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//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/INetwork.hpp>
#include <CommonTestUtils.hpp>
#include <ResolveType.hpp>
#include <doctest/doctest.h>
namespace
{
template<typename armnn::DataType DataType>
armnn::INetworkPtr CreateSubtractionNetwork(const armnn::TensorShape& inputXShape,
const armnn::TensorShape& inputYShape,
const armnn::TensorShape& outputShape,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
using namespace armnn;
INetworkPtr network(INetwork::Create());
TensorInfo inputXTensorInfo(inputXShape, DataType, qScale, qOffset, true);
TensorInfo inputYTensorInfo(inputYShape, DataType, qScale, qOffset, true);
TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
IConnectableLayer* subtraction = network->AddSubtractionLayer("subtraction");
IConnectableLayer* inputX = network->AddInputLayer(0, "inputX");
IConnectableLayer* inputY = network->AddInputLayer(1, "inputY");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(inputX, subtraction, inputXTensorInfo, 0, 0);
Connect(inputY, subtraction, inputYTensorInfo, 0, 1);
Connect(subtraction, output, outputTensorInfo, 0, 0);
return network;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void SubtractionEndToEnd(const std::vector<armnn::BackendId>& backends)
{
using namespace armnn;
const TensorShape& inputXShape = { 2, 2 };
const TensorShape& inputYShape = { 2, 2 };
const TensorShape& outputShape = { 2, 2 };
INetworkPtr network = CreateSubtractionNetwork<ArmnnType>(inputXShape, inputYShape, outputShape);
CHECK(network);
std::vector<T> inputXData{ 10, 11, 12, 13 };
std::vector<T> inputYData{ 5, 7, 6, 8 };
std::vector<T> expectedOutput{ 5, 4, 6, 5 };
std::map<int, std::vector<T>> inputTensorData = {{ 0, inputXData }, {1, inputYData}};
std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
}
template<armnn::DataType ArmnnType>
void SubtractionEndToEndFloat16(const std::vector<armnn::BackendId>& backends)
{
using namespace armnn;
using namespace half_float::literal;
using Half = half_float::half;
const TensorShape& inputXShape = { 2, 2 };
const TensorShape& inputYShape = { 2, 2 };
const TensorShape& outputShape = { 2, 2 };
INetworkPtr network = CreateSubtractionNetwork<ArmnnType>(inputXShape, inputYShape, outputShape);
CHECK(network);
std::vector<Half> inputXData{ 11._h, 12._h,
13._h, 14._h };
std::vector<Half> inputYData{ 5._h, 7._h,
6._h, 8._h };
std::vector<Half> expectedOutput{ 6._h, 5._h,
7._h, 6._h };
std::map<int, std::vector<Half>> inputTensorData = {{ 0, inputXData }, { 1, inputYData }};
std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
}
} // anonymous namespace
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