1 files changed, 9 insertions, 59 deletions

diff --git a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
index 2f14ab9022..19c85f7f33 100644
--- a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
+++ b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
@@ -6,7 +6,8 @@
 #include "NeonLstmFloatWorkload.hpp"
 #include "NeonWorkloadUtils.hpp"
 
-#include "aclCommon/ArmComputeTensorUtils.hpp"
+#include <aclCommon/ArmComputeTensorUtils.hpp>
+#include <aclCommon/ArmComputeUtils.hpp>
 
 #include <armnn/utility/NumericCast.hpp>
 
@@ -16,14 +17,14 @@ namespace armnn
 {
 using namespace armcomputetensorutils;
 
-NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor &descriptor, const WorkloadInfo &info)
+NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descriptor, const WorkloadInfo& info)
         : FloatWorkload<LstmQueueDescriptor>(descriptor, info)
 {
     // Report Profiling Details
     ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonLstmFloatWorkload_Construct",
                                          descriptor.m_Parameters,
                                          info,
-                                         this->GetGuid());
+                                         GetGuid());
 
     arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;
 
@@ -160,36 +161,8 @@ NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor &descript
     float projection_threshold = m_Data.m_Parameters.m_ClippingThresProj;
 
     // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations
-    arm_compute::ActivationLayerInfo activationLayerInfo;
-    if (m_Data.m_Parameters.m_ActivationFunc == 0)
-    {
-        // no activation, do nothing
-    }
-    else if (m_Data.m_Parameters.m_ActivationFunc == 1)
-    {
-        activationLayerInfo = arm_compute::ActivationLayerInfo(
-                arm_compute::ActivationLayerInfo::ActivationFunction::RELU);
-    }
-    else if (m_Data.m_Parameters.m_ActivationFunc == 3)
-    {
-        activationLayerInfo = arm_compute::ActivationLayerInfo(
-                arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0);
-    }
-    else if (m_Data.m_Parameters.m_ActivationFunc == 4)
-    {
-        activationLayerInfo = arm_compute::ActivationLayerInfo(
-                arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0);
-    }
-    else if (m_Data.m_Parameters.m_ActivationFunc == 6)
-    {
-        activationLayerInfo = arm_compute::ActivationLayerInfo(
-                arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC);
-    }
-    else
-    {
-        throw armnn::Exception("Wrong Type of Activation Function!");
-    }
-
+    arm_compute::ActivationLayerInfo activationLayerInfo =
+        ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc);
 
     m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(),
                           m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(),
@@ -273,7 +246,7 @@ NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor &descript
 
 void NeonLstmFloatWorkload::Execute() const
 {
-    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", this->GetGuid());
+    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", GetGuid());
     m_LstmLayer.run();
 }
 
@@ -390,31 +363,8 @@ arm_compute::Status NeonLstmFloatWorkloadValidate(const TensorInfo& input,
     float projection_threshold = descriptor.m_ClippingThresProj;
 
     // for preparing the object for the class ActivationLayerInfo, we need to consider 5 situations
-    arm_compute::ActivationLayerInfo activationLayerInfo;
-    switch (descriptor.m_ActivationFunc)
-    {
-        case 0:
-            // no activation, do nothing
-            break;
-        case 1:
-            activationLayerInfo = arm_compute::ActivationLayerInfo(
-                    arm_compute::ActivationLayerInfo::ActivationFunction::RELU);
-            break;
-        case 3:
-            activationLayerInfo = arm_compute::ActivationLayerInfo(
-                    arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.0);
-            break;
-        case 4:
-            activationLayerInfo = arm_compute::ActivationLayerInfo(
-                    arm_compute::ActivationLayerInfo::ActivationFunction::TANH, 1.0, 1.0);
-            break;
-        case 6:
-            activationLayerInfo = arm_compute::ActivationLayerInfo(
-                    arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC);
-            break;
-        default:
-            throw armnn::Exception("Wrong Type of Activation Function!");
-    }
+    arm_compute::ActivationLayerInfo activationLayerInfo =
+        ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc);
 
     return arm_compute::NELSTMLayer::validate(&aclInputInfo,
                                               &aclInputToForgetWeightsInfo,