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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> 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));
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(
reinterpret_cast<const uint8_t*>(sizeTensorData.data()),
sizeof(int32_t) * sizeTensorData.size())));
buffers.push_back(CreateBuffer(flatBufferBuilder));
std::array<flatbuffers::Offset<Tensor>, 3> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), inputTensorShape.size()),
inputTensorType,
1,
flatBufferBuilder.CreateString("input_tensor"));
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(),
sizeTensorShape.size()),
TensorType_INT32,
2,
flatBufferBuilder.CreateString("size_input_tensor"));
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
inputTensorType,
3,
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, armnnDelegate::FILE_IDENTIFIER);
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 delegateTestInterpreter;
std::vector<char> modelBuffer = CreateResizeTfLiteModel(operatorCode,
::tflite::TensorType_FLOAT32,
input1Shape,
input2NewShape,
input2Shape,
expectedOutputShape);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<float>(input1Values, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(input2NewShape, 1) == kTfLiteOk);
CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
std::vector<float> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(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<float>(input1Values, 0) == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<int32_t>(input2NewShape, 1) == kTfLiteOk);
CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
std::vector<float> armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0);
std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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