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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
#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 <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 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::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::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));
}
template <typename Tensor>
void InitialiseArmComputeTensorEmpty(Tensor& tensor)
{
tensor.allocator()->allocate();
}
// Helper function to obtain byte offset into tensor data
inline size_t GetTensorOffset(const arm_compute::ITensorInfo& info,
uint32_t batchIndex,
uint32_t channelIndex,
uint32_t y,
uint32_t x)
{
arm_compute::Coordinates coords;
coords.set(3, boost::numeric_cast<int>(batchIndex));
coords.set(2, boost::numeric_cast<int>(channelIndex));
coords.set(1, boost::numeric_cast<int>(y));
coords.set(0, boost::numeric_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 batchIndex,
uint32_t channelIndex,
uint32_t y,
uint32_t x)
{
const arm_compute::TensorShape& shape = info.tensor_shape();
uint32_t width = boost::numeric_cast<uint32_t>(shape[0]);
uint32_t height = boost::numeric_cast<uint32_t>(shape[1]);
uint32_t numChannels = boost::numeric_cast<uint32_t>(shape[2]);
return ((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 == 4, "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 = boost::numeric_cast<uint32_t>(shape[0]);
uint32_t height = boost::numeric_cast<uint32_t>(shape[1]);
uint32_t numChannels = boost::numeric_cast<uint32_t>(shape[2]);
uint32_t numBatches = boost::numeric_cast<uint32_t>(shape[3]);
for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex)
{
for (unsigned int channelIndex = 0; channelIndex < numChannels; ++channelIndex)
{
for (unsigned int y = 0; y < height; ++y)
{
// Copy 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, batchIndex, channelIndex, y, 0),
bufferPtr + GetTensorOffset(info, 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 == 4, "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 = boost::numeric_cast<uint32_t>(shape[0]);
uint32_t height = boost::numeric_cast<uint32_t>(shape[1]);
uint32_t numChannels = boost::numeric_cast<uint32_t>(shape[2]);
uint32_t numBatches = boost::numeric_cast<uint32_t>(shape[3]);
for (unsigned int batchIndex = 0; batchIndex < numBatches; ++batchIndex)
{
for (unsigned int channelIndex = 0; channelIndex < numChannels; ++channelIndex)
{
for (unsigned int y = 0; y < height; ++y)
{
// Copy 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, batchIndex, channelIndex, y, 0),
srcData + GetLinearBufferOffset(info, batchIndex, channelIndex, y, 0),
width * sizeof(T));
}
}
}
}
}
} // namespace armcomputetensorutils
} // namespace armnn
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