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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, "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;
}
armnn::INetworkPtr CreateFullyConnectedNetworkNonConstWeightsConstBias(const armnn::TensorInfo& inputTensorInfo,
const armnn::TensorInfo& outputTensorInfo,
const armnn::TensorInfo& weightsTensorInfo,
const armnn::TensorInfo& biasTensorInfo,
const armnn::ConstTensor& biasConstantTensor,
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* biasLayer = network->AddConstantLayer(biasConstantTensor, "Weights");
armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected");
armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);
Connect(weightsInputLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);
Connect(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2);
Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0);
return network;
}
armnn::INetworkPtr CreateFullyConnectedNetworkConstWeightsNonConstBias(const armnn::TensorInfo& inputTensorInfo,
const armnn::TensorInfo& outputTensorInfo,
const armnn::TensorInfo& weightsTensorInfo,
const armnn::TensorInfo& biasTensorInfo,
const armnn::ConstTensor& weightsConstantTensor,
armnn::FullyConnectedDescriptor descriptor)
{
armnn::INetworkPtr network(armnn::INetwork::Create());
armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input");
armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weightsConstantTensor, "Weights");
armnn::IConnectableLayer* biasLayer = network->AddInputLayer(2, "Bias_Input");
armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, "Fully_Connected");
armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0);
Connect(weightsLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1);
Connect(biasLayer, fullyConnectedLayer, biasTensorInfo, 0, 2);
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);
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
void FullyConnectedWithDynamicOrConstantInputsEndToEnd(const std::vector<armnn::BackendId>& backends,
const bool transposeWeights,
const bool constantWeightsOrBias)
{
unsigned int inputWidth = 1;
unsigned int inputHeight = 1;
unsigned int inputChannels = 5;
unsigned int inputNum = 2;
unsigned int outputChannels = 3;
unsigned int outputNum = 2;
unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
unsigned int outputShape[] = { outputNum, outputChannels };
unsigned int weightsShape[] = { inputChannels, outputChannels };
if (transposeWeights)
{
std::swap(weightsShape[0], weightsShape[1]);
}
unsigned int biasShape[] = { outputChannels };
armnn::TensorInfo inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo = armnn::TensorInfo(2, outputShape, armnn::DataType::Float32);
armnn::TensorInfo weightsDesc = armnn::TensorInfo(2, weightsShape, armnn::DataType::Float32);
armnn::TensorInfo biasesDesc = armnn::TensorInfo(1, biasShape, armnn::DataType::Float32);
std::vector<float> input =
{
1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
5.0f, 4.0f, 3.0f, 2.0f, 1.0f
};
std::vector<float> weights =
{
.5f, 2.f, .5f,
.5f, 2.f, 1.f,
.5f, 2.f, 2.f,
.5f, 2.f, 3.f,
.5f, 2.f, 4.f
};
if (transposeWeights)
{
weights =
{
.5f, .5f, .5f, .5f, .5f,
2.f, 2.f, 2.f, 2.f, 2.f,
.5f, 1.f, 2.f, 3.f, 4.f
};
}
std::vector<float> biasValues = std::vector<float>({10.f, 20.f, 30.f});
std::vector<float> expectedOutput =
{
0.5f + 1.0f + 1.5f + 2.0f + 2.5f + biasValues[0],
2.0f + 4.0f + 6.0f + 8.0f + 10.f + biasValues[1],
0.5f + 2.0f + 6.0f + 12.f + 20.f + biasValues[2],
2.5f + 2.0f + 1.5f + 1.0f + 0.5f + biasValues[0],
10.0f + 8.0f + 6.0f + 4.0f + 2.f + biasValues[1],
2.5f + 4.0f + 6.0f + 6.f + 4.f + biasValues[2]
};
FullyConnectedDescriptor descriptor;
descriptor.m_BiasEnabled = true;
descriptor.m_TransposeWeightMatrix = transposeWeights;
descriptor.m_ConstantWeights = constantWeightsOrBias;
if (!constantWeightsOrBias)
{
// Tests non constant weights and constant bias.
ConstTensor biasConstantTensor(biasesDesc, biasValues.data());
armnn::INetworkPtr network = CreateFullyConnectedNetworkNonConstWeightsConstBias(inputTensorInfo,
outputTensorInfo,
weightsDesc,
biasesDesc,
biasConstantTensor,
descriptor);
CHECK(network);
std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {1, weights}};
std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }};
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),
inputTensorData,
expectedOutputTensorData,
backends,
1.0f);
}
else
{
// Tests constant weights and non constant bias.
ConstTensor weightsConstantTensor(weightsDesc, weights.data());
armnn::INetworkPtr network = CreateFullyConnectedNetworkConstWeightsNonConstBias(inputTensorInfo,
outputTensorInfo,
weightsDesc,
biasesDesc,
weightsConstantTensor,
descriptor);
CHECK(network);
std::map<int, std::vector<T>> inputTensorData = {{ 0, input }, {2, biasValues}};
std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutput }};
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network),
inputTensorData,
expectedOutputTensorData,
backends,
1.0f);
}
}
} // anonymous namespace
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