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GETTING_STARTED.md

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Getting Started

Installation

a. [Optional] 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 (CUDA is required):

# CUDA 9.2
conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=9.2 -c pytorch

# CUDA 10.0
conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch

The higher versions are not covered by tests.

c. Clone mmskeleton from github:

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

d. Install mmskeleton:

python setup.py develop

e. Install nms for person estimation:

cd mmskeleton/ops/nms/
python setup_linux.py develop
cd ../../../

f. [Optional] Install mmdetection for person detection:

python setup.py develop --mmdet

In the event of a failure installation, please install mmdetection manually.

g. To verify that mmskeleton and mmdetection installed correctly, use:

python mmskl.py pose_demo [--gpus $GPUS]
# or "python mmskl.py pose_demo_HD [--gpus $GPUS]" for a higher accuracy

An generated video as below will be saved under the prompted path.

Basic usage:

Any application in mmskeleton is described by a configuration file. That can be started by a uniform command:

python mmskl.py $CONFIG_FILE [--options $OPTHION]

which is equivalent to

mmskl $CONFIG_FILE [--options $OPTHION]

Optional arguments options is defined in the configuration file. You can check them via:

mmskl $CONFIG_FILE -h

Example:

See START_RECOGNITION.md for learning how to train a model for skeleton-based action recognitoin.

See CUSTOM_DATASET for building your own skeleton-based dataset.

See CREATE_APPLICATION for creating your own mmskeleton application.