https://github.com/ika-rwth-aachen/tensorflow_cpp
Helpful model wrappers around TensorFlow C++ API
https://github.com/ika-rwth-aachen/tensorflow_cpp
cpp helper ros tensorflow
Last synced: 7 months ago
JSON representation
Helpful model wrappers around TensorFlow C++ API
- Host: GitHub
- URL: https://github.com/ika-rwth-aachen/tensorflow_cpp
- Owner: ika-rwth-aachen
- License: mit
- Created: 2022-09-16T15:35:41.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-05T15:39:51.000Z (about 2 years ago)
- Last Synced: 2025-09-04T19:25:12.572Z (7 months ago)
- Topics: cpp, helper, ros, tensorflow
- Language: C++
- Homepage:
- Size: 983 KB
- Stars: 20
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# tensorflow_cpp
*tensorflow_cpp* is a header-only library that provides helpful wrappers around the [TensorFlow C++ API](https://www.tensorflow.org/api_docs/cc), allowing you to **easily load, inspect, and run saved models and frozen graphs in C++**. The library is easy to integrate into CMake projects, but is also available as a [ROS and ROS2](https://www.ros.org/) package.
> [!IMPORTANT]
> This repository is open-sourced and maintained by the [**Institute for Automotive Engineering (ika) at RWTH Aachen University**](https://www.ika.rwth-aachen.de/).
> **Deep Learning** is one of many research topics within our [*Vehicle Intelligence & Automated Driving*](https://www.ika.rwth-aachen.de/en/competences/fields-of-research/vehicle-intelligence-automated-driving.html) domain.
> If you would like to learn more about how we can support your deep learning or automated driving efforts, feel free to reach out to us!
> *Timo Woopen - Manager Research Area Vehicle Intelligence & Automated Driving*
> *+49 241 80 23549*
> *timo.woopen@ika.rwth-aachen.de*
If you are looking for an easy way to install the TensorFlow C++ API, we suggest that you also check out our repository [*libtensorflow_cc*](https://github.com/ika-rwth-aachen/libtensorflow_cc). There, we provide a pre-built library and a Docker image for easy installation and usage of the TensorFlow C++ API. 
---
- [tensorflow\_cpp](#tensorflow_cpp)
- [Examples](#examples)
- [Installation](#installation)
- [Dependencies](#dependencies)
- [CMake](#cmake)
- [ROS/ROS2](#rosros2)
- [Testing](#testing)
- [Documentation](#documentation)
- [Acknowledgements](#acknowledgements)
- [Notice](#notice)
## Examples
*Loading and running a single-input/single-output model*
```cpp
#include
#include
#include
#include
#include
// load single-input/single-output model
std::string model_path = "/PATH/TO/MODEL";
tensorflow_cpp::Model model;
model.loadModel(model_path);
// log model info
std::cout << model.getInfoString() << std::endl;
// get input/output shape/type, if required
std::vector input_shape = model.getInputShape();
tensorflow::DataType output_type = model.getOutputType();
// ... do something ...
// create and fill input tensor
tensorflow::Tensor input_tensor;
// ... fill input tensor ...
// run model
tensorflow::Tensor output_tensor = model(input_tensor);
```
Loading and running a multi-input/multi-output model
```cpp
#include
#include
#include
#include
#include
// load multi-input/multi-output model
std::string model_path = "/PATH/TO/MODEL";
tensorflow_cpp::Model model;
model.loadModel(model_path);
// log model info
std::cout << model.getInfoString() << std::endl;
// input/output layer names are determined automatically,
// but could potentially have different order than expected
// get input/output shapes/types, if required
std::vector input_shape_1 = model.getNodeShapes()[0];
tensorflow::DataType output_type_2 = model.getNodeTypes()[1];
// ... do something ...
// create and fill input tensors
tensorflow::Tensor input_tensor_1;
tensorflow::Tensor input_tensor_2;
// ... fill input tensors ...
// run model
auto outputs = model({input_tensor_1, input_tensor_2});
tensorflow::Tensor output_tensor_1& = outputs[0];
tensorflow::Tensor output_tensor_2& = outputs[1];
```
Loading and running a multi-input/multi-output model with specific inputs/outputs
```cpp
#include
#include
#include
#include
#include
// load multi-input/multi-output model
std::string model_path = "/PATH/TO/MODEL";
tensorflow_cpp::Model model;
model.loadModel(model_path);
// log model info
std::cout << model.getInfoString() << std::endl;
// set model input/output layer names (see `model.logInfo()`)
const std::string kModelInputName1 = "input1";
const std::string kModelInputName2 = "input2";
const std::string kModelOutputName1 = "output1";
const std::string kModelOutputName2 = "output2";
// get input/output shapes/types, if required
std::vector input_shape_1 = model.getNodeShape(kModelInputName1);
tensorflow::DataType output_type_2 = model.getNodeType(kModelOutputName2);
// ... do something ...
// create and fill input tensors
tensorflow::Tensor input_tensor_1;
tensorflow::Tensor input_tensor_2;
// ... fill input tensors ...
// run model
auto outputs = model({{kModelInputName1, input_tensor_1}, {kModelInputName2, input_tensor_2}}, {kModelOutputName1, kModelOutputName2});
tensorflow::Tensor output_tensor_1& = outputs[kModelOutputName1];
tensorflow::Tensor output_tensor_2& = outputs[kModelOutputName2];
```
## Installation
### Dependencies
*tensorflow_cpp* is a wrapper around the official [TensorFlow C++ API](https://www.tensorflow.org/api_docs/cc). The C++ API including `libtensorflow_cc.so` must be installed on the system.
Instead of having to build the C++ API from source yourself, we recommend to check out our repository [*libtensorflow_cc*](https://github.com/ika-rwth-aachen/libtensorflow_cc). There, we provide a pre-built library and a Docker image for easy installation and usage of the TensorFlow C++ API.
Installation is as easy as the following. Head over to [*libtensorflow_cc*](https://github.com/ika-rwth-aachen/libtensorflow_cc) for more details.
```
ARCH=$(dpkg --print-architecture)
wget https://github.com/ika-rwth-aachen/libtensorflow_cc/releases/download/v2.9.2/libtensorflow-cc_2.9.2-gpu_${ARCH}.deb
sudo dpkg -i libtensorflow-cc_2.9.2-gpu_${ARCH}.deb
ldconfig
```
If you have already installed the C++ API another way, you can use the provided [`TensorFlowConfig.cmake`](cmake/TensorFlowConfig.cmake) to enable the `find_package(TensorFlow REQUIRED)` call in *tensorflow_cpp's* [`CMakeLists.txt`](CMakeLists.txt).
### CMake
1. Clone this repository.
```bash
git clone https://github.com/ika-rwth-aachen/tensorflow_cpp.git
cd tensorflow_cpp
```
2. Install *tensorflow_cpp* system-wide.
```bash
# tensorflow_cpp$
mkdir -p build
cd build
cmake ..
sudo make install
```
3. Use `find_package()` to locate and integrate *tensorflow_cpp* into your CMake project. See the [CMake example project](examples/cmake/).
```cmake
# CMakeLists.txt
find_package(tensorflow_cpp REQUIRED)
# ...
add_executable(foo ...) # / add_library(foo ...)
# ...
target_link_libraries(foo tensorflow_cpp)
```
### ROS/ROS2
1. Clone this repository into your ROS/ROS2 workspace.
```bash
git clone https://github.com/ika-rwth-aachen/tensorflow_cpp.git
cd tensorflow_cpp
```
1. In order to include *tensorflow_cpp* in a ROS/ROS2 package, specify the dependency in its `package.xml` and use `find_package()` in your package's `CMakeLists.txt`.
```xml
tensorflow_cpp
```
```cmake
# CMakeLists.txt
# ROS
find_package(catkin REQUIRED COMPONENTS
tensorflow_cpp
)
# ROS2
find_package(tensorflow_cpp REQUIRED)
ament_target_dependencies( tensorflow_cpp)
```
## Testing
In order to build and run the test cases defined in [`tests/`](tests/), execute the following.
```bash
# tensorflow_cpp$
mkdir -p build
cd build
cmake -DBUILD_TESTING=ON ..
make
ctest
```
## Documentation
[Click here](https://ika-rwth-aachen.github.io/tensorflow_cpp) to be taken to the full API documentation.
The documentation can be generated by running [Doxygen](https://doxygen.nl/).
```bash
# tensorflow_cpp/doc$
doxygen
```
## Acknowledgements
This work is accomplished within the projects [6GEM](https://6gem.de/) (FKZ 16KISK038) and [UNICAR*agil*](https://www.unicaragil.de/) (FKZ 16EMO0284K). We acknowledge the financial support for the projects by the Federal Ministry of Education and Research of Germany (BMBF).
## Notice
This repository is not endorsed by or otherwise affiliated with [TensorFlow](https://www.tensorflow.org) or Google. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. [TensorFlow](https://github.com/tensorflow/tensorflow) is released under the [Apache License 2.0](https://github.com/tensorflow/tensorflow/blob/master/LICENSE).