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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "CommonTestUtils.hpp"
#include <ResolveType.hpp>
#include <armnn/INetwork.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <doctest/doctest.h>
#include <vector>
namespace
{
armnn::INetworkPtr CreateFullyConnectedNetworkNonConstWeights(const armnn::TensorInfo& inputTensorInfo,
const armnn::TensorInfo& outputTensorInfo,
const armnn::TensorInfo& weightsTensorInfo,
armnn::FullyConnectedDescriptor descriptor)
{
armnn::INetworkPtr network(armnn::INetwork::Create());
armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input");
armnn::IConnectableLayer* weightsInputLayer = network->AddInputLayer(1, "Weights_Input");
armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor,
armnn::EmptyOptional(),
armnn::EmptyOptional(),
"Fully_Connected");
armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);
Connect(weightsInputLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);
Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);
return network;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void FullyConnectedWithDynamicWeightsEndToEnd(const std::vector<armnn::BackendId>& backends)
{
using namespace armnn;
armnn::TensorInfo inputTensorInfo({ 1, 1, 2, 3 }, ArmnnType);
inputTensorInfo.SetQuantizationScale(0.1f);
inputTensorInfo.SetQuantizationOffset(63);
armnn::TensorInfo outputTensorInfo({ 1, 2 }, ArmnnType);
outputTensorInfo.SetQuantizationScale(5.f);
outputTensorInfo.SetQuantizationOffset(10);
armnn::TensorInfo weightsTensorInfo({ 2, 6 }, ArmnnType);
weightsTensorInfo.SetQuantizationScale(0.2f);
weightsTensorInfo.SetQuantizationOffset(93);
FullyConnectedDescriptor descriptor;
descriptor.m_ConstantWeights = false;
descriptor.m_BiasEnabled = false;
descriptor.m_TransposeWeightMatrix = true;
std::vector<T> inputData {
-1.2f, 6.1f, -3.5f,
18.8f, -5.5f, 2.9f
};
std::vector<T> weightsData {
-8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f,
23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f
};
std::vector<T> floatExpectedOutputData {
-107.04f, 110.f
};
std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData);
armnn::INetworkPtr network = CreateFullyConnectedNetworkNonConstWeights(inputTensorInfo,
outputTensorInfo,
weightsTensorInfo,
descriptor);
CHECK(network);
std::map<int, std::vector<T>> inputTensorData = {{ 0, inputData }, {1, weightsData}};
std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutputData }};
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),
inputTensorData,
expectedOutputTensorData,
backends,
1.0f);
}
} // anonymous namespace
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