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Diffstat (limited to 'delegate/test/ScatterNdTestHelper.hpp')
| -rw-r--r-- | delegate/test/ScatterNdTestHelper.hpp | 174 |
1 files changed, 174 insertions, 0 deletions
diff --git a/delegate/test/ScatterNdTestHelper.hpp b/delegate/test/ScatterNdTestHelper.hpp new file mode 100644 index 0000000000..5d2cfb011e --- /dev/null +++ b/delegate/test/ScatterNdTestHelper.hpp @@ -0,0 +1,174 @@ +// +// 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
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