IVGCVSW-6440 Print new Optimize and LoadedNetwork profiling points

* Add parent LoadedNetwork profiling point
* Make generic populateParent function to print new descendents in json

Signed-off-by: Kevin May <kevin.may@arm.com>
Change-Id: I41dc876bffae88e61a16d07fb13b062c321e78a6

2 files changed, 23 insertions, 13 deletions

diff --git a/src/backends/backendsCommon/test/JsonPrinterTestImpl.cpp b/src/backends/backendsCommon/test/JsonPrinterTestImpl.cpp
index a1128cfcb9..94855aa7b2 100644
--- a/src/backends/backendsCommon/test/JsonPrinterTestImpl.cpp
+++ b/src/backends/backendsCommon/test/JsonPrinterTestImpl.cpp
@@ -132,7 +132,6 @@ std::string GetSoftmaxProfilerJson(const std::vector<armnn::BackendId>& backends
 
     // build up the structure of the network
     INetworkPtr net(INetwork::Create());
-
     IConnectableLayer* input = net->AddInputLayer(0, "input");
     SoftmaxDescriptor softmaxDescriptor;
     // Set Axis to -1 if CL or Neon until further Axes are supported.
@@ -158,7 +157,9 @@ std::string GetSoftmaxProfilerJson(const std::vector<armnn::BackendId>& backends
     softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
 
     // optimize the network
-    IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
+    armnn::OptimizerOptions optOptions;
+    optOptions.m_ProfilingEnabled = true;
+    IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec(), optOptions);
     if(!optNet)
     {
         FAIL("Error occurred during Optimization, Optimize() returned nullptr.");
@@ -215,6 +216,8 @@ inline void ValidateProfilerJson(std::string& result)
         {
 
             if (sectionVector[i].find("\"ArmNN\":") != std::string::npos
+                || sectionVector[i].find("\"optimize_measurements\":") != std::string::npos
+                || sectionVector[i].find("\"loaded_network_measurements\":") != std::string::npos
                 || sectionVector[i].find("\"inference_measurements\":") != std::string::npos)
             {
                 sectionVector.erase(sectionVector.begin() + static_cast<int>(i));
diff --git a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
index 12a47dcd94..a5f4c7f254 100644
--- a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
+++ b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
@@ -70,6 +70,7 @@ ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescrip
     : BaseWorkload<Convolution2dQueueDescriptor>(descriptor, info)
     , m_ConvolutionLayer(memoryManager)
 {
+    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload");
     const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo();
 
     m_KernelTensor = std::make_unique<arm_compute::CLTensor>();
@@ -97,16 +98,19 @@ ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescrip
 
     const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor);
 
-    m_ConvolutionLayer.configure(clCompileContext,
-                                 &input,
-                                 m_KernelTensor.get(),
-                                 m_BiasTensor.get(),
-                                 &output,
-                                 padStrideInfo,
-                                 arm_compute::WeightsInfo(),
-                                 aclDilationInfo,
-                                 activationInfo,
-                                 isFastMathEnabled);
+    {
+        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload_configure");
+        m_ConvolutionLayer.configure(clCompileContext,
+                                     &input,
+                                     m_KernelTensor.get(),
+                                     m_BiasTensor.get(),
+                                     &output,
+                                     padStrideInfo,
+                                     arm_compute::WeightsInfo(),
+                                     aclDilationInfo,
+                                     activationInfo,
+                                     isFastMathEnabled);
+    }
 
     m_ConvolutionMethod =
         m_ConvolutionLayer.get_convolution_method(input.info(),
@@ -146,7 +150,10 @@ ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescrip
 
     // Force Compute Library to perform the necessary copying and reshaping, after which
     // delete all the input tensors that will no longer be needed
-    m_ConvolutionLayer.prepare();
+    {
+        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload_prepare");
+        m_ConvolutionLayer.prepare();
+    }
     FreeUnusedTensors();
 }