Windows Support (Experimental) (#75)
* Windows basic support * getting_started updated for Windows. * add experimental * install.md restructured to seperate Windows & Linux. * fix problems in install.md * fix mmcv version problem. * Fix fastscnn resize problems. (#82) * Fix fast_scnn resize problems * Fix fast_scnn resize problems 1 * Fix fast_scnn resize problems 2 * test for pascal voc * [Doc] Add annotaion format note (#77) * update pytorch version to 1.6.0 in install.md * del fastscnn_pascal config * del create_symlink=True * Merge instructions for Linux & Windows * mmcv version updated * redundant newline deleted * Update docs/install.md Co-authored-by: Jerry Jiarui XU <xvjiarui0826@gmail.com> * Update docs/install.md Co-authored-by: Jerry Jiarui XU <xvjiarui0826@gmail.com> Co-authored-by: Jerry Jiarui XU <xvjiarui0826@gmail.com>pull/94/head^2
John Zhu
GitHub
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## Installation
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## Requirements
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### Requirements
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- Linux (Windows is not officially supported)
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- Linux or Windows(Experimental)
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- Python 3.6+
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- PyTorch 1.3 or higher
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- [mmcv](https://github.com/open-mmlab/mmcv)
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### Install mmsegmentation
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## Installation
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a. Create a conda virtual environment and activate it.
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@ -17,15 +15,17 @@ conda activate open-mmlab
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```
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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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Here we use PyTorch 1.6.0 and CUDA 10.1.
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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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conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
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```
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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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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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**Install mmcv for Linux:**
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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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@ -33,6 +33,33 @@ The pre-build mmcv-full (with PyTorch 1.5 and CUDA 10.1) can be installed by run
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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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**Install mmcv for Windows (Experimental):**
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For Windows, the installation of MMCV requires native C++ compilers, such as cl.exe. Please add the compiler to %PATH%.
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A typical path for cl.exe looks like the following if you have Windows SDK and Visual Studio installed on your computer:
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```shell
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C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Tools\MSVC\14.26.28801\bin\Hostx86\x64
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```
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Or you should download the cl compiler from web and then set up the path.
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Then, clone mmcv from github and install mmcv via pip:
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```shell
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git clone https://github.com/open-mmlab/mmcv.git
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cd mmcv
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pip install -e .
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```
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Or simply:
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```shell
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pip install mmcv
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```
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Currently, mmcv-full is not supported on Windows.
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d. Install MMSegmentation.
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```shell
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@ -47,24 +74,28 @@ pip install git+https://github.com/open-mmlab/mmsegmentation.git # install the m
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Instead, if you would like to install MMSegmentation in `dev` mode, run following
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```shell
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git clone https://github.com/open-mmlab/mmsegmentation
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git clone https://github.com/open-mmlab/mmsegmentation.git
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cd mmsegmentation
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pip install -e . # or "python setup.py develop"
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```
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Note:
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1. The `version+git_hash` will also be saved in trained models meta, e.g. 0.5.0+c415a2e.
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1. When training or testing models on Windows, please ensure that all the '\\' in paths are replaced with '/'. Add .replace('\\', '/') to your python code wherever path strings occur.
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2. When MMsegmentation is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it.
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2. The `version+git_hash` will also be saved in trained models meta, e.g. 0.5.0+c415a2e.
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3. If you would like to use `opencv-python-headless` instead of `opencv-python`,
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3. When MMsegmentation is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it.
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4. If you would like to use `opencv-python-headless` instead of `opencv-python`,
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you can install it before installing MMCV.
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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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5. 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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## A from-scratch setup script
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### Linux
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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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@ -72,12 +103,31 @@ Here is a full script for setting up mmsegmentation with conda and link the data
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conda create -n open-mmlab python=3.7 -y
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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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conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
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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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git clone https://github.com/open-mmlab/mmsegmentation
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git clone https://github.com/open-mmlab/mmsegmentation.git
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cd mmsegmentation
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pip install -e . # or "python setup.py develop"
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mkdir data
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ln -s $DATA_ROOT data
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```
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### Windows(Experimental)
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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
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%DATA_ROOT%. Notice: It must be an absolute path).
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```shell
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conda create -n open-mmlab python=3.7 -y
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conda activate open-mmlab
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conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
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set PATH=full\path\to\your\cpp\compiler;%PATH%
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pip install mmcv
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git clone https://github.com/open-mmlab/mmsegmentation.git
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cd mmsegmentation
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pip install -e . # or "python setup.py develop"
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mklink /D data %DATA_ROOT%
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```
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@ -3,7 +3,7 @@ import mmcv
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from .version import __version__, version_info
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MMCV_MIN = '1.0.5'
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MMCV_MAX = '1.1.0'
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MMCV_MAX = '1.1.1'
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def digit_version(version_str):
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@ -40,10 +40,12 @@ def collect_env():
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devices[torch.cuda.get_device_name(k)].append(str(k))
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for name, devids in devices.items():
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env_info['GPU ' + ','.join(devids)] = name
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gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
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gcc = gcc.decode('utf-8').strip()
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env_info['GCC'] = gcc
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try:
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gcc = subprocess.check_output('gcc --version | head -n1', shell=True)
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gcc = gcc.decode('utf-8').strip()
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env_info['GCC'] = gcc
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except subprocess.CalledProcessError:
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env_info['GCC'] = 'n/a'
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env_info['PyTorch'] = torch.__version__
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env_info['PyTorch compiling details'] = get_build_config()
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