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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "armnn/INetwork.hpp"
#include "armnnUtils/QuantizeHelper.hpp"
#include <CommonTestUtils.hpp>
#include <ResolveType.hpp>
#include <doctest/doctest.h>
namespace
{
using namespace armnn;
armnn::INetworkPtr CreateTileNetwork(TileDescriptor& descriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo)
{
INetworkPtr network(INetwork::Create());
IConnectableLayer* inputLayer = network->AddInputLayer(0, "input");
IConnectableLayer* tileLayer = network->AddTileLayer(descriptor, "tile");
IConnectableLayer* outputLayer = network->AddOutputLayer(0, "output");
Connect(inputLayer, tileLayer, inputInfo, 0, 0);
Connect(tileLayer, outputLayer, outputInfo, 0, 0);
return network;
}
template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void TileEndToEnd(const std::vector<BackendId>& backends)
{
float qScale = 1.0f;
int32_t qOffset = 0;
bool qConst = true;
const TensorShape inputTensorShape = { 2, 3 };
const TensorShape outputTensorShape = { 4, 6 };
TensorInfo inputInfo (inputTensorShape, ArmnnType, qScale, qOffset, qConst);
TensorInfo outputInfo (outputTensorShape, ArmnnType,qScale, qOffset);
std::vector<T> inputData = armnnUtils::QuantizedVector<T>({
0.f, 1.f, 2.f,
3.f, 4.f, 5.f
}, qScale, qOffset);
std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>({
0.f, 1.f, 2.f, 0.f, 1.f, 2.f,
3.f, 4.f, 5.f, 3.f, 4.f, 5.f,
0.f, 1.f, 2.f, 0.f, 1.f, 2.f,
3.f, 4.f, 5.f, 3.f, 4.f, 5.f
}, qScale, qOffset);
auto descriptor = armnn::TileDescriptor(std::vector<uint32_t>{ 2, 2 });
INetworkPtr network = CreateTileNetwork(descriptor, inputInfo, outputInfo);
std::map<int, std::vector<T>> inputTensor = { { 0, inputData } };
std::map<int, std::vector<T>> expectedOutputTensor = { { 0, expectedOutputData } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensor, expectedOutputTensor, backends);
}
} // anonymous namespace
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