Loading current.txt +2 −2 Original line number Diff line number Diff line Loading @@ -628,9 +628,9 @@ ddcf89cd8ee2df0d32aee55050826446fb64f7aafde0a7cd946c64f61b1a364c android.hardwar 9db064ee44268a876be0367ff771e618362d39ec603b6ecab17e1575725fcd87 android.hardware.neuralnetworks@1.3::IDevice 4167dc3ad35e9cd0d2057d4868c7675ae2c3c9d05bbd614c1f5dccfa5fd68797 android.hardware.neuralnetworks@1.3::IExecutionCallback 2fa3679ad7c94b5e88724adcd560c561041068a4ca565c63830e68101988746a android.hardware.neuralnetworks@1.3::IFencedExecutionCallback 237b23b126a66f3432658020fed78cdd06ba6297459436fe6bae0ba753370833 android.hardware.neuralnetworks@1.3::IPreparedModel 43088ffc71945b463a7279262cfe2e290f6ed2f15d3fd6032798a3be299fb08f android.hardware.neuralnetworks@1.3::IPreparedModel 0439a1fbbec7f16e5e4c653d85ac685d51bfafbae15b8f8cca530acdd7d6a8ce android.hardware.neuralnetworks@1.3::IPreparedModelCallback 2fabd246f985d94a0172dacefb0d6cf19e2aeb2d5f17752653988ef39570a52d android.hardware.neuralnetworks@1.3::types 306fda32ac969fd51d75d066352cadcb769944ec4823be4cdd3f86fdb9e97511 android.hardware.neuralnetworks@1.3::types 3e01d4446cd69fd1c48f8572efd97487bc179564b32bd795800b97bbe10be37b android.hardware.wifi@1.4::IWifi a64467bae843569f0d465c5be7f0c7a5b987985b55a3ef4794dd5afc68538650 android.hardware.wifi.supplicant@1.3::ISupplicant 44445b8a03d7b9e68b2fbd954672c18a8fce9e32851b0692f4f4ab3407f86ecb android.hardware.wifi.supplicant@1.3::ISupplicantStaIface Loading neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp +13 −12 Original line number Diff line number Diff line Loading @@ -42,10 +42,11 @@ using implementation::PreparedModelCallback; Model createModel(const TestModel& testModel) { // Model operands. hidl_vec<Operand> operands(testModel.operands.size()); CHECK_EQ(testModel.referenced.size(), 0u); // Not supported in 1.0. hidl_vec<Operand> operands(testModel.main.operands.size()); size_t constCopySize = 0, constRefSize = 0; for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; DataLocation loc = {}; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { Loading @@ -70,9 +71,9 @@ Model createModel(const TestModel& testModel) { } // Model operations. hidl_vec<Operation> operations(testModel.operations.size()); std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { hidl_vec<Operation> operations(testModel.main.operations.size()); std::transform(testModel.main.operations.begin(), testModel.main.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { return {.type = static_cast<OperationType>(op.type), .inputs = op.inputs, .outputs = op.outputs}; Loading @@ -80,8 +81,8 @@ Model createModel(const TestModel& testModel) { // Constant copies. hidl_vec<uint8_t> operandValues(constCopySize); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -102,8 +103,8 @@ Model createModel(const TestModel& testModel) { reinterpret_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer())); CHECK(mappedPtr != nullptr); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -114,8 +115,8 @@ Model createModel(const TestModel& testModel) { return {.operands = std::move(operands), .operations = std::move(operations), .inputIndexes = testModel.inputIndexes, .outputIndexes = testModel.outputIndexes, .inputIndexes = testModel.main.inputIndexes, .outputIndexes = testModel.main.outputIndexes, .operandValues = std::move(operandValues), .pools = std::move(pools)}; } Loading neuralnetworks/1.0/vts/functional/Utils.cpp +8 −8 Original line number Diff line number Diff line Loading @@ -42,10 +42,10 @@ constexpr uint32_t kOutputPoolIndex = 1; Request createRequest(const TestModel& testModel) { // Model inputs. hidl_vec<RequestArgument> inputs(testModel.inputIndexes.size()); hidl_vec<RequestArgument> inputs(testModel.main.inputIndexes.size()); size_t inputSize = 0; for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) { const auto& op = testModel.operands[testModel.inputIndexes[i]]; for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) { const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]]; if (op.data.size() == 0) { // Omitted input. inputs[i] = {.hasNoValue = true}; Loading @@ -59,10 +59,10 @@ Request createRequest(const TestModel& testModel) { } // Model outputs. hidl_vec<RequestArgument> outputs(testModel.outputIndexes.size()); hidl_vec<RequestArgument> outputs(testModel.main.outputIndexes.size()); size_t outputSize = 0; for (uint32_t i = 0; i < testModel.outputIndexes.size(); i++) { const auto& op = testModel.operands[testModel.outputIndexes[i]]; for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) { const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]]; // In the case of zero-sized output, we should at least provide a one-byte buffer. // This is because zero-sized tensors are only supported internally to the driver, or Loading Loading @@ -90,8 +90,8 @@ Request createRequest(const TestModel& testModel) { CHECK(inputPtr != nullptr); // Copy input data to the memory pool. for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) { const auto& op = testModel.operands[testModel.inputIndexes[i]]; for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) { const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]]; if (op.data.size() > 0) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp +13 −12 Original line number Diff line number Diff line Loading @@ -49,10 +49,11 @@ using V1_0::implementation::PreparedModelCallback; Model createModel(const TestModel& testModel) { // Model operands. hidl_vec<Operand> operands(testModel.operands.size()); CHECK_EQ(testModel.referenced.size(), 0u); // Not supported in 1.1. hidl_vec<Operand> operands(testModel.main.operands.size()); size_t constCopySize = 0, constRefSize = 0; for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; DataLocation loc = {}; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { Loading @@ -77,9 +78,9 @@ Model createModel(const TestModel& testModel) { } // Model operations. hidl_vec<Operation> operations(testModel.operations.size()); std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { hidl_vec<Operation> operations(testModel.main.operations.size()); std::transform(testModel.main.operations.begin(), testModel.main.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { return {.type = static_cast<OperationType>(op.type), .inputs = op.inputs, .outputs = op.outputs}; Loading @@ -87,8 +88,8 @@ Model createModel(const TestModel& testModel) { // Constant copies. hidl_vec<uint8_t> operandValues(constCopySize); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -109,8 +110,8 @@ Model createModel(const TestModel& testModel) { reinterpret_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer())); CHECK(mappedPtr != nullptr); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -121,8 +122,8 @@ Model createModel(const TestModel& testModel) { return {.operands = std::move(operands), .operations = std::move(operations), .inputIndexes = testModel.inputIndexes, .outputIndexes = testModel.outputIndexes, .inputIndexes = testModel.main.inputIndexes, .outputIndexes = testModel.main.outputIndexes, .operandValues = std::move(operandValues), .pools = std::move(pools), .relaxComputationFloat32toFloat16 = testModel.isRelaxed}; Loading neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp +4 −4 Original line number Diff line number Diff line Loading @@ -207,10 +207,10 @@ TestModel createLargeTestModelImpl(TestOperationType op, uint32_t len) { }; return { .operands = std::move(operands), .main = {.operands = std::move(operands), .operations = std::move(operations), .inputIndexes = {1}, .outputIndexes = {len * 2 + 1}, .outputIndexes = {len * 2 + 1}}, .isRelaxed = false, }; } Loading Loading
current.txt +2 −2 Original line number Diff line number Diff line Loading @@ -628,9 +628,9 @@ ddcf89cd8ee2df0d32aee55050826446fb64f7aafde0a7cd946c64f61b1a364c android.hardwar 9db064ee44268a876be0367ff771e618362d39ec603b6ecab17e1575725fcd87 android.hardware.neuralnetworks@1.3::IDevice 4167dc3ad35e9cd0d2057d4868c7675ae2c3c9d05bbd614c1f5dccfa5fd68797 android.hardware.neuralnetworks@1.3::IExecutionCallback 2fa3679ad7c94b5e88724adcd560c561041068a4ca565c63830e68101988746a android.hardware.neuralnetworks@1.3::IFencedExecutionCallback 237b23b126a66f3432658020fed78cdd06ba6297459436fe6bae0ba753370833 android.hardware.neuralnetworks@1.3::IPreparedModel 43088ffc71945b463a7279262cfe2e290f6ed2f15d3fd6032798a3be299fb08f android.hardware.neuralnetworks@1.3::IPreparedModel 0439a1fbbec7f16e5e4c653d85ac685d51bfafbae15b8f8cca530acdd7d6a8ce android.hardware.neuralnetworks@1.3::IPreparedModelCallback 2fabd246f985d94a0172dacefb0d6cf19e2aeb2d5f17752653988ef39570a52d android.hardware.neuralnetworks@1.3::types 306fda32ac969fd51d75d066352cadcb769944ec4823be4cdd3f86fdb9e97511 android.hardware.neuralnetworks@1.3::types 3e01d4446cd69fd1c48f8572efd97487bc179564b32bd795800b97bbe10be37b android.hardware.wifi@1.4::IWifi a64467bae843569f0d465c5be7f0c7a5b987985b55a3ef4794dd5afc68538650 android.hardware.wifi.supplicant@1.3::ISupplicant 44445b8a03d7b9e68b2fbd954672c18a8fce9e32851b0692f4f4ab3407f86ecb android.hardware.wifi.supplicant@1.3::ISupplicantStaIface Loading
neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp +13 −12 Original line number Diff line number Diff line Loading @@ -42,10 +42,11 @@ using implementation::PreparedModelCallback; Model createModel(const TestModel& testModel) { // Model operands. hidl_vec<Operand> operands(testModel.operands.size()); CHECK_EQ(testModel.referenced.size(), 0u); // Not supported in 1.0. hidl_vec<Operand> operands(testModel.main.operands.size()); size_t constCopySize = 0, constRefSize = 0; for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; DataLocation loc = {}; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { Loading @@ -70,9 +71,9 @@ Model createModel(const TestModel& testModel) { } // Model operations. hidl_vec<Operation> operations(testModel.operations.size()); std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { hidl_vec<Operation> operations(testModel.main.operations.size()); std::transform(testModel.main.operations.begin(), testModel.main.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { return {.type = static_cast<OperationType>(op.type), .inputs = op.inputs, .outputs = op.outputs}; Loading @@ -80,8 +81,8 @@ Model createModel(const TestModel& testModel) { // Constant copies. hidl_vec<uint8_t> operandValues(constCopySize); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -102,8 +103,8 @@ Model createModel(const TestModel& testModel) { reinterpret_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer())); CHECK(mappedPtr != nullptr); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -114,8 +115,8 @@ Model createModel(const TestModel& testModel) { return {.operands = std::move(operands), .operations = std::move(operations), .inputIndexes = testModel.inputIndexes, .outputIndexes = testModel.outputIndexes, .inputIndexes = testModel.main.inputIndexes, .outputIndexes = testModel.main.outputIndexes, .operandValues = std::move(operandValues), .pools = std::move(pools)}; } Loading
neuralnetworks/1.0/vts/functional/Utils.cpp +8 −8 Original line number Diff line number Diff line Loading @@ -42,10 +42,10 @@ constexpr uint32_t kOutputPoolIndex = 1; Request createRequest(const TestModel& testModel) { // Model inputs. hidl_vec<RequestArgument> inputs(testModel.inputIndexes.size()); hidl_vec<RequestArgument> inputs(testModel.main.inputIndexes.size()); size_t inputSize = 0; for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) { const auto& op = testModel.operands[testModel.inputIndexes[i]]; for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) { const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]]; if (op.data.size() == 0) { // Omitted input. inputs[i] = {.hasNoValue = true}; Loading @@ -59,10 +59,10 @@ Request createRequest(const TestModel& testModel) { } // Model outputs. hidl_vec<RequestArgument> outputs(testModel.outputIndexes.size()); hidl_vec<RequestArgument> outputs(testModel.main.outputIndexes.size()); size_t outputSize = 0; for (uint32_t i = 0; i < testModel.outputIndexes.size(); i++) { const auto& op = testModel.operands[testModel.outputIndexes[i]]; for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) { const auto& op = testModel.main.operands[testModel.main.outputIndexes[i]]; // In the case of zero-sized output, we should at least provide a one-byte buffer. // This is because zero-sized tensors are only supported internally to the driver, or Loading Loading @@ -90,8 +90,8 @@ Request createRequest(const TestModel& testModel) { CHECK(inputPtr != nullptr); // Copy input data to the memory pool. for (uint32_t i = 0; i < testModel.inputIndexes.size(); i++) { const auto& op = testModel.operands[testModel.inputIndexes[i]]; for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) { const auto& op = testModel.main.operands[testModel.main.inputIndexes[i]]; if (op.data.size() > 0) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading
neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp +13 −12 Original line number Diff line number Diff line Loading @@ -49,10 +49,11 @@ using V1_0::implementation::PreparedModelCallback; Model createModel(const TestModel& testModel) { // Model operands. hidl_vec<Operand> operands(testModel.operands.size()); CHECK_EQ(testModel.referenced.size(), 0u); // Not supported in 1.1. hidl_vec<Operand> operands(testModel.main.operands.size()); size_t constCopySize = 0, constRefSize = 0; for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; DataLocation loc = {}; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { Loading @@ -77,9 +78,9 @@ Model createModel(const TestModel& testModel) { } // Model operations. hidl_vec<Operation> operations(testModel.operations.size()); std::transform(testModel.operations.begin(), testModel.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { hidl_vec<Operation> operations(testModel.main.operations.size()); std::transform(testModel.main.operations.begin(), testModel.main.operations.end(), operations.begin(), [](const TestOperation& op) -> Operation { return {.type = static_cast<OperationType>(op.type), .inputs = op.inputs, .outputs = op.outputs}; Loading @@ -87,8 +88,8 @@ Model createModel(const TestModel& testModel) { // Constant copies. hidl_vec<uint8_t> operandValues(constCopySize); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_COPY) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -109,8 +110,8 @@ Model createModel(const TestModel& testModel) { reinterpret_cast<uint8_t*>(static_cast<void*>(mappedMemory->getPointer())); CHECK(mappedPtr != nullptr); for (uint32_t i = 0; i < testModel.operands.size(); i++) { const auto& op = testModel.operands[i]; for (uint32_t i = 0; i < testModel.main.operands.size(); i++) { const auto& op = testModel.main.operands[i]; if (op.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) { const uint8_t* begin = op.data.get<uint8_t>(); const uint8_t* end = begin + op.data.size(); Loading @@ -121,8 +122,8 @@ Model createModel(const TestModel& testModel) { return {.operands = std::move(operands), .operations = std::move(operations), .inputIndexes = testModel.inputIndexes, .outputIndexes = testModel.outputIndexes, .inputIndexes = testModel.main.inputIndexes, .outputIndexes = testModel.main.outputIndexes, .operandValues = std::move(operandValues), .pools = std::move(pools), .relaxComputationFloat32toFloat16 = testModel.isRelaxed}; Loading
neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp +4 −4 Original line number Diff line number Diff line Loading @@ -207,10 +207,10 @@ TestModel createLargeTestModelImpl(TestOperationType op, uint32_t len) { }; return { .operands = std::move(operands), .main = {.operands = std::move(operands), .operations = std::move(operations), .inputIndexes = {1}, .outputIndexes = {len * 2 + 1}, .outputIndexes = {len * 2 + 1}}, .isRelaxed = false, }; } Loading