Skip to content

Latest commit

 

History

History
30 lines (30 loc) · 1.13 KB

INSTALL.md

File metadata and controls

30 lines (30 loc) · 1.13 KB
Raw

The code was tested on Ubuntu 16.04, with Anaconda Python 3.6 and PyTorch v1.0.0. NVIDIA GPUs are needed for both training and testing. After install Anaconda: 0. [Optional but recommended] create a new conda environment. ~~~ conda create --name KM3D python=3.6 ~~~ And activate the environment. ~~~ conda activate KM3D ~~~

  1. Install pytorch1.0.0: 
    conda install pytorch=1.0.0 torchvision -c pytorch
  2. Install the requirements 
    pip install -r requirements.txt
  3. Compile deformable convolutional (from DCNv2). 
    cd $KM3D_ROOT/src/lib/models/networks/ # [recommended]
# or git clone https://github.com/CharlesShang/DCNv2/ # clone if it is not automatically downloaded by `--recursive`.
cd DCNv2
./make.sh
  4. Compile iou3d (from pointRCNN). GCC>4.9, I have tested it with GCC 5.4.0 and GCC 4.9.4, both of them are ok. 
    cd $KM3D_ROOT/src/lib/utiles/iou3d
python setup.py install