IVGCVSW-4893 Refactor ILayerVisitor using unified interface strategy.
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Signed-off-by: Francis Murtagh <francis.murtagh@arm.com>
Change-Id: Id7bc8255a8e3f9e5aac65d510bec8a559bf37246
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 44e8a38..f261731 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -4,6 +4,7 @@
//
#include "../Serializer.hpp"
+#include "SerializerTestUtils.hpp"
#include <armnn/Descriptors.hpp>
#include <armnn/INetwork.hpp>
@@ -11,6 +12,7 @@
#include <armnn/LstmParams.hpp>
#include <armnn/QuantizedLstmParams.hpp>
#include <armnnDeserializer/IDeserializer.hpp>
+#include <armnn/utility/IgnoreUnused.hpp>
#include <random>
#include <vector>
@@ -19,264 +21,36 @@
using armnnDeserializer::IDeserializer;
-namespace
-{
-
-#define DECLARE_LAYER_VERIFIER_CLASS(name) \
-class name##LayerVerifier : public LayerVerifierBase \
-{ \
-public: \
- name##LayerVerifier(const std::string& layerName, \
- const std::vector<armnn::TensorInfo>& inputInfos, \
- const std::vector<armnn::TensorInfo>& outputInfos) \
- : LayerVerifierBase(layerName, inputInfos, outputInfos) {} \
-\
- void Visit##name##Layer(const armnn::IConnectableLayer* layer, const char* name) override \
- { \
- VerifyNameAndConnections(layer, name); \
- } \
-};
-
-#define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name) \
-class name##LayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::name##Descriptor> \
-{ \
-public: \
- name##LayerVerifier(const std::string& layerName, \
- const std::vector<armnn::TensorInfo>& inputInfos, \
- const std::vector<armnn::TensorInfo>& outputInfos, \
- const armnn::name##Descriptor& descriptor) \
- : LayerVerifierBaseWithDescriptor<armnn::name##Descriptor>( \
- layerName, inputInfos, outputInfos, descriptor) {} \
-\
- void Visit##name##Layer(const armnn::IConnectableLayer* layer, \
- const armnn::name##Descriptor& descriptor, \
- const char* name) override \
- { \
- VerifyNameAndConnections(layer, name); \
- VerifyDescriptor(descriptor); \
- } \
-};
-
-struct DefaultLayerVerifierPolicy
-{
- static void Apply(const std::string)
- {
- BOOST_TEST_MESSAGE("Unexpected layer found in network");
- BOOST_TEST(false);
- }
-};
-
-class LayerVerifierBase : public armnn::LayerVisitorBase<DefaultLayerVerifierPolicy>
-{
-public:
- LayerVerifierBase(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos)
- : m_LayerName(layerName)
- , m_InputTensorInfos(inputInfos)
- , m_OutputTensorInfos(outputInfos) {}
-
- void VisitInputLayer(const armnn::IConnectableLayer*, armnn::LayerBindingId, const char*) override {}
-
- void VisitOutputLayer(const armnn::IConnectableLayer*, armnn::LayerBindingId, const char*) override {}
-
-protected:
- void VerifyNameAndConnections(const armnn::IConnectableLayer* layer, const char* name)
- {
- BOOST_TEST(name == m_LayerName.c_str());
-
- BOOST_TEST(layer->GetNumInputSlots() == m_InputTensorInfos.size());
- BOOST_TEST(layer->GetNumOutputSlots() == m_OutputTensorInfos.size());
-
- for (unsigned int i = 0; i < m_InputTensorInfos.size(); i++)
- {
- const armnn::IOutputSlot* connectedOutput = layer->GetInputSlot(i).GetConnection();
- BOOST_CHECK(connectedOutput);
-
- const armnn::TensorInfo& connectedInfo = connectedOutput->GetTensorInfo();
- BOOST_TEST(connectedInfo.GetShape() == m_InputTensorInfos[i].GetShape());
- BOOST_TEST(
- GetDataTypeName(connectedInfo.GetDataType()) == GetDataTypeName(m_InputTensorInfos[i].GetDataType()));
-
- BOOST_TEST(connectedInfo.GetQuantizationScale() == m_InputTensorInfos[i].GetQuantizationScale());
- BOOST_TEST(connectedInfo.GetQuantizationOffset() == m_InputTensorInfos[i].GetQuantizationOffset());
- }
-
- for (unsigned int i = 0; i < m_OutputTensorInfos.size(); i++)
- {
- const armnn::TensorInfo& outputInfo = layer->GetOutputSlot(i).GetTensorInfo();
- BOOST_TEST(outputInfo.GetShape() == m_OutputTensorInfos[i].GetShape());
- BOOST_TEST(
- GetDataTypeName(outputInfo.GetDataType()) == GetDataTypeName(m_OutputTensorInfos[i].GetDataType()));
-
- BOOST_TEST(outputInfo.GetQuantizationScale() == m_OutputTensorInfos[i].GetQuantizationScale());
- BOOST_TEST(outputInfo.GetQuantizationOffset() == m_OutputTensorInfos[i].GetQuantizationOffset());
- }
- }
-
- void VerifyConstTensors(const std::string& tensorName,
- const armnn::ConstTensor* expectedPtr,
- const armnn::ConstTensor* actualPtr)
- {
- if (expectedPtr == nullptr)
- {
- BOOST_CHECK_MESSAGE(actualPtr == nullptr, tensorName + " should not exist");
- }
- else
- {
- BOOST_CHECK_MESSAGE(actualPtr != nullptr, tensorName + " should have been set");
- if (actualPtr != nullptr)
- {
- const armnn::TensorInfo& expectedInfo = expectedPtr->GetInfo();
- const armnn::TensorInfo& actualInfo = actualPtr->GetInfo();
-
- BOOST_CHECK_MESSAGE(expectedInfo.GetShape() == actualInfo.GetShape(),
- tensorName + " shapes don't match");
- BOOST_CHECK_MESSAGE(
- GetDataTypeName(expectedInfo.GetDataType()) == GetDataTypeName(actualInfo.GetDataType()),
- tensorName + " data types don't match");
-
- BOOST_CHECK_MESSAGE(expectedPtr->GetNumBytes() == actualPtr->GetNumBytes(),
- tensorName + " (GetNumBytes) data sizes do not match");
- if (expectedPtr->GetNumBytes() == actualPtr->GetNumBytes())
- {
- //check the data is identical
- const char* expectedData = static_cast<const char*>(expectedPtr->GetMemoryArea());
- const char* actualData = static_cast<const char*>(actualPtr->GetMemoryArea());
- bool same = true;
- for (unsigned int i = 0; i < expectedPtr->GetNumBytes(); ++i)
- {
- same = expectedData[i] == actualData[i];
- if (!same)
- {
- break;
- }
- }
- BOOST_CHECK_MESSAGE(same, tensorName + " data does not match");
- }
- }
- }
- }
-
-private:
- std::string m_LayerName;
- std::vector<armnn::TensorInfo> m_InputTensorInfos;
- std::vector<armnn::TensorInfo> m_OutputTensorInfos;
-};
-
-template<typename Descriptor>
-class LayerVerifierBaseWithDescriptor : public LayerVerifierBase
-{
-public:
- LayerVerifierBaseWithDescriptor(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor)
- : LayerVerifierBase(layerName, inputInfos, outputInfos)
- , m_Descriptor(descriptor) {}
-
-protected:
- void VerifyDescriptor(const Descriptor& descriptor)
- {
- BOOST_CHECK(descriptor == m_Descriptor);
- }
-
- Descriptor m_Descriptor;
-};
-
-template<typename T>
-void CompareConstTensorData(const void* data1, const void* data2, unsigned int numElements)
-{
- T typedData1 = static_cast<T>(data1);
- T typedData2 = static_cast<T>(data2);
- BOOST_CHECK(typedData1);
- BOOST_CHECK(typedData2);
-
- for (unsigned int i = 0; i < numElements; i++)
- {
- BOOST_TEST(typedData1[i] == typedData2[i]);
- }
-}
-
-void CompareConstTensor(const armnn::ConstTensor& tensor1, const armnn::ConstTensor& tensor2)
-{
- BOOST_TEST(tensor1.GetShape() == tensor2.GetShape());
- BOOST_TEST(GetDataTypeName(tensor1.GetDataType()) == GetDataTypeName(tensor2.GetDataType()));
-
- switch (tensor1.GetDataType())
- {
- case armnn::DataType::Float32:
- CompareConstTensorData<const float*>(
- tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
- break;
- case armnn::DataType::QAsymmU8:
- case armnn::DataType::Boolean:
- CompareConstTensorData<const uint8_t*>(
- tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
- break;
- case armnn::DataType::QSymmS8:
- CompareConstTensorData<const int8_t*>(
- tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
- break;
- case armnn::DataType::Signed32:
- CompareConstTensorData<const int32_t*>(
- tensor1.GetMemoryArea(), tensor2.GetMemoryArea(), tensor1.GetNumElements());
- break;
- default:
- // Note that Float16 is not yet implemented
- BOOST_TEST_MESSAGE("Unexpected datatype");
- BOOST_TEST(false);
- }
-}
-
-armnn::INetworkPtr DeserializeNetwork(const std::string& serializerString)
-{
- std::vector<std::uint8_t> const serializerVector{serializerString.begin(), serializerString.end()};
- return IDeserializer::Create()->CreateNetworkFromBinary(serializerVector);
-}
-
-std::string SerializeNetwork(const armnn::INetwork& network)
-{
- armnnSerializer::ISerializerPtr serializer = armnnSerializer::ISerializer::Create();
-
- serializer->Serialize(network);
-
- std::stringstream stream;
- serializer->SaveSerializedToStream(stream);
-
- std::string serializerString{stream.str()};
- return serializerString;
-}
-
-template<typename DataType>
-static std::vector<DataType> GenerateRandomData(size_t size)
-{
- constexpr bool isIntegerType = std::is_integral<DataType>::value;
- using Distribution =
- typename std::conditional<isIntegerType,
- std::uniform_int_distribution<DataType>,
- std::uniform_real_distribution<DataType>>::type;
-
- static constexpr DataType lowerLimit = std::numeric_limits<DataType>::min();
- static constexpr DataType upperLimit = std::numeric_limits<DataType>::max();
-
- static Distribution distribution(lowerLimit, upperLimit);
- static std::default_random_engine generator;
-
- std::vector<DataType> randomData(size);
- std::generate(randomData.begin(), randomData.end(), []() { return distribution(generator); });
-
- return randomData;
-}
-
-} // anonymous namespace
-
BOOST_AUTO_TEST_SUITE(SerializerTests)
+BOOST_AUTO_TEST_CASE(SerializeAbs)
+{
+ const std::string layerName("abs");
+ const armnn::TensorInfo tensorInfo({1, 2, 3}, armnn::DataType::Float32);
+
+ armnn::INetworkPtr network = armnn::INetwork::Create();
+ armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
+
+ ARMNN_NO_DEPRECATE_WARN_BEGIN
+ armnn::IConnectableLayer* const absLayer = network->AddAbsLayer(layerName.c_str());
+ ARMNN_NO_DEPRECATE_WARN_END
+ armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
+
+ inputLayer->GetOutputSlot(0).Connect(absLayer->GetInputSlot(0));
+ absLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
+
+ inputLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+ absLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+ armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+ BOOST_CHECK(deserializedNetwork);
+
+ LayerVerifierBase verifier(layerName, {tensorInfo}, {tensorInfo});
+ deserializedNetwork->ExecuteStrategy(verifier);
+}
+
BOOST_AUTO_TEST_CASE(SerializeAddition)
{
- DECLARE_LAYER_VERIFIER_CLASS(Addition)
-
const std::string layerName("addition");
const armnn::TensorInfo tensorInfo({1, 2, 3}, armnn::DataType::Float32);
@@ -294,17 +68,16 @@
inputLayer1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
additionLayer->GetOutputSlot(0).SetTensorInfo(tensorInfo);
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
+ std::string serializedNetwork = SerializeNetwork(*network);
+ armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(serializedNetwork);
BOOST_CHECK(deserializedNetwork);
- AdditionLayerVerifier verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeArgMinMax)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(ArgMinMax)
-
const std::string layerName("argminmax");
const armnn::TensorInfo inputInfo({1, 2, 3}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({1, 3}, armnn::DataType::Signed32);
@@ -327,54 +100,15 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- ArgMinMaxLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::ArgMinMaxDescriptor> verifier(layerName,
+ {inputInfo},
+ {outputInfo},
+ descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeBatchNormalization)
{
- using Descriptor = armnn::BatchNormalizationDescriptor;
- class BatchNormalizationLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- BatchNormalizationLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& mean,
- const armnn::ConstTensor& variance,
- const armnn::ConstTensor& beta,
- const armnn::ConstTensor& gamma)
- : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_Mean(mean)
- , m_Variance(variance)
- , m_Beta(beta)
- , m_Gamma(gamma) {}
-
- void VisitBatchNormalizationLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& mean,
- const armnn::ConstTensor& variance,
- const armnn::ConstTensor& beta,
- const armnn::ConstTensor& gamma,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- CompareConstTensor(mean, m_Mean);
- CompareConstTensor(variance, m_Variance);
- CompareConstTensor(beta, m_Beta);
- CompareConstTensor(gamma, m_Gamma);
- }
-
- private:
- armnn::ConstTensor m_Mean;
- armnn::ConstTensor m_Variance;
- armnn::ConstTensor m_Beta;
- armnn::ConstTensor m_Gamma;
- };
-
const std::string layerName("batchNormalization");
const armnn::TensorInfo inputInfo ({ 1, 3, 3, 1 }, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32);
@@ -393,15 +127,21 @@
std::vector<float> betaData({1.0});
std::vector<float> gammaData({0.0});
- armnn::ConstTensor mean(meanInfo, meanData);
- armnn::ConstTensor variance(varianceInfo, varianceData);
- armnn::ConstTensor beta(betaInfo, betaData);
- armnn::ConstTensor gamma(gammaInfo, gammaData);
+ std::vector<armnn::ConstTensor> constants;
+ constants.emplace_back(armnn::ConstTensor(meanInfo, meanData));
+ constants.emplace_back(armnn::ConstTensor(varianceInfo, varianceData));
+ constants.emplace_back(armnn::ConstTensor(betaInfo, betaData));
+ constants.emplace_back(armnn::ConstTensor(gammaInfo, gammaData));
armnn::INetworkPtr network = armnn::INetwork::Create();
armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
armnn::IConnectableLayer* const batchNormalizationLayer =
- network->AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma, layerName.c_str());
+ network->AddBatchNormalizationLayer(descriptor,
+ constants[0],
+ constants[1],
+ constants[2],
+ constants[3],
+ layerName.c_str());
armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
inputLayer->GetOutputSlot(0).Connect(batchNormalizationLayer->GetInputSlot(0));
@@ -413,15 +153,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- BatchNormalizationLayerVerifier verifier(
- layerName, {inputInfo}, {outputInfo}, descriptor, mean, variance, beta, gamma);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptorAndConstants<armnn::BatchNormalizationDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeBatchToSpaceNd)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(BatchToSpaceNd)
-
const std::string layerName("spaceToBatchNd");
const armnn::TensorInfo inputInfo({4, 1, 2, 2}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({1, 1, 4, 4}, armnn::DataType::Float32);
@@ -445,14 +183,15 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- BatchToSpaceNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::BatchToSpaceNdDescriptor> verifier(layerName,
+ {inputInfo},
+ {outputInfo},
+ desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeComparison)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Comparison)
-
const std::string layerName("comparison");
const armnn::TensorShape shape{2, 1, 2, 4};
@@ -479,8 +218,11 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- ComparisonLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::ComparisonDescriptor> verifier(layerName,
+ { inputInfo, inputInfo },
+ { outputInfo },
+ descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeConstant)
@@ -491,22 +233,37 @@
ConstantLayerVerifier(const std::string& layerName,
const std::vector<armnn::TensorInfo>& inputInfos,
const std::vector<armnn::TensorInfo>& outputInfos,
- const armnn::ConstTensor& layerInput)
+ const std::vector<armnn::ConstTensor>& constants)
: LayerVerifierBase(layerName, inputInfos, outputInfos)
- , m_LayerInput(layerInput) {}
+ , m_Constants(constants) {}
- void VisitConstantLayer(const armnn::IConnectableLayer* layer,
- const armnn::ConstTensor& input,
- const char* name) override
+ void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+ const armnn::BaseDescriptor& descriptor,
+ const std::vector<armnn::ConstTensor>& constants,
+ const char* name,
+ const armnn::LayerBindingId id = 0) override
{
- VerifyNameAndConnections(layer, name);
- CompareConstTensor(input, m_LayerInput);
+ armnn::IgnoreUnused(descriptor, id);
+
+ switch (layer->GetType())
+ {
+ case armnn::LayerType::Input: break;
+ case armnn::LayerType::Output: break;
+ case armnn::LayerType::Addition: break;
+ default:
+ {
+ this->VerifyNameAndConnections(layer, name);
+
+ for (std::size_t i = 0; i < constants.size(); i++)
+ {
+ CompareConstTensor(constants[i], m_Constants[i]);
+ }
+ }
+ }
}
- void VisitAdditionLayer(const armnn::IConnectableLayer*, const char*) override {}
-
private:
- armnn::ConstTensor m_LayerInput;
+ const std::vector<armnn::ConstTensor> m_Constants;
};
const std::string layerName("constant");
@@ -532,53 +289,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);
- deserializedNetwork->Accept(verifier);
+ ConstantLayerVerifier verifier(layerName, {}, {info}, {constTensor});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeConvolution2d)
{
- using Descriptor = armnn::Convolution2dDescriptor;
- class Convolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- Convolution2dLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases)
- : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_Weights(weights)
- , m_Biases(biases) {}
-
- void VisitConvolution2dLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- // check weights
- CompareConstTensor(weights, m_Weights);
-
- // check biases
- BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
- BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
- if (biases.has_value() && m_Biases.has_value())
- {
- CompareConstTensor(biases.value(), m_Biases.value());
- }
- }
-
- private:
- armnn::ConstTensor m_Weights;
- armnn::Optional<armnn::ConstTensor> m_Biases;
- };
-
const std::string layerName("convolution2d");
const armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32);
@@ -622,53 +338,14 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
- deserializedNetwork->Accept(verifier);
+ const std::vector<armnn::ConstTensor>& constants {weights, biases};
+ LayerVerifierBaseWithDescriptorAndConstants<armnn::Convolution2dDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeConvolution2dWithPerAxisParams)
{
- using Descriptor = armnn::Convolution2dDescriptor;
- class Convolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- Convolution2dLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases)
- : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_Weights(weights)
- , m_Biases(biases) {}
-
- void VisitConvolution2dLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- // check weights
- CompareConstTensor(weights, m_Weights);
-
- // check biases
- BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
- BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
- if (biases.has_value() && m_Biases.has_value())
- {
- CompareConstTensor(biases.value(), m_Biases.value());
- }
- }
-
- private:
- armnn::ConstTensor m_Weights;
- armnn::Optional<armnn::ConstTensor> m_Biases;
- };
-
using namespace armnn;
const std::string layerName("convolution2dWithPerAxis");
@@ -716,14 +393,14 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
- deserializedNetwork->Accept(verifier);
+ const std::vector<armnn::ConstTensor>& constants {weights, biases};
+ LayerVerifierBaseWithDescriptorAndConstants<Convolution2dDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeDepthToSpace)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(DepthToSpace)
-
const std::string layerName("depthToSpace");
const armnn::TensorInfo inputInfo ({ 1, 8, 4, 12 }, armnn::DataType::Float32);
@@ -747,53 +424,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- DepthToSpaceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::DepthToSpaceDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeDepthwiseConvolution2d)
{
- using Descriptor = armnn::DepthwiseConvolution2dDescriptor;
- class DepthwiseConvolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- DepthwiseConvolution2dLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases) :
- LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor),
- m_Weights(weights),
- m_Biases(biases) {}
-
- void VisitDepthwiseConvolution2dLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- // check weights
- CompareConstTensor(weights, m_Weights);
-
- // check biases
- BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
- BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
- if (biases.has_value() && m_Biases.has_value())
- {
- CompareConstTensor(biases.value(), m_Biases.value());
- }
- }
-
- private:
- armnn::ConstTensor m_Weights;
- armnn::Optional<armnn::ConstTensor> m_Biases;
- };
-
const std::string layerName("depwiseConvolution2d");
const armnn::TensorInfo inputInfo ({ 1, 5, 5, 3 }, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32);
@@ -837,53 +473,14 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
- deserializedNetwork->Accept(verifier);
+ const std::vector<armnn::ConstTensor>& constants {weights, biases};
+ LayerVerifierBaseWithDescriptorAndConstants<armnn::DepthwiseConvolution2dDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeDepthwiseConvolution2dWithPerAxisParams)
{
- using Descriptor = armnn::DepthwiseConvolution2dDescriptor;
- class DepthwiseConvolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- DepthwiseConvolution2dLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases) :
- LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor),
- m_Weights(weights),
- m_Biases(biases) {}
-
- void VisitDepthwiseConvolution2dLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- // check weights
- CompareConstTensor(weights, m_Weights);
-
- // check biases
- BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
- BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
- if (biases.has_value() && m_Biases.has_value())
- {
- CompareConstTensor(biases.value(), m_Biases.value());
- }
- }
-
- private:
- armnn::ConstTensor m_Weights;
- armnn::Optional<armnn::ConstTensor> m_Biases;
- };
-
using namespace armnn;
const std::string layerName("depwiseConvolution2dWithPerAxis");
@@ -933,14 +530,14 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
- deserializedNetwork->Accept(verifier);
+ const std::vector<armnn::ConstTensor>& constants {weights, biases};
+ LayerVerifierBaseWithDescriptorAndConstants<armnn::DepthwiseConvolution2dDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeDequantize)
{
- DECLARE_LAYER_VERIFIER_CLASS(Dequantize)
-
const std::string layerName("dequantize");
const armnn::TensorInfo inputInfo({ 1, 5, 2, 3 }, armnn::DataType::QAsymmU8, 0.5f, 1);
const armnn::TensorInfo outputInfo({ 1, 5, 2, 3 }, armnn::DataType::Float32);
@@ -959,39 +556,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- DequantizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {inputInfo}, {outputInfo});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeDeserializeDetectionPostProcess)
{
- using Descriptor = armnn::DetectionPostProcessDescriptor;
- class DetectionPostProcessLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- DetectionPostProcessLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& anchors)
- : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_Anchors(anchors) {}
-
- void VisitDetectionPostProcessLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& anchors,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- CompareConstTensor(anchors, m_Anchors);
- }
-
- private:
- armnn::ConstTensor m_Anchors;
- };
-
const std::string layerName("detectionPostProcess");
const std::vector<armnn::TensorInfo> inputInfos({
@@ -1051,14 +621,14 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- DetectionPostProcessLayerVerifier verifier(layerName, inputInfos, outputInfos, descriptor, anchors);
- deserializedNetwork->Accept(verifier);
+ const std::vector<armnn::ConstTensor>& constants {anchors};
+ LayerVerifierBaseWithDescriptorAndConstants<armnn::DetectionPostProcessDescriptor> verifier(
+ layerName, inputInfos, outputInfos, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeDivision)
{
- DECLARE_LAYER_VERIFIER_CLASS(Division)
-
const std::string layerName("division");
const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32);
@@ -1079,131 +649,41 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- DivisionLayerVerifier verifier(layerName, {info, info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info, info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
-class EqualLayerVerifier : public LayerVerifierBase
+BOOST_AUTO_TEST_CASE(SerializeDeserializeEqual)
{
-public:
- EqualLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos)
- : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
-
- void VisitComparisonLayer(const armnn::IConnectableLayer* layer,
- const armnn::ComparisonDescriptor& descriptor,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- BOOST_CHECK(descriptor.m_Operation == armnn::ComparisonOperation::Equal);
- }
-
- void VisitEqualLayer(const armnn::IConnectableLayer*, const char*) override
- {
- throw armnn::Exception("EqualLayer should have translated to ComparisonLayer");
- }
-};
-
-// NOTE: Until the deprecated AddEqualLayer disappears this test checks that calling
-// AddEqualLayer places a ComparisonLayer into the serialized format and that
-// when this deserialises we have a ComparisonLayer
-BOOST_AUTO_TEST_CASE(SerializeEqual)
-{
- const std::string layerName("equal");
-
- const armnn::TensorShape shape{2, 1, 2, 4};
-
- const armnn::TensorInfo inputInfo = armnn::TensorInfo(shape, armnn::DataType::Float32);
- const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
+ const std::string layerName("EqualLayer");
+ const armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({2, 1, 2, 4}, armnn::DataType::Float32);
+ const armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({2, 1, 2, 4}, armnn::DataType::Float32);
+ const armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({2, 1, 2, 4}, armnn::DataType::Boolean);
armnn::INetworkPtr network = armnn::INetwork::Create();
- armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0);
- armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(1);
+ armnn::IConnectableLayer* const inputLayer1 = network->AddInputLayer(0);
+ armnn::IConnectableLayer* const inputLayer2 = network->AddInputLayer(1);
ARMNN_NO_DEPRECATE_WARN_BEGIN
armnn::IConnectableLayer* const equalLayer = network->AddEqualLayer(layerName.c_str());
ARMNN_NO_DEPRECATE_WARN_END
armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(0);
- inputLayer0->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(0));
- inputLayer1->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(1));
+ inputLayer1->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(0));
+ inputLayer1->GetOutputSlot(0).SetTensorInfo(inputTensorInfo1);
+ inputLayer2->GetOutputSlot(0).Connect(equalLayer->GetInputSlot(1));
+ inputLayer2->GetOutputSlot(0).SetTensorInfo(inputTensorInfo2);
equalLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-
- inputLayer0->GetOutputSlot(0).SetTensorInfo(inputInfo);
- inputLayer1->GetOutputSlot(0).SetTensorInfo(inputInfo);
- equalLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
+ equalLayer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- EqualLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
- deserializedNetwork->Accept(verifier);
-}
-
-BOOST_AUTO_TEST_CASE(EnsureEqualBackwardCompatibility)
-{
- // The hex data below is a flat buffer containing a simple network with two inputs,
- // an EqualLayer (now deprecated) and an output
- //
- // This test verifies that we can still deserialize this old-style model by replacing
- // the EqualLayer with an equivalent ComparisonLayer
- const std::vector<uint8_t> equalModel =
- {
- 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
- 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
- 0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
- 0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,
- 0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
- 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,
- 0x00, 0x13, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,
- 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,
- 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x65, 0x71, 0x75, 0x61, 0x6C, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
- 0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,
- 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00,
- 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
- 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
- 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
- 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
- 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
- 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00,
- 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
- 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
- 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
- 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
- 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x00, 0x00
- };
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(equalModel.begin(), equalModel.end()));
- BOOST_CHECK(deserializedNetwork);
-
- const armnn::TensorShape shape{ 2, 1, 2, 4 };
-
- const armnn::TensorInfo inputInfo = armnn::TensorInfo(shape, armnn::DataType::Float32);
- const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
-
- EqualLayerVerifier verifier("equal", { inputInfo, inputInfo }, { outputInfo });
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {inputTensorInfo1, inputTensorInfo2}, {outputTensorInfo});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeFill)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Fill)
-
const std::string layerName("fill");
const armnn::TensorInfo inputInfo({4}, armnn::DataType::Signed32);
const armnn::TensorInfo outputInfo({1, 3, 3, 1}, armnn::DataType::Float32);
@@ -1224,15 +704,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- FillLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+ LayerVerifierBaseWithDescriptor<armnn::FillDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeFloor)
{
- DECLARE_LAYER_VERIFIER_CLASS(Floor)
-
const std::string layerName("floor");
const armnn::TensorInfo info({4,4}, armnn::DataType::Float32);
@@ -1250,51 +728,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- FloorLayerVerifier verifier(layerName, {info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeFullyConnected)
{
- using Descriptor = armnn::FullyConnectedDescriptor;
- class FullyConnectedLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- FullyConnectedLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weight,
- const armnn::Optional<armnn::ConstTensor>& bias)
- : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_Weight(weight)
- , m_Bias(bias) {}
-
- void VisitFullyConnectedLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weight,
- const armnn::Optional<armnn::ConstTensor>& bias,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- CompareConstTensor(weight, m_Weight);
-
- BOOST_TEST(bias.has_value() == descriptor.m_BiasEnabled);
- BOOST_TEST(bias.has_value() == m_Bias.has_value());
-
- if (bias.has_value() && m_Bias.has_value())
- {
- CompareConstTensor(bias.value(), m_Bias.value());
- }
- }
-
- private:
- armnn::ConstTensor m_Weight;
- armnn::Optional<armnn::ConstTensor> m_Bias;
- };
-
const std::string layerName("fullyConnected");
const armnn::TensorInfo inputInfo ({ 2, 5, 1, 1 }, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({ 2, 3 }, armnn::DataType::Float32);
@@ -1328,8 +767,10 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- FullyConnectedLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
- deserializedNetwork->Accept(verifier);
+ const std::vector<armnn::ConstTensor> constants {weights, biases};
+ LayerVerifierBaseWithDescriptorAndConstants<armnn::FullyConnectedDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeGather)
@@ -1344,17 +785,26 @@
const GatherDescriptor& descriptor)
: LayerVerifierBaseWithDescriptor<GatherDescriptor>(layerName, inputInfos, outputInfos, descriptor) {}
- void VisitGatherLayer(const armnn::IConnectableLayer* layer,
- const GatherDescriptor& descriptor,
- const char *name) override
+ void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+ const armnn::BaseDescriptor& descriptor,
+ const std::vector<armnn::ConstTensor>& constants,
+ const char* name,
+ const armnn::LayerBindingId id = 0) override
{
- VerifyNameAndConnections(layer, name);
- BOOST_CHECK(descriptor.m_Axis == m_Descriptor.m_Axis);
+ armnn::IgnoreUnused(constants, id);
+ switch (layer->GetType())
+ {
+ case armnn::LayerType::Input: break;
+ case armnn::LayerType::Output: break;
+ case armnn::LayerType::Constant: break;
+ default:
+ {
+ VerifyNameAndConnections(layer, name);
+ const GatherDescriptor& layerDescriptor = static_cast<const GatherDescriptor&>(descriptor);
+ BOOST_CHECK(layerDescriptor.m_Axis == m_Descriptor.m_Axis);
+ }
+ }
}
-
- void VisitConstantLayer(const armnn::IConnectableLayer*,
- const armnn::ConstTensor&,
- const char*) override {}
};
const std::string layerName("gather");
@@ -1390,35 +840,14 @@
BOOST_CHECK(deserializedNetwork);
GatherLayerVerifier verifier(layerName, {paramsInfo, indicesInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
-class GreaterLayerVerifier : public LayerVerifierBase
-{
-public:
- GreaterLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos)
- : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
-
- void VisitComparisonLayer(const armnn::IConnectableLayer* layer,
- const armnn::ComparisonDescriptor& descriptor,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- BOOST_CHECK(descriptor.m_Operation == armnn::ComparisonOperation::Greater);
- }
-
- void VisitGreaterLayer(const armnn::IConnectableLayer*, const char*) override
- {
- throw armnn::Exception("GreaterLayer should have translated to ComparisonLayer");
- }
-};
// NOTE: Until the deprecated AddGreaterLayer disappears this test checks that calling
// AddGreaterLayer places a ComparisonLayer into the serialized format and that
// when this deserialises we have a ComparisonLayer
-BOOST_AUTO_TEST_CASE(SerializeGreater)
+BOOST_AUTO_TEST_CASE(SerializeGreaterDeprecated)
{
const std::string layerName("greater");
@@ -1446,74 +875,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- GreaterLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
+ deserializedNetwork->ExecuteStrategy(verifier);
}
-BOOST_AUTO_TEST_CASE(EnsureGreaterBackwardCompatibility)
-{
- // The hex data below is a flat buffer containing a simple network with two inputs,
- // an GreaterLayer (now deprecated) and an output
- //
- // This test verifies that we can still deserialize this old-style model by replacing
- // the GreaterLayer with an equivalent ComparisonLayer
- const std::vector<uint8_t> greaterModel =
- {
- 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
- 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
- 0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
- 0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,
- 0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
- 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,
- 0x00, 0x19, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,
- 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,
- 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x67, 0x72, 0x65, 0x61, 0x74, 0x65, 0x72, 0x00, 0x02, 0x00, 0x00, 0x00,
- 0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,
- 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,
- 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
- 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
- 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
- 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
- 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
- 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,
- 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
- 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
- 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
- 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
- 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
- 0x02, 0x00, 0x00, 0x00
- };
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(greaterModel.begin(), greaterModel.end()));
- BOOST_CHECK(deserializedNetwork);
-
- const armnn::TensorShape shape{ 1, 2, 2, 2 };
-
- const armnn::TensorInfo inputInfo = armnn::TensorInfo(shape, armnn::DataType::Float32);
- const armnn::TensorInfo outputInfo = armnn::TensorInfo(shape, armnn::DataType::Boolean);
-
- GreaterLayerVerifier verifier("greater", { inputInfo, inputInfo }, { outputInfo });
- deserializedNetwork->Accept(verifier);
-}
BOOST_AUTO_TEST_CASE(SerializeInstanceNormalization)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(InstanceNormalization)
-
const std::string layerName("instanceNormalization");
const armnn::TensorInfo info({ 1, 2, 1, 5 }, armnn::DataType::Float32);
@@ -1538,12 +906,11 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- InstanceNormalizationLayerVerifier verifier(layerName, {info}, {info}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::InstanceNormalizationDescriptor> verifier(
+ layerName, {info}, {info}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
-DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(L2Normalization)
-
BOOST_AUTO_TEST_CASE(SerializeL2Normalization)
{
const std::string l2NormLayerName("l2Normalization");
@@ -1567,8 +934,9 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- L2NormalizationLayerVerifier verifier(l2NormLayerName, {info}, {info}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::L2NormalizationDescriptor> verifier(
+ l2NormLayerName, {info}, {info}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(EnsureL2NormalizationBackwardCompatibility)
@@ -1623,14 +991,13 @@
// Since this variable does not exist in the l2NormalizationModel dump, the default value will be loaded
desc.m_Eps = 1e-12f;
- L2NormalizationLayerVerifier verifier(layerName, {inputInfo}, {inputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::L2NormalizationDescriptor> verifier(
+ layerName, {inputInfo}, {inputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeLogicalBinary)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(LogicalBinary)
-
const std::string layerName("logicalBinaryAnd");
const armnn::TensorShape shape{2, 1, 2, 2};
@@ -1657,14 +1024,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- LogicalBinaryLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::LogicalBinaryDescriptor> verifier(
+ layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeLogicalUnary)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(ElementwiseUnary)
-
const std::string layerName("elementwiseUnaryLogicalNot");
const armnn::TensorShape shape{2, 1, 2, 2};
@@ -1690,15 +1056,14 @@
BOOST_CHECK(deserializedNetwork);
- ElementwiseUnaryLayerVerifier verifier(layerName, { inputInfo }, { outputInfo }, descriptor);
+ LayerVerifierBaseWithDescriptor<armnn::ElementwiseUnaryDescriptor> verifier(
+ layerName, { inputInfo }, { outputInfo }, descriptor);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeLogSoftmax)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(LogSoftmax)
-
const std::string layerName("log_softmax");
const armnn::TensorInfo info({1, 10}, armnn::DataType::Float32);
@@ -1720,14 +1085,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- LogSoftmaxLayerVerifier verifier(layerName, {info}, {info}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::LogSoftmaxDescriptor> verifier(layerName, {info}, {info}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeMaximum)
{
- DECLARE_LAYER_VERIFIER_CLASS(Maximum)
-
const std::string layerName("maximum");
const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
@@ -1748,14 +1111,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- MaximumLayerVerifier verifier(layerName, {info, info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info, info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeMean)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Mean)
-
const std::string layerName("mean");
const armnn::TensorInfo inputInfo({1, 1, 3, 2}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({1, 1, 1, 2}, armnn::DataType::Float32);
@@ -1778,14 +1139,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- MeanLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::MeanDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeMerge)
{
- DECLARE_LAYER_VERIFIER_CLASS(Merge)
-
const std::string layerName("merge");
const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
@@ -1806,8 +1165,8 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- MergeLayerVerifier verifier(layerName, {info, info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info, info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
class MergerLayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor>
@@ -1819,19 +1178,35 @@
const armnn::OriginsDescriptor& descriptor)
: LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor>(layerName, inputInfos, outputInfos, descriptor) {}
- void VisitMergerLayer(const armnn::IConnectableLayer*,
- const armnn::OriginsDescriptor&,
- const char*) override
+ void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+ const armnn::BaseDescriptor& descriptor,
+ const std::vector<armnn::ConstTensor>& constants,
+ const char* name,
+ const armnn::LayerBindingId id = 0) override
{
- throw armnn::Exception("MergerLayer should have translated to ConcatLayer");
- }
-
- void VisitConcatLayer(const armnn::IConnectableLayer* layer,
- const armnn::OriginsDescriptor& descriptor,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
+ armnn::IgnoreUnused(descriptor, constants, id);
+ switch (layer->GetType())
+ {
+ case armnn::LayerType::Input: break;
+ case armnn::LayerType::Output: break;
+ case armnn::LayerType::Merge:
+ {
+ throw armnn::Exception("MergerLayer should have translated to ConcatLayer");
+ break;
+ }
+ case armnn::LayerType::Concat:
+ {
+ VerifyNameAndConnections(layer, name);
+ const armnn::MergerDescriptor& layerDescriptor =
+ static_cast<const armnn::MergerDescriptor&>(descriptor);
+ VerifyDescriptor(layerDescriptor);
+ break;
+ }
+ default:
+ {
+ throw armnn::Exception("Unexpected layer type in Merge test model");
+ }
+ }
}
};
@@ -1870,7 +1245,7 @@
BOOST_CHECK(deserializedNetwork);
MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(EnsureMergerLayerBackwardCompatibility)
@@ -1939,7 +1314,7 @@
armnn::CreateDescriptorForConcatenation(shapes.begin(), shapes.end(), 0);
MergerLayerVerifier verifier("merger", { inputInfo, inputInfo }, { outputInfo }, descriptor);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeConcat)
@@ -1974,13 +1349,11 @@
// NOTE: using the MergerLayerVerifier to ensure that it is a concat layer and not a
// merger layer that gets placed into the graph.
MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeMinimum)
{
- DECLARE_LAYER_VERIFIER_CLASS(Minimum)
-
const std::string layerName("minimum");
const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
@@ -2001,14 +1374,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- MinimumLayerVerifier verifier(layerName, {info, info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info, info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeMultiplication)
{
- DECLARE_LAYER_VERIFIER_CLASS(Multiplication)
-
const std::string layerName("multiplication");
const armnn::TensorInfo info({ 1, 5, 2, 3 }, armnn::DataType::Float32);
@@ -2029,14 +1400,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- MultiplicationLayerVerifier verifier(layerName, {info, info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info, info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializePrelu)
{
- DECLARE_LAYER_VERIFIER_CLASS(Prelu)
-
const std::string layerName("prelu");
armnn::TensorInfo inputTensorInfo ({ 4, 1, 2 }, armnn::DataType::Float32);
@@ -2060,14 +1429,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- PreluLayerVerifier verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeNormalization)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Normalization)
-
const std::string layerName("normalization");
const armnn::TensorInfo info({2, 1, 2, 2}, armnn::DataType::Float32);
@@ -2092,12 +1459,10 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- NormalizationLayerVerifier verifier(layerName, {info}, {info}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::NormalizationDescriptor> verifier(layerName, {info}, {info}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
-DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Pad)
-
BOOST_AUTO_TEST_CASE(SerializePad)
{
const std::string layerName("pad");
@@ -2120,8 +1485,11 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- PadLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::PadDescriptor> verifier(layerName,
+ {inputTensorInfo},
+ {outputTensorInfo},
+ desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(EnsurePadBackwardCompatibility)
@@ -2174,14 +1542,12 @@
armnn::PadDescriptor descriptor({{ 0, 0 }, { 1, 0 }, { 1, 1 }, { 1, 2 }});
- PadLayerVerifier verifier("pad", { inputInfo }, { outputInfo }, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::PadDescriptor> verifier("pad", { inputInfo }, { outputInfo }, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializePermute)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Permute)
-
const std::string layerName("permute");
const armnn::TensorInfo inputTensorInfo({4, 3, 2, 1}, armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({1, 2, 3, 4}, armnn::DataType::Float32);
@@ -2202,14 +1568,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- PermuteLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::PermuteDescriptor> verifier(
+ layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializePooling2d)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Pooling2d)
-
const std::string layerName("pooling2d");
const armnn::TensorInfo inputInfo({1, 2, 2, 1}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({1, 1, 1, 1}, armnn::DataType::Float32);
@@ -2242,14 +1607,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- Pooling2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::Pooling2dDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeQuantize)
{
- DECLARE_LAYER_VERIFIER_CLASS(Quantize)
-
const std::string layerName("quantize");
const armnn::TensorInfo info({ 1, 2, 2, 3 }, armnn::DataType::Float32);
@@ -2267,14 +1631,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- QuantizeLayerVerifier verifier(layerName, {info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeRank)
{
- DECLARE_LAYER_VERIFIER_CLASS(Rank)
-
const std::string layerName("rank");
const armnn::TensorInfo inputInfo({1, 9}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({1}, armnn::DataType::Signed32);
@@ -2293,14 +1655,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- RankLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {inputInfo}, {outputInfo});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeReduceSum)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Reduce)
-
const std::string layerName("Reduce_Sum");
const armnn::TensorInfo inputInfo({1, 1, 3, 2}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({1, 1, 1, 2}, armnn::DataType::Float32);
@@ -2323,14 +1683,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- ReduceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::ReduceDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeReshape)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Reshape)
-
const std::string layerName("reshape");
const armnn::TensorInfo inputInfo({1, 9}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({3, 3}, armnn::DataType::Float32);
@@ -2351,14 +1709,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- ReshapeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::ReshapeDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeResize)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Resize)
-
const std::string layerName("resize");
const armnn::TensorInfo inputInfo = armnn::TensorInfo({1, 3, 5, 5}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo = armnn::TensorInfo({1, 3, 2, 4}, armnn::DataType::Float32);
@@ -2384,8 +1741,8 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- ResizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::ResizeDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
class ResizeBilinearLayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor>
@@ -2398,25 +1755,36 @@
: LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor>(
layerName, inputInfos, outputInfos, descriptor) {}
- void VisitResizeLayer(const armnn::IConnectableLayer* layer,
- const armnn::ResizeDescriptor& descriptor,
- const char* name) override
+ void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+ const armnn::BaseDescriptor& descriptor,
+ const std::vector<armnn::ConstTensor>& constants,
+ const char* name,
+ const armnn::LayerBindingId id = 0) override
{
- VerifyNameAndConnections(layer, name);
-
- BOOST_CHECK(descriptor.m_Method == armnn::ResizeMethod::Bilinear);
- BOOST_CHECK(descriptor.m_TargetWidth == m_Descriptor.m_TargetWidth);
- BOOST_CHECK(descriptor.m_TargetHeight == m_Descriptor.m_TargetHeight);
- BOOST_CHECK(descriptor.m_DataLayout == m_Descriptor.m_DataLayout);
- BOOST_CHECK(descriptor.m_AlignCorners == m_Descriptor.m_AlignCorners);
- BOOST_CHECK(descriptor.m_HalfPixelCenters == m_Descriptor.m_HalfPixelCenters);
- }
-
- void VisitResizeBilinearLayer(const armnn::IConnectableLayer*,
- const armnn::ResizeBilinearDescriptor&,
- const char*) override
- {
- throw armnn::Exception("ResizeBilinearLayer should have translated to ResizeLayer");
+ armnn::IgnoreUnused(descriptor, constants, id);
+ switch (layer->GetType())
+ {
+ case armnn::LayerType::Input: break;
+ case armnn::LayerType::Output: break;
+ case armnn::LayerType::Resize:
+ {
+ VerifyNameAndConnections(layer, name);
+ const armnn::ResizeDescriptor& layerDescriptor =
+ static_cast<const armnn::ResizeDescriptor&>(descriptor);
+ BOOST_CHECK(layerDescriptor.m_Method == armnn::ResizeMethod::Bilinear);
+ BOOST_CHECK(layerDescriptor.m_TargetWidth == m_Descriptor.m_TargetWidth);
+ BOOST_CHECK(layerDescriptor.m_TargetHeight == m_Descriptor.m_TargetHeight);
+ BOOST_CHECK(layerDescriptor.m_DataLayout == m_Descriptor.m_DataLayout);
+ BOOST_CHECK(layerDescriptor.m_AlignCorners == m_Descriptor.m_AlignCorners);
+ BOOST_CHECK(layerDescriptor.m_HalfPixelCenters == m_Descriptor.m_HalfPixelCenters);
+ break;
+ }
+ default:
+ {
+ throw armnn::Exception("Unexpected layer type in test model. ResizeBiliniar "
+ "should have translated to Resize");
+ }
+ }
}
};
@@ -2452,7 +1820,7 @@
BOOST_CHECK(deserializedNetwork);
ResizeBilinearLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(EnsureResizeBilinearBackwardCompatibility)
@@ -2508,13 +1876,11 @@
descriptor.m_TargetHeight = 2u;
ResizeBilinearLayerVerifier verifier("resizeBilinear", { inputInfo }, { outputInfo }, descriptor);
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeSlice)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Slice)
-
const std::string layerName{"slice"};
const armnn::TensorInfo inputInfo = armnn::TensorInfo({3, 2, 3, 1}, armnn::DataType::Float32);
@@ -2537,14 +1903,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- SliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::SliceDescriptor> verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeSoftmax)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Softmax)
-
const std::string layerName("softmax");
const armnn::TensorInfo info({1, 10}, armnn::DataType::Float32);
@@ -2565,14 +1929,12 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- SoftmaxLayerVerifier verifier(layerName, {info}, {info}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::SoftmaxDescriptor> verifier(layerName, {info}, {info}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeSpaceToBatchNd)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(SpaceToBatchNd)
-
const std::string layerName("spaceToBatchNd");
const armnn::TensorInfo inputInfo({2, 1, 2, 4}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({8, 1, 1, 3}, armnn::DataType::Float32);
@@ -2596,14 +1958,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- SpaceToBatchNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::SpaceToBatchNdDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeSpaceToDepth)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(SpaceToDepth)
-
const std::string layerName("spaceToDepth");
const armnn::TensorInfo inputInfo ({ 1, 16, 8, 3 }, armnn::DataType::Float32);
@@ -2627,14 +1988,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- SpaceToDepthLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::SpaceToDepthDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeSplitter)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Splitter)
-
const unsigned int numViews = 3;
const unsigned int numDimensions = 4;
const unsigned int inputShape[] = {1, 18, 4, 4};
@@ -2682,14 +2042,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- SplitterLayerVerifier verifier(layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::ViewsDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeStack)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Stack)
-
const std::string layerName("stack");
armnn::TensorInfo inputTensorInfo ({4, 3, 5}, armnn::DataType::Float32);
@@ -2714,14 +2073,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- StackLayerVerifier verifier(layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::StackDescriptor> verifier(
+ layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeStandIn)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(StandIn)
-
const std::string layerName("standIn");
armnn::TensorInfo tensorInfo({ 1u }, armnn::DataType::Float32);
@@ -2749,14 +2107,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- StandInLayerVerifier verifier(layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::StandInDescriptor> verifier(
+ layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeStridedSlice)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(StridedSlice)
-
const std::string layerName("stridedSlice");
const armnn::TensorInfo inputInfo = armnn::TensorInfo({3, 2, 3, 1}, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo = armnn::TensorInfo({3, 1}, armnn::DataType::Float32);
@@ -2780,14 +2137,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- StridedSliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::StridedSliceDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, desc);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeSubtraction)
{
- DECLARE_LAYER_VERIFIER_CLASS(Subtraction)
-
const std::string layerName("subtraction");
const armnn::TensorInfo info({ 1, 4 }, armnn::DataType::Float32);
@@ -2808,8 +2164,8 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- SubtractionLayerVerifier verifier(layerName, {info, info}, {info});
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBase verifier(layerName, {info, info}, {info});
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeSwitch)
@@ -2820,16 +2176,31 @@
SwitchLayerVerifier(const std::string& layerName,
const std::vector<armnn::TensorInfo>& inputInfos,
const std::vector<armnn::TensorInfo>& outputInfos)
- : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
+ : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
- void VisitSwitchLayer(const armnn::IConnectableLayer* layer, const char* name) override
+ void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+ const armnn::BaseDescriptor& descriptor,
+ const std::vector<armnn::ConstTensor>& constants,
+ const char* name,
+ const armnn::LayerBindingId id = 0) override
{
- VerifyNameAndConnections(layer, name);
+ armnn::IgnoreUnused(descriptor, constants, id);
+ switch (layer->GetType())
+ {
+ case armnn::LayerType::Input: break;
+ case armnn::LayerType::Output: break;
+ case armnn::LayerType::Constant: break;
+ case armnn::LayerType::Switch:
+ {
+ VerifyNameAndConnections(layer, name);
+ break;
+ }
+ default:
+ {
+ throw armnn::Exception("Unexpected layer type in Switch test model");
+ }
+ }
}
-
- void VisitConstantLayer(const armnn::IConnectableLayer*,
- const armnn::ConstTensor&,
- const char*) override {}
};
const std::string layerName("switch");
@@ -2859,13 +2230,11 @@
BOOST_CHECK(deserializedNetwork);
SwitchLayerVerifier verifier(layerName, {info, info}, {info, info});
- deserializedNetwork->Accept(verifier);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeTranspose)
{
- DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(Transpose)
-
const std::string layerName("transpose");
const armnn::TensorInfo inputTensorInfo({4, 3, 2, 1}, armnn::DataType::Float32);
const armnn::TensorInfo outputTensorInfo({1, 2, 3, 4}, armnn::DataType::Float32);
@@ -2886,54 +2255,13 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- TransposeLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
- deserializedNetwork->Accept(verifier);
+ LayerVerifierBaseWithDescriptor<armnn::TransposeDescriptor> verifier(
+ layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeTransposeConvolution2d)
{
- using Descriptor = armnn::TransposeConvolution2dDescriptor;
- class TransposeConvolution2dLayerVerifier : public LayerVerifierBaseWithDescriptor<Descriptor>
- {
- public:
- TransposeConvolution2dLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases)
- : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_Weights(weights)
- , m_Biases(biases)
- {}
-
- void VisitTransposeConvolution2dLayer(const armnn::IConnectableLayer* layer,
- const Descriptor& descriptor,
- const armnn::ConstTensor& weights,
- const armnn::Optional<armnn::ConstTensor>& biases,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
-
- // check weights
- CompareConstTensor(weights, m_Weights);
-
- // check biases
- BOOST_CHECK(biases.has_value() == descriptor.m_BiasEnabled);
- BOOST_CHECK(biases.has_value() == m_Biases.has_value());
-
- if (biases.has_value() && m_Biases.has_value())
- {
- CompareConstTensor(biases.value(), m_Biases.value());
- }
- }
-
- private:
- armnn::ConstTensor m_Weights;
- armnn::Optional<armnn::ConstTensor> m_Biases;
- };
-
const std::string layerName("transposeConvolution2d");
const armnn::TensorInfo inputInfo ({ 1, 7, 7, 1 }, armnn::DataType::Float32);
const armnn::TensorInfo outputInfo({ 1, 9, 9, 1 }, armnn::DataType::Float32);
@@ -2975,8 +2303,10 @@
armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
BOOST_CHECK(deserializedNetwork);
- TransposeConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
- deserializedNetwork->Accept(verifier);
+ const std::vector<armnn::ConstTensor> constants {weights, biases};
+ LayerVerifierBaseWithDescriptorAndConstants<armnn::TransposeConvolution2dDescriptor> verifier(
+ layerName, {inputInfo}, {outputInfo}, descriptor, constants);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_CASE(SerializeDeserializeNonLinearNetwork)
@@ -2991,16 +2321,31 @@
: LayerVerifierBase(layerName, inputInfos, outputInfos)
, m_LayerInput(layerInput) {}
- void VisitConstantLayer(const armnn::IConnectableLayer* layer,
- const armnn::ConstTensor& input,
- const char* name) override
+ void ExecuteStrategy(const armnn::IConnectableLayer* layer,
+ const armnn::BaseDescriptor& descriptor,
+ const std::vector<armnn::ConstTensor>& constants,
+ const char* name,
+ const armnn::LayerBindingId id = 0) override
{
- VerifyNameAndConnections(layer, name);
- CompareConstTensor(input, m_LayerInput);
+ armnn::IgnoreUnused(descriptor, constants, id);
+ switch (layer->GetType())
+ {
+ case armnn::LayerType::Input: break;
+ case armnn::LayerType::Output: break;
+ case armnn::LayerType::Addition: break;
+ case armnn::LayerType::Constant:
+ {
+ VerifyNameAndConnections(layer, name);
+ CompareConstTensor(constants.at(0), m_LayerInput);
+ break;
+ }
+ default:
+ {
+ throw armnn::Exception("Unexpected layer type in test model");
+ }
+ }
}
- void VisitAdditionLayer(const armnn::IConnectableLayer*, const char*) override {}
-
private:
armnn::ConstTensor m_LayerInput;
};
@@ -3029,2125 +2374,7 @@
BOOST_CHECK(deserializedNetwork);
ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);
- deserializedNetwork->Accept(verifier);
-}
-
-class VerifyLstmLayer : public LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor>
-{
-public:
- VerifyLstmLayer(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const armnn::LstmDescriptor& descriptor,
- const armnn::LstmInputParams& inputParams)
- : LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_InputParams(inputParams) {}
-
- void VisitLstmLayer(const armnn::IConnectableLayer* layer,
- const armnn::LstmDescriptor& descriptor,
- const armnn::LstmInputParams& params,
- const char* name)
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
- VerifyInputParameters(params);
- }
-
-protected:
- void VerifyInputParameters(const armnn::LstmInputParams& params)
- {
- VerifyConstTensors(
- "m_InputToInputWeights", m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
- VerifyConstTensors(
- "m_InputToForgetWeights", m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
- VerifyConstTensors(
- "m_InputToCellWeights", m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
- VerifyConstTensors(
- "m_InputToOutputWeights", m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
- VerifyConstTensors(
- "m_RecurrentToInputWeights", m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
- VerifyConstTensors(
- "m_RecurrentToForgetWeights", m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
- VerifyConstTensors(
- "m_RecurrentToCellWeights", m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
- VerifyConstTensors(
- "m_RecurrentToOutputWeights", m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
- VerifyConstTensors(
- "m_CellToInputWeights", m_InputParams.m_CellToInputWeights, params.m_CellToInputWeights);
- VerifyConstTensors(
- "m_CellToForgetWeights", m_InputParams.m_CellToForgetWeights, params.m_CellToForgetWeights);
- VerifyConstTensors(
- "m_CellToOutputWeights", m_InputParams.m_CellToOutputWeights, params.m_CellToOutputWeights);
- VerifyConstTensors(
- "m_InputGateBias", m_InputParams.m_InputGateBias, params.m_InputGateBias);
- VerifyConstTensors(
- "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
- VerifyConstTensors(
- "m_CellBias", m_InputParams.m_CellBias, params.m_CellBias);
- VerifyConstTensors(
- "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
- VerifyConstTensors(
- "m_ProjectionWeights", m_InputParams.m_ProjectionWeights, params.m_ProjectionWeights);
- VerifyConstTensors(
- "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.m_ProjectionBias);
- VerifyConstTensors(
- "m_InputLayerNormWeights", m_InputParams.m_InputLayerNormWeights, params.m_InputLayerNormWeights);
- VerifyConstTensors(
- "m_ForgetLayerNormWeights", m_InputParams.m_ForgetLayerNormWeights, params.m_ForgetLayerNormWeights);
- VerifyConstTensors(
- "m_CellLayerNormWeights", m_InputParams.m_CellLayerNormWeights, params.m_CellLayerNormWeights);
- VerifyConstTensors(
- "m_OutputLayerNormWeights", m_InputParams.m_OutputLayerNormWeights, params.m_OutputLayerNormWeights);
- }
-
-private:
- armnn::LstmInputParams m_InputParams;
-};
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmCifgPeepholeNoProjection)
-{
- armnn::LstmDescriptor descriptor;
- descriptor.m_ActivationFunc = 4;
- descriptor.m_ClippingThresProj = 0.0f;
- descriptor.m_ClippingThresCell = 0.0f;
- descriptor.m_CifgEnabled = true; // if this is true then we DON'T need to set the OptCifgParams
- descriptor.m_ProjectionEnabled = false;
- descriptor.m_PeepholeEnabled = true;
-
- const uint32_t batchSize = 1;
- const uint32_t inputSize = 2;
- const uint32_t numUnits = 4;
- const uint32_t outputSize = numUnits;
-
- armnn::TensorInfo inputWeightsInfo1({numUnits, inputSize}, armnn::DataType::Float32);
- std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
- armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);
-
- std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
- armnn::ConstTensor inputToCellWeights(inputWeightsInfo1, inputToCellWeightsData);
-
- std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
- armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);
-
- armnn::TensorInfo inputWeightsInfo2({numUnits, outputSize}, armnn::DataType::Float32);
- std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
- armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);
-
- std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
- armnn::ConstTensor recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);
-
- std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
- armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);
-
- armnn::TensorInfo inputWeightsInfo3({numUnits}, armnn::DataType::Float32);
- std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
- armnn::ConstTensor cellToForgetWeights(inputWeightsInfo3, cellToForgetWeightsData);
-
- std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
- armnn::ConstTensor cellToOutputWeights(inputWeightsInfo3, cellToOutputWeightsData);
-
- std::vector<float> forgetGateBiasData(numUnits, 1.0f);
- armnn::ConstTensor forgetGateBias(inputWeightsInfo3, forgetGateBiasData);
-
- std::vector<float> cellBiasData(numUnits, 0.0f);
- armnn::ConstTensor cellBias(inputWeightsInfo3, cellBiasData);
-
- std::vector<float> outputGateBiasData(numUnits, 0.0f);
- armnn::ConstTensor outputGateBias(inputWeightsInfo3, outputGateBiasData);
-
- armnn::LstmInputParams params;
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
- params.m_CellToForgetWeights = &cellToForgetWeights;
- params.m_CellToOutputWeights = &cellToOutputWeights;
-
- armnn::INetworkPtr network = armnn::INetwork::Create();
- armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
- armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
- armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
- const std::string layerName("lstm");
- armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
- armnn::IConnectableLayer* const scratchBuffer = network->AddOutputLayer(0);
- armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(1);
- armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(2);
- armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(3);
-
- // connect up
- armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
- armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
- armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 3 }, armnn::DataType::Float32);
-
- inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
- inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
- outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
- outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
- cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
- cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
- lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
- lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
-
- lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
- lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
- lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
- lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
-
- lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
- lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
- BOOST_CHECK(deserializedNetwork);
-
- VerifyLstmLayer checker(
- layerName,
- {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
- descriptor,
- params);
- deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmNoCifgWithPeepholeAndProjection)
-{
- armnn::LstmDescriptor descriptor;
- descriptor.m_ActivationFunc = 4;
- descriptor.m_ClippingThresProj = 0.0f;
- descriptor.m_ClippingThresCell = 0.0f;
- descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
- descriptor.m_ProjectionEnabled = true;
- descriptor.m_PeepholeEnabled = true;
-
- const uint32_t batchSize = 2;
- const uint32_t inputSize = 5;
- const uint32_t numUnits = 20;
- const uint32_t outputSize = 16;
-
- armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
- std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
-
- std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
-
- std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
-
- std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
-
- armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
- std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
-
- std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
-
- std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
-
- std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
-
- armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
- std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
-
- std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
-
- std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
-
- std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
-
- std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
-
- std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
-
- std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);
-
- armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
- std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
- armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
-
- armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
- std::vector<float> projectionBiasData(outputSize, 0.f);
- armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
-
- armnn::LstmInputParams params;
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
-
- // additional params because: descriptor.m_CifgEnabled = false
- params.m_InputToInputWeights = &inputToInputWeights;
- params.m_RecurrentToInputWeights = &recurrentToInputWeights;
- params.m_CellToInputWeights = &cellToInputWeights;
- params.m_InputGateBias = &inputGateBias;
-
- // additional params because: descriptor.m_ProjectionEnabled = true
- params.m_ProjectionWeights = &projectionWeights;
- params.m_ProjectionBias = &projectionBias;
-
- // additional params because: descriptor.m_PeepholeEnabled = true
- params.m_CellToForgetWeights = &cellToForgetWeights;
- params.m_CellToOutputWeights = &cellToOutputWeights;
-
- armnn::INetworkPtr network = armnn::INetwork::Create();
- armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
- armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
- armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
- const std::string layerName("lstm");
- armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
- armnn::IConnectableLayer* const scratchBuffer = network->AddOutputLayer(0);
- armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(1);
- armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(2);
- armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(3);
-
- // connect up
- armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
- armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
- armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
-
- inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
- inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
- outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
- outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
- cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
- cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
- lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
- lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
-
- lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
- lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
- lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
- lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
-
- lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
- lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
- BOOST_CHECK(deserializedNetwork);
-
- VerifyLstmLayer checker(
- layerName,
- {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
- descriptor,
- params);
- deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeLstmNoCifgWithPeepholeWithProjectionWithLayerNorm)
-{
- armnn::LstmDescriptor descriptor;
- descriptor.m_ActivationFunc = 4;
- descriptor.m_ClippingThresProj = 0.0f;
- descriptor.m_ClippingThresCell = 0.0f;
- descriptor.m_CifgEnabled = false; // if this is true then we DON'T need to set the OptCifgParams
- descriptor.m_ProjectionEnabled = true;
- descriptor.m_PeepholeEnabled = true;
- descriptor.m_LayerNormEnabled = true;
-
- const uint32_t batchSize = 2;
- const uint32_t inputSize = 5;
- const uint32_t numUnits = 20;
- const uint32_t outputSize = 16;
-
- armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
- std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
-
- std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
-
- std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
-
- std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
- armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
-
- armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
- std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
-
- std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
-
- std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
-
- std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
-
- armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
- std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
-
- std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
-
- std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
-
- std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
- armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
-
- std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
-
- std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
-
- std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);
-
- armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
- std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
- armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
-
- armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
- std::vector<float> projectionBiasData(outputSize, 0.f);
- armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
-
- std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor inputLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
- std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor forgetLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
- std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor cellLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
- std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
- armnn::ConstTensor outLayerNormWeights(tensorInfo20, forgetGateBiasData);
-
- armnn::LstmInputParams params;
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
-
- // additional params because: descriptor.m_CifgEnabled = false
- params.m_InputToInputWeights = &inputToInputWeights;
- params.m_RecurrentToInputWeights = &recurrentToInputWeights;
- params.m_CellToInputWeights = &cellToInputWeights;
- params.m_InputGateBias = &inputGateBias;
-
- // additional params because: descriptor.m_ProjectionEnabled = true
- params.m_ProjectionWeights = &projectionWeights;
- params.m_ProjectionBias = &projectionBias;
-
- // additional params because: descriptor.m_PeepholeEnabled = true
- params.m_CellToForgetWeights = &cellToForgetWeights;
- params.m_CellToOutputWeights = &cellToOutputWeights;
-
- // additional params because: despriptor.m_LayerNormEnabled = true
- params.m_InputLayerNormWeights = &inputLayerNormWeights;
- params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
- params.m_CellLayerNormWeights = &cellLayerNormWeights;
- params.m_OutputLayerNormWeights = &outLayerNormWeights;
-
- armnn::INetworkPtr network = armnn::INetwork::Create();
- armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
- armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
- armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
- const std::string layerName("lstm");
- armnn::IConnectableLayer* const lstmLayer = network->AddLstmLayer(descriptor, params, layerName.c_str());
- armnn::IConnectableLayer* const scratchBuffer = network->AddOutputLayer(0);
- armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(1);
- armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(2);
- armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(3);
-
- // connect up
- armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
- armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
- armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
-
- inputLayer->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(0));
- inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
- outputStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(1));
- outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
- cellStateIn->GetOutputSlot(0).Connect(lstmLayer->GetInputSlot(2));
- cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
- lstmLayer->GetOutputSlot(0).Connect(scratchBuffer->GetInputSlot(0));
- lstmLayer->GetOutputSlot(0).SetTensorInfo(lstmTensorInfoScratchBuff);
-
- lstmLayer->GetOutputSlot(1).Connect(outputStateOut->GetInputSlot(0));
- lstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
- lstmLayer->GetOutputSlot(2).Connect(cellStateOut->GetInputSlot(0));
- lstmLayer->GetOutputSlot(2).SetTensorInfo(cellStateTensorInfo);
-
- lstmLayer->GetOutputSlot(3).Connect(outputLayer->GetInputSlot(0));
- lstmLayer->GetOutputSlot(3).SetTensorInfo(outputStateTensorInfo);
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
- BOOST_CHECK(deserializedNetwork);
-
- VerifyLstmLayer checker(
- layerName,
- {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
- descriptor,
- params);
- deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(EnsureLstmLayersBackwardCompatibility)
-{
- // The hex data below is a flat buffer containing a lstm layer with no Cifg, with peephole and projection
- // enabled. That data was obtained before additional layer normalization parameters where added to the
- // lstm serializer. That way it can be tested if a lstm model with the old parameter configuration can
- // still be loaded
- const std::vector<uint8_t> lstmNoCifgWithPeepholeAndProjectionModel =
- {
- 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
- 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
- 0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,
- 0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,
- 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,
- 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,
- 0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,
- 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0xE8, 0xD7, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xC8, 0xD6, 0xFF, 0xFF, 0x00, 0x00,
- 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x5E, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xE0, 0xD7, 0xFF, 0xFF,
- 0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x4E, 0xD7, 0xFF, 0xFF, 0x06, 0x00, 0x00, 0x00, 0x10, 0x00,
- 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x40, 0xD8,
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- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x1A, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x10, 0x00, 0x0C, 0x00,
- 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x06, 0x00, 0x07, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x01, 0x01, 0x04, 0x00, 0x00, 0x00, 0x2E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
- 0x22, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6C, 0x73,
- 0x74, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xEC, 0x00, 0x00, 0x00, 0xD0, 0x00, 0x00, 0x00,
- 0xB4, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x88, 0x00, 0x00, 0x00, 0x5C, 0x00, 0x00, 0x00, 0x30, 0x00,
- 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
- 0xA6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,
- 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
- 0x04, 0x00, 0x00, 0x00, 0xCE, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF,
- 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
- 0xB4, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x1A, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00,
- 0xF0, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00,
- 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,
- 0x7E, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00,
- 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
- 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
- 0x68, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xCE, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
- 0x08, 0x00, 0x0E, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,
- 0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00,
- 0x0E, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,
- 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
- 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
- 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x08, 0x00,
- 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,
- 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00,
- 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
- 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,
- 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
- 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
- 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00,
- 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,
- 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00
- };
-
- armnn::INetworkPtr deserializedNetwork =
- DeserializeNetwork(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(),
- lstmNoCifgWithPeepholeAndProjectionModel.end()));
-
- BOOST_CHECK(deserializedNetwork);
-
- // generating the same model parameters which where used to serialize the model (Layer norm is not specified)
- armnn::LstmDescriptor descriptor;
- descriptor.m_ActivationFunc = 4;
- descriptor.m_ClippingThresProj = 0.0f;
- descriptor.m_ClippingThresCell = 0.0f;
- descriptor.m_CifgEnabled = false;
- descriptor.m_ProjectionEnabled = true;
- descriptor.m_PeepholeEnabled = true;
-
- const uint32_t batchSize = 2u;
- const uint32_t inputSize = 5u;
- const uint32_t numUnits = 20u;
- const uint32_t outputSize = 16u;
-
- armnn::TensorInfo tensorInfo20x5({numUnits, inputSize}, armnn::DataType::Float32);
- std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
- armnn::ConstTensor inputToInputWeights(tensorInfo20x5, inputToInputWeightsData);
-
- std::vector<float> inputToForgetWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
- armnn::ConstTensor inputToForgetWeights(tensorInfo20x5, inputToForgetWeightsData);
-
- std::vector<float> inputToCellWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
- armnn::ConstTensor inputToCellWeights(tensorInfo20x5, inputToCellWeightsData);
-
- std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
- armnn::ConstTensor inputToOutputWeights(tensorInfo20x5, inputToOutputWeightsData);
-
- armnn::TensorInfo tensorInfo20({numUnits}, armnn::DataType::Float32);
- std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
- armnn::ConstTensor inputGateBias(tensorInfo20, inputGateBiasData);
-
- std::vector<float> forgetGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
- armnn::ConstTensor forgetGateBias(tensorInfo20, forgetGateBiasData);
-
- std::vector<float> cellBiasData(tensorInfo20.GetNumElements(), 0.0f);
- armnn::ConstTensor cellBias(tensorInfo20, cellBiasData);
-
- std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
- armnn::ConstTensor outputGateBias(tensorInfo20, outputGateBiasData);
-
- armnn::TensorInfo tensorInfo20x16({numUnits, outputSize}, armnn::DataType::Float32);
- std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
- armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
-
- std::vector<float> recurrentToForgetWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
- armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
-
- std::vector<float> recurrentToCellWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
- armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
-
- std::vector<float> recurrentToOutputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
- armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
-
- std::vector<float> cellToInputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
- armnn::ConstTensor cellToInputWeights(tensorInfo20, cellToInputWeightsData);
-
- std::vector<float> cellToForgetWeightsData(tensorInfo20.GetNumElements(), 0.0f);
- armnn::ConstTensor cellToForgetWeights(tensorInfo20, cellToForgetWeightsData);
-
- std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
- armnn::ConstTensor cellToOutputWeights(tensorInfo20, cellToOutputWeightsData);
-
- armnn::TensorInfo tensorInfo16x20({outputSize, numUnits}, armnn::DataType::Float32);
- std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);
- armnn::ConstTensor projectionWeights(tensorInfo16x20, projectionWeightsData);
-
- armnn::TensorInfo tensorInfo16({outputSize}, armnn::DataType::Float32);
- std::vector<float> projectionBiasData(outputSize, 0.0f);
- armnn::ConstTensor projectionBias(tensorInfo16, projectionBiasData);
-
- armnn::LstmInputParams params;
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
-
- // additional params because: descriptor.m_CifgEnabled = false
- params.m_InputToInputWeights = &inputToInputWeights;
- params.m_RecurrentToInputWeights = &recurrentToInputWeights;
- params.m_CellToInputWeights = &cellToInputWeights;
- params.m_InputGateBias = &inputGateBias;
-
- // additional params because: descriptor.m_ProjectionEnabled = true
- params.m_ProjectionWeights = &projectionWeights;
- params.m_ProjectionBias = &projectionBias;
-
- // additional params because: descriptor.m_PeepholeEnabled = true
- params.m_CellToForgetWeights = &cellToForgetWeights;
- params.m_CellToOutputWeights = &cellToOutputWeights;
-
- const std::string layerName("lstm");
- armnn::TensorInfo inputTensorInfo({ batchSize, inputSize }, armnn::DataType::Float32);
- armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits}, armnn::DataType::Float32);
- armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize }, armnn::DataType::Float32);
- armnn::TensorInfo lstmTensorInfoScratchBuff({ batchSize, numUnits * 4 }, armnn::DataType::Float32);
-
- VerifyLstmLayer checker(
- layerName,
- {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
- {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
- descriptor,
- params);
- deserializedNetwork->Accept(checker);
-}
-class VerifyQuantizedLstmLayer : public LayerVerifierBase
-{
-
-public:
- VerifyQuantizedLstmLayer(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const armnn::QuantizedLstmInputParams& inputParams)
- : LayerVerifierBase(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}
-
- void VisitQuantizedLstmLayer(const armnn::IConnectableLayer* layer,
- const armnn::QuantizedLstmInputParams& params,
- const char* name)
- {
- VerifyNameAndConnections(layer, name);
- VerifyInputParameters(params);
- }
-
-protected:
- void VerifyInputParameters(const armnn::QuantizedLstmInputParams& params)
- {
- VerifyConstTensors("m_InputToInputWeights",
- m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
- VerifyConstTensors("m_InputToForgetWeights",
- m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
- VerifyConstTensors("m_InputToCellWeights",
- m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
- VerifyConstTensors("m_InputToOutputWeights",
- m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
- VerifyConstTensors("m_RecurrentToInputWeights",
- m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
- VerifyConstTensors("m_RecurrentToForgetWeights",
- m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
- VerifyConstTensors("m_RecurrentToCellWeights",
- m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
- VerifyConstTensors("m_RecurrentToOutputWeights",
- m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
- VerifyConstTensors("m_InputGateBias",
- m_InputParams.m_InputGateBias, params.m_InputGateBias);
- VerifyConstTensors("m_ForgetGateBias",
- m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
- VerifyConstTensors("m_CellBias",
- m_InputParams.m_CellBias, params.m_CellBias);
- VerifyConstTensors("m_OutputGateBias",
- m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
- }
-
-private:
- armnn::QuantizedLstmInputParams m_InputParams;
-};
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQuantizedLstm)
-{
- const uint32_t batchSize = 1;
- const uint32_t inputSize = 2;
- const uint32_t numUnits = 4;
- const uint32_t outputSize = numUnits;
-
- // Scale/Offset for input/output, cellState In/Out, weights, bias
- float inputOutputScale = 0.0078125f;
- int32_t inputOutputOffset = 128;
-
- float cellStateScale = 0.00048828125f;
- int32_t cellStateOffset = 0;
-
- float weightsScale = 0.00408021f;
- int32_t weightsOffset = 100;
-
- float biasScale = 3.1876640625e-05f;
- int32_t biasOffset = 0;
-
- // The shape of weight data is {outputSize, inputSize} = {4, 2}
- armnn::TensorShape inputToInputWeightsShape = {4, 2};
- std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
- armnn::TensorInfo inputToInputWeightsInfo(inputToInputWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);
-
- armnn::TensorShape inputToForgetWeightsShape = {4, 2};
- std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
- armnn::TensorInfo inputToForgetWeightsInfo(inputToForgetWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);
-
- armnn::TensorShape inputToCellWeightsShape = {4, 2};
- std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
- armnn::TensorInfo inputToCellWeightsInfo(inputToCellWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);
-
- armnn::TensorShape inputToOutputWeightsShape = {4, 2};
- std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
- armnn::TensorInfo inputToOutputWeightsInfo(inputToOutputWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);
-
- // The shape of recurrent weight data is {outputSize, outputSize} = {4, 4}
- armnn::TensorShape recurrentToInputWeightsShape = {4, 4};
- std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
- armnn::TensorInfo recurrentToInputWeightsInfo(recurrentToInputWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);
-
- armnn::TensorShape recurrentToForgetWeightsShape = {4, 4};
- std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
- armnn::TensorInfo recurrentToForgetWeightsInfo(recurrentToForgetWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);
-
- armnn::TensorShape recurrentToCellWeightsShape = {4, 4};
- std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
- armnn::TensorInfo recurrentToCellWeightsInfo(recurrentToCellWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);
-
- armnn::TensorShape recurrentToOutputWeightsShape = {4, 4};
- std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
- armnn::TensorInfo recurrentToOutputWeightsInfo(recurrentToOutputWeightsShape,
- armnn::DataType::QAsymmU8,
- weightsScale,
- weightsOffset);
- armnn::ConstTensor recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);
-
- // The shape of bias data is {outputSize} = {4}
- armnn::TensorShape inputGateBiasShape = {4};
- std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4};
- armnn::TensorInfo inputGateBiasInfo(inputGateBiasShape,
- armnn::DataType::Signed32,
- biasScale,
- biasOffset);
- armnn::ConstTensor inputGateBias(inputGateBiasInfo, inputGateBiasData);
-
- armnn::TensorShape forgetGateBiasShape = {4};
- std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4};
- armnn::TensorInfo forgetGateBiasInfo(forgetGateBiasShape,
- armnn::DataType::Signed32,
- biasScale,
- biasOffset);
- armnn::ConstTensor forgetGateBias(forgetGateBiasInfo, forgetGateBiasData);
-
- armnn::TensorShape cellBiasShape = {4};
- std::vector<int32_t> cellBiasData = {1, 2, 3, 4};
- armnn::TensorInfo cellBiasInfo(cellBiasShape,
- armnn::DataType::Signed32,
- biasScale,
- biasOffset);
- armnn::ConstTensor cellBias(cellBiasInfo, cellBiasData);
-
- armnn::TensorShape outputGateBiasShape = {4};
- std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4};
- armnn::TensorInfo outputGateBiasInfo(outputGateBiasShape,
- armnn::DataType::Signed32,
- biasScale,
- biasOffset);
- armnn::ConstTensor outputGateBias(outputGateBiasInfo, outputGateBiasData);
-
- armnn::QuantizedLstmInputParams params;
- params.m_InputToInputWeights = &inputToInputWeights;
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
- params.m_RecurrentToInputWeights = &recurrentToInputWeights;
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
- params.m_InputGateBias = &inputGateBias;
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
-
- armnn::INetworkPtr network = armnn::INetwork::Create();
- armnn::IConnectableLayer* const inputLayer = network->AddInputLayer(0);
- armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(1);
- armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(2);
- const std::string layerName("QuantizedLstm");
- armnn::IConnectableLayer* const quantizedLstmLayer = network->AddQuantizedLstmLayer(params, layerName.c_str());
- armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(0);
- armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(1);
-
- // Connect up
- armnn::TensorInfo inputTensorInfo({ batchSize, inputSize },
- armnn::DataType::QAsymmU8,
- inputOutputScale,
- inputOutputOffset);
- armnn::TensorInfo cellStateTensorInfo({ batchSize, numUnits },
- armnn::DataType::QSymmS16,
- cellStateScale,
- cellStateOffset);
- armnn::TensorInfo outputStateTensorInfo({ batchSize, outputSize },
- armnn::DataType::QAsymmU8,
- inputOutputScale,
- inputOutputOffset);
-
- inputLayer->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(0));
- inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
-
- cellStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(1));
- cellStateIn->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
- outputStateIn->GetOutputSlot(0).Connect(quantizedLstmLayer->GetInputSlot(2));
- outputStateIn->GetOutputSlot(0).SetTensorInfo(outputStateTensorInfo);
-
- quantizedLstmLayer->GetOutputSlot(0).Connect(cellStateOut->GetInputSlot(0));
- quantizedLstmLayer->GetOutputSlot(0).SetTensorInfo(cellStateTensorInfo);
-
- quantizedLstmLayer->GetOutputSlot(1).Connect(outputLayer->GetInputSlot(0));
- quantizedLstmLayer->GetOutputSlot(1).SetTensorInfo(outputStateTensorInfo);
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
- BOOST_CHECK(deserializedNetwork);
-
- VerifyQuantizedLstmLayer checker(layerName,
- {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
- {cellStateTensorInfo, outputStateTensorInfo},
- params);
-
- deserializedNetwork->Accept(checker);
-}
-
-class VerifyQLstmLayer : public LayerVerifierBaseWithDescriptor<armnn::QLstmDescriptor>
-{
-public:
- VerifyQLstmLayer(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const armnn::QLstmDescriptor& descriptor,
- const armnn::LstmInputParams& inputParams)
- : LayerVerifierBaseWithDescriptor<armnn::QLstmDescriptor>(layerName, inputInfos, outputInfos, descriptor)
- , m_InputParams(inputParams) {}
-
- void VisitQLstmLayer(const armnn::IConnectableLayer* layer,
- const armnn::QLstmDescriptor& descriptor,
- const armnn::LstmInputParams& params,
- const char* name)
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
- VerifyInputParameters(params);
- }
-
-protected:
- void VerifyInputParameters(const armnn::LstmInputParams& params)
- {
- VerifyConstTensors(
- "m_InputToInputWeights", m_InputParams.m_InputToInputWeights, params.m_InputToInputWeights);
- VerifyConstTensors(
- "m_InputToForgetWeights", m_InputParams.m_InputToForgetWeights, params.m_InputToForgetWeights);
- VerifyConstTensors(
- "m_InputToCellWeights", m_InputParams.m_InputToCellWeights, params.m_InputToCellWeights);
- VerifyConstTensors(
- "m_InputToOutputWeights", m_InputParams.m_InputToOutputWeights, params.m_InputToOutputWeights);
- VerifyConstTensors(
- "m_RecurrentToInputWeights", m_InputParams.m_RecurrentToInputWeights, params.m_RecurrentToInputWeights);
- VerifyConstTensors(
- "m_RecurrentToForgetWeights", m_InputParams.m_RecurrentToForgetWeights, params.m_RecurrentToForgetWeights);
- VerifyConstTensors(
- "m_RecurrentToCellWeights", m_InputParams.m_RecurrentToCellWeights, params.m_RecurrentToCellWeights);
- VerifyConstTensors(
- "m_RecurrentToOutputWeights", m_InputParams.m_RecurrentToOutputWeights, params.m_RecurrentToOutputWeights);
- VerifyConstTensors(
- "m_CellToInputWeights", m_InputParams.m_CellToInputWeights, params.m_CellToInputWeights);
- VerifyConstTensors(
- "m_CellToForgetWeights", m_InputParams.m_CellToForgetWeights, params.m_CellToForgetWeights);
- VerifyConstTensors(
- "m_CellToOutputWeights", m_InputParams.m_CellToOutputWeights, params.m_CellToOutputWeights);
- VerifyConstTensors(
- "m_InputGateBias", m_InputParams.m_InputGateBias, params.m_InputGateBias);
- VerifyConstTensors(
- "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.m_ForgetGateBias);
- VerifyConstTensors(
- "m_CellBias", m_InputParams.m_CellBias, params.m_CellBias);
- VerifyConstTensors(
- "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.m_OutputGateBias);
- VerifyConstTensors(
- "m_ProjectionWeights", m_InputParams.m_ProjectionWeights, params.m_ProjectionWeights);
- VerifyConstTensors(
- "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.m_ProjectionBias);
- VerifyConstTensors(
- "m_InputLayerNormWeights", m_InputParams.m_InputLayerNormWeights, params.m_InputLayerNormWeights);
- VerifyConstTensors(
- "m_ForgetLayerNormWeights", m_InputParams.m_ForgetLayerNormWeights, params.m_ForgetLayerNormWeights);
- VerifyConstTensors(
- "m_CellLayerNormWeights", m_InputParams.m_CellLayerNormWeights, params.m_CellLayerNormWeights);
- VerifyConstTensors(
- "m_OutputLayerNormWeights", m_InputParams.m_OutputLayerNormWeights, params.m_OutputLayerNormWeights);
- }
-
-private:
- armnn::LstmInputParams m_InputParams;
-};
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmBasic)
-{
- armnn::QLstmDescriptor descriptor;
-
- descriptor.m_CifgEnabled = true;
- descriptor.m_ProjectionEnabled = false;
- descriptor.m_PeepholeEnabled = false;
- descriptor.m_LayerNormEnabled = false;
-
- descriptor.m_CellClip = 0.0f;
- descriptor.m_ProjectionClip = 0.0f;
-
- descriptor.m_InputIntermediateScale = 0.00001f;
- descriptor.m_ForgetIntermediateScale = 0.00001f;
- descriptor.m_CellIntermediateScale = 0.00001f;
- descriptor.m_OutputIntermediateScale = 0.00001f;
-
- descriptor.m_HiddenStateScale = 0.07f;
- descriptor.m_HiddenStateZeroPoint = 0;
-
- const unsigned int numBatches = 2;
- const unsigned int inputSize = 5;
- const unsigned int outputSize = 4;
- const unsigned int numUnits = 4;
-
- // Scale/Offset quantization info
- float inputScale = 0.0078f;
- int32_t inputOffset = 0;
-
- float outputScale = 0.0078f;
- int32_t outputOffset = 0;
-
- float cellStateScale = 3.5002e-05f;
- int32_t cellStateOffset = 0;
-
- float weightsScale = 0.007f;
- int32_t weightsOffset = 0;
-
- float biasScale = 3.5002e-05f / 1024;
- int32_t biasOffset = 0;
-
- // Weights and bias tensor and quantization info
- armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
- armnn::DataType::QSymmS8,
- weightsScale,
- weightsOffset);
-
- armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
- armnn::DataType::QSymmS8,
- weightsScale,
- weightsOffset);
-
- armnn::TensorInfo biasInfo({numUnits}, armnn::DataType::Signed32, biasScale, biasOffset);
-
- std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
- std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
- std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-
- armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
- armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
- armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
-
- std::vector<int8_t> recurrentToForgetWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
- std::vector<int8_t> recurrentToCellWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
- std::vector<int8_t> recurrentToOutputWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-
- armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
- armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
- armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
-
- std::vector<int32_t> forgetGateBiasData(numUnits, 1);
- std::vector<int32_t> cellBiasData(numUnits, 0);
- std::vector<int32_t> outputGateBiasData(numUnits, 0);
-
- armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
- armnn::ConstTensor cellBias(biasInfo, cellBiasData);
- armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
-
- // Set up params
- armnn::LstmInputParams params;
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
-
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
-
- // Create network
- armnn::INetworkPtr network = armnn::INetwork::Create();
- const std::string layerName("qLstm");
-
- armnn::IConnectableLayer* const input = network->AddInputLayer(0);
- armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
- armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(2);
-
- armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
-
- armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
- armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(1);
- armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(2);
-
- // Input/Output tensor info
- armnn::TensorInfo inputInfo({numBatches , inputSize},
- armnn::DataType::QAsymmS8,
- inputScale,
- inputOffset);
-
- armnn::TensorInfo cellStateInfo({numBatches , numUnits},
- armnn::DataType::QSymmS16,
- cellStateScale,
- cellStateOffset);
-
- armnn::TensorInfo outputStateInfo({numBatches , outputSize},
- armnn::DataType::QAsymmS8,
- outputScale,
- outputOffset);
-
- // Connect input/output slots
- input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
- input->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
- outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
- outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
-
- cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
- cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
- qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
- qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
-
- qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
- BOOST_CHECK(deserializedNetwork);
-
- VerifyQLstmLayer checker(layerName,
- {inputInfo, cellStateInfo, outputStateInfo},
- {outputStateInfo, cellStateInfo, outputStateInfo},
- descriptor,
- params);
-
- deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmCifgLayerNorm)
-{
- armnn::QLstmDescriptor descriptor;
-
- // CIFG params are used when CIFG is disabled
- descriptor.m_CifgEnabled = true;
- descriptor.m_ProjectionEnabled = false;
- descriptor.m_PeepholeEnabled = false;
- descriptor.m_LayerNormEnabled = true;
-
- descriptor.m_CellClip = 0.0f;
- descriptor.m_ProjectionClip = 0.0f;
-
- descriptor.m_InputIntermediateScale = 0.00001f;
- descriptor.m_ForgetIntermediateScale = 0.00001f;
- descriptor.m_CellIntermediateScale = 0.00001f;
- descriptor.m_OutputIntermediateScale = 0.00001f;
-
- descriptor.m_HiddenStateScale = 0.07f;
- descriptor.m_HiddenStateZeroPoint = 0;
-
- const unsigned int numBatches = 2;
- const unsigned int inputSize = 5;
- const unsigned int outputSize = 4;
- const unsigned int numUnits = 4;
-
- // Scale/Offset quantization info
- float inputScale = 0.0078f;
- int32_t inputOffset = 0;
-
- float outputScale = 0.0078f;
- int32_t outputOffset = 0;
-
- float cellStateScale = 3.5002e-05f;
- int32_t cellStateOffset = 0;
-
- float weightsScale = 0.007f;
- int32_t weightsOffset = 0;
-
- float layerNormScale = 3.5002e-05f;
- int32_t layerNormOffset = 0;
-
- float biasScale = layerNormScale / 1024;
- int32_t biasOffset = 0;
-
- // Weights and bias tensor and quantization info
- armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
- armnn::DataType::QSymmS8,
- weightsScale,
- weightsOffset);
-
- armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
- armnn::DataType::QSymmS8,
- weightsScale,
- weightsOffset);
-
- armnn::TensorInfo biasInfo({numUnits},
- armnn::DataType::Signed32,
- biasScale,
- biasOffset);
-
- armnn::TensorInfo layerNormWeightsInfo({numUnits},
- armnn::DataType::QSymmS16,
- layerNormScale,
- layerNormOffset);
-
- // Mandatory params
- std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
- std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
- std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-
- armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
- armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
- armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
-
- std::vector<int8_t> recurrentToForgetWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
- std::vector<int8_t> recurrentToCellWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
- std::vector<int8_t> recurrentToOutputWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-
- armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
- armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
- armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
-
- std::vector<int32_t> forgetGateBiasData(numUnits, 1);
- std::vector<int32_t> cellBiasData(numUnits, 0);
- std::vector<int32_t> outputGateBiasData(numUnits, 0);
-
- armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
- armnn::ConstTensor cellBias(biasInfo, cellBiasData);
- armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
-
- // Layer Norm
- std::vector<int16_t> forgetLayerNormWeightsData =
- GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
- std::vector<int16_t> cellLayerNormWeightsData =
- GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
- std::vector<int16_t> outputLayerNormWeightsData =
- GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-
- armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
- armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
- armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
-
- // Set up params
- armnn::LstmInputParams params;
-
- // Mandatory params
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
-
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
-
- // Layer Norm
- params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
- params.m_CellLayerNormWeights = &cellLayerNormWeights;
- params.m_OutputLayerNormWeights = &outputLayerNormWeights;
-
- // Create network
- armnn::INetworkPtr network = armnn::INetwork::Create();
- const std::string layerName("qLstm");
-
- armnn::IConnectableLayer* const input = network->AddInputLayer(0);
- armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
- armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(2);
-
- armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
-
- armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
- armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(1);
- armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(2);
-
- // Input/Output tensor info
- armnn::TensorInfo inputInfo({numBatches , inputSize},
- armnn::DataType::QAsymmS8,
- inputScale,
- inputOffset);
-
- armnn::TensorInfo cellStateInfo({numBatches , numUnits},
- armnn::DataType::QSymmS16,
- cellStateScale,
- cellStateOffset);
-
- armnn::TensorInfo outputStateInfo({numBatches , outputSize},
- armnn::DataType::QAsymmS8,
- outputScale,
- outputOffset);
-
- // Connect input/output slots
- input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
- input->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
- outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
- outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
-
- cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
- cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
- qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
- qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
-
- qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
- BOOST_CHECK(deserializedNetwork);
-
- VerifyQLstmLayer checker(layerName,
- {inputInfo, cellStateInfo, outputStateInfo},
- {outputStateInfo, cellStateInfo, outputStateInfo},
- descriptor,
- params);
-
- deserializedNetwork->Accept(checker);
-}
-
-BOOST_AUTO_TEST_CASE(SerializeDeserializeQLstmAdvanced)
-{
- armnn::QLstmDescriptor descriptor;
-
- descriptor.m_CifgEnabled = false;
- descriptor.m_ProjectionEnabled = true;
- descriptor.m_PeepholeEnabled = true;
- descriptor.m_LayerNormEnabled = true;
-
- descriptor.m_CellClip = 0.1f;
- descriptor.m_ProjectionClip = 0.1f;
-
- descriptor.m_InputIntermediateScale = 0.00001f;
- descriptor.m_ForgetIntermediateScale = 0.00001f;
- descriptor.m_CellIntermediateScale = 0.00001f;
- descriptor.m_OutputIntermediateScale = 0.00001f;
-
- descriptor.m_HiddenStateScale = 0.07f;
- descriptor.m_HiddenStateZeroPoint = 0;
-
- const unsigned int numBatches = 2;
- const unsigned int inputSize = 5;
- const unsigned int outputSize = 4;
- const unsigned int numUnits = 4;
-
- // Scale/Offset quantization info
- float inputScale = 0.0078f;
- int32_t inputOffset = 0;
-
- float outputScale = 0.0078f;
- int32_t outputOffset = 0;
-
- float cellStateScale = 3.5002e-05f;
- int32_t cellStateOffset = 0;
-
- float weightsScale = 0.007f;
- int32_t weightsOffset = 0;
-
- float layerNormScale = 3.5002e-05f;
- int32_t layerNormOffset = 0;
-
- float biasScale = layerNormScale / 1024;
- int32_t biasOffset = 0;
-
- // Weights and bias tensor and quantization info
- armnn::TensorInfo inputWeightsInfo({numUnits, inputSize},
- armnn::DataType::QSymmS8,
- weightsScale,
- weightsOffset);
-
- armnn::TensorInfo recurrentWeightsInfo({numUnits, outputSize},
- armnn::DataType::QSymmS8,
- weightsScale,
- weightsOffset);
-
- armnn::TensorInfo biasInfo({numUnits},
- armnn::DataType::Signed32,
- biasScale,
- biasOffset);
-
- armnn::TensorInfo peepholeWeightsInfo({numUnits},
- armnn::DataType::QSymmS16,
- weightsScale,
- weightsOffset);
-
- armnn::TensorInfo layerNormWeightsInfo({numUnits},
- armnn::DataType::QSymmS16,
- layerNormScale,
- layerNormOffset);
-
- armnn::TensorInfo projectionWeightsInfo({outputSize, numUnits},
- armnn::DataType::QSymmS8,
- weightsScale,
- weightsOffset);
-
- // Mandatory params
- std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
- std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
- std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
-
- armnn::ConstTensor inputToForgetWeights(inputWeightsInfo, inputToForgetWeightsData);
- armnn::ConstTensor inputToCellWeights(inputWeightsInfo, inputToCellWeightsData);
- armnn::ConstTensor inputToOutputWeights(inputWeightsInfo, inputToOutputWeightsData);
-
- std::vector<int8_t> recurrentToForgetWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
- std::vector<int8_t> recurrentToCellWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
- std::vector<int8_t> recurrentToOutputWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
-
- armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
- armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
- armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
-
- std::vector<int32_t> forgetGateBiasData(numUnits, 1);
- std::vector<int32_t> cellBiasData(numUnits, 0);
- std::vector<int32_t> outputGateBiasData(numUnits, 0);
-
- armnn::ConstTensor forgetGateBias(biasInfo, forgetGateBiasData);
- armnn::ConstTensor cellBias(biasInfo, cellBiasData);
- armnn::ConstTensor outputGateBias(biasInfo, outputGateBiasData);
-
- // CIFG
- std::vector<int8_t> inputToInputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
- std::vector<int8_t> recurrentToInputWeightsData =
- GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
- std::vector<int32_t> inputGateBiasData(numUnits, 1);
-
- armnn::ConstTensor inputToInputWeights(inputWeightsInfo, inputToInputWeightsData);
- armnn::ConstTensor recurrentToInputWeights(recurrentWeightsInfo, recurrentToInputWeightsData);
- armnn::ConstTensor inputGateBias(biasInfo, inputGateBiasData);
-
- // Peephole
- std::vector<int16_t> cellToInputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
- std::vector<int16_t> cellToForgetWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
- std::vector<int16_t> cellToOutputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
-
- armnn::ConstTensor cellToInputWeights(peepholeWeightsInfo, cellToInputWeightsData);
- armnn::ConstTensor cellToForgetWeights(peepholeWeightsInfo, cellToForgetWeightsData);
- armnn::ConstTensor cellToOutputWeights(peepholeWeightsInfo, cellToOutputWeightsData);
-
- // Projection
- std::vector<int8_t> projectionWeightsData = GenerateRandomData<int8_t>(projectionWeightsInfo.GetNumElements());
- std::vector<int32_t> projectionBiasData(outputSize, 1);
-
- armnn::ConstTensor projectionWeights(projectionWeightsInfo, projectionWeightsData);
- armnn::ConstTensor projectionBias(biasInfo, projectionBiasData);
-
- // Layer Norm
- std::vector<int16_t> inputLayerNormWeightsData =
- GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
- std::vector<int16_t> forgetLayerNormWeightsData =
- GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
- std::vector<int16_t> cellLayerNormWeightsData =
- GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
- std::vector<int16_t> outputLayerNormWeightsData =
- GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
-
- armnn::ConstTensor inputLayerNormWeights(layerNormWeightsInfo, inputLayerNormWeightsData);
- armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
- armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
- armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
-
- // Set up params
- armnn::LstmInputParams params;
-
- // Mandatory params
- params.m_InputToForgetWeights = &inputToForgetWeights;
- params.m_InputToCellWeights = &inputToCellWeights;
- params.m_InputToOutputWeights = &inputToOutputWeights;
-
- params.m_RecurrentToForgetWeights = &recurrentToForgetWeights;
- params.m_RecurrentToCellWeights = &recurrentToCellWeights;
- params.m_RecurrentToOutputWeights = &recurrentToOutputWeights;
-
- params.m_ForgetGateBias = &forgetGateBias;
- params.m_CellBias = &cellBias;
- params.m_OutputGateBias = &outputGateBias;
-
- // CIFG
- params.m_InputToInputWeights = &inputToInputWeights;
- params.m_RecurrentToInputWeights = &recurrentToInputWeights;
- params.m_InputGateBias = &inputGateBias;
-
- // Peephole
- params.m_CellToInputWeights = &cellToInputWeights;
- params.m_CellToForgetWeights = &cellToForgetWeights;
- params.m_CellToOutputWeights = &cellToOutputWeights;
-
- // Projection
- params.m_ProjectionWeights = &projectionWeights;
- params.m_ProjectionBias = &projectionBias;
-
- // Layer Norm
- params.m_InputLayerNormWeights = &inputLayerNormWeights;
- params.m_ForgetLayerNormWeights = &forgetLayerNormWeights;
- params.m_CellLayerNormWeights = &cellLayerNormWeights;
- params.m_OutputLayerNormWeights = &outputLayerNormWeights;
-
- // Create network
- armnn::INetworkPtr network = armnn::INetwork::Create();
- const std::string layerName("qLstm");
-
- armnn::IConnectableLayer* const input = network->AddInputLayer(0);
- armnn::IConnectableLayer* const outputStateIn = network->AddInputLayer(1);
- armnn::IConnectableLayer* const cellStateIn = network->AddInputLayer(2);
-
- armnn::IConnectableLayer* const qLstmLayer = network->AddQLstmLayer(descriptor, params, layerName.c_str());
-
- armnn::IConnectableLayer* const outputStateOut = network->AddOutputLayer(0);
- armnn::IConnectableLayer* const cellStateOut = network->AddOutputLayer(1);
- armnn::IConnectableLayer* const outputLayer = network->AddOutputLayer(2);
-
- // Input/Output tensor info
- armnn::TensorInfo inputInfo({numBatches , inputSize},
- armnn::DataType::QAsymmS8,
- inputScale,
- inputOffset);
-
- armnn::TensorInfo cellStateInfo({numBatches , numUnits},
- armnn::DataType::QSymmS16,
- cellStateScale,
- cellStateOffset);
-
- armnn::TensorInfo outputStateInfo({numBatches , outputSize},
- armnn::DataType::QAsymmS8,
- outputScale,
- outputOffset);
-
- // Connect input/output slots
- input->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(0));
- input->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
- outputStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(1));
- outputStateIn->GetOutputSlot(0).SetTensorInfo(cellStateInfo);
-
- cellStateIn->GetOutputSlot(0).Connect(qLstmLayer->GetInputSlot(2));
- cellStateIn->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
- qLstmLayer->GetOutputSlot(0).Connect(outputStateOut->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(0).SetTensorInfo(outputStateInfo);
-
- qLstmLayer->GetOutputSlot(1).Connect(cellStateOut->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(1).SetTensorInfo(cellStateInfo);
-
- qLstmLayer->GetOutputSlot(2).Connect(outputLayer->GetInputSlot(0));
- qLstmLayer->GetOutputSlot(2).SetTensorInfo(outputStateInfo);
-
- armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
- BOOST_CHECK(deserializedNetwork);
-
- VerifyQLstmLayer checker(layerName,
- {inputInfo, cellStateInfo, outputStateInfo},
- {outputStateInfo, cellStateInfo, outputStateInfo},
- descriptor,
- params);
-
- deserializedNetwork->Accept(checker);
+ deserializedNetwork->ExecuteStrategy(verifier);
}
BOOST_AUTO_TEST_SUITE_END()