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Diffstat (limited to 'src/backends/aclCommon/ArmComputeTensorUtils.cpp')
| -rw-r--r-- | src/backends/aclCommon/ArmComputeTensorUtils.cpp | 247 |
1 files changed, 0 insertions, 247 deletions
diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.cpp b/src/backends/aclCommon/ArmComputeTensorUtils.cpp deleted file mode 100644 index 49fef5bf17..0000000000 --- a/src/backends/aclCommon/ArmComputeTensorUtils.cpp +++ /dev/null @@ -1,247 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#include <aclCommon/ArmComputeTensorUtils.hpp> -#include <aclCommon/ArmComputeUtils.hpp> - -#include "armnn/Exceptions.hpp" -#include <armnn/Descriptors.hpp> - -namespace armnn -{ -namespace armcomputetensorutils -{ - -arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType, bool multiScales) -{ - switch(dataType) - { - case armnn::DataType::Boolean: - return arm_compute::DataType::U8; - case armnn::DataType::Float16: - return arm_compute::DataType::F16; - case armnn::DataType::Float32: - return arm_compute::DataType::F32; - case armnn::DataType::QAsymmS8: - return arm_compute::DataType::QASYMM8_SIGNED; - case armnn::DataType::QAsymmU8: - return arm_compute::DataType::QASYMM8; - case armnn::DataType::QSymmS16: - return arm_compute::DataType::QSYMM16; - case armnn::DataType::QSymmS8: - { - return multiScales ? arm_compute::DataType::QSYMM8_PER_CHANNEL : arm_compute::DataType::QSYMM8; - } - ARMNN_NO_DEPRECATE_WARN_BEGIN - case armnn::DataType::QuantizedSymm8PerAxis: - return arm_compute::DataType::QSYMM8_PER_CHANNEL; - ARMNN_NO_DEPRECATE_WARN_END - case armnn::DataType::Signed32: - return arm_compute::DataType::S32; - default: - BOOST_ASSERT_MSG(false, "Unknown data type"); - return arm_compute::DataType::UNKNOWN; - } -} - -arm_compute::Coordinates BuildArmComputeReductionCoordinates(size_t inputDimensions, - unsigned int originalInputRank, - const std::vector<unsigned int>& armnnAxes) -{ - arm_compute::Coordinates outAclCoords; - - if (armnnAxes.empty()) - { - // If no reduction axes were provided, then the input must be reduced along all dimensions. - // Since Compute Library does not accept an empty vector as the reduction dimensions, we then - // manually create a vector including all the input dimensions (in reversed order) as: - // - // { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 } - // - outAclCoords.set_num_dimensions(inputDimensions); - std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () mutable { return d--; }); - } - else - { - // Create a vector of reduction dimensions (in reversed order) with the given reduction axes. - // - // Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any - // dimension correction). - // For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the - // new value for that reduction axis should be 1. - // - // Example: - // ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 } - // ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 } - // ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 } - // - // The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1 - // - outAclCoords.set_num_dimensions(armnnAxes.size()); - std::transform(armnnAxes.begin(), armnnAxes.end(), - outAclCoords.begin(), - [originalInputRank](unsigned int i){ return originalInputRank - i - 1; }); - } - - return outAclCoords; -} - -arm_compute::TensorShape BuildArmComputeTensorShape(const armnn::TensorShape& tensorShape) -{ - arm_compute::TensorShape shape; - - // armnn tensors are (batch, channels, height, width). - // arm_compute tensors are (width, height, channels, batch). - for (unsigned int i = 0; i < tensorShape.GetNumDimensions(); i++) - { - // Note that our dimensions are stored in the opposite order to ACL's. - shape.set(tensorShape.GetNumDimensions() - i - 1, tensorShape[i], false); - - // TensorShape::set() flattens leading ones, so that batch size 1 cannot happen. - // arm_compute tensors expect this. - } - - // prevent arm_compute issue where tensor is flattened to nothing - if (shape.num_dimensions() == 0) - { - shape.set_num_dimensions(1); - } - - return shape; -} - -// Utility function used to build a TensorInfo object, that can be used to initialise -// ARM Compute Tensor and CLTensor allocators. -arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo) -{ - bool multiScales = tensorInfo.HasMultipleQuantizationScales(); - const arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.GetShape()); - const arm_compute::DataType aclDataType = GetArmComputeDataType(tensorInfo.GetDataType(), multiScales); - - const arm_compute::QuantizationInfo aclQuantizationInfo = multiScales ? - arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScales()) : - arm_compute::QuantizationInfo(tensorInfo.GetQuantizationScale(), tensorInfo.GetQuantizationOffset()); - - return arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo); -} - -arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo, - armnn::DataLayout dataLayout) -{ - arm_compute::TensorInfo aclTensorInfo = BuildArmComputeTensorInfo(tensorInfo); - aclTensorInfo.set_data_layout(ConvertDataLayout(dataLayout)); - - return aclTensorInfo; -} - -arm_compute::DataLayout ConvertDataLayout(armnn::DataLayout dataLayout) -{ - switch(dataLayout) - { - case armnn::DataLayout::NHWC : return arm_compute::DataLayout::NHWC; - - case armnn::DataLayout::NCHW : return arm_compute::DataLayout::NCHW; - - default: throw InvalidArgumentException("Unknown armnn::DataLayout: [" + - std::to_string(static_cast<int>(dataLayout)) + "]"); - } -} - -arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDescriptor& descriptor, - bool fpMixedPrecision) -{ - using arm_compute::PoolingType; - using arm_compute::DimensionRoundingType; - using arm_compute::PadStrideInfo; - using arm_compute::PoolingLayerInfo; - using arm_compute::Size2D; - using arm_compute::DataLayout; - - // Resolve ARM Compute layer parameters. - const PoolingType poolingType = ConvertPoolingAlgorithmToAclPoolingType(descriptor.m_PoolType); - - const DataLayout dataLayout = ConvertDataLayout(descriptor.m_DataLayout); - - bool isGlobalPooling = (descriptor.m_StrideX==0 && descriptor.m_StrideY==0); - //use specific constructor if global pooling - if(isGlobalPooling) - { - return arm_compute::PoolingLayerInfo(poolingType, dataLayout); - } - - const DimensionRoundingType rounding = ConvertOutputShapeRoundingToAclDimensionRoundingType( - descriptor.m_OutputShapeRounding); - const PadStrideInfo padStrideInfo(descriptor.m_StrideX, - descriptor.m_StrideY, - descriptor.m_PadLeft, - descriptor.m_PadRight, - descriptor.m_PadTop, - descriptor.m_PadBottom, - rounding); - - const bool excludePadding = (descriptor.m_PaddingMethod == PaddingMethod::Exclude); - - const Size2D poolSize(descriptor.m_PoolWidth, descriptor.m_PoolHeight); - - return arm_compute::PoolingLayerInfo(poolingType, poolSize, dataLayout, padStrideInfo, excludePadding, - fpMixedPrecision); -} - -arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(const NormalizationDescriptor& descriptor) -{ - const arm_compute::NormType normType = - ConvertNormalizationAlgorithmChannelToAclNormType(descriptor.m_NormChannelType); - return arm_compute::NormalizationLayerInfo(normType, - descriptor.m_NormSize, - descriptor.m_Alpha, - descriptor.m_Beta, - descriptor.m_K, - false); -} - -arm_compute::PermutationVector BuildArmComputePermutationVector(const armnn::PermutationVector& perm) -{ - arm_compute::PermutationVector aclPerm; - - unsigned int start = 0; - while ((start < perm.GetSize()) && (start == perm[start])) - { - ++start; - } - - for (unsigned int i = start; i < perm.GetSize(); ++i) - { - aclPerm.set(i - start, perm[i] - start); - } - - return aclPerm; -} - -arm_compute::Size2D BuildArmComputeSize2D(const unsigned int width, const unsigned int height) -{ - return arm_compute::Size2D(width, height); -} - -arm_compute::PixelValue GetPixelValue(arm_compute::ITensor& input, float pixelValue) -{ - switch (input.info()->data_type()) - { - case arm_compute::DataType::F16: - return arm_compute::PixelValue(static_cast<Half>(pixelValue)); - case arm_compute::DataType::F32: - return arm_compute::PixelValue(pixelValue); - case arm_compute::DataType::QASYMM8: - return arm_compute::PixelValue(static_cast<uint8_t>(pixelValue)); - case arm_compute::DataType::QSYMM16: - return arm_compute::PixelValue(static_cast<int16_t>(pixelValue)); - case arm_compute::DataType::QSYMM8_PER_CHANNEL: - return arm_compute::PixelValue(static_cast<int8_t>(pixelValue)); - default: - throw InvalidArgumentException("Unsupported DataType: [" + - std::to_string(static_cast<int>(input.info()->data_type())) + "]"); - } -} - -} // namespace armcomputetensorutils -} // namespace armnn |