| // |
| // Copyright © 2018-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "BatchToSpaceNdLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/backends/WorkloadData.hpp> |
| #include <armnn/backends/WorkloadFactory.hpp> |
| |
| #include <numeric> |
| |
| using namespace armnnUtils; |
| |
| namespace armnn |
| { |
| |
| BatchToSpaceNdLayer::BatchToSpaceNdLayer(const armnn::BatchToSpaceNdDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::BatchToSpaceNd, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> BatchToSpaceNdLayer::CreateWorkload(const IWorkloadFactory& factory) const |
| { |
| BatchToSpaceNdQueueDescriptor descriptor; |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::BatchToSpaceNd, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| BatchToSpaceNdLayer* BatchToSpaceNdLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<BatchToSpaceNdLayer>(graph, m_Param, GetName()); |
| return std::move(layer); |
| } |
| |
| void BatchToSpaceNdLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(1, CHECK_LOCATION()); |
| |
| const TensorShape &outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| auto 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, "BatchToSpaceNdLayer"); |
| } |
| |
| std::vector<TensorShape> BatchToSpaceNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| const TensorShape& inputShape = inputShapes[0]; |
| TensorShape outputShape(inputShape); |
| |
| unsigned int accumulatedBlockShape = std::accumulate(m_Param.m_BlockShape.begin(), |
| m_Param.m_BlockShape.end(), |
| 1U, |
| std::multiplies<>()); |
| outputShape[0] = (inputShape[0] / accumulatedBlockShape) < 1 ? 1 : (inputShape[0] / accumulatedBlockShape) ; |
| |
| // 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; |
| unsigned int cropSize = m_Param.m_Crops[i].first + m_Param.m_Crops[i].second; |
| unsigned int outputSize = inputShape[spatialDimension] * m_Param.m_BlockShape[i]; |
| outputShape[spatialDimension] = outputSize - cropSize; |
| } |
| |
| return std::vector<TensorShape>({ outputShape }); |
| } |
| |
| void BatchToSpaceNdLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| |
| } // namespace armnn |