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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> CreatePadTfLiteModel(
tflite::BuiltinOperator padOperatorCode,
tflite::TensorType tensorType,
tflite::MirrorPadMode paddingMode,
const std::vector<int32_t>& inputTensorShape,
const std::vector<int32_t>& paddingTensorShape,
const std::vector<int32_t>& outputTensorShape,
const std::vector<int32_t>& paddingDim,
const std::vector<T> paddingValue,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ quantScale }),
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
auto inputTensor = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("input"),
quantizationParameters);
auto paddingTensor = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(paddingTensorShape.data(),
paddingTensorShape.size()),
tflite::TensorType_INT32,
1,
flatBufferBuilder.CreateString("padding"));
auto outputTensor = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
2,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, paddingTensor, outputTensor};
std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingDim.data()),
sizeof(int32_t) * paddingDim.size())));
buffers.push_back(CreateBuffer(flatBufferBuilder));
std::vector<int32_t> operatorInputs;
std::vector<int> subgraphInputs;
tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_PadOptions;
flatbuffers::Offset<void> operatorBuiltinOptions;
if (padOperatorCode == tflite::BuiltinOperator_PAD)
{
operatorInputs = {{ 0, 1 }};
subgraphInputs = {{ 0, 1 }};
operatorBuiltinOptions = CreatePadOptions(flatBufferBuilder).Union();
}
else if(padOperatorCode == tflite::BuiltinOperator_MIRROR_PAD)
{
operatorInputs = {{ 0, 1 }};
subgraphInputs = {{ 0, 1 }};
operatorBuiltinOptionsType = BuiltinOptions_MirrorPadOptions;
operatorBuiltinOptions = CreateMirrorPadOptions(flatBufferBuilder, paddingMode).Union();
}
else if (padOperatorCode == tflite::BuiltinOperator_PADV2)
{
buffers.push_back(
CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingValue.data()),
sizeof(T))));
const std::vector<int32_t> shape = { 1 };
auto padValueTensor = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(shape.data(),
shape.size()),
tensorType,
3,
flatBufferBuilder.CreateString("paddingValue"),
quantizationParameters);
tensors.push_back(padValueTensor);
operatorInputs = {{ 0, 1, 3 }};
subgraphInputs = {{ 0, 1, 3 }};
operatorBuiltinOptionsType = BuiltinOptions_PadV2Options;
operatorBuiltinOptions = CreatePadV2Options(flatBufferBuilder).Union();
}
// create operator
const std::vector<int32_t> operatorOutputs{ 2 };
flatbuffers::Offset <Operator> paddingOperator =
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> 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(&paddingOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Pad Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
padOperatorCode);
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 PadTest(tflite::BuiltinOperator padOperatorCode,
tflite::TensorType tensorType,
const std::vector<armnn::BackendId>& backends,
const std::vector<int32_t>& inputShape,
const std::vector<int32_t>& paddingShape,
std::vector<int32_t>& outputShape,
std::vector<T>& inputValues,
std::vector<int32_t>& paddingDim,
std::vector<T>& expectedOutputValues,
T paddingValue,
float quantScale = 1.0f,
int quantOffset = 0,
tflite::MirrorPadMode paddingMode = tflite::MirrorPadMode_SYMMETRIC)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreatePadTfLiteModel<T>(padOperatorCode,
tensorType,
paddingMode,
inputShape,
paddingShape,
outputShape,
paddingDim,
{paddingValue},
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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