| // |
| // Copyright © 2017,2019-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ArgMinMaxLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| |
| #include <armnnUtils/TensorUtils.hpp> |
| |
| #include <armnn/backends/WorkloadData.hpp> |
| #include <armnn/backends/WorkloadFactory.hpp> |
| |
| namespace armnn |
| { |
| |
| ArgMinMaxLayer::ArgMinMaxLayer(const ArgMinMaxDescriptor& param, const char* name) |
| : LayerWithParameters(1, 1, LayerType::ArgMinMax, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> ArgMinMaxLayer::CreateWorkload(const IWorkloadFactory& factory) const |
| { |
| ArgMinMaxQueueDescriptor descriptor; |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::ArgMinMax, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| ArgMinMaxLayer* ArgMinMaxLayer::Clone(Graph& graph) const |
| { |
| return CloneBase<ArgMinMaxLayer>(graph, m_Param, GetName()); |
| } |
| |
| std::vector<TensorShape> ArgMinMaxLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| if (inputShapes.size() != 1) |
| { |
| throw armnn::LayerValidationException("inputShapes' size is \"" + std::to_string(inputShapes.size()) + |
| "\" - should be \"1\"."); |
| } |
| |
| TensorShape inputShape = inputShapes[0]; |
| auto inputNumDimensions = inputShape.GetNumDimensions(); |
| |
| auto axis = m_Param.m_Axis; |
| auto unsignedAxis = armnnUtils::GetUnsignedAxis(inputNumDimensions, axis); |
| |
| if (unsignedAxis > inputNumDimensions) |
| { |
| throw armnn::LayerValidationException("Axis must not be greater than number of input dimensions (\"" |
| + std::to_string(unsignedAxis) + |
| "\" vs \"" |
| + std::to_string(inputNumDimensions) + "\")."); |
| } |
| |
| // 1D input shape results in scalar output |
| if (inputShape.GetNumDimensions() == 1) |
| { |
| std::vector<unsigned int> tensorDimensions(1, 1); |
| TensorShape outputShape(1, tensorDimensions.data()); |
| |
| return std::vector<TensorShape>({ outputShape }); |
| } |
| |
| std::vector<unsigned int> tensorDimensions(inputNumDimensions - 1, 0); |
| for (unsigned int i = 0; i < unsignedAxis; ++i) |
| { |
| tensorDimensions[i] = inputShape[i]; |
| } |
| |
| for (unsigned int i = unsignedAxis + 1; i < inputNumDimensions; ++i) |
| { |
| tensorDimensions[i - 1] = inputShape[i]; |
| } |
| |
| TensorShape outputShape = TensorShape(inputNumDimensions - 1, tensorDimensions.data()); |
| |
| return std::vector<TensorShape>({ outputShape }); |
| } |
| |
| void ArgMinMaxLayer::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, "ArgMinMaxLayer"); |
| } |
| |
| void ArgMinMaxLayer::ExecuteStrategy(IStrategy& strategy) const |
| { |
| strategy.ExecuteStrategy(this, GetParameters(), {}, GetName()); |
| } |
| |
| } // namespace armnn |