1 files changed, 134 insertions, 0 deletions
diff --git a/src/backends/neon/workloads/NeonConvolution2dWorkload.cpp b/src/backends/neon/workloads/NeonConvolution2dWorkload.cpp
new file mode 100644
index 0000000000..c26cdea92b
--- /dev/null
+++ b/src/backends/neon/workloads/NeonConvolution2dWorkload.cpp
@@ -0,0 +1,134 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NeonConvolution2dWorkload.hpp"
+
+#include <backends/CpuTensorHandle.hpp>
+#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
+#include <backends/neon/NeonLayerSupport.hpp>
+
+#include <armnn/Types.hpp>
+#include <armnnUtils/Half.hpp>
+
+namespace armnn
+{
+
+using namespace armcomputetensorutils;
+
+arm_compute::Status NeonConvolution2dWorkloadValidate(const TensorInfo& input,
+    const TensorInfo& output,
+    const Convolution2dDescriptor& descriptor,
+    const TensorInfo& weights,
+    const Optional<TensorInfo>& biases)
+{
+    const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
+    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
+    const arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout);
+
+    arm_compute::TensorInfo aclBiasesInfo;
+    arm_compute::TensorInfo *optionalAclBiasesInfo = nullptr;
+
+    if (descriptor.m_BiasEnabled)
+    {
+        BOOST_ASSERT(biases.has_value());
+
+        aclBiasesInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
+        optionalAclBiasesInfo = &aclBiasesInfo;
+    }
+
+    arm_compute::PadStrideInfo layerInfo = BuildArmComputePadStrideInfo(descriptor);
+
+    return arm_compute::NEConvolutionLayer::validate(&aclInputInfo,
+                                                     &aclWeightsInfo,
+                                                     optionalAclBiasesInfo,
+                                                     &aclOutputInfo,
+                                                     layerInfo);
+}
+
+NeonConvolution2dWorkload::NeonConvolution2dWorkload(
+    const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info,
+    std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
+    : BaseWorkload<Convolution2dQueueDescriptor>(descriptor, info)
+{
+    using arm_compute::NEDirectConvolutionLayer;
+
+    m_Data.ValidateInputsOutputs("NeonConvolution2dWorkload", 1, 1);
+
+    // todo: check tensor shapes match.
+
+    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();
+
+    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
+    input.info()->set_data_layout(aclDataLayout);
+    output.info()->set_data_layout(aclDataLayout);
+
+    m_KernelTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_KernelTensor, m_Data.m_Weight->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
+
+    if (m_Data.m_Parameters.m_BiasEnabled)
+    {
+        m_BiasTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
+    }
+
+    arm_compute::PadStrideInfo padStrideInfo(m_Data.m_Parameters.m_StrideX,
+                                             m_Data.m_Parameters.m_StrideY,
+                                             m_Data.m_Parameters.m_PadLeft,
+                                             m_Data.m_Parameters.m_PadRight,
+                                             m_Data.m_Parameters.m_PadTop,
+                                             m_Data.m_Parameters.m_PadBottom,
+                                             arm_compute::DimensionRoundingType::FLOOR);
+
+    const bool preferDirectConvolution =
+        IsNeonDirectConvolutionPreferred(m_Data.m_Weight->GetTensorInfo(),
+                                         m_Data.m_Parameters);
+
+    if (preferDirectConvolution)
+    {
+        auto directConvolutionLayer = std::make_unique<arm_compute::NEDirectConvolutionLayer>(memoryManager);
+        directConvolutionLayer->configure(&input,
+                                          m_KernelTensor.get(),
+                                          m_BiasTensor.get(),
+                                          &output,
+                                          padStrideInfo);
+        m_ConvolutionLayer.reset(directConvolutionLayer.release());
+    }
+    else
+    {
+        auto convolutionLayer = std::make_unique<arm_compute::NEConvolutionLayer>(memoryManager);
+        convolutionLayer->configure(&input,
+                                    m_KernelTensor.get(),
+                                    m_BiasTensor.get(),
+                                    &output,
+                                    padStrideInfo);
+        m_ConvolutionLayer.reset(convolutionLayer.release());
+    }
+    BOOST_ASSERT(m_ConvolutionLayer);
+
+    InitializeArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight);
+
+    if (m_Data.m_Parameters.m_BiasEnabled)
+    {
+        InitializeArmComputeTensorData(*m_BiasTensor, m_Data.m_Bias);
+    }
+
+    m_ConvolutionLayer->prepare();
+    FreeUnusedTensors();
+}
+
+void NeonConvolution2dWorkload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonConvolution2dWorkload_Execute");
+    m_ConvolutionLayer->run();
+}
+
+void NeonConvolution2dWorkload::FreeUnusedTensors()
+{
+    FreeTensorIfUnused(m_KernelTensor);
+    FreeTensorIfUnused(m_BiasTensor);
+}
+
+} //namespace armnn