| // |
| // Copyright © 2019-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "DepthToSpaceLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| |
| #include <armnn/backends/WorkloadData.hpp> |
| #include <armnn/backends/WorkloadFactory.hpp> |
| |
| #include <numeric> |
| |
| namespace armnn |
| { |
| |
| DepthToSpaceLayer::DepthToSpaceLayer(const DepthToSpaceDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::DepthToSpace, param, name) |
| {} |
| |
| std::unique_ptr<IWorkload> DepthToSpaceLayer::CreateWorkload(const IWorkloadFactory& factory) const |
| { |
| DepthToSpaceQueueDescriptor descriptor; |
| descriptor.m_Parameters.m_BlockSize = m_Param.m_BlockSize; |
| descriptor.m_Parameters.m_DataLayout = m_Param.m_DataLayout; |
| |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::DepthToSpace, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| DepthToSpaceLayer* DepthToSpaceLayer::Clone(Graph& graph) const |
| { |
| return CloneBase<DepthToSpaceLayer>(graph, m_Param, GetName()); |
| } |
| |
| std::vector<TensorShape> DepthToSpaceLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| if (inputShapes.size() != 1) |
| { |
| throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) + |
| "\" - should be \"1\"."); |
| } |
| |
| TensorShape inputShape = inputShapes[0]; |
| TensorShape outputShape(inputShape); |
| |
| armnnUtils::DataLayoutIndexed dimensionIndices(m_Param.m_DataLayout); |
| |
| unsigned int hIndex = dimensionIndices.GetHeightIndex(); |
| unsigned int wIndex = dimensionIndices.GetWidthIndex(); |
| unsigned int cIndex = dimensionIndices.GetChannelsIndex(); |
| |
| outputShape[hIndex] = inputShape[hIndex] * m_Param.m_BlockSize; |
| outputShape[wIndex] = inputShape[wIndex] * m_Param.m_BlockSize; |
| |
| outputShape[cIndex] = inputShape[cIndex] / (m_Param.m_BlockSize * m_Param.m_BlockSize); |
| |
| return std::vector<TensorShape>({ outputShape }); |
| } |
| |
| void DepthToSpaceLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| std::vector<TensorShape> inferredShapes = InferOutputShapes({ |
| GetInputSlot(0).GetTensorInfo().GetShape() }); |
| |
| if (inferredShapes.size() != 1) |
| { |
| throw armnn::LayerValidationException("inferredShapes has " |
| + std::to_string(inferredShapes.size()) + |
| " elements - should only have 1."); |
| } |
| |
| ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "DepthToSpaceLayer"); |
| } |
| |
| void DepthToSpaceLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| |
| } // namespace armnn |