// // Copyright © 2017-2020,2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "BatchToSpaceNd.hpp" #include using namespace armnnUtils; namespace armnn { unsigned int Offset(const TensorShape& shape, unsigned int batch, unsigned int height, unsigned int width, unsigned int channels, const DataLayoutIndexed& dataLayout) { // 3D Tensors unsigned int channelDimension3D = dataLayout.GetDataLayout() == DataLayout::NCHW ? 1 : 2; if (shape.GetNumDimensions() == 3) { return (batch * shape[dataLayout.GetHeightIndex()] + height) * shape[channelDimension3D] + channels; } // 4D Tensors else if (shape.GetNumDimensions() == 4) { if (dataLayout.GetDataLayout() == DataLayout::NHWC) { return ((batch * shape[dataLayout.GetHeightIndex()] + height) * shape[dataLayout.GetWidthIndex()] + width) * shape[dataLayout.GetChannelsIndex()] + channels; } else { return ((batch * shape[dataLayout.GetChannelsIndex()] + channels) * shape[dataLayout.GetHeightIndex()] + height) * shape[dataLayout.GetWidthIndex()] + width; } } else { throw InvalidArgumentException("Tensor rank must be either 3 or 4", CHECK_LOCATION()); } } void BatchToSpaceNd(const TensorInfo& inputInfo, const TensorInfo& outputInfo, const BatchToSpaceNdDescriptor& params, Decoder& inputData, Encoder& outputData) { unsigned int rank = inputInfo.GetNumDimensions(); if (rank != 3 && rank != 4 ) { throw InvalidArgumentException("Tensor rank must be either 3 or 4, but it is " + std::to_string(rank), CHECK_LOCATION()); } DataLayoutIndexed dataLayout = params.m_DataLayout; unsigned int channelDimension3D = params.m_DataLayout == DataLayout::NCHW ? 1 : 2; TensorShape inputShape = inputInfo.GetShape(); TensorShape outputShape = outputInfo.GetShape(); const unsigned int inputBatchSize = inputShape[0]; const unsigned int outputBatchSize = outputShape[0]; const unsigned int channels = (rank == 3) ? inputShape[channelDimension3D] : inputShape[dataLayout.GetChannelsIndex()]; const unsigned int inputHeight = inputShape[dataLayout.GetHeightIndex()]; const unsigned int inputWidth = (rank == 3) ? 1 : inputShape[dataLayout.GetWidthIndex()]; const unsigned int outputHeight = outputShape[dataLayout.GetHeightIndex()]; const unsigned int outputWidth = (rank == 3) ? 1 : outputShape[dataLayout.GetWidthIndex()]; const unsigned int blockHeight = params.m_BlockShape[0]; const unsigned int blockWidth = (rank == 3) ? 1 : params.m_BlockShape[1]; const unsigned int cropsTop = params.m_Crops[0].first; const unsigned int cropsLeft = (rank == 3) ? 0 : params.m_Crops[1].first; for (unsigned int inBatch = 0; inBatch < inputBatchSize; ++inBatch) { const unsigned int outBatch = inBatch % outputBatchSize; const unsigned int spatialOffset = inBatch / outputBatchSize; for (unsigned int inH = 0; inH < inputHeight; ++inH) { const unsigned int outH = inH * blockHeight + spatialOffset / blockWidth - cropsTop; if (outH >= outputHeight) { continue; } for (unsigned int inW = 0; inW < inputWidth; ++inW) { const unsigned int outW = inW * blockWidth + spatialOffset % blockWidth - cropsLeft; if (outW >= outputWidth) { continue; } for (unsigned int c = 0; c < channels; c++) { unsigned int outOffset = Offset(outputShape, outBatch, outH, outW, c, dataLayout); unsigned int inOffset = Offset(inputShape, inBatch, inH, inW, c, dataLayout); outputData[outOffset]; inputData[inOffset]; outputData.Set(inputData.Get()); } } } } } } //namespace armnn