In this example:
- Export traced and scripted models from PyTorch code in Python (
dotnetlibtorch.py) - Create a DLPack tensor in C# (
dotnetlibtorch.cs) - Pass it to C function that uses libtorch and provided model to process the input (
dotnetlibtorch.cpp) - Display the returned tensor in C# (
dotnetlibtorch.cs)
# build C++ library with an exported C function that consumes a DLPack tensor and returns a DLPack tensor
# C++ library dotnetlibtorch.cpp adds 1 to the passed tensor
CMAKE_PREFIX_PATH=$(python3 -c 'import torch; print(torch.__path__[0])')
mkdir -p build
pushd build
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH="$CMAKE_PREFIX_PATH" ..
make
popd# dump export a linear PyTorch model in two versions: jit_scripted_model.pt and jit_traced_model.pt
python3 dotnetlibtorch.py
# run C# caller that only does processing in PyTorch code
dotnet run dotnetlibtorch.cs
# run C# caller that uses JIT scripted model for processing
dotnet run dotnetlibtorch.cs jit_scripted_model.pt
# run C# caller that uses JIT scripted model for processing
dotnet run dotnetlibtorch.cs jit_traced_model.ptBefore passing to libtorch
type_code=kDLInt, bits=32, lanes=1, ndim=2, shape=[2,3], strides=[3,1]
(0, 0) = 0
(0, 1) = 1
(0, 2) = 2
(1, 0) = 3
(1, 1) = 4
(1, 2) = 5
After passing to libtorch
type_code=kDLInt, bits=32, lanes=1, ndim=2, shape=[2,3], strides=[3,1]
(0, 0) = 1
(0, 1) = 2
(0, 2) = 3
(1, 0) = 4
(1, 1) = 5
(1, 2) = 6
Called deleter