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Installation

Requirements

  • Linux or macOS (Windows is not currently officially supported)
  • Python 3.6+
  • PyTorch 1.3+
  • CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
  • GCC 5+
  • mmcv

Install mmdetection

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, e.g.,

conda install pytorch torchvision -c pytorch

Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

E.g.1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

E.g. 2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch

If you build PyTorch from source instead of installing the prebuilt pacakge, you can use more CUDA versions such as 9.0.

c. Install mmcv, we recommend you to install the pre-build mmcv as below.

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

See here for different versions of MMCV compatible to different PyTorch and CUDA versions. Optionally you can choose to compile mmcv from source by the following command

git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
pip install -e .
cd ..

d. Clone the mmdetection repository.

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection

e. Install build requirements and then install mmdetection. (We install our forked version of pycocotools via the github repo instead of pypi for better compatibility with our repo.)

pip install -r requirements/build.txt
pip install -v -e .  # or "python setup.py develop"

If you build mmdetection on macOS, replace the last command with

CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .

Note:

  1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

    Important: Be sure to remove the ./build folder if you reinstall mmdet with a different CUDA/PyTorch version.

    pip uninstall mmdet
rm -rf ./build
find . -name "*.so" | xargs rm

  2. Following the above instructions, mmdetection 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 -v -e . will only install the minimum runtime requirements. To use optional dependencies like albumentations and imagecorruptions either install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -v -e .[optional]). Valid keys for the extras field are: all, tests, build, and optional.

Install with CPU only

The code can be built for CPU only environment (where CUDA isn't available).

In CPU mode you can run the demo/webcam_demo.py for example. However some functionality is gone in this mode:

  • Deformable Convolution
  • Deformable ROI pooling
  • CARAFE: Content-Aware ReAssembly of FEatures
  • nms_cuda
  • sigmoid_focal_loss_cuda

So if you try to run inference with a model containing deformable convolution you will get an error. Note: We set use_torchvision=True on-the-fly in CPU mode for RoIPool and RoIAlign

Another option: Docker Image

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.5, CUDA 10.1
docker build -t mmdetection docker/

Run it with

docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection

A from-scratch setup script

Here is a full script for setting up mmdetection with conda.

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

# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
conda install -c pytorch pytorch torchvision -y

# install the latest mmcv
pip install mmcv-full

# install mmdetection
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -r requirements/build.txt
pip install -v -e .

Using multiple MMDetection versions

The train and test scripts already modify the PYTHONPATH to ensure the script use the MMDetection in the current directory.

To use the default MMDetection installed in the environment rather than that you are working with, you can remove the following line in those scripts

PYTHONPATH="$(dirname $0)/..":$PYTHONPATH