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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#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> CreateComparisonTfLiteModel(tflite::BuiltinOperator comparisonOperatorCode,
tflite::TensorType tensorType,
const std::vector <int32_t>& input0TensorShape,
const std::vector <int32_t>& input1TensorShape,
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>, 3> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
input0TensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("input_0"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
input1TensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("input_1"),
quantizationParameters);
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
::tflite::TensorType_BOOL,
0);
// create operator
tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_EqualOptions;;
flatbuffers::Offset<void> operatorBuiltinOptions = CreateEqualOptions(flatBufferBuilder).Union();
switch (comparisonOperatorCode)
{
case BuiltinOperator_EQUAL:
{
operatorBuiltinOptionsType = BuiltinOptions_EqualOptions;
operatorBuiltinOptions = CreateEqualOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_NOT_EQUAL:
{
operatorBuiltinOptionsType = BuiltinOptions_NotEqualOptions;
operatorBuiltinOptions = CreateNotEqualOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_GREATER:
{
operatorBuiltinOptionsType = BuiltinOptions_GreaterOptions;
operatorBuiltinOptions = CreateGreaterOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_GREATER_EQUAL:
{
operatorBuiltinOptionsType = BuiltinOptions_GreaterEqualOptions;
operatorBuiltinOptions = CreateGreaterEqualOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_LESS:
{
operatorBuiltinOptionsType = BuiltinOptions_LessOptions;
operatorBuiltinOptions = CreateLessOptions(flatBufferBuilder).Union();
break;
}
case BuiltinOperator_LESS_EQUAL:
{
operatorBuiltinOptionsType = BuiltinOptions_LessEqualOptions;
operatorBuiltinOptions = CreateLessEqualOptions(flatBufferBuilder).Union();
break;
}
default:
break;
}
const std::vector<int32_t> operatorInputs{ {0, 1} };
const std::vector<int32_t> operatorOutputs{{2}};
flatbuffers::Offset <Operator> comparisonOperator =
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(&comparisonOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Comparison Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, comparisonOperatorCode);
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>
void ComparisonTest(tflite::BuiltinOperator comparisonOperatorCode,
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<bool>& expectedOutputValues,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace tflite;
std::vector<char> modelBuffer = CreateComparisonTfLiteModel(comparisonOperatorCode,
tensorType,
input0Shape,
input1Shape,
outputShape,
quantScale,
quantOffset);
const Model* tfLiteModel = GetModel(modelBuffer.data());
// Create TfLite Interpreters
std::unique_ptr<Interpreter> armnnDelegateInterpreter;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&armnnDelegateInterpreter) == kTfLiteOk);
CHECK(armnnDelegateInterpreter != nullptr);
CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
std::unique_ptr<Interpreter> tfLiteInterpreter;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&tfLiteInterpreter) == kTfLiteOk);
CHECK(tfLiteInterpreter != nullptr);
CHECK(tfLiteInterpreter->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(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
// Set input data
auto tfLiteDelegateInput0Id = tfLiteInterpreter->inputs()[0];
auto tfLiteDelageInput0Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput0Id);
for (unsigned int i = 0; i < input0Values.size(); ++i)
{
tfLiteDelageInput0Data[i] = input0Values[i];
}
auto tfLiteDelegateInput1Id = tfLiteInterpreter->inputs()[1];
auto tfLiteDelageInput1Data = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInput1Id);
for (unsigned int i = 0; i < input1Values.size(); ++i)
{
tfLiteDelageInput1Data[i] = input1Values[i];
}
auto armnnDelegateInput0Id = armnnDelegateInterpreter->inputs()[0];
auto armnnDelegateInput0Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput0Id);
for (unsigned int i = 0; i < input0Values.size(); ++i)
{
armnnDelegateInput0Data[i] = input0Values[i];
}
auto armnnDelegateInput1Id = armnnDelegateInterpreter->inputs()[1];
auto armnnDelegateInput1Data = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInput1Id);
for (unsigned int i = 0; i < input1Values.size(); ++i)
{
armnnDelegateInput1Data[i] = input1Values[i];
}
// Run EnqueWorkload
CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
// Compare output data
auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<bool>(tfLiteDelegateOutputId);
auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<bool>(armnnDelegateOutputId);
for (size_t i = 0; i < expectedOutputValues.size(); i++)
{
CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]);
CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]);
CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]);
}
}
} // anonymous namespace
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