1 files changed, 119 insertions, 0 deletions
diff --git a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
new file mode 100644
index 0000000000..521711becc
--- /dev/null
+++ b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp
@@ -0,0 +1,119 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ClConvolution2dWorkload.hpp"
+
+#include "ClWorkloadUtils.hpp"
+
+#include <backends/cl/ClLayerSupport.hpp>
+#include <backends/cl/ClTensorHandle.hpp>
+#include <backends/cl/ClLayerSupport.hpp>
+#include <backends/aclCommon/ArmComputeUtils.hpp>
+#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
+#include <backends/CpuTensorHandle.hpp>
+
+#include <arm_compute/runtime/CL/functions/CLConvolutionLayer.h>
+
+namespace armnn
+{
+using namespace armcomputetensorutils;
+
+arm_compute::Status ClConvolution2dWorkloadValidate(const TensorInfo& input,
+                                                    const TensorInfo& output,
+                                                    const Convolution2dDescriptor& descriptor,
+                                                    const TensorInfo& weights,
+                                                    const boost::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.is_initialized());
+
+        aclBiasesInfo = BuildArmComputeTensorInfo(biases.get(), descriptor.m_DataLayout);
+        optionalAclBiasesInfo = &aclBiasesInfo;
+    }
+
+    arm_compute::PadStrideInfo layerInfo = BuildArmComputePadStrideInfo(descriptor);
+
+    return arm_compute::CLConvolutionLayer::validate(&aclInputInfo,
+                                                     &aclWeightsInfo,
+                                                     optionalAclBiasesInfo,
+                                                     &aclOutputInfo,
+                                                     layerInfo);
+}
+
+ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescriptor& descriptor,
+    const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
+    : BaseWorkload<Convolution2dQueueDescriptor>(descriptor, info)
+    , m_ConvolutionLayer(memoryManager)
+{
+    // todo: check tensor shapes match.
+    const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo();
+
+    m_KernelTensor = std::make_unique<arm_compute::CLTensor>();
+    BuildArmComputeTensor(*m_KernelTensor, weightInfo, 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);
+
+    if (m_Data.m_Parameters.m_BiasEnabled)
+    {
+        m_BiasTensor = std::make_unique<arm_compute::CLTensor>();
+        BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
+    }
+
+    m_Data.ValidateInputsOutputs("ClConvolution2dWorkload", 1, 1);
+
+    arm_compute::ICLTensor& input  = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
+    arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(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_ConvolutionLayer.configure(&input,
+                                 m_KernelTensor.get(),
+                                 m_BiasTensor.get(),
+                                 &output,
+                                 padStrideInfo);
+
+    InitializeArmComputeClTensorData(*m_KernelTensor, m_Data.m_Weight);
+
+    if (m_BiasTensor)
+    {
+        InitializeArmComputeClTensorData(*m_BiasTensor, m_Data.m_Bias);
+    }
+
+    // Force Compute Library to perform the necessary copying and reshaping, after which
+    // delete all the input tensors that will no longer be needed
+    m_ConvolutionLayer.prepare();
+    FreeUnusedTensors();
+}
+
+void ClConvolution2dWorkload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT_CL("ClConvolution2dWorkload_Execute");
+
+    m_ConvolutionLayer.run();
+}
+
+void ClConvolution2dWorkload::FreeUnusedTensors()
+{
+    FreeTensorIfUnused(m_KernelTensor);
+    FreeTensorIfUnused(m_BiasTensor);
+}
+
+} //namespace armnn