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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "CommonTestUtils.hpp"
#include <armnn/INetwork.hpp>
#include <armnn/TypesUtils.hpp>
#include <ResolveType.hpp>
namespace
{
armnn::INetworkPtr CreateFillNetwork(const armnn::TensorInfo& inputTensorInfo,
const armnn::TensorInfo& outputTensorInfo,
armnn::FillDescriptor descriptor)
{
armnn::INetworkPtr network(armnn::INetwork::Create());
armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input");
armnn::IConnectableLayer* fillLayer = network->AddFillLayer(descriptor, "Fill");
armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
Connect(inputLayer, fillLayer, inputTensorInfo, 0, 0);
Connect(fillLayer, outputLayer, outputTensorInfo, 0, 0);
return network;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void FillEndToEnd(const std::vector<armnn::BackendId>& backends)
{
using namespace armnn;
FillDescriptor descriptor;
descriptor.m_Value = 9;
std::vector<int32_t> inputData {
1, 1, 5, 3
};
std::vector<float> floatExpectedOutputData {
9, 9, 9, 9, 9,
9, 9, 9, 9, 9,
9, 9, 9, 9, 9
};
std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData);
TensorInfo inputInfo ({ 4 }, DataType::Signed32);
TensorInfo outputInfo({ 1, 1, 5, 3 }, ArmnnType);
armnn::INetworkPtr network = CreateFillNetwork(inputInfo, outputInfo, descriptor);
BOOST_TEST_CHECKPOINT("create a network");
std::map<int, std::vector<int32_t>> inputTensorData = {{ 0, inputData }};
std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutputData }};
EndToEndLayerTestImpl<DataType::Signed32, ArmnnType>(move(network),
inputTensorData,
expectedOutputTensorData,
backends);
}
} // anonymous namespace
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