[Fix] Fix docstrings and README (#2280)

* docstrings and .md fixes

* add minkresnet to readme

* fix cross in zh readme

* fix lint

README.md

@@ -60,15 +60,15 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 
 - **High efficiency**
 
-  It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/notes/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
+  It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/notes/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `✗`.
 
   |       Methods       | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) | [votenet](https://github.com/facebookresearch/votenet) | [Det3D](https://github.com/poodarchu/Det3D) |
   | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
-  |       VoteNet       |      358      |                          ×                           |                           77                           |                      ×                      |
-  |  PointPillars-car   |      141      |                          ×                           |                           ×                            |                     140                     |
-  | PointPillars-3class |      107      |                          44                          |                           ×                            |                      ×                      |
-  |       SECOND        |      40       |                          30                          |                           ×                            |                      ×                      |
-  |       Part-A2       |      17       |                          14                          |                           ×                            |                      ×                      |
+  |       VoteNet       |      358      |                          ✗                           |                           77                           |                      ✗                      |
+  |  PointPillars-car   |      141      |                          ✗                           |                           ✗                            |                     140                     |
+  | PointPillars-3class |      107      |                          44                          |                           ✗                            |                      ✗                      |
+  |       SECOND        |      40       |                          30                          |                           ✗                            |                      ✗                      |
+  |       Part-A2       |      17       |                          14                          |                           ✗                            |                      ✗                      |
 
 Like [MMDetection](https://github.com/open-mmlab/mmdetection) and [MMCV](https://github.com/open-mmlab/mmcv), MMDetection3D can also be used as a library to support different projects on top of it.
 
@@ -110,6 +110,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         <li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
         <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
         <li>DLA (CVPR'2018)</li>
+        <li>MinkResNet (CVPR'2019)</li>
       </ul>
       </td>
       <td>
@@ -178,6 +179,10 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
           <li><a href="configs/pgd">PGD (CoRL'2021)</a></li>
           <li><a href="configs/monoflex">MonoFlex (CVPR'2021)</a></li>
         </ul>
+        <li><b>Indoor</b></li>
+        <ul>
+          <li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li>
+        </ul>
       </td>
       <td>
         <li><b>Outdoor</b></li>
@@ -204,31 +209,31 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
   </tbody>
 </table>
 
-|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA | MinkResNet |
-| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: | :--------: |
-| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
-| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
-| SA-SSD        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| FCAF3D        |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✓      |
-| PV-RCNN       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+|               | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet |
+| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: |
+|    SECOND     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+| PointPillars  |   ✗    |     ✗      |   ✓    |   ✗   |    ✓    |  ✗  |     ✗      |
+|  FreeAnchor   |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
+|    VoteNet    |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    H3DNet     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|     3DSSD     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    Part-A2    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    MVXNet     |   ✓    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|  CenterPoint  |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|      SSN      |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
+|   ImVoteNet   |   ✓    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    FCOS3D     |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|  PointNet++   |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+| Group-Free-3D |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|  ImVoxelNet   |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    PAConv     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|     DGCNN     |   ✗    |     ✗      |   ✗    |   ✓   |    ✗    |  ✗  |     ✗      |
+|     SMOKE     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
+|      PGD      |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|   MonoFlex    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
+|    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |
+|    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
 
 **Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
 

README_zh-CN.md

@@ -59,15 +59,15 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 
 - **性能高**
 
-  训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/notes/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
+  训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/notes/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `✗`。
 
   |       Methods       | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) | [votenet](https://github.com/facebookresearch/votenet) | [Det3D](https://github.com/poodarchu/Det3D) |
   | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
-  |       VoteNet       |      358      |                          ×                           |                           77                           |                      ×                      |
-  |  PointPillars-car   |      141      |                          ×                           |                           ×                            |                     140                     |
-  | PointPillars-3class |      107      |                          44                          |                           ×                            |                      ×                      |
-  |       SECOND        |      40       |                          30                          |                           ×                            |                      ×                      |
-  |       Part-A2       |      17       |                          14                          |                           ×                            |                      ×                      |
+  |       VoteNet       |      358      |                          ✗                           |                           77                           |                      ✗                      |
+  |  PointPillars-car   |      141      |                          ✗                           |                           ✗                            |                     140                     |
+  | PointPillars-3class |      107      |                          44                          |                           ✗                            |                      ✗                      |
+  |       SECOND        |      40       |                          30                          |                           ✗                            |                      ✗                      |
+  |       Part-A2       |      17       |                          14                          |                           ✗                            |                      ✗                      |
 
 和 [MMDetection](https://github.com/open-mmlab/mmdetection)，[MMCV](https://github.com/open-mmlab/mmcv) 一样，MMDetection3D 也可以作为一个库去支持各式各样的项目。
 
@@ -109,6 +109,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
         <li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
         <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
         <li>DLA (CVPR'2018)</li>
+        <li>MinkResNet (CVPR'2019)</li>
       </ul>
       </td>
       <td>
@@ -176,6 +177,10 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
           <li><a href="configs/pgd">PGD (CoRL'2021)</a></li>
           <li><a href="configs/monoflex">MonoFlex (CVPR'2021)</a></li>
         </ul>
+        <li><b>Indoor</b></li>
+        <ul>
+          <li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li>
+        </ul>
       </td>
       <td>
         <li><b>室外</b></li>
@@ -202,30 +207,31 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
   </tbody>
 </table>
 
-|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA | MinkResNet |
-| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: | :--------: |
-| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
-| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
-| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
-| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
-| SA-SSD        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
-| FCAF3D        |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✓      |
+|               | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet |
+| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: |
+|    SECOND     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+| PointPillars  |   ✗    |     ✗      |   ✓    |   ✗   |    ✓    |  ✗  |     ✗      |
+|  FreeAnchor   |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
+|    VoteNet    |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    H3DNet     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|     3DSSD     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    Part-A2    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    MVXNet     |   ✓    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|  CenterPoint  |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|      SSN      |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
+|   ImVoteNet   |   ✓    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    FCOS3D     |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|  PointNet++   |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+| Group-Free-3D |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|  ImVoxelNet   |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    PAConv     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|     DGCNN     |   ✗    |     ✗      |   ✗    |   ✓   |    ✗    |  ✗  |     ✗      |
+|     SMOKE     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
+|      PGD      |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|   MonoFlex    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
+|    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |
+|    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
 
 **注意：**[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的基于 2D 检测的 **300+ 个模型，40+ 的论文算法**在 MMDetection3D 中都可以被训练或使用。
 

configs/fcaf3d/README.md

@@ -22,24 +22,24 @@ We implement FCAF3D and provide the result and checkpoints on the ScanNet and SU
 
 |                      Backbone                      | Mem (GB) | Inf time (fps) |   AP@0.25    |    AP@0.5    |                                                                                                                                                          Download                                                                                                                                                           |
 | :------------------------------------------------: | :------: | :------------: | :----------: | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [MinkResNet34](./fcaf3d_8x2_scannet-3d-18class.py) |   10.5   |      8.0       | 69.7(70.7\*) | 55.2(56.0\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_scannet-3d-18class/fcaf3d_8x2_scannet-3d-18class_20220805_084956.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_scannet-3d-18class/fcaf3d_8x2_scannet-3d-18class_20220805_084956.log.json) |
+| [MinkResNet34](./fcaf3d_8x2_scannet-3d-18class.py) |   10.5   |      15.7      | 69.7(70.7\*) | 55.2(56.0\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_scannet-3d-18class/fcaf3d_8x2_scannet-3d-18class_20220805_084956.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_scannet-3d-18class/fcaf3d_8x2_scannet-3d-18class_20220805_084956.log.json) |
 
 ### SUN RGB-D
 
 |                      Backbone                      | Mem (GB) | Inf time (fps) |   AP@0.25    |    AP@0.5    |                                                                                                                                                          Download                                                                                                                                                           |
 | :------------------------------------------------: | :------: | :------------: | :----------: | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [MinkResNet34](./fcaf3d_8x2_sunrgbd-3d-10class.py) |   6.3    |      15.6      | 63.8(63.8\*) | 47.3(48.2\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_sunrgbd-3d-10class/fcaf3d_8x2_sunrgbd-3d-10class_20220805_165017.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_sunrgbd-3d-10class/fcaf3d_8x2_sunrgbd-3d-10class_20220805_165017.log.json) |
+| [MinkResNet34](./fcaf3d_8x2_sunrgbd-3d-10class.py) |   6.3    |      17.9      | 63.8(63.8\*) | 47.3(48.2\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_sunrgbd-3d-10class/fcaf3d_8x2_sunrgbd-3d-10class_20220805_165017.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_sunrgbd-3d-10class/fcaf3d_8x2_sunrgbd-3d-10class_20220805_165017.log.json) |
 
 ### S3DIS
 
 |                     Backbone                     | Mem (GB) | Inf time (fps) |   AP@0.25    |    AP@0.5    |                                                                                                                                                    Download                                                                                                                                                     |
 | :----------------------------------------------: | :------: | :------------: | :----------: | :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| [MinkResNet34](./fcaf3d_2xb8_s3dis-3d-5class.py) |   23.5   |      4.2       | 67.4(64.9\*) | 45.7(43.8\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_s3dis-3d-5class/fcaf3d_8x2_s3dis-3d-5class_20220805_121957.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_s3dis-3d-5class/fcaf3d_8x2_s3dis-3d-5class_20220805_121957.log.json) |
+| [MinkResNet34](./fcaf3d_2xb8_s3dis-3d-5class.py) |   23.5   |      10.9      | 67.4(64.9\*) | 45.7(43.8\*) | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_s3dis-3d-5class/fcaf3d_8x2_s3dis-3d-5class_20220805_121957.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/fcaf3d/fcaf3d_8x2_s3dis-3d-5class/fcaf3d_8x2_s3dis-3d-5class_20220805_121957.log.json) |
 
 **Note**
 
 - We report the results across 5 train runs followed by 5 test runs. * means the results reported in the paper.
-- Inference time is given for a single NVidia GeForce GTX 1080 Ti GPU. All models are trained on 2 GPUs.
+- Inference time is given for a single NVidia RTX 4090 GPU. All models are trained on 2 GPUs.
 
 ## Citation
 

mmdet3d/models/backbones/mink_resnet.py

 # Copyright (c) OpenMMLab. All rights reserved.
 # Follow https://github.com/NVIDIA/MinkowskiEngine/blob/master/examples/resnet.py # noqa
 # and mmcv.cnn.ResNet
+from typing import List, Union
+
 try:
     import MinkowskiEngine as ME
+    from MinkowskiEngine import SparseTensor
     from MinkowskiEngine.modules.resnet_block import BasicBlock, Bottleneck
 except ImportError:
     # blocks are used in the static part of MinkResNet
-    ME = BasicBlock = Bottleneck = None
+    ME = BasicBlock = Bottleneck = SparseTensor = None
 
 import torch.nn as nn
+from mmengine.model import BaseModule
 
 from mmdet3d.registry import MODELS
 
 
 @MODELS.register_module()
-class MinkResNet(nn.Module):
+class MinkResNet(BaseModule):
     r"""Minkowski ResNet backbone. See `4D Spatio-Temporal ConvNets
     <https://arxiv.org/abs/1904.08755>`_ for more details.
 
@@ -33,7 +37,11 @@ class MinkResNet(nn.Module):
         152: (Bottleneck, (3, 8, 36, 3))
     }
 
-    def __init__(self, depth, in_channels, num_stages=4, pool=True):
+    def __init__(self,
+                 depth: int,
+                 in_channels: int,
+                 num_stages: int = 4,
+                 pool: bool = True):
         super(MinkResNet, self).__init__()
         if ME is None:
             raise ImportError(
@@ -57,12 +65,13 @@ class MinkResNet(nn.Module):
             self.maxpool = ME.MinkowskiMaxPooling(
                 kernel_size=2, stride=2, dimension=3)
 
-        for i, num_blocks in enumerate(stage_blocks):
+        for i in range(len(stage_blocks)):
             setattr(
                 self, f'layer{i + 1}',
                 self._make_layer(block, 64 * 2**i, stage_blocks[i], stride=2))
 
     def init_weights(self):
+        """Initialize weights."""
         for m in self.modules():
             if isinstance(m, ME.MinkowskiConvolution):
                 ME.utils.kaiming_normal_(
@@ -72,7 +81,19 @@ class MinkResNet(nn.Module):
                 nn.init.constant_(m.bn.weight, 1)
                 nn.init.constant_(m.bn.bias, 0)
 
-    def _make_layer(self, block, planes, blocks, stride):
+    def _make_layer(self, block: Union[BasicBlock, Bottleneck], planes: int,
+                    blocks: int, stride: int) -> nn.Module:
+        """Make single level of residual blocks.
+
+        Args:
+            block (BasicBlock | Bottleneck): Residual block class.
+            planes (int): Number of convolution filters.
+            blocks (int): Number of blocks in the layers.
+            stride (int): Stride of the first convolutional layer.
+
+        Returns:
+            nn.Module: With residual blocks.
+        """
         downsample = None
         if stride != 1 or self.inplanes != planes * block.expansion:
             downsample = nn.Sequential(
@@ -92,11 +113,11 @@ class MinkResNet(nn.Module):
                 downsample=downsample,
                 dimension=3))
         self.inplanes = planes * block.expansion
-        for i in range(1, blocks):
+        for _ in range(1, blocks):
             layers.append(block(self.inplanes, planes, stride=1, dimension=3))
         return nn.Sequential(*layers)
 
-    def forward(self, x):
+    def forward(self, x: SparseTensor) -> List[SparseTensor]:
         """Forward pass of ResNet.
 
         Args:

mmdet3d/models/dense_heads/fcaf3d_head.py

@@ -34,7 +34,7 @@ class FCAF3DHead(Base3DDenseHead):
 
     Args:
         num_classes (int): Number of classes.
-        in_channels (int): Number of channels in input tensors.
+        in_channels (tuple(int)): Number of channels in input tensors.
         out_channels (int): Number of channels in the neck output tensors.
         num_reg_outs (int): Number of regression layer channels.
         voxel_size (float): Voxel size in meters.
@@ -59,7 +59,7 @@ class FCAF3DHead(Base3DDenseHead):
 
     def __init__(self,
                  num_classes: int,
-                 in_channels: int,
+                 in_channels: Tuple[int],
                  out_channels: int,
                  num_reg_outs: int,
                  voxel_size: float,
@@ -340,8 +340,8 @@ class FCAF3DHead(Base3DDenseHead):
             batch_gt_instances_3d (list[:obj:`InstanceData`]): Batch of
                 gt_instance_3d.  It usually includes ``bboxes_3d``、`
                 `labels_3d``、``depths``、``centers_2d`` and attributes.
-            batch_img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
+            batch_input_metas (list[dict]): Meta information of each input,
+                e.g., image size, scaling factor, etc.
             batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
                 Batch of gt_instances_ignore. It includes ``bboxes`` attribute
                 data that is ignored during training and testing.