diff options
Diffstat (limited to 'src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp')
| -rw-r--r-- | src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp | 84 |
1 files changed, 34 insertions, 50 deletions
diff --git a/src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp b/src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp index 42a476c6ca..b122be62ce 100644 --- a/src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp +++ b/src/backends/neon/workloads/NeonDepthwiseConvolutionWorkload.cpp @@ -33,11 +33,18 @@ arm_compute::Status NeonDepthwiseConvolutionWorkloadValidate(const TensorInfo& i const Optional<TensorInfo>& biases, const ActivationDescriptor* activationDescriptor) { - const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); - const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); - - // ArmNN's weight format is usually [ M, I, H, W ] but for depthwise its [ 1, H, W, I*M] - // Permute to [ 1, I * M, H, W ] (if NCHW), as required by the compute library + const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); + const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); + + // ArmNN format for weights for depthwise is [1, H, W, C] independently of the input/output layout + // + // ACL format for weights for depthwise is: + // - [1, H, W, C] for [N, H, W, C] input/output layout (matches with ArmNN) + // - [1, C, H, W] for [N, C, H, W] input/output layout + // + // Therefore ArmNN weights have to be permuted when input/output layout is [N, C, H, W] to pass them to ACL. + // The PermuteDepthwiseConv2dWeights backend optimization takes care of this, but it has not been performed yet, + // so we do the permute here for the TensorInfo weights. unsigned int aclDepthMultiplier; TensorInfo weightsPermuted; std::tie(weightsPermuted, aclDepthMultiplier) = Convert1HWOTensorInfoToAcl(weights, input, descriptor.m_DataLayout); @@ -47,11 +54,9 @@ arm_compute::Status NeonDepthwiseConvolutionWorkloadValidate(const TensorInfo& i arm_compute::TensorInfo aclBiasesInfo; arm_compute::TensorInfo* optionalAclBiasesInfo = nullptr; - if (descriptor.m_BiasEnabled) { ARMNN_ASSERT(biases.has_value()); - aclBiasesInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout); optionalAclBiasesInfo = &aclBiasesInfo; } @@ -78,40 +83,34 @@ NeonDepthwiseConvolutionWorkload::NeonDepthwiseConvolutionWorkload( const WorkloadInfo& info) : NeonBaseWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info) { - // ArmNN's weight format for depthwise is [ 1, H, W, I*M ] - auto& weightInfo = m_Data.m_Weight->GetTensorInfo(); - - ConstTensor weightsPermuted; - unsigned int depthMultiplier; - std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightInfo.GetNumBytes()]); - std::tie(weightsPermuted, depthMultiplier) = Convert1HWOTensorToAcl(m_Data.m_Weight, - info.m_InputTensorInfos[0], - m_Data.m_Parameters.m_DataLayout, - permuteBuffer.get()); - - // Convert the weights into the compute library format - m_KernelTensor = std::make_unique<arm_compute::Tensor>(); - BuildArmComputeTensor(*m_KernelTensor, weightsPermuted.GetInfo(), m_Data.m_Parameters.m_DataLayout); - + arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); + arm_compute::ITensor& weights = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor(); + arm_compute::ITensor* biasesPtr = nullptr; 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); + biasesPtr = &PolymorphicDowncast<IAclTensorHandle *>(m_Data.m_Inputs[2])->GetTensor(); } - const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D( - m_Data.m_Parameters.m_DilationX, m_Data.m_Parameters.m_DilationY); + arm_compute::ITensorInfo* weightsInfo = weights.info(); + arm_compute::ITensorInfo* inputInfo = input.info(); + auto weightsShape = weightsInfo->tensor_shape(); + auto inputShape = inputInfo->tensor_shape(); - m_Data.ValidateInputsOutputs("NeonDepthwiseConvolutionWorkload", 1, 1); + // The PermuteDepthwiseConv2dWeights backend optimization has been performed, + // converting weights to have the same data layout as input. + unsigned int depthMultiplier = + ComputeDepthwiseConv2dDepthMultiplier(m_Data.m_Parameters.m_DataLayout, weightsShape, inputShape); - IAclTensorHandle* inputTensorHandle = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0]); - IAclTensorHandle* outputTensorHandle = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0]); + const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D( + m_Data.m_Parameters.m_DilationX, m_Data.m_Parameters.m_DilationY); - arm_compute::ITensor& input = inputTensorHandle->GetTensor(); - arm_compute::ITensor& output = outputTensorHandle->GetTensor(); + uint32_t numInputs = m_Data.m_Parameters.m_BiasEnabled ? 3: 2; + m_Data.ValidateInputsOutputs("NeonDepthwiseConvolutionWorkload", numInputs, 1); arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); input.info()->set_data_layout(aclDataLayout); + weights.info()->set_data_layout(aclDataLayout); output.info()->set_data_layout(aclDataLayout); arm_compute::PadStrideInfo padStrideInfo = BuildArmComputePadStrideInfo(m_Data.m_Parameters); @@ -121,8 +120,8 @@ NeonDepthwiseConvolutionWorkload::NeonDepthwiseConvolutionWorkload( m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer>(); static_cast<arm_compute::NEDepthwiseConvolutionLayer*>( m_pDepthwiseConvolutionLayer.get())->configure(&input, - m_KernelTensor.get(), - m_BiasTensor.get(), + &weights, + biasesPtr, &output, padStrideInfo, depthMultiplier, @@ -144,34 +143,19 @@ NeonDepthwiseConvolutionWorkload::NeonDepthwiseConvolutionWorkload( ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonDepthwiseConvolution2dWorkload_Construct", descriptor.m_Parameters, detailsInfo, - this->GetGuid()); + GetGuid()); ARMNN_ASSERT(m_pDepthwiseConvolutionLayer); - ScopedTensorHandle weightsPermutedHandle(weightsPermuted); - InitializeArmComputeTensorData(*m_KernelTensor, &weightsPermutedHandle); - - if (m_Data.m_Parameters.m_BiasEnabled) - { - InitializeArmComputeTensorData(*m_BiasTensor, m_Data.m_Bias); - } - m_pDepthwiseConvolutionLayer->prepare(); - FreeUnusedTensors(); } void NeonDepthwiseConvolutionWorkload::Execute() const { - ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonDepthwiseConvolutionWorkload_Execute", this->GetGuid()); + ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonDepthwiseConvolutionWorkload_Execute", GetGuid()); ARMNN_ASSERT(m_pDepthwiseConvolutionLayer); m_pDepthwiseConvolutionLayer->run(); } -void NeonDepthwiseConvolutionWorkload::FreeUnusedTensors() -{ - FreeTensorIfUnused(m_KernelTensor); - FreeTensorIfUnused(m_BiasTensor); -} - } //namespace armnn |