IVGCVSW-1897 : build infrastructure for the src/backends folder

Change-Id: I7ebafb675ccc77ad54d1deb01412a8379a5356bb

1 files changed, 94 insertions, 0 deletions

diff --git a/src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp b/src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp
new file mode 100644
index 0000000000..1ec1417a58
--- /dev/null
+++ b/src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp
@@ -0,0 +1,94 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NeonDepthwiseConvolutionFloatWorkload.hpp"
+#include "backends/NeonLayerSupport.hpp"
+#include "backends/CpuTensorHandle.hpp"
+#include "backends/ArmComputeTensorUtils.hpp"
+
+
+namespace armnn
+{
+using namespace armcomputetensorutils;
+
+NeonDepthwiseConvolutionFloatWorkload::NeonDepthwiseConvolutionFloatWorkload(
+    const DepthwiseConvolution2dQueueDescriptor& descriptor,
+    const WorkloadInfo& info)
+    : FloatWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info)
+{
+    const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo();
+
+    m_KernelTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_KernelTensor, weightInfo);
+
+    if (m_Data.m_Parameters.m_BiasEnabled)
+    {
+        m_BiasTensor = std::make_unique<arm_compute::Tensor>();
+        BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo());
+    }
+
+    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);
+
+    m_Data.ValidateInputsOutputs("NeonDepthwiseConvolutionFloatWorkload", 1, 1);
+
+    arm_compute::ITensor& input  = static_cast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ITensor& output = static_cast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+
+    bool use3x3Optimisation = weightInfo.GetShape()[3] == 3 && weightInfo.GetShape()[2] == 3;
+    if (use3x3Optimisation)
+    {
+        m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer3x3>();
+        static_cast<arm_compute::NEDepthwiseConvolutionLayer3x3*>(
+            m_pDepthwiseConvolutionLayer.get())->configure(&input,
+                                                           m_KernelTensor.get(),
+                                                           m_BiasTensor.get(),
+                                                           &output,
+                                                           padStrideInfo);
+    }
+    else
+    {
+        m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer>();
+        static_cast<arm_compute::NEDepthwiseConvolutionLayer*>(
+            m_pDepthwiseConvolutionLayer.get())->configure(&input,
+                                                           m_KernelTensor.get(),
+                                                           m_BiasTensor.get(),
+                                                           &output,
+                                                           padStrideInfo);
+    }
+
+    BOOST_ASSERT(m_pDepthwiseConvolutionLayer);
+
+    InitializeArmComputeTensorDataForFloatTypes(*m_KernelTensor, m_Data.m_Weight);
+
+    if (m_BiasTensor)
+    {
+        InitializeArmComputeTensorDataForFloatTypes(*m_BiasTensor, m_Data.m_Bias);
+    }
+
+    m_pDepthwiseConvolutionLayer->prepare();
+    FreeUnusedTensors();
+}
+
+void NeonDepthwiseConvolutionFloatWorkload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonDepthwiseConvolutionFloatWorkload_Execute");
+    BOOST_ASSERT(m_pDepthwiseConvolutionLayer);
+
+    m_pDepthwiseConvolutionLayer->run();
+}
+
+void NeonDepthwiseConvolutionFloatWorkload::FreeUnusedTensors()
+{
+    FreeTensorIfUnused(m_KernelTensor);
+    FreeTensorIfUnused(m_BiasTensor);
+}
+
+} //namespace armnn