The code was tested on Ubuntu 16.04, with Anaconda Python 3.6 and PyTorch v1.7.0. NVIDIA GPUs are needed for both training and testing.
After install Anaconda:
[Optional but recommended] create a new conda environment.
conda create --name EllipseNet python=3.6
And activate the environment.
conda activate EllipseNet
Install pytorch:
pip install torch==1.7.0 torchvision==0.8.0
# PYTORCH=/path/to/pytorch # usually ~/anaconda3/envs/EllipseNet/lib/python3.6/site-packages/
# for pytorch v0.4.0
sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
# for pytorch v0.4.1
sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
Install COCOAPI:
# COCOAPI=/path/to/clone/cocoapi
git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
cd $COCOAPI/PythonAPI
make
python setup.py install --user
Clone this repo:
EllipseNet_ROOT=/path/to/clone/EllipseNet
git clone https://git.openi.org.cn/capepoint/EllipseNet $EllipseNet_ROOT
Install the requirements
pip install -r requirements.txt
Compile deformable convolutional (from DCNv2).
cd $EllipseNet_ROOT/src/lib/models/networks/DCNv2
./make.sh
Download pertained models for testing.
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