test_tensorflow.cpp

/*
 
 Test for successful dynamic linkage with Tensorflow, either as an API, or as a 'main' exec
 
*/

#include <vector>


#include <tensorflow/core/public/session.h>
#include <tensorflow/core/platform/env.h>
#include <tensorflow/cc/saved_model/loader.h>


#include <tensorflow/cc/ops/standard_ops.h>

int test_link_tensorflow_cc_api() {
    tensorflow::Session* session;
    tensorflow::SessionOptions opts;
    tensorflow::Status status = tensorflow::NewSession(opts, &session);
    return 0;
}

int main() {
    test_link_tensorflow_cc_api();
    return 0;
}

// a computation example taken from "https://joe-antognini.github.io/machine-learning/windows-tf-project" :

using namespace tensorflow;


// Build a computation graph that takes a tensor of shape [?, 2] and
// multiplies it by a hard-coded matrix.
static GraphDef CreateGraphDef(){
    Scope root = Scope::NewRootScope();
    
    auto X = ops::Placeholder(root.WithOpName("x"), DT_FLOAT,
                              ops::Placeholder::Shape({ -1, 2 }));
    auto A = ops::Const(root, { { 3.f, 2.f },{ -1.f, 0.f } });
    
    auto Y = ops::MatMul(root.WithOpName("y"), A, X,
                         ops::MatMul::TransposeB(true));
    
    GraphDef def;
    TF_CHECK_OK(root.ToGraphDef(&def));
    
    return def;
}

int test_link_tensorflow_cc_computation(){
    GraphDef graph_def = CreateGraphDef();
    
    // Start up the session
    SessionOptions options;
    std::unique_ptr<Session> session(NewSession(options));
    TF_CHECK_OK(session->Create(graph_def));
    
    // Define some data.  This needs to be converted to an Eigen Tensor to be
    // fed into the placeholder.  Note that this will be broken up into two
    // separate vectors of length 2: [1, 2] and [3, 4], which will separately
    // be multiplied by the matrix.
    std::vector<float> data = { 1, 2, 3, 4 };
    auto mapped_X_ = Eigen::TensorMap<Eigen::Tensor<float, 2, Eigen::RowMajor>>
    (&data[0], 2, 2);
    auto eigen_X_ = Eigen::Tensor<float, 2, Eigen::RowMajor>(mapped_X_);
    
    Tensor X_(DT_FLOAT, TensorShape({ 2, 2 }));
    X_.tensor<float, 2>() = eigen_X_;
    
    std::vector<Tensor> outputs;
    TF_CHECK_OK(session->Run({ { "x", X_ } }, { "y" }, {}, &outputs));
    
    // Get the result and print it out
    Tensor Y_ = outputs[0];
    std::cout << Y_.tensor<float, 2>() << std::endl;
    
    auto closeStatus = session->Close();
    return 0;
}