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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <armnn/INetwork.hpp>
#include <armnn/Tensor.hpp>
#include <armnn/INetworkQuantizer.hpp>
#include <armnn/Types.hpp>
#include "../LayerVisitorBase.hpp"
#include "../Network.hpp"
#include "../Graph.hpp"
#include <boost/test/unit_test.hpp>
namespace armnn
{
BOOST_AUTO_TEST_SUITE(Quantizer)
void VisitLayersTopologically(const INetwork* inputNetwork, ILayerVisitor& visitor)
{
auto network = boost::polymorphic_downcast<const Network*>(inputNetwork);
auto graph = network->GetGraph().TopologicalSort();
for (auto layer : graph)
{
layer->Accept(visitor);
}
}
BOOST_AUTO_TEST_CASE(QuantizeAddition)
{
class TestQuantization : public LayerVisitorBase<VisitorThrowingPolicy>
{
public:
virtual void VisitAdditionLayer(const IConnectableLayer* layer,
const char* name = nullptr)
{
TensorInfo info = layer->GetOutputSlot(0).GetTensorInfo();
BOOST_TEST((info.GetDataType() == DataType::QuantisedAsymm8));
BOOST_TEST((info.GetQuantizationOffset() == 128));
// Based off current static value [-20.0f, 20.0f]
BOOST_CHECK_CLOSE(info.GetQuantizationScale(), 40.0f/255.0f, 0.000001f );
}
virtual void VisitInputLayer(const IConnectableLayer* layer,
LayerBindingId id,
const char* name = nullptr)
{
TensorInfo info = layer->GetOutputSlot(0).GetTensorInfo();
BOOST_TEST((info.GetDataType() == DataType::QuantisedAsymm8));
BOOST_TEST((info.GetQuantizationOffset() == 128));
// Based off current default [-15.0f, 15.0f]
BOOST_CHECK_CLOSE(info.GetQuantizationScale(), 30.0f/255.0f, 0.000001f );
}
virtual void VisitOutputLayer(const IConnectableLayer* layer,
LayerBindingId id,
const char* name = nullptr)
{}
};
auto network = INetwork::Create();
// Add the layers
IConnectableLayer* input0 = network->AddInputLayer(0);
IConnectableLayer* input1 = network->AddInputLayer(1);
IConnectableLayer* addition = network->AddAdditionLayer();
IConnectableLayer* output = network->AddOutputLayer(2);
// Establish connections
input0->GetOutputSlot(0).Connect(addition->GetInputSlot(0));
input1->GetOutputSlot(0).Connect(addition->GetInputSlot(1));
addition->GetOutputSlot(0).Connect(output->GetInputSlot(0));
//Set TensorInfo
TensorShape shape{1U};
TensorInfo info(shape, DataType::Float32);
input0->GetOutputSlot(0).SetTensorInfo(info);
input1->GetOutputSlot(0).SetTensorInfo(info);
addition->GetOutputSlot(0).SetTensorInfo(info);
auto quantizedNetwork = INetworkQuantizer::Create(network.get())->ExportNetwork();
TestQuantization validator;
VisitLayersTopologically(quantizedNetwork.get(), validator);
}
BOOST_AUTO_TEST_CASE(QuantizeBatchNorm)
{
class TestQuantization : public LayerVisitorBase<VisitorThrowingPolicy>
{
public:
virtual void VisitBatchNormalizationLayer(const IConnectableLayer* layer,
const BatchNormalizationDescriptor& desc,
const ConstTensor& mean,
const ConstTensor& variance,
const ConstTensor& beta,
const ConstTensor& gamma,
const char* name = nullptr)
{
TensorInfo info = layer->GetOutputSlot(0).GetTensorInfo();
BOOST_TEST((info.GetDataType() == DataType::QuantisedAsymm8));
BOOST_TEST((info.GetQuantizationOffset() == 128));
// Based off current static value [-15.0f, 15.0f]
BOOST_CHECK_CLOSE(info.GetQuantizationScale(), 30.0f/255.0f, 0.000001f );
//Test constants
BOOST_TEST((mean.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
BOOST_TEST((variance.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
BOOST_TEST((beta.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
BOOST_TEST((gamma.GetInfo().GetDataType() == DataType::QuantisedAsymm8));
BOOST_CHECK_CLOSE(mean.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
BOOST_CHECK_CLOSE(variance.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
BOOST_CHECK_CLOSE(beta.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
BOOST_CHECK_CLOSE(gamma.GetInfo().GetQuantizationScale(), 3.0f/255.0f, 0.000001f);
BOOST_TEST((mean.GetInfo().GetQuantizationOffset() == 85));
}
virtual void VisitInputLayer(const IConnectableLayer* layer,
LayerBindingId id,
const char* name = nullptr)
{}
virtual void VisitOutputLayer(const IConnectableLayer* layer,
LayerBindingId id,
const char* name = nullptr)
{}
};
auto network = INetwork::Create();
TensorShape shape{3U};
TensorInfo info(shape, DataType::Float32);
std::vector<float> meanData{-1.0f, 1.5f, 2.0f};
std::vector<float> varData{-1.0f, 1.5f, 2.0f};
std::vector<float> betaData{-1.0f, 1.5f, 2.0f};
std::vector<float> gammaData{-1.0f, 1.5f, 2.0f};
ConstTensor mean(info, meanData);
ConstTensor var(info, varData);
ConstTensor beta(info, betaData);
ConstTensor gamma(info, gammaData);
BatchNormalizationDescriptor desc;
// Add the layers
IConnectableLayer* input0 = network->AddInputLayer(0);
IConnectableLayer* batchNorm = network->AddBatchNormalizationLayer(desc, mean, var, beta, gamma);
IConnectableLayer* output = network->AddOutputLayer(1);
// Establish connections
input0->GetOutputSlot(0).Connect(batchNorm->GetInputSlot(0));
batchNorm->GetOutputSlot(0).Connect(output->GetInputSlot(0));
//Set TensorInfo
input0->GetOutputSlot(0).SetTensorInfo(info);
batchNorm->GetOutputSlot(0).SetTensorInfo(info);
auto quantizedNetwork = INetworkQuantizer::Create(network.get())->ExportNetwork();
TestQuantization validator;
VisitLayersTopologically(quantizedNetwork.get(), validator);
}
BOOST_AUTO_TEST_SUITE_END()
} //namespace armnn
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