[Enhance] Move train_cfg test_cfg to model

configs/_base_/models/3dssd.py

@@ -65,17 +65,16 @@
  type='SmoothL1Loss', reduction='sum', loss_weight=1.0),
  corner_loss=dict(
  type='SmoothL1Loss', reduction='sum', loss_weight=1.0),
- vote_loss=dict(type='SmoothL1Loss', reduction='sum', loss_weight=1.0)))
-
-# model training and testing settings
-train_cfg = dict(
- sample_mod='spec', pos_distance_thr=10.0, expand_dims_length=0.05)
-test_cfg = dict(
- nms_cfg=dict(type='nms', iou_thr=0.1),
- sample_mod='spec',
- score_thr=0.0,
- per_class_proposal=True,
- max_output_num=100)
+ vote_loss=dict(type='SmoothL1Loss', reduction='sum', loss_weight=1.0)),
+ # model training and testing settings
+ train_cfg=dict(
+ sample_mod='spec', pos_distance_thr=10.0, expand_dims_length=0.05),
+ test_cfg=dict(
+ nms_cfg=dict(type='nms', iou_thr=0.1),
+ sample_mod='spec',
+ score_thr=0.0,
+ per_class_proposal=True,
+ max_output_num=100))
 
 # optimizer
 # This schedule is mainly used by models on indoor dataset,

configs/_base_/models/cascade_mask_rcnn_r50_fpn.py

@@ -105,96 +105,96 @@
  conv_out_channels=256,
  num_classes=80,
  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,
- match_low_quality=True,
- 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,
- match_low_quality=False,
- 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,
- match_low_quality=False,
- 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(
+ 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=0.7,
- min_pos_iou=0.7,
- match_low_quality=False,
+ neg_iou_thr=0.3,
+ min_pos_iou=0.3,
+ match_low_quality=True,
  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=0.05,
- 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,
+ match_low_quality=False,
+ 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,
+ match_low_quality=False,
+ 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,
+ match_low_quality=False,
+ 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=0.05,
+ nms=dict(type='nms', iou_threshold=0.5),
+ max_per_img=100,
+ mask_thr_binary=0.5)))

configs/_base_/models/centerpoint_01voxel_second_secfpn_nus.py

@@ -56,28 +56,28 @@
  type='SeparateHead', init_bias=-2.19, final_kernel=3),
  loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
  loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
- norm_bbox=True))
-# model training and testing settings
-train_cfg = dict(
- pts=dict(
- grid_size=[1024, 1024, 40],
- voxel_size=voxel_size,
- out_size_factor=8,
- dense_reg=1,
- gaussian_overlap=0.1,
- max_objs=500,
- min_radius=2,
- code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]))
-test_cfg = dict(
- pts=dict(
- post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
- max_per_img=500,
- max_pool_nms=False,
- min_radius=[4, 12, 10, 1, 0.85, 0.175],
- score_threshold=0.1,
- out_size_factor=8,
- voxel_size=voxel_size[:2],
- nms_type='rotate',
- pre_max_size=1000,
- post_max_size=83,
- nms_thr=0.2))
+ norm_bbox=True),
+ # model training and testing settings
+ train_cfg=dict(
+  pts=dict(
+  grid_size=[1024, 1024, 40],
+  voxel_size=voxel_size,
+  out_size_factor=8,
+  dense_reg=1,
+  gaussian_overlap=0.1,
+  max_objs=500,
+  min_radius=2,
+  code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2])),
+ test_cfg=dict(
+  pts=dict(
+  post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
+  max_per_img=500,
+  max_pool_nms=False,
+  min_radius=[4, 12, 10, 1, 0.85, 0.175],
+  score_threshold=0.1,
+  out_size_factor=8,
+  voxel_size=voxel_size[:2],
+  nms_type='rotate',
+  pre_max_size=1000,
+  post_max_size=83,
+  nms_thr=0.2)))

configs/_base_/models/centerpoint_02pillar_second_secfpn_nus.py

@@ -55,29 +55,29 @@
  type='SeparateHead', init_bias=-2.19, final_kernel=3),
  loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
  loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
- norm_bbox=True))
-# model training and testing settings
-train_cfg = dict(
- pts=dict(
- grid_size=[512, 512, 1],
- voxel_size=voxel_size,
- out_size_factor=4,
- dense_reg=1,
- gaussian_overlap=0.1,
- max_objs=500,
- min_radius=2,
- code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]))
-test_cfg = dict(
- pts=dict(
- post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
- max_per_img=500,
- max_pool_nms=False,
- min_radius=[4, 12, 10, 1, 0.85, 0.175],
- score_threshold=0.1,
- pc_range=[-51.2, -51.2],
- out_size_factor=4,
- voxel_size=voxel_size[:2],
- nms_type='rotate',
- pre_max_size=1000,
- post_max_size=83,
- nms_thr=0.2))
+ norm_bbox=True),
+ # model training and testing settings
+ train_cfg=dict(
+  pts=dict(
+  grid_size=[512, 512, 1],
+  voxel_size=voxel_size,
+  out_size_factor=4,
+  dense_reg=1,
+  gaussian_overlap=0.1,
+  max_objs=500,
+  min_radius=2,
+  code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2])),
+ test_cfg=dict(
+  pts=dict(
+  post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
+  max_per_img=500,
+  max_pool_nms=False,
+  min_radius=[4, 12, 10, 1, 0.85, 0.175],
+  score_threshold=0.1,
+  pc_range=[-51.2, -51.2],
+  out_size_factor=4,
+  voxel_size=voxel_size[:2],
+  nms_type='rotate',
+  pre_max_size=1000,
+  post_max_size=83,
+  nms_thr=0.2)))

configs/_base_/models/h3dnet.py

@@ -311,32 +311,31 @@
  reduction='none',
  loss_weight=5.0),
  primitive_center_loss=dict(
- type='MSELoss', reduction='none', loss_weight=1.0))))
-
-# model training and testing settings
-train_cfg = dict(
- rpn=dict(pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mod='vote'),
- rpn_proposal=dict(use_nms=False),
- rcnn=dict(
- pos_distance_thr=0.3,
- neg_distance_thr=0.6,
- sample_mod='vote',
- far_threshold=0.6,
- near_threshold=0.3,
- mask_surface_threshold=0.3,
- label_surface_threshold=0.3,
- mask_line_threshold=0.3,
- label_line_threshold=0.3))
-
-test_cfg = dict(
- rpn=dict(
- sample_mod='seed',
- nms_thr=0.25,
- score_thr=0.05,
- per_class_proposal=True,
- use_nms=False),
- rcnn=dict(
- sample_mod='seed',
- nms_thr=0.25,
- score_thr=0.05,
- per_class_proposal=True))
+ type='MSELoss', reduction='none', loss_weight=1.0))),
+ # model training and testing settings
+ train_cfg=dict(
+ rpn=dict(
+ pos_distance_thr=0.3, neg_distance_thr=0.6, sample_mod='vote'),
+ rpn_proposal=dict(use_nms=False),
+ rcnn=dict(
+ pos_distance_thr=0.3,
+ neg_distance_thr=0.6,
+ sample_mod='vote',
+ far_threshold=0.6,
+ near_threshold=0.3,
+ mask_surface_threshold=0.3,
+ label_surface_threshold=0.3,
+ mask_line_threshold=0.3,
+ label_line_threshold=0.3)),
+ test_cfg=dict(
+ rpn=dict(
+ sample_mod='seed',
+ nms_thr=0.25,
+ score_thr=0.05,
+ per_class_proposal=True,
+ use_nms=False),
+ rcnn=dict(
+ sample_mod='seed',
+ nms_thr=0.25,
+ score_thr=0.05,
+ per_class_proposal=True)))

configs/_base_/models/hv_pointpillars_fpn_lyft.py

@@ -17,6 +17,6 @@
  num_classes=9,
  anchor_generator=dict(
  ranges=[[-80, -80, -1.8, 80, 80, -1.8]], custom_values=[]),
- bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7)))
-# model training settings (based on nuScenes model settings)
-train_cfg = dict(pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]))
+ bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder', code_size=7)),
+ # model training settings (based on nuScenes model settings)
+ train_cfg=dict(pts=dict(code_weight=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])))