MLCE-103: MDK changes for running multiple inferences qasymm8

Change-Id: I34e8e0736e133ffb5f931ce3b5f5bfa4e2c975c2
Signed-off-by: Pablo Tello <pablo.tello@arm.com>

2 files changed, 79 insertions, 28 deletions

diff --git a/tests/InferenceModel.hpp b/tests/InferenceModel.hpp
index cb6daefa9e..72c5edac29 100644
--- a/tests/InferenceModel.hpp
+++ b/tests/InferenceModel.hpp
@@ -393,6 +393,7 @@ public:
         std::vector<std::string> m_ComputeDevices;
         bool m_VisualizePostOptimizationModel;
         bool m_EnableFp16TurboMode;
+        std::string m_Labels;
 
         std::vector<armnn::BackendId> GetComputeDevicesAsBackendIds()
         {
@@ -417,6 +418,9 @@ public:
             ("compute,c", po::value<std::vector<std::string>>(&options.m_ComputeDevices)->
                 default_value(defaultComputes, boost::algorithm::join(defaultComputes, ", "))->
                 multitoken(), backendsMessage.c_str())
+            ("labels,l", po::value<std::string>(&options.m_Labels),
+                "Text file containing one image filename - correct label pair per line, "
+                "used to test the accuracy of the network.")
             ("visualize-optimized-model,v",
                 po::value<bool>(&options.m_VisualizePostOptimizationModel)->default_value(false),
              "Produce a dot file useful for visualizing the graph post optimization."
diff --git a/tests/TfLiteMobilenetQuantized-Armnn/TfLiteMobilenetQuantized-Armnn.cpp b/tests/TfLiteMobilenetQuantized-Armnn/TfLiteMobilenetQuantized-Armnn.cpp
index 36b1d14008..220964d061 100644
--- a/tests/TfLiteMobilenetQuantized-Armnn/TfLiteMobilenetQuantized-Armnn.cpp
+++ b/tests/TfLiteMobilenetQuantized-Armnn/TfLiteMobilenetQuantized-Armnn.cpp
@@ -6,41 +6,88 @@
 #include "../ImagePreprocessor.hpp"
 #include "armnnTfLiteParser/ITfLiteParser.hpp"
 
+#include "boost/program_options.hpp"
+#include <fstream>
+
 using namespace armnnTfLiteParser;
 
+std::vector<ImageSet> ParseDataset(const std::string& filename)
+{
+    std::ifstream read(filename);
+    std::vector<ImageSet> imageSet;
+    if (read.is_open())
+    {
+        // Get the images and the correct corresponding label from the given file
+        for (std::string line; std::getline(read, line);)
+        {
+            stringstream ss(line);
+            std::string image_name;
+            std::string label;
+            getline(ss, image_name, ' ');
+            getline(ss, label, ' ');
+            imageSet.push_back(ImageSet(image_name, std::stoi(label)));
+        }
+    }
+    else
+    {
+        // Use the default images
+        imageSet.push_back(ImageSet("Dog.jpg", 209));
+        // top five predictions in tensorflow:
+        // -----------------------------------
+        // 209:Labrador retriever 0.949995
+        // 160:Rhodesian ridgeback 0.0270182
+        // 208:golden retriever 0.0192866
+        // 853:tennis ball 0.000470382
+        // 239:Greater Swiss Mountain dog 0.000464451
+        imageSet.push_back(ImageSet("Cat.jpg", 283));
+        // top five predictions in tensorflow:
+        // -----------------------------------
+        // 283:tiger cat 0.579016
+        // 286:Egyptian cat 0.319676
+        // 282:tabby, tabby cat 0.0873346
+        // 288:lynx, catamount 0.011163
+        // 289:leopard, Panthera pardus 0.000856755
+        imageSet.push_back(ImageSet("shark.jpg", 3));
+        // top five predictions in tensorflow:
+        // -----------------------------------
+        // 3:great white shark, white shark, ... 0.996926
+        // 4:tiger shark, Galeocerdo cuvieri 0.00270528
+        // 149:killer whale, killer, orca, ... 0.000121848
+        // 395:sturgeon 7.78977e-05
+        // 5:hammerhead, hammerhead shark 6.44127e-055
+    };
+    return imageSet;
+}
+
+std::string GetLabelsFilenameFromOptions(int argc, char* argv[])
+{
+    namespace po = boost::program_options;
+    po::options_description desc("Validation Options");
+    std::string fn("");
+    desc.add_options()
+        ("labels", po::value<std::string>(&fn), "Filename of a text file where in each line contains an image "
+            "filename and the correct label the network should predict when fed that image");
+    po::variables_map vm;
+    po::parsed_options parsed = po::command_line_parser(argc, argv).options(desc).allow_unregistered().run();
+    po::store(parsed, vm);
+    if (vm.count("labels"))
+    {
+        fn = vm["labels"].as<std::string>();
+    }
+    return fn;
+}
+
+
 int main(int argc, char* argv[])
 {
     int retVal = EXIT_FAILURE;
     try
     {
         // Coverity fix: The following code may throw an exception of type std::length_error.
-        std::vector<ImageSet> imageSet =
-        {
-            {"Dog.jpg", 209},
-            // top five predictions in tensorflow:
-            // -----------------------------------
-            // 209:Labrador retriever 0.949995
-            // 160:Rhodesian ridgeback 0.0270182
-            // 208:golden retriever 0.0192866
-            // 853:tennis ball 0.000470382
-            // 239:Greater Swiss Mountain dog 0.000464451
-            {"Cat.jpg", 283},
-            // top five predictions in tensorflow:
-            // -----------------------------------
-            // 283:tiger cat 0.579016
-            // 286:Egyptian cat 0.319676
-            // 282:tabby, tabby cat 0.0873346
-            // 288:lynx, catamount 0.011163
-            // 289:leopard, Panthera pardus 0.000856755
-            {"shark.jpg", 3},
-            // top five predictions in tensorflow:
-            // -----------------------------------
-            // 3:great white shark, white shark, ... 0.996926
-            // 4:tiger shark, Galeocerdo cuvieri 0.00270528
-            // 149:killer whale, killer, orca, ... 0.000121848
-            // 395:sturgeon 7.78977e-05
-            // 5:hammerhead, hammerhead shark 6.44127e-055
-        };
+        const std::string labels_file = GetLabelsFilenameFromOptions(argc,argv);
+        std::vector<ImageSet> imageSet = ParseDataset(labels_file);
+        std::vector<unsigned int> indices(imageSet.size());
+        std::generate(indices.begin(), indices.end(), [n = 0] () mutable { return n++; });
 
         armnn::TensorShape inputTensorShape({ 1, 224, 224, 3  });
 
@@ -57,7 +104,7 @@ int main(int argc, char* argv[])
                      true,                                // model is binary
                      "input",                             // input tensor name
                      "MobilenetV1/Predictions/Reshape_1", // output tensor name
-                     { 0, 1, 2 },                         // test images to test with as above
+                     indices,                             // vector of indices to select which images to validate
                      [&imageSet](const char* dataDir, const ModelType & model) {
                          // we need to get the input quantization parameters from
                          // the parsed model