IVGCVSW-1921: move common Acl code to a separate folder
Change-Id: I400be8e7c0cc5a31eb9d2a7396da145d50d51b6e
diff --git a/src/backends/aclCommon/ArmComputeTensorUtils.cpp b/src/backends/aclCommon/ArmComputeTensorUtils.cpp
new file mode 100644
index 0000000..d48408c
--- /dev/null
+++ b/src/backends/aclCommon/ArmComputeTensorUtils.cpp
@@ -0,0 +1,163 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
+#include <backends/aclCommon/ArmComputeUtils.hpp>
+
+#include "armnn/Exceptions.hpp"
+#include <armnn/Descriptors.hpp>
+
+namespace armnn
+{
+namespace armcomputetensorutils
+{
+
+arm_compute::DataType GetArmComputeDataType(armnn::DataType dataType)
+{
+ switch(dataType)
+ {
+ case armnn::DataType::Float16:
+ return arm_compute::DataType::F16;
+ 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::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::TensorInfo BuildArmComputeTensorInfo(const armnn::TensorInfo& tensorInfo,
+ armnn::DataLayout dataLayout)
+{
+ 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());
+
+ arm_compute::TensorInfo clTensorInfo(aclTensorShape, 1, aclDataType, aclQuantizationInfo);
+ clTensorInfo.set_data_layout(ConvertDataLayout(dataLayout));
+
+ return clTensorInfo;
+}
+
+arm_compute::PoolingLayerInfo BuildArmComputePoolingLayerInfo(const Pooling2dDescriptor& descriptor)
+{
+ using arm_compute::PoolingType;
+ using arm_compute::DimensionRoundingType;
+ using arm_compute::PadStrideInfo;
+ using arm_compute::PoolingLayerInfo;
+ using arm_compute::Size2D;
+
+ // Resolve ARM Compute layer parameters.
+ const PoolingType poolingType = ConvertPoolingAlgorithmToAclPoolingType(descriptor.m_PoolType);
+
+ bool isGlobalPooling = (descriptor.m_StrideX==0 && descriptor.m_StrideY==0);
+ //use specific constructor if global pooling
+ if(isGlobalPooling)
+ {
+ return arm_compute::PoolingLayerInfo(poolingType);
+ }
+
+ 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, 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.GetSize()) && (start == perm[start]))
+ {
+ ++start;
+ }
+
+ for (unsigned int i = start; i < perm.GetSize(); ++i)
+ {
+ aclPerm.set(i - start, perm[i] - start);
+ }
+
+ return aclPerm;
+}
+
+} // namespace armcomputetensorutils
+} // namespace armnn