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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
{
std::vector<char> CreatePooling2dTfLiteModel(
tflite::BuiltinOperator poolingOperatorCode,
tflite::TensorType tensorType,
const std::vector <int32_t>& inputTensorShape,
const std::vector <int32_t>& outputTensorShape,
tflite::Padding padding = tflite::Padding_SAME,
int32_t strideWidth = 0,
int32_t strideHeight = 0,
int32_t filterWidth = 0,
int32_t filterHeight = 0,
tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
flatbuffers::Offset<tflite::Buffer> buffers[3] = {CreateBuffer(flatBufferBuilder),
CreateBuffer(flatBufferBuilder),
CreateBuffer(flatBufferBuilder)};
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ quantScale }),
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
flatbuffers::Offset<Tensor> tensors[2] {
CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape),
tensorType,
1,
flatBufferBuilder.CreateString("input"),
quantizationParameters),
CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape),
tensorType,
2,
flatBufferBuilder.CreateString("output"),
quantizationParameters)
};
// create operator
tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_Pool2DOptions;
flatbuffers::Offset<void> operatorBuiltinOptions = CreatePool2DOptions(flatBufferBuilder,
padding,
strideWidth,
strideHeight,
filterWidth,
filterHeight,
fusedActivation).Union();
const std::vector<int32_t> operatorInputs{0};
const std::vector<int32_t> operatorOutputs{1};
flatbuffers::Offset <Operator> poolingOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs),
operatorBuiltinOptionsType,
operatorBuiltinOptions);
const int subgraphInputs[1] = {0};
const int subgraphOutputs[1] = {1};
flatbuffers::Offset <SubGraph> subgraph =
CreateSubGraph(flatBufferBuilder,
flatBufferBuilder.CreateVector(tensors, 2),
flatBufferBuilder.CreateVector<int32_t>(subgraphInputs, 1),
flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs, 1),
flatBufferBuilder.CreateVector(&poolingOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Pooling2d Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, poolingOperatorCode);
flatbuffers::Offset <Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&operatorCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers, 3));
flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
template <typename T>
void Pooling2dTest(tflite::BuiltinOperator poolingOperatorCode,
tflite::TensorType tensorType,
std::vector<armnn::BackendId>& backends,
std::vector<int32_t>& inputShape,
std::vector<int32_t>& outputShape,
std::vector<T>& inputValues,
std::vector<T>& expectedOutputValues,
tflite::Padding padding = tflite::Padding_SAME,
int32_t strideWidth = 0,
int32_t strideHeight = 0,
int32_t filterWidth = 0,
int32_t filterHeight = 0,
tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreatePooling2dTfLiteModel(poolingOperatorCode,
tensorType,
inputShape,
outputShape,
padding,
strideWidth,
strideHeight,
filterWidth,
filterHeight,
fusedActivation,
quantScale,
quantOffset);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 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(modelBuffer, backends);
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 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, outputShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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