| // |
| // Copyright © 2024 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 |
| { |
| if (inputShapes.size() != 2) |
| { |
| throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) + |
| "\" - should be \"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. |
| if (dim0 != dim1 && dim0 != 1 && dim1 != 1) |
| { |
| throw armnn::Exception("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).GetTensorInfo().GetShape(), |
| GetInputSlot(1).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, GetLayerTypeAsCString(GetType())); |
| } |
| |
| void ElementwiseBinaryLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| } // namespace armnn |