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-rw-r--r--src/armnn/backends/ArmComputeTensorUtils.hpp199
1 files changed, 0 insertions, 199 deletions
diff --git a/src/armnn/backends/ArmComputeTensorUtils.hpp b/src/armnn/backends/ArmComputeTensorUtils.hpp
deleted file mode 100644
index 572e310ecf..0000000000
--- a/src/armnn/backends/ArmComputeTensorUtils.hpp
+++ /dev/null
@@ -1,199 +0,0 @@
-//
-// 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 <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();
-}
-
-/// 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 batchIndex,
- uint32_t channelIndex,
- uint32_t y,
- uint32_t x)
-{
- arm_compute::Coordinates coords;
- 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 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]);
- 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 = 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]);
-
- 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, 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 = 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]);
-
- 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, batchIndex, channelIndex, y, 0),
- srcData + GetLinearBufferOffset(info, 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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