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139 lines
6.3 KiB
139 lines
6.3 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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std::vector<char> CreateCastTfLiteModel(tflite::TensorType inputTensorType,
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tflite::TensorType outputTensorType,
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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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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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inputTensorType,
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1,
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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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outputTensorType,
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2,
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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> castOperator =
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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_CastOptions,
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CreateCastOptions(flatBufferBuilder).Union());
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flatbuffers::Offset<flatbuffers::String> modelDescription =
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flatBufferBuilder.CreateString("ArmnnDelegate: CAST Operator Model");
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flatbuffers::Offset<OperatorCode> operatorCode =
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CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_CAST);
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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(&castOperator, 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, typename K>
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void CastTest(tflite::TensorType inputTensorType,
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tflite::TensorType outputTensorType,
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std::vector<armnn::BackendId>& backends,
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std::vector<int32_t>& shape,
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std::vector<T>& inputValues,
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std::vector<K>& expectedOutputValues,
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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 = CreateCastTfLiteModel(inputTensorType,
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outputTensorType,
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shape,
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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<K> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<K>(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, backends);
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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<K> armnnOutputValues = armnnInterpreter.GetOutputResult<K>(0);
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std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
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armnnDelegate::CompareOutputData<K>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
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armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, shape);
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tfLiteInterpreter.Cleanup();
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armnnInterpreter.Cleanup();
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}
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} // anonymous namespace
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