// // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // #include "Merger.hpp" #include "RefWorkloadUtils.hpp" namespace armnn { template <> void CopyValue(const float& source, const TensorInfo& sourceInfo, float& dest, const TensorInfo& destInfo) { dest = source; } template <> void CopyValue(const uint8_t& source, const TensorInfo& sourceInfo, uint8_t& dest, const TensorInfo& destInfo) { if (sourceInfo.GetQuantizationScale() != destInfo.GetQuantizationScale() || sourceInfo.GetQuantizationOffset() != destInfo.GetQuantizationOffset()) { // Dequantize value according to sourceInfo params float dequantizedValue = armnn::Dequantize(source, sourceInfo.GetQuantizationScale(), sourceInfo.GetQuantizationOffset()); // Quantize again according to destInfo paramns dest = armnn::Quantize(dequantizedValue, destInfo.GetQuantizationScale(), destInfo.GetQuantizationOffset()); } else { dest = source; } } template void Merger(const MergerQueueDescriptor& data) { const TensorInfo& outputInfo0 = GetTensorInfo(data.m_Outputs[0]); for (unsigned int index = 0 ; index < outputInfo0.GetNumElements(); ++index) { unsigned int indices[MaxNumOfTensorDimensions] = { 0 }; unsigned int indexRemainder = index; unsigned int dimensionStride = outputInfo0.GetNumElements(); for (unsigned int i = 0; i < outputInfo0.GetNumDimensions(); i++) { dimensionStride /= outputInfo0.GetShape()[i]; indices[i] = indexRemainder / dimensionStride; // Use integer division to round down. indexRemainder -= indices[i] * dimensionStride; } for (unsigned int viewIdx = 0; viewIdx < data.m_ViewOrigins.size(); ++viewIdx) { MergerQueueDescriptor::ViewOrigin const& view = data.m_ViewOrigins[viewIdx]; //Split view extents are defined by the size of (the corresponding) input tensor. const TensorInfo& inputInfo = GetTensorInfo(data.m_Inputs[viewIdx]); BOOST_ASSERT(inputInfo.GetNumDimensions() == outputInfo0.GetNumDimensions()); // Check all dimensions to see if this element is inside the given input view. bool insideView = true; for (unsigned int i = 0; i < inputInfo.GetNumDimensions(); i++) { if (indices[i] < view.m_Origin[i]) { insideView = false; } if (indices[i] >= view.m_Origin[i] + inputInfo.GetShape()[i]) { insideView = false; } } if (insideView) { unsigned int inIndex = 0; unsigned int dimensionStride = 1; for (unsigned int i = inputInfo.GetNumDimensions(); i-- > 0;) { inIndex += dimensionStride * (indices[i] - view.m_Origin[i]); dimensionStride *= inputInfo.GetShape()[i]; } CopyValue((GetInputTensorData(viewIdx, data))[inIndex], GetTensorInfo(data.m_Inputs[viewIdx]), (GetOutputTensorData(0, data))[index], outputInfo0); //What should we do if input views overlap on the output tensor? //We could error, take the average, or shm else... //For now just stop after finding first view (input) that matches. break; } } } } template void Merger(const MergerQueueDescriptor& data); template void Merger(const MergerQueueDescriptor& data); } //namespace armnn