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raw_mmdetection

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mmdetection3d/.github/pull_request_template.md 0 → 100644
View file @ 7aa442d5
Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
## Motivation
Please describe the motivation of this PR and the goal you want to achieve through this PR.
## Modification
Please briefly describe what modification is made in this PR.
## BC-breaking (Optional)
Does the modification introduce changes that break the back-compatibility of the downstream repos?
If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
## Use cases (Optional)
If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.
## Checklist
1. Pre-commit or other linting tools are used to fix the potential lint issues.
2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects.
4. The documentation has been modified accordingly, like docstring or example tutorials.
mmdetection3d/.github/workflows/deploy.yml 0 → 100644
View file @ 7aa442d5
name: deploy
on: push
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
build-n-publish:
runs-on: ubuntu-latest
if: startsWith(github.event.ref, 'refs/tags')
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.7
uses: actions/setup-python@v2
with:
python-version: 3.7
- name: Install torch
run: pip install torch
- name: Install wheel
run: pip install wheel
- name: Build MMDet3D
run: python setup.py sdist bdist_wheel
- name: Publish distribution to PyPI
run: |
pip install twine
twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}
mmdetection3d/.github/workflows/lint.yml 0 → 100644
View file @ 7aa442d5
name: lint
on: [push, pull_request]
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python 3.7
uses: actions/setup-python@v2
with:
python-version: 3.7
- name: Install pre-commit hook
run: |
pip install pre-commit
pre-commit install
- name: Linting
run: pre-commit run --all-files
- name: Check docstring coverage
run: |
pip install interrogate
interrogate -v --ignore-init-method --ignore-magic --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 90 mmdet3d
mmdetection3d/.github/workflows/merge_stage_test.yml 0 → 100644
View file @ 7aa442d5
name: merge_stage_test
on:
push:
paths-ignore:
- 'README.md'
- 'README_zh-CN.md'
- 'docs/**'
- 'demo/**'
- '.dev_scripts/**'
- '.circleci/**'
branches:
- dev-1.x
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
build_cpu_py:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.8, 3.9]
torch: [1.8.1]
include:
- torch: 1.8.1
torchvision: 0.9.1
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Install PyTorch
run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
- name: Install MMEngine
run: pip install git+https://github.com/open-mmlab/mmengine.git@main
- name: Install MMCV
run: |
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
- name: Install MMDet
run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
- name: Install other dependencies
run: pip install -r requirements/tests.txt
- name: Build and install
run: rm -rf .eggs && pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmdet3d -m pytest tests/
coverage xml
coverage report -m
build_cpu_pt:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.7]
torch: [1.8.1, 1.9.1, 1.10.1, 1.11.0, 1.12.0, 1.13.0]
include:
- torch: 1.8.1
torchvision: 0.9.1
- torch: 1.9.1
torchvision: 0.10.1
- torch: 1.10.1
torchvision: 0.11.2
- torch: 1.11.0
torchvision: 0.12.0
- torch: 1.12.0
torchvision: 0.13.0
- torch: 1.13.0
torchvision: 0.14.0
- python-version: 3.8
torch: 2.0.0
torchvision: 0.15.1
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Install PyTorch
run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
- name: Install MMEngine
run: pip install git+https://github.com/open-mmlab/mmengine.git@main
- name: Install MMCV
run: |
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
- name: Install MMDet
run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
- name: Install other dependencies
run: pip install -r requirements/tests.txt
- name: Build and install
run: rm -rf .eggs && pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmdet3d -m pytest tests/
coverage xml
coverage report -m
# Only upload coverage report for python3.7 && pytorch1.8.1 cpu
- name: Upload coverage to Codecov
if: ${{matrix.torch == '1.8.1' && matrix.python-version == '3.7'}}
uses: codecov/codecov-action@v1.0.14
with:
file: ./coverage.xml
flags: unittests
env_vars: OS,PYTHON
name: codecov-umbrella
fail_ci_if_error: false
build_cu102:
runs-on: ubuntu-22.04
container:
image: pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel
strategy:
matrix:
python-version: [3.7]
include:
- torch: 1.8.1
cuda: 10.2
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Fetch GPG keys
run: |
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
- name: Install system dependencies
run: apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
- name: Install mmdet3d dependencies
run: |
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
- name: Build and install
run: pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmdet3d -m pytest tests/
coverage xml
coverage report -m
build_cu116:
runs-on: ubuntu-22.04
container:
image: pytorch/pytorch:1.13.0-cuda11.6-cudnn8-devel
strategy:
matrix:
python-version: [3.7]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Fetch GPG keys
run: |
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
- name: Install system dependencies
run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
- name: Install mmdet3d dependencies
run: |
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
- name: Build and install
run: pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmcv -m pytest tests
coverage xml
coverage report -m
build_cu117:
runs-on: ubuntu-22.04
container:
image: pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
strategy:
matrix:
python-version: [3.9]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Fetch GPG keys
run: |
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
- name: Install system dependencies
run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
- name: Install mmdet3d dependencies
run: |
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
- name: Build and install
run: pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmcv -m pytest tests
coverage xml
coverage report -m
build_windows:
runs-on: windows-2022
strategy:
matrix:
python-version: [3.7]
platform: [cpu, cu111]
torch: [1.8.1]
torchvision: [0.9.1]
include:
- python-version: 3.8
platform: cu117
torch: 2.0.0
torchvision: 0.15.1
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: python -m pip install pip --upgrade && pip install wheel
- name: Install lmdb
run: pip install lmdb
- name: Install PyTorch
run: pip install torch==${{matrix.torch}}+${{matrix.platform}} torchvision==${{matrix.torchvision}}+${{matrix.platform}} -f https://download.pytorch.org/whl/${{matrix.platform}}/torch_stable.html
- name: Install mmdet3d dependencies
run: |
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
- name: Build and install
run: pip install -e .
- name: Run unittests and generate coverage report
run: pytest tests/
mmdetection3d/.github/workflows/pr_stage_test.yml 0 → 100644
View file @ 7aa442d5
name: pr_stage_test
on:
pull_request:
paths-ignore:
- 'README.md'
- 'README_zh-CN.md'
- 'docs/**'
- 'demo/**'
- '.dev_scripts/**'
- '.circleci/**'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
build_cpu:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.7]
include:
- torch: 1.8.1
torchvision: 0.9.1
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: python -m pip install pip --upgrade && pip install wheel
- name: Install PyTorch
run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
- name: Install MMEngine
run: pip install git+https://github.com/open-mmlab/mmengine.git@main
- name: Install MMCV
run: |
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
- name: Install MMDet
run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
- name: Install other dependencies
run: pip install -r requirements/tests.txt
- name: Build and install
run: rm -rf .eggs && pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmdet3d -m pytest tests/
coverage xml
coverage report -m
# Upload coverage report for python3.7 && pytorch1.8.1 cpu
- name: Upload coverage to Codecov
uses: codecov/codecov-action@v1.0.14
with:
file: ./coverage.xml
flags: unittests
env_vars: OS,PYTHON
name: codecov-umbrella
fail_ci_if_error: false
build_cu102:
runs-on: ubuntu-22.04
container:
image: pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel
strategy:
matrix:
python-version: [3.7]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Fetch GPG keys
run: |
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
- name: Install system dependencies
run: apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
- name: Install mmdet3d dependencies
run: |
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
- name: Build and install
run: pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmdet3d -m pytest tests/
coverage xml
coverage report -m
build_cu117:
runs-on: ubuntu-22.04
container:
image: pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
strategy:
matrix:
python-version: [3.9]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Fetch GPG keys
run: |
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
- name: Install system dependencies
run: apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
- name: Install mmdet3d dependencies
run: |
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
- name: Build and install
run: pip install -e .
- name: Run unittests and generate coverage report
run: |
coverage run --branch --source mmdet3d -m pytest tests/
coverage xml
coverage report -m
build_windows:
runs-on: windows-2022
strategy:
matrix:
python-version: [3.7]
platform: [cpu, cu111]
torch: [1.8.1]
torchvision: [0.9.1]
include:
- python-version: 3.8
platform: cu117
torch: 2.0.0
torchvision: 0.15.1
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: python -m pip install pip --upgrade && pip install wheel
- name: Install lmdb
run: pip install lmdb
- name: Install PyTorch
run: pip install torch==${{matrix.torch}}+${{matrix.platform}} torchvision==${{matrix.torchvision}}+${{matrix.platform}} -f https://download.pytorch.org/whl/${{matrix.platform}}/torch_stable.html
- name: Install mmdet3d dependencies
run: |
pip install git+https://github.com/open-mmlab/mmengine.git@main
pip install -U openmim
mim install 'mmcv >= 2.0.0rc4'
pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
pip install -r requirements/tests.txt
- name: Build and install
run: pip install -e .
- name: Run unittests and generate coverage report
run: pytest tests/
mmdetection3d/.github/workflows/test_mim.yml 0 → 100644
View file @ 7aa442d5
name: test-mim
on:
push:
paths:
- 'model-index.yml'
- 'configs/**'
pull_request:
paths:
- 'model-index.yml'
- 'configs/**'
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
jobs:
build_cpu:
runs-on: ubuntu-22.04
strategy:
matrix:
python-version: [3.7]
torch: [1.8.1]
include:
- torch: 1.8.1
torch_version: torch1.8
torchvision: 0.9.1
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Upgrade pip
run: pip install pip --upgrade && pip install wheel
- name: Install PyTorch
run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
- name: Install openmim
run: pip install openmim
- name: Build and install
run: rm -rf .eggs && mim install -e .
- name: test commands of mim
run: mim search mmdet3d
mmdetection3d/.gitignore 0 → 100644
View file @ 7aa442d5
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
*.ipynb
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/en/_build/
docs/zh_cn/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
# cython generated cpp
data
.vscode
.idea
# custom
*.pkl
*.pkl.json
*.log.json
work_dirs/
exps/
*~
mmdet3d/.mim
# Pytorch
*.pth
# demo
*.jpg
*.png
data/s3dis/Stanford3dDataset_v1.2_Aligned_Version/
data/scannet/scans/
data/sunrgbd/OFFICIAL_SUNRGBD/
*.obj
*.ply
# Waymo evaluation
mmdet3d/evaluation/functional/waymo_utils/compute_detection_metrics_main
mmdet3d/evaluation/functional/waymo_utils/compute_detection_let_metrics_main
mmdet3d/evaluation/functional/waymo_utils/compute_segmentation_metrics_main
mmdetection3d/.pre-commit-config-zh-cn.yaml 0 → 100644
View file @ 7aa442d5
repos:
- repo: https://gitee.com/openmmlab/mirrors-flake8
rev: 5.0.4
hooks:
- id: flake8
- repo: https://gitee.com/openmmlab/mirrors-isort
rev: 5.11.5
hooks:
- id: isort
- repo: https://gitee.com/openmmlab/mirrors-yapf
rev: v0.32.0
hooks:
- id: yapf
- repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
rev: v4.3.0
hooks:
- id: trailing-whitespace
- id: check-yaml
- id: end-of-file-fixer
- id: requirements-txt-fixer
- id: double-quote-string-fixer
- id: check-merge-conflict
- id: fix-encoding-pragma
args: ["--remove"]
- id: mixed-line-ending
args: ["--fix=lf"]
- repo: https://gitee.com/openmmlab/mirrors-codespell
rev: v2.2.1
hooks:
- id: codespell
- repo: https://gitee.com/openmmlab/mirrors-mdformat
rev: 0.7.9
hooks:
- id: mdformat
args: ["--number"]
additional_dependencies:
- mdformat-openmmlab
- mdformat_frontmatter
- linkify-it-py
- repo: https://gitee.com/openmmlab/mirrors-docformatter
rev: v1.3.1
hooks:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://gitee.com/openmmlab/pre-commit-hooks
rev: v0.2.0
hooks:
- id: check-algo-readme
- id: check-copyright
args: ["mmdet3d"]
mmdetection3d/.pre-commit-config.yaml 0 → 100644
View file @ 7aa442d5
repos:
- repo: https://github.com/PyCQA/flake8
rev: 5.0.4
hooks:
- id: flake8
- repo: https://github.com/PyCQA/isort
rev: 5.11.5
hooks:
- id: isort
- repo: https://github.com/pre-commit/mirrors-yapf
rev: v0.32.0
hooks:
- id: yapf
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.3.0
hooks:
- id: trailing-whitespace
- id: check-yaml
- id: end-of-file-fixer
- id: requirements-txt-fixer
- id: double-quote-string-fixer
- id: check-merge-conflict
- id: fix-encoding-pragma
args: ["--remove"]
- id: mixed-line-ending
args: ["--fix=lf"]
- repo: https://github.com/codespell-project/codespell
rev: v2.2.1
hooks:
- id: codespell
- repo: https://github.com/executablebooks/mdformat
rev: 0.7.9
hooks:
- id: mdformat
args: [ "--number" ]
additional_dependencies:
- mdformat-openmmlab
- mdformat_frontmatter
- linkify-it-py
- repo: https://github.com/myint/docformatter
rev: v1.3.1
hooks:
- id: docformatter
args: ["--in-place", "--wrap-descriptions", "79"]
- repo: https://github.com/open-mmlab/pre-commit-hooks
rev: v0.2.0 # Use the ref you want to point at
hooks:
- id: check-algo-readme
- id: check-copyright
args: ["mmdet3d"] # replace the dir_to_check with your expected directory to check
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version: 2
build:
os: ubuntu-22.04
tools:
python: "3.8"
formats:
- epub
python:
install:
- requirements: requirements/docs.txt
- requirements: requirements/readthedocs.txt
mmdetection3d/CITATION.cff 0 → 100644
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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
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 mmdet3d/.mim/dataset-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>
[![PyPI](https://img.shields.io/pypi/v/mmdet3d)](https://pypi.org/project/mmdet3d)
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[📘Documentation](https://mmdetection3d.readthedocs.io/en/latest/) |
[🛠️Installation](https://mmdetection3d.readthedocs.io/en/latest/get_started.html) |
[👀Model Zoo](https://mmdetection3d.readthedocs.io/en/latest/model_zoo.html) |
[🆕Update News](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog.html) |
[🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection3d/projects) |
[🤔Reporting Issues](https://github.com/open-mmlab/mmdetection3d/issues/new/choose)
</div>
<div align="center">
English | [简体中文](README_zh-CN.md)
</div>
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</div>
## Introduction
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](https://openmmlab.com/) project.
The main branch works with **PyTorch 1.8+**.
![demo image](resources/mmdet3d_outdoor_demo.gif)
<details open>
<summary>Major features</summary>
- **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/main/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/3.x/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/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 | ✗ | ✗ |
</details>
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.
## What's New
### Highlight
In version 1.4, MMDetecion3D refactors the Waymo dataset and accelerates the preprocessing, training/testing setup, and evaluation of Waymo dataset. We also extends the support for camera-based, such as Monocular and BEV, 3D object detection models on Waymo. A detailed description of the Waymo data information is provided [here](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html).
Besides, in version 1.4, MMDetection3D provides [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo_mmdet3d_after_1x4/waymo_mini.tar.gz) to help community users get started with Waymo and use it for quick iterative development.
**v1.4.0** was released in 8/1/2024：
- Support the training of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects`
- Support [Nerf-Det](https://arxiv.org/abs/2307.14620) in `projects`
- Refactor Waymo dataset
**v1.3.0** was released in 18/10/2023:
- Support [CENet](https://arxiv.org/abs/2207.12691) in `projects`
- Enhance demos with new 3D inferencers
**v1.2.0** was released in 4/7/2023
- Support [New Config Type](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) in `mmdet3d/configs`
- Support the inference of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects`
- Support downloading datasets from [OpenDataLab](https://opendatalab.com/) using `mim`
**v1.1.1** was released in 30/5/2023:
- Support [TPVFormer](https://arxiv.org/pdf/2302.07817.pdf) in `projects`
- Support the training of BEVFusion in `projects`
- Support lidar-based 3D semantic segmentation benchmark
## Installation
Please refer to [Installation](https://mmdetection3d.readthedocs.io/en/latest/get_started.html) for installation instructions.
## Getting Started
For detailed user guides and advanced guides, please refer to our [documentation](https://mmdetection3d.readthedocs.io/en/latest/):
<details>
<summary>User Guides</summary>
- [Train & Test](https://mmdetection3d.readthedocs.io/en/latest/user_guides/index.html#train-test)
- [Learn about Configs](https://mmdetection3d.readthedocs.io/en/latest/user_guides/config.html)
- [Coordinate System](https://mmdetection3d.readthedocs.io/en/latest/user_guides/coord_sys_tutorial.html)
- [Dataset Preparation](https://mmdetection3d.readthedocs.io/en/latest/user_guides/dataset_prepare.html)
- [Customize Data Pipelines](https://mmdetection3d.readthedocs.io/en/latest/user_guides/data_pipeline.html)
- [Test and Train on Standard Datasets](https://mmdetection3d.readthedocs.io/en/latest/user_guides/train_test.html)
- [Inference](https://mmdetection3d.readthedocs.io/en/latest/user_guides/inference.html)
- [Train with Customized Datasets](https://mmdetection3d.readthedocs.io/en/latest/user_guides/new_data_model.html)
- [Useful Tools](https://mmdetection3d.readthedocs.io/en/latest/user_guides/index.html#useful-tools)
</details>
<details>
<summary>Advanced Guides</summary>
- [Datasets](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/index.html#datasets)
- [KITTI Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/kitti.html)
- [NuScenes Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/nuscenes.html)
- [Lyft Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/lyft.html)
- [Waymo Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html)
- [SUN RGB-D Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/sunrgbd.html)
- [ScanNet Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/scannet.html)
- [S3DIS Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/s3dis.html)
- [SemanticKITTI Dataset](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/semantickitti.html)
- [Supported Tasks](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/index.html#supported-tasks)
- [LiDAR-Based 3D Detection](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/supported_tasks/lidar_det3d.html)
- [Vision-Based 3D Detection](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/supported_tasks/vision_det3d.html)
- [LiDAR-Based 3D Semantic Segmentation](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/supported_tasks/lidar_sem_seg3d.html)
- [Customization](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/index.html#customization)
- [Customize Datasets](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_dataset.html)
- [Customize Models](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_models.html)
- [Customize Runtime Settings](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/customize_runtime.html)
</details>
## Overview of 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>
<li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
<li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></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>
</td>
</tr>
</tbody>
</table>
<div align="center">
<b>Architectures</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="middle">
<td>
<b>LiDAR-based 3D Object Detection</b>
</td>
<td>
<b>Camera-based 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>
<li><a href="configs/pv_rcnn">PV-RCNN (CVPR'2020)</a></li>
<li><a href="projects/CenterFormer">CenterFormer (ECCV'2022)</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>
<li><a href="projects/TR3D">TR3D (ArXiv'2023)</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>
<li><a href="projects/DETR3D">DETR3D (CoRL'2021)</a></li>
<li><a href="projects/PETR">PETR (ECCV'2022)</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>
<ul>
<li><a href="configs/mvxnet">MVXNet (ICRA'2019)</a></li>
<li><a href="projects/BEVFusion">BEVFusion (ICRA'2023)</a></li>
</ul>
<li><b>Indoor</b></li>
<ul>
<li><a href="configs/imvotenet">ImVoteNet (CVPR'2020)</a></li>
</ul>
</td>
<td>
<li><b>Outdoor</b></li>
<ul>
<li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
<li><a href="configs/spvcnn">SPVCNN (ECCV'2020)</a></li>
<li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
<li><a href="projects/TPVFormer">TPVFormer (CVPR'2023)</a></li>
</ul>
<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 | VoVNet | Swin-T | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | MinkUNet |
| :-----------: | :----: | :----: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :--------: | :------: |
| 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 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| MinkUNet | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| SPVCNN | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| BEVFusion | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| CenterFormer | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TR3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| DETR3D | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PETR | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TPVFormer | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
**Note:** All the about **500+ models, methods of 90+ 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.
## FAQ
Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.
## Contributing
We appreciate all contributions to improve MMDetection3D. Please refer to [CONTRIBUTING.md](docs/en/notes/contribution_guides.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.
## 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}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in OpenMMLab
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training 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.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series 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.
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation 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.
mmdetection3d/README_zh-CN.md 0 → 100644
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<div>&nbsp;</div>
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<b><font size="5">OpenMMLab 官网</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
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<i><font size="4">TRY IT OUT</font></i>
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[📘使用文档](https://mmdetection3d.readthedocs.io/zh_CN/latest/) |
[🛠️安装教程](https://mmdetection3d.readthedocs.io/zh_CN/latest/get_started.html) |
[👀模型库](https://mmdetection3d.readthedocs.io/zh_CN/latest/model_zoo.html) |
[🆕更新日志](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog.html) |
[🚀进行中的项目](https://github.com/open-mmlab/mmdetection3d/projects) |
[🤔报告问题](https://github.com/open-mmlab/mmdetection3d/issues/new/choose)
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[English](README.md) | 简体中文
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</div>
## 简介
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](https://openmmlab.com/) 项目的一部分。
主分支代码目前支持 PyTorch 1.8 以上的版本。
![demo image](resources/mmdet3d_outdoor_demo.gif)
<details open>
<summary>主要特性</summary>
- **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/main/configs/nuimages)。
- **与 2D 检测器的自然整合**
[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/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/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 | ✗ | ✗ |
</details>
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。
## 最新进展
### 亮点
在1.4版本中，MMDetecion3D 重构了 Waymo 数据集, 加速了 Waymo 数据集的预处理、训练/测试启动、验证的速度。并且在 Waymo 上拓展了对 单目/BEV 等基于相机的三维目标检测模型的支持。在[这里](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html)提供了对 Waymo 数据信息的详细解读。
此外，在1.4版本中，MMDetection3D 提供了 [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo_mmdet3d_after_1x4/waymo_mini.tar.gz) 来帮助社区用户上手 Waymo 并用于快速迭代开发。
**v1.4.0** 版本已经在 2024.1.8 发布：
- 在 `projects` 中支持了 [DSVT](<(https://arxiv.org/abs/2301.06051)>) 的训练
- 在 `projects` 中支持了 [Nerf-Det](https://arxiv.org/abs/2307.14620)
- 重构了 Waymo 数据集
**v1.3.0** 版本已经在 2023.10.18 发布：
- 在 `projects` 中支持 [CENet](https://arxiv.org/abs/2207.12691)
- 使用新的 3D inferencers 增强演示代码效果
**v1.2.0** 版本已经在 2023.7.4 发布：
- 在 `mmdet3d/configs`中支持 [新Config样式](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta)
- 在 `projects` 中支持 [DSVT](<(https://arxiv.org/abs/2301.06051)>) 的推理
- 支持通过 `mim` 从 [OpenDataLab](https://opendatalab.com/) 下载数据集
**v1.1.1** 版本已经在 2023.5.30 发布：
- 在 `projects` 中支持 [TPVFormer](https://arxiv.org/pdf/2302.07817.pdf)
- 在 `projects` 中支持 BEVFusion 的训练
- 支持基于激光雷达的 3D 语义分割基准
## 安装
请参考[快速入门文档](https://mmdetection3d.readthedocs.io/zh_CN/latest/get_started.html)进行安装。
## 教程
<details>
<summary>用户指南</summary>
- [训练 & 测试](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/index.html#train-test)
- [学习配置文件](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/config.html)
- [坐标系](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/coord_sys_tutorial.html)
- [数据预处理](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/dataset_prepare.html)
- [自定义数据预处理流程](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/data_pipeline.html)
- [在标注数据集上测试和训练](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/train_test.html)
- [推理](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/inference.html)
- [在自定义数据集上进行训练](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/new_data_model.html)
- [实用工具](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/index.html#useful-tools)
</details>
<details>
<summary>进阶教程</summary>
- [数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/index.html#datasets)
- [KITTI 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/kitti.html)
- [NuScenes 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/nuscenes.html)
- [Lyft 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/lyft.html)
- [Waymo 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/waymo.html)
- [SUN RGB-D 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/sunrgbd.html)
- [ScanNet 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/scannet.html)
- [S3DIS 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/s3dis.html)
- [SemanticKITTI 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/semantickitti.html)
- [支持的任务](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/index.html#supported-tasks)
- [基于激光雷达的 3D 检测](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/supported_tasks/lidar_det3d.html)
- [基于视觉的 3D 检测](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/supported_tasks/vision_det3d.html)
- [基于激光雷达的 3D 语义分割](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/supported_tasks/lidar_sem_seg3d.html)
- [自定义项目](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/index.html#customization)
- [自定义数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/customize_dataset.html)
- [自定义模型](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/customize_models.html)
- [自定义运行时配置](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/customize_runtime.html)
</details>
## 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>
<li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
<li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></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>
</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>激光雷达 3D 目标检测</b>
</td>
<td>
<b>相机 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>
<li><a href="configs/pv_rcnn">PV-RCNN (CVPR'2020)</a></li>
<li><a href="projects/CenterFormer">CenterFormer (ECCV'2022)</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>
<li><a href="projects/TR3D">TR3D (ArXiv'2023)</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>
<li><a href="projects/DETR3D">DETR3D (CoRL'2021)</a></li>
<li><a href="projects/PETR">PETR (ECCV'2022)</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>
<ul>
<li><a href="configs/mvxnet">MVXNet (ICRA'2019)</a></li>
<li><a href="projects/BEVFusion">BEVFusion (ICRA'2023)</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>室外</b></li>
<ul>
<li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
<li><a href="configs/spvcnn">SPVCNN (ECCV'2020)</a></li>
<li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
<li><a href="projects/TPVFormer">TPVFormer (CVPR'2023)</a></li>
</ul>
<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 | VoVNet | Swin-T | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | MinkUNet |
| :-----------: | :----: | :----: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :--------: | :------: |
| 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 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| MinkUNet | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| SPVCNN | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| BEVFusion | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| CenterFormer | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TR3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| DETR3D | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PETR | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TPVFormer | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
**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。
## 常见问题
请参考 [FAQ](docs/zh_cn/notes/faq.md) 了解其他用户的常见问题。
## 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](docs/en/notes/contribution_guides.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。
## 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}
}
```
## 开源许可证
该项目采用 [Apache 2.0 开源许可证](LICENSE)。
## OpenMMLab 的其他项目
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
- [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
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
- [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
- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）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)，扫描下方微信二维码添加喵喵好友，进入 MMDetection3D 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】
<div align="center">
<img src="https://user-images.githubusercontent.com/58739961/187154320-f3312cdf-31f2-4316-9dbb-8d7b0e1b7e08.jpg" height="400" /> <img src="https://github.com/open-mmlab/mmdetection3d/assets/62195058/dfb3f6a9-25c6-47a5-936b-3f1d7347a42b" height="400" />
</div>
我们会在 OpenMMLab 社区为大家
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干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
mmdetection3d/configs/3dssd/3dssd_4xb4_kitti-3d-car.py 0 → 100644
View file @ 7aa442d5
_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)
backend_args = None
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),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
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='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_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='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=4, dataset=dict(dataset=dict(pipeline=train_pipeline, )))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
# 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
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=lr, weight_decay=0.),
clip_grad=dict(max_norm=35, norm_type=2),
)
# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=80, val_interval=2)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=80,
by_epoch=True,
milestones=[45, 60],
gamma=0.1)
]
mmdetection3d/configs/3dssd/README.md 0 → 100644
View file @ 7aa442d5
# 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_4xb4_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}
}
```
mmdetection3d/configs/3dssd/metafile.yml 0 → 100644
View file @ 7aa442d5
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_4xb4_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
mmdetection3d/configs/_base_/datasets/kitti-3d-3class.py 0 → 100644
View file @ 7aa442d5
# 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)
metainfo = dict(classes=class_names)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
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,
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4, # x, y, z, intensity
use_dim=4,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
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='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_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='Pack3DDetInputs', 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,
backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=6,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='kitti_infos_train.pkl',
data_prefix=dict(pts='training/velodyne_reduced'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
# 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',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdetection3d/configs/_base_/datasets/kitti-3d-car.py 0 → 100644
View file @ 7aa442d5
# 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)
metainfo = dict(classes=class_names)
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
# Method 2: Use backend_args, file_client_args in versions before 1.1.0
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection3d/',
# 'data/': 's3://openmmlab/datasets/detection3d/'
# }))
backend_args = None
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),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
backend_args=backend_args)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4, # x, y, z, intensity
use_dim=4,
backend_args=backend_args),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
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='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_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='Pack3DDetInputs', 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,
backend_args=backend_args),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=6,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='kitti_infos_train.pkl',
data_prefix=dict(pts='training/velodyne_reduced'),
pipeline=train_pipeline,
modality=input_modality,
test_mode=False,
metainfo=metainfo,
# 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',
backend_args=backend_args)))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR',
backend_args=backend_args))
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
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