[Fix] Fix invalid configs (#2477)

* fix invalid configs

* fix metafile in nuimages

* remove breakpoint

* minor fix

configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-flip-tta-cyclic-20e_nus-3d.py

-_base_ = './centerpoint_voxel0075_second_secfpn_' \
-         'head-dcn-circlenms_8xb4_cyclic-20e_nus-3d.py'
+_base_ = './centerpoint_voxel0075_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py'  # noqa: E501
 
 point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
 # Using calibration info convert the Lidar-coordinate point cloud range to the

configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-flip-tta-cyclic-20e_nus-3d.py

-_base_ = './centerpoint_voxel0075_second_secfpn' \
-         '_head-dcn_8xb4-cyclic-20e_nus-3d.py'
+_base_ = \
+    './centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py'
 
 point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
 # Using calibration info convert the Lidar-coordinate point cloud range to the

configs/centerpoint/centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-tta-cyclic-20e_nus-3d.py

-_base_ = './centerpoint_voxel0075_second_secfpn' \
-         '_head-dcn_8xb4-cyclic-20e_nus-3d.py'
+_base_ = \
+    './centerpoint_voxel0075_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py'
 
 model = dict(test_cfg=dict(pts=dict(use_rotate_nms=True, max_num=500)))
 

configs/dfm/multiview-dfm_r101-dcn_centerhead_16xb2_waymoD5-3d-3class.py

-_base_ = ['./multiview-dfm_r101_dcn_2x16_waymoD5-3d-3class.py']
+_base_ = ['./multiview-dfm_r101-dcn_16xb2_waymoD5-3d-3class.py']
 
 model = dict(
     bbox_head=dict(

configs/dynamic_voxelization/second_dv_secfpn_8xb6-80e_kitti-3d-car.py

-_base_ = '../second/hv_second_secfpn_6x8_80e_kitti-3d-car.py'
+_base_ = '../second/second_hv_secfpn_8xb6-80e_kitti-3d-car.py'
 
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 voxel_size = [0.05, 0.05, 0.1]

configs/nuimages/cascade-mask-rcnn-r50-fpn_coco-20e_nuim.py

-_base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
+_base_ = './cascade-mask-rcnn_r50_fpn_1x_nuim.py'
 
 # learning policy
 lr_config = dict(step=[16, 19])

configs/nuimages/cascade-mask-rcnn_r101_fpn_1x_nuim.py

-_base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
+_base_ = './cascade-mask-rcnn_r50_fpn_1x_nuim.py'
 model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

configs/nuimages/cascade-mask-rcnn_r50_fpn_1x_nuim.py

 _base_ = [
-    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
-    '../_base_/datasets/nuim_instance.py',
-    '../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
+    '../_base_/models/cascade-mask-rcnn_r50_fpn.py',
+    '../_base_/datasets/nuim-instance.py',
+    '../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py'
 ]
 model = dict(
     roi_head=dict(

configs/nuimages/cascade-mask-rcnn_r50_fpn_coco-20e-1x_nuim.py

-_base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
+_base_ = './cascade-mask-rcnn_r50_fpn_1x_nuim.py'
 
 load_from = 'http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth'  # noqa

configs/nuimages/cascade-mask-rcnn_x101_32x4d_fpn_1x_nuim.py

-_base_ = './cascade_mask_rcnn_r50_fpn_1x_nuim.py'
+_base_ = './cascade-mask-rcnn_r50_fpn_1x_nuim.py'
 model = dict(
     pretrained='open-mmlab://resnext101_32x4d',
     backbone=dict(

configs/nuimages/htc_r50_fpn_1x_nuim.py

-_base_ = './htc_without_semantic_r50_fpn_1x_nuim.py'
+_base_ = './htc_r50_fpn_head-without-semantic_1x_nuim.py'
 model = dict(
     roi_head=dict(
         semantic_roi_extractor=dict(

configs/nuimages/mask-rcnn_r101_fpn_1x_nuim.py

-_base_ = './mask_rcnn_r50_fpn_1x_nuim.py'
+_base_ = './mask-rcnn_r50_fpn_1x_nuim.py'
 model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

configs/nuimages/mask-rcnn_r50_fpn_coco-2x_1x_nus-2d.py

 _base_ = [
-    '../_base_/models/mask_rcnn_r50_fpn.py',
-    '../_base_/datasets/nuim_instance.py',
-    '../_base_/schedules/mmdet_schedule_1x.py', '../_base_/default_runtime.py'
+    '../_base_/models/mask-rcnn_r50_fpn.py',
+    '../_base_/datasets/nuim-instance.py',
+    '../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py'
 ]
 model = dict(
     roi_head=dict(

configs/nuimages/mask-rcnn_x101_32x4d_fpn_1x_nuim.py

-_base_ = './mask_rcnn_r50_fpn_1x_nuim.py'
+_base_ = './mask-rcnn_r50_fpn_1x_nuim.py'
 model = dict(
     pretrained='open-mmlab://resnext101_32x4d',
     backbone=dict(

configs/nuimages/metafile.yml

@@ -23,9 +23,9 @@ Collections:
       Version: v2.0.0
 
 Models:
-  - Name: mask_rcnn_r50_fpn_1x_nuim
+  - Name: mask-rcnn_r50_fpn_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/mask_rcnn_r50_fpn_1x_nuim.py
+    Config: configs/nuimages/mask-rcnn_r50_fpn_1x_nuim.py
     Metadata:
       Training Memory (GB): 7.4
       Training Resources: 8x TITAN Xp
@@ -40,9 +40,9 @@ Models:
           Mask AP: 38.4
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_fpn_1x_nuim/mask_rcnn_r50_fpn_1x_nuim_20201008_195238-e99f5182.pth
 
-  - Name: mask_rcnn_r50_fpn_coco-2x_1x_nuim
+  - Name: mask-rcnn_r50_fpn_coco-2x_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/mask_rcnn_r50_fpn_coco-2x_1x_nuim.py
+    Config: configs/nuimages/mask-rcnn_r50_fpn_coco-2x_1x_nuim.py
     Metadata:
       Training Memory (GB): 7.4
       Training Resources: 8x TITAN Xp
@@ -57,9 +57,9 @@ Models:
           Mask AP: 40.5
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_fpn_coco-2x_1x_nuim/mask_rcnn_r50_fpn_coco-2x_1x_nuim_20201008_195238-b1742a60.pth
 
-  - Name: mask_rcnn_r50_caffe_fpn_1x_nuim
+  - Name: mask-rcnn_r50_caffe_fpn_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/mask_rcnn_r50_caffe_fpn_1x_nuim.py
+    Config: configs/nuimages/mask-rcnn_r50_caffe_fpn_1x_nuim.py
     Metadata:
       Training Memory (GB): 7.0
       Training Resources: 8x TITAN Xp
@@ -74,9 +74,9 @@ Models:
           Mask AP: 38.2
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_1x_nuim/
 
-  - Name: mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim
+  - Name: mask-rcnn_r50_caffe_fpn_coco-3x_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim.py
+    Config: configs/nuimages/mask-rcnn_r50_caffe_fpn_coco-3x_1x_nuim.py
     Metadata:
       Training Memory (GB): 7.0
       Training Resources: 8x TITAN Xp
@@ -91,9 +91,9 @@ Models:
           Mask AP: 40.8
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_1x_nuim_20201008_195305-661a992e.pth
 
-  - Name: mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim
+  - Name: mask-rcnn_r50_caffe_fpn_coco-3x_20e_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim.py
+    Config: configs/nuimages/mask-rcnn_r50_caffe_fpn_coco-3x_20e_nuim.py
     Metadata:
       Training Memory (GB): 7.0
       Training Resources: 8x TITAN Xp
@@ -108,7 +108,7 @@ Models:
           Mask AP: 41.3
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim/mask_rcnn_r50_caffe_fpn_coco-3x_20e_nuim_20201009_125002-5529442c.pth
 
-  - Name: mask_rcnn_r101_fpn_1x_nuim
+  - Name: mask-rcnn_r101_fpn_1x_nuim
     In Collection: Mask R-CNN
     Config: configs/nuimages/mask-rcnn_r101_fpn_1x_nuim.py
     Metadata:
@@ -125,7 +125,7 @@ Models:
           Mask AP: 39.1
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_r101_fpn_1x_nuim/mask_rcnn_r101_fpn_1x_nuim_20201024_134803-65c7623a.pth
 
-  - Name: mask_rcnn_x101_32x4d_fpn_1x_nuim
+  - Name: mask-rcnn_x101_32x4d_fpn_1x_nuim
     In Collection: Mask R-CNN
     Config: configs/nuimages/mask-rcnn_x101_32x4d_fpn_1x_nuim.py
     Metadata:
@@ -142,9 +142,9 @@ Models:
           Mask AP: 40.5
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/mask_rcnn_x101_32x4d_fpn_1x_nuim/mask_rcnn_x101_32x4d_fpn_1x_nuim_20201024_135741-b699ab37.pth
 
-  - Name: cascade_mask_rcnn_r50_fpn_1x_nuim
+  - Name: cascade-mask-rcnn_r50_fpn_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/cascade_mask_rcnn_r50_fpn_1x_nuim.py
+    Config: configs/nuimages/cascade-mask-rcnn_r50_fpn_1x_nuim.py
     Metadata:
       Training Memory (GB): 8.9
       Training Resources: 8x TITAN Xp
@@ -159,9 +159,9 @@ Models:
           Mask AP: 40.4
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_1x_nuim/cascade_mask_rcnn_r50_fpn_1x_nuim_20201008_195342-1147c036.pth
 
-  - Name: cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim
+  - Name: cascade-mask-rcnn_r50_fpn_coco-20e_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim.py
+    Config: configs/nuimages/cascade-mask-rcnn_r50_fpn_coco-20e_1x_nuim.py
     Metadata:
       Training Memory (GB): 8.9
       Training Resources: 8x TITAN Xp
@@ -176,9 +176,9 @@ Models:
           Mask AP: 42.2
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim/cascade_mask_rcnn_r50_fpn_coco-20e_1x_nuim_20201009_124158-ad0540e3.pth
 
-  - Name: cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim
+  - Name: cascade-mask-rcnn_r50_fpn_coco-20e_20e_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim.py
+    Config: configs/nuimages/cascade-mask-rcnn_r50_fpn_coco-20e_20e_nuim.py
     Metadata:
       Training Memory (GB): 8.9
       Training Resources: 8x TITAN Xp
@@ -193,9 +193,9 @@ Models:
           Mask AP: 42.2
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim_20201009_124951-40963960.pth
 
-  - Name: cascade_mask_rcnn_r101_fpn_1x_nuim
+  - Name: cascade-mask-rcnn_r101_fpn_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/cascade_mask_rcnn_r101_fpn_1x_nuim.py
+    Config: configs/nuimages/cascade-mask-rcnn_r101_fpn_1x_nuim.py
     Metadata:
       Training Memory (GB): 12.5
       Training Resources: 8x TITAN Xp
@@ -210,9 +210,9 @@ Models:
           Mask AP: 40.7
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/nuimages_semseg/cascade_mask_rcnn_r101_fpn_1x_nuim/cascade_mask_rcnn_r101_fpn_1x_nuim_20201024_134804-45215b1e.pth
 
-  - Name: cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim
+  - Name: cascade-mask-rcnn_x101_32x4d_fpn_1x_nuim
     In Collection: Mask R-CNN
-    Config: configs/nuimages/cascade_mask_rcnn_x101_32x4d_fpn_1x_nuim.py
+    Config: configs/nuimages/cascade-mask-rcnn_x101_32x4d_fpn_1x_nuim.py
     Metadata:
       Training Memory (GB): 14.9
       Training Resources: 8x TITAN Xp

configs/pgd/pgd_r101_fpn-head_dcn_16xb3_waymoD5-mono3d.py

-_base_ = [
-    '../_base_/datasets/waymoD5-mono3d-3class.py', '../_base_/models/pgd.py',
-    '../_base_/schedules/mmdet-schedule-1x.py', '../_base_/default_runtime.py'
-]
-# model settings
-model = dict(
-    backbone=dict(
-        type='mmdet.ResNet',
-        depth=101,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch',
-        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'),
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, False, True, True)),
-    neck=dict(num_outs=3),
-    bbox_head=dict(
-        num_classes=3,
-        bbox_code_size=7,
-        pred_attrs=False,
-        pred_velo=False,
-        pred_bbox2d=True,
-        use_onlyreg_proj=True,
-        strides=(8, 16, 32),
-        regress_ranges=((-1, 128), (128, 256), (256, 1e8)),
-        group_reg_dims=(2, 1, 3, 1, 16,
-                        4),  # offset, depth, size, rot, kpts, bbox2d
-        reg_branch=(
-            (256, ),  # offset
-            (256, ),  # depth
-            (256, ),  # size
-            (256, ),  # rot
-            (256, ),  # kpts
-            (256, )  # bbox2d
-        ),
-        centerness_branch=(256, ),
-        loss_cls=dict(
-            type='mmdet.FocalLoss',
-            use_sigmoid=True,
-            gamma=2.0,
-            alpha=0.25,
-            loss_weight=1.0),
-        loss_bbox=dict(
-            type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
-        loss_dir=dict(
-            type='mmdet.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
-        loss_centerness=dict(
-            type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
-        use_depth_classifier=True,
-        depth_branch=(256, ),
-        depth_range=(0, 50),
-        depth_unit=10,
-        division='uniform',
-        depth_bins=6,
-        pred_keypoints=True,
-        weight_dim=1,
-        loss_depth=dict(
-            type='UncertainSmoothL1Loss', alpha=1.0, beta=3.0,
-            loss_weight=1.0),
-        loss_bbox2d=dict(
-            type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.0),
-        loss_consistency=dict(type='mmdet.GIoULoss', loss_weight=0.0),
-        bbox_coder=dict(
-            type='PGDBBoxCoder',
-            base_depths=((41.01, 18.44), ),
-            base_dims=(
-                (4.73, 1.77, 2.08),
-                (0.91, 1.74, 0.84),
-                (1.81, 1.77, 0.84),
-            ),
-            code_size=7)),
-    # set weight 1.0 for base 7 dims (offset, depth, size, rot)
-    # 0.2 for 16-dim keypoint offsets and 1.0 for 4-dim 2D distance targets
-    train_cfg=dict(code_weight=[
-        1.0, 1.0, 0.2, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
-        0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0
-    ]),
-    test_cfg=dict(nms_pre=100, nms_thr=0.05, score_thr=0.001, max_per_img=20))
-
-# optimizer
-optim_wrapper = dict(
-    optimizer=dict(
-        type='SGD',
-        lr=0.008,
-    ),
-    paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.),
-    clip_grad=dict(max_norm=35, norm_type=2))
-
-param_scheduler = [
-    dict(
-        type='LinearLR',
-        start_factor=1.0 / 3,
-        by_epoch=False,
-        begin=0,
-        end=500),
-    dict(
-        type='MultiStepLR',
-        begin=0,
-        end=24,
-        by_epoch=True,
-        milestones=[16, 22],
-        gamma=0.1)
-]
-
-train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=24)
-val_cfg = dict(type='ValLoop')
-test_cfg = dict(type='TestLoop')
-auto_scale_lr = dict(base_batch_size=48)

configs/regnet/pointpillars_hv_regnet-400mf_secfpn_sbn-all_8xb2-2x_lyft-3d.py

-_base_ = './hv_pointpillars_regnet-400mf_fpn_sbn-all_2x8_2x_lyft-3d.py'
+_base_ = './pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb2-2x_lyft-3d.py'
 # model settings
 model = dict(
     pts_neck=dict(

configs/regnet/pointpillars_hv_regnet-400mf_secfpn_sbn-all_8xb4-2x_nus-3d.py

-_base_ = './hv_pointpillars_regnet-400mf_fpn_sbn-all_4x8_2x_nus-3d.py'
+_base_ = './pointpillars_hv_regnet-400mf_fpn_sbn-all_8xb4-2x_nus-3d.py'
 # model settings
 model = dict(
     pts_neck=dict(

configs/regnet/pointpillars_hv_regnet-400mf_secfpn_sbn-all_range100_8xb2-2x_lyft-3d.py

 _base_ = \
-    './hv_pointpillars_regnet-400mf_fpn_sbn-all_range100_2x8_2x_lyft-3d.py'
+    './pointpillars_hv_regnet-400mf_fpn_sbn-all_range100_8xb2-2x_lyft-3d.py'
 # model settings
 model = dict(
     pts_neck=dict(