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314 lines
15 KiB
314 lines
15 KiB
//
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// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
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// SPDX-License-Identifier: MIT
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//
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#pragma once
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#include "TestUtils.hpp"
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#include <armnn_delegate.hpp>
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#include <DelegateTestInterpreter.hpp>
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#include <flatbuffers/flatbuffers.h>
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#include <tensorflow/lite/kernels/register.h>
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#include <tensorflow/lite/version.h>
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#include <schema_generated.h>
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#include <doctest/doctest.h>
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namespace
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{
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struct StreamRedirector
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{
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public:
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StreamRedirector(std::ostream &stream, std::streambuf *newStreamBuffer)
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: m_Stream(stream), m_BackupBuffer(m_Stream.rdbuf(newStreamBuffer)) {}
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~StreamRedirector() { m_Stream.rdbuf(m_BackupBuffer); }
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private:
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std::ostream &m_Stream;
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std::streambuf *m_BackupBuffer;
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};
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std::vector<char> CreateAddDivTfLiteModel(tflite::TensorType tensorType,
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const std::vector<int32_t>& tensorShape,
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float quantScale = 1.0f,
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int quantOffset = 0)
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{
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using namespace tflite;
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flatbuffers::FlatBufferBuilder flatBufferBuilder;
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std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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auto quantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>({ quantScale }),
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flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
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std::array<flatbuffers::Offset<Tensor>, 5> tensors;
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tensors[0] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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1,
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flatBufferBuilder.CreateString("input_0"),
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quantizationParameters);
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tensors[1] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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2,
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flatBufferBuilder.CreateString("input_1"),
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quantizationParameters);
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tensors[2] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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3,
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flatBufferBuilder.CreateString("input_2"),
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quantizationParameters);
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tensors[3] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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4,
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flatBufferBuilder.CreateString("add"),
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quantizationParameters);
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tensors[4] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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5,
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flatBufferBuilder.CreateString("output"),
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quantizationParameters);
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// create operator
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tflite::BuiltinOptions addBuiltinOptionsType = tflite::BuiltinOptions_AddOptions;
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flatbuffers::Offset<void> addBuiltinOptions =
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CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union();
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tflite::BuiltinOptions divBuiltinOptionsType = tflite::BuiltinOptions_DivOptions;
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flatbuffers::Offset<void> divBuiltinOptions =
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CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union();
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std::array<flatbuffers::Offset<Operator>, 2> operators;
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const std::vector<int32_t> addInputs{0, 1};
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const std::vector<int32_t> addOutputs{3};
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operators[0] = CreateOperator(flatBufferBuilder,
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0,
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flatBufferBuilder.CreateVector<int32_t>(addInputs.data(), addInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(addOutputs.data(), addOutputs.size()),
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addBuiltinOptionsType,
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addBuiltinOptions);
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const std::vector<int32_t> divInputs{3, 2};
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const std::vector<int32_t> divOutputs{4};
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operators[1] = CreateOperator(flatBufferBuilder,
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1,
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flatBufferBuilder.CreateVector<int32_t>(divInputs.data(), divInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(divOutputs.data(), divOutputs.size()),
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divBuiltinOptionsType,
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divBuiltinOptions);
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const std::vector<int> subgraphInputs{0, 1, 2};
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const std::vector<int> subgraphOutputs{4};
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flatbuffers::Offset<SubGraph> subgraph =
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CreateSubGraph(flatBufferBuilder,
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flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
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flatBufferBuilder.CreateVector(operators.data(), operators.size()));
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flatbuffers::Offset<flatbuffers::String> modelDescription =
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flatBufferBuilder.CreateString("ArmnnDelegate: Add and Div Operator Model");
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std::array<flatbuffers::Offset<OperatorCode>, 2> codes;
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codes[0] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_ADD);
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codes[1] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_DIV);
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flatbuffers::Offset<Model> flatbufferModel =
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CreateModel(flatBufferBuilder,
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TFLITE_SCHEMA_VERSION,
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flatBufferBuilder.CreateVector(codes.data(), codes.size()),
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flatBufferBuilder.CreateVector(&subgraph, 1),
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modelDescription,
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flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
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flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
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return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
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flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
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}
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std::vector<char> CreateCosTfLiteModel(tflite::TensorType tensorType,
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const std::vector <int32_t>& tensorShape,
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float quantScale = 1.0f,
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int quantOffset = 0)
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{
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using namespace tflite;
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flatbuffers::FlatBufferBuilder flatBufferBuilder;
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std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
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buffers.push_back(CreateBuffer(flatBufferBuilder));
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auto quantizationParameters =
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CreateQuantizationParameters(flatBufferBuilder,
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0,
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0,
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flatBufferBuilder.CreateVector<float>({quantScale}),
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flatBufferBuilder.CreateVector<int64_t>({quantOffset}));
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std::array<flatbuffers::Offset<Tensor>, 2> tensors;
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tensors[0] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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0,
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flatBufferBuilder.CreateString("input"),
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quantizationParameters);
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tensors[1] = CreateTensor(flatBufferBuilder,
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flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
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tensorShape.size()),
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tensorType,
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0,
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flatBufferBuilder.CreateString("output"),
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quantizationParameters);
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const std::vector<int32_t> operatorInputs({0});
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const std::vector<int32_t> operatorOutputs({1});
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flatbuffers::Offset<Operator> ceilOperator =
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CreateOperator(flatBufferBuilder,
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0,
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flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
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BuiltinOptions_NONE);
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flatbuffers::Offset<flatbuffers::String> modelDescription =
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flatBufferBuilder.CreateString("ArmnnDelegate: CEIL Operator Model");
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flatbuffers::Offset<OperatorCode> operatorCode =
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CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_COS);
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const std::vector<int32_t> subgraphInputs({0});
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const std::vector<int32_t> subgraphOutputs({1});
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flatbuffers::Offset<SubGraph> subgraph =
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CreateSubGraph(flatBufferBuilder,
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flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
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flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
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flatBufferBuilder.CreateVector(&ceilOperator, 1));
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flatbuffers::Offset<Model> flatbufferModel =
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CreateModel(flatBufferBuilder,
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TFLITE_SCHEMA_VERSION,
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flatBufferBuilder.CreateVector(&operatorCode, 1),
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flatBufferBuilder.CreateVector(&subgraph, 1),
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modelDescription,
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flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
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flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
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return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
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flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
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}
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template <typename T>
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void DelegateOptionTest(tflite::TensorType tensorType,
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std::vector<int32_t>& tensorShape,
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std::vector<T>& input0Values,
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std::vector<T>& input1Values,
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std::vector<T>& input2Values,
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std::vector<T>& expectedOutputValues,
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const armnnDelegate::DelegateOptions& delegateOptions,
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float quantScale = 1.0f,
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int quantOffset = 0)
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{
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using namespace delegateTestInterpreter;
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std::vector<char> modelBuffer = CreateAddDivTfLiteModel(tensorType,
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tensorShape,
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quantScale,
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quantOffset);
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// Setup interpreter with just TFLite Runtime.
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auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
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CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(tfLiteInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk);
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CHECK(tfLiteInterpreter.FillInputTensor<T>(input1Values, 1) == kTfLiteOk);
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CHECK(tfLiteInterpreter.FillInputTensor<T>(input2Values, 2) == kTfLiteOk);
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CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
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// Setup interpreter with Arm NN Delegate applied.
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auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, delegateOptions);
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CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(armnnInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk);
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CHECK(armnnInterpreter.FillInputTensor<T>(input1Values, 1) == kTfLiteOk);
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CHECK(armnnInterpreter.FillInputTensor<T>(input2Values, 2) == kTfLiteOk);
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CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
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armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
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armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, tensorShape);
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tfLiteInterpreter.Cleanup();
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armnnInterpreter.Cleanup();
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}
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template <typename T>
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void DelegateOptionNoFallbackTest(tflite::TensorType tensorType,
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std::vector<int32_t>& tensorShape,
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std::vector<T>& inputValues,
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std::vector<T>& expectedOutputValues,
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const armnnDelegate::DelegateOptions& delegateOptions,
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float quantScale = 1.0f,
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int quantOffset = 0)
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{
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using namespace delegateTestInterpreter;
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std::vector<char> modelBuffer = CreateCosTfLiteModel(tensorType,
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tensorShape,
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quantScale,
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quantOffset);
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// Setup interpreter with just TFLite Runtime.
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auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
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CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
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CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
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tfLiteInterpreter.Cleanup();
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try
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{
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auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, delegateOptions);
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CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
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CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
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CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
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std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
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std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
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armnnInterpreter.Cleanup();
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armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
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armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, tensorShape);
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}
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catch (const armnn::Exception& e)
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{
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// Forward the exception message to std::cout
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std::cout << e.what() << std::endl;
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}
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}
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} // anonymous namespace
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