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//
// Copyright © 2021, 2023-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <DelegateTestInterpreter.hpp>
#include <tensorflow/lite/version.h>
namespace
{
std::vector<char> CreatePackTfLiteModel(tflite::BuiltinOperator packOperatorCode,
tflite::TensorType tensorType,
std::vector<int32_t>& inputTensorShape,
const std::vector <int32_t>& outputTensorShape,
const int32_t inputTensorNum,
unsigned int axis = 0,
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));
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ quantScale }),
flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
std::vector<int32_t> operatorInputs{};
const std::vector<int32_t> operatorOutputs{inputTensorNum};
std::vector<int> subgraphInputs{};
const std::vector<int> subgraphOutputs{inputTensorNum};
std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1);
for (int i = 0; i < inputTensorNum; ++i)
{
tensors[i] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
tensorType,
1,
flatBufferBuilder.CreateString("input" + std::to_string(i)),
quantizationParameters);
// Add number of inputs to vector.
operatorInputs.push_back(i);
subgraphInputs.push_back(i);
}
// Create output tensor
tensors[inputTensorNum] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
0,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
// create operator
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_PackOptions;
flatbuffers::Offset<void> operatorBuiltinOptions =
CreatePackOptions(flatBufferBuilder, inputTensorNum, axis).Union();
flatbuffers::Offset <Operator> packOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
operatorBuiltinOptionsType,
operatorBuiltinOptions);
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(&packOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Pack Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, packOperatorCode);
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 PackTest(tflite::BuiltinOperator packOperatorCode,
tflite::TensorType tensorType,
std::vector<int32_t>& inputShape,
std::vector<int32_t>& expectedOutputShape,
std::vector<std::vector<T>>& inputValues,
std::vector<T>& expectedOutputValues,
const std::vector<armnn::BackendId>& backends = {},
unsigned int axis = 0,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreatePackTfLiteModel(packOperatorCode,
tensorType,
inputShape,
expectedOutputShape,
inputValues.size(),
axis,
quantScale,
quantOffset);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
// Setup interpreter with Arm NN Delegate applied.
auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends));
CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
// Set input data for all input tensors.
for (unsigned int i = 0; i < inputValues.size(); ++i)
{
auto inputTensorValues = inputValues[i];
CHECK(tfLiteInterpreter.FillInputTensor<T>(inputTensorValues, i) == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<T>(inputTensorValues, i) == kTfLiteOk);
}
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
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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