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get_started.md

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Prerequisites

  • Linux or macOS
  • Python 3.6+
  • PyTorch 1.3+
  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
  • GCC 5+
  • MMCV

Note: You need to run pip uninstall mmcv first if you have mmcv installed. If mmcv and mmcv-full are both installed, there will be ModuleNotFoundError.

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 semask python=3.7 -y
conda activate semask

conda install pytorch=1.6.0 torchvision cudatoolkit=10.1 -c pytorch
pip install mmcv-full==latest+torch1.5.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html
git clone https://github.com/Picsart-AI-Research/SeMask-Segmentation
cd SeMask-Segmentation/SeMask-FPN
pip install -e .  # or "python setup.py develop"

mkdir data
ln -s $DATA_ROOT data

Note:

  1. The version+git_hash will also be saved in trained models meta, e.g. 0.5.0+c415a2e.
  2. When MMsegmentation is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it.
  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.