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//
// Copyright © 2022, 2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "EndToEndTestImpl.hpp"
#include <armnnUtils/QuantizeHelper.hpp>
#include <ResolveType.hpp>
#include <CommonTestUtils.hpp>
#include <armnnTestUtils/DataLayoutUtils.hpp>
#include <map>
#include <vector>
namespace
{
armnn::INetworkPtr CreateConstConvolution2dNetwork(const armnn::Convolution2dDescriptor& descriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& weightsInfo,
const armnn::TensorInfo& biasInfo,
const armnn::TensorInfo& outputInfo,
const armnn::ConstTensor& weights,
const armnn::ConstTensor& biases,
bool biasEnabled)
{
using namespace armnn;
INetworkPtr network(INetwork::Create());
IConnectableLayer* input = network->AddInputLayer(0, "input");
IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights");
IConnectableLayer* convolution2d = network->AddConvolution2dLayer(descriptor, "convolution2d");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(input, convolution2d, inputInfo, 0, 0);
Connect(weightsLayer, convolution2d, weightsInfo, 0, 1);
if(biasEnabled)
{
armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias");
Connect(biasLayer, convolution2d, biasInfo, 0, 2);
}
Connect(convolution2d, output, outputInfo, 0, 0);
return network;
}
template<DataType ArmnnIType, DataType ArmnnWType = ArmnnIType, DataType ArmnnBType = ArmnnIType,
DataType ArmnnOType = ArmnnIType>
void Convolution2dEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout,
bool biasEnabled = true)
{
using namespace armnn;
using IT = ResolveType<ArmnnIType>;
using WT = ResolveType<ArmnnWType>;
using BT = ResolveType<ArmnnBType>;
using OT = ResolveType<ArmnnOType>;
const float qScale = 1.0f;
const int32_t qOffset = IsQuantizedType<IT>() ? 10 : 0; // offset must be zero for non-quantized types
TensorInfo inputInfo( { 1, 5, 5, 1 }, ArmnnIType, qScale, qOffset, true);
TensorInfo weightsInfo({ 1, 3, 3, 1 }, ArmnnWType, qScale, qOffset, true);
TensorInfo biasesInfo( { 1 }, ArmnnBType, qScale * qScale, 0, true);
TensorInfo outputInfo( { 1, 3, 3, 1 }, ArmnnOType, qScale, qOffset);
std::vector<float> inputData =
{
1, 5, 2, 3, 5,
8, 7, 3, 6, 3,
3, 3, 9, 1, 9,
4, 1, 8, 1, 3,
6, 8, 1, 9, 2
};
std::vector<float> weightsData =
{
4, 5, 6,
0, 0, 0,
3, 2, 1
};
std::vector<float> biasesData = { 1 };
float bias = biasEnabled ? biasesData[0] : 0;
std::vector<float> expectedOutputData =
{
65 + bias, 76 + bias, 91 + bias,
107 + bias, 99 + bias, 89 + bias,
116 + bias, 98 + bias, 118 + bias
};
Convolution2dDescriptor descriptor;
descriptor.m_PadLeft = 0;
descriptor.m_PadRight = 0;
descriptor.m_PadTop = 0;
descriptor.m_PadBottom = 0;
descriptor.m_StrideX = 1;
descriptor.m_StrideY = 1;
descriptor.m_BiasEnabled = biasEnabled;
descriptor.m_DataLayout = dataLayout;
if (dataLayout == DataLayout::NCHW)
{
PermuteTensorNhwcToNchw(inputInfo, inputData);
PermuteTensorNhwcToNchw(weightsInfo, weightsData);
PermuteTensorNhwcToNchw(outputInfo, expectedOutputData);
}
// Convert data
std::vector<IT> qInputData = armnnUtils::QuantizedVector<IT>(inputData, qScale, qOffset);
std::vector<WT> qWeightsData = armnnUtils::QuantizedVector<WT>(weightsData, qScale, qOffset);
std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0);
std::vector<OT> qExpectedOutputData = armnnUtils::QuantizedVector<OT>(expectedOutputData, qScale, qOffset);
ConstTensor weights(weightsInfo, qWeightsData);
ConstTensor biases(biasesInfo, qBiasesData);
INetworkPtr network = CreateConstConvolution2dNetwork(descriptor,
inputInfo,
weightsInfo,
biasesInfo,
outputInfo,
weights,
biases,
biasEnabled);
EndToEndLayerTestImpl<ArmnnIType, ArmnnOType>(std::move(network),
{{ 0, qInputData }},
{{ 0, qExpectedOutputData }},
backends);
}
} // anonymous namespace
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