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//
// Copyright © 2022 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<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void Convolution2dEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout,
bool biasEnabled = true)
{
using namespace armnn;
const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
TensorInfo inputInfo({ 1, 5, 5, 1 }, ArmnnType, qScale, qOffset, true);
TensorInfo outputInfo({ 1, 3, 3, 1 }, ArmnnType, qScale, qOffset);
TensorInfo weightsInfo({ 1, 3, 3, 1 }, ArmnnType, qScale, qOffset, true);
TensorInfo biasesInfo({ 1 }, ArmnnType, qScale * qScale, 0, true);
std::vector<float> inputData =
{
1.0f, 5.0f, 2.0f, 3.0f, 5.0f,
8.0f, 7.0f, 3.0f, 6.0f, 3.0f,
3.0f, 3.0f, 9.0f, 1.0f, 9.0f,
4.0f, 1.0f, 8.0f, 1.0f, 3.0f,
6.0f, 8.0f, 1.0f, 9.0f, 2.0f
};
std::vector<float> weightsData =
{
4.0f, 5.0f, 6.0f,
0.0f, 0.0f, 0.0f,
3.0f, 2.0f, 1.0f
};
std::vector<float> biasesData = { 1.0f };
float bias = biasEnabled ? biasesData[0] : 0.0f;
std::vector<float> expectedOutputData =
{
65.0f + bias, 76.0f + bias, 91.0f + bias,
107.0f + bias, 99.0f + bias, 89.0f + bias,
116.0f + bias, 98.0f + bias, 118.0f + 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);
}
// Quantize data
std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset);
std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
std::vector<T> qBiasesData = armnnUtils::QuantizedVector<T>(biasesData, qScale * qScale, 0);
ConstTensor weights(weightsInfo, qWeightsData);
ConstTensor biases(biasesInfo, qBiasesData);
INetworkPtr network = CreateConstConvolution2dNetwork(descriptor,
inputInfo,
weightsInfo,
biasesInfo,
outputInfo,
weights,
biases,
biasEnabled);
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
{{ 0, qInputData }},
{{ 0, qExpectedOutputData }},
backends);
}
} // anonymous namespace
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