IVGCVSW-2454 Merge together the pluggable backends work (was in a

separate branch) and master

 * Brings in all the changes done for the pluggable backends
 * Added sub-graph support and tests
 * Added precompiled layer support and tests
 * Moved BackendSettings to a separate file
 * Removed the backend-specific code
 * Ported DebugLayer and associated functionality
 * Included fixes to make those changes work with master

Change-Id: Id7028fa7917527b844628d5aff5732e3d94c0488

3 files changed, 9 insertions, 11 deletions

diff --git a/tests/ExecuteNetwork/ExecuteNetwork.cpp b/tests/ExecuteNetwork/ExecuteNetwork.cpp
index cfddc38a99..7d6aafcfbf 100644
--- a/tests/ExecuteNetwork/ExecuteNetwork.cpp
+++ b/tests/ExecuteNetwork/ExecuteNetwork.cpp
@@ -463,9 +463,9 @@ int main(int argc, const char* argv[])
              "as they are expected to be defined in the file for each test in particular.")
             ("concurrent,n", po::bool_switch()->default_value(false),
              "Whether or not the test cases should be executed in parallel")
-            ("model-format,f", po::value(&modelFormat),
+            ("model-format,f", po::value(&modelFormat)->required(),
              "caffe-binary, caffe-text, onnx-binary, onnx-text, tflite-binary, tensorflow-binary or tensorflow-text.")
-            ("model-path,m", po::value(&modelPath), "Path to model file, e.g. .caffemodel, .prototxt,"
+            ("model-path,m", po::value(&modelPath)->required(), "Path to model file, e.g. .caffemodel, .prototxt,"
              " .tflite, .onnx")
             ("compute,c", po::value<std::vector<std::string>>()->multitoken(),
              backendsMessage.c_str())
diff --git a/tests/InferenceTest.cpp b/tests/InferenceTest.cpp
index 8733bc53a0..7413de97dd 100644
--- a/tests/InferenceTest.cpp
+++ b/tests/InferenceTest.cpp
@@ -34,8 +34,6 @@ bool ParseCommandLine(int argc, char** argv, IInferenceTestCaseProvider& testCas
 {
     namespace po = boost::program_options;
 
-    std::string computeDeviceStr;
-
     po::options_description desc("Options");
 
     try
diff --git a/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp b/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
index e23dbdc9d4..84d5292195 100644
--- a/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
+++ b/tests/TfLiteVGG16Quantized-Armnn/TfLiteVGG16Quantized-Armnn.cpp
@@ -23,7 +23,7 @@ int main(int argc, char* argv[])
             {"shark.jpg", 669},
         };
 
-        armnn::TensorShape inputTensorShape({ 2, 224, 224, 3 });
+        armnn::TensorShape inputTensorShape({ 1, 224, 224, 3 });
 
         using DataType = uint8_t;
         using DatabaseType = ImagePreprocessor<DataType>;
@@ -34,11 +34,11 @@ int main(int argc, char* argv[])
         retVal = armnn::test::ClassifierInferenceTestMain<DatabaseType,
                                                           ParserType>(
                      argc, argv,
-                     "vgg_16_u8.tflite",                  // model name
-                     true,                                // model is binary
-                     "content_vgg/concat",                // input tensor name
-                     "content_vgg/prob",                  // output tensor name
-                     { 0, 1, 2 },                         // test images to test with as above
+                     "vgg_16_u8_batch1.tflite", // model name
+                     true,                      // model is binary
+                     "content_vgg/concat",      // input tensor name
+                     "content_vgg/prob",        // output tensor name
+                     { 0, 1, 2 },               // test images to test with as above
                      [&imageSet](const char* dataDir, const ModelType & model) {
                          // we need to get the input quantization parameters from
                          // the parsed model
@@ -53,7 +53,7 @@ int main(int argc, char* argv[])
                              {{0, 0, 0}},
                              {{1, 1, 1}},
                              DatabaseType::DataFormat::NCHW,
-                             2);
+                             1);
                      },
                      &inputTensorShape);
     }