aboutsummaryrefslogtreecommitdiff
path: root/src/armnn/test/QuantizerTest.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'src/armnn/test/QuantizerTest.cpp')
-rw-r--r--src/armnn/test/QuantizerTest.cpp123
1 files changed, 90 insertions, 33 deletions
diff --git a/src/armnn/test/QuantizerTest.cpp b/src/armnn/test/QuantizerTest.cpp
index 67d0f95292..a932698674 100644
--- a/src/armnn/test/QuantizerTest.cpp
+++ b/src/armnn/test/QuantizerTest.cpp
@@ -33,6 +33,70 @@ const float g_SymmS8QuantizationBase = 127.0f;
const float g_SymmS16QuantizationBase = 32767.0f;
const float g_TestTolerance = 0.000001f;
+class TestConnectionPreservation : public LayerVisitorBase<VisitorNoThrowPolicy>
+{
+public:
+ TestConnectionPreservation(INetwork* network)
+ : LayerVisitorBase<VisitorNoThrowPolicy>()
+ , m_Network(network)
+ {}
+
+ void VisitAdditionLayer(const IConnectableLayer* layer, const char*) override
+ {
+ CheckLayerName(layer->GetInputSlot(0).GetConnection()->GetOwningLayerGuid(), "reLU1");
+ CheckLayerName(layer->GetInputSlot(1).GetConnection()->GetOwningLayerGuid(), "reLU2");
+ }
+
+ void CheckLayerName(LayerGuid guid, std::string expectedName)
+ {
+ auto graph = m_Network->pNetworkImpl->GetGraph();
+ bool guidFound = false;
+ for (Layer* layer : graph)
+ {
+ if (layer->GetGuid() == guid)
+ {
+ BOOST_CHECK_EQUAL(layer->GetName(), expectedName.c_str());
+ guidFound = true;
+ break;
+ }
+ }
+ if (!guidFound)
+ {
+ BOOST_FAIL("No layer matching the GUID was found");
+ }
+ }
+
+private:
+ INetwork* m_Network;
+};
+
+void VisitLayersTopologically(const INetwork* inputNetwork, IStrategy& visitor)
+{
+ auto graph = inputNetwork->pNetworkImpl->GetGraph().TopologicalSort();
+
+ ApplyStrategyToLayers(graph, visitor);
+}
+
+TensorInfo GetInputTensorInfo(const INetwork* network)
+{
+ for (auto&& inputLayer : network->pNetworkImpl->GetGraph().GetInputLayers())
+ {
+ ARMNN_ASSERT_MSG(inputLayer->GetNumOutputSlots() == 1, "Input layer should have exactly 1 output slot");
+ return inputLayer->GetOutputSlot(0).GetTensorInfo();
+ }
+ throw InvalidArgumentException("Network has no input layers");
+}
+
+TensorInfo GetInputTensorInfo(const NetworkImpl* network)
+{
+ for (auto&& inputLayer : network->GetGraph().GetInputLayers())
+ {
+ ARMNN_ASSERT_MSG(inputLayer->GetNumOutputSlots() == 1, "Input layer should have exactly 1 output slot");
+ return inputLayer->GetOutputSlot(0).GetTensorInfo();
+ }
+ throw InvalidArgumentException("Network has no input layers");
+}
+
BOOST_AUTO_TEST_SUITE(Quantizer)
class TestQuantization : public IStrategy
@@ -473,14 +537,6 @@ private:
QuantizerOptions m_QuantizerOptions;
};
-void VisitLayersTopologically(const INetwork* inputNetwork, IStrategy& strategy)
-{
- auto network = PolymorphicDowncast<const Network*>(inputNetwork);
- auto graph = network->GetGraph().TopologicalSort();
-
- ApplyStrategyToLayers(graph, strategy);
-}
-
void TestNetwork(INetwork* network, const TensorShape inShape, const TensorShape outShape)
{
const QuantizerOptions qAsymmU8Options(DataType::QAsymmU8);
@@ -596,21 +652,11 @@ INetworkPtr CreateNetworkWithInputOutputLayers()
return network;
}
-TensorInfo GetInputTensorInfo(const Network* network)
-{
- for (auto&& inputLayer : network->GetGraph().GetInputLayers())
- {
- ARMNN_ASSERT_MSG(inputLayer->GetNumOutputSlots() == 1, "Input layer should have exactly 1 output slot");
- return inputLayer->GetOutputSlot(0).GetTensorInfo();
- }
- throw InvalidArgumentException("Network has no input layers");
-}
-
BOOST_AUTO_TEST_CASE(InputOutputLayerDynamicQuant)
{
INetworkPtr network = CreateNetworkWithInputOutputLayers();
- armnn::TensorInfo tensorInfo = GetInputTensorInfo(PolymorphicDowncast<const Network*>(network.get()));
+ armnn::TensorInfo tensorInfo = GetInputTensorInfo(network.get());
// Outliers -56 and 98
std::vector<float> inputData({0, 0, 0, -56, 98, 0, 0, 0});
@@ -870,7 +916,7 @@ BOOST_AUTO_TEST_CASE(OverrideInputRangeEmptyNetwork)
RangeTracker ranges;
RangeTracker::MinMaxRange minMaxRange(-12.3f, 45.6f); // Range to use for the override
- Network network; // Empty network
+ NetworkImpl network; // Empty network
auto inputLayers = network.GetGraph().GetInputLayers(); // Empty list of input layers
OverrideInputRangeStrategy overrideInputRangeStrategy(ranges, 0, minMaxRange);
@@ -884,7 +930,7 @@ BOOST_AUTO_TEST_CASE(OverrideInputRangeNoInputLayers)
RangeTracker ranges;
MinMaxRange minMaxRange(-12.3f, 45.6f); // Range to use for the override
- Network network;
+ NetworkImpl network;
network.AddAdditionLayer(); // Network with no input layers
auto inputLayers = network.GetGraph().GetInputLayers(); // Empty list of input layers
@@ -899,7 +945,7 @@ BOOST_AUTO_TEST_CASE(OverrideInputRangeInputLayers)
RangeTracker ranges;
MinMaxRange minMaxRange(-12.3f, 45.6f); // Range to use for the override
- Network network;
+ NetworkImpl network;
// Adding the layers
IConnectableLayer* input0 = network.AddInputLayer(0);
@@ -2117,16 +2163,25 @@ BOOST_AUTO_TEST_CASE(TestConnectionPreservationAfterDynamicQuant)
Graph m_Graph;
};
- INetworkPtr network = INetwork::Create();
+ class TestNetwork : public INetwork
+ {
+ public :
+ NetworkImpl* GetPNetworkImpl()
+ {
+ return pNetworkImpl.get();
+ }
+ };
+
+ TestNetwork testNetwork;
- IConnectableLayer* inputLayer = network->AddInputLayer(0,"inputLayer1");
+ IConnectableLayer* inputLayer = testNetwork.AddInputLayer(0,"inputLayer1");
armnn::ActivationDescriptor ReLUDesc;
ReLUDesc.m_Function = ActivationFunction::ReLu;
- IConnectableLayer* reLULayer1 = network->AddActivationLayer(ReLUDesc, "reLU1");
- IConnectableLayer* reLULayer2 = network->AddActivationLayer(ReLUDesc, "reLU2");
- IConnectableLayer* addLayer1 = network->AddAdditionLayer("addLayer1");
- IConnectableLayer* outputLayer = network->AddOutputLayer(0,"outPutLayer1");
+ IConnectableLayer* reLULayer1 = testNetwork.AddActivationLayer(ReLUDesc, "reLU1");
+ IConnectableLayer* reLULayer2 = testNetwork.AddActivationLayer(ReLUDesc, "reLU2");
+ IConnectableLayer* addLayer1 = testNetwork.AddAdditionLayer("addLayer1");
+ IConnectableLayer* outputLayer = testNetwork.AddOutputLayer(0,"outPutLayer1");
inputLayer->GetOutputSlot(0).Connect(reLULayer1->GetInputSlot(0));
reLULayer1->GetOutputSlot(0).Connect(reLULayer2->GetInputSlot(0));
@@ -2139,12 +2194,12 @@ BOOST_AUTO_TEST_CASE(TestConnectionPreservationAfterDynamicQuant)
reLULayer2->GetOutputSlot(0).SetTensorInfo(TensorInfo(TensorShape({1, 2, 2, 1}), DataType::Float32));
addLayer1->GetOutputSlot(0).SetTensorInfo(TensorInfo(TensorShape({1, 2, 2, 1}), DataType::Float32));
- TestConnectionPreservation strategy1(PolymorphicDowncast<const Network*>(network.get())->GetGraph());
- VisitLayersTopologically(network.get(), strategy1);
+ TestConnectionPreservation strategy1(testNetwork.GetPNetworkImpl()->GetGraph());
+ VisitLayersTopologically(&testNetwork, strategy1);
- armnn::INetworkQuantizerPtr quantizer = armnn::INetworkQuantizer::Create(network.get());
+ armnn::INetworkQuantizerPtr quantizer = armnn::INetworkQuantizer::Create(&testNetwork);
- armnn::TensorInfo tensorInfo = GetInputTensorInfo(PolymorphicDowncast<const Network*>(network.get()));
+ armnn::TensorInfo tensorInfo = GetInputTensorInfo(&testNetwork);
std::vector<float> inputData({0, 2, 0, 4});
armnn::ConstTensor inputTensor(tensorInfo, inputData.data());
@@ -2155,7 +2210,9 @@ BOOST_AUTO_TEST_CASE(TestConnectionPreservationAfterDynamicQuant)
INetworkPtr quantNetwork = quantizer->ExportNetwork();
- TestConnectionPreservation strategy2(PolymorphicDowncast<const Network*>(quantNetwork.get())->GetGraph());
+ TestNetwork* testQuantNetwork = static_cast<TestNetwork*>(quantNetwork.get());
+
+ TestConnectionPreservation strategy2(testQuantNetwork->GetPNetworkImpl()->GetGraph());
VisitLayersTopologically(quantNetwork.get(), strategy2);
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-01 18:19:50 +0000