Merge "Remove the data type from the OperationType enum." into oc-mr1-dev (5a9c0c31) · Commits · e / os / android_hardware_interfaces

neuralnetworks/1.0/types.hal

+44 −37

Original line number	Diff line number	Diff line
		@@ -32,7 +32,7 @@ enum OperandType : uint32_t {
		UINT32 = 7,
		TENSOR_FLOAT16 = 8,
		TENSOR_FLOAT32 = 9,
		TENSOR_SYMMETRICAL_QUANT8 = 10,
		TENSOR_QUANT8_ASYMM = 10,
		};

		// The type of operations. Unlike the operation types found in
		@@ -41,39 +41,39 @@ enum OperandType : uint32_t {
		// TODO: Currently they are the same. Add a conversion when finalizing the model.
		// When modifying, be sure to update HAL_NUM_OPERATION_TYPES in HalIntefaces.h.
		enum OperationType : uint32_t {
		AVERAGE_POOL_FLOAT32 = 0,
		CONCATENATION_FLOAT32 = 1,
		CONV_FLOAT32 = 2,
		DEPTHWISE_CONV_FLOAT32 = 3,
		MAX_POOL_FLOAT32 = 4,
		L2_POOL_FLOAT32 = 5,
		DEPTH_TO_SPACE_FLOAT32 = 6,
		SPACE_TO_DEPTH_FLOAT32 = 7,
		LOCAL_RESPONSE_NORMALIZATION_FLOAT32 = 8,
		SOFTMAX_FLOAT32 = 9,
		RESHAPE_FLOAT32 = 10,
		SPLIT_FLOAT32 = 11,
		FAKE_QUANT_FLOAT32 = 12,
		ADD_FLOAT32 = 13,
		FULLY_CONNECTED_FLOAT32 = 14,
		CAST_FLOAT32 = 15,
		MUL_FLOAT32 = 16,
		L2_NORMALIZATION_FLOAT32 = 17,
		LOGISTIC_FLOAT32 = 18,
		RELU_FLOAT32 = 19,
		RELU6_FLOAT32 = 20,
		RELU1_FLOAT32 = 21,
		TANH_FLOAT32 = 22,
		DEQUANTIZE_FLOAT32 = 23,
		FLOOR_FLOAT32 = 24,
		GATHER_FLOAT32 = 25,
		RESIZE_BILINEAR_FLOAT32 = 26,
		LSH_PROJECTION_FLOAT32 = 27,
		LSTM_FLOAT32 = 28,
		SVDF_FLOAT32 = 29,
		RNN_FLOAT32 = 30,
		N_GRAM_FLOAT32 = 31,
		LOOKUP_FLOAT32 = 32,
		AVERAGE_POOL = 0,
		CONCATENATION = 1,
		CONV = 2,
		DEPTHWISE_CONV = 3,
		MAX_POOL = 4,
		L2_POOL = 5,
		DEPTH_TO_SPACE = 6,
		SPACE_TO_DEPTH = 7,
		LOCAL_RESPONSE_NORMALIZATION = 8,
		SOFTMAX = 9,
		RESHAPE = 10,
		SPLIT = 11,
		FAKE_QUANT = 12,
		ADD = 13,
		FULLY_CONNECTED = 14,
		CAST = 15,
		MUL = 16,
		L2_NORMALIZATION = 17,
		LOGISTIC = 18,
		RELU = 19,
		RELU6 = 20,
		RELU1 = 21,
		TANH = 22,
		DEQUANTIZE = 23,
		FLOOR = 24,
		GATHER = 25,
		RESIZE_BILINEAR = 26,
		LSH_PROJECTION = 27,
		LSTM = 28,
		SVDF = 29,
		RNN = 30,
		N_GRAM = 31,
		LOOKUP = 32,
		};

		// Two special values that can be used instead of a regular poolIndex.
		@@ -102,9 +102,16 @@ struct PerformanceInfo {
		float powerUsage; // in picoJoules
		};

		struct OperationTuple {
		// The type of operation.
		OperationType operationType;
		// The input data type of operation.
		OperandType operandType;
		};

		// The capabilities of a driver.
		struct Capabilities {
		vec<OperationType> supportedOperationTypes;
		vec<OperationTuple> supportedOperationTuples;
		// TODO Do the same for baseline model IDs
		bool cachesCompilation;
		// TODO revisit the data types and scales.
		@@ -142,8 +149,8 @@ struct Operand {

		// Describes one operation of the graph.
		struct Operation {
		// The type of operation.
		OperationType type;
		// The tuple describing the operation type and input type.
		OperationTuple opTuple;
		// Describes the table that contains the indexes of the inputs of the
		// operation. The offset is the index in the operandIndexes table.
		vec<uint32_t> inputs;

neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp

+8 −6

Original line number	Diff line number	Diff line
		@@ -66,8 +66,8 @@ TEST_F(NeuralnetworksHidlTest, StatusTest) {
		// initialization
		TEST_F(NeuralnetworksHidlTest, InitializeTest) {
		Return<void> ret = device->initialize([](const Capabilities& capabilities) {
		EXPECT_NE(nullptr, capabilities.supportedOperationTypes.data());
		EXPECT_NE(0ull, capabilities.supportedOperationTypes.size());
		EXPECT_NE(nullptr, capabilities.supportedOperationTuples.data());
		EXPECT_NE(0ull, capabilities.supportedOperationTuples.size());
		EXPECT_EQ(0u, static_cast<uint32_t>(capabilities.cachesCompilation) & ~0x1);
		EXPECT_LT(0.0f, capabilities.bootupTime);
		EXPECT_LT(0.0f, capabilities.float16Performance.execTime);
		@@ -92,7 +92,7 @@ Model createTestModel() {

		const std::vector<Operand> operands = {
		{
		.type = OperandType::FLOAT32,
		.type = OperandType::TENSOR_FLOAT32,
		.dimensions = {1, 2, 2, 1},
		.numberOfConsumers = 1,
		.scale = 0.0f,
		@@ -102,7 +102,7 @@ Model createTestModel() {
		.length = 0},
		},
		{
		.type = OperandType::FLOAT32,
		.type = OperandType::TENSOR_FLOAT32,
		.dimensions = {1, 2, 2, 1},
		.numberOfConsumers = 1,
		.scale = 0.0f,
		@@ -112,7 +112,7 @@ Model createTestModel() {
		.length = size},
		},
		{
		.type = OperandType::FLOAT32,
		.type = OperandType::TENSOR_FLOAT32,
		.dimensions = {1, 2, 2, 1},
		.numberOfConsumers = 0,
		.scale = 0.0f,
		@@ -124,7 +124,9 @@ Model createTestModel() {
		};

		const std::vector<Operation> operations = {{
		.type = OperationType::ADD_FLOAT32, .inputs = {operand1, operand2}, .outputs = {operand3},
		.opTuple = {OperationType::ADD, OperandType::TENSOR_FLOAT32},
		.inputs = {operand1, operand2},
		.outputs = {operand3},
		}};

		const std::vector<uint32_t> inputIndexes = {operand1};