add run model with multi input layers

Author: rangsimanketkaew

README.md

@@ -22,3 +22,4 @@ meaning that it lacks information and a tutorial about how to use TensorFlow API
 2. [TensorFlow Serving](./tensorflow_serving.md)
 3. [Tutorial: Introduction to TensorFlow Tensor](./tensorflow_tensor.md)
 4. [Tutorial: load saved model](./load_model_tensorflow_cpp.md)
+5. [Code: load and run model with multiple input layer](./run_model_multi_input.cpp)

run_model_multi_input.cpp

@@ -0,0 +1,99 @@
+/*
+Load and run a saved TensorFlow's multiple-input model
+
+g++ -Wall -fPIC -D_GLIBCXX_USE_CXX11_ABI=1 run_model.cpp -o run_model.o \
+    -I/usr/local/tensorflow-2.11/include/ -L/usr/local/tensorflow-2.11/lib \ 
+    -ltensorflow_cc -ltensorflow_framework 
+
+Rangsiman Ketkaew
+*/
+
+#include <tensorflow/cc/saved_model/loader.h>
+#include <tensorflow/cc/saved_model/tag_constants.h>
+
+using namespace tensorflow;
+
+int main()
+{
+
+    // load the whole folder
+    std::string export_dir = "./model/";
+
+    // Load
+    SavedModelBundle model_bundle;
+    SessionOptions session_options = SessionOptions();
+    RunOptions run_options = RunOptions();
+    Status status = LoadSavedModel(session_options, run_options, export_dir, {kSavedModelTagServe}, &model_bundle);
+
+    if (!status.ok())
+    {
+        std::cerr << "Failed: " << status;
+    }
+
+    auto sig_map = model_bundle.GetSignatures();
+    auto model_def = sig_map.at("serving_default");
+
+    for (auto const &p : sig_map)
+    {
+        std::cout << "key: " << p.first.c_str() << std::endl;
+    }
+    for (auto const &p : model_def.inputs())
+    {
+        std::cout << "key: " << p.first.c_str() << " " << p.second.name().c_str() << std::endl;
+    }
+    for (auto const &p : model_def.outputs())
+    {
+        std::cout << "key: " << p.first.c_str() << " " << p.second.name().c_str() << std::endl;
+    }
+
+    auto input_tensor_1_name = "serving_default_args_0:0";
+    auto input_tensor_2_name = "serving_default_args_0_1:0";
+    auto output_tensor_1_name = "StatefulPartitionedCall:0";
+    auto output_tensor_2_name = "StatefulPartitionedCall:1";
+
+    // Create output placeholder tensors
+    // I use the size of input that exactly matchs the size of input from Python model
+    // Use saved_model_cli to check
+    tensorflow::Tensor input_1(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, 394}));
+    tensorflow::Tensor input_2(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, 99}));
+
+    auto input_mat_1 = input_1.matrix<float>();
+    // assign random value
+    for (unsigned i = 0; i < 394; ++i)
+        input_mat_1(0, i) = 1.0;
+    auto input_mat_2 = input_2.matrix<float>();
+    // assign random value
+    for (unsigned i = 0; i < 99; ++i)
+        input_mat_2(0, i) = 2.0;
+
+    typedef std::vector<std::pair<std::string, tensorflow::Tensor>> tensor_dict;
+    tensor_dict feed_dict = {
+        {input_tensor_1_name, input_1},
+        {input_tensor_2_name, input_2}};
+
+    // Create output placeholder tensors for results
+    std::vector<tensorflow::Tensor> outputs;
+    std::vector<std::string> output_names = {output_tensor_1_name, output_tensor_2_name};
+    // Running inference
+    tensorflow::Status status_run = model_bundle.session->Run(feed_dict,
+                                                              output_names,
+                                                              {},
+                                                              &outputs);
+    // Check if session is successfully loaded
+    if (!status_run.ok())
+    {
+        std::cerr << "Failed: " << status_run;
+    }
+    else
+    {
+        std::cout << "Passed: " << status_run << std::endl;
+    }
+
+    std::cout << "input 1         " << input_1.DebugString() << std::endl;
+    std::cout << "input 2         " << input_2.DebugString() << std::endl;
+    std::cout << "output          " << outputs[0].DebugString() << std::endl;
+    std::cout << "dense/kernel:0  " << outputs[1].DebugString() << std::endl;
+    // std::cout << "dense/bias:0    " << outputs[2].DebugString() << std::endl;
+
+    return 0;
+}

session.cpp

@@ -1,3 +1,7 @@
+/*
+Test TensorFlow sesssion
+*/
+
 #include <tensorflow/core/platform/env.h>
 #include <tensorflow/core/public/session.h>