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tensorflow-setup.md

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Below are instructions on how to setup completion3d training in tensorflow. New networks can be added following the template in tensorflow/models/TopNet.py and adding corresponding import statements in tensorflow/main.py and tensorflow/utils/train_utils.py Feel free to submit new model additions to the benchmark as a pull request.

Clone Repository

git clone [email protected]:lynetcha/completion3d.git

Install CUDA and CUDNN

Instructions below assume CUDA 9.0 is installed in /usr/local/cuda

export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

Tensorflow Python Environment

cd completion3d/tensorflow
PYTHON_BIN=/path/to/python3.6
virtualenv -p $PYTHON_BIN comp3d_tf_venv
source comp3d_tf_venv/bin/activate
pip install -r ../requirements/tensorflow-requirements.txt

Build Chamfer and EMD functions

cd utils/pc_distance
make
cd ../../../

Run Tensorflow Training/Testing

cd tensorflow

Link data (see data-setup.md)

ln -s /path/to/data data

Modify parameters in run.sh

chmod +x run.sh
./run.sh

Benchmark submission instructions

To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.