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Add pypi deployment (open-mmlab#11)
* add pypi deployment * remove useless jobs * fixed typo and cleanup * delete build.tx * add change log Co-authored-by: Jiarui XU <[email protected]>
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‎.github/CONTRIBUTING.md‎

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- [yapf](https://github.com/google/yapf): formatter
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- [isort](https://github.com/timothycrosley/isort): sort imports
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Style configurations of yapf and isort can be found in [.style.yapf](../.style.yapf) and [.isort.cfg](../.isort.cfg).
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Style configurations of yapf and isort can be found in [setup.cfg](../setup.cfg) and [.isort.cfg](../.isort.cfg).
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We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`,
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fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.

‎.github/workflows/build.yml‎

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# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
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# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
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name: build
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on: [push, pull_request]
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jobs:
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lint:
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runs-on: ubuntu-latest
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steps:
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python-version: 3.7
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install CUDA
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if: ${{matrix.torch == '1.5.0+cu101'}}
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run: |
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export INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb
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wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER}
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sudo dpkg -i ${INSTALLER}
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wget https://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/7fa2af80.pub
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sudo apt-key add 7fa2af80.pub
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sudo apt update -qq
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sudo apt install -y cuda-${CUDA_SHORT/./-} cuda-cufft-dev-${CUDA_SHORT/./-}
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sudo apt clean
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export CUDA_HOME=/usr/local/cuda-${CUDA_SHORT}
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export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/include:${LD_LIBRARY_PATH}
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export PATH=${CUDA_HOME}/bin:${PATH}
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sudo apt-get install -y ninja-build
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- name: Install Pillow
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if: ${{matrix.torchvision == '0.4.2+cpu'}}
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run: pip install Pillow==6.2.2
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- name: Install PyTorch
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run: pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
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- name: Install mmseg dependencies
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run: |
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pip install mmcv-full==latest+torch${{matrix.torch}} -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
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pip install -r requirements.txt
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- name: Build and install
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run: rm -rf .eggs && pip install -e .
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- name: Run unittests and generate coverage report
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run: |
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coverage run --branch --source mmseg -m pytest tests/
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coverage xml
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coverage report -m --omit="mmseg/utils/*","mmseg/apis/*"
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# Only upload coverage report for python3.7 && pytorch1.5
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- name: Upload coverage to Codecov
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if: ${{matrix.torch == '1.5.0+cu101' && matrix.python-version == '3.7'}}
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uses: codecov/[email protected]
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with:
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file: ./coverage.xml
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flags: unittests
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env_vars: OS,PYTHON
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name: codecov-umbrella
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fail_ci_if_error: false
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- uses: actions/checkout@v2
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v2
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install CUDA
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if: ${{matrix.torch == '1.5.0+cu101'}}
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run: |
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export INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb
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wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER}
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sudo dpkg -i ${INSTALLER}
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wget https://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/7fa2af80.pub
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sudo apt-key add 7fa2af80.pub
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sudo apt update -qq
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sudo apt install -y cuda-${CUDA_SHORT/./-} cuda-cufft-dev-${CUDA_SHORT/./-}
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sudo apt clean
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export CUDA_HOME=/usr/local/cuda-${CUDA_SHORT}
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export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/include:${LD_LIBRARY_PATH}
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export PATH=${CUDA_HOME}/bin:${PATH}
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sudo apt-get install -y ninja-build
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- name: Install Pillow
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if: ${{matrix.torchvision == '0.4.2+cpu'}}
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run: pip install Pillow==6.2.2
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- name: Install PyTorch
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run: pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
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- name: Install mmseg dependencies
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run: |
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pip install mmcv-full==latest+torch${{matrix.torch}} -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
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pip install -r requirements.txt
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- name: Build and install
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run: rm -rf .eggs && pip install -e .
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- name: Run unittests and generate coverage report
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run: |
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coverage run --branch --source mmseg -m pytest tests/
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coverage xml
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coverage report -m
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# Only upload coverage report for python3.7 && pytorch1.5
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- name: Upload coverage to Codecov
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if: ${{matrix.torch == '1.5.0+cu101' && matrix.python-version == '3.7'}}
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uses: codecov/[email protected]
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with:
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file: ./coverage.xml
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flags: unittests
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env_vars: OS,PYTHON
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name: codecov-umbrella
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fail_ci_if_error: false

‎.github/workflows/deploy.yml‎

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name: deploy
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on: push
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jobs:
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build-n-publish:
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runs-on: ubuntu-latest
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if: startsWith(github.event.ref, 'refs/tags')
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steps:
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- uses: actions/checkout@v2
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- name: Set up Python 3.7
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uses: actions/setup-python@v2
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with:
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python-version: 3.7
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- name: Build MMSegmentation
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run: python setup.py sdist bdist_wheel
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- name: Publish distribution to PyPI
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run: |
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pip install twine
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twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}

‎.style.yapf‎

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‎README.md‎

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This project is released under the [Apache 2.0 license](LICENSE).
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## Changelog
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v0.5.0 was released in 10/7/2020.
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## Benchmark and model zoo
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Results and models are available in the [model zoo](docs/model_zoo.md).

‎docs/install.md‎

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b. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/).
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Here we use PyTorch 1.5.0 and CUDA 10.1.
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You may also switch to other version by specifying version number.
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You may also switch to other version by specifying the version number.
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```shell
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conda install pytorch=1.5.0 torchvision cudatoolkit=10.1 -c pytorch
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```
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c. Clone the mmsegmentation repository.
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c. Install [MMCV](https://mmcv.readthedocs.io/en/latest/) following the [official instructions](https://mmcv.readthedocs.io/en/latest/#installation).
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Either `mmcv` or `mmcv-full` is compatible with MMSegmentation, but for methods like CCNet and PSANet, CUDA ops in `mmcv-full` is required
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```shell
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git clone http://github.com/open-mmlab/mmsegmentation
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cd mmsegmentation
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```
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d. Install [MMCV](https://mmcv.readthedocs.io/en/latest/).
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Either *mmcv* or *mmcv-full* is compatible with MMSegmentation, but for methods like CCNet and PSANet, CUDA ops in *mmcv-full* is required
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The pre-build mmcv-full (with PyTorch 1.5 and CUDA 10.1) can be installed by running: (other available versions could be found [here](https://mmcv.readthedocs.io/en/latest/#install-with-pip))
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The pre-build *mmcv-full* could be installed by running: (available versions could be found [here](https://mmcv.readthedocs.io/en/latest/#install-with-pip))
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```
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```shell
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pip install mmcv-full==latest+torch1.5.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
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```
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Optionally, you could also install lite version by running:
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```
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pip install mmcv
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```
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or build full version from source:
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```
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pip install mmcv-full
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d. Install MMSegmentation.
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```shell
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pip install mmseg # install the latest release
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```
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e. Install build requirements and then install MMSegmentation.
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or
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```shell
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pip install -r requirements/build.txt # or "pip install -r requirements.txt" for everything.
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pip install -e . # or "python setup.py develop"
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pip install git+https://github.com/open-mmlab/mmsegmentation.git # install the master branch
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Note:
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4. Some dependencies are optional. Simply running `pip install -e .` will only install the minimum runtime requirements.
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To use optional dependencies like `cityscapessripts` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.
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### A from-scratch setup script
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Here is a full script for setting up mmsegmentation with conda and link the dataset path (supposing that your dataset path is $DATA_ROOT).
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conda activate open-mmlab
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conda install pytorch=1.5.0 torchvision cudatoolkit=10.1 -c pytorch
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git clone http://github.com/open-mmlab/mmsegmentation
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cd mmsegmentation
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pip install mmcv-full==latest+torch1.5.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
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‎docs/model_zoo.md‎

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### DeepLabV3
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Please refer to [DeepLabV3](https://github.com/open-mmlab/mmsegmentatio/blob/master/configs/deeplabv3) for details.
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Please refer to [DeepLabV3](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3) for details.
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### PSANet
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Please refer to [PSANet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet) for details.
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### DeepLabV3+
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Please refer to [DeepLabV3+](https://github.com/open-mmlab/mmsegmentatio/blob/master/configs/deeplabv3plus) for details.
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Please refer to [DeepLabV3+](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus) for details.
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### UPerNet
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Please refer to [UPerNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet) for details.
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### NonLocal Net
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Please refer to [NonLocal Net](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nlnet) for details.
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### EncNet
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Please refer to [NonLocal Net](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet) for details.
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### CCNet
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‎mmseg/__init__.py‎

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from .version import __version__, short_version
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from .version import __version__, short_version, version_info
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__all__ = ['__version__', 'short_version']
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__all__ = ['__version__', 'short_version', 'version_info']

‎requirements.txt‎

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-r requirements/build.txt
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-r requirements/optional.txt
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-r requirements/runtime.txt
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-r requirements/tests.txt

‎requirements/build.txt‎

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