[Fix] Fix demo and visualization

README.md

@@ -82,7 +82,7 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 - **Support indoor/outdoor 3D detection out of box**
 
   It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI.
-  For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/latest/configs/nuimages).
+  For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/nuimages).
 
 - **Natural integration with 2D detection**
 

README_zh-CN.md

@@ -78,7 +78,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 - **支持户内/户外的数据集**
 
   支持室内/室外的 3D 检测数据集，包括 ScanNet，SUNRGB-D，Waymo，nuScenes，Lyft，KITTI。
-  对于 nuScenes 数据集，我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/latest/configs/nuimages)。
+  对于 nuScenes 数据集，我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/nuimages)。
 
 - **与 2D 检测器的自然整合**
 

configs/pointnet2/README.md

@@ -35,7 +35,7 @@ We implement PointNet++ and provide the result and checkpoints on ScanNet and S3
 
 - Since ScanNet dataset doesn't provide ground-truth labels for the test set, users can only evaluate test set performance by submitting to its online benchmark [website](http://kaldir.vc.in.tum.de/scannet_benchmark/). However, users are only allowed to submit once every two weeks. Therefore, we currently report val set mIoU. Test set performance may be added in the future.
 
-- To generate submission file for ScanNet online benchmark, you need to modify the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126). Change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'`, and then simply run:
+- To generate submission file for ScanNet online benchmark, you need to modify the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/_base_/datasets/scannet-seg.py#L126). Change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'`, and then simply run:
 
   ```shell
   python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --format-only --options 'txt_prefix=exps/pointnet2_scannet_results'

configs/second/README.md

@@ -22,10 +22,10 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset.
 
 |                              Backbone                               |  Class  |  Lr schd   | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                                                             Download                                                                                                                                                                                             |
 | :-----------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-|        [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-car.py)        |   Car   | cyclic 80e |   5.4    |                | 79.07 |               [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238.log.json)               |
-|  [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   2.9    |                | 78.72 |       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)\| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)        |
-|      [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-3class.py)       | 3 Class | cyclic 80e |   5.4    |                | 65.74 |         [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017-ae782e87.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017log.json)          |
-| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   2.9    |                | 67.4  | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json) |
+|        [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-car.py)        |   Car   | cyclic 80e |   5.4    |                | 78.2  |                       [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-car/second_hv_secfpn_8xb6-80e_kitti-3d-car-75d9305e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-car/second_hv_secfpn_8xb6-80e_kitti-3d-car-20230420_191750.log)                        |
+|  [SECFPN (FP16)](./second_hv_secfpn_8xb6-amp-80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   2.9    |                | 78.72 |       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)\| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)        |
+|      [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-3class.py)       | 3 Class | cyclic 80e |   5.4    |                | 65.3  |                 [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class/second_hv_secfpn_8xb6-80e_kitti-3d-3class-b086d0a3.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class/second_hv_secfpn_8xb6-80e_kitti-3d-3class-20230420_221130.log)                  |
+| [SECFPN (FP16)](./second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   2.9    |                | 67.4  | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json) |
 
 ### Waymo
 
@@ -38,7 +38,7 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset.
 
 Note:
 
-- See more details about metrics and data split on Waymo [HERE](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/pointpillars). For implementation details, we basically follow the original settings. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.
+- See more details about metrics and data split on Waymo [HERE](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/pointpillars). For implementation details, we basically follow the original settings. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.
 - `FP16` means Mixed Precision (FP16) is adopted in training.
 
 ## Citation

configs/second/metafile.yml

@@ -25,8 +25,8 @@ Models:
       - Task: 3D Object Detection
         Dataset: KITTI
         Metrics:
-          mAP: 79.07
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth
+          mAP: 78.2
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-car/second_hv_secfpn_8xb6-80e_kitti-3d-car-75d9305e.pth
 
   - Name: second_hv_secfpn_8xb6-80e_kitti-3d-3class
     In Collection: SECOND
@@ -39,8 +39,8 @@ Models:
       - Task: 3D Object Detection
         Dataset: KITTI
         Metrics:
-          mAP: 65.74
-    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017-ae782e87.pth
+          mAP: 65.3
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class/second_hv_secfpn_8xb6-80e_kitti-3d-3class-b086d0a3.pth
 
   - Name: second_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class
     In Collection: SECOND

docs/en/advanced_guides/datasets/scannet.md

@@ -337,12 +337,12 @@ train_pipeline = [
 ## Testing and Making a Submission
 
 By default, our codebase evaluates semantic segmentation results on the validation set.
-If you would like to test the model performance on the online benchmark, add `--format-only` flag in the evaluation script and change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'` in the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126). Remember to specify the `txt_prefix` as the directory to save the testing results.
+If you would like to test the model performance on the online benchmark, add `--format-only` flag in the evaluation script and change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'` in the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/_base_/datasets/scannet-seg.py#L126). Remember to specify the `txt_prefix` as the directory to save the testing results.
 
 Taking PointNet++ (SSG) on ScanNet for example, the following command can be used to do inference on test set:
 
 ```
-./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py \
+./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet-seg.py \
     work_dirs/pointnet2_ssg/latest.pth --format-only \
     --eval-options txt_prefix=work_dirs/pointnet2_ssg/test_submission
 ```

docs/en/advanced_guides/supported_tasks/lidar_sem_seg3d.md

@@ -74,14 +74,14 @@ The evaluation results will be printed in the command like:
 In addition, you can also evaluate a specific model checkpoint after training is finished. Simply run scripts like the following:
 
 ```
-./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py work_dirs/pointnet2_ssg/latest.pth 8
+./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet-seg.py work_dirs/pointnet2_ssg/latest.pth 8
 ```
 
 ## Testing and Making a Submission
 
 If you would like to only conduct inference or test the model performance on the online benchmark,
 you should change `ann_file='scannet_infos_val.pkl'` to `ann_file='scannet_infos_test.pkl'` in the
-ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/configs/_base_/datasets/scannet_seg-3d-20class.py#L129). Remember to
+ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/configs/_base_/datasets/scannet-seg.py#L129). Remember to
 specify the `submission_prefix` in the `test_evaluator`,
 e.g., adding `test_evaluator = dict(type='SegMetric', submission_prefix=work_dirs/pointnet2_ssg/test_submission`) or just add `--cfg-options test_evaluator.submission_prefix=work_dirs/pointnet2_ssg/test_submission` in the end of command.
 After generating the results, you can basically compress the folder and upload to the [ScanNet evaluation server](http://kaldir.vc.in.tum.de/scannet_benchmark/semantic_label_3d).

docs/en/model_zoo.md

@@ -10,75 +10,75 @@
 
 ### SECOND
 
-Please refer to [SECOND](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/second) for details. We provide SECOND baselines on KITTI and Waymo datasets.
+Please refer to [SECOND](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/second) for details. We provide SECOND baselines on KITTI and Waymo datasets.
 
 ### PointPillars
 
-Please refer to [PointPillars](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars) for details. We provide pointpillars baselines on KITTI, nuScenes, Lyft, and Waymo datasets.
+Please refer to [PointPillars](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/pointpillars) for details. We provide pointpillars baselines on KITTI, nuScenes, Lyft, and Waymo datasets.
 
 ### Part-A2
 
-Please refer to [Part-A2](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/parta2) for details.
+Please refer to [Part-A2](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/parta2) for details.
 
 ### VoteNet
 
-Please refer to [VoteNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet) for details. We provide VoteNet baselines on ScanNet and SUNRGBD datasets.
+Please refer to [VoteNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/votenet) for details. We provide VoteNet baselines on ScanNet and SUNRGBD datasets.
 
 ### Dynamic Voxelization
 
-Please refer to [Dynamic Voxelization](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/dynamic_voxelization) for details.
+Please refer to [Dynamic Voxelization](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/dynamic_voxelization) for details.
 
 ### MVXNet
 
-Please refer to [MVXNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/mvxnet) for details.
+Please refer to [MVXNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/mvxnet) for details.
 
 ### RegNetX
 
-Please refer to [RegNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/regnet) for details. We provide pointpillars baselines with RegNetX backbones on nuScenes and Lyft datasets currently.
+Please refer to [RegNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/regnet) for details. We provide pointpillars baselines with RegNetX backbones on nuScenes and Lyft datasets currently.
 
 ### nuImages
 
-We also support baseline models on [nuImages dataset](https://www.nuscenes.org/nuimages). Please refer to [nuImages](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages) for details. We report Mask R-CNN, Cascade Mask R-CNN and HTC results currently.
+We also support baseline models on [nuImages dataset](https://www.nuscenes.org/nuimages). Please refer to [nuImages](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/nuimages) for details. We report Mask R-CNN, Cascade Mask R-CNN and HTC results currently.
 
 ### H3DNet
 
-Please refer to [H3DNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/h3dnet) for details.
+Please refer to [H3DNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/h3dnet) for details.
 
 ### 3DSSD
 
-Please refer to [3DSSD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/3dssd) for details.
+Please refer to [3DSSD](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/3dssd) for details.
 
 ### CenterPoint
 
-Please refer to [CenterPoint](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/centerpoint) for details.
+Please refer to [CenterPoint](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/centerpoint) for details.
 
 ### SSN
 
-Please refer to [SSN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/ssn) for details. We provide pointpillars with shape-aware grouping heads used in SSN on the nuScenes and Lyft datasets currently.
+Please refer to [SSN](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/ssn) for details. We provide pointpillars with shape-aware grouping heads used in SSN on the nuScenes and Lyft datasets currently.
 
 ### ImVoteNet
 
-Please refer to [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/imvotenet) for details. We provide ImVoteNet baselines on SUNRGBD dataset.
+Please refer to [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/imvotenet) for details. We provide ImVoteNet baselines on SUNRGBD dataset.
 
 ### FCOS3D
 
-Please refer to [FCOS3D](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcos3d) for details. We provide FCOS3D baselines on the nuScenes dataset.
+Please refer to [FCOS3D](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/fcos3d) for details. We provide FCOS3D baselines on the nuScenes dataset.
 
 ### PointNet++
 
-Please refer to [PointNet++](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointnet2) for details. We provide PointNet++ baselines on ScanNet and S3DIS datasets.
+Please refer to [PointNet++](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/pointnet2) for details. We provide PointNet++ baselines on ScanNet and S3DIS datasets.
 
 ### Group-Free-3D
 
-Please refer to [Group-Free-3D](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/groupfree3d) for details. We provide Group-Free-3D baselines on ScanNet dataset.
+Please refer to [Group-Free-3D](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/groupfree3d) for details. We provide Group-Free-3D baselines on ScanNet dataset.
 
 ### ImVoxelNet
 
-Please refer to [ImVoxelNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/imvoxelnet) for details. We provide ImVoxelNet baselines on KITTI dataset.
+Please refer to [ImVoxelNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/imvoxelnet) for details. We provide ImVoxelNet baselines on KITTI dataset.
 
 ### PAConv
 
-Please refer to [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/paconv) for details. We provide PAConv baselines on S3DIS dataset.
+Please refer to [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/paconv) for details. We provide PAConv baselines on S3DIS dataset.
 
 ### DGCNN
 
@@ -102,11 +102,11 @@ Please refer to [MonoFlex](https://github.com/open-mmlab/mmdetection3d/tree/v1.0
 
 ### SA-SSD
 
-Please refer to [SA-SSD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/sassd) for details. We provide SA-SSD baselines on the KITTI dataset.
+Please refer to [SA-SSD](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/sassd) for details. We provide SA-SSD baselines on the KITTI dataset.
 
 ### FCAF3D
 
-Please refer to [FCAF3D](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcaf3d) for details. We provide FCAF3D baselines on the ScanNet, S3DIS, and SUN RGB-D datasets.
+Please refer to [FCAF3D](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/fcaf3d) for details. We provide FCAF3D baselines on the ScanNet, S3DIS, and SUN RGB-D datasets.
 
 ### PV-RCNN