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author | David Beck <david.beck@arm.com> | 2018-09-24 15:59:27 +0100 |
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committer | Matthew Bentham <matthew.bentham@arm.com> | 2018-10-10 16:16:57 +0100 |
commit | 0dbe0ee25312b728d77383d11c465156e64ae757 (patch) | |
tree | af37a9802e3ad551e1bf63f7636508cde7a41643 /src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp | |
parent | b4540bef0b0327683fe8e63f727c1212800dc2a9 (diff) | |
download | armnn-0dbe0ee25312b728d77383d11c465156e64ae757.tar.gz |
IVGCVSW-1899 : Neon backend folder structure
armnn:149855
Change-Id: I26e8cf83422a65049386a5ebdb6d0001627aefaa
Diffstat (limited to 'src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp')
-rw-r--r-- | src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp | 96 |
1 files changed, 96 insertions, 0 deletions
diff --git a/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp b/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp new file mode 100644 index 0000000000..2383e78df3 --- /dev/null +++ b/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp @@ -0,0 +1,96 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "NeonBatchNormalizationFloatWorkload.hpp" +#include <backends/CpuTensorHandle.hpp> +#include <backends/aclCommon/ArmComputeTensorUtils.hpp> +#include <armnn/ArmNN.hpp> + +namespace armnn +{ +using namespace armcomputetensorutils; + + +arm_compute::Status NeonBatchNormalizationValidate(const TensorInfo& input, + const TensorInfo& output, + const TensorInfo& mean, + const TensorInfo& var, + const TensorInfo& beta, + const TensorInfo& gamma, + const BatchNormalizationDescriptor& descriptor) +{ + const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input); + const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output); + const arm_compute::TensorInfo aclMeanInfo = BuildArmComputeTensorInfo(mean); + const arm_compute::TensorInfo aclVarInfo = BuildArmComputeTensorInfo(var); + const arm_compute::TensorInfo aclBetaInfo = BuildArmComputeTensorInfo(beta); + const arm_compute::TensorInfo aclGammaInfo = BuildArmComputeTensorInfo(gamma); + + return arm_compute::NEBatchNormalizationLayer::validate(&aclInputInfo, + &aclOutputInfo, + &aclMeanInfo, + &aclVarInfo, + &aclBetaInfo, + &aclGammaInfo, + descriptor.m_Eps); +} + +NeonBatchNormalizationFloatWorkload::NeonBatchNormalizationFloatWorkload( + const BatchNormalizationQueueDescriptor& descriptor, const WorkloadInfo& info) + : FloatWorkload<BatchNormalizationQueueDescriptor>(descriptor, info) +{ + m_Data.ValidateInputsOutputs("NeonBatchNormalizationFloatWorkload", 1, 1); + + arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); + + m_Mean = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_Mean, m_Data.m_Mean->GetTensorInfo()); + + m_Variance = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_Variance, m_Data.m_Variance->GetTensorInfo()); + + m_Gamma = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_Gamma, m_Data.m_Gamma->GetTensorInfo()); + + m_Beta = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_Beta, m_Data.m_Beta->GetTensorInfo()); + + m_Layer.configure(&input, + &output, + m_Mean.get(), + m_Variance.get(), + m_Beta.get(), + m_Gamma.get(), + m_Data.m_Parameters.m_Eps); + + InitializeArmComputeTensorDataForFloatTypes(*m_Mean, m_Data.m_Mean); + InitializeArmComputeTensorDataForFloatTypes(*m_Variance, m_Data.m_Variance); + InitializeArmComputeTensorDataForFloatTypes(*m_Gamma, m_Data.m_Gamma); + InitializeArmComputeTensorDataForFloatTypes(*m_Beta, m_Data.m_Beta); + + // Force Compute Library to perform the necessary copying and reshaping, after which + // delete all the input tensors that will no longer be needed + m_Layer.prepare(); + FreeUnusedTensors(); +} + +void NeonBatchNormalizationFloatWorkload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonBatchNormalizationFloatWorkload_Execute"); + m_Layer.run(); +} + +void NeonBatchNormalizationFloatWorkload::FreeUnusedTensors() +{ + FreeTensorIfUnused(m_Mean); + FreeTensorIfUnused(m_Variance); + FreeTensorIfUnused(m_Gamma); + FreeTensorIfUnused(m_Beta); +} + +} //namespace armnn + + |