IVGCVSW-1784: Rename float32 workloads for ACL

Change-Id: I98bdfe9cb12c663d1d5cfa456e2cc967d70ab22b

1 files changed, 70 insertions, 0 deletions

diff --git a/src/armnn/backends/NeonWorkloads/NeonNormalizationFloatWorkload.cpp b/src/armnn/backends/NeonWorkloads/NeonNormalizationFloatWorkload.cpp
new file mode 100644
index 0000000000..8c2a87d8bc
--- /dev/null
+++ b/src/armnn/backends/NeonWorkloads/NeonNormalizationFloatWorkload.cpp
@@ -0,0 +1,70 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#include "NeonNormalizationFloatWorkload.hpp"
+#include "backends/NeonLayerSupport.hpp"
+#include "backends/ArmComputeUtils.hpp"
+#include "backends/ArmComputeTensorUtils.hpp"
+
+namespace armnn
+{
+
+arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo& input,
+                                                      const TensorInfo& output,
+                                                      const NormalizationDescriptor& descriptor)
+{
+    const arm_compute::TensorInfo aclInput = armcomputetensorutils::BuildArmComputeTensorInfo(input);
+    const arm_compute::TensorInfo aclOutput = armcomputetensorutils::BuildArmComputeTensorInfo(output);
+
+    arm_compute::NormalizationLayerInfo normalizationInfo =
+            armcomputetensorutils::BuildArmComputeNormalizationLayerInfo(descriptor);
+
+    return arm_compute::NENormalizationLayer::validate(&aclInput, &aclOutput, normalizationInfo);
+}
+
+NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor& descriptor,
+                                                   const WorkloadInfo& info,
+                                                   std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
+    : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info)
+    , m_NormalizationLayer(memoryManager)
+{
+    m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1);
+    std::string reasonIfUnsupported;
+    if (!IsNeonNormalizationDescParamsSupported(&reasonIfUnsupported, m_Data.m_Parameters))
+    {
+        throw UnimplementedException(reasonIfUnsupported);
+    }
+
+    // Input and output tensors have to have the same dimensionality.
+    if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1]
+        || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0]
+        || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3]
+        || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2])
+    {
+        throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality.");
+    }
+
+    arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+
+    const arm_compute::NormType normType =
+        ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType);
+    arm_compute::NormalizationLayerInfo normalizationInfo(normType,
+                                                          m_Data.m_Parameters.m_NormSize,
+                                                          m_Data.m_Parameters.m_Alpha,
+                                                          m_Data.m_Parameters.m_Beta,
+                                                          m_Data.m_Parameters.m_K,
+                                                          false);
+
+    m_NormalizationLayer.configure(&input, &output, normalizationInfo);
+}
+
+void NeonNormalizationFloatWorkload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonNormalizationFloatWorkload_Execute");
+    m_NormalizationLayer.run();
+}
+
+} //namespace armnn