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//
// Copyright © 2020, 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> CreateElementwiseBinaryTfLiteModel(tflite::BuiltinOperator binaryOperatorCode,
tflite::ActivationFunctionType activationType,
tflite::TensorType tensorType,
const std::vector <int32_t>& input0TensorShape,
const std::vector <int32_t>& input1TensorShape,
const std::vector <int32_t>& outputTensorShape,
std::vector<T>& input1Values,
bool constantInput = false,
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));
if (constantInput)
{
buffers.push_back(
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(input1Values.data()),
sizeof(T) * input1Values.size())));
}
else
{
buffers.push_back(CreateBuffer(flatBufferBuilder));
}
buffers.push_back(CreateBuffer(flatBufferBuilder));
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ quantScale }),
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
std::array<flatbuffers::Offset<Tensor>, 3> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
input0TensorShape.size()),
tensorType,
1,
flatBufferBuilder.CreateString("input_0"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
input1TensorShape.size()),
tensorType,
2,
flatBufferBuilder.CreateString("input_1"),
quantizationParameters);
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
3,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
// create operator
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
flatbuffers::Offset<void> operatorBuiltinOptions = 0;
switch (binaryOperatorCode)
{
case BuiltinOperator_ADD:
{
operatorBuiltinOptionsType = BuiltinOptions_AddOptions;
operatorBuiltinOptions = CreateAddOptions(flatBufferBuilder, activationType).Union();
break;
}
case BuiltinOperator_DIV:
{
operatorBuiltinOptionsType = BuiltinOptions_DivOptions;
operatorBuiltinOptions = CreateDivOptions(flatBufferBuilder, activationType).Union();
break;
}
case BuiltinOperator_MAXIMUM:
{
operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions;
operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_MINIMUM:
{
operatorBuiltinOptionsType = BuiltinOptions_MaximumMinimumOptions;
operatorBuiltinOptions = CreateMaximumMinimumOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_MUL:
{
operatorBuiltinOptionsType = BuiltinOptions_MulOptions;
operatorBuiltinOptions = CreateMulOptions(flatBufferBuilder, activationType).Union();
break;
}
case BuiltinOperator_SUB:
{
operatorBuiltinOptionsType = BuiltinOptions_SubOptions;
operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union();
break;
}
case BuiltinOperator_POW:
{
operatorBuiltinOptionsType = BuiltinOptions_PowOptions;
operatorBuiltinOptions = CreatePowOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_SQUARED_DIFFERENCE:
{
operatorBuiltinOptionsType = BuiltinOptions_SquaredDifferenceOptions;
operatorBuiltinOptions = CreateSquaredDifferenceOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_FLOOR_DIV:
{
operatorBuiltinOptionsType = tflite::BuiltinOptions_FloorDivOptions;
operatorBuiltinOptions = CreateSubOptions(flatBufferBuilder, activationType).Union();
break;
}
default:
break;
}
const std::vector<int32_t> operatorInputs{0, 1};
const std::vector<int32_t> operatorOutputs{2};
flatbuffers::Offset <Operator> elementwiseBinaryOperator =
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(&elementwiseBinaryOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Elementwise Binary Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, binaryOperatorCode);
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 ElementwiseBinaryTest(tflite::BuiltinOperator binaryOperatorCode,
tflite::ActivationFunctionType activationType,
tflite::TensorType tensorType,
std::vector<armnn::BackendId>& backends,
std::vector<int32_t>& input0Shape,
std::vector<int32_t>& input1Shape,
std::vector<int32_t>& outputShape,
std::vector<T>& input0Values,
std::vector<T>& input1Values,
std::vector<T>& expectedOutputValues,
float quantScale = 1.0f,
int quantOffset = 0,
bool constantInput = false)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreateElementwiseBinaryTfLiteModel<T>(binaryOperatorCode,
activationType,
tensorType,
input0Shape,
input1Shape,
outputShape,
input1Values,
constantInput,
quantScale,
quantOffset);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(input1Values, 1) == 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.FillInputTensor<T>(input0Values, 0) == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<T>(input1Values, 1) == 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, outputShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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