diff options
Diffstat (limited to 'src/backends/cl/workloads/ClConvolution2dWorkload.cpp')
| -rw-r--r-- | src/backends/cl/workloads/ClConvolution2dWorkload.cpp | 57 |
1 files changed, 19 insertions, 38 deletions
diff --git a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp index e3d679a773..762645bfba 100644 --- a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp +++ b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp @@ -28,7 +28,7 @@ arm_compute::Status ClConvolution2dWorkloadValidate(const TensorInfo& input, bool isFastMathEnabled, const ActivationDescriptor* activationDescriptor) { - // The implemented workload does support both const and non const + // The arm_compute::CLConvolutionLayer supports both const and non const // weights. However, in the case of non const weights we'd have to call // prepare or configure for each inference which we're not setup to do just yet. if (!weights.IsConstant()) @@ -39,7 +39,8 @@ arm_compute::Status ClConvolution2dWorkloadValidate(const TensorInfo& input, 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 aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout); + aclWeightsInfo.set_are_values_constant(weights.IsConstant()); const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(descriptor.m_DilationX, descriptor.m_DilationY); @@ -57,6 +58,7 @@ arm_compute::Status ClConvolution2dWorkloadValidate(const TensorInfo& input, "ArmNN ClConvolution2dWorkload does not support non constant bias."}; } aclBiasesInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout); + aclBiasesInfo.set_are_values_constant(biases.value().IsConstant()); optionalAclBiasesInfo = &aclBiasesInfo; } @@ -85,31 +87,31 @@ ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescrip , m_ConvolutionLayer(memoryManager) { ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload"); - const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo(); - m_Data.ValidateInputsOutputs("ClConvolution2dWorkload", 1, 1); - - m_KernelTensor = std::make_unique<arm_compute::CLTensor>(); - BuildArmComputeTensor(*m_KernelTensor, weightInfo, m_Data.m_Parameters.m_DataLayout); const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(m_Data.m_Parameters.m_DilationX, m_Data.m_Parameters.m_DilationY); - 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); - } + uint32_t numInputs = m_Data.m_Parameters.m_BiasEnabled ? 3: 2; + m_Data.ValidateInputsOutputs("ClConvolution2dWorkload", numInputs, 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::ICLTensor& weights = static_cast<IClTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); + arm_compute::ICLTensor* bias = nullptr; + if (m_Data.m_Parameters.m_BiasEnabled) + { + bias = &static_cast<IClTensorHandle*>(m_Data.m_Inputs[2])->GetTensor(); + } // Create Proxy tensor and set the initial tensor handle to it m_InputProxy = std::make_unique<ICLTensorProxy>(&input); m_OutputProxy = std::make_unique<ICLTensorProxy>(&output); + arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); input.info()->set_data_layout(aclDataLayout); output.info()->set_data_layout(aclDataLayout); + weights.info()->set_data_layout(aclDataLayout); arm_compute::PadStrideInfo padStrideInfo = BuildArmComputePadStrideInfo(m_Data.m_Parameters); @@ -119,8 +121,8 @@ ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescrip ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload_configure"); m_ConvolutionLayer.configure(clCompileContext, m_InputProxy.get(), - m_KernelTensor.get(), - m_BiasTensor.get(), + &weights, + bias, m_OutputProxy.get(), padStrideInfo, arm_compute::WeightsInfo(), @@ -131,7 +133,7 @@ ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescrip m_ConvolutionMethod = m_ConvolutionLayer.get_convolution_method(input.info(), - m_KernelTensor->info(), + weights.info(), output.info(), padStrideInfo, arm_compute::WeightsInfo(), @@ -156,27 +158,12 @@ ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescrip ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClConvolution2dWorkload_Construct", descriptor.m_Parameters, detailsInfo, - this->GetGuid()); - - 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 - { - ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClConvolution2dWorkload_prepare"); - m_ConvolutionLayer.prepare(); - } - FreeUnusedTensors(); + GetGuid()); } void ClConvolution2dWorkload::Execute() const { - ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClConvolution2dWorkload_Execute", this->GetGuid()); + ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClConvolution2dWorkload_Execute", GetGuid()); RunClFunction(m_ConvolutionLayer, CHECK_LOCATION()); } @@ -185,12 +172,6 @@ arm_compute::ConvolutionMethod ClConvolution2dWorkload::GetConvolutionMethod() c return m_ConvolutionMethod; } -void ClConvolution2dWorkload::FreeUnusedTensors() -{ - FreeTensorIfUnused(m_KernelTensor); - FreeTensorIfUnused(m_BiasTensor); -} - void ClConvolution2dWorkload::Reconfigure() { arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |