Deep-Learning-with-PyTorch

468 CHAPTER 15 Deploying to production
You can download a C++ library of PyTorch from the PyTorch page. But given that
we already have PyTorch installed, 10 we might as well use that; it comes with all we
need for C++. We need to know where our PyTorch installation lives, so open Python
and check torch.__file__, which may say /usr/local/lib/python3.7/dist-packages/
torch/__init__.py. This means the CMake files we need are in /usr/local/lib/
python3.7/dist-packages/torch/share/cmake/.
While using CMake seems like overkill for a single source file project, linking to
PyTorch is a bit complex; so we just use the following as a boilerplate CMake file. 11
Listing 15.12
CMakeLists.txt
cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(cyclegan-jit)
We need Torch.
find_package(Torch REQUIRED)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
add_executable(cyclegan-jit cyclegan_jit.cpp)
target_link_libraries(cyclegan-jit pthread jpeg X11)
target_link_libraries(cyclegan-jit "${TORCH_LIBRARIES}")
set_property(TARGET cyclegan-jit PROPERTY CXX_STANDARD 14)
Project name. Replace it with your
own here and on the other lines.
We want to compile
an executable named
cyclegan-jit from the
cyclegan_jit.cpp
source file.
Links to the bits required
for CImg. CImg itself is allinclude,
so it does not
appear here.
It is best to make a build directory as a subdirectory of where the source code resides
and then in it run CMake as 12 CMAKE_PREFIX_PATH=/usr/local/lib/python3.7/
dist-packages/torch/share/cmake/ cmake .. and finally make. This will build the
cyclegan-jit program, which we can then run as follows:
./cyclegan-jit ../traced_zebra_model.pt
../../data/p1ch2/horse.jpg /tmp/z.jpg
We just ran our PyTorch model without Python. Awesome! If you want to ship your
application, you likely want to copy the libraries from /usr/local/lib/python3.7/distpackages/torch/lib
into where your executable is, so that they will always be found.
15.4.2 C++ from the start: The C++ API
The C++ modular API is intended to feel a lot like the Python one. To get a taste, we
will translate the CycleGAN generator into a model natively defined in C++, but without
the JIT. We do, however, need the pretrained weights, so we’ll save a traced version
of the model (and here it is important to trace not a function but the model).
10 We hope you have not been slacking off about trying out things you read.
11 The code directory has a bit longer version to work around Windows issues.
12 You might have to replace the path with where your PyTorch or LibTorch installation is located. Note that
the C++ library can be more picky than the Python one in terms of compatibility: If you are using a CUDAenabled
library, you need to have the matching CUDA headers installed. If you get cryptic error messages
about “Caffe2 using CUDA,” you need to install a CPU-only version of the library, but CMake found a CUDAenabled
one.