1 files changed, 94 insertions, 0 deletions
diff --git a/src/armnn/layers/ScatterNdLayer.cpp b/src/armnn/layers/ScatterNdLayer.cpp
new file mode 100644
index 0000000000..a0b270fba5
--- /dev/null
+++ b/src/armnn/layers/ScatterNdLayer.cpp
@@ -0,0 +1,94 @@
+//
+// 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 &param, 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().GetNumDimensions();
+        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