1 files changed, 135 insertions, 0 deletions
diff --git a/src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp b/src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp
new file mode 100644
index 0000000000..8b1feaa93c
--- /dev/null
+++ b/src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp
@@ -0,0 +1,135 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "NeonDepthwiseConvolutionWorkload.hpp"
+
+#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
+#include <backends/neon/NeonLayerSupport.hpp>
+#include <backends/CpuTensorHandle.hpp>
+
+namespace armnn
+{
+
+using namespace armcomputetensorutils;
+
+arm_compute::Status NeonDepthwiseConvolutionWorkloadValidate(const TensorInfo& input,
+    const TensorInfo& output,
+    const DepthwiseConvolution2dDescriptor& 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;
+    }
+
+    const arm_compute::PadStrideInfo aclPadStrideInfo =
+        BuildArmComputePadStrideInfo(descriptor);
+    const unsigned int aclDepthMultiplier = weights.GetShape()[0];
+
+    return arm_compute::NEDepthwiseConvolutionLayer::validate(&aclInputInfo,
+                                                              &aclWeightsInfo,
+                                                              optionalAclBiasesInfo,
+                                                              &aclOutputInfo,
+                                                              aclPadStrideInfo,
+                                                              aclDepthMultiplier);
+}
+
+NeonDepthwiseConvolutionWorkload::NeonDepthwiseConvolutionWorkload(
+    const DepthwiseConvolution2dQueueDescriptor& descriptor,
+    const WorkloadInfo& info)
+    : BaseWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info)
+{
+    const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo();
+
+    m_KernelTensor = std::make_unique<arm_compute::Tensor>();
+    BuildArmComputeTensor(*m_KernelTensor, weightInfo, 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);
+
+    m_Data.ValidateInputsOutputs("NeonDepthwiseConvolutionWorkload", 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();
+
+    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
+    input.info()->set_data_layout(aclDataLayout);
+    output.info()->set_data_layout(aclDataLayout);
+
+    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);
+
+    InitializeArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight);
+
+    if (m_BiasTensor)
+    {
+        InitializeArmComputeTensorData(*m_BiasTensor, m_Data.m_Bias);
+    }
+
+    m_pDepthwiseConvolutionLayer->prepare();
+    FreeUnusedTensors();
+}
+
+void NeonDepthwiseConvolutionWorkload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonDepthwiseConvolutionWorkload_Execute");
+    BOOST_ASSERT(m_pDepthwiseConvolutionLayer);
+
+    m_pDepthwiseConvolutionLayer->run();
+}
+
+void NeonDepthwiseConvolutionWorkload::FreeUnusedTensors()
+{
+    FreeTensorIfUnused(m_KernelTensor);
+    FreeTensorIfUnused(m_BiasTensor);
+}
+
+} //namespace armnn