diff options
Diffstat (limited to 'src/armnn/layers/SpaceToBatchNdLayer.cpp')
-rw-r--r-- | src/armnn/layers/SpaceToBatchNdLayer.cpp | 31 |
1 files changed, 12 insertions, 19 deletions
diff --git a/src/armnn/layers/SpaceToBatchNdLayer.cpp b/src/armnn/layers/SpaceToBatchNdLayer.cpp index 151b6a5301..a758617e2e 100644 --- a/src/armnn/layers/SpaceToBatchNdLayer.cpp +++ b/src/armnn/layers/SpaceToBatchNdLayer.cpp @@ -1,15 +1,11 @@ // -// Copyright © 2017 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2017,2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "SpaceToBatchNdLayer.hpp" #include "LayerCloneBase.hpp" -#include <armnn/TypesUtils.hpp> - -#include <armnnUtils/DataLayoutIndexed.hpp> - #include <armnn/backends/WorkloadData.hpp> #include <armnn/backends/WorkloadFactory.hpp> @@ -42,9 +38,7 @@ SpaceToBatchNdLayer* SpaceToBatchNdLayer::Clone(Graph& graph) const std::vector<TensorShape> SpaceToBatchNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const { - ARMNN_ASSERT(inputShapes.size() == 1); - - TensorShape inputShape = inputShapes[0]; + const TensorShape inputShape = inputShapes[0]; TensorShape outputShape(inputShape); outputShape[0] = inputShape[0] * std::accumulate(m_Param.m_BlockShape.begin(), @@ -52,17 +46,16 @@ std::vector<TensorShape> SpaceToBatchNdLayer::InferOutputShapes(const std::vecto 1U, std::multiplies<>()); - DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout; - unsigned int heightIndex = dimensionIndices.GetHeightIndex(); - unsigned int widthIndex = dimensionIndices.GetWidthIndex(); - - std::pair<unsigned int, unsigned int> heightPad = m_Param.m_PadList[0]; - std::pair<unsigned int, unsigned int> widthPad = m_Param.m_PadList[1]; - - outputShape[heightIndex] = - (inputShape[heightIndex] + heightPad.first + heightPad.second) / m_Param.m_BlockShape[0]; - outputShape[widthIndex] = - (inputShape[widthIndex] + widthPad.first + widthPad.second) / m_Param.m_BlockShape[1]; + // In a 4D tensor, there will be 2 spatialDimensions (H and W), and the for loop will run twice. + // In a 3D tensor, there will be 1 spatialDimensions, and the for loop will run once. + unsigned int firstSpatialDimension = m_Param.m_DataLayout == DataLayout::NCHW ? 2 : 1; + for (unsigned int i = 0; i < m_Param.m_BlockShape.size(); ++i) + { + unsigned int spatialDimension = firstSpatialDimension + i; + outputShape[spatialDimension] = + (inputShape[spatialDimension] + m_Param.m_PadList[i].first + m_Param.m_PadList[i].second) + / m_Param.m_BlockShape[i]; + } return std::vector<TensorShape>({ outputShape }); } |