Run TensorFlow models in c++ without Bazel, without TensorFlow installation and without compiling Tensorflow. Perform tensor manipulation, use eager execution and run saved models directly from C++.

// Read the graph
cppflow::model model("saved_model_folder");

// Load an image
auto input = cppflow::decode_jpeg(cppflow::read_file(std::string("image.jpg")));

// Cast it to float, normalize to range [0, 1], and add batch_dimension
input = cppflow::cast(input, TF_UINT8, TF_FLOAT);
input = input / 255.f;
input = cppflow::expand_dims(input, 0);

// Run
auto output = model(input);

// Show the predicted class
std::cout << cppflow::arg_max(output, 1) << std::endl;

You can take a look to the examples to see a full example on how to load a deep network and feed it with a sample image.

CppFlow uses Tensorflow C API to run the models, meaning you can use it without installing Tensorflow and without compiling the whole Tensorflow repository with bazel, you just need to download the C API. With this project you can manage and run your models in C++ without worrying about void, malloc or free. With CppFlow you easily can:

• Open saved models created with Python
• Execute Tensorflow neural networks in C++
• Perform tensor manipulation directly from C++

How To Run It

Since it uses TensorFlow 2 C API you just have to download it, check the docs to see a guide on how to do it.

You can either install the library system wide or you can just place the contents of the archive in a folder called libtensorflow2 in your HOME directory.

Afterwards, you can run the examples:

git clone [email protected]:serizba/cppflow.git
cd cppflow/examples/load_model
mkdir build
cd build
cmake ..
make
./example

Documentation

Check the docs at https://serizba.github.io/cppflow/.

There you can find quickstart guides and more information about how to install the library and run the examples.

Development

CppFlow is basically a wrapper over Tensorflow C API. The basic class, tensor is a wrapper of a TF eager tensor, and it just constains a pointer to its TF representation.

The TF C API provides the tools to call all the TF raw ops, but using them is confusing. CppFlow includes a facade over these functions, so they can be called easily as normal C++ functions. To achieve this, the file ops contains (mostly) all the TF raw ops functions, but with a simple C++ interface. This file has been generated automatically using a small script.

CppFlow also includes a wrapper on TF saved models, the model class, so they can be easily opened and executed.

There are still many things to implement... some of them may be:

• Model complex invoking
• Model eager API: Calling model with the eager API instead of the TF_SessionRun API. I have tried using TF_GraphToFunction but I could not achieve it.
• Cover more raw_ops: Currently, the generator that creates the raw_ops facade converts many of the raw_ops but not all of them. Improve the generator to cover these cases (which are marked in the generator code).
• Include testing

Cppflow 1

You can also use the older version of this work.

Remark

CppFlow is not related with TensorFlow. The CppFlow icon is a modified version of the TensorFlow logo. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.