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Diffstat (limited to 'src/armnn/layers/ElementwiseBinaryLayer.cpp')
-rw-r--r-- | src/armnn/layers/ElementwiseBinaryLayer.cpp | 89 |
1 files changed, 89 insertions, 0 deletions
diff --git a/src/armnn/layers/ElementwiseBinaryLayer.cpp b/src/armnn/layers/ElementwiseBinaryLayer.cpp new file mode 100644 index 0000000000..ae1813f33a --- /dev/null +++ b/src/armnn/layers/ElementwiseBinaryLayer.cpp @@ -0,0 +1,89 @@ +// +// Copyright © 2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ElementwiseBinaryLayer.hpp" + +#include "LayerCloneBase.hpp" + +namespace armnn +{ + +ElementwiseBinaryLayer::ElementwiseBinaryLayer(const ElementwiseBinaryDescriptor& param, const char* name) + : LayerWithParameters(2, 1, LayerType::ElementwiseBinary, param, name) +{ +} + +std::unique_ptr<IWorkload> ElementwiseBinaryLayer::CreateWorkload(const IWorkloadFactory& factory) const +{ + ElementwiseBinaryQueueDescriptor descriptor; + SetAdditionalInfo(descriptor); + + return factory.CreateWorkload(LayerType::ElementwiseBinary, descriptor, PrepInfoAndDesc(descriptor)); +} + +ElementwiseBinaryLayer* ElementwiseBinaryLayer::Clone(Graph& graph) const +{ + return CloneBase<ElementwiseBinaryLayer>(graph, m_Param, GetName()); +} + +std::vector<TensorShape> ElementwiseBinaryLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const +{ + ARMNN_ASSERT(inputShapes.size() == 2); + TensorShape input0 = inputShapes[0]; + TensorShape input1 = inputShapes[1]; + + if (inputShapes[0].GetNumDimensions() < inputShapes[1].GetNumDimensions()) + { + input1 = inputShapes[0]; + input0 = inputShapes[1]; + } + + unsigned int numDims = input0.GetNumDimensions(); + unsigned int shiftedDims = input0.GetNumDimensions() - input1.GetNumDimensions(); + + // Get the max of the inputs. + std::vector<unsigned int> dims(numDims); + for (unsigned int i = shiftedDims; i < numDims; i++) + { + unsigned int dim0 = input0[i]; + unsigned int dim1 = input1[i - shiftedDims]; + + // Validate inputs are broadcast compatible. + ARMNN_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1, + "Dimensions should either match or one should be of size 1."); + + dims[i] = std::max(dim0, dim1); + } + + // Fill in the rest of the shifted dimensions. + for (unsigned int i = 0; i < shiftedDims; i++) + { + dims[i] = input0[i]; + } + + return std::vector<TensorShape>({ TensorShape(numDims, dims.data()) }); +} + +void ElementwiseBinaryLayer::ValidateTensorShapesFromInputs() +{ + VerifyLayerConnections(2, CHECK_LOCATION()); + + const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); + + VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); + + auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(), + GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() }); + + ARMNN_ASSERT(inferredShapes.size() == 1); + + ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, GetLayerTypeAsCString(GetType())); +} + +void ElementwiseBinaryLayer::ExecuteStrategy(IStrategy& strategy) const +{ + strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); +} +} // namespace armnn |