[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

@@ -87,9 +87,9 @@ 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(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
+    # model training and testing settings
+    train_cfg=dict(
         pts=dict(
             assigner=[
                 dict(  # for Pedestrian
@@ -116,8 +116,8 @@ train_cfg = dict(
             ],
             allowed_border=0,
             pos_weight=-1,
-        debug=False))
-test_cfg = dict(
+            debug=False)),
+    test_cfg=dict(
         pts=dict(
             use_rotate_nms=True,
             nms_across_levels=False,
@@ -125,7 +125,7 @@ test_cfg = dict(
             score_thr=0.1,
             min_bbox_size=0,
             nms_pre=100,
-        max_num=50))
+            max_num=50)))
 
 # dataset settings
 dataset_type = 'KittiDataset'

configs/nuimages/htc_without_semantic_r50_fpn_1x_nuim.py

@@ -130,9 +130,9 @@ 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(
+        ]),
+    # model training and testing settings
+    train_cfg=dict(
         rpn=dict(
             assigner=dict(
                 type='MaxIoUAssigner',
@@ -205,8 +205,8 @@ train_cfg = dict(
                 mask_size=28,
                 pos_weight=-1,
                 debug=False)
-    ])
-test_cfg = dict(
+        ]),
+    test_cfg=dict(
         rpn=dict(
             nms_across_levels=False,
             nms_pre=1000,
@@ -218,4 +218,4 @@ test_cfg = dict(
             score_thr=0.001,
             nms=dict(type='nms', iou_threshold=0.5),
             max_per_img=100,
-        mask_thr_binary=0.5))
+            mask_thr_binary=0.5)))

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

@@ -112,9 +112,9 @@ model = dict(
                 type='CrossEntropyLoss',
                 use_sigmoid=True,
                 reduction='sum',
-                loss_weight=1.0))))
-# model training and testing settings
-train_cfg = dict(
+                loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
         rpn=dict(
             assigner=[
                 dict(  # for Pedestrian
@@ -153,21 +153,24 @@ train_cfg = dict(
             assigner=[
                 dict(  # for Pedestrian
                     type='MaxIoUAssigner',
-                iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
+                    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'),
+                    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'),
+                    iou_calculator=dict(
+                        type='BboxOverlaps3D', coordinate='lidar'),
                     pos_iou_thr=0.55,
                     neg_iou_thr=0.55,
                     min_pos_iou=0.55,
@@ -183,8 +186,8 @@ train_cfg = dict(
                 add_gt_as_proposals=False,
                 return_iou=True),
             cls_pos_thr=0.75,
-        cls_neg_thr=0.25))
-test_cfg = dict(
+            cls_neg_thr=0.25)),
+    test_cfg=dict(
         rpn=dict(
             nms_pre=1024,
             nms_post=100,
@@ -193,7 +196,10 @@ test_cfg = dict(
             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))
+            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

@@ -17,9 +17,9 @@ 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(
+        bbox_head=dict(num_classes=1)),
+    # model training and testing settings
+    train_cfg=dict(
         _delete_=True,
         rpn=dict(
             assigner=dict(
@@ -57,8 +57,8 @@ train_cfg = dict(
                 add_gt_as_proposals=False,
                 return_iou=True),
             cls_pos_thr=0.75,
-        cls_neg_thr=0.25))
-test_cfg = dict(
+            cls_neg_thr=0.25)),
+    test_cfg=dict(
         rpn=dict(
             nms_pre=1024,
             nms_post=100,
@@ -67,7 +67,10 @@ test_cfg = dict(
             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))
+            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

@@ -12,9 +12,9 @@ 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(
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
         _delete_=True,
         assigner=dict(
             type='MaxIoUAssigner',
@@ -25,7 +25,7 @@ train_cfg = dict(
             ignore_iof_thr=-1),
         allowed_border=0,
         pos_weight=-1,
-    debug=False)
+        debug=False))
 
 # dataset settings
 dataset_type = 'KittiDataset'

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

@@ -19,10 +19,9 @@ 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(
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
         _delete_=True,
         pts=dict(
             assigner=dict(
@@ -35,4 +34,4 @@ train_cfg = dict(
             allowed_border=0,
             code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
             pos_weight=-1,
-        debug=False))
+            debug=False)))

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

@@ -16,10 +16,9 @@ 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(
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
         _delete_=True,
         pts=dict(
             assigner=dict(
@@ -32,4 +31,4 @@ train_cfg = dict(
             allowed_border=0,
             code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
             pos_weight=-1,
-        debug=False))
+            debug=False)))

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

@@ -14,9 +14,9 @@ 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(
+            reshape_out=True)),
+    # model training and testing settings
+    train_cfg=dict(
         _delete_=True,
         assigner=dict(
             type='MaxIoUAssigner',
@@ -27,4 +27,4 @@ train_cfg = dict(
             ignore_iof_thr=-1),
         allowed_border=0,
         pos_weight=-1,
-    debug=False)
+        debug=False))

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

@@ -155,10 +155,9 @@ 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(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
+    # model training and testing settings
+    train_cfg=dict(
         _delete_=True,
         pts=dict(
             assigner=[
@@ -236,4 +235,4 @@ train_cfg = dict(
             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))
+            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
+            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='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
+        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',