1 files changed, 59 insertions, 9 deletions

diff --git a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
index 19c85f7f33..2f14ab9022 100644
--- a/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
+++ b/src/backends/neon/workloads/NeonLstmFloatWorkload.cpp
@@ -6,8 +6,7 @@
 #include "NeonLstmFloatWorkload.hpp"
 #include "NeonWorkloadUtils.hpp"
 
-#include <aclCommon/ArmComputeTensorUtils.hpp>
-#include <aclCommon/ArmComputeUtils.hpp>
+#include "aclCommon/ArmComputeTensorUtils.hpp"
 
 #include <armnn/utility/NumericCast.hpp>
 
@@ -17,14 +16,14 @@ 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,
-                                         GetGuid());
+                                         this->GetGuid());
 
     arm_compute::LSTMParams<arm_compute::ITensor> lstm_param;
 
@@ -161,8 +160,36 @@ 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 =
-        ConvertLstmActivationFuncToAclLayerInfo(m_Data.m_Parameters.m_ActivationFunc);
+    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!");
+    }
+
 
     m_LstmLayer.configure(&input, m_InputToForgetWeightsTensor.get(), m_InputToCellWeightsTensor.get(),
                           m_InputToOutputWeightsTensor.get(), m_RecurrentToForgetWeightsTensor.get(),
@@ -246,7 +273,7 @@ NeonLstmFloatWorkload::NeonLstmFloatWorkload(const LstmQueueDescriptor& descript
 
 void NeonLstmFloatWorkload::Execute() const
 {
-    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", GetGuid());
+    ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonLstmFloatWorkload_Execute", this->GetGuid());
     m_LstmLayer.run();
 }
 
@@ -363,8 +390,31 @@ 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 =
-        ConvertLstmActivationFuncToAclLayerInfo(descriptor.m_ActivationFunc);
+    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!");
+    }
 
     return arm_compute::NELSTMLayer::validate(&aclInputInfo,
                                               &aclInputToForgetWeightsInfo,