Merge "Add control flow support to NNAPI VTS tests" (5efd0f71) · Commits · e / os / android_hardware_interfaces

neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp

+13 −12

Original line number	Diff line number	Diff line
		@@ -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) {
		@@ -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};
		@@ -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();
		@@ -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();
		@@ -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)};
		}

neuralnetworks/1.0/vts/functional/Utils.cpp

+8 −8

Original line number	Diff line number	Diff line
		@@ -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};
		@@ -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
		@@ -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();

neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp

+13 −12

Original line number	Diff line number	Diff line
		@@ -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) {
		@@ -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};
		@@ -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();
		@@ -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();
		@@ -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};

neuralnetworks/1.2/vts/functional/CompilationCachingTests.cpp

+4 −4

Original line number	Diff line number	Diff line
		@@ -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,
		};
		}

neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp

+18 −17

Original line number	Diff line number	Diff line
		@@ -75,10 +75,11 @@ struct TestConfig {

		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) {
		@@ -110,9 +111,9 @@ 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};
		@@ -120,8 +121,8 @@ 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();
		@@ -142,8 +143,8 @@ 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();
		@@ -154,15 +155,15 @@ 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};
		}

		static bool isOutputSizeGreaterThanOne(const TestModel& testModel, uint32_t index) {
		const auto byteSize = testModel.operands[testModel.outputIndexes[index]].data.size();
		const auto byteSize = testModel.main.operands[testModel.main.outputIndexes[index]].data.size();
		return byteSize > 1u;
		}

		@@ -302,17 +303,17 @@ void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestMo
		// either empty, or have the same number of elements as the number of outputs.
		ASSERT_EQ(ErrorStatus::NONE, executionStatus);
		ASSERT_TRUE(outputShapes.size() == 0 \|\|
		outputShapes.size() == testModel.outputIndexes.size());
		outputShapes.size() == testModel.main.outputIndexes.size());
		break;
		case OutputType::UNSPECIFIED:
		// If the model output operands are not fully specified, outputShapes must have
		// the same number of elements as the number of outputs.
		ASSERT_EQ(ErrorStatus::NONE, executionStatus);
		ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
		ASSERT_EQ(outputShapes.size(), testModel.main.outputIndexes.size());
		break;
		case OutputType::INSUFFICIENT:
		ASSERT_EQ(ErrorStatus::OUTPUT_INSUFFICIENT_SIZE, executionStatus);
		ASSERT_EQ(outputShapes.size(), testModel.outputIndexes.size());
		ASSERT_EQ(outputShapes.size(), testModel.main.outputIndexes.size());
		ASSERT_FALSE(outputShapes[0].isSufficient);
		return;
		}
		@@ -320,7 +321,7 @@ void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestMo
		// Go through all outputs, check returned output shapes.
		for (uint32_t i = 0; i < outputShapes.size(); i++) {
		EXPECT_TRUE(outputShapes[i].isSufficient);
		const auto& expect = testModel.operands[testModel.outputIndexes[i]].dimensions;
		const auto& expect = testModel.main.operands[testModel.main.outputIndexes[i]].dimensions;
		const std::vector<uint32_t> actual = outputShapes[i].dimensions;
		EXPECT_EQ(expect, actual);
		}