Evaluating the Convolutional Social Pooling (CSP) work using the PyTorch C++ frontend
You can create an Anaconda environment, install CMake and the C++ distributions of PyTorch as follows:
1- Download the C++ distributions of PyTorch (LibTorch) ZIP archive:
wget https://download.pytorch.org/libtorch/nightly/cpu/libtorch-shared-with-deps-latest.zip
unzip libtorch-shared-with-deps-latest.zip
Note that the above link has CPU-only LibTorch.
2- Create and activate a new conda environment:
conda create --name cpp-pytorch
conda activate cpp-pytorch
3- Install CMake:
conda install -c anaconda cmake
CMake is the recommended build system and will be well supported into the future.
This repository includes a 'eval.cpp' script that loads a traced model and evaluates it on a given set of inputs. The model is already traced and saved at '/traced-models' and a 'CMakeLists.txt' file is provide to build the application. Follow these steps to test the code:
1- Clone the repository to your local machine.
2- Run the following commands to build the application from within the cloned repository folder:
mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH=/absolute/path/to/libtorch ..
cmake --build . --config Release
where /absolute/path/to/libtorch should be the ABSOLUTE path to the unzipped LibTorch distribution.
3- Execute the resulting binary found in the build folder:
./csp-cpp "../traced-models/traced_net_model.pt"
1- PyTorch documentation
https://pytorch.org/cppdocs/installing.html
2- CSP Code
https://github.com/nachiket92/conv-social-pooling