| // |
| // Copyright © 2017-2020,2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "BatchToSpaceNd.hpp" |
| |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| |
| 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<float>& inputData, |
| Encoder<float>& 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 |