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//
// Copyright © 2022-2023 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 <flatbuffers/flexbuffers.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/kernels/custom_ops_register.h>
#include <tensorflow/lite/version.h>
#include <doctest/doctest.h>
namespace
{
#if defined(ARMNN_POST_TFLITE_2_5)
std::vector<uint8_t> CreateCustomOptions(int, int, int, int, int, int, TfLitePadding);
std::vector<char> CreatePooling3dTfLiteModel(
std::string poolType,
tflite::TensorType tensorType,
const std::vector<int32_t>& inputTensorShape,
const std::vector<int32_t>& outputTensorShape,
TfLitePadding padding = kTfLitePaddingSame,
int32_t strideWidth = 0,
int32_t strideHeight = 0,
int32_t strideDepth = 0,
int32_t filterWidth = 0,
int32_t filterHeight = 0,
int32_t filterDepth = 0,
tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
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));
buffers.push_back(CreateBuffer(flatBufferBuilder));
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ quantScale }),
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
// Create the input and output tensors
std::array<flatbuffers::Offset<Tensor>, 2> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("input"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
// Create the custom options from the function below
std::vector<uint8_t> customOperatorOptions = CreateCustomOptions(strideHeight, strideWidth, strideDepth,
filterHeight, filterWidth, filterDepth, padding);
// opCodeIndex is created as a uint8_t to avoid map lookup
uint8_t opCodeIndex = 0;
// Set the operator name based on the PoolType passed in from the test case
std::string opName = "";
if (poolType == "kMax")
{
opName = "MaxPool3D";
}
else
{
opName = "AveragePool3D";
}
// To create a custom operator code you pass in the builtin code for custom operators and the name of the custom op
flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCodeDirect(flatBufferBuilder,
tflite::BuiltinOperator_CUSTOM,
opName.c_str());
// Create the Operator using the opCodeIndex and custom options. Also sets builtin options to none.
const std::vector<int32_t> operatorInputs{ 0 };
const std::vector<int32_t> operatorOutputs{ 1 };
flatbuffers::Offset<Operator> poolingOperator =
CreateOperator(flatBufferBuilder,
opCodeIndex,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
tflite::BuiltinOptions_NONE,
0,
flatBufferBuilder.CreateVector<uint8_t>(customOperatorOptions),
tflite::CustomOptionsFormat_FLEXBUFFERS);
// Create the subgraph using the operator created above.
const std::vector<int> subgraphInputs{ 0 };
const std::vector<int> subgraphOutputs{ 1 };
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(&poolingOperator, 1));
flatbuffers::Offset<flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Pooling3d Operator Model");
// Create the model using operatorCode and the subgraph.
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 Pooling3dTest(std::string poolType,
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,
TfLitePadding padding = kTfLitePaddingSame,
int32_t strideWidth = 0,
int32_t strideHeight = 0,
int32_t strideDepth = 0,
int32_t filterWidth = 0,
int32_t filterHeight = 0,
int32_t filterDepth = 0,
tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace delegateTestInterpreter;
// Create the single op model buffer
std::vector<char> modelBuffer = CreatePooling3dTfLiteModel(poolType,
tensorType,
inputShape,
outputShape,
padding,
strideWidth,
strideHeight,
strideDepth,
filterWidth,
filterHeight,
filterDepth,
fusedActivation,
quantScale,
quantOffset);
std::string opType = "";
if (poolType == "kMax")
{
opType = "MaxPool3D";
}
else
{
opType = "AveragePool3D";
}
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer, opType);
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, opType);
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();
}
// Function to create the flexbuffer custom options for the custom pooling3d operator.
std::vector<uint8_t> CreateCustomOptions(int strideHeight, int strideWidth, int strideDepth,
int filterHeight, int filterWidth, int filterDepth, TfLitePadding padding)
{
auto flex_builder = std::make_unique<flexbuffers::Builder>();
size_t map_start = flex_builder->StartMap();
flex_builder->String("data_format", "NDHWC");
// Padding is created as a key and padding type. Only VALID and SAME supported
if (padding == kTfLitePaddingValid)
{
flex_builder->String("padding", "VALID");
}
else
{
flex_builder->String("padding", "SAME");
}
// Vector of filter dimensions in order ( 1, Depth, Height, Width, 1 )
auto start = flex_builder->StartVector("ksize");
flex_builder->Add(1);
flex_builder->Add(filterDepth);
flex_builder->Add(filterHeight);
flex_builder->Add(filterWidth);
flex_builder->Add(1);
// EndVector( start, bool typed, bool fixed)
flex_builder->EndVector(start, true, false);
// Vector of stride dimensions in order ( 1, Depth, Height, Width, 1 )
auto stridesStart = flex_builder->StartVector("strides");
flex_builder->Add(1);
flex_builder->Add(strideDepth);
flex_builder->Add(strideHeight);
flex_builder->Add(strideWidth);
flex_builder->Add(1);
// EndVector( stridesStart, bool typed, bool fixed)
flex_builder->EndVector(stridesStart, true, false);
flex_builder->EndMap(map_start);
flex_builder->Finish();
return flex_builder->GetBuffer();
}
#endif
} // anonymous namespace
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