1 files changed, 88 insertions, 0 deletions
diff --git a/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.cpp b/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.cpp
new file mode 100644
index 0000000000..5099965a24
--- /dev/null
+++ b/src/armnn/backends/NeonWorkloads/NeonConvolution2dBaseWorkload.cpp
@@ -0,0 +1,88 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+
+#include "backends/CpuTensorHandle.hpp"
+#include "backends/ArmComputeTensorUtils.hpp"
+#include "backends/NeonLayerSupport.hpp"
+
+#include "NeonConvolution2dBaseWorkload.hpp"
+
+namespace armnn
+{
+
+template<armnn::DataType dataType>
+NeonConvolution2dBaseWorkload<dataType>::NeonConvolution2dBaseWorkload(const Convolution2dQueueDescriptor& descriptor,
+                                                                       const WorkloadInfo& info)
+    : TypedWorkload<Convolution2dQueueDescriptor, dataType>(descriptor, info)
+{
+    using arm_compute::NEDirectConvolutionLayer;
+    using namespace armcomputetensorutils;
+
+    ValidateData();
+
+    // 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();
+
+    BuildArmComputeTensor(m_KernelTensor, m_Data.m_Weight->GetTensorInfo());
+
+    arm_compute::Tensor* optionalBiasTensor = nullptr;
+    if (m_Data.m_Parameters.m_BiasEnabled)
+    {
+        BuildArmComputeTensor(m_BiasTensor, m_Data.m_Bias->GetTensorInfo());
+        optionalBiasTensor = &m_BiasTensor;
+    }
+
+    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>();
+        directConvolutionLayer->configure(&input,
+                                          &m_KernelTensor,
+                                          optionalBiasTensor,
+                                          &output,
+                                          padStrideInfo);
+        m_ConvolutionLayer.reset(directConvolutionLayer.release());
+    }
+    else
+    {
+        auto convolutionLayer = std::make_unique<arm_compute::NEConvolutionLayer>();
+        convolutionLayer->configure(&input,
+                                    &m_KernelTensor,
+                                    optionalBiasTensor,
+                                    &output,
+                                    padStrideInfo);
+        m_ConvolutionLayer.reset(convolutionLayer.release());
+    }
+    BOOST_ASSERT(m_ConvolutionLayer);
+
+    using Type = ResolveType<dataType>;
+
+    InitialiseArmComputeTensorData(m_KernelTensor, m_Data.m_Weight->template GetConstTensor<Type>());
+    if (m_Data.m_Parameters.m_BiasEnabled)
+    {
+        InitialiseArmComputeTensorData(m_BiasTensor, m_Data.m_Bias->template GetConstTensor<Type>());
+    }
+}
+
+// Generate known implementations for linker
+template class NeonConvolution2dBaseWorkload<DataType::Float32>;
+template class NeonConvolution2dBaseWorkload<DataType::QuantisedAsymm8>;
+
+} //namespace armnn
+
+