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Tensorflow C/C++ inference with Debian packages on Ubuntu x86_64 and Raspberry Pi

  • The generic and optimized x86_64 builds are based on the "TensorFlow CMake" component in tensorflow/contrib/cmake directory.
  • The CUDA x86_64 build is based on tensorflow_cc project, thanks to FloopCZ.
  • The Raspberry Pi build is based on the "TensorFlow Makefile" component in tensorflow/contrib/makefile directory.
  • Two targets were tested: Ubuntu Bionic (x86_64) and Raspberry Pi Ubuntu MATE Xenial (armhf).
  • Generic, optimized and Raspberry Pi packages contain both static and shared Tensorflow libraries. The choice is on your side. However, the CUDA builds only ship shared libraries.
  • CPU can be used for inference, GPU support is only in the CUDA packages.
  • Debian packages are generated from the built binary files for distribution.
  • The build contains e.g. the C/C++ API to load model "snapshots" or frozen models. Other parts of the C++ API is included, but they are untested.
  • CUDA support with Tensorflow 1.14.0 requires to all GPU memory available for inference by default otherwise it drops error message. To allow_growth option must be set to allow multiple processes doing inference on the same GPU. See in the example codes below.

Status

I trained a simple CNN model with TFLearn (and Tensorflow 1.10) for a 2-label classification and the inference works on Ubuntu and Raspberry Pi, however, there are differences in the accuracies:

Platform Class 1 Class 2
Python 95.26 % 95.69 %
Ubuntu C/C++ 95.12 % 95.57 %
Raspberry Pi C++ 97.23 % 83.73 %

These results are generated with the same frozen graph (.pb file). The C/C++ inference does not provide 100 % reproducible results vs. the Python inference.

Releases

Dependencies for generic and optimized packages

  • You must install some dependencies for Ubuntu Bionic x86_64:
sudo apt-get install libdouble-conversion-dev libfarmhash-dev libre2-dev libgif-dev libpng-dev libsqlite3-dev libsnappy-dev liblmdb-dev libicu-dev libjsoncpp-dev

Dependencies for the latest CUDA packages

You must install CUDA 10.1 from the official Nvidia repositories and Bazel 0.25.2 for Tensorflow. Other configurations are not supported.

Dependencies for Raspberry Pi packages

Nothing.

Installation

  • You can download the releases from here.

Manual compilation

Requirements

sudo apt-get install make g++-6 gcc-6 cmake git dpkg-dev debhelper quilt python3 autogen autoconf libtool fakeroot golang pxz libicu-dev python python-future ccache

Current Protobuf in Tensorflow 1.14.0 does not compile with gcc 7.3+/8. We must install gcc 6 to build Tensorflow. Be sure that "gcc -v" and "g++ -v" shows a 6.x version: e.g. "gcc version 6.5.0 20181026 (Ubuntu 6.5.0-2ubuntu1~18.04)" before building the packages. Using the packages does not require these specific gcc/g++ versions.

  • These build dependencies must be installed on Raspberry Pi:
sudo apt-get install make g++-6 cmake git dpkg-dev debhelper quilt python3 autogen autoconf libtool fakeroot

Note: Gcc 6 is needed for the build process on the Raspberry Pi because gcc 5 has a linking bug and Tensorflow does not compile with the shipped gcc 5 in Ubuntu Xenial. Gcc 6 can be installed from this PPA.

  • Tensorflow must be compiled natively on the Raspberry Pi. It takes a lot time (6-8 hours?), but extra swap space is not necessary over the usual 1 GB. I restricted the build process to 2 parallel jobs to avoid unresponsive Raspberry Pi because of running out of memory.

Compilation steps

  • Clone this repository and enter to its directory:
git clone https://github.com/kecsap/tensorflow_cpp_packaging && cd tensorflow_cpp_packaging
  • Clone the Tensorflow Github repository.

Latest master:

./1_clone_tensorflow.sh

A specific branch or tag can be also checked out, for example Tensorflow 1.6.0:

./1_clone_tensorflow.sh v1.6.0
  • Make the python package to extract include files for C++. Define NO_TF_PACKAGING_CUDA environmental variable to disable CUDA support or use TF_PACKAGING_LEGACY_CPU for older CPUs (AMD FX):
./2_make_wheel_for_headers.sh
  • Compile the Tensorflow C++ libraries for the desired platform:

Generic build for Ubuntu x86_64, optimized for minimum Haswell of Intel and Piledriver of AMD (AVX, FMA instructions):

./3_build_tensorflow_cpp_generic_x86_64.sh

Optimized build for Ubuntu x86_64, optimized for minimum Skylake of Intel and Excavator of AMD (AVX, AVX2, FMA instructions):

./3_build_tensorflow_cpp_optimized_x86_64.sh

Generic/CUDA build for Ubuntu x86_64, optimized for minimum Haswell of Intel and Piledriver of AMD (AVX, FMA instructions). The generic build had to be compiled first to get all C++ headers correctly:

./3_build_tensorflow_cpp_generic_x86_64.sh
./3_build_tensorflow_cpp_generic_cuda_x86_64.sh

Optimized/CUDA build for Ubuntu x86_64, optimized for minimum Skylake of Intel and Excavator of AMD (AVX, AVX2, FMA instructions). The optimized build had to be compiled first to get all C++ headers correctly:

./3_build_tensorflow_cpp_optimized_x86_64.sh
./3_build_tensorflow_cpp_optimized_cuda_x86_64.sh

Raspberry Pi build:

./3_build_tensorflow_cpp_arm_rpi.sh
  • Generate debian packages:

Generic build for Ubuntu x86_64:

./4_generate_generic_x86_64_package.sh

Optimized build for Ubuntu x86_64:

./4_generate_optimized_x86_64_package.sh

Generic/CUDA build for Ubuntu x86_64:

./4_generate_generic_cuda_x86_64_package.sh

Optimized/CUDA build for Ubuntu x86_64:

./4_generate_optimized_cuda_x86_64_package.sh

Raspberry Pi build:

./4_generate_arm_rpi_package.sh

Save a checkpoint in Python (TFLearn)

The standard, saved checkpoints in TFLearn are not good because they contain the training ops and those must be deleted before saving a checkpoint. I provide an example how the checkpoints can be saved inside a TFLearn callback:

...
model = tflearn.DNN(network)

class MonitorCallback(tflearn.callbacks.Callback):
  # Create an other session to clone the model and avoid effecting the training process
  with tf.Session() as second_sess:
    # Clone the current model
    model2 = model
    # Delete the training ops
    del tf.get_collection_ref(tf.GraphKeys.TRAIN_OPS)[:]
    # Save the checkpoint
    model2.save('checkpoint_'+str(training_state.step)+".ckpt")
    # Write a text protobuf to have a human-readable form of the model
    tf.train.write_graph(second_sess.graph_def, '.', 'checkpoint_'+str(training_state.step)+".pbtxt", as_text = True)
  return

mycb = MonitorCallback()
model.fit({'input': X}, {'target': Y}, n_epoch=500, run_id="mymodel", callbacks=mycb)
...

Freeze a checkpoint (snapshot) into a model in Python

Use the provided utility script in this repository at utils/tf_freezer.py:

./tf_freezer.py -c my_checkpoint -p final_model.pb -i Input/X -o Fc2/Sigmoid

The above example assumes that you have three files related to a checkpoint inside the directory with my_checkpoint prefix (e.g. my_checkpoint.meta). The script will create a frozen protobuf model (final_model.pb). You must specify the input and output tensors with -i and -o. The default input tensor name is Input/X and the default output tensor name is FullyConnected/Sigmoid.

CMake support

Once you installed the Debian package of this project, CMake support is provided for Tensorflow inference in your C++ project.

# Find tensorflow-cpp
FIND_PACKAGE(TensorFlowCpp REQUIRED PATHS /usr/lib/cmake/tensorflow-cpp)

# Add the tensorflow-cpp paths to the include directories
INCLUDE_DIRECTORIES(${TENSORFLOWCPP_INCLUDE_DIRS})

# Add a target with your inference codes
ADD_EXECUTABLE(my_nice_app my_nice_app.cpp)

# Link your application against the shared Tensorflow C++ library
TARGET_LINK_LIBRARIES(my_nice_app ${TENSORFLOWCPP_LIBRARIES})
# OR link your application against the static Tensorflow C++ library
#TARGET_LINK_LIBRARIES(my_nice_app ${TENSORFLOWCPP_STATIC_LIBRARIES})

Load a checkpoint in C++

#include <tensorflow/core/protobuf/meta_graph.pb.h>
#include <tensorflow/core/public/session.h>

tensorflow::MetaGraphDef GraphDef;
tensorflow::Session* Session = nullptr;

bool LoadGraph()
{
  const std::string PathToGraph = "my_checkpoint.meta";
  const std::string CheckpointPrefix = "my_checkpoint";
  // Ignore info logs
  char EnvStr[] = "TF_CPP_MIN_LOG_LEVEL=1";
  
  putenv(EnvStr);
  // Dynamic GPU memory allocation
  tensorflow::SessionOptions SessionOptions;
  
  SessionOptions.config.mutable_gpu_options()->set_allow_growth(true);
  Session = tensorflow::NewSession(SessionOptions);
  if (Session == nullptr)
  {
    printf("Could not create Tensorflow session.\n");
    return false;
  }

  // Read in the protobuf graph
  tensorflow::Status Status;

  Status = ReadBinaryProto(tensorflow::Env::Default(), PathToGraph, &GraphDef);
  if (!Status.ok())
  {
    printf("Error reading graph definition from %s: %s\n", PathToGraph.c_str(), Status.ToString().c_str());
    return false;
  }

  // Add the graph to the session
  Status = Session->Create(GraphDef.graph_def());
  if (!Status.ok())
  {
    printf("Error creating graph: %s\n", Status.ToString().c_str());
    return false;
  }

  // Read weights from the saved checkpoint
  tensorflow::Tensor CheckpointPathTensor(tensorflow::DT_STRING, tensorflow::TensorShape());

  CheckpointPathTensor.scalar<std::string>()() = CheckpointPrefix;
  Status = Session->Run(
        {{ GraphDef.saver_def().filename_tensor_name(), CheckpointPathTensor },},
        {},
        { GraphDef.saver_def().restore_op_name() },
        nullptr);

  if (!Status.ok())
  {
    printf("Error loading checkpoint from %s: %s\n", CheckpointPrefix.c_str(), Status.ToString().c_str());
    return false;
  }
  return true;
}

Load a frozen model in C++

Loading a frozen model is much more simple than loading a checkpoint:

#include <tensorflow/core/protobuf/meta_graph.pb.h>
#include <tensorflow/core/public/session.h>

tensorflow::GraphDef GraphDef;
tensorflow::Session* Session = nullptr;

bool LoadGraph()
{
  // Read in the protobuf graph we exported
  tensorflow::Status Status;

  Status = tensorflow::ReadBinaryProto(tensorflow::Env::Default(), "my_model.pb", &GraphDef);
  if (!Status.ok())
  {
    printf("Error reading graph definition from %s: %s\n", "my_model.pb", Status.ToString().c_str());
    return false;
  }

  Session = tensorflow::NewSession(tensorflow::SessionOptions());
  if (Session == nullptr)
  {
    printf("Could not create Tensorflow session.\n");
    return false;
  }

  // Add the graph to the session
  Status = Session->Create(GraphDef);
  if (!Status.ok())
  {
    printf("Error creating graph: %s\n", Status.ToString().c_str());
    return false;
  }
  return true;
}

Inference in C++

int Predict()
{
  // The input tensor in this example is an image with resolution 160x96
  tensorflow::Tensor X(tensorflow::DT_FLOAT, tensorflow::TensorShape({ 1, 96, 160, 1 }));
  // Replace YOUR_INPUT_TENSOR with the input tensor name in your network.
  std::vector<std::pair<std::string, tensorflow::Tensor>> Input = { { "YOUR_INPUT_TENSOR", X} };
  std::vector<tensorflow::Tensor> Outputs;
  float* XData = X.flat<float>().data();

  for (int i = 0; i < 160*96; ++i)
  {
    // Read your image and set the input data
    XData[i] = (float)YOUR_PIXELS;
  }

  // Replace YOUR_OUTPUT_TENSOR with your output tensor name
  tensorflow::Status Status = Session->Run(Input, { "YOUR_OUTPUT_TENSOR" }, {}, &Outputs);

  if (!Status.ok())
  {
    printf("Error in prediction: %s\n", Status.ToString().c_str());
    return -1;
  }
  if (Outputs.size() != 1)
  {
    printf("Missing prediction! (%d)\n", (int)Outputs.size());
    return -1;
  }

  // This example has a final, output layer with two neurons for classification with softmax.
  // The two neurons are inspected in this example instead of the softmax tensor
  auto Item = Outputs[0].shaped<float, 2>({ 1, 2 }); // { 1, 2 } -> One sample+2 label classes

  printf("First neuron output: %1.4\n", (float)Item(0, 0));
  printf("Second neuron output: %1.4\n", (float)Item(0, 1));
  if ((float)Item(0, 0) > (float)Item(0, 1))
  {
    return 0;
  }
  return 1;
}

Load a frozen model in C

Loading a frozen model is with the C API:

#include <tensorflow/c/c_api.h>

TF_Status* Status = NULL;
TF_Graph* Graph = NULL;
TF_ImportGraphDefOptions* GraphDefOpts = NULL;
TF_Session* Session = NULL;
TF_SessionOptions* SessionOpts = NULL;
TF_Buffer* GraphDef = NULL;

bool LoadGraph()
{
  // The protobuf is loaded with C API here
  unsigned char CurrentFile[YOUR_BUFFER_SIZE];
  
  LOAD YOUR PROTOBUF FILE HERE TO CurrentFile

  Status = TF_NewStatus();
  Graph = TF_NewGraph();
  GraphDef.reset(TF_NewBuffer());
  GraphDef->data = &CurrentFile;
  GraphDef->length = YOUR_BUFFER_SIZE;
  // Deallocator is skipped in this example
  GraphDef->data_deallocator = NULL;
  GraphDefOpts = TF_NewImportGraphDefOptions();
  TF_GraphImportGraphDef(Graph, GraphDef.get(), GraphDefOpts, Status);
  if (TF_GetCode(Status) != TF_OK)
  {
    printf("Tensorflow status %d - %s\n", TF_GetCode(Status), TF_Message(Status));
    return false;
  }
  SessionOpts = TF_NewSessionOptions();
  Session = TF_NewSession(Graph, SessionOpts, Status);
  if (TF_GetCode(Status) != TF_OK)
  {
    printf("Tensorflow status %d - %s\n", TF_GetCode(Status), TF_Message(Status));
    return false;
  }
  return true;
}

Inference in C

int Predict()
{
  // Make prediction with C API
  float* InputData = (float*)malloc(sizeof(float)*96*160);

  for (int i = 0; i < image.GetImageDataSize(); ++i)
  {
    InputData[i] = (float)YOUR_PIXELS;
  }

  // Input node
  int64_t InputDim[] = { 1, 96, 160, 1 };
  TF_Tensor* ImageTensor = TF_NewTensor(TF_FLOAT, InputDim, 4, InputData, 96*160*sizeof(float), NULL, NULL);
  TF_Output Input = { TF_GraphOperationByName(Graph, "YOUR_INPUT_TENSOR") };
  TF_Tensor* InputValues[] = { ImageTensor };

  // Output node
  TF_Output Output = { TF_GraphOperationByName(Graph, "YOUR_OUTPUT_TENSOR") };
  TF_Tensor* OutputValues = NULL;

  // Run prediction
  TF_SessionRun(Session, NULL,
                &Input, &InputValues[0], 1,
                &Output, &OutputValues, 1,
                NULL, 0, NULL, Status);

  delete InputData;
  InputData = NULL;
  if (TF_GetCode(Status) != TF_OK)
  {
    printf("Tensorflow status %d - %s\n", TF_GetCode(Status), TF_Message(Status));
    return false;
  }
  // This example has a final in argmax classification.
  int Result = (int)*(long long*)TF_TensorData(OutputValues);
  
  printf("Argmax output: %d\n", Result);
  return Result;
}

Remember to release the allocated resources in the end:

  TF_DeleteGraph(Graph);
  TF_DeleteSession(Session, Status);
  TF_DeleteSessionOptions(SessionOpts);
  TF_DeleteStatus(Status);
  TF_DeleteImportGraphDefOptions(GraphDefOpts);

Conclusion

  • The tensorflow/contrib/makefile/cmake is not really tested officially, only some demos were run on iOS/Android. If you don't mind the minor differences in performance, it is suitable for you.

Contact

Open an issue or drop an email: csaba.kertesz@gmail.com