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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> CreateGatherTfLiteModel(tflite::TensorType tensorType,
std::vector<int32_t>& paramsShape,
std::vector<int32_t>& indicesShape,
const std::vector<int32_t>& expectedOutputShape,
int32_t axis,
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));
buffers.push_back(CreateBuffer(flatBufferBuilder));
auto quantizationParameters =
CreateQuantizationParameters(flatBufferBuilder,
0,
0,
flatBufferBuilder.CreateVector<float>({quantScale}),
flatBufferBuilder.CreateVector<int64_t>({quantOffset}));
std::array<flatbuffers::Offset<Tensor>, 3> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(paramsShape.data(),
paramsShape.size()),
tensorType,
1,
flatBufferBuilder.CreateString("params"),
quantizationParameters);
tensors[1] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(indicesShape.data(),
indicesShape.size()),
::tflite::TensorType_INT32,
2,
flatBufferBuilder.CreateString("indices"),
quantizationParameters);
tensors[2] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(expectedOutputShape.data(),
expectedOutputShape.size()),
tensorType,
3,
flatBufferBuilder.CreateString("output"),
quantizationParameters);
// create operator
tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_GatherOptions;
flatbuffers::Offset<void> operatorBuiltinOptions = CreateGatherOptions(flatBufferBuilder).Union();
const std::vector<int> operatorInputs{{0, 1}};
const std::vector<int> operatorOutputs{2};
flatbuffers::Offset<Operator> controlOperator =
CreateOperator(flatBufferBuilder,
0,
flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(),
operatorInputs.size()),
flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(),
operatorOutputs.size()),
operatorBuiltinOptionsType,
operatorBuiltinOptions);
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(&controlOperator, 1));
flatbuffers::Offset<flatbuffers::String> modelDescription =
flatBufferBuilder.CreateString("ArmnnDelegate: GATHER Operator Model");
flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
BuiltinOperator_GATHER);
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 GatherTest(tflite::TensorType tensorType,
std::vector<armnn::BackendId>& backends,
std::vector<int32_t>& paramsShape,
std::vector<int32_t>& indicesShape,
std::vector<int32_t>& expectedOutputShape,
int32_t axis,
std::vector<T>& paramsValues,
std::vector<int32_t>& indicesValues,
std::vector<T>& expectedOutputValues,
float quantScale = 1.0f,
int quantOffset = 0)
{
using namespace delegateTestInterpreter;
std::vector<char> modelBuffer = CreateGatherTfLiteModel(tensorType,
paramsShape,
indicesShape,
expectedOutputShape,
axis,
quantScale,
quantOffset);
// Setup interpreter with just TFLite Runtime.
auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<T>(paramsValues, 0) == kTfLiteOk);
CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(indicesValues, 1) == 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>(paramsValues, 0) == kTfLiteOk);
CHECK(armnnInterpreter.FillInputTensor<int32_t>(indicesValues, 1) == 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, expectedOutputShape);
tfLiteInterpreter.Cleanup();
armnnInterpreter.Cleanup();
}
} // anonymous namespace
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