| // |
| // Copyright © 2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <tensorflow/lite/version.h> |
| |
| namespace |
| { |
| |
| std::vector<char> CreateScatterNdTfLiteModel(tflite::TensorType tensorType, |
| const std::vector<int32_t>& indicesShape, |
| const std::vector<int32_t>& updatesShape, |
| const std::vector<int32_t>& shapeShape, |
| const std::vector<int32_t>& outputShape, |
| const std::vector<int32_t>& shapeData, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); // indices |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); // updates |
| buffers.push_back(CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(shapeData.data()), |
| sizeof(int32_t) * shapeData.size()))); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); // output |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(), |
| indicesShape.size()), |
| TensorType_INT32, |
| 1, |
| flatBufferBuilder.CreateString("indices_tensor"), |
| quantizationParameters); |
| |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(updatesShape.data(), |
| updatesShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("updates_tensor"), |
| quantizationParameters); |
| |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(shapeShape.data(), |
| shapeShape.size()), |
| TensorType_INT32, |
| 3, |
| flatBufferBuilder.CreateString("shape_tensor"), |
| quantizationParameters); |
| |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), |
| outputShape.size()), |
| tensorType, |
| 4, |
| flatBufferBuilder.CreateString("output_tensor"), |
| quantizationParameters); |
| |
| // Create Operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ScatterNdOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateScatterNdOptions(flatBufferBuilder).Union(); |
| |
| const std::vector<int> operatorInputs { 0, 1, 2 }; |
| const std::vector<int> operatorOutputs { 3 }; |
| |
| flatbuffers::Offset<Operator> scatterNdOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOptions); |
| |
| const std::vector<int> subgraphInputs{ 0, 1, 2 }; |
| const std::vector<int> subgraphOutputs{ 3 }; |
| flatbuffers::Offset <SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| flatBufferBuilder.CreateVector(&scatterNdOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: ScatterNd Operator Model"); |
| flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, |
| tflite::BuiltinOperator_SCATTER_ND); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&opCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template<typename T> |
| void ScatterNdTestImpl(tflite::TensorType tensorType, |
| std::vector<int32_t>& indicesShape, |
| std::vector<int32_t>& indicesValues, |
| std::vector<int32_t>& updatesShape, |
| std::vector<T>& updatesValues, |
| std::vector<int32_t>& shapeShape, |
| std::vector<int32_t>& shapeValue, |
| std::vector<int32_t>& expectedOutputShape, |
| std::vector<T>& expectedOutputValues, |
| const std::vector<armnn::BackendId>& backends = {}, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer = CreateScatterNdTfLiteModel(tensorType, |
| indicesShape, |
| updatesShape, |
| shapeShape, |
| expectedOutputShape, |
| shapeValue, |
| quantScale, |
| quantOffset); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(indicesValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(updatesValues, 1) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(shapeValue, 2) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<int32_t>(indicesValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(updatesValues, 1) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<int32_t>(shapeValue, 2) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |