mmsegmentation/docs/install.md

Installation

Requirements

• Linux (Windows is not officially supported)
• Python 3.6+
• PyTorch 1.3 or higher
• mmcv

Install mmsegmentation

a. Create a conda virtual environment and activate it.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

b. Install PyTorch and torchvision following the official instructions. Here we use PyTorch 1.5.0 and CUDA 10.1. You may also switch to other version by specifying the version number.

conda install pytorch=1.5.0 torchvision cudatoolkit=10.1 -c pytorch

c. Install MMCV following the official instructions. Either mmcv or mmcv-full is compatible with MMSegmentation, but for methods like CCNet and PSANet, CUDA ops in mmcv-full is required

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)

pip install mmcv-full==latest+torch1.5.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html

d. Install MMSegmentation.

pip install mmseg # install the latest release

or

pip install git+https://github.com/open-mmlab/mmsegmentation.git # install the master branch

Note:

1. The git commit id will be written to the version number with step e, e.g. 0.5.0+c415a2e. The version will also be saved in trained models. It is recommended that you run step e each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

2. Following the above instructions, mmsegmentation is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).

3. If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.

4. Some dependencies are optional. Simply running pip install -e . will only install the minimum runtime requirements. 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.

A from-scratch setup script

Here is a full script for setting up mmsegmentation with conda and link the dataset path (supposing that your dataset path is $DATA_ROOT).

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

conda install pytorch=1.5.0 torchvision cudatoolkit=10.1 -c pytorch
pip install mmcv-full==latest+torch1.5.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html
pip install git+https://github.com/open-mmlab/mmsegmentation.git

mkdir data
ln -s $DATA_ROOT data