict(
                 padding=1,
                 groups=4),
             init_bias=-2.19,
-            final_kernel=3)))
-
-test_cfg = dict(pts=dict(nms_type='circle'))
+            final_kernel=3)),
+    test_cfg=dict(pts=dict(nms_type='circle')))

configs/centerpoint/centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py

@@ -15,10 +15,10 @@ class_names = [
 
 model = dict(
     pts_voxel_layer=dict(point_cloud_range=point_cloud_range),
-    pts_bbox_head=dict(bbox_coder=dict(pc_range=point_cloud_range[:2])))
-# model training and testing settings
-train_cfg = dict(pts=dict(point_cloud_range=point_cloud_range))
-test_cfg = dict(pts=dict(pc_range=point_cloud_range[:2]))
+    pts_bbox_head=dict(bbox_coder=dict(pc_range=point_cloud_range[:2])),
+    # model training and testing settings
+    train_cfg=dict(pts=dict(point_cloud_range=point_cloud_range)),
+    test_cfg=dict(pts=dict(pc_range=point_cloud_range[:2])))
 
 dataset_type = 'NuScenesDataset'
 data_root = 'data/nuscenes/'

configs/centerpoint/centerpoint_01voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py

 _base_ = ['./centerpoint_01voxel_second_secfpn_4x8_cyclic_20e_nus.py']
 
-test_cfg = dict(pts=dict(nms_type='circle'))
+model = dict(test_cfg=dict(pts=dict(nms_type='circle')))

configs/centerpoint/centerpoint_01voxel_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py

@@ -12,6 +12,5 @@ model = dict(
                 padding=1,
                 groups=4),
             init_bias=-2.19,
-            final_kernel=3)))
-
-test_cfg = dict(pts=dict(nms_type='circle'))
+            final_kernel=3)),
+    test_cfg=dict(pts=dict(nms_type='circle')))

configs/centerpoint/centerpoint_02pillar_second_secfpn_4x8_cyclic_20e_nus.py

@@ -16,10 +16,10 @@ class_names = [
 model = dict(
     pts_voxel_layer=dict(point_cloud_range=point_cloud_range),
     pts_voxel_encoder=dict(point_cloud_range=point_cloud_range),
-    pts_bbox_head=dict(bbox_coder=dict(pc_range=point_cloud_range[:2])))
-# model training and testing settings
-train_cfg = dict(pts=dict(point_cloud_range=point_cloud_range))
-test_cfg = dict(pts=dict(pc_range=point_cloud_range[:2]))
+    pts_bbox_head=dict(bbox_coder=dict(pc_range=point_cloud_range[:2])),
+    # model training and testing settings
+    train_cfg=dict(pts=dict(point_cloud_range=point_cloud_range)),
+    test_cfg=dict(pts=dict(pc_range=point_cloud_range[:2])))
 
 dataset_type = 'NuScenesDataset'
 data_root = 'data/nuscenes/'

configs/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus.py

 _base_ = ['./centerpoint_02pillar_second_secfpn_4x8_cyclic_20e_nus.py']
 
-test_cfg = dict(pts=dict(nms_type='circle'))
+model = dict(test_cfg=dict(pts=dict(nms_type='circle')))

configs/centerpoint/centerpoint_02pillar_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus.py

@@ -12,6 +12,5 @@ model = dict(
                 padding=1,
                 groups=4),
             init_bias=-2.19,
-            final_kernel=3)))
-
-test_cfg = dict(pts=dict(nms_type='circle'))
+            final_kernel=3)),
+    test_cfg=dict(pts=dict(nms_type='circle')))

configs/free_anchor/README.md

@@ -70,10 +70,10 @@ model = dict(
             loss_weight=1.0),
         loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.8),
         loss_dir=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)))
-# model training and testing settings
-train_cfg = dict(
-    pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 0.25]))
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
+    # model training and testing settings
+    train_cfg = dict(
+        pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 0.25])))
 ```
 
 ## Results

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

@@ -42,7 +42,7 @@ model = dict(
             loss_weight=1.0),
         loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.8),
         loss_dir=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)))
-# model training and testing settings
-train_cfg = dict(
-    pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 0.25]))
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
+    # model training and testing settings
+    train_cfg=dict(
+        pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.25, 0.25])))

configs/mvxnet/dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class.py

@@ -87,45 +87,45 @@ 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)))
-# 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))
+            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'

configs/nuimages/htc_without_semantic_r50_fpn_1x_nuim.py

@@ -130,92 +130,92 @@ model = dict(
                 num_classes=10,
                 loss_mask=dict(
                     type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
-        ]))
-# model training and testing settings
-train_cfg = dict(
-    rpn=dict(
-        assigner=dict(
-            type='MaxIoUAssigner',
-            pos_iou_thr=0.7,
-            neg_iou_thr=0.3,
-            min_pos_iou=0.3,
-            ignore_iof_thr=-1),
-        sampler=dict(
-            type='RandomSampler',
-            num=256,
-            pos_fraction=0.5,
-            neg_pos_ub=-1,
-            add_gt_as_proposals=False),
-        allowed_border=0,
-        pos_weight=-1,
-        debug=False),
-    rpn_proposal=dict(
-        nms_across_levels=False,
-        nms_pre=2000,
-        nms_post=2000,
-        max_num=2000,
-        nms_thr=0.7,
-        min_bbox_size=0),
-    rcnn=[
-        dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.5,
-                min_pos_iou=0.5,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
-            pos_weight=-1,
-            debug=False),
-        dict(
-            assigner=dict(
-                type='MaxIoUAssigner',
-                pos_iou_thr=0.6,
-                neg_iou_thr=0.6,
-                min_pos_iou=0.6,
-                ignore_iof_thr=-1),
-            sampler=dict(
-                type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
-                neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
-            pos_weight=-1,
-            debug=False),
-        dict(
+        ]),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
             assigner=dict(
                 type='MaxIoUAssigner',
                 pos_iou_thr=0.7,
-                neg_iou_thr=0.7,
-                min_pos_iou=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
                 ignore_iof_thr=-1),
             sampler=dict(
                 type='RandomSampler',
-                num=512,
-                pos_fraction=0.25,
+                num=256,
+                pos_fraction=0.5,
                 neg_pos_ub=-1,
-                add_gt_as_proposals=True),
-            mask_size=28,
+                add_gt_as_proposals=False),
+            allowed_border=0,
             pos_weight=-1,
-            debug=False)
-    ])
-test_cfg = dict(
-    rpn=dict(
-        nms_across_levels=False,
-        nms_pre=1000,
-        nms_post=1000,
-        max_num=1000,
-        nms_thr=0.7,
-        min_bbox_size=0),
-    rcnn=dict(
-        score_thr=0.001,
-        nms=dict(type='nms', iou_threshold=0.5),
-        max_per_img=100,
-        mask_thr_binary=0.5))
+            debug=False),
+        rpn_proposal=dict(
+            nms_across_levels=False,
+            nms_pre=2000,
+            nms_post=2000,
+            max_num=2000,
+            nms_thr=0.7,
+            min_bbox_size=0),
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.5,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.6,
+                    min_pos_iou=0.6,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.7,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False)
+        ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_across_levels=False,
+            nms_pre=1000,
+            nms_post=1000,
+            max_num=1000,
+            nms_thr=0.7,
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.001,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))

configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py

@@ -112,88 +112,94 @@ model = dict(
                 type='CrossEntropyLoss',
                 use_sigmoid=True,
                 reduction='sum',
-                loss_weight=1.0))))
-# model training and testing settings
-train_cfg = dict(
-    rpn=dict(
-        assigner=[
-            dict(  # for Pedestrian
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.35,
-                min_pos_iou=0.35,
-                ignore_iof_thr=-1),
-            dict(  # for Cyclist
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.35,
-                min_pos_iou=0.35,
-                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),
-    rpn_proposal=dict(
-        nms_pre=9000,
-        nms_post=512,
-        max_num=512,
-        nms_thr=0.8,
-        score_thr=0,
-        use_rotate_nms=False),
-    rcnn=dict(
-        assigner=[
-            dict(  # for Pedestrian
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
-                pos_iou_thr=0.55,
-                neg_iou_thr=0.55,
-                min_pos_iou=0.55,
-                ignore_iof_thr=-1),
-            dict(  # for Cyclist
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
-                pos_iou_thr=0.55,
-                neg_iou_thr=0.55,
-                min_pos_iou=0.55,
-                ignore_iof_thr=-1),
-            dict(  # for Car
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
-                pos_iou_thr=0.55,
-                neg_iou_thr=0.55,
-                min_pos_iou=0.55,
-                ignore_iof_thr=-1)
-        ],
-        sampler=dict(
-            type='IoUNegPiecewiseSampler',
-            num=128,
-            pos_fraction=0.55,
-            neg_piece_fractions=[0.8, 0.2],
-            neg_iou_piece_thrs=[0.55, 0.1],
-            neg_pos_ub=-1,
-            add_gt_as_proposals=False,
-            return_iou=True),
-        cls_pos_thr=0.75,
-        cls_neg_thr=0.25))
-test_cfg = dict(
-    rpn=dict(
-        nms_pre=1024,
-        nms_post=100,
-        max_num=100,
-        nms_thr=0.7,
-        score_thr=0,
-        use_rotate_nms=True),
-    rcnn=dict(
-        use_rotate_nms=True, use_raw_score=True, nms_thr=0.01, score_thr=0.1))
+                loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=[
+                dict(  # for Pedestrian
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.35,
+                    min_pos_iou=0.35,
+                    ignore_iof_thr=-1),
+                dict(  # for Cyclist
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.35,
+                    min_pos_iou=0.35,
+                    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),
+        rpn_proposal=dict(
+            nms_pre=9000,
+            nms_post=512,
+            max_num=512,
+            nms_thr=0.8,
+            score_thr=0,
+            use_rotate_nms=False),
+        rcnn=dict(
+            assigner=[
+                dict(  # for Pedestrian
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(
+                        type='BboxOverlaps3D', coordinate='lidar'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.55,
+                    min_pos_iou=0.55,
+                    ignore_iof_thr=-1),
+                dict(  # for Cyclist
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(
+                        type='BboxOverlaps3D', coordinate='lidar'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.55,
+                    min_pos_iou=0.55,
+                    ignore_iof_thr=-1),
+                dict(  # for Car
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(
+                        type='BboxOverlaps3D', coordinate='lidar'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.55,
+                    min_pos_iou=0.55,
+                    ignore_iof_thr=-1)
+            ],
+            sampler=dict(
+                type='IoUNegPiecewiseSampler',
+                num=128,
+                pos_fraction=0.55,
+                neg_piece_fractions=[0.8, 0.2],
+                neg_iou_piece_thrs=[0.55, 0.1],
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False,
+                return_iou=True),
+            cls_pos_thr=0.75,
+            cls_neg_thr=0.25)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1024,
+            nms_post=100,
+            max_num=100,
+            nms_thr=0.7,
+            score_thr=0,
+            use_rotate_nms=True),
+        rcnn=dict(
+            use_rotate_nms=True,
+            use_raw_score=True,
+            nms_thr=0.01,
+            score_thr=0.1)))
 
 # dataset settings
 dataset_type = 'KittiDataset'

configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py

@@ -17,57 +17,60 @@ model = dict(
     roi_head=dict(
         num_classes=1,
         semantic_head=dict(num_classes=1),
-        bbox_head=dict(num_classes=1)))
-# model training and testing settings
-train_cfg = dict(
-    _delete_=True,
-    rpn=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),
-    rpn_proposal=dict(
-        nms_pre=9000,
-        nms_post=512,
-        max_num=512,
-        nms_thr=0.8,
-        score_thr=0,
-        use_rotate_nms=False),
-    rcnn=dict(
-        assigner=dict(  # for Car
-            type='MaxIoUAssigner',
-            iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
-            pos_iou_thr=0.55,
-            neg_iou_thr=0.55,
-            min_pos_iou=0.55,
-            ignore_iof_thr=-1),
-        sampler=dict(
-            type='IoUNegPiecewiseSampler',
-            num=128,
-            pos_fraction=0.55,
-            neg_piece_fractions=[0.8, 0.2],
-            neg_iou_piece_thrs=[0.55, 0.1],
-            neg_pos_ub=-1,
-            add_gt_as_proposals=False,
-            return_iou=True),
-        cls_pos_thr=0.75,
-        cls_neg_thr=0.25))
-test_cfg = dict(
-    rpn=dict(
-        nms_pre=1024,
-        nms_post=100,
-        max_num=100,
-        nms_thr=0.7,
-        score_thr=0,
-        use_rotate_nms=True),
-    rcnn=dict(
-        use_rotate_nms=True, use_raw_score=True, nms_thr=0.01, score_thr=0.1))
+        bbox_head=dict(num_classes=1)),
+    # model training and testing settings
+    train_cfg=dict(
+        _delete_=True,
+        rpn=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),
+        rpn_proposal=dict(
+            nms_pre=9000,
+            nms_post=512,
+            max_num=512,
+            nms_thr=0.8,
+            score_thr=0,
+            use_rotate_nms=False),
+        rcnn=dict(
+            assigner=dict(  # for Car
+                type='MaxIoUAssigner',
+                iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
+                pos_iou_thr=0.55,
+                neg_iou_thr=0.55,
+                min_pos_iou=0.55,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='IoUNegPiecewiseSampler',
+                num=128,
+                pos_fraction=0.55,
+                neg_piece_fractions=[0.8, 0.2],
+                neg_iou_piece_thrs=[0.55, 0.1],
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False,
+                return_iou=True),
+            cls_pos_thr=0.75,
+            cls_neg_thr=0.25)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1024,
+            nms_post=100,
+            max_num=100,
+            nms_thr=0.7,
+            score_thr=0,
+            use_rotate_nms=True),
+        rcnn=dict(
+            use_rotate_nms=True,
+            use_raw_score=True,
+            nms_thr=0.01,
+            score_thr=0.1)))
 
 # dataset settings
 dataset_type = 'KittiDataset'

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

@@ -12,20 +12,20 @@ model = dict(
             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)))
-# model training and testing settings
-train_cfg = dict(
-    _delete_=True,
-    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)
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
+        _delete_=True,
+        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))
 
 # dataset settings
 dataset_type = 'KittiDataset'

configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymo-3d-car.py

@@ -19,20 +19,19 @@ model = dict(
             ranges=[[-74.88, -74.88, -0.0345, 74.88, 74.88, -0.0345]],
             sizes=[[2.08, 4.73, 1.77]],
             rotations=[0, 1.57],
-            reshape_out=True)))
-
-# model training and testing settings
-train_cfg = dict(
-    _delete_=True,
-    pts=dict(
-        assigner=dict(
-            type='MaxIoUAssigner',
-            iou_calculator=dict(type='BboxOverlapsNearest3D'),
-            pos_iou_thr=0.55,
-            neg_iou_thr=0.4,
-            min_pos_iou=0.4,
-            ignore_iof_thr=-1),
-        allowed_border=0,
-        code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
-        pos_weight=-1,
-        debug=False))
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
+        _delete_=True,
+        pts=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                pos_iou_thr=0.55,
+                neg_iou_thr=0.4,
+                min_pos_iou=0.4,
+                ignore_iof_thr=-1),
+            allowed_border=0,
+            code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+            pos_weight=-1,
+            debug=False)))

configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-car.py

@@ -16,20 +16,19 @@ model = dict(
             ranges=[[-74.88, -74.88, -0.0345, 74.88, 74.88, -0.0345]],
             sizes=[[2.08, 4.73, 1.77]],
             rotations=[0, 1.57],
-            reshape_out=True)))
-
-# model training and testing settings
-train_cfg = dict(
-    _delete_=True,
-    pts=dict(
-        assigner=dict(
-            type='MaxIoUAssigner',
-            iou_calculator=dict(type='BboxOverlapsNearest3D'),
-            pos_iou_thr=0.55,
-            neg_iou_thr=0.4,
-            min_pos_iou=0.4,
-            ignore_iof_thr=-1),
-        allowed_border=0,
-        code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
-        pos_weight=-1,
-        debug=False))
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
+        _delete_=True,
+        pts=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                pos_iou_thr=0.55,
+                neg_iou_thr=0.4,
+                min_pos_iou=0.4,
+                ignore_iof_thr=-1),
+            allowed_border=0,
+            code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
+            pos_weight=-1,
+            debug=False)))

configs/second/hv_second_secfpn_6x8_80e_kitti-3d-car.py

@@ -14,17 +14,17 @@ model = dict(
             ranges=[[0, -40.0, -1.78, 70.4, 40.0, -1.78]],
             sizes=[[1.6, 3.9, 1.56]],
             rotations=[0, 1.57],
-            reshape_out=True)))
-# model training and testing settings
-train_cfg = dict(
-    _delete_=True,
-    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)
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
+        _delete_=True,
+        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))

configs/ssn/hv_ssn_secfpn_sbn-all_2x16_2x_nus-3d.py

@@ -155,85 +155,84 @@ model = dict(
             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(
-    _delete_=True,
-    pts=dict(
-        assigner=[
-            dict(  # bicycle
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.35,
-                min_pos_iou=0.35,
-                ignore_iof_thr=-1),
-            dict(  # motorcycle
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.3,
-                min_pos_iou=0.3,
-                ignore_iof_thr=-1),
-            dict(  # pedestrian
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.6,
-                neg_iou_thr=0.4,
-                min_pos_iou=0.4,
-                ignore_iof_thr=-1),
-            dict(  # traffic cone
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.6,
-                neg_iou_thr=0.4,
-                min_pos_iou=0.4,
-                ignore_iof_thr=-1),
-            dict(  # barrier
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.55,
-                neg_iou_thr=0.4,
-                min_pos_iou=0.4,
-                ignore_iof_thr=-1),
-            dict(  # 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),
-            dict(  # truck
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.55,
-                neg_iou_thr=0.4,
-                min_pos_iou=0.4,
-                ignore_iof_thr=-1),
-            dict(  # trailer
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.35,
-                min_pos_iou=0.35,
-                ignore_iof_thr=-1),
-            dict(  # bus
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.55,
-                neg_iou_thr=0.4,
-                min_pos_iou=0.4,
-                ignore_iof_thr=-1),
-            dict(  # construction vehicle
-                type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlapsNearest3D'),
-                pos_iou_thr=0.5,
-                neg_iou_thr=0.35,
-                min_pos_iou=0.35,
-                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))
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
+    # model training and testing settings
+    train_cfg=dict(
+        _delete_=True,
+        pts=dict(
+            assigner=[
+                dict(  # bicycle
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.35,
+                    min_pos_iou=0.35,
+                    ignore_iof_thr=-1),
+                dict(  # motorcycle
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.3,
+                    min_pos_iou=0.3,
+                    ignore_iof_thr=-1),
+                dict(  # pedestrian
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.4,
+                    min_pos_iou=0.4,
+                    ignore_iof_thr=-1),
+                dict(  # traffic cone
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.4,
+                    min_pos_iou=0.4,
+                    ignore_iof_thr=-1),
+                dict(  # barrier
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.4,
+                    min_pos_iou=0.4,
+                    ignore_iof_thr=-1),
+                dict(  # 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),
+                dict(  # truck
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.4,
+                    min_pos_iou=0.4,
+                    ignore_iof_thr=-1),
+                dict(  # trailer
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.35,
+                    min_pos_iou=0.35,
+                    ignore_iof_thr=-1),
+                dict(  # bus
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.55,
+                    neg_iou_thr=0.4,
+                    min_pos_iou=0.4,
+                    ignore_iof_thr=-1),
+                dict(  # construction vehicle
+                    type='MaxIoUAssigner',
+                    iou_calculator=dict(type='BboxOverlapsNearest3D'),
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.35,
+                    min_pos_iou=0.35,
+                    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)))

docs/tutorials/config.md

@@ -44,6 +44,32 @@ For `1x`/`2x`, initial learning rate decays by a factor of 10 at the 8/16th and
 For `20e`, initial learning rate decays by a factor of 10 at the 16th and 19th epochs.
 - `{dataset}`: dataset like `nus-3d`, `kitti-3d`, `lyft-3d`, `scannet-3d`, `sunrgbd-3d`. We also indicate the number of classes we are using if there exist multiple settings, e.g., `kitti-3d-3class` and `kitti-3d-car` means training on KITTI dataset with 3 classes and single class, respectively.
 
+## Deprecated train_cfg/test_cfg
+
+Following MMDetection, the `train_cfg` and `test_cfg` are deprecated in config file, please specify them in the model config. The original config structure is as below.
+
+```python
+# deprecated
+model = dict(
+   type=...,
+   ...
+)
+train_cfg=dict(...)
+test_cfg=dict(...)
+```
+
+The migration example is as below.
+
+```python
+# recommended
+model = dict(
+   type=...,
+   ...
+   train_cfg=dict(...),
+   test_cfg=dict(...)
+)
+```
+
 ## An example of VoteNet
 
 ```python
@@ -144,16 +170,16 @@ model = dict(
         semantic_loss=dict(  # Config to semantic loss
             type='CrossEntropyLoss',  # Type of loss
             reduction='sum',  # Specifies the reduction to apply to the output
-            loss_weight=1.0)))  # Loss weight of the semantic loss
-train_cfg = dict(  # Config of training hyperparameters for votenet
-    pos_distance_thr=0.3,  # distance >= threshold 0.3 will be taken as positive samples
-    neg_distance_thr=0.6,  # distance < threshold 0.6 will be taken as positive samples
-    sample_mod='vote')  # Mode of the sampling method
-test_cfg = dict(  # Config of testing hyperparameters for votenet
-    sample_mod='seed',  # Mode of the sampling method
-    nms_thr=0.25,  # The threshold to be used during NMS
-    score_thr=0.8,  # Threshold to filter out boxes
-    per_class_proposal=False)  # Whether to use per_class_proposal
+            loss_weight=1.0)),  # Loss weight of the semantic loss
+    train_cfg = dict(  # Config of training hyperparameters for votenet
+        pos_distance_thr=0.3,  # distance >= threshold 0.3 will be taken as positive samples
+        neg_distance_thr=0.6,  # distance < threshold 0.6 will be taken as positive samples
+        sample_mod='vote'),  # Mode of the sampling method
+    test_cfg = dict(  # Config of testing hyperparameters for votenet
+        sample_mod='seed',  # Mode of the sampling method
+        nms_thr=0.25,  # The threshold to be used during NMS
+        score_thr=0.8,  # Threshold to filter out boxes
+        per_class_proposal=False))  # Whether to use per_class_proposal
 dataset_type = 'ScanNetDataset'  # Type of the dataset
 data_root = './data/scannet/'  # Root path of the data
 class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',