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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
{
std::vector<char> CreateReduceTfLiteModel(tflite::BuiltinOperator reduceOperatorCode,
tflite::TensorType tensorType,
std::vector<int32_t>& input0TensorShape,
std::vector<int32_t>& input1TensorShape,
const std::vector <int32_t>& outputTensorShape,
std::vector<int32_t>& axisData,
const bool keepDims,
float quantScale = 1.0f,
int quantOffset = 0,
bool kTfLiteNoQuantizationForQuantized = false)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
flatbuffers::Offset<tflite::Buffer> buffers[4] = {
CreateBuffer(flatBufferBuilder),
CreateBuffer(flatBufferBuilder),
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()),
sizeof(int32_t) * axisData.size())),
CreateBuffer(flatBufferBuilder)
};
flatbuffers::Offset<tflite::QuantizationParameters> quantizationParametersAxis
= CreateQuantizationParameters(flatBufferBuilder);
flatbuffers::Offset<tflite::QuantizationParameters> quantizationParameters;
if (kTfLiteNoQuantizationForQuantized)
{
if ((quantScale == 1 || quantScale == 0) && quantOffset == 0)
{
// Creates quantization parameter with quantization.type = kTfLiteNoQuantization
quantizationParameters = CreateQuantizationParameters(flatBufferBuilder);
}
else
{
// Creates quantization parameter with quantization.type != kTfLiteNoQuantization
quantizationParameters = CreateQuantizationParameters(
flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({quantScale}),
flatBufferBuilder.CreateVector<int64_t>({quantOffset}));
}
}
else
{
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"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
input1TensorShape.size()),
::tflite::TensorType_INT32,
2,
flatBufferBuilder.CreateString("axis"),
quantizationParametersAxis);
// Create output tensor
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
3,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
// Create operator. Reduce operations MIN, MAX, SUM, MEAN, PROD uses ReducerOptions.
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_ReducerOptions;
flatbuffers::Offset<void> operatorBuiltinOptions = CreateReducerOptions(flatBufferBuilder, keepDims).Union();
const std::vector<int> operatorInputs{ {0, 1} };
const std::vector<int> operatorOutputs{ 2 };
flatbuffers::Offset <Operator> reduceOperator =
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(&reduceOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Reduce Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, reduceOperatorCode);
flatbuffers::Offset <Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&operatorCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers, 4));
flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
template <typename T>
void ReduceTest(tflite::BuiltinOperator reduceOperatorCode,
tflite::TensorType tensorType,
std::vector<armnn::BackendId>& backends,
std::vector<int32_t>& input0Shape,
std::vector<int32_t>& input1Shape,
std::vector<int32_t>& expectedOutputShape,
std::vector<T>& input0Values,
std::vector<int32_t>& input1Values,
std::vector<T>& expectedOutputValues,
const bool keepDims,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBufferArmNN = CreateReduceTfLiteModel(reduceOperatorCode,
tensorType,
input0Shape,
input1Shape,
expectedOutputShape,
input1Values,
keepDims,
quantScale,
quantOffset,
false);
std::vector<char> modelBufferTFLite = CreateReduceTfLiteModel(reduceOperatorCode,
tensorType,
input0Shape,
input1Shape,
expectedOutputShape,
input1Values,
keepDims,
quantScale,
quantOffset,
true);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBufferTFLite);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(input0Values, 0) == 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(modelBufferArmNN, backends);
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<T>(input0Values, 0) == 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, expectedOutputShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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