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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/Tensor.hpp>
#include <armnn/DescriptorsFwd.hpp>
#include <arm_compute/core/ITensor.h>
#include <arm_compute/core/TensorInfo.h>
#include <arm_compute/core/Types.h>
#include <arm_compute/core/Size2D.h>
#include <Half.hpp>
#include <boost/cast.hpp>
namespace armnn
{
class ITensorHandle;
namespace armcomputetensorutils
{
/// Utility function to map an armnn::DataType to corresponding arm_compute::DataType.
arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType);
/// Utility function used to set up an arm_compute::Coordinates from a vector of ArmNN Axes for reduction functions
arm_compute::Coordinates BuildArmComputeReductionCoordinates(size_t inputDimensions,
unsigned int originalInputRank,
const std::vector<unsigned int>& armnnAxes);
/// Utility function used to setup an arm_compute::TensorShape object from an armnn::TensorShape.
arm_compute::TensorShape BuildArmComputeTensorShape(const armnn::TensorShape& tensorShape);
/// Utility function used to setup an arm_compute::ITensorInfo object whose dimensions are based on the given
/// armnn::ITensorInfo.
arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo);
/// Utility function used to setup an arm_compute::ITensorInfo object whose dimensions are based on the given
/// armnn::ITensorInfo.
/// armnn::DataLayout.
arm_compute::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo,
armnn::DataLayout dataLayout);
/// Utility function used to convert armnn::DataLayout to arm_compute::DataLayout
/// armnn::DataLayout.
arm_compute::DataLayout ConvertDataLayout(armnn::DataLayout dataLayout);
/// Utility function used to setup an arm_compute::PoolingLayerInfo object from an armnn::Pooling2dDescriptor.
arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDescriptor& descriptor);
/// Utility function to setup an arm_compute::NormalizationLayerInfo object from an armnn::NormalizationDescriptor.
arm_compute::NormalizationLayerInfo BuildArmComputeNormalizationLayerInfo(const NormalizationDescriptor& desc);
/// Utility function used to setup an arm_compute::PermutationVector object from an armnn::PermutationVector.
arm_compute::PermutationVector BuildArmComputePermutationVector(const armnn::PermutationVector& vector);
/// Utility function used to setup an arm_compute::Size2D object from width and height values.
arm_compute::Size2D BuildArmComputeSize2D(const unsigned int width, const unsigned int height);
/// Gets the appropriate PixelValue for the input DataType
arm_compute::PixelValue GetPixelValue(arm_compute::ITensor& input, float pixelValue);
/// Utility function used to setup an arm_compute::PadStrideInfo object from an armnn layer descriptor.
template <typename Descriptor>
arm_compute::PadStrideInfo BuildArmComputePadStrideInfo(const Descriptor &descriptor)
{
return arm_compute::PadStrideInfo(descriptor.m_StrideX,
descriptor.m_StrideY,
descriptor.m_PadLeft,
descriptor.m_PadRight,
descriptor.m_PadTop,
descriptor.m_PadBottom,
arm_compute::DimensionRoundingType::FLOOR);
}
/// Sets up the given ArmCompute tensor's dimensions based on the given ArmNN tensor.
template <typename Tensor>
void BuildArmComputeTensor(Tensor& tensor, const armnn::TensorInfo& tensorInfo)
{
tensor.allocator()->init(BuildArmComputeTensorInfo(tensorInfo));
}
/// Sets up the given ArmCompute tensor's dimensions based on the given ArmNN tensor.
template <typename Tensor>
void BuildArmComputeTensor(Tensor& tensor, const armnn::TensorInfo& tensorInfo, DataLayout dataLayout)
{
tensor.allocator()->init(BuildArmComputeTensorInfo(tensorInfo, dataLayout));
}
template <typename Tensor>
void InitialiseArmComputeTensorEmpty(Tensor& tensor)
{
tensor.allocator()->allocate();
}
/// Utility function to free unused tensors after a workload is configured and prepared
template <typename Tensor>
void FreeTensorIfUnused(std::unique_ptr<Tensor>& tensor)
{
if (tensor && !tensor->is_used())
{
tensor.reset(nullptr);
}
}
// Helper function to obtain byte offset into tensor data
inline size_t GetTensorOffset(const arm_compute::ITensorInfo& info,
uint32_t depthIndex,
uint32_t batchIndex,
uint32_t channelIndex,
uint32_t y,
uint32_t x)
{
arm_compute::Coordinates coords;
coords.set(4, static_cast<int>(depthIndex));
coords.set(3, static_cast<int>(batchIndex));
coords.set(2, static_cast<int>(channelIndex));
coords.set(1, static_cast<int>(y));
coords.set(0, static_cast<int>(x));
return info.offset_element_in_bytes(coords);
}
// Helper function to obtain element offset into data buffer representing tensor data (assuming no strides).
inline size_t GetLinearBufferOffset(const arm_compute::ITensorInfo& info,
uint32_t depthIndex,
uint32_t batchIndex,
uint32_t channelIndex,
uint32_t y,
uint32_t x)
{
const arm_compute::TensorShape& shape = info.tensor_shape();
uint32_t width = static_cast<uint32_t>(shape[0]);
uint32_t height = static_cast<uint32_t>(shape[1]);
uint32_t numChannels = static_cast<uint32_t>(shape[2]);
uint32_t numBatches = static_cast<uint32_t>(shape[3]);
return (((depthIndex * numBatches + batchIndex) * numChannels + channelIndex) * height + y) * width + x;
}
template <typename T>
void CopyArmComputeITensorData(const arm_compute::ITensor& srcTensor, T* dstData)
{
// If MaxNumOfTensorDimensions is increased, this loop will need fixing.
static_assert(MaxNumOfTensorDimensions == 5, "Please update CopyArmComputeITensorData");
{
const arm_compute::ITensorInfo& info = *srcTensor.info();
const arm_compute::TensorShape& shape = info.tensor_shape();
const uint8_t* const bufferPtr = srcTensor.buffer();
uint32_t width = static_cast<uint32_t>(shape[0]);
uint32_t height = static_cast<uint32_t>(shape[1]);
uint32_t numChannels = static_cast<uint32_t>(shape[2]);
uint32_t numBatches = static_cast<uint32_t>(shape[3]);
uint32_t depth = static_cast<uint32_t>(shape[4]);
for (unsigned int depthIndex = 0; depthIndex < depth; ++depthIndex)
{
for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex)
{
for (unsigned int channelIndex = 0; channelIndex < numChannels; ++channelIndex)
{
for (unsigned int y = 0; y < height; ++y)
{
// Copies one row from arm_compute tensor buffer to linear memory buffer.
// A row is the largest contiguous region we can copy, as the tensor data may be using strides.
memcpy(
dstData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0),
bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0),
width * sizeof(T));
}
}
}
}
}
}
template <typename T>
void CopyArmComputeITensorData(const T* srcData, arm_compute::ITensor& dstTensor)
{
// If MaxNumOfTensorDimensions is increased, this loop will need fixing.
static_assert(MaxNumOfTensorDimensions == 5, "Please update CopyArmComputeITensorData");
{
const arm_compute::ITensorInfo& info = *dstTensor.info();
const arm_compute::TensorShape& shape = info.tensor_shape();
uint8_t* const bufferPtr = dstTensor.buffer();
uint32_t width = static_cast<uint32_t>(shape[0]);
uint32_t height = static_cast<uint32_t>(shape[1]);
uint32_t numChannels = static_cast<uint32_t>(shape[2]);
uint32_t numBatches = static_cast<uint32_t>(shape[3]);
uint32_t depth = static_cast<uint32_t>(shape[4]);
for (unsigned int depthIndex = 0; depthIndex < depth; ++depthIndex)
{
for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex)
{
for (unsigned int channelIndex = 0; channelIndex < numChannels; ++channelIndex)
{
for (unsigned int y = 0; y < height; ++y)
{
// Copies one row from linear memory buffer to arm_compute tensor buffer.
// A row is the largest contiguous region we can copy, as the tensor data may be using strides.
memcpy(
bufferPtr + GetTensorOffset(info, depthIndex, batchIndex, channelIndex, y, 0),
srcData + GetLinearBufferOffset(info, depthIndex, batchIndex, channelIndex, y, 0),
width * sizeof(T));
}
}
}
}
}
}
/// Construct a TensorShape object from an ArmCompute object based on arm_compute::Dimensions.
/// \tparam ArmComputeType Any type that implements the Dimensions interface
/// \tparam T Shape value type
/// \param shapelike An ArmCompute object that implements the Dimensions interface
/// \param initial A default value to initialise the shape with
/// \return A TensorShape object filled from the Acl shapelike object.
template<typename ArmComputeType, typename T>
TensorShape GetTensorShape(const ArmComputeType& shapelike, T initial)
{
std::vector<unsigned int> s(MaxNumOfTensorDimensions, initial);
for (unsigned int i=0; i < shapelike.num_dimensions(); ++i)
{
s[(shapelike.num_dimensions()-1)-i] = boost::numeric_cast<unsigned int>(shapelike[i]);
}
return TensorShape(boost::numeric_cast<unsigned int>(shapelike.num_dimensions()), s.data());
};
/// Get the strides from an ACL strides object
inline TensorShape GetStrides(const arm_compute::Strides& strides)
{
return GetTensorShape(strides, 0U);
}
/// Get the shape from an ACL shape object
inline TensorShape GetShape(const arm_compute::TensorShape& shape)
{
return GetTensorShape(shape, 1U);
}
} // namespace armcomputetensorutils
} // namespace armnn
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