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| cmake_minimum_required(VERSION 3.10) | ||
| project(run_model) | ||
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| option(USE_TORCHVISION "Whether to link to torchvision" OFF) | ||
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| find_package(Torch REQUIRED) | ||
| if(USE_TORCHVISION) | ||
| find_package(TorchVision REQUIRED) | ||
| endif() | ||
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| add_executable(run_model run_model.cpp) | ||
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| target_link_libraries(run_model "${TORCH_LIBRARIES}") | ||
| if(USE_TORCHVISION) | ||
| target_link_libraries(run_model TorchVision::TorchVision) | ||
| endif() | ||
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| set_property(TARGET run_model PROPERTY CXX_STANDARD 17) |
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| Using torchvision models in C++ | ||
| =============================== | ||
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| This is a minimal example of getting TorchVision models to work in C++ with | ||
| Torchscript. The model is first scripted in Python and exported to a file, and | ||
| then loaded in C++. For a similar tutorial, see [this | ||
| tutorial](https://pytorch.org/tutorials/advanced/cpp_export.html). | ||
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| In order to successfully compile this example, make sure you have ``LibTorch`` | ||
| installed. You can either: | ||
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| - Install PyTorch normally | ||
| - Or download the LibTorch C++ distribution. | ||
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| In both cases refer [here](https://pytorch.org/get-started/locally/) the | ||
| corresponding install or download instructions. | ||
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| Some torchvision models only depend on PyTorch operators, and can be used in C++ | ||
| without depending on the torchvision lib. Other models rely on torchvision's C++ | ||
| operators like NMS, RoiAlign (typically the detection models) and those need to | ||
| be linked against the torchvision lib. | ||
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| We'll first see the simpler case of running a model without the torchvision lib | ||
| dependency. | ||
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| Running a model that doesn't need torchvision lib | ||
| ------------------------------------------------- | ||
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| Create a ``build`` directory inside the current one. | ||
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| ```bash | ||
| mkdir build | ||
| cd build | ||
| ``` | ||
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| Then run `python ../trace_model.py` which should create a `resnet18.pt` file in | ||
| the build directory. This is the scripted model that will be used in the C++ | ||
| code. | ||
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| We can now start building with CMake. We have to tell CMake where it can find | ||
| the necessary PyTorch resources. If you installed PyTorch normally, you can do: | ||
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| ```bash | ||
| TORCH_PATH=$(python -c "import pathlib, torch; print(pathlib.Path(torch.__path__[0]))") | ||
| Torch_DIR="${TORCH_PATH}/share/cmake/Torch" # there should be .cmake files in there | ||
| cmake .. -DTorch_DIR=$Torch_DIR | ||
| ``` | ||
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| If instead you downloaded the LibTorch somewhere, you can do: | ||
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| ```bash | ||
| cmake .. -DCMAKE_PREFIX_PATH=/path/to/libtorch | ||
| ``` | ||
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| Then `cmake --build .` and you should now be able to run | ||
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| ```bash | ||
| ./run_model resnet18.pt | ||
| ``` | ||
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| If you try to run the model with a model that depends on the torchvision lib, like | ||
| `./run_model fasterrcnn_resnet50_fpn.pt`, you should get a runtime error. This is | ||
| because the executable wasn't linked against the torchvision lib. | ||
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| Running a model that needs torchvision lib | ||
| ------------------------------------------ | ||
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| First, we need to build the torchvision lib. To build the torchvision lib go to | ||
| the root of the torchvision project and run: | ||
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| ```bash | ||
| mkdir build | ||
| cd build | ||
| cmake .. -DCMAKE_PREFIX_PATH=/path/to/libtorch # or -DTorch_DIR= if you installed PyTorch normally, see above | ||
| cmake --build . | ||
| cmake --install . | ||
| ``` | ||
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| You may want to pass `-DCMAKE_INSTALL_PREFIX=/path/to/libtorchvision` for | ||
| cmake to copy/install the files to a specific location (e.g. `$CONDA_PREFIX`). | ||
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| **DISCLAIMER**: the `libtorchvision` library includes the torchvision | ||
| custom ops as well as most of the C++ torchvision APIs. Those APIs do not come | ||
| with any backward-compatibility guarantees and may change from one version to | ||
| the next. Only the Python APIs are stable and with backward-compatibility | ||
| guarantees. So, if you need stability within a C++ environment, your best bet is | ||
| to export the Python APIs via torchscript. | ||
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| Now that libtorchvision is built and installed we can tell our project to use | ||
| and link to it via the `-DUSE_TORCHVISION` flag. We also need to tell CMake | ||
| where to find it, just like we did with LibTorch, e.g.: | ||
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| ```bash | ||
| cmake .. -DTorch_DIR=$Torch_DIR -DTorchVision_DIR=path/to/libtorchvision -DUSE_TORCHVISION=ON | ||
| cmake --build . | ||
| ``` | ||
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| Now the `run_model` executable should be able to run the | ||
| `fasterrcnn_resnet50_fpn.pt` file. |
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