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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ClDepthwiseConvolutionWorkload.hpp"
#include "TypeUtils.hpp"
#include "ClWorkloadUtils.hpp"
#include <backends/aclCommon/ArmComputeUtils.hpp>
#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
#include <backends/cl/ClTensorHandle.hpp>
#include <backends/CpuTensorHandle.hpp>
#include <arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h>
namespace armnn
{
using namespace armcomputetensorutils;
arm_compute::Status ClDepthwiseConvolutionWorkloadValidate(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::CLDepthwiseConvolutionLayer::validate(&aclInputInfo,
&aclWeightsInfo,
optionalAclBiasesInfo,
&aclOutputInfo,
aclPadStrideInfo,
aclDepthMultiplier);
}
ClDepthwiseConvolutionWorkload::ClDepthwiseConvolutionWorkload(
const DepthwiseConvolution2dQueueDescriptor& descriptor,
const WorkloadInfo& info)
: BaseWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info)
{
auto& weightInfo = m_Data.m_Weight->GetTensorInfo();
m_KernelTensor = std::make_unique<arm_compute::CLTensor>();
BuildArmComputeTensor(*m_KernelTensor, weightInfo, m_Data.m_Parameters.m_DataLayout);
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);
}
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);
std::string name = std::string("ClDepthwiseConvolutionWorkload");
m_Data.ValidateInputsOutputs(name, 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);
const unsigned int depthMultiplier = weightInfo.GetShape()[0];
const unsigned int widthIndex = (m_Data.m_Parameters.m_DataLayout == DataLayout::NCHW) ? 3 : 2;
const unsigned int heightIndex = (m_Data.m_Parameters.m_DataLayout == DataLayout::NCHW) ? 2 : 1;
//Check for optimisation opportunities.
bool use3x3Optimisation = (weightInfo.GetShape()[widthIndex] == 3) && (weightInfo.GetShape()[heightIndex] == 3);
if (use3x3Optimisation)
{
m_DepthwiseConvolutionLayer = std::make_unique<arm_compute::CLDepthwiseConvolutionLayer3x3>();
static_cast<arm_compute::CLDepthwiseConvolutionLayer3x3*>(m_DepthwiseConvolutionLayer.get())->configure(
&input,
m_KernelTensor.get(),
m_BiasTensor.get(),
&output,
padStrideInfo,
depthMultiplier);
}
else
{
m_DepthwiseConvolutionLayer = std::make_unique<arm_compute::CLDepthwiseConvolutionLayer>();
static_cast<arm_compute::CLDepthwiseConvolutionLayer*>(m_DepthwiseConvolutionLayer.get())->configure(
&input,
m_KernelTensor.get(),
m_BiasTensor.get(),
&output,
padStrideInfo,
depthMultiplier);
}
BOOST_ASSERT(m_DepthwiseConvolutionLayer);
InitializeArmComputeClTensorData(*m_KernelTensor, m_Data.m_Weight);
if (m_BiasTensor)
{
InitializeArmComputeClTensorData(*m_BiasTensor, m_Data.m_Bias);
}
m_DepthwiseConvolutionLayer->prepare();
FreeUnusedTensors();
}
void ClDepthwiseConvolutionWorkload::FreeUnusedTensors()
{
FreeTensorIfUnused(m_KernelTensor);
FreeTensorIfUnused(m_BiasTensor);
}
void ClDepthwiseConvolutionWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_CL("ClDepthwiseConvolutionWorkload_Execute");
BOOST_ASSERT(m_DepthwiseConvolutionLayer);
RunClFunction(*m_DepthwiseConvolutionLayer, CHECK_LOCATION());
}
} // namespace armnn
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