From ce3e84a8d449cbf31cee57e30f0eef6a96c0ce94 Mon Sep 17 00:00:00 2001 From: telsoa01 Date: Fri, 31 Aug 2018 09:31:35 +0100 Subject: Release 18.08 --- test/Android.mk | 166 ++++-- test/Concurrent.cpp | 6 +- test/Convolution2D.cpp | 10 +- test/DriverTestHelpers.cpp | 26 +- test/DriverTestHelpers.hpp | 23 +- test/FullyConnected.cpp | 16 +- test/GenericLayerTests.cpp | 222 ++++--- test/Lstm.cpp | 1397 ++++++++++++++++++++++++++++++++++++++++++++ test/Merger.cpp | 160 +++-- test/Tests.cpp | 8 +- test/UtilsTests.cpp | 11 +- 11 files changed, 1826 insertions(+), 219 deletions(-) create mode 100644 test/Lstm.cpp (limited to 'test') diff --git a/test/Android.mk b/test/Android.mk index 97e9a903..1cef3787 100644 --- a/test/Android.mk +++ b/test/Android.mk @@ -12,55 +12,72 @@ NN_HEADER_PATH := $(LOCAL_PATH)/../../../../frameworks/ml/nn/runtime/include ARMNN_HEADER_PATH := $(LOCAL_PATH)/../armnn/include ARMNN_DRIVER_HEADER_PATH := $(LOCAL_PATH)/.. +########################## +# armnn-driver-tests@1.0 # +########################## include $(CLEAR_VARS) -LOCAL_C_INCLUDES := \ - $(OPENCL_HEADER_PATH) \ - $(NN_HEADER_PATH) \ - $(ARMNN_HEADER_PATH) \ - $(ARMNN_DRIVER_HEADER_PATH) +LOCAL_MODULE := armnn-driver-tests@1.0 +LOCAL_MODULE_TAGS := eng optional +LOCAL_ARM_MODE := arm +LOCAL_PROPRIETARY_MODULE := true +# Mark source files as dependent on Android.mk +LOCAL_ADDITIONAL_DEPENDENCIES := $(LOCAL_PATH)/Android.mk + +LOCAL_C_INCLUDES := \ + $(OPENCL_HEADER_PATH) \ + $(NN_HEADER_PATH) \ + $(ARMNN_HEADER_PATH) \ + $(ARMNN_DRIVER_HEADER_PATH) LOCAL_CFLAGS := \ - -std=c++14 \ - -fexceptions \ - -Werror \ - -UNDEBUG - -LOCAL_SRC_FILES := \ - Tests.cpp \ - UtilsTests.cpp \ - Concurrent.cpp \ - Convolution2D.cpp \ - FullyConnected.cpp \ - GenericLayerTests.cpp \ - DriverTestHelpers.cpp \ - SystemProperties.cpp \ - Merger.cpp \ - TestTensor.cpp + -std=c++14 \ + -fexceptions \ + -Werror \ + -O0 \ + -UNDEBUG +ifeq ($(PLATFORM_VERSION),9) +# Required to build with the changes made to the Android ML framework starting from Android P, +# regardless of the HAL version used for the build. +LOCAL_CFLAGS+= \ + -DARMNN_ANDROID_P +endif -LOCAL_STATIC_LIBRARIES := \ - libarmnn-driver \ - libneuralnetworks_common \ - libarmnn \ - libboost_log \ - libboost_system \ - libboost_unit_test_framework \ - libboost_thread \ - armnn-arm_compute - -LOCAL_SHARED_LIBRARIES := \ - libbase \ - libhidlbase \ - libhidltransport \ - libhidlmemory \ - liblog \ - libtextclassifier_hash \ - libutils \ - android.hardware.neuralnetworks@1.0 \ - android.hidl.allocator@1.0 \ - android.hidl.memory@1.0 \ - libOpenCL +LOCAL_SRC_FILES := \ + Tests.cpp \ + UtilsTests.cpp \ + Concurrent.cpp \ + Convolution2D.cpp \ + FullyConnected.cpp \ + GenericLayerTests.cpp \ + DriverTestHelpers.cpp \ + SystemProperties.cpp \ + Lstm.cpp \ + Merger.cpp \ + TestTensor.cpp +LOCAL_STATIC_LIBRARIES := \ + libarmnn-driver@1.0 \ + libneuralnetworks_common \ + libarmnn \ + libboost_log \ + libboost_system \ + libboost_unit_test_framework \ + libboost_thread \ + armnn-arm_compute + +LOCAL_SHARED_LIBRARIES := \ + libbase \ + libhidlbase \ + libhidltransport \ + libhidlmemory \ + liblog \ + libtextclassifier_hash \ + libutils \ + android.hardware.neuralnetworks@1.0 \ + android.hidl.allocator@1.0 \ + android.hidl.memory@1.0 \ + libOpenCL ifeq ($(PLATFORM_VERSION),9) # Required to build the 1.0 version of the NN Driver on Android P and later versions, # as the 1.0 version of the NN API needs the 1.1 HAL headers to be included regardless. @@ -68,18 +85,71 @@ LOCAL_SHARED_LIBRARIES+= \ android.hardware.neuralnetworks@1.1 endif -LOCAL_MODULE := armnn-driver-tests +include $(BUILD_EXECUTABLE) -LOCAL_MODULE_TAGS := eng optional +########################## +# armnn-driver-tests@1.1 # +########################## +include $(CLEAR_VARS) +LOCAL_MODULE := armnn-driver-tests@1.1 +LOCAL_MODULE_TAGS := eng optional LOCAL_ARM_MODE := arm - +LOCAL_PROPRIETARY_MODULE := true # Mark source files as dependent on Android.mk LOCAL_ADDITIONAL_DEPENDENCIES := $(LOCAL_PATH)/Android.mk -LOCAL_PROPRIETARY_MODULE := true +LOCAL_C_INCLUDES := \ + $(OPENCL_HEADER_PATH) \ + $(NN_HEADER_PATH) \ + $(ARMNN_HEADER_PATH) \ + $(ARMNN_DRIVER_HEADER_PATH) -include $(BUILD_EXECUTABLE) +LOCAL_CFLAGS := \ + -std=c++14 \ + -fexceptions \ + -Werror \ + -O0 \ + -UNDEBUG \ + -DARMNN_ANDROID_P \ + -DARMNN_ANDROID_NN_V1_1 + +LOCAL_SRC_FILES := \ + Tests.cpp \ + UtilsTests.cpp \ + Concurrent.cpp \ + Convolution2D.cpp \ + FullyConnected.cpp \ + GenericLayerTests.cpp \ + DriverTestHelpers.cpp \ + SystemProperties.cpp \ + Lstm.cpp \ + Merger.cpp \ + TestTensor.cpp +LOCAL_STATIC_LIBRARIES := \ + libarmnn-driver@1.1 \ + libneuralnetworks_common \ + libarmnn \ + libboost_log \ + libboost_system \ + libboost_unit_test_framework \ + libboost_thread \ + armnn-arm_compute + +LOCAL_SHARED_LIBRARIES := \ + libbase \ + libhidlbase \ + libhidltransport \ + libhidlmemory \ + liblog \ + libtextclassifier_hash \ + libutils \ + android.hardware.neuralnetworks@1.0 \ + android.hardware.neuralnetworks@1.1 \ + android.hidl.allocator@1.0 \ + android.hidl.memory@1.0 \ + libOpenCL +include $(BUILD_EXECUTABLE) diff --git a/test/Concurrent.cpp b/test/Concurrent.cpp index c2d58bde..e4940537 100644 --- a/test/Concurrent.cpp +++ b/test/Concurrent.cpp @@ -11,7 +11,9 @@ BOOST_AUTO_TEST_SUITE(ConcurrentDriverTests) using ArmnnDriver = armnn_driver::ArmnnDriver; using DriverOptions = armnn_driver::DriverOptions; using namespace android::nn; +using namespace android::hardware; using namespace driverTestHelpers; +using namespace armnn_driver; // Add our own test for concurrent execution // The main point of this test is to check that multiple requests can be @@ -22,7 +24,7 @@ BOOST_AUTO_TEST_CASE(ConcurrentExecute) ALOGI("ConcurrentExecute: entry"); auto driver = std::make_unique(DriverOptions(armnn::Compute::CpuRef)); - V1_0::Model model = {}; + neuralnetworks::V1_0::Model model = {}; // add operands int32_t actValue = 0; @@ -37,7 +39,7 @@ BOOST_AUTO_TEST_CASE(ConcurrentExecute) // make the fully connected operation model.operations.resize(1); - model.operations[0].type = V1_0::OperationType::FULLY_CONNECTED; + model.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; model.operations[0].inputs = hidl_vec{0, 1, 2, 3}; model.operations[0].outputs = hidl_vec{4}; diff --git a/test/Convolution2D.cpp b/test/Convolution2D.cpp index cc301bc9..fe28aa44 100644 --- a/test/Convolution2D.cpp +++ b/test/Convolution2D.cpp @@ -6,13 +6,13 @@ #include #include -#include "OperationsUtils.h" +#include BOOST_AUTO_TEST_SUITE(Convolution2DTests) -using ArmnnDriver = armnn_driver::ArmnnDriver; -using DriverOptions = armnn_driver::DriverOptions; +using namespace android::hardware; using namespace driverTestHelpers; +using namespace armnn_driver; namespace { @@ -20,7 +20,7 @@ namespace void PaddingTestImpl(android::nn::PaddingScheme paddingScheme) { auto driver = std::make_unique(DriverOptions(armnn::Compute::CpuRef)); - V1_0::Model model = {}; + neuralnetworks::V1_0::Model model = {}; uint32_t outSize = paddingScheme == android::nn::kPaddingSame ? 2 : 1; @@ -39,7 +39,7 @@ void PaddingTestImpl(android::nn::PaddingScheme paddingScheme) // make the convolution operation model.operations.resize(1); - model.operations[0].type = V1_0::OperationType::CONV_2D; + model.operations[0].type = neuralnetworks::V1_0::OperationType::CONV_2D; model.operations[0].inputs = hidl_vec{0, 1, 2, 3, 4, 5, 6}; model.operations[0].outputs = hidl_vec{7}; diff --git a/test/DriverTestHelpers.cpp b/test/DriverTestHelpers.cpp index d2d380a7..82087961 100644 --- a/test/DriverTestHelpers.cpp +++ b/test/DriverTestHelpers.cpp @@ -25,10 +25,12 @@ std::ostream& operator<<(std::ostream& os, ErrorStatus stat) } // namespace android::hardware } // namespace android - namespace driverTestHelpers { +using namespace android::hardware; +using namespace armnn_driver; + Return ExecutionCallback::notify(ErrorStatus status) { (void)status; @@ -107,13 +109,13 @@ void AddPoolAndSetData(uint32_t size, Request& request, const float* data) memcpy(dst, data, size * sizeof(float)); } -void AddOperand(V1_0::Model& model, const Operand& op) +void AddOperand(neuralnetworks::V1_0::Model& model, const Operand& op) { model.operands.resize(model.operands.size() + 1); model.operands[model.operands.size() - 1] = op; } -void AddIntOperand(V1_0::Model& model, int32_t value) +void AddIntOperand(neuralnetworks::V1_0::Model& model, int32_t value) { DataLocation location = {}; location.offset = model.operandValues.size(); @@ -131,10 +133,12 @@ void AddIntOperand(V1_0::Model& model, int32_t value) AddOperand(model, op); } -void AddInputOperand(V1_0::Model& model, hidl_vec dimensions) +void AddInputOperand(neuralnetworks::V1_0::Model& model, + hidl_vec dimensions, + neuralnetworks::V1_0::OperandType operandType) { Operand op = {}; - op.type = OperandType::TENSOR_FLOAT32; + op.type = operandType; op.dimensions = dimensions; op.lifetime = OperandLifeTime::MODEL_INPUT; @@ -144,10 +148,13 @@ void AddInputOperand(V1_0::Model& model, hidl_vec dimensions) model.inputIndexes[model.inputIndexes.size() - 1] = model.operands.size() - 1; } -void AddOutputOperand(V1_0::Model& model, hidl_vec dimensions) +void AddOutputOperand(neuralnetworks::V1_0::Model& model, + hidl_vec dimensions, + neuralnetworks::V1_0::OperandType operandType) { Operand op = {}; - op.type = OperandType::TENSOR_FLOAT32; + op.type = operandType; + op.scale = operandType == neuralnetworks::V1_0::OperandType::TENSOR_QUANT8_ASYMM ? 1.f / 255.f : 0.f; op.dimensions = dimensions; op.lifetime = OperandLifeTime::MODEL_OUTPUT; @@ -158,7 +165,7 @@ void AddOutputOperand(V1_0::Model& model, hidl_vec dimensions) } -android::sp PrepareModelWithStatus(const V1_0::Model& model, +android::sp PrepareModelWithStatus(const neuralnetworks::V1_0::Model& model, armnn_driver::ArmnnDriver& driver, ErrorStatus & prepareStatus, ErrorStatus expectedStatus) @@ -176,7 +183,7 @@ android::sp PrepareModelWithStatus(const V1_0::Model& model, return cb->GetPreparedModel(); } -android::sp PrepareModel(const V1_0::Model& model, +android::sp PrepareModel(const neuralnetworks::V1_0::Model& model, armnn_driver::ArmnnDriver& driver) { ErrorStatus prepareStatus = ErrorStatus::NONE; @@ -187,6 +194,7 @@ ErrorStatus Execute(android::sp preparedModel, const Request& request, ErrorStatus expectedStatus) { + BOOST_TEST(preparedModel.get() != nullptr); android::sp cb(new ExecutionCallback()); ErrorStatus execStatus = preparedModel->execute(request, cb); BOOST_TEST(execStatus == expectedStatus); diff --git a/test/DriverTestHelpers.hpp b/test/DriverTestHelpers.hpp index 57541a35..ccb6b983 100644 --- a/test/DriverTestHelpers.hpp +++ b/test/DriverTestHelpers.hpp @@ -72,9 +72,9 @@ android::sp AddPoolAndGetData(uint32_t size, Request& request); void AddPoolAndSetData(uint32_t size, Request& request, const float* data); -void AddOperand(V1_0::Model& model, const Operand& op); +void AddOperand(::android::hardware::neuralnetworks::V1_0::Model& model, const Operand& op); -void AddIntOperand(V1_0::Model& model, int32_t value); +void AddIntOperand(::android::hardware::neuralnetworks::V1_0::Model& model, int32_t value); template OperandType TypeToOperandType(); @@ -86,7 +86,10 @@ template<> OperandType TypeToOperandType(); template -void AddTensorOperand(V1_0::Model& model, hidl_vec dimensions, T* values) +void AddTensorOperand(::android::hardware::neuralnetworks::V1_0::Model& model, + hidl_vec dimensions, + T* values, + OperandType operandType = OperandType::TENSOR_FLOAT32) { uint32_t totalElements = 1; for (uint32_t dim : dimensions) @@ -99,7 +102,7 @@ void AddTensorOperand(V1_0::Model& model, hidl_vec dimensions, T* valu location.length = totalElements * sizeof(T); Operand op = {}; - op.type = TypeToOperandType(); + op.type = operandType; op.dimensions = dimensions; op.lifetime = OperandLifeTime::CONSTANT_COPY; op.location = location; @@ -113,14 +116,18 @@ void AddTensorOperand(V1_0::Model& model, hidl_vec dimensions, T* valu AddOperand(model, op); } -void AddInputOperand(V1_0::Model& model, hidl_vec dimensions); +void AddInputOperand(::android::hardware::neuralnetworks::V1_0::Model& model, + hidl_vec dimensions, + ::android::hardware::neuralnetworks::V1_0::OperandType operandType = OperandType::TENSOR_FLOAT32); -void AddOutputOperand(V1_0::Model& model, hidl_vec dimensions); +void AddOutputOperand(::android::hardware::neuralnetworks::V1_0::Model& model, + hidl_vec dimensions, + ::android::hardware::neuralnetworks::V1_0::OperandType operandType = OperandType::TENSOR_FLOAT32); -android::sp PrepareModel(const V1_0::Model& model, +android::sp PrepareModel(const ::android::hardware::neuralnetworks::V1_0::Model& model, armnn_driver::ArmnnDriver& driver); -android::sp PrepareModelWithStatus(const V1_0::Model& model, +android::sp PrepareModelWithStatus(const ::android::hardware::neuralnetworks::V1_0::Model& model, armnn_driver::ArmnnDriver& driver, ErrorStatus & prepareStatus, ErrorStatus expectedStatus=ErrorStatus::NONE); diff --git a/test/FullyConnected.cpp b/test/FullyConnected.cpp index 4feda30b..20a350c5 100644 --- a/test/FullyConnected.cpp +++ b/test/FullyConnected.cpp @@ -8,9 +8,9 @@ BOOST_AUTO_TEST_SUITE(FullyConnectedTests) -using ArmnnDriver = armnn_driver::ArmnnDriver; -using DriverOptions = armnn_driver::DriverOptions; +using namespace android::hardware; using namespace driverTestHelpers; +using namespace armnn_driver; // Add our own test here since we fail the fc tests which Google supplies (because of non-const weights) BOOST_AUTO_TEST_CASE(FullyConnected) @@ -19,7 +19,7 @@ BOOST_AUTO_TEST_CASE(FullyConnected) // but that uses slightly weird dimensions which I don't think we need to support for now auto driver = std::make_unique(DriverOptions(armnn::Compute::CpuRef)); - V1_0::Model model = {}; + neuralnetworks::V1_0::Model model = {}; // add operands int32_t actValue = 0; @@ -34,7 +34,7 @@ BOOST_AUTO_TEST_CASE(FullyConnected) // make the fully connected operation model.operations.resize(1); - model.operations[0].type = V1_0::OperationType::FULLY_CONNECTED; + model.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; model.operations[0].inputs = hidl_vec{0, 1, 2, 3}; model.operations[0].outputs = hidl_vec{4}; @@ -90,7 +90,7 @@ BOOST_AUTO_TEST_CASE(TestFullyConnected4dInput) sup = supported; }; - V1_0::Model model = {}; + neuralnetworks::V1_0::Model model = {}; // operands int32_t actValue = 0; @@ -113,7 +113,7 @@ BOOST_AUTO_TEST_CASE(TestFullyConnected4dInput) model.operations.resize(1); - model.operations[0].type = V1_0::OperationType::FULLY_CONNECTED; + model.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; model.operations[0].inputs = hidl_vec{0,1,2,3}; model.operations[0].outputs = hidl_vec{4}; @@ -177,7 +177,7 @@ BOOST_AUTO_TEST_CASE(TestFullyConnected4dInputReshape) sup = supported; }; - V1_0::Model model = {}; + neuralnetworks::V1_0::Model model = {}; // operands int32_t actValue = 0; @@ -200,7 +200,7 @@ BOOST_AUTO_TEST_CASE(TestFullyConnected4dInputReshape) model.operations.resize(1); - model.operations[0].type = V1_0::OperationType::FULLY_CONNECTED; + model.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; model.operations[0].inputs = hidl_vec{0,1,2,3}; model.operations[0].outputs = hidl_vec{4}; diff --git a/test/GenericLayerTests.cpp b/test/GenericLayerTests.cpp index 7116f0b0..aa91ce15 100644 --- a/test/GenericLayerTests.cpp +++ b/test/GenericLayerTests.cpp @@ -8,189 +8,233 @@ BOOST_AUTO_TEST_SUITE(GenericLayerTests) -using ArmnnDriver = armnn_driver::ArmnnDriver; -using DriverOptions = armnn_driver::DriverOptions; +using namespace android::hardware; using namespace driverTestHelpers; +using namespace armnn_driver; BOOST_AUTO_TEST_CASE(GetSupportedOperations) { auto driver = std::make_unique(DriverOptions(armnn::Compute::CpuRef)); - ErrorStatus error; - std::vector sup; + ErrorStatus errorStatus; + std::vector supported; - ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector& supported) + auto cb = [&](ErrorStatus _errorStatus, const std::vector& _supported) { - error = status; - sup = supported; + errorStatus = _errorStatus; + supported = _supported; }; - V1_0::Model model1 = {}; + neuralnetworks::V1_0::Model model0 = {}; - // add operands + // Add operands int32_t actValue = 0; float weightValue[] = {2, 4, 1}; float biasValue[] = {4}; - AddInputOperand(model1, hidl_vec{1, 3}); + AddInputOperand (model0, hidl_vec{1, 3}); + AddTensorOperand(model0, hidl_vec{1, 3}, weightValue); + AddTensorOperand(model0, hidl_vec{1}, biasValue); + AddIntOperand (model0, actValue); + AddOutputOperand(model0, hidl_vec{1, 1}); + + model0.operations.resize(1); + + // Make a correct fully connected operation + model0.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; + model0.operations[0].inputs = hidl_vec{0, 1, 2, 3}; + model0.operations[0].outputs = hidl_vec{4}; + + driver->getSupportedOperations(model0, cb); + BOOST_TEST((int)errorStatus == (int)ErrorStatus::NONE); + BOOST_TEST(supported.size() == (size_t)1); + BOOST_TEST(supported[0] == true); + + neuralnetworks::V1_0::Model model1 = {}; + + AddInputOperand (model1, hidl_vec{1, 3}); AddTensorOperand(model1, hidl_vec{1, 3}, weightValue); AddTensorOperand(model1, hidl_vec{1}, biasValue); - AddIntOperand(model1, actValue); + AddIntOperand (model1, actValue); AddOutputOperand(model1, hidl_vec{1, 1}); - // make a correct fully connected operation model1.operations.resize(2); - model1.operations[0].type = V1_0::OperationType::FULLY_CONNECTED; + + // Make a correct fully connected operation + model1.operations[0].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; model1.operations[0].inputs = hidl_vec{0, 1, 2, 3}; model1.operations[0].outputs = hidl_vec{4}; - // make an incorrect fully connected operation - AddIntOperand(model1, actValue); + // Add an incorrect fully connected operation + AddIntOperand (model1, actValue); AddOutputOperand(model1, hidl_vec{1, 1}); - model1.operations[1].type = V1_0::OperationType::FULLY_CONNECTED; - model1.operations[1].inputs = hidl_vec{4}; + model1.operations[1].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; + model1.operations[1].inputs = hidl_vec{4}; // Only 1 input operand, expected 4 model1.operations[1].outputs = hidl_vec{5}; driver->getSupportedOperations(model1, cb); - BOOST_TEST((int)error == (int)ErrorStatus::NONE); - BOOST_TEST(sup[0] == true); - BOOST_TEST(sup[1] == false); - // Broadcast add/mul are not supported - V1_0::Model model2 = {}; - - AddInputOperand(model2, hidl_vec{1, 1, 3, 4}); - AddInputOperand(model2, hidl_vec{4}); +#if defined(ARMNN_ANDROID_P) + // In Android P, android::nn::validateModel returns INVALID_ARGUMENT, because of the wrong number of inputs for the + // fully connected layer (1 instead of 4) + BOOST_TEST((int)errorStatus == (int)ErrorStatus::INVALID_ARGUMENT); + BOOST_TEST(supported.empty()); +#else + // In Android O, android::nn::validateModel indicates that the second (wrong) fully connected layer in unsupported + // in the vector of flags returned by the callback + BOOST_TEST((int)errorStatus == (int)ErrorStatus::NONE); + BOOST_TEST(supported.size() == (size_t)2); + BOOST_TEST(supported[0] == true); + BOOST_TEST(supported[1] == false); +#endif + + // Test Broadcast on add/mul operators + neuralnetworks::V1_0::Model model2 = {}; + + AddInputOperand (model2, hidl_vec{1, 1, 3, 4}); + AddInputOperand (model2, hidl_vec{4}); + AddIntOperand (model2, actValue); AddOutputOperand(model2, hidl_vec{1, 1, 3, 4}); AddOutputOperand(model2, hidl_vec{1, 1, 3, 4}); model2.operations.resize(2); - model2.operations[0].type = V1_0::OperationType::ADD; - model2.operations[0].inputs = hidl_vec{0,1}; - model2.operations[0].outputs = hidl_vec{2}; + model2.operations[0].type = neuralnetworks::V1_0::OperationType::ADD; + model2.operations[0].inputs = hidl_vec{0, 1, 2}; + model2.operations[0].outputs = hidl_vec{3}; - model2.operations[1].type = V1_0::OperationType::MUL; - model2.operations[1].inputs = hidl_vec{0,1}; - model2.operations[1].outputs = hidl_vec{3}; + model2.operations[1].type = neuralnetworks::V1_0::OperationType::MUL; + model2.operations[1].inputs = hidl_vec{0, 1, 2}; + model2.operations[1].outputs = hidl_vec{4}; driver->getSupportedOperations(model2, cb); - BOOST_TEST((int)error == (int)ErrorStatus::NONE); - BOOST_TEST(sup[0] == false); - BOOST_TEST(sup[1] == false); + BOOST_TEST((int)errorStatus == (int)ErrorStatus::NONE); + BOOST_TEST(supported.size() == (size_t)2); + BOOST_TEST(supported[0] == true); + BOOST_TEST(supported[1] == true); - V1_0::Model model3 = {}; + neuralnetworks::V1_0::Model model3 = {}; - // Add unsupported operation, should return no error but we don't support it - AddInputOperand(model3, hidl_vec{1, 1, 1, 8}); - AddIntOperand(model3, 2); + AddInputOperand (model3, hidl_vec{1, 1, 1, 8}); + AddIntOperand (model3, 2); AddOutputOperand(model3, hidl_vec{1, 2, 2, 2}); + model3.operations.resize(1); - model3.operations[0].type = V1_0::OperationType::DEPTH_TO_SPACE; - model1.operations[0].inputs = hidl_vec{0, 1}; + + // Add unsupported operation, should return no error but we don't support it + model3.operations[0].type = neuralnetworks::V1_0::OperationType::DEPTH_TO_SPACE; + model3.operations[0].inputs = hidl_vec{0, 1}; model3.operations[0].outputs = hidl_vec{2}; driver->getSupportedOperations(model3, cb); - BOOST_TEST((int)error == (int)ErrorStatus::NONE); - BOOST_TEST(sup[0] == false); + BOOST_TEST((int)errorStatus == (int)ErrorStatus::NONE); + BOOST_TEST(supported.size() == (size_t)1); + BOOST_TEST(supported[0] == false); + + neuralnetworks::V1_0::Model model4 = {}; - // Add invalid operation - V1_0::Model model4 = {}; AddIntOperand(model4, 0); + model4.operations.resize(1); - model4.operations[0].type = static_cast(100); + + // Add invalid operation + model4.operations[0].type = static_cast(100); model4.operations[0].outputs = hidl_vec{0}; driver->getSupportedOperations(model4, cb); - BOOST_TEST((int)error == (int)ErrorStatus::INVALID_ARGUMENT); + BOOST_TEST((int)errorStatus == (int)ErrorStatus::INVALID_ARGUMENT); + BOOST_TEST(supported.empty()); } // The purpose of this test is to ensure that when encountering an unsupported operation -// it is skipped and getSupportedOperations() continues (rather than failing and stopping). -// As per IVGCVSW-710. +// it is skipped and getSupportedOperations() continues (rather than failing and stopping). +// As per IVGCVSW-710. BOOST_AUTO_TEST_CASE(UnsupportedLayerContinueOnFailure) { auto driver = std::make_unique(DriverOptions(armnn::Compute::CpuRef)); - ErrorStatus error; - std::vector sup; + ErrorStatus errorStatus; + std::vector supported; - ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector& supported) + auto cb = [&](ErrorStatus _errorStatus, const std::vector& _supported) { - error = status; - sup = supported; + errorStatus = _errorStatus; + supported = _supported; }; - V1_0::Model model = {}; + neuralnetworks::V1_0::Model model = {}; - // operands + // Operands int32_t actValue = 0; float weightValue[] = {2, 4, 1}; float biasValue[] = {4}; - // broadcast add is unsupported at the time of writing this test, but any unsupported layer will do - AddInputOperand(model, hidl_vec{1, 1, 3, 4}); - AddInputOperand(model, hidl_vec{4}); + // HASHTABLE_LOOKUP is unsupported at the time of writing this test, but any unsupported layer will do + AddInputOperand (model, hidl_vec{1, 1, 3, 4}, neuralnetworks::V1_0::OperandType::TENSOR_INT32); + AddInputOperand (model, hidl_vec{4}, neuralnetworks::V1_0::OperandType::TENSOR_INT32); + AddInputOperand (model, hidl_vec{1, 1, 3, 4}); AddOutputOperand(model, hidl_vec{1, 1, 3, 4}); + AddOutputOperand(model, hidl_vec{1, 1, 3, 4}, neuralnetworks::V1_0::OperandType::TENSOR_QUANT8_ASYMM); - // fully connected - AddInputOperand(model, hidl_vec{1, 3}); + // Fully connected is supported + AddInputOperand (model, hidl_vec{1, 3}); AddTensorOperand(model, hidl_vec{1, 3}, weightValue); AddTensorOperand(model, hidl_vec{1}, biasValue); - AddIntOperand(model, actValue); + AddIntOperand (model, actValue); AddOutputOperand(model, hidl_vec{1, 1}); - // broadcast mul is unsupported + // EMBEDDING_LOOKUP is unsupported AddOutputOperand(model, hidl_vec{1, 1, 3, 4}); model.operations.resize(3); - // unsupported - model.operations[0].type = V1_0::OperationType::ADD; - model.operations[0].inputs = hidl_vec{0,1}; - model.operations[0].outputs = hidl_vec{2}; + // Unsupported + model.operations[0].type = neuralnetworks::V1_0::OperationType::HASHTABLE_LOOKUP; + model.operations[0].inputs = hidl_vec{0, 1, 2}; + model.operations[0].outputs = hidl_vec{3, 4}; - // supported - model.operations[1].type = V1_0::OperationType::FULLY_CONNECTED; - model.operations[1].inputs = hidl_vec{3, 4, 5, 6}; - model.operations[1].outputs = hidl_vec{7}; + // Supported + model.operations[1].type = neuralnetworks::V1_0::OperationType::FULLY_CONNECTED; + model.operations[1].inputs = hidl_vec{5, 6, 7, 8}; + model.operations[1].outputs = hidl_vec{9}; - // unsupported - model.operations[2].type = V1_0::OperationType::MUL; - model.operations[2].inputs = hidl_vec{0,1}; - model.operations[2].outputs = hidl_vec{8}; + // Unsupported + model.operations[2].type = neuralnetworks::V1_0::OperationType::EMBEDDING_LOOKUP; + model.operations[2].inputs = hidl_vec{1, 2}; + model.operations[2].outputs = hidl_vec{10}; - // we are testing that the unsupported layers return false and the test continues - // rather than failing and stopping. + // We are testing that the unsupported layers return false and the test continues rather than failing and stopping driver->getSupportedOperations(model, cb); - BOOST_TEST((int)error == (int)ErrorStatus::NONE); - BOOST_TEST(sup[0] == false); - BOOST_TEST(sup[1] == true); - BOOST_TEST(sup[2] == false); + BOOST_TEST((int)errorStatus == (int)ErrorStatus::NONE); + BOOST_TEST(supported.size() == (size_t)3); + BOOST_TEST(supported[0] == false); + BOOST_TEST(supported[1] == true); + BOOST_TEST(supported[2] == false); } // The purpose of this test is to ensure that when encountering an failure -// during mem pool mapping we properly report an error to the framework via a callback +// during mem pool mapping we properly report an error to the framework via a callback BOOST_AUTO_TEST_CASE(ModelToINetworkConverterMemPoolFail) { auto driver = std::make_unique(armnn::Compute::CpuRef); - ErrorStatus error; - std::vector sup; + ErrorStatus errorStatus; + std::vector supported; - ArmnnDriver::getSupportedOperations_cb cb = [&](ErrorStatus status, const std::vector& supported) + auto cb = [&](ErrorStatus _errorStatus, const std::vector& _supported) { - error = status; - sup = supported; + errorStatus = _errorStatus; + supported = _supported; }; - V1_0::Model model = {}; + neuralnetworks::V1_0::Model model = {}; model.pools = hidl_vec{hidl_memory("Unsuported hidl memory type", nullptr, 0)}; - //memory pool mapping should fail, we should report an error + // Memory pool mapping should fail, we should report an error driver->getSupportedOperations(model, cb); - BOOST_TEST((int)error == (int)ErrorStatus::GENERAL_FAILURE); + BOOST_TEST((int)errorStatus != (int)ErrorStatus::NONE); + BOOST_TEST(supported.empty()); } BOOST_AUTO_TEST_SUITE_END() diff --git a/test/Lstm.cpp b/test/Lstm.cpp new file mode 100644 index 00000000..1b6ef60d --- /dev/null +++ b/test/Lstm.cpp @@ -0,0 +1,1397 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// +#include "DriverTestHelpers.hpp" +#include +#include +#include + +#include "OperationsUtils.h" + +#include + +BOOST_AUTO_TEST_SUITE(LstmTests) + +using ArmnnDriver = armnn_driver::ArmnnDriver; +using DriverOptions = armnn_driver::DriverOptions; +using namespace driverTestHelpers; +using namespace android::hardware; + +namespace +{ + +template +RequestArgument CreateRequestArgument(std::vector value, unsigned int poolIndex) +{ + DataLocation inputInloc = {}; + inputInloc.poolIndex = poolIndex; + inputInloc.offset = 0; + inputInloc.length = value.size() * sizeof(T); + RequestArgument inputRequestArgument = {}; + inputRequestArgument.location = inputInloc; + inputRequestArgument.dimensions = hidl_vec{}; + return inputRequestArgument; +} + +// Returns true if the relative difference between two float values is less than the tolerance value given. +// This is used because the floating point comparison tolerance (set on each BOOST_AUTO_TEST_CASE) does not work! +bool TolerantCompareEqual(float a, float b, float tolerance = 0.00001f) +{ + float rd; + if (a == 0.0f) + { + rd = fabs(b); + } + else if (b == 0.0f) + { + rd = fabs(a); + } + else + { + rd = boost::math::relative_difference(a, b); + } + return rd < tolerance; +} + +} // namespace + +// Add our own tests here since we fail the lstm tests which Google supplies (because of non-const weights) + +void LstmTestImpl(hidl_vec inputDimensions, + std::vector inputValue, + hidl_vec inputToInputWeightsDimensions, + float* inputToInputWeightsValue, + hidl_vec inputToForgetWeightsDimensions, + float* inputToForgetWeightsValue, + hidl_vec inputToCellWeightsDimensions, + float* inputToCellWeightsValue, + hidl_vec inputToOutputWeightsDimensions, + float* inputToOutputWeightsValue, + hidl_vec recurrentToInputWeightsDimensions, + float* recurrentToInputWeightsValue, + hidl_vec recurrentToForgetWeightsDimensions, + float* recurrentToForgetWeightsValue, + hidl_vec recurrentToCellWeightsDimensions, + float* recurrentToCellWeightsValue, + hidl_vec recurrentToOutputWeightsDimensions, + float* recurrentToOutputWeightsValue, + hidl_vec cellToInputWeightsDimensions, + float* cellToInputWeightsValue, + hidl_vec cellToForgetWeightsDimensions, + float* cellToForgetWeightsValue, + hidl_vec cellToOutputWeightsDimensions, + float* cellToOutputWeightsValue, + hidl_vec inputGateBiasDimensions, + float* inputGateBiasValue, + hidl_vec forgetGateBiasDimensions, + float* forgetGateBiasValue, + hidl_vec cellBiasDimensions, + float* cellBiasValue, + hidl_vec outputGateBiasDimensions, + float* outputGateBiasValue, + hidl_vec projectionWeightsDimensions, + float* projectionWeightsValue, + hidl_vec projectionBiasDimensions, + float* projectionBiasValue, + hidl_vec outputStateInDimensions, + std::vector outputStateInValue, + hidl_vec cellStateInDimensions, + std::vector cellStateInValue, + hidl_vec activationFunctionDimensions, + int32_t* activationFunctionValue, + hidl_vec cellClippingThresholdDimensions, + float* cellClippingThresholdValue, + hidl_vec projectionClippingThresholdDimensions, + float* projectionClippingThresholdValue, + hidl_vec scratchBufferDimensions, + std::vector scratchBufferValue, + hidl_vec outputStateOutDimensions, + std::vector outputStateOutValue, + hidl_vec cellStateOutDimensions, + std::vector cellStateOutValue, + hidl_vec outputDimensions, + std::vector outputValue) +{ + auto driver = std::make_unique(DriverOptions(armnn::Compute::GpuAcc)); + neuralnetworks::V1_0::Model model = {}; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + AddInputOperand(model, inputDimensions); + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + AddTensorOperand(model, inputToInputWeightsDimensions, inputToInputWeightsValue); + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + AddTensorOperand(model, inputToForgetWeightsDimensions, inputToForgetWeightsValue); + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + AddTensorOperand(model, inputToCellWeightsDimensions, inputToCellWeightsValue); + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + AddTensorOperand(model, inputToOutputWeightsDimensions, inputToOutputWeightsValue); + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + AddTensorOperand(model, recurrentToInputWeightsDimensions, recurrentToInputWeightsValue); + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + AddTensorOperand(model, recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue); + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + AddTensorOperand(model, recurrentToCellWeightsDimensions, recurrentToCellWeightsValue); + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + AddTensorOperand(model, recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue); + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, cellToInputWeightsDimensions, cellToInputWeightsValue); + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, cellToForgetWeightsDimensions, cellToForgetWeightsValue); + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, cellToOutputWeightsDimensions, cellToOutputWeightsValue); + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, inputGateBiasDimensions, inputGateBiasValue); + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, forgetGateBiasDimensions, forgetGateBiasValue); + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, cellBiasDimensions, cellBiasValue); + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + AddTensorOperand(model, outputGateBiasDimensions, outputGateBiasValue); + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + AddTensorOperand(model, projectionWeightsDimensions, projectionWeightsValue); + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + AddTensorOperand(model, projectionBiasDimensions, projectionBiasValue); + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + AddInputOperand(model, outputStateInDimensions); + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + AddInputOperand(model, cellStateInDimensions); + + // constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + AddTensorOperand(model, activationFunctionDimensions, + activationFunctionValue, OperandType::INT32); + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + AddTensorOperand(model, cellClippingThresholdDimensions, + cellClippingThresholdValue, OperandType::FLOAT32); + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + AddTensorOperand(model, projectionClippingThresholdDimensions, + projectionClippingThresholdValue, OperandType::FLOAT32); + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + AddOutputOperand(model, scratchBufferDimensions); + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + AddOutputOperand(model, outputStateOutDimensions); + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + AddOutputOperand(model, cellStateOutDimensions); + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + AddOutputOperand(model, outputDimensions); + + // make the lstm operation + model.operations.resize(1); + model.operations[0].type = neuralnetworks::V1_0::OperationType::LSTM; + model.operations[0].inputs = + hidl_vec {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22}; + model.operations[0].outputs = hidl_vec {23, 24, 25, 26}; + + // define the input values + hidl_vec inputArguments; + inputArguments.resize(3); + + inputArguments[0] = CreateRequestArgument(inputValue, 0); + inputArguments[1] = CreateRequestArgument(outputStateInValue, 1); + inputArguments[2] = CreateRequestArgument(cellStateInValue, 2); + + // define the expected output values + hidl_vec outputArguments; + outputArguments.resize(4); + + outputArguments[0] = CreateRequestArgument(scratchBufferValue, 3); + outputArguments[1] = CreateRequestArgument(outputStateOutValue, 4); + outputArguments[2] = CreateRequestArgument(cellStateOutValue, 5); + outputArguments[3] = CreateRequestArgument(outputValue, 6); + + Request request = {}; + request.inputs = inputArguments; + request.outputs = outputArguments; + + // set the input data + AddPoolAndSetData(inputValue.size(), request, inputValue.data()); + AddPoolAndSetData(outputStateInValue.size(), request, outputStateInValue.data()); + AddPoolAndSetData(cellStateInValue.size(), request, cellStateInValue.data()); + + // add memory for the outputs + AddPoolAndGetData(scratchBufferValue.size(), request); + android::sp outputStateOutMemory = AddPoolAndGetData(outputStateOutValue.size(), request); + float* outputStateOutData = static_cast(static_cast(outputStateOutMemory->getPointer())); + android::sp cellStateOutMemory = AddPoolAndGetData(cellStateOutValue.size(), request); + float* cellStateOutData = static_cast(static_cast(cellStateOutMemory->getPointer())); + android::sp outputMemory = AddPoolAndGetData(outputValue.size(), request); + float* outputData = static_cast(static_cast(outputMemory->getPointer())); + + // make the prepared model and run the execution + android::sp preparedModel = PrepareModel(model, *driver); + if (preparedModel.get() != nullptr) + { + Execute(preparedModel, request); + } + + // check the results + for (size_t i = 0; i < outputStateOutValue.size(); ++i) + { + BOOST_TEST(TolerantCompareEqual(outputStateOutValue[i], outputStateOutData[i]), + "outputStateOut[" << i << "]: " << outputStateOutValue[i] << " != " << outputStateOutData[i]); + } + for (size_t i = 0; i < cellStateOutValue.size(); ++i) + { + BOOST_TEST(TolerantCompareEqual(cellStateOutValue[i], cellStateOutData[i]), + "cellStateOut[" << i << "]: " << cellStateOutValue[i] << " != " << cellStateOutData[i]); + } + for (size_t i = 0; i < outputValue.size(); ++i) + { + BOOST_TEST(TolerantCompareEqual(outputValue[i], outputData[i]), + "output[" << i << "]: " << outputValue[i] << " != " << outputData[i]); + } +} + +BOOST_AUTO_TEST_CASE(LstmNoCifgNoPeepholeNoProjection) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions({1, 2}); + std::vector inputValue {2.0f, 3.0f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions({4, 2}); + float inputToInputWeightsValue[] = {-0.45018822f, -0.02338299f, + -0.08705890f, -0.34550029f, + 0.04266912f, -0.15680569f, + -0.34856534f, 0.43890524f}; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions({4, 2}); + float inputToForgetWeightsValue[] = { 0.09701663f, 0.20334584f, + -0.50592935f, -0.31343272f, + -0.40032279f, 0.44781327f, + 0.01387155f, -0.35593212f}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions({4, 2}); + float inputToCellWeightsValue[] = {-0.50013041f, 0.13702840f, + 0.11810488f, 0.20131630f, + -0.20583314f, 0.44344562f, + 0.22077113f, -0.29909778f}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions({4, 2}); + float inputToOutputWeightsValue[] = {-0.25065863f, -0.28290087f, + 0.04613829f, 0.40525138f, + 0.44272184f, 0.03897077f, + -0.15568960f, 0.19487578f}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions({4, 4}); + float recurrentToInputWeightsValue[] = {-0.00635350f, -0.20423880f, 0.31454784f, -0.35746509f, + 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, + -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, + 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions({4, 4}); + float recurrentToForgetWeightsValue[] = {-0.48684245f, -0.06655136f, 0.42224967f, 0.21126390f, + 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, + 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, + 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions({4, 4}); + float recurrentToCellWeightsValue[] = {-0.34074140f, 0.24443203f, -0.20785320f, 0.26320225f, + 0.05695659f, -0.00123841f, -0.47447860f, -0.35869038f, + -0.06418842f, -0.13502428f, -0.50176400f, 0.22830659f, + -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions({4, 4}); + float recurrentToOutputWeightsValue[] = { 0.43385774f, -0.17194885f, 0.27182370f, 0.09215671f, + 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, + 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, + -0.51818722f, -0.15390486f, 0.04681480f, 0.39922136f}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions({0}); + float cellToInputWeightsValue[] = {}; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions({0}); + float cellToForgetWeightsValue[] = {}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions({0}); + float cellToOutputWeightsValue[] = {}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions({4}); + float inputGateBiasValue[] = {0.0f, 0.0f, 0.0f, 0.0f}; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions({4}); + float forgetGateBiasValue[] = {1.0f, 1.0f, 1.0f, 1.0f}; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions({4}); + float cellBiasValue[] = {0.0f, 0.0f, 0.0f, 0.0f}; + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions({4}); + float outputGateBiasValue[] = {0.0f, 0.0f, 0.0f, 0.0f}; + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions({0}); + float projectionWeightsValue[] = {}; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions({0}); + float projectionBiasValue[] = {}; + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions({1, 4}); + std::vector outputStateInValue {0, 0, 0, 0}; + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions({1, 4}); + std::vector cellStateInValue {0, 0, 0, 0}; + + // constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions({}); + int32_t activationFunctionValue[] = {4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions({}); + float cellClippingThresholdValue[] = {0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions({}); + float projectionClippingThresholdValue[] = {0.0f}; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + hidl_vec scratchBufferDimensions({1, 12}); + std::vector scratchBufferValue {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions({1, 4}); + std::vector outputStateOutValue {-0.0297319f, 0.122947f, 0.208851f, -0.153588f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions({1, 4}); + std::vector cellStateOutValue {-0.145439f, 0.157475f, 0.293663f, -0.277353f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions({1, 4}); + std::vector outputValue {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue); +} + +BOOST_AUTO_TEST_CASE(LstmCifgPeepholeNoProjection) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm2.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm2.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions({1, 2}); + std::vector inputValue {2.0f, 3.0f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions({0}); + float inputToInputWeightsValue[] = {}; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions({4, 2}); + float inputToForgetWeightsValue[] = {-0.55291498f, -0.42866567f, + 0.13056988f, -0.36333650f, + -0.22755712f, 0.28253698f, + 0.24407166f, 0.33826375f}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions({4, 2}); + float inputToCellWeightsValue[] = {-0.49770179f, -0.27711356f, + -0.09624726f, 0.05100781f, + 0.04717243f, 0.48944736f, + -0.38535351f, -0.17212132f}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions({4, 2}); + float inputToOutputWeightsValue[] = { 0.10725588f, -0.02335852f, + -0.55932593f, -0.09426838f, + -0.44257352f, 0.54939759f, + 0.01533556f, 0.42751634f}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions({0}); // VTS was {4, 4} -> {0} ? + float recurrentToInputWeightsValue[] = {}; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions({4, 4}); + float recurrentToForgetWeightsValue[] = {-0.13832897f, -0.05151010f, -0.23590070f, -0.16661474f, + -0.14340827f, 0.36986142f, 0.23414481f, 0.55899000f, + 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, + -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions({4, 4}); + float recurrentToCellWeightsValue[] = { 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, + 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, + -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, + -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions({4, 4}); + float recurrentToOutputWeightsValue[] = { 0.41613156f, 0.42610586f, -0.16495961f, -0.56638730f, + 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, + 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, + 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions({0}); + float cellToInputWeightsValue[] = {}; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions({4}); + float cellToForgetWeightsValue[] = {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions({4}); + float cellToOutputWeightsValue[] = {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions({0}); // VTS was {4} -> {0} ? + float inputGateBiasValue[] = {}; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions({4}); + float forgetGateBiasValue[] = {1.0f, 1.0f, 1.0f, 1.0f}; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions({4}); + float cellBiasValue[] = {0.0f, 0.0f, 0.0f, 0.0f}; + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions({4}); + float outputGateBiasValue[] = {0.0f, 0.0f, 0.0f, 0.0f}; + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions({0}); + float projectionWeightsValue[] = {}; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions({0}); + float projectionBiasValue[] = {}; + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions({1, 4}); + std::vector outputStateInValue {0, 0, 0, 0}; + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions({1, 4}); + std::vector cellStateInValue {0, 0, 0, 0}; + + // constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions({}); + int32_t activationFunctionValue[] = {4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions({}); + float cellClippingThresholdValue[] = {0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions({}); + float projectionClippingThresholdValue[] = {0.0f}; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + hidl_vec scratchBufferDimensions({1, 16}); // VTS was {1, 12} -> {1, 16} + std::vector scratchBufferValue {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions({1, 4}); + std::vector outputStateOutValue {-0.364445f, -0.00352185f, 0.128866f, -0.0516365f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions({1, 4}); + std::vector cellStateOutValue {-0.760444f, -0.0180416f, 0.182264f, -0.0649371f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions({1, 4}); + std::vector outputValue {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue); +} + +BOOST_AUTO_TEST_CASE(LstmNoCifgPeepholeProjection) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm3.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm3.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions({2, 5}); + std::vector inputValue {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, + 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions({20, 5}); + float inputToInputWeightsValue[] = { 0.0213936830f, 0.0612455100f, 0.0469051670f, -0.0146576770f, -0.0314946300f, + 0.0917180300f, 0.1464780100f, 0.1079719300f, -0.0057968358f, 0.0019193048f, + -0.2726754000f, 0.1015402900f, -0.0185398850f, 0.0803498850f, -0.1026238500f, + -0.0225997870f, -0.0912115500f, -0.0086759670f, -0.0452061030f, -0.0821282000f, + -0.0080459520f, 0.0154780810f, 0.0552172470f, 0.0387195870f, 0.0441536270f, + -0.0645324300f, 0.0503182500f, -0.0469351080f, -0.0081644309f, 0.0145742260f, + -0.1671009000f, -0.1551955200f, -0.1681979700f, -0.1397126900f, -0.1195305900f, + 0.2500548700f, -0.2279098300f, 0.0098550870f, -0.0281409580f, -0.1120069800f, + 0.1129540800f, -0.0035217577f, 0.0544850750f, 0.0518469500f, 0.0647112060f, + 0.1098919300f, 0.1167478600f, 0.0349060700f, 0.0772735700f, 0.1139058500f, + -0.1863375000f, -0.1034451000f, -0.1394518900f, -0.0494012270f, -0.1876706300f, + 0.0424839030f, 0.1423355200f, 0.1383258100f, 0.1835016500f, 0.1454560300f, + -0.0285457040f, 0.0249395310f, 0.0509297180f, 0.0076203286f, -0.0029723682f, + -0.0424842240f, -0.1182759600f, -0.0917110400f, -0.1080862800f, -0.1632798800f, + -0.2273378000f, -0.0993647000f, -0.0171551070f, 0.0023917493f, 0.0492727640f, + 0.0038534778f, 0.0547645050f, 0.0897537840f, 0.0694723400f, 0.0801447600f, + -0.0454423400f, -0.0497073000f, -0.0713563100f, -0.0489291060f, -0.0040420120f, + -0.0092840260f, 0.0180420540f, 0.0036860977f, -0.0742730200f, -0.1143460400f, + -0.0189954560f, 0.0314875430f, 0.0128349080f, 0.0199777540f, 0.0442566540f, + -0.3929261300f, -0.1851933400f, -0.1165128100f, -0.0680989200f, 0.0113736770f}; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions({20, 5}); + float inputToForgetWeightsValue[] = {-0.0018401089f, -0.0048522370f, 0.0369842400f, 0.0141817040f, 0.0282732360f, + -0.0167261940f, -0.0524975900f, -0.1020426100f, 0.0086106600f, -0.0409795050f, + -0.0098991870f, 0.0192389200f, -0.0281772690f, -0.0853510300f, -0.1458549500f, + 0.1066256700f, -0.0190973100f, -0.0178835340f, -0.0047269356f, -0.0451033230f, + 0.0030784295f, 0.0767847750f, 0.0746369600f, 0.0945313950f, 0.0814421000f, + -0.1225789900f, -0.0339457580f, -0.0313034650f, 0.0456306260f, 0.0684388700f, + -0.1349294500f, -0.0124800070f, -0.0811829000f, -0.0722449900f, -0.0962879100f, + 0.0451009460f, 0.0012300825f, 0.0139646620f, 0.0993723940f, 0.0254305900f, + 0.0695832400f, 0.0342572960f, 0.0482646000f, 0.0626799700f, 0.0526250680f, + 0.1278466600f, 0.0707789700f, 0.0257259350f, 0.0416500900f, 0.0724190500f, + 0.0186686440f, -0.0373772940f, -0.0627778300f, -0.0883363600f, -0.0401206050f, + -0.0114055860f, -0.0078083350f, -0.0103013860f, -0.0051021670f, 0.0277174640f, + 0.0548342300f, 0.1144911100f, 0.1128965200f, 0.1093983900f, 0.1339650600f, + -0.0840216600f, -0.0190146200f, -0.0446783040f, -0.0772056500f, 0.0143500630f, + -0.1175795800f, -0.0652038000f, -0.0818573300f, -0.0767543240f, -0.0926143750f, + 0.1040549100f, 0.0529603360f, 0.0357558950f, 0.0358393860f, -0.0125405530f, + 0.0368812980f, 0.0291337600f, 0.0342015900f, 0.0544844700f, -0.0545233530f, + 0.0258271500f, 0.0232735500f, -0.0118571790f, -0.0011980024f, -0.0346417170f, + -0.0261250940f, -0.1758261500f, -0.1592365700f, -0.2748677400f, -0.0006143371f, + 0.0001771948f, -8.470171e-05f, 0.0265180700f, 0.0457907650f, 0.069564960f}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions({20, 5}); + float inputToCellWeightsValue[] = {-0.0458028300f, -0.0954946200f, -0.0324189850f, -0.0645463300f, -0.0435284530f, + 0.0430185870f, -0.0491523440f, -0.1241814400f, -0.0789854750f, -0.0759688900f, + 0.0194843620f, -0.1143496200f, -0.0074034138f, -0.0631484400f, -0.0929814950f, + 0.0062155537f, -0.0250343380f, -0.0028890965f, 0.0489295270f, 0.0623507500f, + 0.1066591800f, -0.0320367920f, -0.0850591600f, -0.1084335800f, -0.1300243300f, + -0.0368164370f, -0.0213013400f, -0.0165182390f, 0.0047691227f, -0.0025825808f, + 0.0660178660f, 0.0299915340f, -0.1065283600f, -0.1037554000f, -0.1305607100f, + -0.0326664300f, -0.0337024140f, -0.0064734240f, -0.0461169200f, 0.0144193390f, + -0.0251743230f, 0.0396852000f, 0.0817775060f, 0.0615746800f, 0.1021009500f, + -0.0096581940f, 0.0465117170f, 0.0360390600f, 0.0069369148f, 0.0159600950f, + -0.0650766600f, 0.0955159800f, 0.0535688360f, 0.0640871400f, 0.1283566700f, + -0.0087143290f, -0.2021196600f, -0.1209367400f, 0.0294504720f, 0.2849013000f, + -0.0292279010f, 0.1164364000f, -0.0856026300f, 0.0994178600f, -0.0369995650f, + -0.0288426260f, -0.0033637602f, -0.0170129020f, -0.0972086500f, -0.1119335100f, + -0.0291551170f, -0.0179360340f, -0.0097689360f, -0.0422332400f, -0.0361596350f, + 0.0650511200f, -0.0217428920f, -0.0233772120f, -0.0722136400f, -0.0643055200f, + 0.0545386500f, 0.0911498140f, 0.0638733100f, 0.0075183930f, 0.0559609530f, + 0.0697793440f, 0.0464111680f, 0.1050991100f, 0.0746389400f, 0.0075130584f, + 0.0128509820f, 0.0455543100f, 0.0569556880f, 0.0655528500f, 0.0508014560f, + -0.0098626830f, 0.0082677200f, -0.0265556090f, -0.0073611983f, -0.0014897042f}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions({20, 5}); + float inputToOutputWeightsValue[] = {-0.0998932000f, -0.0720195600f, -0.0528037730f, -0.1562959300f, -0.1500191800f, + -0.0765075100f, 0.0235985500f, -0.0751553550f, -0.0803770900f, -0.1509353400f, + 0.0295175520f, -0.0475139300f, 0.0103505310f, -0.0266485100f, -0.0168397220f, + -0.0231211630f, 0.0077019283f, 0.0128512570f, -0.0504064900f, -0.0129761000f, + -0.0217377470f, -0.0383057930f, -0.0687058600f, -0.0148124700f, -0.0012853940f, + 0.1012423600f, 0.0831228350f, 0.0533130060f, -0.0622356460f, -0.0756371540f, + -0.0278339030f, 0.0297749710f, 0.1130802000f, 0.0921890600f, 0.0950613500f, + -0.0866657640f, -0.0371627060f, -0.0388809140f, -0.0358328450f, -0.0144815640f, + -0.0982500300f, -0.1204856900f, -0.0976655860f, -0.0528763300f, -0.0964047000f, + -0.1136642900f, 0.0357775050f, 0.1356881900f, 0.0524513830f, 0.0506493040f, + 0.0579895100f, -0.0218523350f, -0.0998488440f, 0.0147404750f, -0.0788979460f, + 0.0497469900f, 0.0141604730f, 0.0697393200f, 0.0496494200f, 0.0333646460f, + 0.0819012400f, 0.0255353670f, 0.0508931650f, 0.0485142540f, 0.0694581300f, + -0.0789075640f, -0.0670761600f, -0.1184450800f, -0.0998668800f, -0.0750940300f, + 0.0626322600f, 0.1492558700f, 0.2018843600f, 0.1209845100f, 0.1463941500f, + 0.0015017595f, -0.0142673820f, -0.0341725700f, 0.0127114680f, 0.0028300495f, + -0.0247584820f, -0.0509854800f, -0.0821182000f, 0.0142256720f, 0.0215441580f, + 0.0894972500f, 0.0750526800f, -0.0020780868f, 0.0490825800f, 0.0647629500f, + -0.0229070630f, 0.0275624560f, 0.0401857350f, 0.0195675770f, -0.0155987390f, + -0.0490973030f, -0.0171218660f, -0.0833682340f, -0.0233200200f, -0.084095600f}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions({20, 16}); + float recurrentToInputWeightsValue[] = { + -0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, // 00 + -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, + -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, + -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, + 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, // 01 + 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, + -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, + 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, + -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, // 02 + 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, + -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, + 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, + -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, // 03 + 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, + -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, + -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, + -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, // 04 + -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, + -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f, + 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f, + 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f, // 05 + 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f, + 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f, + 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f, + -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f, // 06 + -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f, + 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f, + -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f, + -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f, // 07 + -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f, + -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f, + -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f, + -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f, // 08 + 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f, + -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f, + 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f, + -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f, // 09 + -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f, + 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f, + -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f, + -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f, // 10 + 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f, + -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f, + 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f, + 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f, // 11 + 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f, + 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f, + 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f, + 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f, // 12 + -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f, + -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f, + -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f, + -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f, // 13 + -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f, + 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f, + 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f, + -0.017081132f, 0.019358726f, 0.0027079724f, 0.004635139f, // 14 + 0.062634714f, -0.02338735f, -0.039547626f, -0.02050681f, + 0.03385117f, -0.083611414f, 0.002862572f, -0.09421313f, + 0.058618143f, -0.08598433f, 0.00972939f, 0.023867095f, + -0.053934585f, -0.023203006f, 0.07452513f, -0.048767887f, // 15 + -0.07314807f, -0.056307215f, -0.10433547f, -0.06440842f, + 0.04328182f, 0.04389765f, -0.020006588f, -0.09076438f, + -0.11652589f, -0.021705797f, 0.03345259f, -0.010329105f, + -0.025767034f, 0.013057034f, -0.07316461f, -0.10145612f, // 16 + 0.06358255f, 0.18531723f, 0.07759293f, 0.12006465f, + 0.1305557f, 0.058638252f, -0.03393652f, 0.09622831f, + -0.16253184f, -2.4580743e-06f, 0.079869635f, -0.070196845f, + -0.005644518f, 0.06857898f, -0.12598175f, -0.035084512f, // 17 + 0.03156317f, -0.12794146f, -0.031963028f, 0.04692781f, + 0.030070418f, 0.0071660685f, -0.095516115f, -0.004643372f, + 0.040170413f, -0.062104587f, -0.0037324072f, 0.0554317f, + 0.08184801f, -0.019164372f, 0.06791302f, 0.034257166f, // 18 + -0.10307039f, 0.021943003f, 0.046745934f, 0.0790918f, + -0.0265588f, -0.007824208f, 0.042546265f, -0.00977924f, + -0.0002440307f, -0.017384544f, -0.017990116f, 0.12252321f, + -0.014512694f, -0.08251313f, 0.08861942f, 0.13589665f, // 19 + 0.026351685f, 0.012641483f, 0.07466548f, 0.044301085f, + -0.045414884f, -0.051112458f, 0.03444247f, -0.08502782f, + -0.04106223f, -0.028126027f, 0.028473156f, 0.10467447f}; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions({20, 16}); + float recurrentToForgetWeightsValue[] = { + -0.057784554f, -0.026057621f, -0.068447545f, -0.022581743f, // 00 + 0.14811787f, 0.10826372f, 0.09471067f, 0.03987225f, + -0.0039523416f, 0.00030638507f, 0.053185795f, 0.10572994f, + 0.08414449f, -0.022036452f, -0.00066928595f, -0.09203576f, + 0.032950465f, -0.10985798f, -0.023809856f, 0.0021431844f, // 01 + -0.02196096f, -0.00326074f, 0.00058621005f, -0.074678116f, + -0.06193199f, 0.055729095f, 0.03736828f, 0.020123724f, + 0.061878487f, -0.04729229f, 0.034919553f, -0.07585433f, + -0.04421272f, -0.044019096f, 0.085488975f, 0.04058006f, // 02 + -0.06890133f, -0.030951202f, -0.024628663f, -0.07672815f, + 0.034293607f, 0.08556707f, -0.05293577f, -0.033561368f, + -0.04899627f, 0.0241671f, 0.015736353f, -0.095442444f, + -0.029564252f, 0.016493602f, -0.035026584f, 0.022337519f, // 03 + -0.026871363f, 0.004780428f, 0.0077918363f, -0.03601621f, + 0.016435321f, -0.03263031f, -0.09543275f, -0.047392778f, + 0.013454138f, 0.028934088f, 0.01685226f, -0.086110644f, + -0.046250615f, -0.01847454f, 0.047608484f, 0.07339695f, // 04 + 0.034546845f, -0.04881143f, 0.009128804f, -0.08802852f, + 0.03761666f, 0.008096139f, -0.014454086f, 0.014361001f, + -0.023502491f, -0.0011840804f, -0.07607001f, 0.001856849f, + -0.06509276f, -0.006021153f, -0.08570962f, -0.1451793f, // 05 + 0.060212336f, 0.055259194f, 0.06974018f, 0.049454916f, + -0.027794661f, -0.08077226f, -0.016179763f, 0.1169753f, + 0.17213494f, -0.0056326236f, -0.053934924f, -0.0124349f, + -0.11520337f, 0.05409887f, 0.088759385f, 0.0019655675f, // 06 + 0.0042065294f, 0.03881498f, 0.019844765f, 0.041858196f, + -0.05695512f, 0.047233116f, 0.038937137f, -0.06542224f, + 0.014429736f, -0.09719407f, 0.13908425f, -0.05379757f, + 0.012321099f, 0.082840554f, -0.029899208f, 0.044217527f, // 07 + 0.059855383f, 0.07711018f, -0.045319796f, 0.0948846f, + -0.011724666f, -0.0033288454f, -0.033542685f, -0.04764985f, + -0.13873616f, 0.040668588f, 0.034832682f, -0.015319203f, + -0.018715994f, 0.046002675f, 0.0599172f, -0.043107376f, // 08 + 0.0294216f, -0.002314414f, -0.022424703f, 0.0030315618f, + 0.0014641669f, 0.0029166266f, -0.11878115f, 0.013738511f, + 0.12375372f, -0.0006038222f, 0.029104086f, 0.087442465f, + 0.052958444f, 0.07558703f, 0.04817258f, 0.044462286f, // 09 + -0.015213451f, -0.08783778f, -0.0561384f, -0.003008196f, + 0.047060397f, -0.002058388f, 0.03429439f, -0.018839769f, + 0.024734668f, 0.024614193f, -0.042046934f, 0.09597743f, + -0.0043254104f, 0.04320769f, 0.0064070094f, -0.0019131786f, // 10 + -0.02558259f, -0.022822596f, -0.023273505f, -0.02464396f, + -0.10991725f, -0.006240552f, 0.0074488563f, 0.024044557f, + 0.04383914f, -0.046476185f, 0.028658995f, 0.060410924f, + 0.050786525f, 0.009452605f, -0.0073054377f, -0.024810238f, // 11 + 0.0052906186f, 0.0066939713f, -0.0020913032f, 0.014515517f, + 0.015898481f, 0.021362653f, -0.030262267f, 0.016587038f, + -0.011442813f, 0.041154444f, -0.007631438f, -0.03423484f, + -0.010977775f, 0.036152758f, 0.0066366293f, 0.11915515f, // 12 + 0.02318443f, -0.041350313f, 0.021485701f, -0.10906167f, + -0.028218046f, -0.00954771f, 0.020531068f, -0.11995105f, + -0.03672871f, 0.024019798f, 0.014255957f, -0.05221243f, + -0.00661567f, -0.04630967f, 0.033188973f, 0.10107534f, // 13 + -0.014027541f, 0.030796422f, -0.10270911f, -0.035999842f, + 0.15443139f, 0.07684145f, 0.036571592f, -0.035900835f, + -0.0034699554f, 0.06209149f, 0.015920248f, -0.031122351f, + -0.03858649f, 0.01849943f, 0.13872518f, 0.01503974f, // 14 + 0.069941424f, -0.06948533f, -0.0088794185f, 0.061282158f, + -0.047401894f, 0.03100163f, -0.041533746f, -0.10430945f, + 0.044574402f, -0.01425562f, -0.024290353f, 0.034563623f, + 0.05866852f, 0.023947537f, -0.09445152f, 0.035450947f, // 15 + 0.02247216f, -0.0042998926f, 0.061146557f, -0.10250651f, + 0.020881841f, -0.06747029f, 0.10062043f, -0.0023941975f, + 0.03532124f, -0.016341697f, 0.09685456f, -0.016764693f, + 0.051808182f, 0.05875331f, -0.04536488f, 0.001626336f, // 16 + -0.028892258f, -0.01048663f, -0.009793449f, -0.017093895f, + 0.010987891f, 0.02357273f, -0.00010856845f, 0.0099760275f, + -0.001845119f, -0.03551521f, 0.0018358806f, 0.05763657f, + -0.01769146f, 0.040995963f, 0.02235177f, -0.060430344f, // 17 + 0.11475477f, -0.023854522f, 0.10071741f, 0.0686208f, + -0.014250481f, 0.034261297f, 0.047418304f, 0.08562733f, + -0.030519066f, 0.0060542435f, 0.014653856f, -0.038836084f, + 0.04096551f, 0.032249358f, -0.08355519f, -0.026823482f, // 18 + 0.056386515f, -0.010401743f, -0.028396193f, 0.08507674f, + 0.014410365f, 0.020995233f, 0.17040324f, 0.11511526f, + 0.02459721f, 0.0066619175f, 0.025853224f, -0.023133837f, + -0.081302024f, 0.017264642f, -0.009585969f, 0.09491168f, // 19 + -0.051313367f, 0.054532815f, -0.014298593f, 0.10657464f, + 0.007076659f, 0.10964551f, 0.0409152f, 0.008275321f, + -0.07283536f, 0.07937492f, 0.04192024f, -0.1075027f}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions({20, 16}); + float recurrentToCellWeightsValue[] = { + -0.037322544f, 0.018592842f, 0.0056175636f, -0.06253426f, + 0.055647098f, -0.05713207f, -0.05626563f, 0.005559383f, + 0.03375411f, -0.025757805f, -0.088049285f, 0.06017052f, + -0.06570978f, 0.007384076f, 0.035123326f, -0.07920549f, + 0.053676967f, 0.044480428f, -0.07663568f, 0.0071805613f, + 0.08089997f, 0.05143358f, 0.038261272f, 0.03339287f, + -0.027673481f, 0.044746667f, 0.028349208f, 0.020090483f, + -0.019443132f, -0.030755889f, -0.0040000007f, 0.04465846f, + -0.021585021f, 0.0031670958f, 0.0053199246f, -0.056117613f, + -0.10893326f, 0.076739706f, -0.08509834f, -0.027997585f, + 0.037871376f, 0.01449768f, -0.09002357f, -0.06111149f, + -0.046195522f, 0.0422062f, -0.005683705f, -0.1253618f, + -0.012925729f, -0.04890792f, 0.06985068f, 0.037654128f, + 0.03398274f, -0.004781977f, 0.007032333f, -0.031787455f, + 0.010868644f, -0.031489216f, 0.09525667f, 0.013939797f, + 0.0058680447f, 0.0167067f, 0.02668468f, -0.04797466f, + -0.048885044f, -0.12722108f, 0.035304096f, 0.06554885f, + 0.00972396f, -0.039238118f, -0.05159735f, -0.11329045f, + 0.1613692f, -0.03750952f, 0.06529313f, -0.071974665f, + -0.11769596f, 0.015524369f, -0.0013754242f, -0.12446318f, + 0.02786344f, -0.014179351f, 0.005264273f, 0.14376344f, + 0.015983658f, 0.03406988f, -0.06939408f, 0.040699873f, + 0.02111075f, 0.09669095f, 0.041345075f, -0.08316494f, + -0.07684199f, -0.045768797f, 0.032298047f, -0.041805092f, + 0.0119405f, 0.0061010392f, 0.12652606f, 0.0064572375f, + -0.024950314f, 0.11574242f, 0.04508852f, -0.04335324f, + 0.06760663f, -0.027437469f, 0.07216407f, 0.06977076f, + -0.05438599f, 0.034033038f, -0.028602652f, 0.05346137f, + 0.043184172f, -0.037189785f, 0.10420091f, 0.00882477f, + -0.054019816f, -0.074273005f, -0.030617684f, -0.0028467078f, + 0.024302477f, -0.0038869337f, 0.005332455f, 0.0013399826f, + 0.04361412f, -0.007001822f, 0.09631092f, -0.06702025f, + -0.042049985f, -0.035070654f, -0.04103342f, -0.10273396f, + 0.0544271f, 0.037184782f, -0.13150354f, -0.0058036847f, + -0.008264958f, 0.042035464f, 0.05891794f, 0.029673764f, + 0.0063542654f, 0.044788733f, 0.054816857f, 0.062257513f, + -0.00093483756f, 0.048938446f, -0.004952862f, -0.007730018f, + -0.04043371f, -0.017094059f, 0.07229206f, -0.023670016f, + -0.052195564f, -0.025616996f, -0.01520939f, 0.045104615f, + -0.007376126f, 0.003533447f, 0.006570588f, 0.056037236f, + 0.12436656f, 0.051817212f, 0.028532185f, -0.08686856f, + 0.11868599f, 0.07663395f, -0.07323171f, 0.03463402f, + -0.050708205f, -0.04458982f, -0.11590894f, 0.021273347f, + 0.1251325f, -0.15313013f, -0.12224372f, 0.17228661f, + 0.023029093f, 0.086124025f, 0.006445803f, -0.03496501f, + 0.028332196f, 0.04449512f, -0.042436164f, -0.026587414f, + -0.006041347f, -0.09292539f, -0.05678812f, 0.03897832f, + 0.09465633f, 0.008115513f, -0.02171956f, 0.08304309f, + 0.071401566f, 0.019622514f, 0.032163795f, -0.004167056f, + 0.02295182f, 0.030739572f, 0.056506045f, 0.004612461f, + 0.06524936f, 0.059999723f, 0.046395954f, -0.0045512207f, + -0.1335546f, -0.030136576f, 0.11584653f, -0.014678886f, + 0.0020118146f, -0.09688814f, -0.0790206f, 0.039770417f, + -0.0329582f, 0.07922767f, 0.029322514f, 0.026405897f, + 0.04207835f, -0.07073373f, 0.063781224f, 0.0859677f, + -0.10925287f, -0.07011058f, 0.048005477f, 0.03438226f, + -0.09606514f, -0.006669445f, -0.043381985f, 0.04240257f, + -0.06955775f, -0.06769346f, 0.043903265f, -0.026784198f, + -0.017840602f, 0.024307009f, -0.040079936f, -0.019946516f, + 0.045318738f, -0.12233574f, 0.026170589f, 0.0074471775f, + 0.15978073f, 0.10185836f, 0.10298046f, -0.015476589f, + -0.039390966f, -0.072174534f, 0.0739445f, -0.1211869f, + -0.0347889f, -0.07943156f, 0.014809798f, -0.12412325f, + -0.0030663363f, 0.039695457f, 0.0647603f, -0.08291318f, + -0.018529687f, -0.004423833f, 0.0037507233f, 0.084633216f, + -0.01514876f, -0.056505352f, -0.012800942f, -0.06994386f, + 0.012962922f, -0.031234352f, 0.07029052f, 0.016418684f, + 0.03618972f, 0.055686004f, -0.08663945f, -0.017404709f, + -0.054761406f, 0.029065743f, 0.052404847f, 0.020238016f, + 0.0048197987f, -0.0214882f, 0.07078733f, 0.013016777f, + 0.06262858f, 0.009184685f, 0.020785125f, -0.043904778f, + -0.0270329f, -0.03299152f, -0.060088247f, -0.015162964f, + -0.001828936f, 0.12642565f, -0.056757294f, 0.013586685f, + 0.09232601f, -0.035886683f, 0.06000002f, 0.05229691f, + -0.052580316f, -0.082029596f, -0.010794592f, 0.012947712f, + -0.036429964f, -0.085508935f, -0.13127148f, -0.017744139f, + 0.031502828f, 0.036232427f, -0.031581745f, 0.023051167f, + -0.05325106f, -0.03421577f, 0.028793324f, -0.034633752f, + -0.009881397f, -0.043551125f, -0.018609839f, 0.0019097115f, + -0.008799762f, 0.056595087f, 0.0022273948f, 0.055752404f}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions({20, 16}); + float recurrentToOutputWeightsValue[] = { + 0.025825322f, -0.05813119f, 0.09495884f, -0.045984812f, + -0.01255415f, -0.0026479573f, -0.08196161f, -0.054914974f, + -0.0046604523f, -0.029587349f, -0.044576716f, -0.07480124f, + -0.082868785f, 0.023254942f, 0.027502948f, -0.0039728214f, + -0.08683098f, -0.08116779f, -0.014675607f, -0.037924774f, + -0.023314456f, -0.007401714f, -0.09255757f, 0.029460307f, + -0.08829125f, -0.005139627f, -0.08989442f, -0.0555066f, + 0.13596267f, -0.025062224f, -0.048351806f, -0.03850004f, + 0.07266485f, -0.022414139f, 0.05940088f, 0.075114764f, + 0.09597592f, -0.010211725f, -0.0049794707f, -0.011523867f, + -0.025980417f, 0.072999895f, 0.11091378f, -0.081685916f, + 0.014416728f, 0.043229222f, 0.034178585f, -0.07530371f, + 0.035837382f, -0.085607f, -0.007721233f, -0.03287832f, + -0.043848954f, -0.06404588f, -0.06632928f, -0.073643476f, + 0.008214239f, -0.045984086f, 0.039764922f, 0.03474462f, + 0.060612556f, -0.080590084f, 0.049127717f, 0.04151091f, + -0.030063879f, 0.008801774f, -0.023021035f, -0.019558564f, + 0.05158114f, -0.010947698f, -0.011825728f, 0.0075720972f, + 0.0699727f, -0.0039981045f, 0.069350146f, 0.08799282f, + 0.016156472f, 0.035502106f, 0.11695009f, 0.006217345f, + 0.13392477f, -0.037875112f, 0.025745004f, 0.08940699f, + -0.00924166f, 0.0046702605f, -0.036598757f, -0.08811812f, + 0.10522024f, -0.032441203f, 0.008176899f, -0.04454919f, + 0.07058152f, 0.0067963637f, 0.039206743f, 0.03259838f, + 0.03725492f, -0.09515802f, 0.013326398f, -0.052055415f, + -0.025676316f, 0.03198509f, -0.015951829f, -0.058556724f, + 0.036879618f, 0.043357447f, 0.028362012f, -0.05908629f, + 0.0059240665f, -0.04995891f, -0.019187413f, 0.0276265f, + -0.01628143f, 0.0025863599f, 0.08800015f, 0.035250366f, + -0.022165963f, -0.07328642f, -0.009415526f, -0.07455109f, + 0.11690406f, 0.0363299f, 0.07411125f, 0.042103454f, + -0.009660886f, 0.019076364f, 0.018299393f, -0.046004917f, + 0.08891175f, 0.0431396f, -0.026327137f, -0.051502608f, + 0.08979574f, -0.051670972f, 0.04940282f, -0.07491107f, + -0.021240504f, 0.022596184f, -0.034280192f, 0.060163025f, + -0.058211457f, -0.051837247f, -0.01349775f, -0.04639988f, + -0.035936575f, -0.011681591f, 0.064818054f, 0.0073146066f, + -0.021745546f, -0.043124277f, -0.06471268f, -0.07053354f, + -0.029321948f, -0.05330136f, 0.016933719f, -0.053782392f, + 0.13747959f, -0.1361751f, -0.11569455f, 0.0033329215f, + 0.05693899f, -0.053219706f, 0.063698f, 0.07977434f, + -0.07924483f, 0.06936997f, 0.0034815092f, -0.007305279f, + -0.037325785f, -0.07251102f, -0.033633437f, -0.08677009f, + 0.091591336f, -0.14165086f, 0.021752775f, 0.019683983f, + 0.0011612234f, -0.058154266f, 0.049996935f, 0.0288841f, + -0.0024567875f, -0.14345716f, 0.010955264f, -0.10234828f, + 0.1183656f, -0.0010731248f, -0.023590032f, -0.072285876f, + -0.0724771f, -0.026382286f, -0.0014920527f, 0.042667855f, + 0.0018776858f, 0.02986552f, 0.009814309f, 0.0733756f, + 0.12289186f, 0.018043943f, -0.0458958f, 0.049412545f, + 0.033632483f, 0.05495232f, 0.036686596f, -0.013781798f, + -0.010036754f, 0.02576849f, -0.08307328f, 0.010112348f, + 0.042521734f, -0.05869831f, -0.071689695f, 0.03876447f, + -0.13275425f, -0.0352966f, -0.023077697f, 0.10285965f, + 0.084736146f, 0.15568255f, -0.00040734606f, 0.027835453f, + -0.10292561f, -0.032401145f, 0.10053256f, -0.026142767f, + -0.08271222f, -0.0030240538f, -0.016368777f, 0.1070414f, + 0.042672627f, 0.013456989f, -0.0437609f, -0.022309763f, + 0.11576483f, 0.04108048f, 0.061026827f, -0.0190714f, + -0.0869359f, 0.037901703f, 0.0610107f, 0.07202949f, + 0.01675338f, 0.086139716f, -0.08795751f, -0.014898893f, + -0.023771819f, -0.01965048f, 0.007955471f, -0.043740474f, + 0.03346837f, -0.10549954f, 0.090567775f, 0.042013682f, + -0.03176985f, 0.12569028f, -0.02421228f, -0.029526481f, + 0.023851605f, 0.031539805f, 0.05292009f, -0.02344001f, + -0.07811758f, -0.08834428f, 0.10094801f, 0.16594367f, + -0.06861939f, -0.021256343f, -0.041093912f, -0.06669611f, + 0.035498552f, 0.021757556f, -0.09302526f, -0.015403468f, + -0.06614931f, -0.051798206f, -0.013874718f, 0.03630673f, + 0.010412845f, -0.08077351f, 0.046185967f, 0.0035662893f, + 0.03541868f, -0.094149634f, -0.034814864f, 0.003128424f, + -0.020674974f, -0.03944324f, -0.008110165f, -0.11113267f, + 0.08484226f, 0.043586485f, 0.040582247f, 0.0968012f, + -0.065249965f, -0.028036479f, 0.0050708856f, 0.0017462453f, + 0.0326779f, 0.041296225f, 0.09164146f, -0.047743853f, + -0.015952192f, -0.034451712f, 0.084197424f, -0.05347844f, + -0.11768019f, 0.085926116f, -0.08251791f, -0.045081906f, + 0.0948852f, 0.068401024f, 0.024856757f, 0.06978981f, + -0.057309967f, -0.012775832f, -0.0032452994f, 0.01977615f, + -0.041040014f, -0.024264973f, 0.063464895f, 0.05431621f}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions({20}); + float cellToInputWeightsValue[] = {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, + -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, + -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f, -0.052169047f, + 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f}; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions({20}); + float cellToForgetWeightsValue[] = {-0.01998659f, -0.15568835f, -0.24248174f, -0.012770197f, 0.041331276f, + -0.072311886f, -0.052123554f, -0.0066330447f, -0.043891653f, 0.036225766f, + -0.047248036f, 0.021479502f, 0.033189066f, 0.11952997f, -0.020432774f, + 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions({20}); + float cellToOutputWeightsValue[] = {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, + -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, + -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, + 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions({20}); + float inputGateBiasValue[] = {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, + -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, + -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, + 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f}; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions({20}); + float forgetGateBiasValue[] = {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, + 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, + 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, + 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f}; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions({20}); + float cellBiasValue[] = {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, + -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, + -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, + 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f}; + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions({20}); + float outputGateBiasValue[] = {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, + 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, + 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, + -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f}; + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions({16, 20}); + float projectionWeightsValue[] = { + -0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, + 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, + -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, + -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, + 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, + 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, + 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, + 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, + -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, + -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, + -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, + 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, + 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, + 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, + 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, + 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, + -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, + 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, + -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, + 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, + -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, + -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, + 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, + -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, + 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, + -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, + -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, + 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, + -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, + -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, + -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, + 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, + 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, + -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, + 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, + 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, + 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, + 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, + 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, + -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, + -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, + 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, + -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, + -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, + 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, + 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, + 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, + -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, + -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, + -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, + 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, + -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, + 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, + 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, + -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, + -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, + -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, + 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, + -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, + -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, + -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, + 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, + 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, + 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions({0}); + float projectionBiasValue[] = {}; + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions({2, 16}); + std::vector outputStateInValue {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions({2, 20}); + std::vector cellStateInValue {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + + // constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions({}); + int32_t activationFunctionValue[] = {4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions({}); + float cellClippingThresholdValue[] = {0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions({}); + float projectionClippingThresholdValue[] = {0.0f}; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + hidl_vec scratchBufferDimensions({2, 60}); + std::vector scratchBufferValue {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions({2, 16}); + std::vector outputStateOutValue { + -0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835577f, -0.0211779f, 0.0283512f, -0.0114597f, + 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415119f, 0.017147f, 0.0134203f, + -0.013869f, 0.0287268f, -0.00334694f, 0.00733397f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, + 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.0216801f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions({2, 20}); + std::vector cellStateOutValue { + -0.0531632f, -0.0118138f, 0.0870833f, 0.0347929f, -0.076144f, + -0.0659219f, -0.0463811f, 0.0141307f, -0.0127706f, -0.03782f, + -0.00402401f, -0.00571876f, -0.187957f, -0.0247127f, 0.0711425f, + 0.008244f, 0.0492649f, 0.126972f, 0.0933097f, 0.29848f, + -0.0966178f, -0.114417f, 0.0387229f, 0.0453255f, -0.181286f, + -0.0651251f, -0.0996879f, -0.00276995f, 0.0617558f, -0.0100728f, + 0.056304f, -0.077416f, -0.162858f, -0.0541251f, 0.0571202f, + -0.0525331f, 0.0724297f, 0.171029f, 0.141738f, 0.295483f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions({2, 16}); + std::vector outputValue { + -0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, + 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, + -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, + 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue); +} + +BOOST_AUTO_TEST_CASE(LstmCifgPeepholeNoProjectionBatch2) +{ + // This replicates android/frameworks/ml/nn/runtime/test/generated/vts_models/lstm2.model.cpp + // with values from android/frameworks/ml/nn/runtime/test/generated/examples/lstm2.example.cpp + // and weights, biases and scalars passed as CONSTANT_COPY tensors (instead of MODEL_INPUT tensors). + // The batch size has been increased to 2 (it was 1 in the VTS test) with appropriate input and output values added. + + uint32_t batchSize = 2; + uint32_t inputSize = 2; + uint32_t numUnits = 4; + uint32_t outputSize = numUnits; + + // Inputs: + // 00: The input: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, input_size], where + // “batch_size” corresponds to the batching dimension, and “input_size” is the size of the input. + hidl_vec inputDimensions({batchSize, inputSize}); + std::vector inputValue {2.0f, 3.0f, 3.0f, 4.0f}; + + // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size], where “num_units” corresponds to the number of cell units. + hidl_vec inputToInputWeightsDimensions({0}); + float inputToInputWeightsValue[] = {}; + // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToForgetWeightsDimensions({numUnits, inputSize}); + float inputToForgetWeightsValue[] = {-0.55291498f, -0.42866567f, + 0.13056988f, -0.36333650f, + -0.22755712f, 0.28253698f, + 0.24407166f, 0.33826375f}; + // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units, input_size]. + hidl_vec inputToCellWeightsDimensions({numUnits, inputSize}); + float inputToCellWeightsValue[] = {-0.49770179f, -0.27711356f, + -0.09624726f, 0.05100781f, + 0.04717243f, 0.48944736f, + -0.38535351f, -0.17212132f}; + // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, input_size]. + hidl_vec inputToOutputWeightsDimensions({numUnits, inputSize}); + float inputToOutputWeightsValue[] = { 0.10725588f, -0.02335852f, + -0.55932593f, -0.09426838f, + -0.44257352f, 0.54939759f, + 0.01533556f, 0.42751634f}; + // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e., + // “num_units”), or the second dimension of the “projection_weights”, if defined. + hidl_vec recurrentToInputWeightsDimensions({0}); // VTS was {4, 4} -> {0} ? + float recurrentToInputWeightsValue[] = {}; + // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToForgetWeightsDimensions({numUnits, outputSize}); + float recurrentToForgetWeightsValue[] = {-0.13832897f, -0.05151010f, -0.23590070f, -0.16661474f, + -0.14340827f, 0.36986142f, 0.23414481f, 0.55899000f, + 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, + -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}; + // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToCellWeightsDimensions({numUnits, outputSize}); + float recurrentToCellWeightsValue[] = { 0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, + 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, + -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, + -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}; + // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [num_units, output_size]. + hidl_vec recurrentToOutputWeightsDimensions({numUnits, outputSize}); + float recurrentToOutputWeightsValue[] = { 0.41613156f, 0.42610586f, -0.16495961f, -0.56638730f, + 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, + 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, + 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}; + // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToInputWeightsDimensions({0}); + float cellToInputWeightsValue[] = {}; + // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToForgetWeightsDimensions({numUnits}); + float cellToForgetWeightsValue[] = {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}; + // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellToOutputWeightsDimensions({numUnits}); + float cellToOutputWeightsValue[] = {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}; + // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec inputGateBiasDimensions({0}); // VTS was {4} -> {0} ? + float inputGateBiasValue[] = {}; + // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec forgetGateBiasDimensions({4}); + float forgetGateBiasValue[] = {1.0f, 1.0f, 1.0f, 1.0f}; + // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec cellBiasDimensions({numUnits}); + float cellBiasValue[] = {0.0f, 0.0f, 0.0f, 0.0f}; + // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [num_units]. + hidl_vec outputGateBiasDimensions({numUnits}); + float outputGateBiasValue[] = {0.0f, 0.0f, 0.0f, 0.0f}; + // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape + // [output_size, num_units]. + hidl_vec projectionWeightsDimensions({0}); + float projectionWeightsValue[] = {}; + // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [output_size]. + hidl_vec projectionBiasDimensions({0}); + float projectionBiasValue[] = {}; + + // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateInDimensions({batchSize, outputSize}); + std::vector outputStateInValue {0, 0, 0, 0, 0, 0, 0, 0}; + // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateInDimensions({batchSize, numUnits}); + std::vector cellStateInValue {0, 0, 0, 0, 0, 0, 0, 0}; + + // constant scalar values (the VTS test adds these as tensors of dim {}) + // 20: The activation function: A value indicating the activation function: + // 0: None; 1: Relu; 3: Relu6; 4: Tanh; 6: Sigmoid. + hidl_vec activationFunctionDimensions({}); + int32_t activationFunctionValue[] = {4}; + // 21: The clipping threshold: for the cell state, such that values are bound within [-cell_clip, cell_clip]. + // If set to 0.0 then clipping is disabled. + hidl_vec cellClippingThresholdDimensions({}); + float cellClippingThresholdValue[] = {0.0f}; + // 22: The clipping threshold: for the output from the projection layer, such that values are bound within + // [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled. + hidl_vec projectionClippingThresholdDimensions({}); + float projectionClippingThresholdValue[] = {0.0f}; + + // Outputs: + // 0: The scratch buffer: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units * 4] with + // CIFG, or [batch_size, num_units * 3] without CIFG. + hidl_vec scratchBufferDimensions({batchSize, numUnits * 4}); + std::vector scratchBufferValue {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; + // 1: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. + hidl_vec outputStateOutDimensions({batchSize, outputSize}); + std::vector outputStateOutValue {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, + -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}; + // 2: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, num_units]. + hidl_vec cellStateOutDimensions({batchSize, numUnits}); + std::vector cellStateOutValue {-0.76044439f, -0.01804161f, 0.18226376f, -0.06493707f, + -0.90477051f, -0.04355603f, 0.18475688f, -0.04158677f}; + // 3: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_FLOAT32, of shape [batch_size, output_size]. This is + // effectively the same as the current “output state (out)” value. + hidl_vec outputDimensions({batchSize, outputSize}); + std::vector outputValue {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, + -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f}; + + LstmTestImpl(inputDimensions, inputValue, + inputToInputWeightsDimensions, inputToInputWeightsValue, + inputToForgetWeightsDimensions, inputToForgetWeightsValue, + inputToCellWeightsDimensions, inputToCellWeightsValue, + inputToOutputWeightsDimensions, inputToOutputWeightsValue, + recurrentToInputWeightsDimensions, recurrentToInputWeightsValue, + recurrentToForgetWeightsDimensions, recurrentToForgetWeightsValue, + recurrentToCellWeightsDimensions, recurrentToCellWeightsValue, + recurrentToOutputWeightsDimensions, recurrentToOutputWeightsValue, + cellToInputWeightsDimensions, cellToInputWeightsValue, + cellToForgetWeightsDimensions, cellToForgetWeightsValue, + cellToOutputWeightsDimensions, cellToOutputWeightsValue, + inputGateBiasDimensions, inputGateBiasValue, + forgetGateBiasDimensions, forgetGateBiasValue, + cellBiasDimensions, cellBiasValue, + outputGateBiasDimensions, outputGateBiasValue, + projectionWeightsDimensions, projectionWeightsValue, + projectionBiasDimensions, projectionBiasValue, + outputStateInDimensions, outputStateInValue, + cellStateInDimensions, cellStateInValue, + activationFunctionDimensions, activationFunctionValue, + cellClippingThresholdDimensions, cellClippingThresholdValue, + projectionClippingThresholdDimensions, projectionClippingThresholdValue, + scratchBufferDimensions, scratchBufferValue, + outputStateOutDimensions, outputStateOutValue, + cellStateOutDimensions, cellStateOutValue, + outputDimensions, outputValue); +} + +BOOST_AUTO_TEST_SUITE_END() diff --git a/test/Merger.cpp b/test/Merger.cpp index 48253604..aeaff0cb 100644 --- a/test/Merger.cpp +++ b/test/Merger.cpp @@ -4,28 +4,33 @@ // #include "DriverTestHelpers.hpp" #include "TestTensor.hpp" +#include #include +#include #include BOOST_AUTO_TEST_SUITE(MergerTests) -using ArmnnDriver = armnn_driver::ArmnnDriver; -using DriverOptions = armnn_driver::DriverOptions; +using namespace android::hardware; using namespace driverTestHelpers; +using namespace armnn_driver; namespace { +static const boost::array COMPUTE_DEVICES = {{ armnn::Compute::CpuRef, armnn::Compute::GpuAcc }}; + void MergerTestImpl(const std::vector & inputs, int32_t concatAxis, const TestTensor & expectedOutputTensor, + armnn::Compute computeDevice, ErrorStatus expectedPrepareStatus=ErrorStatus::NONE, ErrorStatus expectedExecStatus=ErrorStatus::NONE) { - std::unique_ptr driver = std::make_unique(DriverOptions(armnn::Compute::CpuRef)); - V1_0::Model model{}; + std::unique_ptr driver = std::make_unique(DriverOptions(computeDevice)); + neuralnetworks::V1_0::Model model{}; hidl_vec modelInputIds; modelInputIds.resize(inputs.size()+1); @@ -40,7 +45,7 @@ MergerTestImpl(const std::vector & inputs, // make the concat operation model.operations.resize(1); - model.operations[0].type = V1_0::OperationType::CONCATENATION; + model.operations[0].type = neuralnetworks::V1_0::OperationType::CONCATENATION; model.operations[0].inputs = modelInputIds; model.operations[0].outputs = hidl_vec{static_cast(inputs.size()+1)}; @@ -130,7 +135,8 @@ MergerTestImpl(const std::vector & inputs, } // namespace -BOOST_AUTO_TEST_CASE(SimpleConcatAxis0) + +BOOST_DATA_TEST_CASE(SimpleConcatAxis0, COMPUTE_DEVICES) { int32_t axis = 0; TestTensor aIn{armnn::TensorShape{1,1,1,1},{0}}; @@ -139,10 +145,10 @@ BOOST_AUTO_TEST_CASE(SimpleConcatAxis0) TestTensor expected{armnn::TensorShape{3,1,1,1},{0,1,2}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(ConcatAxis0_NoInterleave) +BOOST_DATA_TEST_CASE(ConcatAxis0_NoInterleave, COMPUTE_DEVICES) { int32_t axis = 0; TestTensor aIn{armnn::TensorShape{2,1,2,1},{0, 1, @@ -159,10 +165,10 @@ BOOST_AUTO_TEST_CASE(ConcatAxis0_NoInterleave) 8, 9, 10, 11}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(SimpleConcatAxis1) +BOOST_DATA_TEST_CASE(SimpleConcatAxis1, COMPUTE_DEVICES) { int32_t axis = 1; TestTensor aIn{armnn::TensorShape{1,1,1,1},{0}}; @@ -171,10 +177,10 @@ BOOST_AUTO_TEST_CASE(SimpleConcatAxis1) TestTensor expected{armnn::TensorShape{1,3,1,1},{0,1,2}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(ConcatAxis1_NoInterleave) +BOOST_DATA_TEST_CASE(ConcatAxis1_NoInterleave, COMPUTE_DEVICES) { int32_t axis = 1; TestTensor aIn{armnn::TensorShape{1,2,2,1},{0, 1, @@ -191,10 +197,10 @@ BOOST_AUTO_TEST_CASE(ConcatAxis1_NoInterleave) 8, 9, 10, 11}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(SimpleConcatAxis1_DoInterleave) +BOOST_DATA_TEST_CASE(SimpleConcatAxis1_DoInterleave, COMPUTE_DEVICES) { int32_t axis = 1; TestTensor aIn{armnn::TensorShape{2,2,1,1},{0, 1, @@ -207,10 +213,10 @@ BOOST_AUTO_TEST_CASE(SimpleConcatAxis1_DoInterleave) TestTensor expected{armnn::TensorShape{2,6,1,1},{0, 1, 4, 5, 6, 10, 2, 3, 7, 8, 9, 11}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(SimpleConcatAxis2) +BOOST_DATA_TEST_CASE(SimpleConcatAxis2, COMPUTE_DEVICES) { int32_t axis = 2; TestTensor aIn{armnn::TensorShape{1,1,1,1},{0}}; @@ -219,10 +225,10 @@ BOOST_AUTO_TEST_CASE(SimpleConcatAxis2) TestTensor expected{armnn::TensorShape{1,1,3,1},{0,1,2}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(ConcatAxis2_NoInterleave) +BOOST_DATA_TEST_CASE(ConcatAxis2_NoInterleave, COMPUTE_DEVICES) { int32_t axis = 2; TestTensor aIn{armnn::TensorShape{1,1,2,2},{0, 1, @@ -239,10 +245,10 @@ BOOST_AUTO_TEST_CASE(ConcatAxis2_NoInterleave) 8, 9, 10, 11}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(SimpleConcatAxis2_DoInterleave) +BOOST_DATA_TEST_CASE(SimpleConcatAxis2_DoInterleave, COMPUTE_DEVICES) { int32_t axis = 2; TestTensor aIn{armnn::TensorShape{1,2,2,1},{0, 1, @@ -255,10 +261,10 @@ BOOST_AUTO_TEST_CASE(SimpleConcatAxis2_DoInterleave) TestTensor expected{armnn::TensorShape{1,2,6,1},{0, 1, 4, 5, 6, 10, 2, 3, 7, 8, 9, 11}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(SimpleConcatAxis3) +BOOST_DATA_TEST_CASE(SimpleConcatAxis3, COMPUTE_DEVICES) { int32_t axis = 3; TestTensor aIn{armnn::TensorShape{1,1,1,1},{0}}; @@ -267,10 +273,10 @@ BOOST_AUTO_TEST_CASE(SimpleConcatAxis3) TestTensor expected{armnn::TensorShape{1,1,1,3},{0,1,2}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(SimpleConcatAxis3_DoInterleave) +BOOST_DATA_TEST_CASE(SimpleConcatAxis3_DoInterleave, COMPUTE_DEVICES) { int32_t axis = 3; TestTensor aIn{armnn::TensorShape{1,1,2,2},{0, 1, @@ -283,10 +289,10 @@ BOOST_AUTO_TEST_CASE(SimpleConcatAxis3_DoInterleave) TestTensor expected{armnn::TensorShape{1,1,2,6},{0, 1, 4, 5, 6, 10, 2, 3, 7, 8, 9, 11}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } -BOOST_AUTO_TEST_CASE(AxisTooBig) +BOOST_DATA_TEST_CASE(AxisTooBig, COMPUTE_DEVICES) { int32_t axis = 4; TestTensor aIn{armnn::TensorShape{1,1,1,1},{0}}; @@ -296,10 +302,10 @@ BOOST_AUTO_TEST_CASE(AxisTooBig) // see: https://www.tensorflow.org/api_docs/python/tf/concat TestTensor uncheckedOutput{armnn::TensorShape{1,1,1,1},{0}}; ErrorStatus expectedParserStatus = ErrorStatus::GENERAL_FAILURE; - MergerTestImpl({&aIn, &bIn}, axis, uncheckedOutput, expectedParserStatus); + MergerTestImpl({&aIn, &bIn}, axis, uncheckedOutput, sample, expectedParserStatus); } -BOOST_AUTO_TEST_CASE(AxisTooSmall) +BOOST_DATA_TEST_CASE(AxisTooSmall, COMPUTE_DEVICES) { int32_t axis = -5; TestTensor aIn{armnn::TensorShape{1,1,1,1},{0}}; @@ -309,20 +315,20 @@ BOOST_AUTO_TEST_CASE(AxisTooSmall) // see: https://www.tensorflow.org/api_docs/python/tf/concat TestTensor uncheckedOutput{armnn::TensorShape{1,1,1,1},{0}}; ErrorStatus expectedParserStatus = ErrorStatus::GENERAL_FAILURE; - MergerTestImpl({&aIn, &bIn}, axis, uncheckedOutput, expectedParserStatus); + MergerTestImpl({&aIn, &bIn}, axis, uncheckedOutput, sample, expectedParserStatus); } -BOOST_AUTO_TEST_CASE(TooFewInputs) +BOOST_DATA_TEST_CASE(TooFewInputs, COMPUTE_DEVICES) { int32_t axis = 0; TestTensor aIn{armnn::TensorShape{1,1,1,1},{0}}; // We need at least two tensors to concatenate ErrorStatus expectedParserStatus = ErrorStatus::GENERAL_FAILURE; - MergerTestImpl({&aIn}, axis, aIn, expectedParserStatus); + MergerTestImpl({&aIn}, axis, aIn, sample, expectedParserStatus); } -BOOST_AUTO_TEST_CASE(MismatchedInputDimensions) +BOOST_DATA_TEST_CASE(MismatchedInputDimensions, COMPUTE_DEVICES) { int32_t axis = 3; TestTensor aIn{armnn::TensorShape{1,1,2,2},{0, 1, @@ -336,10 +342,10 @@ BOOST_AUTO_TEST_CASE(MismatchedInputDimensions) // The input dimensions must be compatible ErrorStatus expectedParserStatus = ErrorStatus::GENERAL_FAILURE; - MergerTestImpl({&aIn, &bIn, &mismatched}, axis, expected, expectedParserStatus); + MergerTestImpl({&aIn, &bIn, &mismatched}, axis, expected, sample, expectedParserStatus); } -BOOST_AUTO_TEST_CASE(MismatchedInputRanks) +BOOST_DATA_TEST_CASE(MismatchedInputRanks, COMPUTE_DEVICES) { int32_t axis = 2; TestTensor aIn{armnn::TensorShape{1,1,2},{0,1}}; @@ -348,10 +354,10 @@ BOOST_AUTO_TEST_CASE(MismatchedInputRanks) // The input dimensions must be compatible ErrorStatus expectedParserStatus = ErrorStatus::GENERAL_FAILURE; - MergerTestImpl({&aIn, &bIn}, axis, expected, expectedParserStatus); + MergerTestImpl({&aIn, &bIn}, axis, expected, sample, expectedParserStatus); } -BOOST_AUTO_TEST_CASE(MismatchedOutputDimensions) +BOOST_DATA_TEST_CASE(MismatchedOutputDimensions, COMPUTE_DEVICES) { int32_t axis = 3; TestTensor aIn{armnn::TensorShape{1,1,2,2},{0, 1, @@ -366,10 +372,10 @@ BOOST_AUTO_TEST_CASE(MismatchedOutputDimensions) // The input and output dimensions must be compatible ErrorStatus expectedParserStatus = ErrorStatus::GENERAL_FAILURE; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, mismatched, expectedParserStatus); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, mismatched, sample, expectedParserStatus); } -BOOST_AUTO_TEST_CASE(MismatchedOutputRank) +BOOST_DATA_TEST_CASE(MismatchedOutputRank, COMPUTE_DEVICES) { int32_t axis = 3; TestTensor aIn{armnn::TensorShape{1,1,2,2},{0, 1, @@ -384,10 +390,10 @@ BOOST_AUTO_TEST_CASE(MismatchedOutputRank) // The input and output ranks must match ErrorStatus expectedParserStatus = ErrorStatus::GENERAL_FAILURE; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, mismatched, expectedParserStatus); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, mismatched, sample, expectedParserStatus); } -BOOST_AUTO_TEST_CASE(ValidNegativeAxis) +BOOST_DATA_TEST_CASE(ValidNegativeAxis, COMPUTE_DEVICES) { // this is the same as 3 // see: https://www.tensorflow.org/api_docs/python/tf/concat @@ -402,7 +408,79 @@ BOOST_AUTO_TEST_CASE(ValidNegativeAxis) TestTensor expected{armnn::TensorShape{1,1,2,6},{0, 1, 4, 5, 6, 10, 2, 3, 7, 8, 9, 11}}; - MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected); + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); +} + +BOOST_DATA_TEST_CASE(SimpleConcatAxisZero3D, COMPUTE_DEVICES) +{ + int32_t axis = 0; + TestTensor aIn{armnn::TensorShape{1,1,1},{0}}; + TestTensor bIn{armnn::TensorShape{1,1,1},{1}}; + TestTensor cIn{armnn::TensorShape{1,1,1},{2}}; + + TestTensor expected{armnn::TensorShape{3,1,1},{0,1,2}}; + + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); +} + +BOOST_DATA_TEST_CASE(SimpleConcatAxisOne3D, COMPUTE_DEVICES) +{ + int32_t axis = 1; + TestTensor aIn{armnn::TensorShape{1,1,1},{0}}; + TestTensor bIn{armnn::TensorShape{1,1,1},{1}}; + TestTensor cIn{armnn::TensorShape{1,1,1},{2}}; + + TestTensor expected{armnn::TensorShape{1,3,1},{0,1,2}}; + + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); +} + +BOOST_DATA_TEST_CASE(SimpleConcatAxisTwo3D, COMPUTE_DEVICES) +{ + int32_t axis = 2; + TestTensor aIn{armnn::TensorShape{1,1,1},{0}}; + TestTensor bIn{armnn::TensorShape{1,1,1},{1}}; + TestTensor cIn{armnn::TensorShape{1,1,1},{2}}; + + TestTensor expected{armnn::TensorShape{1,1,3},{0,1,2}}; + + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); +} + +BOOST_DATA_TEST_CASE(SimpleConcatAxisZero2D, COMPUTE_DEVICES) +{ + int32_t axis = 0; + TestTensor aIn{armnn::TensorShape{1,1},{0}}; + TestTensor bIn{armnn::TensorShape{1,1},{1}}; + TestTensor cIn{armnn::TensorShape{1,1},{2}}; + + TestTensor expected{armnn::TensorShape{3,1},{0,1,2}}; + + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); +} + +BOOST_DATA_TEST_CASE(SimpleConcatAxisOne2D, COMPUTE_DEVICES) +{ + int32_t axis = 1; + TestTensor aIn{armnn::TensorShape{1,1},{0}}; + TestTensor bIn{armnn::TensorShape{1,1},{1}}; + TestTensor cIn{armnn::TensorShape{1,1},{2}}; + + TestTensor expected{armnn::TensorShape{1,3},{0,1,2}}; + + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); +} + +BOOST_DATA_TEST_CASE(SimpleConcatAxisZero1D, COMPUTE_DEVICES) +{ + int32_t axis = 0; + TestTensor aIn{armnn::TensorShape{1},{0}}; + TestTensor bIn{armnn::TensorShape{1},{1}}; + TestTensor cIn{armnn::TensorShape{1},{2}}; + + TestTensor expected{armnn::TensorShape{3},{0,1,2}}; + + MergerTestImpl({&aIn, &bIn, &cIn}, axis, expected, sample); } BOOST_AUTO_TEST_SUITE_END() diff --git a/test/Tests.cpp b/test/Tests.cpp index 3fa8e125..df98b2ca 100644 --- a/test/Tests.cpp +++ b/test/Tests.cpp @@ -11,9 +11,9 @@ BOOST_AUTO_TEST_SUITE(DriverTests) -using ArmnnDriver = armnn_driver::ArmnnDriver; -using DriverOptions = armnn_driver::DriverOptions; +using namespace android::hardware; using namespace driverTestHelpers; +using namespace armnn_driver; BOOST_AUTO_TEST_CASE(Init) { @@ -31,9 +31,9 @@ BOOST_AUTO_TEST_CASE(TestCapabilities) auto driver = std::make_unique(DriverOptions(armnn::Compute::CpuRef)); ErrorStatus error; - V1_0::Capabilities cap; + neuralnetworks::V1_0::Capabilities cap; - ArmnnDriver::getCapabilities_cb cb = [&](ErrorStatus status, const V1_0::Capabilities& capabilities) + auto cb = [&](ErrorStatus status, const neuralnetworks::V1_0::Capabilities& capabilities) { error = status; cap = capabilities; diff --git a/test/UtilsTests.cpp b/test/UtilsTests.cpp index e7e6cde7..72b6d361 100644 --- a/test/UtilsTests.cpp +++ b/test/UtilsTests.cpp @@ -16,9 +16,10 @@ BOOST_AUTO_TEST_SUITE(UtilsTests) -using namespace armnn_driver; -using namespace android::nn; using namespace android; +using namespace android::nn; +using namespace android::hardware; +using namespace armnn_driver; // The following are helpers for writing unit tests for the driver. namespace @@ -28,9 +29,9 @@ struct ExportNetworkGraphFixture { public: // Setup: set the output dump directory and an empty dummy model (as only its memory address is used). - // Defaulting the output dump directory to "/sdcard" because it should exist and be writable in all deployments. + // Defaulting the output dump directory to "/data" because it should exist and be writable in all deployments. ExportNetworkGraphFixture() - : ExportNetworkGraphFixture("/sdcard") + : ExportNetworkGraphFixture("/data") {} ExportNetworkGraphFixture(const std::string& requestInputsAndOutputsDumpDir) : m_RequestInputsAndOutputsDumpDir(requestInputsAndOutputsDumpDir) @@ -95,7 +96,7 @@ public: } std::string m_RequestInputsAndOutputsDumpDir; - V1_0::Model m_Model; + neuralnetworks::V1_0::Model m_Model; private: std::string m_FileName; -- cgit v1.2.1