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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/model.h>
#include <tensorflow/lite/schema/schema_generated.h>
#include <tensorflow/lite/version.h>
#include <doctest/doctest.h>
namespace
{
std::vector<char> CreateShapeTfLiteModel(tflite::TensorType inputTensorType,
tflite::TensorType outputTensorType,
const std::vector<int32_t>& inputTensorShape,
const std::vector<int32_t>& outputTensorShape,
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, flatBufferBuilder.CreateVector({})));
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ quantScale }),
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
std::array<flatbuffers::Offset<Tensor>, 2> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
inputTensorType,
0,
flatBufferBuilder.CreateString("input"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
outputTensorType,
0,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
const std::vector<int32_t> operatorInputs({ 0 });
const std::vector<int32_t> operatorOutputs({ 1 });
flatbuffers::Offset<Operator> shapeOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(),
operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(),
operatorOutputs.size()),
BuiltinOptions_ShapeOptions,
CreateShapeOptions(flatBufferBuilder, outputTensorType).Union());
flatbuffers::Offset<flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: SHAPE Operator Model");
flatbuffers::Offset<OperatorCode> operatorCode =
CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_SHAPE);
const std::vector<int32_t> subgraphInputs({ 0 });
const std::vector<int32_t> subgraphOutputs({ 1 });
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(&shapeOperator, 1));
flatbuffers::Offset<Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&operatorCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
flatBufferBuilder.Finish(flatbufferModel);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
template<typename T, typename K>
void ShapeTest(tflite::TensorType inputTensorType,
tflite::TensorType outputTensorType,
std::vector<armnn::BackendId>& backends,
std::vector<int32_t>& inputShape,
std::vector<T>& inputValues,
std::vector<K>& expectedOutputValues,
std::vector<int32_t>& expectedOutputShape,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace tflite;
std::vector<char> modelBuffer = CreateShapeTfLiteModel(inputTensorType,
outputTensorType,
inputShape,
expectedOutputShape,
quantScale,
quantOffset);
const Model* tfLiteModel = GetModel(modelBuffer.data());
// Create TfLite Interpreters
std::unique_ptr<Interpreter> armnnDelegate;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&armnnDelegate) == kTfLiteOk);
CHECK(armnnDelegate != nullptr);
CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk);
std::unique_ptr<Interpreter> tfLiteDelegate;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&tfLiteDelegate) == kTfLiteOk);
CHECK(tfLiteDelegate != nullptr);
CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk);
// Create the ArmNN Delegate
armnnDelegate::DelegateOptions delegateOptions(backends);
std::unique_ptr < TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete) >
theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
armnnDelegate::TfLiteArmnnDelegateDelete);
CHECK(theArmnnDelegate != nullptr);
// Modify armnnDelegateInterpreter to use armnnDelegate
CHECK(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
// Set input data
armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues);
armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues);
// Run EnqueWorkload
CHECK(tfLiteDelegate->Invoke() == kTfLiteOk);
CHECK(armnnDelegate->Invoke() == kTfLiteOk);
// Compare output data
armnnDelegate::CompareOutputData<K>(tfLiteDelegate,
armnnDelegate,
expectedOutputShape,
expectedOutputValues,
0);
tfLiteDelegate.reset(nullptr);
armnnDelegate.reset(nullptr);
}
} // anonymous namespace
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