Bump version to v1.1.0rc2

Bump to v1.1.0rc2

configs/paconv/paconv_ssg-cuda_8xb8-cosine-200e_s3dis-seg.py

@@ -58,5 +58,4 @@ train_pipeline = [
 train_dataloader = dict(batch_size=8, dataset=dict(pipeline=train_pipeline))
 
 # runtime settings
-val_cfg = dict(interval=1)
-train_cfg = dict(by_epoch=True, max_epochs=200)
+train_cfg = dict(by_epoch=True, max_epochs=200, val_interval=1)

configs/paconv/paconv_ssg_8xb8-cosine-150e_s3dis-seg.py

@@ -56,4 +56,3 @@ train_pipeline = [
 ]
 
 train_dataloader = dict(batch_size=8, dataset=dict(pipeline=train_pipeline))
-val_cfg = dict(interval=1)

configs/parta2/README.md

@@ -22,8 +22,8 @@ We implement Part-A^2 and provide its results and checkpoints on KITTI dataset.
 
 |                            Backbone                             |  Class  |  Lr schd   | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                                                                   Download                                                                                                                                                                                                   |
 | :-------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [SECFPN](./PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   4.1    |                | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017.log.json) |
-|  [SECFPN](./PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   4.0    |                | 79.08 |       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017-cb7ff621.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017.log.json)       |
+| [SECFPN](./parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   4.1    |                | 68.33 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017.log.json) |
+|  [SECFPN](./parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   4.0    |                | 79.08 |       [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017-cb7ff621.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car_20210831_022017.log.json)       |
 
 ## Citation
 

configs/parta2/metafile.yml

@@ -16,9 +16,9 @@ Collections:
       Version: v0.5.0
 
 Models:
-  - Name: hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class
+  - Name: parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class
     In Collection: Part-A^2
-    Config: configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
+    Config: configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
     Metadata:
       Training Memory (GB): 4.1
     Results:
@@ -28,9 +28,9 @@ Models:
           mAP: 68.33
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class_20210831_022017-454a5344.pth
 
-  - Name: hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car
+  - Name: parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car
     In Collection: Part-A^2
-    Config: configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py
+    Config: configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py
     Metadata:
       Training Memory (GB): 4.0
     Results:

configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py → configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py

@@ -84,7 +84,7 @@ train_dataloader = dict(
             data_prefix=dict(pts='training/velodyne_reduced'),
             pipeline=train_pipeline,
             modality=input_modality,
-            metainfo=dict(CLASSES=class_names),
+            metainfo=dict(classes=class_names),
             box_type_3d='LiDAR',
             test_mode=False)))
 test_dataloader = dict(
@@ -100,7 +100,7 @@ test_dataloader = dict(
         data_prefix=dict(pts='training/velodyne_reduced'),
         pipeline=test_pipeline,
         modality=input_modality,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         box_type_3d='LiDAR',
         test_mode=True))
 val_dataloader = dict(
@@ -116,7 +116,7 @@ val_dataloader = dict(
         data_prefix=dict(pts='training/velodyne_reduced'),
         pipeline=eval_pipeline,
         modality=input_modality,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         box_type_3d='LiDAR',
         test_mode=True))
 val_evaluator = dict(

configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py → configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-car.py

-_base_ = './PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py'
+_base_ = './parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py'
 
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]  # velodyne coordinates, x, y, z
 
@@ -124,19 +124,15 @@ test_pipeline = [
             dict(type='RandomFlip3D'),
             dict(
                 type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 
 train_dataloader = dict(
     dataset=dict(
         dataset=dict(
-            pipeline=train_pipeline, metainfo=dict(CLASSES=class_names))))
+            pipeline=train_pipeline, metainfo=dict(classes=class_names))))
 test_dataloader = dict(
-    dataset=dict(pipeline=test_pipeline, metainfo=dict(CLASSES=class_names)))
-val_dataloader = dict(dataset=dict(metainfo=dict(CLASSES=class_names)))
+    dataset=dict(pipeline=test_pipeline, metainfo=dict(classes=class_names)))
+val_dataloader = dict(dataset=dict(metainfo=dict(classes=class_names)))
 find_unused_parameters = True

configs/pgd/metafile.yml

@@ -16,7 +16,7 @@ Collections:
       Version: v1.0.0
 
 Models:
-  - Name: pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d
+  - Name: pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d.py
     Metadata:
@@ -28,7 +28,7 @@ Models:
           mAP: 18.33
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_20211022_102608-8a97533b.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d.py
     Metadata:
@@ -41,7 +41,7 @@ Models:
           NDS: 39.3
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_20211116_195350-f4b5eec2.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d_finetune
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d_finetune.py
     Metadata:
@@ -54,7 +54,7 @@ Models:
           NDS: 41.1
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune/pgd_r101_caffe_fpn_gn-head_2x16_1x_nus-mono3d_finetune_20211118_093245-fd419681.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d.py
     Metadata:
@@ -67,7 +67,7 @@ Models:
           NDS: 40.9
     Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d/pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_20211112_125314-cb677266.pth
 
-  - Name: pgd_r101_caffe_fpn_gn-head_2x16_2x_nus-mono3d_finetune
+  - Name: pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d_finetune
     In Collection: PGD
     Config: configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-2x_nus-mono3d_finetune.py
     Metadata:

configs/pgd/pgd_r101-caffe_fpn_head-gn_16xb2-1x_nus-mono3d.py

@@ -19,7 +19,8 @@ model = dict(
             (),  # velo
             (256, )  # bbox2d
         ),
-        loss_depth=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
+        loss_depth=dict(
+            type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
         bbox_coder=dict(
             type='PGDBBoxCoder',
             base_depths=((31.99, 21.12), (37.15, 24.63), (39.69, 23.97),
@@ -43,8 +44,6 @@ class_names = [
     'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
     'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
 ]
-img_norm_cfg = dict(
-    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
 train_pipeline = [
     dict(type='LoadImageFromFileMono3D'),
     dict(
@@ -57,11 +56,8 @@ train_pipeline = [
         with_bbox_depth=True),
     dict(type='Resize', img_scale=(1600, 900), keep_ratio=True),
     dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
     dict(
-        type='Collect3D',
+        type='Pack3DDetInputs',
         keys=[
             'img', 'gt_bboxes', 'gt_bboxes_labels', 'attr_labels',
             'gt_bboxes_3d', 'gt_labels_3d', 'centers2d', 'depths'
@@ -75,14 +71,8 @@ test_pipeline = [
         flip=False,
         transforms=[
             dict(type='RandomFlip3D'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['img']),
-        ])
+        ]),
+    dict(type='Pack3DDetInputs', keys=['img']),
 ]
 data = dict(
     samples_per_gpu=2,

configs/pgd/pgd_r101_fpn-head_dcn_16xb3_waymoD5-fov-mono3d.py

+_base_ = [
+    '../_base_/datasets/waymoD5-fov-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)
+]
+total_epochs = 24
+runner = dict(max_epochs=total_epochs)
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=24)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')

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)
+]
+total_epochs = 24
+runner = dict(max_epochs=total_epochs)
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=24)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')

configs/pgd/pgd_r101_fpn-head_dcn_16xb3_waymoD5-mv-mono3d.py

+_base_ = [
+    '../_base_/datasets/waymoD5-mv-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)
+]
+total_epochs = 24
+runner = dict(max_epochs=total_epochs)
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=24)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')

configs/point_rcnn/metafile.yml

@@ -16,7 +16,7 @@ Collections:
       Version: v1.0.0
 
 Models:
-  - Name: point-rcnn_8xb2_kitti-3d-3class.py
+  - Name: point-rcnn_8xb2_kitti-3d-3class
     In Collection: PointRCNN
     Config: configs/point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py
     Metadata:

configs/point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py

@@ -7,7 +7,7 @@ _base_ = [
 dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 input_modality = dict(use_lidar=True, use_camera=False)
 

configs/pointnet2/metafile.yml

@@ -15,7 +15,7 @@ Collections:
       Version: v0.14.0
 
 Models:
-  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only.py
+  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only.py
     Metadata:
@@ -28,7 +28,7 @@ Models:
           mIoU: 53.91
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_xyz-only_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143628-4e341a48.pth
 
-  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py
+  - Name: pointnet2_ssg_2xb16-cosine-200e_scannet-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py
     Metadata:
@@ -41,7 +41,7 @@ Models:
           mIoU: 54.44
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644-ee73704a.pth
 
-  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only.py
+  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only.py
     Metadata:
@@ -54,7 +54,7 @@ Models:
           mIoU: 54.26
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_xyz-only_16x2_cosine_250e_scannet_seg-3d-20class_20210514_143838-b4a3cf89.pth
 
-  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
+  - Name: pointnet2_msg_2xb16-cosine-250e_scannet-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
     Metadata:
@@ -67,7 +67,7 @@ Models:
           mIoU: 55.05
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class/pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class_20210514_144009-24477ab1.pth
 
-  - Name: pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py
+  - Name: pointnet2_ssg_2xb16-cosine-50e_s3dis-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py
     Metadata:
@@ -80,7 +80,7 @@ Models:
           mIoU: 56.93
     Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class/pointnet2_ssg_16x2_cosine_50e_s3dis_seg-3d-13class_20210514_144205-995d0119.pth
 
-  - Name: pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
+  - Name: pointnet2_msg_2xb16-cosine-80e_s3dis-seg
     In Collection: PointNet++
     Config: configs/pointnet/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
     Metadata:

configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only.py

@@ -101,7 +101,6 @@ val_dataloader = test_dataloader
 
 # runtime settings
 default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=5))
-val_cfg = dict(interval=5)
 
 # PointNet2-MSG needs longer training time than PointNet2-SSG
-train_cfg = dict(by_epoch=True, max_epochs=250)
+train_cfg = dict(by_epoch=True, max_epochs=250, val_interval=5)

configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py

@@ -30,7 +30,6 @@ train_dataloader = dict(batch_size=16)
 
 # runtime settings
 default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=5))
-val_cfg = dict(interval=5)
 
 # PointNet2-MSG needs longer training time than PointNet2-SSG
-train_cfg = dict(by_epoch=True, max_epochs=250)
+train_cfg = dict(by_epoch=True, max_epochs=250, val_interval=5)

configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

@@ -21,7 +21,6 @@ train_dataloader = dict(batch_size=16)
 
 # runtime settings
 default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=2))
-val_cfg = dict(interval=2)
 
 # PointNet2-MSG needs longer training time than PointNet2-SSG
-train_cfg = dict(by_epoch=True, max_epochs=80)
+train_cfg = dict(by_epoch=True, max_epochs=80, val_interval=2)

configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only.py

@@ -101,4 +101,4 @@ val_dataloader = test_dataloader
 
 # runtime settings
 default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=5))
-val_cfg = dict(interval=5)
+train_cfg = dict(val_interval=5)

configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py

@@ -30,4 +30,4 @@ train_dataloader = dict(batch_size=16)
 
 # runtime settings
 default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=5))
-val_cfg = dict(interval=5)
+train_cfg = dict(val_interval=5)

configs/pointnet2/pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py

@@ -17,8 +17,8 @@ model = dict(
         batch_size=24))
 
 # data settings
-train_dataloader = dict(batch_size=6)
+train_dataloader = dict(batch_size=16)
 
 # runtime settings
-default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=2), )
-val_cfg = dict(interval=2)
+default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=2))
+train_cfg = dict(val_interval=2)