| // |
| // Copyright © 2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ScatterNdLayer.hpp" |
| #include "LayerCloneBase.hpp" |
| |
| #include <armnn/TypesUtils.hpp> |
| #include <armnn/backends/WorkloadData.hpp> |
| #include <armnn/backends/WorkloadFactory.hpp> |
| |
| namespace armnn |
| { |
| |
| ScatterNdLayer::ScatterNdLayer(const ScatterNdDescriptor ¶m, const char* name) |
| : LayerWithParameters(3, 1, LayerType::ScatterNd, param, name) |
| { |
| } |
| |
| std::unique_ptr<IWorkload> ScatterNdLayer::CreateWorkload(const armnn::IWorkloadFactory& factory) const |
| { |
| ScatterNdQueueDescriptor descriptor; |
| SetAdditionalInfo(descriptor); |
| |
| return factory.CreateWorkload(LayerType::ScatterNd, descriptor, PrepInfoAndDesc(descriptor)); |
| } |
| |
| ScatterNdLayer* ScatterNdLayer::Clone(Graph& graph) const |
| { |
| auto layer = CloneBase<ScatterNdLayer>(graph, m_Param, GetName()); |
| |
| return std::move(layer); |
| } |
| |
| std::vector<TensorShape> ScatterNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const |
| { |
| const auto inputDims = inputShapes[0].GetNumDimensions(); |
| |
| std::vector<unsigned int> dimSizes(inputDims); |
| |
| for (unsigned i = 0; i < inputDims; ++i) |
| { |
| dimSizes[i] = inputShapes[0][i]; |
| } |
| |
| TensorShape outputShape({ inputDims, dimSizes.data() }); |
| |
| return std::vector<TensorShape>({ outputShape }); |
| } |
| |
| void ScatterNdLayer::ValidateTensorShapesFromInputs() |
| { |
| VerifyLayerConnections(3, CHECK_LOCATION()); |
| |
| const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape(); |
| |
| VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod); |
| |
| if (m_Param.m_InputEnabled) |
| { |
| std::vector<TensorShape> inferredShapes = InferOutputShapes( |
| {GetInputSlot(0).GetTensorInfo().GetShape(), |
| GetInputSlot(1).GetTensorInfo().GetShape(), |
| GetInputSlot(2).GetTensorInfo().GetShape()}); |
| |
| if (inferredShapes.size() != 1) { |
| throw armnn::LayerValidationException("inferredShape has " + |
| std::to_string(inferredShapes.size()) + |
| " elements - should only have 1."); |
| } |
| |
| ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "ScatterLayer"); |
| } |
| else |
| { |
| // No input tensor, only shape provided via input slot |
| // In this case, we cannot validate the output shape from the input shape, but we can |
| // validate that the dimensions of shape and output tensor matched |
| unsigned int shapeDims = GetInputSlot(0).GetTensorInfo().GetShape().GetNumElements(); |
| unsigned int outputDims = GetOutputSlot(0).GetTensorInfo().GetNumDimensions(); |
| |
| if (shapeDims != outputDims) |
| { |
| throw armnn::LayerValidationException("shape dimension " + |
| std::to_string(shapeDims) + |
| " and output dimension " + |
| std::to_string(outputDims) + |
| " are not matched."); |
| } |
| } |
| } |
| |
| } // namespace armnn |