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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
#include "ArmComputeTensorUtils.hpp"
#include "ArmComputeUtils.hpp"
#include <armnn/Descriptors.hpp>
namespace armnn
{
namespace armcomputetensorutils
{
arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType)
{
switch(dataType)
{
case armnn::DataType::Float32:
{
return arm_compute::DataType::F32;
}
case armnn::DataType::QuantisedAsymm8:
{
return arm_compute::DataType::QASYMM8;
}
case armnn::DataType::Signed32:
{
return arm_compute::DataType::S32;
}
default:
{
BOOST_ASSERT_MSG(false, "Unknown data type");
return arm_compute::DataType::UNKNOWN;
}
}
}
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]);
// 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)
{
const arm_compute::TensorShape aclTensorShape = BuildArmComputeTensorShape(tensorInfo.GetShape());
const arm_compute::DataType aclDataType = GetArmComputeDataType(tensorInfo.GetDataType());
const arm_compute::QuantizationInfo aclQuantizationInfo(tensorInfo.GetQuantizationScale(),
tensorInfo.GetQuantizationOffset());
return arm_compute::TensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);
}
arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDescriptor& descriptor)
{
using arm_compute::PoolingType;
using arm_compute::DimensionRoundingType;
using arm_compute::PadStrideInfo;
using arm_compute::PoolingLayerInfo;
// Resolve ARM Compute layer parameters
const PoolingType poolingType = ConvertPoolingAlgorithmToAclPoolingType(descriptor.m_PoolType);
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);
return arm_compute::PoolingLayerInfo(poolingType, descriptor.m_PoolWidth, padStrideInfo, excludePadding);
}
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[start]) && (start < perm.GetSize()))
{
++start;
}
for (unsigned int i = start; i < perm.GetSize(); ++i)
{
aclPerm.set(i - start, perm[i] - start);
}
return aclPerm;
}
} // namespace armcomputetensorutils
} // namespace armnn
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