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//
// Copyright © 2021, 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> CreateSpaceDepthTfLiteModel(tflite::BuiltinOperator spaceDepthOperatorCode,
tflite::TensorType tensorType,
const std::vector <int32_t>& inputTensorShape,
const std::vector <int32_t>& outputTensorShape,
int32_t blockSize)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({ 1.0f }),
flatBufferBuilder.CreateVector<int64_t>({ 0 }));
std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder));
buffers.push_back(CreateBuffer(flatBufferBuilder));
std::array<flatbuffers::Offset<Tensor>, 2> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
tensorType,
1,
flatBufferBuilder.CreateString("input"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
2,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
const std::vector<int32_t> operatorInputs({0});
const std::vector<int32_t> operatorOutputs({1});
flatbuffers::Offset<Operator> spaceDepthOperator;
flatbuffers::Offset<flatbuffers::String> modelDescription;
flatbuffers::Offset<OperatorCode> operatorCode;
switch (spaceDepthOperatorCode)
{
case tflite::BuiltinOperator_SPACE_TO_DEPTH:
spaceDepthOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
BuiltinOptions_SpaceToDepthOptions,
CreateSpaceToDepthOptions(flatBufferBuilder, blockSize).Union());
modelDescription = flatBufferBuilder.CreateString("ArmnnDelegate: SPACE_TO_DEPTH Operator Model");
operatorCode = CreateOperatorCode(flatBufferBuilder,
tflite::BuiltinOperator_SPACE_TO_DEPTH);
break;
case tflite::BuiltinOperator_DEPTH_TO_SPACE:
spaceDepthOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
BuiltinOptions_DepthToSpaceOptions,
CreateDepthToSpaceOptions(flatBufferBuilder, blockSize).Union());
flatBufferBuilder.CreateString("ArmnnDelegate: DEPTH_TO_SPACE Operator Model");
operatorCode = CreateOperatorCode(flatBufferBuilder,
tflite::BuiltinOperator_DEPTH_TO_SPACE);
break;
default:
break;
}
const std::vector<int32_t> subgraphInputs({0});
const std::vector<int32_t> 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(&spaceDepthOperator, 1));
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 SpaceDepthTest(tflite::BuiltinOperator spaceDepthOperatorCode,
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,
int32_t blockSize = 2)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreateSpaceDepthTfLiteModel(spaceDepthOperatorCode,
tensorType,
inputShape,
outputShape,
blockSize);
// 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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