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//
// Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>
#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/version.h>
#include <schema_generated.h>
#include <doctest/doctest.h>
namespace
{
template <typename T>
std::vector<char> CreateFillTfLiteModel(tflite::BuiltinOperator fillOperatorCode,
tflite::TensorType tensorType,
const std::vector<int32_t>& inputShape,
const std::vector <int32_t>& tensorShape,
const std::vector<T> fillValue)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(tensorShape.data()),
sizeof(int32_t) * tensorShape.size())));
buffers.push_back(
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(fillValue.data()),
sizeof(T) * fillValue.size())));
buffers.push_back(CreateBuffer(flatBufferBuilder));
std::array<flatbuffers::Offset<Tensor>, 3> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputShape.data(),
inputShape.size()),
tflite::TensorType_INT32,
1,
flatBufferBuilder.CreateString("dims"));
std::vector<int32_t> fillShape = {};
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(fillShape.data(),
fillShape.size()),
tensorType,
2,
flatBufferBuilder.CreateString("value"));
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
tensorShape.size()),
tensorType,
3,
flatBufferBuilder.CreateString("output"));
tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FillOptions;
flatbuffers::Offset<void> operatorBuiltinOptions = CreateFillOptions(flatBufferBuilder).Union();
// create operator
const std::vector<int> operatorInputs{ {0, 1} };
const std::vector<int> operatorOutputs{ 2 };
flatbuffers::Offset <Operator> fillOperator =
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} };
const std::vector<int> subgraphOutputs{ 2 };
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(&fillOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Fill Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
fillOperatorCode);
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, armnnDelegate::FILE_IDENTIFIER);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
template <typename T>
void FillTest(tflite::BuiltinOperator fillOperatorCode,
tflite::TensorType tensorType,
const std::vector<armnn::BackendId>& backends,
std::vector<int32_t >& inputShape,
std::vector<int32_t >& tensorShape,
std::vector<T>& expectedOutputValues,
T fillValue)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreateFillTfLiteModel<T>(fillOperatorCode,
tensorType,
inputShape,
tensorShape,
{fillValue});
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == 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, backends);
CHECK(armnnInterpreter.AllocateTensors() == 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, tensorShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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