- 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>=1.2.4, <1.3 |
2.8.0 | mmcv-full>=1.2.4, <1.3 |
2.7.0 | 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
.
-
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
.
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
- Modulated Deformable Convolution
- ROI pooling
- Deformable ROI pooling
- CARAFE: Content-Aware ReAssembly of FEatures
- SyncBatchNorm
- CrissCrossAttention: Criss-Cross Attention
- MaskedConv2d
- Temporal Interlace Shift
- nms_cuda
- sigmoid_focal_loss_cuda
- bbox_overlaps
So if you try to run inference with a model containing above ops you will get an error. The following table lists the related methods that cannot inference on CPU due to dependency on these operators
Operator | Model |
---|---|
Deformable Convolution/Modulated Deformable Convolution | DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS |
MaskedConv2d | Guided Anchoring |
CARAFE | CARAFE |
SyncBatchNorm | ResNeSt |
Notice: MMDetection does not support training with CPU for now.
We provide a Dockerfile to build an image. Ensure that you are using docker version >=19.03.
# 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
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 .
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
To verify whether MMDetection and the required environment are installed correctly, we can run sample Python code to initialize a detector and run inference a demo image:
from mmdet.apis import init_detector, inference_detector
config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
# download the checkpoint from model zoo and put it in `checkpoints/`
# url: http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
device = 'cuda:0'
# init a detector
model = init_detector(config_file, checkpoint_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.