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Commit 7aa442d5 authored by raojy's avatar raojy
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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
mmdetection3d/.readthedocs.yml 0 → 100644
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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
mmdetection3d/LICENSE 0 → 100644
View file @ 7aa442d5
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mmdetection3d/MANIFEST.in 0 → 100644
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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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This diff is collapsed.
mmdetection3d/README_zh-CN.md 0 → 100644
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This diff is collapsed.
mmdetection3d/configs/3dssd/3dssd_4xb4_kitti-3d-car.py 0 → 100644
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_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
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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_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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