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version: 2
formats: all
python:
version: 3.7
install:
- requirements: requirements/docs.txt
- requirements: requirements/runtime.txt
- requirements: requirements/readthedocs.txt
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cff-version: 1.2.0
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authors:
- name: "MMDetection3D Contributors"
title: "OpenMMLab's Next-generation Platform for General 3D Object Detection"
date-released: 2020-07-23
url: "https://github.com/open-mmlab/mmdetection3d"
license: Apache-2.0
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include mmdet3d/.mim/model-index.yml
include requirements/*.txt
recursive-include mmdet3d/.mim/ops *.cpp *.cu *.h *.cc
recursive-include mmdet3d/.mim/configs *.py *.yml
recursive-include mmdet3d/.mim/tools *.sh *.py
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<div align="center">
<img src="resources/mmdet3d-logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
</div>
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/latest/)
[![badge](https://github.com/open-mmlab/mmdetection3d/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection3d/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmdetection3d/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection3d)
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
**News**: We released the codebase v1.0.0rc4.
Note: We are going through large refactoring to provide simpler and more unified usage of many modules.
The compatibilities of models are broken due to the unification and simplification of coordinate systems. For now, most models are benchmarked with similar performance, though few models are still being benchmarked. In this version, we update some of the model checkpoints after the refactor of coordinate systems. See more details in the [Changelog](docs/en/changelog.md).
In the [nuScenes 3D detection challenge](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.
Code and models for the best vision-only method, [FCOS3D](https://arxiv.org/abs/2104.10956), have been released. Please stay tuned for [MoCa](https://arxiv.org/abs/2012.12741).
MMDeploy has supported some MMDetection3d model deployment.
Documentation: https://mmdetection3d.readthedocs.io/
## Introduction
English | [简体中文](README_zh-CN.md)
The master branch works with **PyTorch 1.3+**.
MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is
a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk/).
![demo image](resources/mmdet3d_outdoor_demo.gif)
### Major features
- **Support multi-modality/single-modality detectors out of box**
It directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc.
- **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/master/configs/nuimages).
- **Natural integration with 2D detection**
All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
- **High efficiency**
It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/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 | ✗ | ✗ |
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.
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
v1.0.0rc4 was released in 8/8/2022.
- Support [FCAF3D](https://arxiv.org/pdf/2112.00322.pdf)
Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
## Benchmark and model zoo
Results and models are available in the [model zoo](docs/en/model_zoo.md).
<div align="center">
<b>Components</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Backbones</b>
</td>
<td>
<b>Heads</b>
</td>
<td>
<b>Features</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/pointnet2">PointNet (CVPR'2017)</a></li>
<li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
<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>
<ul>
<li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/dynamic_voxelization">Dynamic Voxelization (CoRL'2019)</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
<div align="center">
<b>Architectures</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="middle">
<td>
<b>3D Object Detection</b>
</td>
<td>
<b>Monocular 3D Object Detection</b>
</td>
<td>
<b>Multi-modal 3D Object Detection</b>
</td>
<td>
<b>3D Semantic Segmentation</b>
</td>
</tr>
<tr valign="top">
<td>
<li><b>Outdoor</b></li>
<ul>
<li><a href="configs/second">SECOND (Sensor'2018)</a></li>
<li><a href="configs/pointpillars">PointPillars (CVPR'2019)</a></li>
<li><a href="configs/ssn">SSN (ECCV'2020)</a></li>
<li><a href="configs/3dssd">3DSSD (CVPR'2020)</a></li>
<li><a href="configs/sassd">SA-SSD (CVPR'2020)</a></li>
<li><a href="configs/point_rcnn">PointRCNN (CVPR'2019)</a></li>
<li><a href="configs/parta2">Part-A2 (TPAMI'2020)</a></li>
<li><a href="configs/centerpoint">CenterPoint (CVPR'2021)</a></li>
</ul>
<li><b>Indoor</b></li>
<ul>
<li><a href="configs/votenet">VoteNet (ICCV'2019)</a></li>
<li><a href="configs/h3dnet">H3DNet (ECCV'2020)</a></li>
<li><a href="configs/groupfree3d">Group-Free-3D (ICCV'2021)</a></li>
<li><a href="configs/fcaf3d">FCAF3D (ECCV'2022)</a></li>
</ul>
</td>
<td>
<li><b>Outdoor</b></li>
<ul>
<li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li>
<li><a href="configs/smoke">SMOKE (CVPRW'2020)</a></li>
<li><a href="configs/fcos3d">FCOS3D (ICCVW'2021)</a></li>
<li><a href="configs/pgd">PGD (CoRL'2021)</a></li>
<li><a href="configs/monoflex">MonoFlex (CVPR'2021)</a></li>
</ul>
</td>
<td>
<li><b>Outdoor</b></li>
<ul>
<li><a href="configs/mvxnet">MVXNet (ICRA'2019)</a></li>
</ul>
<li><b>Indoor</b></li>
<ul>
<li><a href="configs/imvotenet">ImVoteNet (CVPR'2020)</a></li>
</ul>
</td>
<td>
<li><b>Indoor</b></li>
<ul>
<li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
<li><a href="configs/paconv">PAConv (CVPR'2021)</a></li>
<li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
</ul>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
| | 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 | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
## Installation
Please refer to [getting_started.md](docs/en/getting_started.md) for installation.
## Get Started
Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/1_exist_data_model.md) and [with customized dataset](docs/en/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/tutorials/config.md), [adding new dataset](docs/en/tutorials/customize_dataset.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing models](docs/en/tutorials/customize_models.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [Waymo dataset](docs/en/datasets/waymo_det.md).
Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
## Model deployment
Now MMDeploy has supported some MMDetection3D model deployment. Please refer to [model_deployment.md](docs/en/tutorials/model_deployment.md) for more details.
## Citation
If you find this project useful in your research, please consider cite:
```latex
@misc{mmdet3d2020,
title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
author={MMDetection3D Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
year={2020}
}
```
## Contributing
We appreciate all contributions to improve MMDetection3D. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
## Acknowledgement
MMDetection3D is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new 3D detectors.
## Projects in OpenMMLab
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
docker-hub/FlashOCC/Flashocc/mmdetection3d/README_zh-CN.md 0 → 100644
View file @ ba3cd005
<div align="center">
<img src="resources/mmdet3d-logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab 官网</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab 开放平台</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
</div>
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/latest/)
[![badge](https://github.com/open-mmlab/mmdetection3d/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection3d/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmdetection3d/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection3d)
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
**新闻**: 我们发布了版本 v1.0.0rc4.
说明：我们正在进行大规模的重构，以提供对许多模块更简单、更统一的使用。
由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。在这个版本中，我们更新了部分坐标系重构后的模型权重文件。您可以在 [变更日志](docs/en/changelog.md) 中查看更多详细信息。
在第三届 [nuScenes 3D 检测挑战赛](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any)（第五届 AI Driving Olympics, NeurIPS 2020）中，我们获得了最佳 PKL 奖、第三名和最好的纯视觉的结果，相关的代码和模型将会在不久后发布。
最好的纯视觉方法 [FCOS3D](https://arxiv.org/abs/2104.10956) 的代码和模型已经发布。请继续关注我们的多模态检测器 [MoCa](https://arxiv.org/abs/2012.12741)。
MMDeploy 已经支持了部分 MMDetection3D 模型的部署。
文档: https://mmdetection3d.readthedocs.io/
## 简介
[English](README.md) | 简体中文
主分支代码目前支持 PyTorch 1.3 以上的版本。
MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代面向3D检测的平台. 它是 OpenMMlab 项目的一部分，这个项目由香港中文大学多媒体实验室和商汤科技联合发起.
![demo image](resources/mmdet3d_outdoor_demo.gif)
### 主要特性
- **支持多模态/单模态的检测器**
支持多模态/单模态检测器，包括 MVXNet，VoteNet，PointPillars 等。
- **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/master/configs/nuimages).
- **与 2D 检测器的自然整合**
[MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
- **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/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 | ✗ | ✗ |
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.
## 开源许可证
该项目采用 [Apache 2.0 开源许可证](LICENSE)。
## 更新日志
最新的版本 v1.0.0rc4 在 2022.8.8 发布。
- 支持 [FCAF3D](https://arxiv.org/pdf/2112.00322.pdf)
如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/zh_cn/changelog.md)。
## 基准测试和模型库
测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
<div align="center">
<b>模块组件</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>主干网络</b>
</td>
<td>
<b>检测头</b>
</td>
<td>
<b>特性</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/pointnet2">PointNet (CVPR'2017)</a></li>
<li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
<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>
<ul>
<li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
</ul>
</td>
<td>
<ul>
<li><a href="configs/dynamic_voxelization">Dynamic Voxelization (CoRL'2019)</a></li>
</ul>
</td>
</tr>
</tbody>
</table>
<div align="center">
<b>算法模型</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="middle">
<td>
<b>3D 目标检测</b>
</td>
<td>
<b>单目 3D 目标检测</b>
</td>
<td>
<b>多模态 3D 目标检测</b>
</td>
<td>
<b>3D 语义分割</b>
</td>
</tr>
<tr valign="top">
<td>
<li><b>室外</b></li>
<ul>
<li><a href="configs/second">SECOND (Sensor'2018)</a></li>
<li><a href="configs/pointpillars">PointPillars (CVPR'2019)</a></li>
<li><a href="configs/ssn">SSN (ECCV'2020)</a></li>
<li><a href="configs/3dssd">3DSSD (CVPR'2020)</a></li>
<li><a href="configs/sassd">SA-SSD (CVPR'2020)</a></li>
<li><a href="configs/point_rcnn">PointRCNN (CVPR'2019)</a></li>
<li><a href="configs/parta2">Part-A2 (TPAMI'2020)</a></li>
<li><a href="configs/centerpoint">CenterPoint (CVPR'2021)</a></li>
</ul>
<li><b>室内</b></li>
<ul>
<li><a href="configs/votenet">VoteNet (ICCV'2019)</a></li>
<li><a href="configs/h3dnet">H3DNet (ECCV'2020)</a></li>
<li><a href="configs/groupfree3d">Group-Free-3D (ICCV'2021)</a></li>
<li><a href="configs/fcaf3d">FCAF3D (ECCV'2022)</a></li>
</ul>
</td>
<td>
<li><b>室外</b></li>
<ul>
<li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li>
<li><a href="configs/smoke">SMOKE (CVPRW'2020)</a></li>
<li><a href="configs/fcos3d">FCOS3D (ICCVW'2021)</a></li>
<li><a href="configs/pgd">PGD (CoRL'2021)</a></li>
<li><a href="configs/monoflex">MonoFlex (CVPR'2021)</a></li>
</ul>
</td>
<td>
<li><b>室外</b></li>
<ul>
<li><a href="configs/mvxnet">MVXNet (ICRA'2019)</a></li>
</ul>
<li><b>室内</b></li>
<ul>
<li><a href="configs/imvotenet">ImVoteNet (CVPR'2020)</a></li>
</ul>
</td>
<td>
<li><b>室内</b></li>
<ul>
<li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
<li><a href="configs/paconv">PAConv (CVPR'2021)</a></li>
<li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
</ul>
</ul>
</td>
</tr>
</td>
</tr>
</tbody>
</table>
| | 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 | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
**注意：** [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/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。
## 安装
请参考[快速入门文档](docs/zh_cn/getting_started.md)进行安装。
## 快速入门
请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对[已有数据集](docs/zh_cn/1_exist_data_model.md)和[新数据集](docs/zh_cn/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/tutorials/config.md), [增加数据集支持](docs/zh_cn/tutorials/customize_dataset.md), [设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md), [增加自定义模型](docs/zh_cn/tutorials/customize_models.md), [增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)和 [Waymo 数据集](docs/zh_cn/tutorials/waymo.md).
请参考 [FAQ](docs/zh_cn/faq.md) 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时，请查看[兼容性文档](docs/zh_cn/compatibility.md)以知晓每个版本引入的不与之前版本兼容的更新。
## 模型部署
现在 MMDeploy 已经支持了一些 MMDetection3D 模型的部署。请参考 [model_deployment.md](docs/zh_cn/tutorials/model_deployment.md)了解更多细节。
## 引用
如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMdetection3D
```latex
@misc{mmdet3d2020,
title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
author={MMDetection3D Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
year={2020}
}
```
## 贡献指南
我们感谢所有的贡献者为改进和提升 MMDetection3D 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
## 致谢
MMDetection3D 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新的 3D 检测模型。
## OpenMMLab 的其他项目
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
## 欢迎加入 OpenMMLab 社区
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
<div align="center">
<img src="/resources/zhihu_qrcode.jpg" height="400" /> <img src="/resources/qq_group_qrcode.png" height="400" />
</div>
我们会在 OpenMMLab 社区为大家
- 📢 分享 AI 框架的前沿核心技术
- 💻 解读 PyTorch 常用模块源码
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干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/3dssd/3dssd_4x4_kitti-3d-car.py 0 → 100644
View file @ ba3cd005
_base_ = [
'../_base_/models/3dssd.py', '../_base_/datasets/kitti-3d-car.py',
'../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
point_cloud_range = [0, -40, -5, 70, 40, 3]
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
classes=class_names,
sample_groups=dict(Car=15))
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
file_client_args=file_client_args),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='ObjectNoise',
num_try=100,
translation_std=[1.0, 1.0, 0],
global_rot_range=[0.0, 0.0],
rot_range=[-1.0471975511965976, 1.0471975511965976]),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.9, 1.1]),
# 3DSSD can get a higher performance without this transform
# dict(type='BackgroundPointsFilter', bbox_enlarge_range=(0.5, 2.0, 0.5)),
dict(type='PointSample', num_points=16384),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointSample', num_points=16384),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(dataset=dict(pipeline=train_pipeline)),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
evaluation = dict(interval=2)
# model settings
model = dict(
bbox_head=dict(
num_classes=1,
bbox_coder=dict(
type='AnchorFreeBBoxCoder', num_dir_bins=12, with_rot=True)))
# optimizer
lr = 0.002 # max learning rate
optimizer = dict(type='AdamW', lr=lr, weight_decay=0)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(policy='step', warmup=None, step=[45, 60])
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=80)
# yapf:disable
log_config = dict(
interval=30,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/3dssd/README.md 0 → 100644
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# 3DSSD: Point-based 3D Single Stage Object Detector
> [3DSSD: Point-based 3D Single Stage Object Detector](https://arxiv.org/abs/2002.10187)
<!-- [ALGORITHM] -->
## Abstract
Currently, there have been many kinds of voxel-based 3D single stage detectors, while point-based single stage methods are still underexplored. In this paper, we first present a lightweight and effective point-based 3D single stage object detector, named 3DSSD, achieving a good balance between accuracy and efficiency. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. We novelly propose a fusion sampling strategy in downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet with our demand of accuracy and speed. Our paradigm is an elegant single stage anchor-free framework, showing great superiority to other existing methods. We evaluate 3DSSD on widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well, with inference speed more than 25 FPS, 2x faster than former state-of-the-art point-based methods.
<div align=center>
<img src="https://user-images.githubusercontent.com/30491025/143854187-54ed1257-a046-4764-81cd-d2c8404137d3.png" width="800"/>
</div>
## Introduction
We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
Some settings in our implementation are different from the [official implementation](https://github.com/Jia-Research-Lab/3DSSD), which bring marginal differences to the performance on KITTI datasets in our experiments. To simplify and unify the models of our implementation, we skip them in our models. These differences are listed as below:
1. We keep the scenes without any object while the official code skips these scenes in training. In the official implementation, only 3229 and 3394 samples are used as training and validation sets, respectively. In our implementation, we keep using 3712 and 3769 samples as training and validation sets, respectively, as those used for all the other models in our implementation on KITTI datasets.
2. We do not modify the decay of `batch normalization` during training.
3. While using [`DataBaseSampler`](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/dbsampler.py#L80) for data augmentation, the official code uses road planes as reference to place the sampled objects while we do not.
4. We perform detection using LIDAR coordinates while the official code uses camera coordinates.
## Results and models
### KITTI
| Backbone | Class | Lr schd | Mem (GB) | Inf time (fps) | mAP | Download |
| :-------------------------------------------: | :---: | :-----: | :------: | :------------: | :----------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [PointNet2SAMSG](./3dssd_4x4_kitti-3d-car.py) | Car | 72e | 4.7 | | 78.58(81.27)<sup>1</sup> | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828-b89c8fc4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828.log.json) |
\[1\]: We report two different 3D object detection performance here. 78.58mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implementation in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
## Citation
```latex
@inproceedings{yang20203dssd,
author = {Zetong Yang and Yanan Sun and Shu Liu and Jiaya Jia},
title = {3DSSD: Point-based 3D Single Stage Object Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2020}
}
```
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/3dssd/metafile.yml 0 → 100644
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Collections:
- Name: 3DSSD
Metadata:
Training Data: KITTI
Training Techniques:
- AdamW
Training Resources: 4x TITAN X
Architecture:
- PointNet++
Paper:
URL: https://arxiv.org/abs/2002.10187
Title: '3DSSD: Point-based 3D Single Stage Object Detector'
README: configs/3dssd/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/models/detectors/ssd3dnet.py#L7
Version: v0.6.0
Models:
- Name: 3dssd_4x4_kitti-3d-car
In Collection: 3DSSD
Config: configs/3dssd/3dssd_4x4_kitti-3d-car.py
Metadata:
Training Memory (GB): 4.7
Results:
- Task: 3D Object Detection
Dataset: KITTI
Metrics:
mAP: 78.58
Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828-b89c8fc4.pth
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/coco_instance.py 0 → 100644
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dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
evaluation = dict(metric=['bbox', 'segm'])
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# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
input_modality = dict(use_lidar=True, use_camera=False)
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/kitti/':
# 's3://openmmlab/datasets/detection3d/kitti/',
# 'data/kitti/':
# 's3://openmmlab/datasets/detection3d/kitti/'
# }))
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
classes=class_names,
sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
file_client_args=file_client_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
file_client_args=file_client_args),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='ObjectNoise',
num_try=100,
translation_std=[1.0, 1.0, 0.5],
global_rot_range=[0.0, 0.0],
rot_range=[-0.78539816, 0.78539816]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
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'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict(
samples_per_gpu=6,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR',
file_client_args=file_client_args)),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR',
file_client_args=file_client_args),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR',
file_client_args=file_client_args))
evaluation = dict(interval=1, pipeline=eval_pipeline)
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/kitti-3d-car.py 0 → 100644
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# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
classes=class_names,
sample_groups=dict(Car=15))
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
file_client_args=file_client_args),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='ObjectNoise',
num_try=100,
translation_std=[1.0, 1.0, 0.5],
global_rot_range=[0.0, 0.0],
rot_range=[-0.78539816, 0.78539816]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.78539816, 0.78539816],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
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'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict(
samples_per_gpu=6,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR')),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True,
box_type_3d='LiDAR'))
evaluation = dict(interval=1, pipeline=eval_pipeline)
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/kitti-mono3d.py 0 → 100644
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dataset_type = 'KittiMonoDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
input_modality = dict(use_lidar=False, use_camera=True)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=False,
with_bbox_3d=True,
with_label_3d=True,
with_bbox_depth=True),
dict(type='Resize', img_scale=(1242, 375), 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',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_bboxes_3d', 'gt_labels_3d',
'centers2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='MultiScaleFlipAug',
img_scale=(1242, 375),
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']),
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train_mono3d.coco.json',
info_file=data_root + 'kitti_infos_train.pkl',
img_prefix=data_root,
classes=class_names,
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
box_type_3d='Camera'),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val_mono3d.coco.json',
info_file=data_root + 'kitti_infos_val.pkl',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
box_type_3d='Camera'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val_mono3d.coco.json',
info_file=data_root + 'kitti_infos_val.pkl',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
box_type_3d='Camera'))
evaluation = dict(interval=2)
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# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-80, -80, -5, 80, 80, 3]
# For Lyft we usually do 9-class detection
class_names = [
'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
'bicycle', 'pedestrian', 'animal'
]
dataset_type = 'LyftDataset'
data_root = 'data/lyft/'
# Input modality for Lyft dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/lyft/': 's3://lyft/lyft/',
# 'data/lyft/': 's3://lyft/lyft/'
# }))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
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'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_test.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True))
# For Lyft dataset, we usually evaluate the model at the end of training.
# Since the models are trained by 24 epochs by default, we set evaluation
# interval to be 24. Please change the interval accordingly if you do not
# use a default schedule.
evaluation = dict(interval=24, pipeline=eval_pipeline)
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/nuim_instance.py 0 → 100644
View file @ ba3cd005
dataset_type = 'CocoDataset'
data_root = 'data/nuimages/'
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1280, 720), (1920, 1080)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1600, 900),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/nuimages_v1.0-train.json',
img_prefix=data_root,
classes=class_names,
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline))
evaluation = dict(metric=['bbox', 'segm'])
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/nus-3d.py 0 → 100644
View file @ ba3cd005
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-50, -50, -5, 50, 50, 3]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
# Input modality for nuScenes dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/nuscenes/': 's3://nuscenes/nuscenes/',
# 'data/nuscenes/': 's3://nuscenes/nuscenes/'
# }))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
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'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR'),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'))
# For nuScenes dataset, we usually evaluate the model at the end of training.
# Since the models are trained by 24 epochs by default, we set evaluation
# interval to be 24. Please change the interval accordingly if you do not
# use a default schedule.
evaluation = dict(interval=24, pipeline=eval_pipeline)
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/nus-mono3d.py 0 → 100644
View file @ ba3cd005
dataset_type = 'NuScenesMonoDataset'
data_root = 'data/nuscenes/'
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
# Input modality for nuScenes dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='LoadAnnotations3D',
with_bbox=True,
with_label=True,
with_attr_label=True,
with_bbox_3d=True,
with_label_3d=True,
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',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'attr_labels', 'gt_bboxes_3d',
'gt_labels_3d', 'centers2d', 'depths'
]),
]
test_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='MultiScaleFlipAug',
scale_factor=1.0,
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']),
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadImageFromFileMono3D'),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['img'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_train_mono3d.coco.json',
img_prefix=data_root,
classes=class_names,
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
box_type_3d='Camera'),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
box_type_3d='Camera'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
img_prefix=data_root,
classes=class_names,
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
box_type_3d='Camera'))
evaluation = dict(interval=2)
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/range100_lyft-3d.py 0 → 100644
View file @ ba3cd005
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-100, -100, -5, 100, 100, 3]
# For Lyft we usually do 9-class detection
class_names = [
'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
'bicycle', 'pedestrian', 'animal'
]
dataset_type = 'LyftDataset'
data_root = 'data/lyft/'
# Input modality for Lyft dataset, this is consistent with the submission
# format which requires the information in input_modality.
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/lyft/': 's3://lyft/lyft/',
# 'data/lyft/': 's3://lyft/lyft/'
# }))
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
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'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
file_client_args=file_client_args),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'lyft_infos_test.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True))
# For Lyft dataset, we usually evaluate the model at the end of training.
# Since the models are trained by 24 epochs by default, we set evaluation
# interval to be 24. Please change the interval accordingly if you do not
# use a default schedule.
evaluation = dict(interval=24, pipeline=eval_pipeline)
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/s3dis-3d-5class.py 0 → 100644
View file @ ba3cd005
# dataset settings
dataset_type = 'S3DISDataset'
data_root = './data/s3dis/'
class_names = ('table', 'chair', 'sofa', 'bookcase', 'board')
train_area = [1, 2, 3, 4, 6]
test_area = 5
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='PointSample', num_points=40000),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(
type='GlobalRotScaleTrans',
# following ScanNet dataset the rotation range is 5 degrees
rot_range=[-0.087266, 0.087266],
scale_ratio_range=[1.0, 1.0],
shift_height=True),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointSample', num_points=40000),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='DEPTH',
shift_height=False,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type='ConcatDataset',
datasets=[
dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + f's3dis_infos_Area_{i}.pkl',
pipeline=train_pipeline,
filter_empty_gt=False,
classes=class_names,
box_type_3d='Depth') for i in train_area
],
separate_eval=False)),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
box_type_3d='Depth'))
evaluation = dict(pipeline=eval_pipeline)
docker-hub/FlashOCC/Flashocc/mmdetection3d/configs/_base_/datasets/s3dis_seg-3d-13class.py 0 → 100644
View file @ ba3cd005
# dataset settings
dataset_type = 'S3DISSegDataset'
data_root = './data/s3dis/'
class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/s3dis/':
# 's3://openmmlab/datasets/detection3d/s3dis_processed/',
# 'data/s3dis/':
# 's3://openmmlab/datasets/detection3d/s3dis_processed/'
# }))
num_points = 4096
train_area = [1, 2, 3, 4, 6]
test_area = 5
train_pipeline = [
dict(
type='LoadPointsFromFile',
file_client_args=file_client_args,
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
file_client_args=file_client_args,
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=tuple(range(len(class_names))),
max_cat_id=13),
dict(
type='IndoorPatchPointSample',
num_points=num_points,
block_size=1.0,
ignore_index=len(class_names),
use_normalized_coord=True,
enlarge_size=0.2,
min_unique_num=None),
dict(type='NormalizePointsColor', color_mean=None),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
file_client_args=file_client_args,
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(type='NormalizePointsColor', color_mean=None),
dict(
# a wrapper in order to successfully call test function
# actually we don't perform test-time-aug
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
translation_std=[0, 0, 0]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.0,
flip_ratio_bev_vertical=0.0),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
# we need to load gt seg_mask!
eval_pipeline = [
dict(
type='LoadPointsFromFile',
file_client_args=file_client_args,
coord_type='DEPTH',
shift_height=False,
use_color=True,
load_dim=6,
use_dim=[0, 1, 2, 3, 4, 5]),
dict(
type='LoadAnnotations3D',
file_client_args=file_client_args,
with_bbox_3d=False,
with_label_3d=False,
with_mask_3d=False,
with_seg_3d=True),
dict(
type='PointSegClassMapping',
valid_cat_ids=tuple(range(len(class_names))),
max_cat_id=13),
dict(
type='DefaultFormatBundle3D',
with_label=False,
class_names=class_names),
dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
]
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
# train on area 1, 2, 3, 4, 6
# test on area 5
train=dict(
type=dataset_type,
data_root=data_root,
ann_files=[
data_root + f's3dis_infos_Area_{i}.pkl' for i in train_area
],
pipeline=train_pipeline,
classes=class_names,
test_mode=False,
ignore_index=len(class_names),
scene_idxs=[
data_root + f'seg_info/Area_{i}_resampled_scene_idxs.npy'
for i in train_area
],
file_client_args=file_client_args),
val=dict(
type=dataset_type,
data_root=data_root,
ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
ignore_index=len(class_names),
scene_idxs=data_root +
f'seg_info/Area_{test_area}_resampled_scene_idxs.npy',
file_client_args=file_client_args),
test=dict(
type=dataset_type,
data_root=data_root,
ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl',
pipeline=test_pipeline,
classes=class_names,
test_mode=True,
ignore_index=len(class_names),
file_client_args=file_client_args))
evaluation = dict(pipeline=eval_pipeline)
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