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//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/INetwork.hpp>
#include <armnn/Types.hpp>
#include <CommonTestUtils.hpp>
#include <ResolveType.hpp>
#include <doctest/doctest.h>
namespace
{
using namespace armnn;
template<typename armnn::DataType DataType>
armnn::INetworkPtr CreatePooling2dNetwork(const armnn::TensorShape& inputShape,
const armnn::TensorShape& outputShape,
PaddingMethod padMethod = PaddingMethod::Exclude,
PoolingAlgorithm poolAlg = PoolingAlgorithm::Max,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
INetworkPtr network(INetwork::Create());
TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true);
TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset, true);
Pooling2dDescriptor descriptor;
descriptor.m_PoolType = poolAlg;
descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3;
descriptor.m_StrideX = descriptor.m_StrideY = 1;
descriptor.m_PadLeft = 1;
descriptor.m_PadRight = 1;
descriptor.m_PadTop = 1;
descriptor.m_PadBottom = 1;
descriptor.m_PaddingMethod = padMethod;
descriptor.m_DataLayout = DataLayout::NHWC;
IConnectableLayer* pool = network->AddPooling2dLayer(descriptor, "pool");
IConnectableLayer* input = network->AddInputLayer(0, "input");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(input, pool, inputTensorInfo, 0, 0);
Connect(pool, output, outputTensorInfo, 0, 0);
return network;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void MaxPool2dEndToEnd(const std::vector<armnn::BackendId>& backends,
PaddingMethod padMethod = PaddingMethod::Exclude)
{
const TensorShape& inputShape = { 1, 3, 3, 1 };
const TensorShape& outputShape = { 1, 3, 3, 1 };
INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape, padMethod);
CHECK(network);
std::vector<T> inputData{ 1, 2, 3,
4, 5, 6,
7, 8, 9 };
std::vector<T> expectedOutput{ 5, 6, 6,
8, 9, 9,
8, 9, 9 };
std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
}
template<armnn::DataType ArmnnType>
void MaxPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends)
{
using namespace half_float::literal;
using Half = half_float::half;
const TensorShape& inputShape = { 1, 3, 3, 1 };
const TensorShape& outputShape = { 1, 3, 3, 1 };
INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape);
CHECK(network);
std::vector<Half> inputData{ 1._h, 2._h, 3._h,
4._h, 5._h, 6._h,
7._h, 8._h, 9._h };
std::vector<Half> expectedOutput{ 5._h, 6._h, 6._h,
8._h, 9._h, 9._h,
8._h, 9._h, 9._h };
std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } };
std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void AvgPool2dEndToEnd(const std::vector<armnn::BackendId>& backends,
PaddingMethod padMethod = PaddingMethod::Exclude)
{
const TensorShape& inputShape = { 1, 3, 3, 1 };
const TensorShape& outputShape = { 1, 3, 3, 1 };
INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(
inputShape, outputShape, padMethod, PoolingAlgorithm::Average);
CHECK(network);
std::vector<T> inputData{ 1, 2, 3,
4, 5, 6,
7, 8, 9 };
std::vector<T> expectedOutput;
if (padMethod == PaddingMethod::Exclude)
{
expectedOutput = { 3.f , 3.5f, 4.f ,
4.5f, 5.f , 5.5f,
6.f , 6.5f, 7.f };
}
else
{
expectedOutput = { 1.33333f, 2.33333f, 1.77778f,
3.f , 5.f , 3.66667f,
2.66667f, 4.33333f, 3.11111f };
}
std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } };
std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
inputTensorData,
expectedOutputData,
backends,
0.00001f);
}
template<armnn::DataType ArmnnType>
void AvgPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends,
PaddingMethod padMethod = PaddingMethod::IgnoreValue)
{
using namespace half_float::literal;
using Half = half_float::half;
const TensorShape& inputShape = { 1, 3, 3, 1 };
const TensorShape& outputShape = { 1, 3, 3, 1 };
INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(
inputShape, outputShape, padMethod, PoolingAlgorithm::Average);
CHECK(network);
std::vector<Half> inputData{ 1._h, 2._h, 3._h,
4._h, 5._h, 6._h,
7._h, 8._h, 9._h };
std::vector<Half> expectedOutput{ 1.33333_h, 2.33333_h, 1.77778_h,
3._h , 5._h , 3.66667_h,
2.66667_h, 4.33333_h, 3.11111_h };
std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } };
std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } };
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
inputTensorData,
expectedOutputData,
backends,
0.00001f);
}
} // anonymous namespace
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