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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <armnn_delegate.hpp>
#include <flatbuffers/flatbuffers.h>
#include <tensorflow/lite/interpreter.h>
#include <tensorflow/lite/kernels/register.h>
#include <tensorflow/lite/model.h>
#include <tensorflow/lite/schema/schema_generated.h>
#include <tensorflow/lite/version.h>
#include <doctest/doctest.h>
namespace
{
std::vector<char> CreateResizeTfLiteModel(tflite::BuiltinOperator operatorCode,
tflite::TensorType inputTensorType,
const std::vector <int32_t>& inputTensorShape,
const std::vector <int32_t>& sizeTensorData,
const std::vector <int32_t>& sizeTensorShape,
const std::vector <int32_t>& outputTensorShape)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
buffers.push_back(CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(
reinterpret_cast<const uint8_t*>(sizeTensorData.data()),
sizeof(int32_t) * sizeTensorData.size())));
std::array<flatbuffers::Offset<Tensor>, 3> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()),
inputTensorType,
0,
flatBufferBuilder.CreateString("input_tensor"));
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(),
sizeTensorShape.size()),
TensorType_INT32,
1,
flatBufferBuilder.CreateString("size_input_tensor"));
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
inputTensorType,
0,
flatBufferBuilder.CreateString("output_tensor"));
// Create Operator
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
flatbuffers::Offset<void> operatorBuiltinOption = 0;
switch (operatorCode)
{
case BuiltinOperator_RESIZE_BILINEAR:
{
operatorBuiltinOption = CreateResizeBilinearOptions(flatBufferBuilder, false, false).Union();
operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeBilinearOptions;
break;
}
case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR:
{
operatorBuiltinOption = CreateResizeNearestNeighborOptions(flatBufferBuilder, false, false).Union();
operatorBuiltinOptionsType = tflite::BuiltinOptions_ResizeNearestNeighborOptions;
break;
}
default:
break;
}
const std::vector<int> operatorInputs{0, 1};
const std::vector<int> operatorOutputs{2};
flatbuffers::Offset <Operator> resizeOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
operatorBuiltinOptionsType,
operatorBuiltinOption);
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(&resizeOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: Resize Biliniar Operator Model");
flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode);
flatbuffers::Offset <Model> flatbufferModel =
CreateModel(flatBufferBuilder,
TFLITE_SCHEMA_VERSION,
flatBufferBuilder.CreateVector(&opCode, 1),
flatBufferBuilder.CreateVector(&subgraph, 1),
modelDescription,
flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
flatBufferBuilder.Finish(flatbufferModel);
return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
}
void ResizeFP32TestImpl(tflite::BuiltinOperator operatorCode,
std::vector<armnn::BackendId>& backends,
std::vector<float>& input1Values,
std::vector<int32_t> input1Shape,
std::vector<int32_t> input2NewShape,
std::vector<int32_t> input2Shape,
std::vector<float>& expectedOutputValues,
std::vector<int32_t> expectedOutputShape)
{
using namespace tflite;
std::vector<char> modelBuffer = CreateResizeTfLiteModel(operatorCode,
::tflite::TensorType_FLOAT32,
input1Shape,
input2NewShape,
input2Shape,
expectedOutputShape);
const Model* tfLiteModel = GetModel(modelBuffer.data());
// The model will be executed using tflite and using the armnn delegate so that the outputs
// can be compared.
// Create TfLite Interpreter with armnn delegate
std::unique_ptr<Interpreter> armnnDelegateInterpreter;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&armnnDelegateInterpreter) == kTfLiteOk);
CHECK(armnnDelegateInterpreter != nullptr);
CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
// Create TfLite Interpreter without armnn delegate
std::unique_ptr<Interpreter> tfLiteInterpreter;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
(&tfLiteInterpreter) == kTfLiteOk);
CHECK(tfLiteInterpreter != nullptr);
CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
// Create the ArmNN Delegate
armnnDelegate::DelegateOptions delegateOptions(backends);
std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
armnnDelegate::TfLiteArmnnDelegateDelete);
CHECK(theArmnnDelegate != nullptr);
// Modify armnnDelegateInterpreter to use armnnDelegate
CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
// Set input data for the armnn interpreter
armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input1Values);
armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input2NewShape);
// Set input data for the tflite interpreter
armnnDelegate::FillInput(tfLiteInterpreter, 0, input1Values);
armnnDelegate::FillInput(tfLiteInterpreter, 1, input2NewShape);
// Run EnqueWorkload
CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
// Compare output data
auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateOutputId);
auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateOutputId);
for (size_t i = 0; i < expectedOutputValues.size(); i++)
{
CHECK(expectedOutputValues[i] == doctest::Approx(armnnDelegateOutputData[i]));
CHECK(armnnDelegateOutputData[i] == doctest::Approx(tfLiteDelageOutputData[i]));
}
armnnDelegateInterpreter.reset(nullptr);
}
} // anonymous namespace
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