conda create --name openmmlab python=3.8 -y
conda activate openmmlab
Step 2. Install PyTorch following official instructions, e.g.
On GPU platforms:
conda install pytorch torchvision -c pytorch
Warning
This command will automatically install the latest version PyTorch and cudatoolkit, please check whether they match your environment.
On CPU platforms:
conda install pytorch torchvision cpuonly -c pytorch
Step 3. Install MMEngine and MMCV using MIM.
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.1"
Note that some of the demo scripts in MMPose require MMDetection (mmdet) for human detection. If you want to run these demo scripts with mmdet, you can easily install mmdet as a dependency by running:
mim install "mmdet>=3.1.0"
Here are the version correspondences between mmdet, mmpose and mmcv:
mmdet 2.x <=> mmpose 0.x <=> mmcv 1.x
mmdet 3.x <=> mmpose 1.x <=> mmcv 2.x
If you encounter version incompatibility issues, please check the correspondence using pip list | grep mm and upgrade or downgrade the dependencies accordingly. Please note that mmcv-full is only for mmcv 1.x, so please uninstall it first, and then use mim install mmcv to install mmcv 2.x.
Best Practices
Build MMPose from source
To develop and run mmpose directly, install it from source:
git clone https://github.com/open-mmlab/mmpose.git
cd mmpose
pip install -r requirements.txt
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.
Install as a Python package
To use mmpose as a dependency or third-party package, install it with pip:
mim install "mmpose>=1.1.0"
Verify the installation
To verify that MMPose is installed correctly, you can run an inference demo with the following steps.
Step 1. We need to download config and checkpoint files.
mim download mmpose --config td-hm_hrnet-w48_8xb32-210e_coco-256x192 --dest .
The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files td-hm_hrnet-w48_8xb32-210e_coco-256x192.py and td-hm_hrnet-w48_8xb32-210e_coco-256x192-0e67c616_20220913.pth in your current folder.
Step 2. Run the inference demo.
Option (A). If you install mmpose from source, just run the following command under the folder $MMPOSE:
python demo/image_demo.py \
tests/data/coco/000000000785.jpg \
td-hm_hrnet-w48_8xb32-210e_coco-256x192.py \
td-hm_hrnet-w48_8xb32-210e_coco-256x192-0e67c616_20220913.pth \
--out-file vis_results.jpg \
--draw-heatmap
If everything goes fine, you will be able to get the following visualization result from vis_results.jpg in your current folder, which displays the predicted keypoints and heatmaps overlaid on the person in the image.
Option (B). If you install mmpose with pip, open you python interpreter and copy & paste the following codes.
from mmpose.apis import inference_topdown, init_model
from mmpose.utils import register_all_modules
register_all_modules()
config_file = 'td-hm_hrnet-w48_8xb32-210e_coco-256x192.py'
checkpoint_file = 'td-hm_hrnet-w48_8xb32-210e_coco-256x192-0e67c616_20220913.pth'
model = init_model(config_file, checkpoint_file, device='cpu') # or device='cuda:0'
# please prepare an image with person
results = inference_topdown(model, 'demo.jpg')
The demo.jpg can be downloaded from Github.
The inference results will be a list of PoseDataSample, and the predictions are in the pred_instances, indicating the detected keypoint locations and scores.
MMCV version should match PyTorch version strictly. If you encounter the following issues:
No module named ‘mmcv.ops’
No module named ‘mmcv._ext’
It means that the current PyTorch version does not match the CUDA version. You can check the CUDA version using nvidia-smi, and it should match the +cu1xx in PyTorch version in pip list | grep torch. Otherwise, you need to uninstall PyTorch and reinstall it, then reinstall MMCV (the installation order CAN NOT be swapped).
Customize Installation
CUDA versions
When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:
For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.
Please make sure the GPU driver satisfies the minimum version requirements. See this table for more information.
Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIA’s website, and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in conda install command.
Install MMEngine without MIM
To install MMEngine with pip instead of MIM, please follow MMEngine installation guides.
For example, you can install MMEngine by the following command.
pip install mmengine
Install MMCV without MIM
MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.
To install MMCV with pip instead of MIM, please follow MMCV installation guides. This requires manually specifying a find-url based on PyTorch version and its CUDA version.
For example, the following command install mmcv built for PyTorch 1.10.x and CUDA 11.3.
pip install 'mmcv>=2.0.1' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
Install on CPU-only platforms
MMPose can be built for CPU only environment. In CPU mode you can train, test or inference a model.
However, some functionalities are missing in this mode, usually GPU-compiled ops like Deformable Convolution. Most models in MMPose don’t depend on these ops, but if you try to train/test/infer a model containing these ops, an error will be raised.
Install on Google Colab
Google Colab usually has PyTorch installed,
thus we only need to install MMEngine, MMCV and MMPose with the following commands.
Step 1. Install MMEngine and MMCV using MIM.
!pip3 install openmim
!mim install mmengine
!mim install "mmcv>=2.0.1"
Step 2. Install MMPose from the source.
!git clone https://github.com/open-mmlab/mmpose.git
%cd mmpose
!pip install -e .
Step 3. Verification.
import mmpose
print(mmpose.__version__)
# Example output: 1.1.0
Note that within Jupyter, the exclamation mark ! is used to call external executables and %cd is a magic command to change the current working directory of Python.
Using MMPose with Docker
We provide a Dockerfile to build an image. Ensure that your docker version >=19.03.
# build an image with PyTorch 1.8.0, CUDA 10.1, CUDNN 7.
# If you prefer other versions, just modified the Dockerfile
docker build -t mmpose docker/
Important: Make sure you’ve installed the nvidia-container-toolkit.
Run it with
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmpose/data mmpose
{DATA_DIR} is your local folder containing all the datasets for mmpose.
If you encounter the error message like permission denied, please add sudo at the start of the command and try it again.
Trouble shooting
If you have some issues during the installation, please first view the FAQ page.
You may open an issue on GitHub if no solution is found.
