commit 07810fc2fcdd34db74222d90cc73ef12a88e7b78
author Mike Kelly <mike.kelly@arm.com> Thu Nov 12 10:58:48 2020 +0000
committer Jim Flynn <jim.flynn@arm.com> Fri Nov 13 14:25:30 2020 +0000
tree 8becef8453674822d079815b06ae37310b97d2cf
parent 8502adeafbbb1db0acefa62560d93453e38dcadb

IVGCVSW-5328-5329 Fuse Activation

 * Added Fused Activation Optimization to both CL and Neon backends.
 * Added Fused Activation support to all the CL and Neon workloads
   that support it.
 * Changed ProfilingTest network to be a Convolution layer
   followed by an Abs layer rather than an Activation layer.
 * Added IBackendInternal::OptimizeSubgraphView function that can accept a
   ModelOptions.
 * Network will now call OptimizeSubgraphView passing in the ModelOptions.

Signed-off-by: Keith Davis <keith.davis@arm.com>
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib536ac3cbafc7d9b35c139ad9a65b7735262cd9d

src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp

diff --git a/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp b/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp
index e808c60..31489a0 100644
--- a/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp
+++ b/src/backends/neon/workloads/NeonFullyConnectedWorkload.cpp
@@ -6,9 +6,12 @@
 #include "NeonFullyConnectedWorkload.hpp"
 
 #include "NeonWorkloadUtils.hpp"
+
 #include <aclCommon/ArmComputeTensorUtils.hpp>
 #include <aclCommon/ArmComputeUtils.hpp>
+
 #include <armnn/utility/PolymorphicDowncast.hpp>
+
 #include <backendsCommon/CpuTensorHandle.hpp>
 
 #include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h>
@@ -21,7 +24,8 @@
                                                        const TensorInfo& output,
                                                        const TensorInfo& weights,
                                                        const TensorInfo& biases,
-                                                       const FullyConnectedDescriptor& descriptor)
+                                                       const FullyConnectedDescriptor& descriptor,
+                                                       const ActivationDescriptor* activationDescriptor)
 {
     const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input);
     const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output);
@@ -36,8 +40,7 @@
     }
 
     const arm_compute::FullyConnectedLayerInfo fullyConnectedLayerInfo =
-        ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor);
-
+        ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor, activationDescriptor);
 
     return arm_compute::NEFullyConnectedLayer::validate(&aclInput,
                                                         &aclWeights,
@@ -64,9 +67,10 @@
         BuildArmComputeTensor(*m_BiasesTensor, m_Data.m_Bias->GetTensorInfo());
     }
 
-    // Construct
-    arm_compute::FullyConnectedLayerInfo fc_info;
-    fc_info.transpose_weights = m_Data.m_Parameters.m_TransposeWeightMatrix;
+    const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor);
+
+    arm_compute::FullyConnectedLayerInfo fc_info  =
+            ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor.m_Parameters, activationInfo);
 
     auto layer = std::make_unique<arm_compute::NEFullyConnectedLayer>(memoryManager);
     layer->configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info);