Prerequisites¶

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

The compatible MMDetection and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues.

MMDetection version	MMCV version
master	mmcv-full>=1.1.5, <=1.3
2.6.0	mmcv-full>=1.1.5, <=1.3
2.5.0	mmcv-full>=1.1.5, <=1.3
2.4.0	mmcv-full>=1.1.1, <=1.3
2.3.0	mmcv-full==1.0.5
2.3.0rc0	mmcv-full>=1.0.2
2.2.1	mmcv==0.6.2
2.2.0	mmcv==0.6.2
2.1.0	mmcv>=0.5.9, <=0.6.1
2.0.0	mmcv>=0.5.1, <=0.5.8

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.

Installation¶

Create a conda virtual environment and activate it.

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

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.

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

pip install mmcv-full==latest+torch1.6.0+cu101 -f https://download.openmmlab.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
MMCV_WITH_OPS=1 pip install -e .  # package mmcv-full will be installed after this step
cd ..

Or directly run

pip install mmcv-full

Clone the MMDetection repository.

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

Install build requirements and then install MMDetection.

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

Note:

a. 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.

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

c. 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.

Another option: Docker Image¶

We provide a Dockerfile to build an image.

# build an image with PyTorch 1.6, 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¶

Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMDetection with conda.

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

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit==10.1 -c pytorch -y

# install the latest mmcv
pip install mmcv-full==latest+torch1.6.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html

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

Developing with 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

Verification¶

To verify whether MMDetection and the required environment are installed correctly, we can run sample python codes to initialize a detector and inference a demo image:

from mmdet.apis import init_detector, inference_detector

config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
device = 'cuda:0'
# init a detector
model = init_detector(config_file, device=device)
# inference the demo image
inference_detector(model, 'demo/demo.jpg')

The above code is supposed to run successfully upon you finish the installation.