6 #include "../Serializer.hpp" 18 #include <boost/test/unit_test.hpp> 25 #define DECLARE_LAYER_VERIFIER_CLASS(name) \ 26 class name##LayerVerifier : public LayerVerifierBase \ 29 name##LayerVerifier(const std::string& layerName, \ 30 const std::vector<armnn::TensorInfo>& inputInfos, \ 31 const std::vector<armnn::TensorInfo>& outputInfos) \ 32 : LayerVerifierBase(layerName, inputInfos, outputInfos) {} \ 34 void Visit##name##Layer(const armnn::IConnectableLayer* layer, const char* name) override \ 36 VerifyNameAndConnections(layer, name); \ 40 #define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name) \ 41 class name##LayerVerifier : public LayerVerifierBaseWithDescriptor<armnn::name##Descriptor> \ 44 name##LayerVerifier(const std::string& layerName, \ 45 const std::vector<armnn::TensorInfo>& inputInfos, \ 46 const std::vector<armnn::TensorInfo>& outputInfos, \ 47 const armnn::name##Descriptor& descriptor) \ 48 : LayerVerifierBaseWithDescriptor<armnn::name##Descriptor>( \ 49 layerName, inputInfos, outputInfos, descriptor) {} \ 51 void Visit##name##Layer(const armnn::IConnectableLayer* layer, \ 52 const armnn::name##Descriptor& descriptor, \ 53 const char* name) override \ 55 VerifyNameAndConnections(layer, name); \ 56 VerifyDescriptor(descriptor); \ 60 struct DefaultLayerVerifierPolicy
62 static void Apply(
const std::string)
64 BOOST_TEST_MESSAGE(
"Unexpected layer found in network");
72 LayerVerifierBase(
const std::string& layerName,
73 const std::vector<armnn::TensorInfo>& inputInfos,
74 const std::vector<armnn::TensorInfo>& outputInfos)
75 : m_LayerName(layerName)
76 , m_InputTensorInfos(inputInfos)
77 , m_OutputTensorInfos(outputInfos) {}
86 BOOST_TEST(name == m_LayerName.c_str());
91 for (
unsigned int i = 0; i < m_InputTensorInfos.size(); i++)
94 BOOST_CHECK(connectedOutput);
97 BOOST_TEST(connectedInfo.
GetShape() == m_InputTensorInfos[i].GetShape());
101 BOOST_TEST(connectedInfo.
GetQuantizationScale() == m_InputTensorInfos[i].GetQuantizationScale());
105 for (
unsigned int i = 0; i < m_OutputTensorInfos.size(); i++)
108 BOOST_TEST(outputInfo.
GetShape() == m_OutputTensorInfos[i].GetShape());
117 void VerifyConstTensors(
const std::string& tensorName,
121 if (expectedPtr ==
nullptr)
123 BOOST_CHECK_MESSAGE(actualPtr ==
nullptr, tensorName +
" should not exist");
127 BOOST_CHECK_MESSAGE(actualPtr !=
nullptr, tensorName +
" should have been set");
128 if (actualPtr !=
nullptr)
134 tensorName +
" shapes don't match");
137 tensorName +
" data types don't match");
140 tensorName +
" (GetNumBytes) data sizes do not match");
144 const char* expectedData =
static_cast<const char*
>(expectedPtr->
GetMemoryArea());
145 const char* actualData =
static_cast<const char*
>(actualPtr->
GetMemoryArea());
147 for (
unsigned int i = 0; i < expectedPtr->
GetNumBytes(); ++i)
149 same = expectedData[i] == actualData[i];
155 BOOST_CHECK_MESSAGE(same, tensorName +
" data does not match");
162 std::string m_LayerName;
163 std::vector<armnn::TensorInfo> m_InputTensorInfos;
164 std::vector<armnn::TensorInfo> m_OutputTensorInfos;
167 template<
typename Descriptor>
168 class LayerVerifierBaseWithDescriptor :
public LayerVerifierBase
171 LayerVerifierBaseWithDescriptor(
const std::string& layerName,
172 const std::vector<armnn::TensorInfo>& inputInfos,
173 const std::vector<armnn::TensorInfo>& outputInfos,
174 const Descriptor& descriptor)
175 : LayerVerifierBase(layerName, inputInfos, outputInfos)
176 , m_Descriptor(descriptor) {}
179 void VerifyDescriptor(
const Descriptor& descriptor)
181 BOOST_CHECK(descriptor == m_Descriptor);
184 Descriptor m_Descriptor;
188 void CompareConstTensorData(
const void* data1,
const void* data2,
unsigned int numElements)
190 T typedData1 =
static_cast<T
>(data1);
191 T typedData2 =
static_cast<T
>(data2);
192 BOOST_CHECK(typedData1);
193 BOOST_CHECK(typedData2);
195 for (
unsigned int i = 0; i < numElements; i++)
197 BOOST_TEST(typedData1[i] == typedData2[i]);
209 CompareConstTensorData<const float*>(
214 CompareConstTensorData<const uint8_t*>(
218 CompareConstTensorData<const int8_t*>(
222 CompareConstTensorData<const int32_t*>(
227 BOOST_TEST_MESSAGE(
"Unexpected datatype");
234 std::vector<std::uint8_t>
const serializerVector{serializerString.begin(), serializerString.end()};
235 return IDeserializer::Create()->CreateNetworkFromBinary(serializerVector);
243 std::stringstream stream;
246 std::string serializerString{stream.str()};
247 return serializerString;
250 template<
typename DataType>
251 static std::vector<DataType> GenerateRandomData(
size_t size)
253 constexpr
bool isIntegerType = std::is_integral<DataType>::value;
255 typename std::conditional<isIntegerType,
256 std::uniform_int_distribution<DataType>,
257 std::uniform_real_distribution<DataType>>::type;
259 static constexpr
DataType lowerLimit = std::numeric_limits<DataType>::min();
260 static constexpr
DataType upperLimit = std::numeric_limits<DataType>::max();
262 static Distribution distribution(lowerLimit, upperLimit);
263 static std::default_random_engine generator;
265 std::vector<DataType> randomData(size);
266 std::generate(randomData.begin(), randomData.end(), []() {
return distribution(generator); });
279 const std::string layerName(
"addition");
296 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
297 BOOST_CHECK(deserializedNetwork);
299 AdditionLayerVerifier verifier(layerName, {tensorInfo, tensorInfo}, {tensorInfo});
300 deserializedNetwork->Accept(verifier);
307 const std::string layerName(
"argminmax");
313 descriptor.m_Axis = 1;
326 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
327 BOOST_CHECK(deserializedNetwork);
329 ArgMinMaxLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
330 deserializedNetwork->Accept(verifier);
336 class BatchNormalizationLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
339 BatchNormalizationLayerVerifier(
const std::string& layerName,
340 const std::vector<armnn::TensorInfo>& inputInfos,
341 const std::vector<armnn::TensorInfo>& outputInfos,
342 const Descriptor& descriptor,
347 : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
349 , m_Variance(variance)
354 const Descriptor& descriptor,
359 const char* name)
override 361 VerifyNameAndConnections(layer, name);
362 VerifyDescriptor(descriptor);
364 CompareConstTensor(mean, m_Mean);
365 CompareConstTensor(variance, m_Variance);
366 CompareConstTensor(beta, m_Beta);
367 CompareConstTensor(gamma, m_Gamma);
377 const std::string layerName(
"batchNormalization");
387 descriptor.
m_Eps = 0.0010000000475f;
390 std::vector<float> meanData({5.0});
391 std::vector<float> varianceData({2.0});
392 std::vector<float> betaData({1.0});
393 std::vector<float> gammaData({0.0});
403 network->AddBatchNormalizationLayer(descriptor, mean, variance, beta, gamma, layerName.c_str());
412 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
413 BOOST_CHECK(deserializedNetwork);
415 BatchNormalizationLayerVerifier verifier(
416 layerName, {inputInfo}, {outputInfo}, descriptor, mean, variance, beta, gamma);
417 deserializedNetwork->Accept(verifier);
424 const std::string layerName(
"spaceToBatchNd");
430 desc.m_BlockShape = {2, 2};
431 desc.m_Crops = {{0, 0}, {0, 0}};
444 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
445 BOOST_CHECK(deserializedNetwork);
447 BatchToSpaceNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
448 deserializedNetwork->Accept(verifier);
455 const std::string layerName(
"comparison");
478 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
479 BOOST_CHECK(deserializedNetwork);
481 ComparisonLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
482 deserializedNetwork->Accept(verifier);
487 class ConstantLayerVerifier :
public LayerVerifierBase
490 ConstantLayerVerifier(
const std::string& layerName,
491 const std::vector<armnn::TensorInfo>& inputInfos,
492 const std::vector<armnn::TensorInfo>& outputInfos,
494 : LayerVerifierBase(layerName, inputInfos, outputInfos)
495 , m_LayerInput(layerInput) {}
499 const char* name)
override 501 VerifyNameAndConnections(layer, name);
502 CompareConstTensor(input, m_LayerInput);
511 const std::string layerName(
"constant");
514 std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements());
531 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
532 BOOST_CHECK(deserializedNetwork);
534 ConstantLayerVerifier verifier(layerName, {}, {info}, constTensor);
535 deserializedNetwork->Accept(verifier);
541 class Convolution2dLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
544 Convolution2dLayerVerifier(
const std::string& layerName,
545 const std::vector<armnn::TensorInfo>& inputInfos,
546 const std::vector<armnn::TensorInfo>& outputInfos,
547 const Descriptor& descriptor,
550 : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
552 , m_Biases(biases) {}
555 const Descriptor& descriptor,
558 const char* name)
override 560 VerifyNameAndConnections(layer, name);
561 VerifyDescriptor(descriptor);
564 CompareConstTensor(weights, m_Weights);
567 BOOST_CHECK(biases.
has_value() == descriptor.m_BiasEnabled);
568 BOOST_CHECK(biases.
has_value() == m_Biases.has_value());
570 if (biases.
has_value() && m_Biases.has_value())
572 CompareConstTensor(biases.
value(), m_Biases.value());
581 const std::string layerName(
"convolution2d");
588 std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());
591 std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());
609 network->AddConvolution2dLayer(descriptor,
621 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
622 BOOST_CHECK(deserializedNetwork);
624 Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
625 deserializedNetwork->Accept(verifier);
631 class Convolution2dLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
634 Convolution2dLayerVerifier(
const std::string& layerName,
635 const std::vector<armnn::TensorInfo>& inputInfos,
636 const std::vector<armnn::TensorInfo>& outputInfos,
637 const Descriptor& descriptor,
640 : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
642 , m_Biases(biases) {}
645 const Descriptor& descriptor,
648 const char* name)
override 650 VerifyNameAndConnections(layer, name);
651 VerifyDescriptor(descriptor);
654 CompareConstTensor(weights, m_Weights);
657 BOOST_CHECK(biases.
has_value() == descriptor.m_BiasEnabled);
658 BOOST_CHECK(biases.
has_value() == m_Biases.has_value());
660 if (biases.
has_value() && m_Biases.has_value())
662 CompareConstTensor(biases.
value(), m_Biases.value());
671 using namespace armnn;
673 const std::string layerName(
"convolution2dWithPerAxis");
674 const TensorInfo inputInfo ({ 1, 3, 1, 2 }, DataType::QAsymmU8, 0.55f, 128);
675 const TensorInfo outputInfo({ 1, 3, 1, 3 }, DataType::QAsymmU8, 0.75f, 128);
677 const std::vector<float> quantScales{ 0.75f, 0.65f, 0.85f };
678 constexpr
unsigned int quantDimension = 0;
680 const TensorInfo kernelInfo({ 3, 1, 1, 2 }, DataType::QSymmS8, quantScales, quantDimension);
682 const std::vector<float> biasQuantScales{ 0.25f, 0.50f, 0.75f };
683 const TensorInfo biasInfo({ 3 }, DataType::Signed32, biasQuantScales, quantDimension);
685 std::vector<int8_t> kernelData = GenerateRandomData<int8_t>(kernelInfo.GetNumElements());
687 std::vector<int32_t> biasData = GenerateRandomData<int32_t>(biasInfo.GetNumElements());
703 network->AddConvolution2dLayer(descriptor,
715 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
716 BOOST_CHECK(deserializedNetwork);
718 Convolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
719 deserializedNetwork->Accept(verifier);
726 const std::string layerName(
"depthToSpace");
746 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
747 BOOST_CHECK(deserializedNetwork);
749 DepthToSpaceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
750 deserializedNetwork->Accept(verifier);
756 class DepthwiseConvolution2dLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
759 DepthwiseConvolution2dLayerVerifier(
const std::string& layerName,
760 const std::vector<armnn::TensorInfo>& inputInfos,
761 const std::vector<armnn::TensorInfo>& outputInfos,
762 const Descriptor& descriptor,
765 LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor),
770 const Descriptor& descriptor,
773 const char* name)
override 775 VerifyNameAndConnections(layer, name);
776 VerifyDescriptor(descriptor);
779 CompareConstTensor(weights, m_Weights);
782 BOOST_CHECK(biases.
has_value() == descriptor.m_BiasEnabled);
783 BOOST_CHECK(biases.
has_value() == m_Biases.has_value());
785 if (biases.
has_value() && m_Biases.has_value())
787 CompareConstTensor(biases.
value(), m_Biases.value());
796 const std::string layerName(
"depwiseConvolution2d");
803 std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());
806 std::vector<int32_t> biasesData = GenerateRandomData<int32_t>(biasesInfo.GetNumElements());
824 network->AddDepthwiseConvolution2dLayer(descriptor,
836 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
837 BOOST_CHECK(deserializedNetwork);
839 DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
840 deserializedNetwork->Accept(verifier);
846 class DepthwiseConvolution2dLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
849 DepthwiseConvolution2dLayerVerifier(
const std::string& layerName,
850 const std::vector<armnn::TensorInfo>& inputInfos,
851 const std::vector<armnn::TensorInfo>& outputInfos,
852 const Descriptor& descriptor,
855 LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor),
860 const Descriptor& descriptor,
863 const char* name)
override 865 VerifyNameAndConnections(layer, name);
866 VerifyDescriptor(descriptor);
869 CompareConstTensor(weights, m_Weights);
872 BOOST_CHECK(biases.
has_value() == descriptor.m_BiasEnabled);
873 BOOST_CHECK(biases.
has_value() == m_Biases.has_value());
875 if (biases.
has_value() && m_Biases.has_value())
877 CompareConstTensor(biases.
value(), m_Biases.value());
886 using namespace armnn;
888 const std::string layerName(
"depwiseConvolution2dWithPerAxis");
889 const TensorInfo inputInfo ({ 1, 3, 3, 2 }, DataType::QAsymmU8, 0.55f, 128);
890 const TensorInfo outputInfo({ 1, 2, 2, 4 }, DataType::QAsymmU8, 0.75f, 128);
892 const std::vector<float> quantScales{ 0.75f, 0.80f, 0.90f, 0.95f };
893 const unsigned int quantDimension = 0;
894 TensorInfo kernelInfo({ 2, 2, 2, 2 }, DataType::QSymmS8, quantScales, quantDimension);
896 const std::vector<float> biasQuantScales{ 0.25f, 0.35f, 0.45f, 0.55f };
897 constexpr
unsigned int biasQuantDimension = 0;
898 TensorInfo biasInfo({ 4 }, DataType::Signed32, biasQuantScales, biasQuantDimension);
900 std::vector<int8_t> kernelData = GenerateRandomData<int8_t>(kernelInfo.GetNumElements());
902 std::vector<int32_t> biasData = GenerateRandomData<int32_t>(biasInfo.GetNumElements());
920 network->AddDepthwiseConvolution2dLayer(descriptor,
932 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
933 BOOST_CHECK(deserializedNetwork);
935 DepthwiseConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
936 deserializedNetwork->Accept(verifier);
943 const std::string layerName(
"dequantize");
958 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
959 BOOST_CHECK(deserializedNetwork);
961 DequantizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});
962 deserializedNetwork->Accept(verifier);
968 class DetectionPostProcessLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
971 DetectionPostProcessLayerVerifier(
const std::string& layerName,
972 const std::vector<armnn::TensorInfo>& inputInfos,
973 const std::vector<armnn::TensorInfo>& outputInfos,
974 const Descriptor& descriptor,
976 : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
977 , m_Anchors(anchors) {}
980 const Descriptor& descriptor,
982 const char* name)
override 984 VerifyNameAndConnections(layer, name);
985 VerifyDescriptor(descriptor);
987 CompareConstTensor(anchors, m_Anchors);
994 const std::string layerName(
"detectionPostProcess");
996 const std::vector<armnn::TensorInfo> inputInfos({
1001 const std::vector<armnn::TensorInfo> outputInfos({
1022 const std::vector<float> anchorsData({
1023 0.5f, 0.5f, 1.0f, 1.0f,
1024 0.5f, 0.5f, 1.0f, 1.0f,
1025 0.5f, 0.5f, 1.0f, 1.0f,
1026 0.5f, 10.5f, 1.0f, 1.0f,
1027 0.5f, 10.5f, 1.0f, 1.0f,
1028 0.5f, 100.5f, 1.0f, 1.0f
1034 network->AddDetectionPostProcessLayer(descriptor, anchors, layerName.c_str());
1036 for (
unsigned int i = 0; i < 2; i++)
1043 for (
unsigned int i = 0; i < 4; i++)
1050 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1051 BOOST_CHECK(deserializedNetwork);
1053 DetectionPostProcessLayerVerifier verifier(layerName, inputInfos, outputInfos, descriptor, anchors);
1054 deserializedNetwork->Accept(verifier);
1061 const std::string layerName(
"division");
1078 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1079 BOOST_CHECK(deserializedNetwork);
1081 DivisionLayerVerifier verifier(layerName, {
info,
info}, {info});
1082 deserializedNetwork->Accept(verifier);
1085 class EqualLayerVerifier :
public LayerVerifierBase
1088 EqualLayerVerifier(
const std::string& layerName,
1089 const std::vector<armnn::TensorInfo>& inputInfos,
1090 const std::vector<armnn::TensorInfo>& outputInfos)
1091 : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
1095 const char* name)
override 1097 VerifyNameAndConnections(layer, name);
1103 throw armnn::Exception(
"EqualLayer should have translated to ComparisonLayer");
1112 const std::string layerName(
"equal");
1129 equalLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
1133 equalLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
1135 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1136 BOOST_CHECK(deserializedNetwork);
1138 EqualLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
1139 deserializedNetwork->Accept(verifier);
1149 const std::vector<uint8_t> equalModel =
1151 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
1152 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
1153 0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,
1154 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
1155 0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,
1156 0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
1157 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
1158 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1159 0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,
1160 0x00, 0x13, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,
1161 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,
1162 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x65, 0x71, 0x75, 0x61, 0x6C, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
1163 0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,
1164 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,
1165 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00,
1166 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
1167 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,
1168 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
1169 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
1170 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
1171 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
1172 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
1173 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,
1174 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
1175 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
1176 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1177 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00,
1178 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
1179 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
1180 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
1181 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
1182 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1183 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
1184 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
1185 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
1186 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
1187 0x04, 0x00, 0x00, 0x00
1190 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(equalModel.begin(), equalModel.end()));
1191 BOOST_CHECK(deserializedNetwork);
1198 EqualLayerVerifier verifier(
"equal", { inputInfo, inputInfo }, { outputInfo });
1199 deserializedNetwork->Accept(verifier);
1206 const std::string layerName(
"fill");
1223 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1224 BOOST_CHECK(deserializedNetwork);
1226 FillLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
1228 deserializedNetwork->Accept(verifier);
1235 const std::string layerName(
"floor");
1249 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1250 BOOST_CHECK(deserializedNetwork);
1252 FloorLayerVerifier verifier(layerName, {
info}, {
info});
1253 deserializedNetwork->Accept(verifier);
1259 class FullyConnectedLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
1262 FullyConnectedLayerVerifier(
const std::string& layerName,
1263 const std::vector<armnn::TensorInfo>& inputInfos,
1264 const std::vector<armnn::TensorInfo>& outputInfos,
1265 const Descriptor& descriptor,
1268 : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
1273 const Descriptor& descriptor,
1276 const char* name)
override 1278 VerifyNameAndConnections(layer, name);
1279 VerifyDescriptor(descriptor);
1281 CompareConstTensor(weight, m_Weight);
1283 BOOST_TEST(bias.
has_value() == descriptor.m_BiasEnabled);
1284 BOOST_TEST(bias.
has_value() == m_Bias.has_value());
1286 if (bias.
has_value() && m_Bias.has_value())
1288 CompareConstTensor(bias.
value(), m_Bias.value());
1297 const std::string layerName(
"fullyConnected");
1303 std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());
1304 std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());
1315 network->AddFullyConnectedLayer(descriptor,
1327 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1328 BOOST_CHECK(deserializedNetwork);
1330 FullyConnectedLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
1331 deserializedNetwork->Accept(verifier);
1337 class GatherLayerVerifier :
public LayerVerifierBaseWithDescriptor<GatherDescriptor>
1340 GatherLayerVerifier(
const std::string& layerName,
1341 const std::vector<armnn::TensorInfo>& inputInfos,
1342 const std::vector<armnn::TensorInfo>& outputInfos,
1344 : LayerVerifierBaseWithDescriptor<GatherDescriptor>(layerName, inputInfos, outputInfos, descriptor) {}
1348 const char *name)
override 1350 VerifyNameAndConnections(layer, name);
1351 BOOST_CHECK(descriptor.
m_Axis == m_Descriptor.m_Axis);
1356 const char*)
override {}
1359 const std::string layerName(
"gather");
1366 paramsInfo.SetQuantizationScale(1.0f);
1367 paramsInfo.SetQuantizationOffset(0);
1368 outputInfo.SetQuantizationScale(1.0f);
1369 outputInfo.SetQuantizationOffset(0);
1371 const std::vector<int32_t>& indicesData = {7, 6, 5};
1388 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1389 BOOST_CHECK(deserializedNetwork);
1391 GatherLayerVerifier verifier(layerName, {paramsInfo, indicesInfo}, {outputInfo}, descriptor);
1392 deserializedNetwork->Accept(verifier);
1395 class GreaterLayerVerifier :
public LayerVerifierBase
1398 GreaterLayerVerifier(
const std::string& layerName,
1399 const std::vector<armnn::TensorInfo>& inputInfos,
1400 const std::vector<armnn::TensorInfo>& outputInfos)
1401 : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
1405 const char* name)
override 1407 VerifyNameAndConnections(layer, name);
1413 throw armnn::Exception(
"GreaterLayer should have translated to ComparisonLayer");
1422 const std::string layerName(
"greater");
1439 equalLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
1443 equalLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
1445 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1446 BOOST_CHECK(deserializedNetwork);
1448 GreaterLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo });
1449 deserializedNetwork->Accept(verifier);
1459 const std::vector<uint8_t> greaterModel =
1461 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
1462 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
1463 0xCC, 0x01, 0x00, 0x00, 0x20, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,
1464 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
1465 0x60, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0xFE, 0xFE, 0xFF, 0xFF, 0x04, 0x00,
1466 0x00, 0x00, 0x06, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xEA, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
1467 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
1468 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1469 0x64, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xB4, 0xFE, 0xFF, 0xFF, 0x00, 0x00,
1470 0x00, 0x19, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,
1471 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,
1472 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x67, 0x72, 0x65, 0x61, 0x74, 0x65, 0x72, 0x00, 0x02, 0x00, 0x00, 0x00,
1473 0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,
1474 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x04, 0x08, 0x00, 0x00, 0x00,
1475 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,
1476 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
1477 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,
1478 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
1479 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
1480 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
1481 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
1482 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
1483 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,
1484 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
1485 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
1486 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1487 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,
1488 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
1489 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
1490 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
1491 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
1492 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1493 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
1494 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
1495 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
1496 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
1497 0x02, 0x00, 0x00, 0x00
1500 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(greaterModel.begin(), greaterModel.end()));
1501 BOOST_CHECK(deserializedNetwork);
1508 GreaterLayerVerifier verifier(
"greater", { inputInfo, inputInfo }, { outputInfo });
1509 deserializedNetwork->Accept(verifier);
1516 const std::string layerName(
"instanceNormalization");
1521 descriptor.m_Beta = 0.1f;
1522 descriptor.m_Eps = 0.0001f;
1528 network->AddInstanceNormalizationLayer(descriptor, layerName.c_str());
1537 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1538 BOOST_CHECK(deserializedNetwork);
1540 InstanceNormalizationLayerVerifier verifier(layerName, {
info}, {
info}, descriptor);
1541 deserializedNetwork->Accept(verifier);
1548 const std::string l2NormLayerName(
"l2Normalization");
1553 desc.m_Eps = 0.0001f;
1566 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1567 BOOST_CHECK(deserializedNetwork);
1569 L2NormalizationLayerVerifier verifier(l2NormLayerName, {
info}, {
info}, desc);
1570 deserializedNetwork->Accept(verifier);
1580 const std::vector<uint8_t> l2NormalizationModel =
1582 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
1583 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
1584 0x3C, 0x01, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
1585 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,
1586 0x04, 0x00, 0x00, 0x00, 0xD6, 0xFE, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00,
1587 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
1588 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
1589 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1590 0x4C, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x44, 0xFF, 0xFF, 0xFF, 0x00, 0x00,
1591 0x00, 0x20, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
1592 0x20, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
1593 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
1594 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x1F, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x20, 0x00,
1595 0x00, 0x00, 0x0F, 0x00, 0x00, 0x00, 0x6C, 0x32, 0x4E, 0x6F, 0x72, 0x6D, 0x61, 0x6C, 0x69, 0x7A, 0x61, 0x74,
1596 0x69, 0x6F, 0x6E, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00,
1597 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
1598 0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00,
1599 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
1600 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1601 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
1602 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
1603 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
1604 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
1605 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1606 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
1607 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
1608 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
1609 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
1610 0x05, 0x00, 0x00, 0x00, 0x00
1614 DeserializeNetwork(std::string(l2NormalizationModel.begin(), l2NormalizationModel.end()));
1615 BOOST_CHECK(deserializedNetwork);
1617 const std::string layerName(
"l2Normalization");
1623 desc.m_Eps = 1e-12f;
1625 L2NormalizationLayerVerifier verifier(layerName, {inputInfo}, {inputInfo}, desc);
1626 deserializedNetwork->Accept(verifier);
1633 const std::string layerName(
"logicalBinaryAnd");
1645 armnn::IConnectableLayer*
const logicalBinaryLayer = network->AddLogicalBinaryLayer(descriptor, layerName.c_str());
1656 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1657 BOOST_CHECK(deserializedNetwork);
1659 LogicalBinaryLayerVerifier verifier(layerName, { inputInfo, inputInfo }, { outputInfo }, descriptor);
1660 deserializedNetwork->Accept(verifier);
1667 const std::string layerName(
"elementwiseUnaryLogicalNot");
1679 network->AddElementwiseUnaryLayer(descriptor, layerName.c_str());
1688 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1690 BOOST_CHECK(deserializedNetwork);
1692 ElementwiseUnaryLayerVerifier verifier(layerName, { inputInfo }, { outputInfo }, descriptor);
1694 deserializedNetwork->Accept(verifier);
1701 const std::string layerName(
"log_softmax");
1705 descriptor.
m_Beta = 1.0f;
1706 descriptor.m_Axis = -1;
1719 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1720 BOOST_CHECK(deserializedNetwork);
1722 LogSoftmaxLayerVerifier verifier(layerName, {
info}, {
info}, descriptor);
1723 deserializedNetwork->Accept(verifier);
1730 const std::string layerName(
"maximum");
1747 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1748 BOOST_CHECK(deserializedNetwork);
1750 MaximumLayerVerifier verifier(layerName, {
info,
info}, {info});
1751 deserializedNetwork->Accept(verifier);
1758 const std::string layerName(
"mean");
1763 descriptor.
m_Axis = { 2 };
1764 descriptor.m_KeepDims =
true;
1777 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1778 BOOST_CHECK(deserializedNetwork);
1780 MeanLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
1781 deserializedNetwork->Accept(verifier);
1788 const std::string layerName(
"merge");
1805 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
1806 BOOST_CHECK(deserializedNetwork);
1808 MergeLayerVerifier verifier(layerName, {
info,
info}, {info});
1809 deserializedNetwork->Accept(verifier);
1812 class MergerLayerVerifier :
public LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor>
1815 MergerLayerVerifier(
const std::string& layerName,
1816 const std::vector<armnn::TensorInfo>& inputInfos,
1817 const std::vector<armnn::TensorInfo>& outputInfos,
1819 : LayerVerifierBaseWithDescriptor<armnn::OriginsDescriptor>(layerName, inputInfos, outputInfos, descriptor) {}
1823 const char*)
override 1825 throw armnn::Exception(
"MergerLayer should have translated to ConcatLayer");
1830 const char* name)
override 1832 VerifyNameAndConnections(layer, name);
1833 VerifyDescriptor(descriptor);
1842 const std::string layerName(
"merger");
1846 const std::vector<armnn::TensorShape> shapes({inputInfo.
GetShape(), inputInfo.
GetShape()});
1861 mergerLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
1865 mergerLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
1867 std::string mergerLayerNetwork = SerializeNetwork(*network);
1869 BOOST_CHECK(deserializedNetwork);
1871 MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);
1872 deserializedNetwork->Accept(verifier);
1882 const std::vector<uint8_t> mergerModel =
1884 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
1885 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
1886 0x38, 0x02, 0x00, 0x00, 0x8C, 0x01, 0x00, 0x00, 0x70, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00,
1887 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
1888 0xF4, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x04, 0x00,
1889 0x00, 0x00, 0x9A, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x7E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00,
1890 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
1891 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1892 0xF8, 0xFE, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x48, 0xFE, 0xFF, 0xFF, 0x00, 0x00,
1893 0x00, 0x1F, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
1894 0x68, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
1895 0x0C, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
1896 0x02, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x22, 0xFF, 0xFF, 0xFF, 0x04, 0x00,
1897 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1898 0x00, 0x00, 0x00, 0x00, 0x3E, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00,
1899 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x36, 0xFF, 0xFF, 0xFF,
1900 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x1E, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1C, 0x00,
1901 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x6D, 0x65, 0x72, 0x67, 0x65, 0x72, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
1902 0x5C, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x34, 0xFF,
1903 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x92, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
1904 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00,
1905 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
1906 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00,
1907 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
1908 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00,
1909 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00,
1910 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
1911 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
1912 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00,
1913 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
1914 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
1915 0x00, 0x00, 0x66, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1916 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00,
1917 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
1918 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00,
1919 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00,
1920 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00,
1921 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
1922 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00,
1923 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00,
1924 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
1925 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
1926 0x02, 0x00, 0x00, 0x00
1929 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(mergerModel.begin(), mergerModel.end()));
1930 BOOST_CHECK(deserializedNetwork);
1935 const std::vector<armnn::TensorShape> shapes({inputInfo.
GetShape(), inputInfo.
GetShape()});
1940 MergerLayerVerifier verifier(
"merger", { inputInfo, inputInfo }, { outputInfo }, descriptor);
1941 deserializedNetwork->Accept(verifier);
1946 const std::string layerName(
"concat");
1950 const std::vector<armnn::TensorShape> shapes({inputInfo.
GetShape(), inputInfo.
GetShape()});
1969 std::string concatLayerNetwork = SerializeNetwork(*network);
1971 BOOST_CHECK(deserializedNetwork);
1975 MergerLayerVerifier verifier(layerName, {inputInfo, inputInfo}, {outputInfo}, descriptor);
1976 deserializedNetwork->Accept(verifier);
1983 const std::string layerName(
"minimum");
2000 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2001 BOOST_CHECK(deserializedNetwork);
2003 MinimumLayerVerifier verifier(layerName, {
info,
info}, {info});
2004 deserializedNetwork->Accept(verifier);
2011 const std::string layerName(
"multiplication");
2028 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2029 BOOST_CHECK(deserializedNetwork);
2031 MultiplicationLayerVerifier verifier(layerName, {
info,
info}, {info});
2032 deserializedNetwork->Accept(verifier);
2039 const std::string layerName(
"prelu");
2059 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2060 BOOST_CHECK(deserializedNetwork);
2062 PreluLayerVerifier verifier(layerName, {inputTensorInfo, alphaTensorInfo}, {outputTensorInfo});
2063 deserializedNetwork->Accept(verifier);
2070 const std::string layerName(
"normalization");
2075 desc.m_NormSize = 3;
2091 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2092 BOOST_CHECK(deserializedNetwork);
2094 NormalizationLayerVerifier verifier(layerName, {
info}, {
info}, desc);
2095 deserializedNetwork->Accept(verifier);
2102 const std::string layerName(
"pad");
2119 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2120 BOOST_CHECK(deserializedNetwork);
2122 PadLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, desc);
2123 deserializedNetwork->Accept(verifier);
2134 const std::vector<uint8_t> padModel =
2136 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
2137 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
2138 0x54, 0x01, 0x00, 0x00, 0x6C, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
2139 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xD0, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,
2140 0x04, 0x00, 0x00, 0x00, 0x96, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x9E, 0xFF, 0xFF, 0xFF, 0x04, 0x00,
2141 0x00, 0x00, 0x72, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
2142 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00,
2143 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2C, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00,
2144 0x00, 0x00, 0x00, 0x00, 0x24, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x16, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00,
2145 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x4C, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,
2146 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00,
2147 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
2148 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00,
2149 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00, 0x00, 0x00,
2150 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00,
2151 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x70, 0x61, 0x64, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00,
2152 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
2153 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
2154 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x05, 0x00,
2155 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00,
2156 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00,
2157 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00,
2158 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
2159 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00,
2160 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
2161 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00,
2162 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
2163 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,
2164 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00,
2165 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00
2168 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(std::string(padModel.begin(), padModel.end()));
2169 BOOST_CHECK(deserializedNetwork);
2176 PadLayerVerifier verifier(
"pad", { inputInfo }, { outputInfo }, descriptor);
2177 deserializedNetwork->Accept(verifier);
2184 const std::string layerName(
"permute");
2201 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2202 BOOST_CHECK(deserializedNetwork);
2204 PermuteLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
2205 deserializedNetwork->Accept(verifier);
2212 const std::string layerName(
"pooling2d");
2219 desc.m_PadBottom = 0;
2221 desc.m_PadRight = 0;
2225 desc.m_PoolHeight = 2;
2226 desc.m_PoolWidth = 2;
2241 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2242 BOOST_CHECK(deserializedNetwork);
2244 Pooling2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
2245 deserializedNetwork->Accept(verifier);
2252 const std::string layerName(
"quantize");
2266 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2267 BOOST_CHECK(deserializedNetwork);
2269 QuantizeLayerVerifier verifier(layerName, {
info}, {
info});
2270 deserializedNetwork->Accept(verifier);
2277 const std::string layerName(
"rank");
2292 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2293 BOOST_CHECK(deserializedNetwork);
2295 RankLayerVerifier verifier(layerName, {inputInfo}, {outputInfo});
2296 deserializedNetwork->Accept(verifier);
2303 const std::string layerName(
"reshape");
2320 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2321 BOOST_CHECK(deserializedNetwork);
2323 ReshapeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
2324 deserializedNetwork->Accept(verifier);
2331 const std::string layerName(
"resize");
2337 desc.m_TargetHeight = 2;
2339 desc.m_AlignCorners =
true;
2340 desc.m_HalfPixelCenters =
true;
2353 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2354 BOOST_CHECK(deserializedNetwork);
2356 ResizeLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
2357 deserializedNetwork->Accept(verifier);
2360 class ResizeBilinearLayerVerifier :
public LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor>
2363 ResizeBilinearLayerVerifier(
const std::string& layerName,
2364 const std::vector<armnn::TensorInfo>& inputInfos,
2365 const std::vector<armnn::TensorInfo>& outputInfos,
2367 : LayerVerifierBaseWithDescriptor<armnn::ResizeBilinearDescriptor>(
2368 layerName, inputInfos, outputInfos, descriptor) {}
2372 const char* name)
override 2374 VerifyNameAndConnections(layer, name);
2377 BOOST_CHECK(descriptor.
m_TargetWidth == m_Descriptor.m_TargetWidth);
2378 BOOST_CHECK(descriptor.
m_TargetHeight == m_Descriptor.m_TargetHeight);
2379 BOOST_CHECK(descriptor.
m_DataLayout == m_Descriptor.m_DataLayout);
2380 BOOST_CHECK(descriptor.
m_AlignCorners == m_Descriptor.m_AlignCorners);
2386 const char*)
override 2388 throw armnn::Exception(
"ResizeBilinearLayer should have translated to ResizeLayer");
2397 const std::string layerName(
"resizeBilinear");
2403 desc.m_TargetHeight = 2u;
2404 desc.m_AlignCorners =
true;
2405 desc.m_HalfPixelCenters =
true;
2415 resizeLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
2418 resizeLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
2420 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2421 BOOST_CHECK(deserializedNetwork);
2423 ResizeBilinearLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
2424 deserializedNetwork->Accept(verifier);
2434 const std::vector<uint8_t> resizeBilinearModel =
2436 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
2437 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
2438 0x50, 0x01, 0x00, 0x00, 0x74, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00,
2439 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xD4, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x0B,
2440 0x04, 0x00, 0x00, 0x00, 0xC2, 0xFE, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00,
2441 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x8A, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
2442 0x10, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00,
2443 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
2444 0x38, 0xFF, 0xFF, 0xFF, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0xFF, 0xFF, 0xFF, 0x00, 0x00,
2445 0x00, 0x1A, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00,
2446 0x34, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x12, 0x00, 0x08, 0x00, 0x0C, 0x00,
2447 0x07, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x04, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
2448 0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0E, 0x00,
2449 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1C, 0x00, 0x00, 0x00,
2450 0x20, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x72, 0x65, 0x73, 0x69, 0x7A, 0x65, 0x42, 0x69, 0x6C, 0x69,
2451 0x6E, 0x65, 0x61, 0x72, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
2452 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00,
2453 0x00, 0x00, 0x52, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
2454 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00,
2455 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
2456 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
2457 0x00, 0x09, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00,
2458 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00,
2459 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
2460 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
2461 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00,
2462 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00,
2463 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
2464 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x05, 0x00,
2465 0x00, 0x00, 0x05, 0x00, 0x00, 0x00
2469 DeserializeNetwork(std::string(resizeBilinearModel.begin(), resizeBilinearModel.end()));
2470 BOOST_CHECK(deserializedNetwork);
2477 descriptor.m_TargetHeight = 2u;
2479 ResizeBilinearLayerVerifier verifier(
"resizeBilinear", { inputInfo }, { outputInfo }, descriptor);
2480 deserializedNetwork->Accept(verifier);
2487 const std::string layerName{
"slice"};
2506 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2507 BOOST_CHECK(deserializedNetwork);
2509 SliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor);
2510 deserializedNetwork->Accept(verifier);
2517 const std::string layerName(
"softmax");
2521 descriptor.
m_Beta = 1.0f;
2534 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2535 BOOST_CHECK(deserializedNetwork);
2537 SoftmaxLayerVerifier verifier(layerName, {
info}, {
info}, descriptor);
2538 deserializedNetwork->Accept(verifier);
2545 const std::string layerName(
"spaceToBatchNd");
2551 desc.m_BlockShape = {2, 2};
2552 desc.m_PadList = {{0, 0}, {2, 0}};
2565 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2566 BOOST_CHECK(deserializedNetwork);
2568 SpaceToBatchNdLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
2569 deserializedNetwork->Accept(verifier);
2576 const std::string layerName(
"spaceToDepth");
2596 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2597 BOOST_CHECK(deserializedNetwork);
2599 SpaceToDepthLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
2600 deserializedNetwork->Accept(verifier);
2607 const unsigned int numViews = 3;
2608 const unsigned int numDimensions = 4;
2609 const unsigned int inputShape[] = {1, 18, 4, 4};
2610 const unsigned int outputShape[] = {1, 6, 4, 4};
2613 unsigned int splitterDimSizes[4] = {
static_cast<unsigned int>(inputShape[0]),
2614 static_cast<unsigned int>(inputShape[1]),
2615 static_cast<unsigned int>(inputShape[2]),
2616 static_cast<unsigned int>(inputShape[3])};
2617 splitterDimSizes[1] /= numViews;
2620 for (
unsigned int g = 0; g < numViews; ++g)
2624 for (
unsigned int dimIdx=0; dimIdx < 4; dimIdx++)
2626 desc.
SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]);
2630 const std::string layerName(
"splitter");
2651 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2652 BOOST_CHECK(deserializedNetwork);
2654 SplitterLayerVerifier verifier(layerName, {inputInfo}, {outputInfo, outputInfo, outputInfo}, desc);
2655 deserializedNetwork->Accept(verifier);
2662 const std::string layerName(
"stack");
2683 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2684 BOOST_CHECK(deserializedNetwork);
2686 StackLayerVerifier verifier(layerName, {inputTensorInfo, inputTensorInfo}, {outputTensorInfo}, descriptor);
2687 deserializedNetwork->Accept(verifier);
2694 const std::string layerName(
"standIn");
2718 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2719 BOOST_CHECK(deserializedNetwork);
2721 StandInLayerVerifier verifier(layerName, { tensorInfo, tensorInfo }, { tensorInfo, tensorInfo }, descriptor);
2722 deserializedNetwork->Accept(verifier);
2729 const std::string layerName(
"stridedSlice");
2733 armnn::StridedSliceDescriptor desc({0, 0, 1, 0}, {1, 1, 1, 1}, {1, 1, 1, 1});
2735 desc.m_ShrinkAxisMask = (1 << 1) | (1 << 2);
2749 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2750 BOOST_CHECK(deserializedNetwork);
2752 StridedSliceLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, desc);
2753 deserializedNetwork->Accept(verifier);
2760 const std::string layerName(
"subtraction");
2777 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2778 BOOST_CHECK(deserializedNetwork);
2780 SubtractionLayerVerifier verifier(layerName, {
info,
info}, {info});
2781 deserializedNetwork->Accept(verifier);
2786 class SwitchLayerVerifier :
public LayerVerifierBase
2789 SwitchLayerVerifier(
const std::string& layerName,
2790 const std::vector<armnn::TensorInfo>& inputInfos,
2791 const std::vector<armnn::TensorInfo>& outputInfos)
2792 : LayerVerifierBase(layerName, inputInfos, outputInfos) {}
2796 VerifyNameAndConnections(layer, name);
2801 const char*)
override {}
2804 const std::string layerName(
"switch");
2807 std::vector<float> constantData = GenerateRandomData<float>(
info.GetNumElements());
2827 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2828 BOOST_CHECK(deserializedNetwork);
2830 SwitchLayerVerifier verifier(layerName, {
info,
info}, {info, info});
2831 deserializedNetwork->Accept(verifier);
2838 const std::string layerName(
"transpose");
2855 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2856 BOOST_CHECK(deserializedNetwork);
2858 TransposeLayerVerifier verifier(layerName, {inputTensorInfo}, {outputTensorInfo}, descriptor);
2859 deserializedNetwork->Accept(verifier);
2865 class TransposeConvolution2dLayerVerifier :
public LayerVerifierBaseWithDescriptor<Descriptor>
2868 TransposeConvolution2dLayerVerifier(
const std::string& layerName,
2869 const std::vector<armnn::TensorInfo>& inputInfos,
2870 const std::vector<armnn::TensorInfo>& outputInfos,
2871 const Descriptor& descriptor,
2874 : LayerVerifierBaseWithDescriptor<Descriptor>(layerName, inputInfos, outputInfos, descriptor)
2875 , m_Weights(weights)
2880 const Descriptor& descriptor,
2883 const char* name)
override 2885 VerifyNameAndConnections(layer, name);
2886 VerifyDescriptor(descriptor);
2889 CompareConstTensor(weights, m_Weights);
2892 BOOST_CHECK(biases.
has_value() == descriptor.m_BiasEnabled);
2893 BOOST_CHECK(biases.
has_value() == m_Biases.has_value());
2895 if (biases.
has_value() && m_Biases.has_value())
2897 CompareConstTensor(biases.
value(), m_Biases.value());
2906 const std::string layerName(
"transposeConvolution2d");
2913 std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements());
2916 std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements());
2932 network->AddTransposeConvolution2dLayer(descriptor,
2944 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2945 BOOST_CHECK(deserializedNetwork);
2947 TransposeConvolution2dLayerVerifier verifier(layerName, {inputInfo}, {outputInfo}, descriptor, weights, biases);
2948 deserializedNetwork->Accept(verifier);
2953 class ConstantLayerVerifier :
public LayerVerifierBase
2956 ConstantLayerVerifier(
const std::string& layerName,
2957 const std::vector<armnn::TensorInfo>& inputInfos,
2958 const std::vector<armnn::TensorInfo>& outputInfos,
2960 : LayerVerifierBase(layerName, inputInfos, outputInfos)
2961 , m_LayerInput(layerInput) {}
2965 const char* name)
override 2967 VerifyNameAndConnections(layer, name);
2968 CompareConstTensor(input, m_LayerInput);
2977 const std::string layerName(
"constant");
2980 std::vector<float> constantData = GenerateRandomData<float>(
info.GetNumElements());
2997 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
2998 BOOST_CHECK(deserializedNetwork);
3000 ConstantLayerVerifier verifier(layerName, {}, {
info}, constTensor);
3001 deserializedNetwork->Accept(verifier);
3004 class VerifyLstmLayer :
public LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor>
3007 VerifyLstmLayer(
const std::string& layerName,
3008 const std::vector<armnn::TensorInfo>& inputInfos,
3009 const std::vector<armnn::TensorInfo>& outputInfos,
3012 : LayerVerifierBaseWithDescriptor<armnn::LstmDescriptor>(layerName, inputInfos, outputInfos, descriptor)
3013 , m_InputParams(inputParams) {}
3020 VerifyNameAndConnections(layer, name);
3021 VerifyDescriptor(descriptor);
3022 VerifyInputParameters(params);
3051 "m_InputGateBias", m_InputParams.m_InputGateBias, params.
m_InputGateBias);
3053 "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.
m_ForgetGateBias);
3055 "m_CellBias", m_InputParams.m_CellBias, params.
m_CellBias);
3057 "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.
m_OutputGateBias);
3061 "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.
m_ProjectionBias);
3086 const uint32_t batchSize = 1;
3087 const uint32_t inputSize = 2;
3088 const uint32_t numUnits = 4;
3089 const uint32_t outputSize = numUnits;
3092 std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
3093 armnn::ConstTensor inputToForgetWeights(inputWeightsInfo1, inputToForgetWeightsData);
3095 std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
3098 std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo1.GetNumElements());
3099 armnn::ConstTensor inputToOutputWeights(inputWeightsInfo1, inputToOutputWeightsData);
3102 std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
3103 armnn::ConstTensor recurrentToForgetWeights(inputWeightsInfo2, recurrentToForgetWeightsData);
3105 std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
3106 armnn::ConstTensor recurrentToCellWeights(inputWeightsInfo2, recurrentToCellWeightsData);
3108 std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo2.GetNumElements());
3109 armnn::ConstTensor recurrentToOutputWeights(inputWeightsInfo2, recurrentToOutputWeightsData);
3112 std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
3115 std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(inputWeightsInfo3.GetNumElements());
3118 std::vector<float> forgetGateBiasData(numUnits, 1.0f);
3121 std::vector<float> cellBiasData(numUnits, 0.0f);
3124 std::vector<float> outputGateBiasData(numUnits, 0.0f);
3144 const std::string layerName(
"lstm");
3178 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
3179 BOOST_CHECK(deserializedNetwork);
3181 VerifyLstmLayer checker(
3183 {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
3184 {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
3187 deserializedNetwork->Accept(checker);
3200 const uint32_t batchSize = 2;
3201 const uint32_t inputSize = 5;
3202 const uint32_t numUnits = 20;
3203 const uint32_t outputSize = 16;
3206 std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3209 std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3212 std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3215 std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3219 std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3222 std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3225 std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3228 std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3232 std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3233 armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
3235 std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3236 armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
3238 std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3239 armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
3241 std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3242 armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
3244 std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3247 std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3250 std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3254 std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
3258 std::vector<float> projectionBiasData(outputSize, 0.f);
3290 const std::string layerName(
"lstm");
3324 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
3325 BOOST_CHECK(deserializedNetwork);
3327 VerifyLstmLayer checker(
3329 {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
3330 {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
3333 deserializedNetwork->Accept(checker);
3347 const uint32_t batchSize = 2;
3348 const uint32_t inputSize = 5;
3349 const uint32_t numUnits = 20;
3350 const uint32_t outputSize = 16;
3353 std::vector<float> inputToInputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3356 std::vector<float> inputToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3359 std::vector<float> inputToCellWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3362 std::vector<float> inputToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x5.GetNumElements());
3366 std::vector<float> inputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3369 std::vector<float> forgetGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3372 std::vector<float> cellBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3375 std::vector<float> outputGateBiasData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3379 std::vector<float> recurrentToInputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3380 armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
3382 std::vector<float> recurrentToForgetWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3383 armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
3385 std::vector<float> recurrentToCellWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3386 armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
3388 std::vector<float> recurrentToOutputWeightsData = GenerateRandomData<float>(tensorInfo20x16.GetNumElements());
3389 armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
3391 std::vector<float> cellToInputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3394 std::vector<float> cellToForgetWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3397 std::vector<float> cellToOutputWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3401 std::vector<float> projectionWeightsData = GenerateRandomData<float>(tensorInfo16x20.GetNumElements());
3405 std::vector<float> projectionBiasData(outputSize, 0.f);
3408 std::vector<float> inputLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3411 std::vector<float> forgetLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3414 std::vector<float> cellLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3417 std::vector<float> outLayerNormWeightsData = GenerateRandomData<float>(tensorInfo20.GetNumElements());
3455 const std::string layerName(
"lstm");
3489 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
3490 BOOST_CHECK(deserializedNetwork);
3492 VerifyLstmLayer checker(
3494 {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
3495 {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
3498 deserializedNetwork->Accept(checker);
3507 const std::vector<uint8_t> lstmNoCifgWithPeepholeAndProjectionModel =
3509 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x0A, 0x00,
3510 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x2C, 0x00, 0x00, 0x00, 0x38, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
3511 0xDC, 0x29, 0x00, 0x00, 0x38, 0x29, 0x00, 0x00, 0xB4, 0x28, 0x00, 0x00, 0x94, 0x01, 0x00, 0x00, 0x3C, 0x01,
3512 0x00, 0x00, 0xE0, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
3513 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00,
3514 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x70, 0xD6, 0xFF, 0xFF,
3515 0x00, 0x00, 0x00, 0x0B, 0x04, 0x00, 0x00, 0x00, 0x06, 0xD7, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x88, 0xD7,
3516 0xFF, 0xFF, 0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xF6, 0xD6, 0xFF, 0xFF, 0x07, 0x00, 0x00, 0x00,
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4074 0x01, 0x01, 0x04, 0x00, 0x00, 0x00, 0x2E, 0xFE, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
4075 0x22, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6C, 0x73,
4076 0x74, 0x6D, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0xEC, 0x00, 0x00, 0x00, 0xD0, 0x00, 0x00, 0x00,
4077 0xB4, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x88, 0x00, 0x00, 0x00, 0x5C, 0x00, 0x00, 0x00, 0x30, 0x00,
4078 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0xFF, 0xFF, 0xFF, 0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
4079 0xA6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,
4080 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3C, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
4081 0x04, 0x00, 0x00, 0x00, 0xCE, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
4082 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x64, 0xFF, 0xFF, 0xFF,
4083 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xF6, 0xFD, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
4084 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
4085 0xB4, 0xFE, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0x1A, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
4086 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x50, 0x00, 0x00, 0x00,
4087 0xF0, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00,
4088 0x10, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
4089 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00,
4090 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xE8, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,
4091 0x7E, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00,
4092 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x76, 0xFF, 0xFF, 0xFF, 0x02, 0x00, 0x00, 0x00,
4093 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00,
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4095 0x68, 0xFF, 0xFF, 0xFF, 0x04, 0x00, 0x00, 0x00, 0xCE, 0xFE, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00,
4096 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
4097 0x08, 0x00, 0x0E, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x0C, 0x00,
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4099 0x08, 0x00, 0x0E, 0x00, 0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x01, 0x00,
4100 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0C, 0x00, 0x10, 0x00, 0x14, 0x00,
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4102 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
4103 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
4104 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x6E, 0xFF, 0xFF, 0xFF, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00,
4105 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x08, 0x00,
4106 0x0C, 0x00, 0x07, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x04, 0x00, 0x00, 0x00,
4107 0xF6, 0xFF, 0xFF, 0xFF, 0x0C, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x0A, 0x00, 0x04, 0x00, 0x06, 0x00,
4108 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0E, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
4109 0x0C, 0x00, 0x10, 0x00, 0x0E, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00,
4110 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
4111 0x01, 0x00, 0x00, 0x00, 0x0C, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0A, 0x00, 0x00, 0x00, 0x04, 0x00, 0x08, 0x00,
4112 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0A, 0x00, 0x10, 0x00, 0x08, 0x00, 0x07, 0x00, 0x0C, 0x00,
4113 0x0A, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00,
4114 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00
4118 DeserializeNetwork(std::string(lstmNoCifgWithPeepholeAndProjectionModel.begin(),
4119 lstmNoCifgWithPeepholeAndProjectionModel.end()));
4121 BOOST_CHECK(deserializedNetwork);
4132 const uint32_t batchSize = 2u;
4133 const uint32_t inputSize = 5u;
4134 const uint32_t numUnits = 20u;
4135 const uint32_t outputSize = 16u;
4138 std::vector<float> inputToInputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
4141 std::vector<float> inputToForgetWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
4144 std::vector<float> inputToCellWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
4147 std::vector<float> inputToOutputWeightsData(tensorInfo20x5.GetNumElements(), 0.0f);
4151 std::vector<float> inputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
4154 std::vector<float> forgetGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
4157 std::vector<float> cellBiasData(tensorInfo20.GetNumElements(), 0.0f);
4160 std::vector<float> outputGateBiasData(tensorInfo20.GetNumElements(), 0.0f);
4164 std::vector<float> recurrentToInputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
4165 armnn::ConstTensor recurrentToInputWeights(tensorInfo20x16, recurrentToInputWeightsData);
4167 std::vector<float> recurrentToForgetWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
4168 armnn::ConstTensor recurrentToForgetWeights(tensorInfo20x16, recurrentToForgetWeightsData);
4170 std::vector<float> recurrentToCellWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
4171 armnn::ConstTensor recurrentToCellWeights(tensorInfo20x16, recurrentToCellWeightsData);
4173 std::vector<float> recurrentToOutputWeightsData(tensorInfo20x16.GetNumElements(), 0.0f);
4174 armnn::ConstTensor recurrentToOutputWeights(tensorInfo20x16, recurrentToOutputWeightsData);
4176 std::vector<float> cellToInputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
4179 std::vector<float> cellToForgetWeightsData(tensorInfo20.GetNumElements(), 0.0f);
4182 std::vector<float> cellToOutputWeightsData(tensorInfo20.GetNumElements(), 0.0f);
4186 std::vector<float> projectionWeightsData(tensorInfo16x20.GetNumElements(), 0.0f);
4190 std::vector<float> projectionBiasData(outputSize, 0.0f);
4218 const std::string layerName(
"lstm");
4224 VerifyLstmLayer checker(
4226 {inputTensorInfo, outputStateTensorInfo, cellStateTensorInfo},
4227 {lstmTensorInfoScratchBuff, outputStateTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
4230 deserializedNetwork->Accept(checker);
4232 class VerifyQuantizedLstmLayer :
public LayerVerifierBase
4236 VerifyQuantizedLstmLayer(
const std::string& layerName,
4237 const std::vector<armnn::TensorInfo>& inputInfos,
4238 const std::vector<armnn::TensorInfo>& outputInfos,
4240 : LayerVerifierBase(layerName, inputInfos, outputInfos), m_InputParams(inputParams) {}
4246 VerifyNameAndConnections(layer, name);
4247 VerifyInputParameters(params);
4253 VerifyConstTensors(
"m_InputToInputWeights",
4255 VerifyConstTensors(
"m_InputToForgetWeights",
4257 VerifyConstTensors(
"m_InputToCellWeights",
4259 VerifyConstTensors(
"m_InputToOutputWeights",
4261 VerifyConstTensors(
"m_RecurrentToInputWeights",
4263 VerifyConstTensors(
"m_RecurrentToForgetWeights",
4265 VerifyConstTensors(
"m_RecurrentToCellWeights",
4267 VerifyConstTensors(
"m_RecurrentToOutputWeights",
4269 VerifyConstTensors(
"m_InputGateBias",
4271 VerifyConstTensors(
"m_ForgetGateBias",
4273 VerifyConstTensors(
"m_CellBias",
4274 m_InputParams.m_CellBias, params.
m_CellBias);
4275 VerifyConstTensors(
"m_OutputGateBias",
4285 const uint32_t batchSize = 1;
4286 const uint32_t inputSize = 2;
4287 const uint32_t numUnits = 4;
4288 const uint32_t outputSize = numUnits;
4291 float inputOutputScale = 0.0078125f;
4292 int32_t inputOutputOffset = 128;
4294 float cellStateScale = 0.00048828125f;
4295 int32_t cellStateOffset = 0;
4297 float weightsScale = 0.00408021f;
4298 int32_t weightsOffset = 100;
4300 float biasScale = 3.1876640625e-05f;
4301 int32_t biasOffset = 0;
4305 std::vector<uint8_t> inputToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
4310 armnn::ConstTensor inputToInputWeights(inputToInputWeightsInfo, inputToInputWeightsData);
4313 std::vector<uint8_t> inputToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
4318 armnn::ConstTensor inputToForgetWeights(inputToForgetWeightsInfo, inputToForgetWeightsData);
4321 std::vector<uint8_t> inputToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
4326 armnn::ConstTensor inputToCellWeights(inputToCellWeightsInfo, inputToCellWeightsData);
4329 std::vector<uint8_t> inputToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8};
4334 armnn::ConstTensor inputToOutputWeights(inputToOutputWeightsInfo, inputToOutputWeightsData);
4338 std::vector<uint8_t> recurrentToInputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
4343 armnn::ConstTensor recurrentToInputWeights(recurrentToInputWeightsInfo, recurrentToInputWeightsData);
4346 std::vector<uint8_t> recurrentToForgetWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
4351 armnn::ConstTensor recurrentToForgetWeights(recurrentToForgetWeightsInfo, recurrentToForgetWeightsData);
4354 std::vector<uint8_t> recurrentToCellWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
4359 armnn::ConstTensor recurrentToCellWeights(recurrentToCellWeightsInfo, recurrentToCellWeightsData);
4362 std::vector<uint8_t> recurrentToOutputWeightsData = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16};
4367 armnn::ConstTensor recurrentToOutputWeights(recurrentToOutputWeightsInfo, recurrentToOutputWeightsData);
4371 std::vector<int32_t> inputGateBiasData = {1, 2, 3, 4};
4379 std::vector<int32_t> forgetGateBiasData = {1, 2, 3, 4};
4387 std::vector<int32_t> cellBiasData = {1, 2, 3, 4};
4395 std::vector<int32_t> outputGateBiasData = {1, 2, 3, 4};
4420 const std::string layerName(
"QuantizedLstm");
4454 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
4455 BOOST_CHECK(deserializedNetwork);
4457 VerifyQuantizedLstmLayer checker(layerName,
4458 {inputTensorInfo, cellStateTensorInfo, outputStateTensorInfo},
4459 {cellStateTensorInfo, outputStateTensorInfo},
4462 deserializedNetwork->Accept(checker);
4465 class VerifyQLstmLayer :
public LayerVerifierBaseWithDescriptor<armnn::QLstmDescriptor>
4468 VerifyQLstmLayer(
const std::string& layerName,
4469 const std::vector<armnn::TensorInfo>& inputInfos,
4470 const std::vector<armnn::TensorInfo>& outputInfos,
4473 : LayerVerifierBaseWithDescriptor<armnn::QLstmDescriptor>(layerName, inputInfos, outputInfos, descriptor)
4474 , m_InputParams(inputParams) {}
4481 VerifyNameAndConnections(layer, name);
4482 VerifyDescriptor(descriptor);
4483 VerifyInputParameters(params);
4512 "m_InputGateBias", m_InputParams.m_InputGateBias, params.
m_InputGateBias);
4514 "m_ForgetGateBias", m_InputParams.m_ForgetGateBias, params.
m_ForgetGateBias);
4516 "m_CellBias", m_InputParams.m_CellBias, params.
m_CellBias);
4518 "m_OutputGateBias", m_InputParams.m_OutputGateBias, params.
m_OutputGateBias);
4522 "m_ProjectionBias", m_InputParams.m_ProjectionBias, params.
m_ProjectionBias);
4557 const unsigned int numBatches = 2;
4558 const unsigned int inputSize = 5;
4559 const unsigned int outputSize = 4;
4560 const unsigned int numUnits = 4;
4563 float inputScale = 0.0078f;
4564 int32_t inputOffset = 0;
4566 float outputScale = 0.0078f;
4567 int32_t outputOffset = 0;
4569 float cellStateScale = 3.5002e-05f;
4570 int32_t cellStateOffset = 0;
4572 float weightsScale = 0.007f;
4573 int32_t weightsOffset = 0;
4575 float biasScale = 3.5002e-05f / 1024;
4576 int32_t biasOffset = 0;
4591 std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4592 std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4593 std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4599 std::vector<int8_t> recurrentToForgetWeightsData =
4600 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4601 std::vector<int8_t> recurrentToCellWeightsData =
4602 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4603 std::vector<int8_t> recurrentToOutputWeightsData =
4604 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4606 armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
4607 armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
4608 armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
4610 std::vector<int32_t> forgetGateBiasData(numUnits, 1);
4611 std::vector<int32_t> cellBiasData(numUnits, 0);
4612 std::vector<int32_t> outputGateBiasData(numUnits, 0);
4634 const std::string layerName(
"qLstm");
4681 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
4682 BOOST_CHECK(deserializedNetwork);
4684 VerifyQLstmLayer checker(layerName,
4685 {inputInfo, cellStateInfo, outputStateInfo},
4686 {outputStateInfo, cellStateInfo, outputStateInfo},
4690 deserializedNetwork->Accept(checker);
4714 const unsigned int numBatches = 2;
4715 const unsigned int inputSize = 5;
4716 const unsigned int outputSize = 4;
4717 const unsigned int numUnits = 4;
4720 float inputScale = 0.0078f;
4721 int32_t inputOffset = 0;
4723 float outputScale = 0.0078f;
4724 int32_t outputOffset = 0;
4726 float cellStateScale = 3.5002e-05f;
4727 int32_t cellStateOffset = 0;
4729 float weightsScale = 0.007f;
4730 int32_t weightsOffset = 0;
4732 float layerNormScale = 3.5002e-05f;
4733 int32_t layerNormOffset = 0;
4735 float biasScale = layerNormScale / 1024;
4736 int32_t biasOffset = 0;
4760 std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4761 std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4762 std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4768 std::vector<int8_t> recurrentToForgetWeightsData =
4769 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4770 std::vector<int8_t> recurrentToCellWeightsData =
4771 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4772 std::vector<int8_t> recurrentToOutputWeightsData =
4773 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4775 armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
4776 armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
4777 armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
4779 std::vector<int32_t> forgetGateBiasData(numUnits, 1);
4780 std::vector<int32_t> cellBiasData(numUnits, 0);
4781 std::vector<int32_t> outputGateBiasData(numUnits, 0);
4788 std::vector<int16_t> forgetLayerNormWeightsData =
4789 GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
4790 std::vector<int16_t> cellLayerNormWeightsData =
4791 GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
4792 std::vector<int16_t> outputLayerNormWeightsData =
4793 GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
4795 armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
4796 armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
4797 armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
4822 const std::string layerName(
"qLstm");
4869 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
4870 BOOST_CHECK(deserializedNetwork);
4872 VerifyQLstmLayer checker(layerName,
4873 {inputInfo, cellStateInfo, outputStateInfo},
4874 {outputStateInfo, cellStateInfo, outputStateInfo},
4878 deserializedNetwork->Accept(checker);
4901 const unsigned int numBatches = 2;
4902 const unsigned int inputSize = 5;
4903 const unsigned int outputSize = 4;
4904 const unsigned int numUnits = 4;
4907 float inputScale = 0.0078f;
4908 int32_t inputOffset = 0;
4910 float outputScale = 0.0078f;
4911 int32_t outputOffset = 0;
4913 float cellStateScale = 3.5002e-05f;
4914 int32_t cellStateOffset = 0;
4916 float weightsScale = 0.007f;
4917 int32_t weightsOffset = 0;
4919 float layerNormScale = 3.5002e-05f;
4920 int32_t layerNormOffset = 0;
4922 float biasScale = layerNormScale / 1024;
4923 int32_t biasOffset = 0;
4957 std::vector<int8_t> inputToForgetWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4958 std::vector<int8_t> inputToCellWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4959 std::vector<int8_t> inputToOutputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4965 std::vector<int8_t> recurrentToForgetWeightsData =
4966 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4967 std::vector<int8_t> recurrentToCellWeightsData =
4968 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4969 std::vector<int8_t> recurrentToOutputWeightsData =
4970 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4972 armnn::ConstTensor recurrentToForgetWeights(recurrentWeightsInfo, recurrentToForgetWeightsData);
4973 armnn::ConstTensor recurrentToCellWeights(recurrentWeightsInfo, recurrentToCellWeightsData);
4974 armnn::ConstTensor recurrentToOutputWeights(recurrentWeightsInfo, recurrentToOutputWeightsData);
4976 std::vector<int32_t> forgetGateBiasData(numUnits, 1);
4977 std::vector<int32_t> cellBiasData(numUnits, 0);
4978 std::vector<int32_t> outputGateBiasData(numUnits, 0);
4985 std::vector<int8_t> inputToInputWeightsData = GenerateRandomData<int8_t>(inputWeightsInfo.GetNumElements());
4986 std::vector<int8_t> recurrentToInputWeightsData =
4987 GenerateRandomData<int8_t>(recurrentWeightsInfo.GetNumElements());
4988 std::vector<int32_t> inputGateBiasData(numUnits, 1);
4991 armnn::ConstTensor recurrentToInputWeights(recurrentWeightsInfo, recurrentToInputWeightsData);
4995 std::vector<int16_t> cellToInputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
4996 std::vector<int16_t> cellToForgetWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
4997 std::vector<int16_t> cellToOutputWeightsData = GenerateRandomData<int16_t>(peepholeWeightsInfo.GetNumElements());
5000 armnn::ConstTensor cellToForgetWeights(peepholeWeightsInfo, cellToForgetWeightsData);
5001 armnn::ConstTensor cellToOutputWeights(peepholeWeightsInfo, cellToOutputWeightsData);
5004 std::vector<int8_t> projectionWeightsData = GenerateRandomData<int8_t>(projectionWeightsInfo.GetNumElements());
5005 std::vector<int32_t> projectionBiasData(outputSize, 1);
5011 std::vector<int16_t> inputLayerNormWeightsData =
5012 GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
5013 std::vector<int16_t> forgetLayerNormWeightsData =
5014 GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
5015 std::vector<int16_t> cellLayerNormWeightsData =
5016 GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
5017 std::vector<int16_t> outputLayerNormWeightsData =
5018 GenerateRandomData<int16_t>(layerNormWeightsInfo.GetNumElements());
5020 armnn::ConstTensor inputLayerNormWeights(layerNormWeightsInfo, inputLayerNormWeightsData);
5021 armnn::ConstTensor forgetLayerNormWeights(layerNormWeightsInfo, forgetLayerNormWeightsData);
5022 armnn::ConstTensor cellLayerNormWeights(layerNormWeightsInfo, cellLayerNormWeightsData);
5023 armnn::ConstTensor outputLayerNormWeights(layerNormWeightsInfo, outputLayerNormWeightsData);
5063 const std::string layerName(
"qLstm");
5110 armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(SerializeNetwork(*network));
5111 BOOST_CHECK(deserializedNetwork);
5113 VerifyQLstmLayer checker(layerName,
5114 {inputInfo, cellStateInfo, outputStateInfo},
5115 {outputStateInfo, cellStateInfo, outputStateInfo},
5119 deserializedNetwork->Accept(checker);
BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)
uint32_t m_PadBottom
Padding bottom value in the height dimension.
bool m_BiasEnabled
Enable/disable bias.
virtual unsigned int GetNumOutputSlots() const =0
Returns the number of connectable output slots.
bool m_ProjectionEnabled
Enable/disable the projection layer.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
float Dequantize(QuantizedType value, float scale, int32_t offset)
Dequantize an 8-bit data type into a floating point data type.
A ViewsDescriptor for the SplitterLayer.
Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.
float m_ScaleW
Center size encoding scale weight.
bool m_BiasEnabled
Enable/disable bias.
virtual unsigned int GetNumInputSlots() const =0
Returns the number of connectable input slots.
void Slice(const TensorInfo &inputInfo, const SliceDescriptor &descriptor, const void *inputData, void *outputData, unsigned int dataTypeSize)
A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer.
const TensorShape & GetShape() const
uint32_t m_PadBottom
Padding bottom value in the height dimension.
float m_ClippingThresProj
Clipping threshold value for the projection.
A ReshapeDescriptor for the ReshapeLayer.
#define ARMNN_NO_DEPRECATE_WARN_BEGIN
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
void Fill(Encoder< float > &output, const TensorShape &desiredOutputShape, const float value)
Creates a tensor and fills it with a scalar value.
A ComparisonDescriptor for the ComparisonLayer.
float m_ScaleX
Center size encoding scale x.
uint32_t m_TargetWidth
Target width value.
bool m_TransposeWeightMatrix
Enable/disable transpose weight matrix.
bool m_PeepholeEnabled
Enable/disable peephole.
A Convolution2dDescriptor for the Convolution2dLayer.
uint32_t m_PadLeft
Padding left value in the width dimension.
float m_HiddenStateScale
Hidden State quantization scale.
bool m_BiasEnabled
Enable/disable bias.
const TensorShape & GetShape() const
float m_OutputIntermediateScale
Output intermediate quantization scale.
ResizeMethod m_Method
The Interpolation method to use (Bilinear, NearestNeighbor).
float m_Gamma
Gamma, the scale scalar value applied for the normalized tensor. Defaults to 1.0. ...
float m_Beta
Exponentiation value.
The padding fields don't count and are ignored.
float m_Eps
Value to add to the variance. Used to avoid dividing by zero.
ArgMinMaxFunction m_Function
Specify if the function is to find Min or Max.
uint32_t m_DetectionsPerClass
Detections per classes, used in Regular NMS.
armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32)
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Main network class which provides the interface for building up a neural network. ...
unsigned int GetNumElements() const
A LogicalBinaryDescriptor for the LogicalBinaryLayer.
void Serialize(const armnn::INetwork &inNetwork) override
Serializes the network to ArmNN SerializedGraph.
void Transpose(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)
void DepthToSpace(const TensorInfo &inputInfo, const DepthToSpaceDescriptor &descriptor, const void *inputData, void *outputData, unsigned int dataTypeSize)
uint32_t m_PadRight
Padding right value in the width dimension.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
MemoryType GetMemoryArea() const
void ArgMinMax(Decoder< float > &in, OUT *out, const TensorInfo &inputTensorInfo, const TensorInfo &outputTensorInfo, ArgMinMaxFunction function, int axis)
Copyright (c) 2020 ARM Limited.
uint32_t m_PadBottom
Padding bottom value in the height dimension.
uint32_t m_DilationY
Dilation along y axis.
int32_t m_EndMask
End mask value.
A SpaceToDepthDescriptor for the SpaceToDepthLayer.
uint32_t m_DilationY
Dilation factor value for height dimension.
#define DECLARE_LAYER_VERIFIER_CLASS(name)
A BatchToSpaceNdDescriptor for the BatchToSpaceNdLayer.
int LayerBindingId
Type of identifiers for bindable layers (inputs, outputs).
void Stack(const StackQueueDescriptor &data, std::vector< std::unique_ptr< Decoder< float >>> &inputs, Encoder< float > &output)
virtual void SetTensorInfo(const TensorInfo &tensorInfo)=0
constexpr const char * GetDataTypeName(DataType dataType)
A ResizeDescriptor for the ResizeLayer.
BOOST_AUTO_TEST_CASE(SerializeAddition)
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
uint32_t m_MaxClassesPerDetection
Maximum numbers of classes per detection, used in Fast NMS.
std::vector< unsigned int > m_Axis
Values for the dimensions to reduce.
A StackDescriptor for the StackLayer.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
uint32_t m_PadTop
Padding top value in the height dimension.
uint32_t m_MaxDetections
Maximum numbers of detections.
A PadDescriptor for the PadLayer.
void Permute(const armnn::TensorShape &dstShape, const armnn::PermutationVector &mappings, const void *src, void *dst, size_t dataTypeSize)
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
bool m_LayerNormEnabled
Enable/disable layer normalization.
float m_NmsIouThreshold
Intersection over union threshold.
An LstmDescriptor for the LstmLayer.
#define ARMNN_NO_DEPRECATE_WARN_END
uint32_t m_DilationX
Dilation factor value for width dimension.
uint32_t m_PadTop
Padding top value in the height dimension.
Status SetViewSize(uint32_t view, uint32_t coord, uint32_t value)
Set the size of the views.
An output connection slot for a layer.
A L2NormalizationDescriptor for the L2NormalizationLayer.
int32_t GetQuantizationOffset() const
An ArgMinMaxDescriptor for ArgMinMaxLayer.
float GetQuantizationScale() const
DataType GetDataType() const
An OriginsDescriptor for the ConcatLayer.
float m_ProjectionClip
Clipping threshold value for the projection.
bool has_value() const noexcept
A FullyConnectedDescriptor for the FullyConnectedLayer.
bool m_BiasEnabled
Enable/disable bias.
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
float m_InputIntermediateScale
Input intermediate quantization scale.
uint32_t m_TargetWidth
Target width value.
A GatherDescriptor for the GatherLayer.
bool m_PeepholeEnabled
Enable/disable peephole.
uint32_t m_NumClasses
Number of classes.
bool m_HalfPixelCenters
Half Pixel Centers.
uint32_t m_PadTop
Padding top value in the height dimension.
A StandInDescriptor for the StandIn layer.
A QLstmDescriptor for the QLstmLayer.
QuantizedType Quantize(float value, float scale, int32_t offset)
Quantize a floating point data type into an 8-bit data type.
bool m_UseRegularNms
Use Regular NMS.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
void LogSoftmax(Decoder< float > &input, Encoder< float > &output, const TensorInfo &inputInfo, const LogSoftmaxDescriptor &descriptor)
const TensorInfo & GetInfo() const
uint32_t m_TargetHeight
Target height value.
uint32_t m_ActivationFunc
The activation function to use.
A SliceDescriptor for the SliceLayer.
Visitor base class with empty implementations.
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
void SpaceToBatchNd(const TensorInfo &inputInfo, const TensorInfo &outputInfo, const SpaceToBatchNdDescriptor ¶ms, Decoder< float > &inputData, Encoder< float > &outputData)
bool SaveSerializedToStream(std::ostream &stream) override
Serializes the SerializedGraph to the stream.
float m_ClippingThresCell
Clipping threshold value for the cell state.
unsigned int m_BlockSize
Scalar specifying the input block size. It must be >= 1.
float m_ForgetIntermediateScale
Forget intermediate quantization scale.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
float m_ScaleH
Center size encoding scale height.
ComparisonOperation m_Operation
Specifies the comparison operation to execute.
A SpaceToBatchNdDescriptor for the SpaceToBatchNdLayer.
float m_CellClip
Clipping threshold value for the cell state.
uint32_t m_DilationX
Dilation along x axis.
BOOST_AUTO_TEST_SUITE_END()
bool m_CifgEnabled
Enable/disable cifg (coupled input & forget gate).
uint32_t m_PadLeft
Padding left value in the width dimension.
void StridedSlice(const TensorInfo &inputInfo, const StridedSliceDescriptor ¶ms, const void *inputData, void *outputData, unsigned int dataTypeSize)
bool m_AlignCorners
Aligned corners.
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
int32_t m_Axis
The axis in params to gather indices from.
A ElementwiseUnaryDescriptor for the ElementwiseUnaryLayer.
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Base class for all ArmNN exceptions so that users can filter to just those.
bool m_ProjectionEnabled
Enable/disable the projection layer.
virtual const IInputSlot & GetInputSlot(unsigned int index) const =0
Get a const input slot handle by slot index.
A MeanDescriptor for the MeanLayer.
void Mean(const armnn::TensorInfo &inputInfo, const armnn::TensorInfo &outputInfo, const std::vector< unsigned int > &axis, Decoder< float > &input, Encoder< float > &output)
bool m_LayerNormEnabled
Enable/disable layer normalization.
uint32_t m_PadRight
Padding right value in the width dimension.
A TransposeDescriptor for the TransposeLayer.
A StridedSliceDescriptor for the StridedSliceLayer.
virtual const TensorInfo & GetTensorInfo() const =0
virtual const IOutputSlot & GetOutputSlot(unsigned int index) const =0
Get the const output slot handle by slot index.
void SpaceToDepth(const TensorInfo &inputInfo, const TensorInfo &outputInfo, const SpaceToDepthDescriptor ¶ms, Decoder< float > &inputData, Encoder< float > &outputData)
float m_ScaleY
Center size encoding scale y.
OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)
Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...
#define DECLARE_LAYER_VERIFIER_CLASS_WITH_DESCRIPTOR(name)
float m_NmsScoreThreshold
NMS score threshold.
std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr
void BatchToSpaceNd(const DataLayoutIndexed &dataLayout, const TensorInfo &inputTensorInfo, const TensorInfo &outputTensorInfo, const std::vector< unsigned int > &blockShape, const std::vector< std::pair< unsigned int, unsigned int >> &cropsData, Decoder< float > &inputDecoder, Encoder< float > &outputEncoder)
void Pad(const TensorInfo &inputInfo, const TensorInfo &outputInfo, const PadQueueDescriptor &data)
virtual int Connect(IInputSlot &destination)=0
DataType GetDataType() const
A Pooling2dDescriptor for the Pooling2dLayer.
A NormalizationDescriptor for the NormalizationLayer.
void Pooling2d(Decoder< float > &rInputDecoder, Encoder< float > &rOutputEncoder, const TensorInfo &inputInfo, const TensorInfo &outputInfo, const Pooling2dDescriptor ¶ms)
Computes the Pooling2d operation.
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
An InstanceNormalizationDescriptor for InstanceNormalizationLayer.
A ResizeBilinearDescriptor for the ResizeBilinearLayer.
void Splitter(const SplitterQueueDescriptor &data)
float m_CellIntermediateScale
Cell intermediate quantization scale.
void Softmax(Decoder< float > &in, Encoder< float > &out, const TensorInfo &inputTensorInfo, float beta, int axis)
Computes the softmax function on some inputs, into outputs, with a shape given by tensorInfo...
static INetworkPtr Create(NetworkOptions networkOptions={})
A SoftmaxDescriptor for the SoftmaxLayer.
bool m_CifgEnabled
Enable/disable CIFG (coupled input & forget gate).
Status SetViewOriginCoord(uint32_t view, uint32_t coord, uint32_t value)
Set the view origin coordinates.
void Resize(Decoder< float > &in, const TensorInfo &inputInfo, Encoder< float > &out, const TensorInfo &outputInfo, DataLayoutIndexed dataLayout, armnn::ResizeMethod resizeMethod, bool alignCorners, bool halfPixelCenters)
A DepthwiseConvolution2dDescriptor for the DepthwiseConvolution2dLayer.
A FillDescriptor for the FillLayer.
A BatchNormalizationDescriptor for the BatchNormalizationLayer.
uint32_t m_PadLeft
Padding left value in the width dimension.
unsigned int GetNumBytes() const
A PermuteDescriptor for the PermuteLayer.
uint32_t m_PadRight
Padding right value in the width dimension.
int32_t m_HiddenStateZeroPoint
Hidden State zero point.
std::vector< float > anchors({ 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 10.5f, 1.0f, 1.0f, 0.5f, 100.5f, 1.0f, 1.0f })