harmony 鸿蒙neural_network_runtime_type.h

  • 2025-06-16
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neural_network_runtime_type.h

概述

Neural Network Runtime定义的结构体和枚举值。

引用文件：<neural_network_runtime/neural_network_runtime_type.h>

库： libneural_network_runtime.so

系统能力： SystemCapability.Ai.NeuralNetworkRuntime

起始版本： 9

相关模块：NeuralNetworkRuntime

汇总

结构体

名称 描述
struct OH_NN_UInt32Array 该结构体用于存储32位无符号整型数组。
struct OH_NN_QuantParam 量化信息。
struct OH_NN_Tensor 张量结构体。
struct OH_NN_Memory 内存结构体。

类型定义

名称 描述
typedef struct OH_NNModel OH_NNModel 模型句柄。
typedef struct OH_NNCompilation OH_NNCompilation 编译器句柄。
typedef struct OH_NNExecutor OH_NNExecutor 执行器句柄。
typedef struct NN_QuantParam NN_QuantParam 量化参数的句柄。
typedef struct NN_TensorDesc NN_TensorDesc Tensor描述的句柄。
typedef struct NN_Tensor NN_Tensor Tensor句柄。
typedef void(* NN_OnRunDone) (void *userData, OH_NN_ReturnCode errCode, void *outputTensor[], int32_t outputCount) 异步推理结束后的回调处理函数句柄。
typedef void(* NN_OnServiceDied) (void *userData) 异步推理执行期间设备驱动服务异常终止时的回调处理函数句柄。
typedef struct OH_NN_UInt32Array OH_NN_UInt32Array 该结构体用于存储32位无符号整型数组。
typedef struct OH_NN_QuantParam OH_NN_QuantParam 量化信息。
typedef struct OH_NN_Tensor OH_NN_Tensor 张量结构体。
typedef struct OH_NN_Memory OH_NN_Memory 内存结构体。

枚举

名称 描述
OH_NN_PerformanceMode {
OH_NN_PERFORMANCE_NONE = 0, OH_NN_PERFORMANCE_LOW = 1, OH_NN_PERFORMANCE_MEDIUM = 2, OH_NN_PERFORMANCE_HIGH = 3,OH_NN_PERFORMANCE_EXTREME = 4
}		 硬件的性能模式。
OH_NN_Priority { OH_NN_PRIORITY_NONE = 0, OH_NN_PRIORITY_LOW = 1, OH_NN_PRIORITY_MEDIUM = 2, OH_NN_PRIORITY_HIGH = 3 } 模型推理任务优先级。
OH_NN_ReturnCode {
OH_NN_SUCCESS = 0, OH_NN_FAILED = 1, OH_NN_INVALID_PARAMETER = 2, OH_NN_MEMORY_ERROR = 3,
OH_NN_OPERATION_FORBIDDEN = 4, OH_NN_NULL_PTR = 5, OH_NN_INVALID_FILE = 6, OH_NN_UNAVALIDABLE_DEVICE = 7,
OH_NN_INVALID_PATH = 8, OH_NN_TIMEOUT = 9, OH_NN_UNSUPPORTED = 10, OH_NN_CONNECTION_EXCEPTION = 11,
OH_NN_SAVE_CACHE_EXCEPTION = 12, OH_NN_DYNAMIC_SHAPE = 13, OH_NN_UNAVAILABLE_DEVICE = 14
}		 Neural Network Runtime 定义的错误码类型。
OH_NN_FuseType : int8_t { OH_NN_FUSED_NONE = 0, OH_NN_FUSED_RELU = 1, OH_NN_FUSED_RELU6 = 2 } Neural Network Runtime 融合算子中激活函数的类型。
OH_NN_Format { OH_NN_FORMAT_NONE = 0, OH_NN_FORMAT_NCHW = 1, OH_NN_FORMAT_NHWC = 2, OH_NN_FORMAT_ND = 3 } 张量数据的排布类型。
OH_NN_DeviceType { OH_NN_OTHERS = 0, OH_NN_CPU = 1, OH_NN_GPU = 2, OH_NN_ACCELERATOR = 3 } Neural Network Runtime 支持的设备类型。
OH_NN_DataType {
OH_NN_UNKNOWN = 0, OH_NN_BOOL = 1, OH_NN_INT8 = 2, OH_NN_INT16 = 3,OH_NN_INT32 = 4, OH_NN_INT64 = 5, OH_NN_UINT8 = 6, OH_NN_UINT16 = 7,OH_NN_UINT32 = 8, OH_NN_UINT64 = 9, OH_NN_FLOAT16 = 10, OH_NN_FLOAT32 = 11,OH_NN_FLOAT64 = 12
}		 Neural Network Runtime 支持的数据类型。
OH_NN_OperationType {
OH_NN_OPS_ADD = 1, OH_NN_OPS_AVG_POOL = 2, OH_NN_OPS_BATCH_NORM = 3, OH_NN_OPS_BATCH_TO_SPACE_ND = 4,
OH_NN_OPS_BIAS_ADD = 5, OH_NN_OPS_CAST = 6, OH_NN_OPS_CONCAT = 7, OH_NN_OPS_CONV2D = 8,
OH_NN_OPS_CONV2D_TRANSPOSE = 9, OH_NN_OPS_DEPTHWISE_CONV2D_NATIVE = 10, OH_NN_OPS_DIV = 11, OH_NN_OPS_ELTWISE = 12,
OH_NN_OPS_EXPAND_DIMS = 13, OH_NN_OPS_FILL = 14, OH_NN_OPS_FULL_CONNECTION = 15, OH_NN_OPS_GATHER = 16,
OH_NN_OPS_HSWISH = 17, OH_NN_OPS_LESS_EQUAL = 18, OH_NN_OPS_MATMUL = 19, OH_NN_OPS_MAXIMUM = 20,
OH_NN_OPS_MAX_POOL = 21, OH_NN_OPS_MUL = 22, OH_NN_OPS_ONE_HOT = 23, OH_NN_OPS_PAD = 24,
OH_NN_OPS_POW = 25, OH_NN_OPS_SCALE = 26, OH_NN_OPS_SHAPE = 27, OH_NN_OPS_SIGMOID = 28,
OH_NN_OPS_SLICE = 29, OH_NN_OPS_SOFTMAX = 30, OH_NN_OPS_SPACE_TO_BATCH_ND = 31, OH_NN_OPS_SPLIT = 32,
OH_NN_OPS_SQRT = 33, OH_NN_OPS_SQUARED_DIFFERENCE = 34, OH_NN_OPS_SQUEEZE = 35, OH_NN_OPS_STACK = 36,
OH_NN_OPS_STRIDED_SLICE = 37, OH_NN_OPS_SUB = 38, OH_NN_OPS_TANH = 39, OH_NN_OPS_TILE = 40,
OH_NN_OPS_TRANSPOSE = 41, OH_NN_OPS_REDUCE_MEAN = 42, OH_NN_OPS_RESIZE_BILINEAR = 43, OH_NN_OPS_RSQRT = 44,
OH_NN_OPS_RESHAPE = 45, OH_NN_OPS_PRELU = 46, OH_NN_OPS_RELU = 47, OH_NN_OPS_RELU6 = 48,
OH_NN_OPS_LAYER_NORM = 49, OH_NN_OPS_REDUCE_PROD = 50, OH_NN_OPS_REDUCE_ALL = 51, OH_NN_OPS_QUANT_DTYPE_CAST = 52,
OH_NN_OPS_TOP_K = 53, OH_NN_OPS_ARG_MAX = 54, OH_NN_OPS_UNSQUEEZE = 55, OH_NN_OPS_GELU = 56,
OH_NN_OPS_UNSTACK = 57, OH_NN_OPS_ABS = 58, OH_NN_OPS_ERF = 59, OH_NN_OPS_EXP = 60,
OH_NN_OPS_LESS = 61, OH_NN_OPS_SELECT = 62, OH_NN_OPS_SQUARE = 63, OH_NN_OPS_FLATTEN = 64,
OH_NN_OPS_DEPTH_TO_SPACE = 65, OH_NN_OPS_RANGE = 66, OH_NN_OPS_INSTANCE_NORM = 67, OH_NN_OPS_CONSTANT_OF_SHAPE = 68,
OH_NN_OPS_BROADCAST_TO = 69, OH_NN_OPS_EQUAL = 70, OH_NN_OPS_GREATER = 71, OH_NN_OPS_NOT_EQUAL = 72,
OH_NN_OPS_GREATER_EQUAL = 73, OH_NN_OPS_LEAKY_RELU = 74, OH_NN_OPS_LSTM = 75, OH_NN_OPS_CLIP = 76,
OH_NN_OPS_ALL = 77, OH_NN_OPS_ASSERT = 78, OH_NN_OPS_COS = 79, OH_NN_OPS_LOG = 80,
OH_NN_OPS_LOGICAL_AND = 81, OH_NN_OPS_LOGICAL_NOT = 82, OH_NN_OPS_MOD = 83, OH_NN_OPS_NEG = 84,
OH_NN_OPS_RECIPROCAL = 85, OH_NN_OPS_SIN = 86, OH_NN_OPS_WHERE = 87, OH_NN_OPS_SPARSE_TO_DENSE = 88,
OH_NN_OPS_LOGICAL_OR = 89, OH_NN_OPS_CEIL = 90, OH_NN_OPS_CROP = 91, OH_NN_OPS_DETECTION_POST_PROCESS = 92,
OH_NN_OPS_FLOOR = 93, OH_NN_OPS_L2_NORMALIZE = 94, OH_NN_OPS_LOG_SOFTMAX = 95, OH_NN_OPS_LRN = 96,
OH_NN_OPS_MINIMUM = 97, OH_NN_OPS_RANK = 98, OH_NN_OPS_REDUCE_MAX = 99, OH_NN_OPS_REDUCE_MIN = 100,
OH_NN_OPS_REDUCE_SUM = 101, OH_NN_OPS_ROUND = 102, OH_NN_OPS_SCATTER_ND = 103, OH_NN_OPS_SPACE_TO_DEPTH = 104,
OH_NN_OPS_SWISH = 105, OH_NN_OPS_REDUCE_L2 = 106, OH_NN_OPS_HARD_SIGMOID = 107, OH_NN_OPS_GATHER_ND = 108
}		 Neural Network Runtime 支持算子的类型。
OH_NN_TensorType {
OH_NN_TENSOR = 0, OH_NN_ADD_ACTIVATIONTYPE = 1, OH_NN_AVG_POOL_KERNEL_SIZE = 2, OH_NN_AVG_POOL_STRIDE = 3,
OH_NN_AVG_POOL_PAD_MODE = 4, OH_NN_AVG_POOL_PAD = 5, OH_NN_AVG_POOL_ACTIVATION_TYPE = 6, OH_NN_BATCH_NORM_EPSILON = 7,
OH_NN_BATCH_TO_SPACE_ND_BLOCKSIZE = 8, OH_NN_BATCH_TO_SPACE_ND_CROPS = 9, OH_NN_CONCAT_AXIS = 10, OH_NN_CONV2D_STRIDES = 11,
OH_NN_CONV2D_PAD = 12, OH_NN_CONV2D_DILATION = 13, OH_NN_CONV2D_PAD_MODE = 14, OH_NN_CONV2D_ACTIVATION_TYPE = 15,
OH_NN_CONV2D_GROUP = 16, OH_NN_CONV2D_TRANSPOSE_STRIDES = 17, OH_NN_CONV2D_TRANSPOSE_PAD = 18, OH_NN_CONV2D_TRANSPOSE_DILATION = 19,
OH_NN_CONV2D_TRANSPOSE_OUTPUT_PADDINGS = 20, OH_NN_CONV2D_TRANSPOSE_PAD_MODE = 21, OH_NN_CONV2D_TRANSPOSE_ACTIVATION_TYPE = 22, OH_NN_CONV2D_TRANSPOSE_GROUP = 23,
OH_NN_DEPTHWISE_CONV2D_NATIVE_STRIDES = 24, OH_NN_DEPTHWISE_CONV2D_NATIVE_PAD = 25, OH_NN_DEPTHWISE_CONV2D_NATIVE_DILATION = 26, OH_NN_DEPTHWISE_CONV2D_NATIVE_PAD_MODE = 27,
OH_NN_DEPTHWISE_CONV2D_NATIVE_ACTIVATION_TYPE = 28, OH_NN_DIV_ACTIVATIONTYPE = 29, OH_NN_ELTWISE_MODE = 30, OH_NN_FULL_CONNECTION_AXIS = 31,
OH_NN_FULL_CONNECTION_ACTIVATIONTYPE = 32, OH_NN_MATMUL_TRANSPOSE_A = 33, OH_NN_MATMUL_TRANSPOSE_B = 34, OH_NN_MATMUL_ACTIVATION_TYPE = 35,
OH_NN_MAX_POOL_KERNEL_SIZE = 36, OH_NN_MAX_POOL_STRIDE = 37, OH_NN_MAX_POOL_PAD_MODE = 38, OH_NN_MAX_POOL_PAD = 39,
OH_NN_MAX_POOL_ACTIVATION_TYPE = 40, OH_NN_MUL_ACTIVATION_TYPE = 41, OH_NN_ONE_HOT_AXIS = 42, OH_NN_PAD_CONSTANT_VALUE = 43,
OH_NN_SCALE_ACTIVATIONTYPE = 44, OH_NN_SCALE_AXIS = 45, OH_NN_SOFTMAX_AXIS = 46, OH_NN_SPACE_TO_BATCH_ND_BLOCK_SHAPE = 47,
OH_NN_SPACE_TO_BATCH_ND_PADDINGS = 48, OH_NN_SPLIT_AXIS = 49, OH_NN_SPLIT_OUTPUT_NUM = 50, OH_NN_SPLIT_SIZE_SPLITS = 51,
OH_NN_SQUEEZE_AXIS = 52, OH_NN_STACK_AXIS = 53, OH_NN_STRIDED_SLICE_BEGIN_MASK = 54, OH_NN_STRIDED_SLICE_END_MASK = 55,
OH_NN_STRIDED_SLICE_ELLIPSIS_MASK = 56, OH_NN_STRIDED_SLICE_NEW_AXIS_MASK = 57, OH_NN_STRIDED_SLICE_SHRINK_AXIS_MASK = 58, OH_NN_SUB_ACTIVATIONTYPE = 59,
OH_NN_REDUCE_MEAN_KEEP_DIMS = 60, OH_NN_RESIZE_BILINEAR_NEW_HEIGHT = 61, OH_NN_RESIZE_BILINEAR_NEW_WIDTH = 62, OH_NN_RESIZE_BILINEAR_PRESERVE_ASPECT_RATIO = 63,
OH_NN_RESIZE_BILINEAR_COORDINATE_TRANSFORM_MODE = 64, OH_NN_RESIZE_BILINEAR_EXCLUDE_OUTSIDE = 65, OH_NN_LAYER_NORM_BEGIN_NORM_AXIS = 66, OH_NN_LAYER_NORM_EPSILON = 67,
OH_NN_LAYER_NORM_BEGIN_PARAM_AXIS = 68, OH_NN_LAYER_NORM_ELEMENTWISE_AFFINE = 69, OH_NN_REDUCE_PROD_KEEP_DIMS = 70, OH_NN_REDUCE_ALL_KEEP_DIMS = 71,
OH_NN_QUANT_DTYPE_CAST_SRC_T = 72, OH_NN_QUANT_DTYPE_CAST_DST_T = 73, OH_NN_TOP_K_SORTED = 74, OH_NN_ARG_MAX_AXIS = 75,
OH_NN_ARG_MAX_KEEPDIMS = 76, OH_NN_UNSQUEEZE_AXIS = 77, OH_NN_UNSTACK_AXIS = 78, OH_NN_FLATTEN_AXIS = 79,
OH_NN_DEPTH_TO_SPACE_BLOCK_SIZE = 80, OH_NN_DEPTH_TO_SPACE_MODE = 81, OH_NN_RANGE_START = 82, OH_NN_RANGE_LIMIT = 83,
OH_NN_RANGE_DELTA = 84, OH_NN_CONSTANT_OF_SHAPE_DATA_TYPE = 85, OH_NN_CONSTANT_OF_SHAPE_VALUE = 86, OH_NN_BROADCAST_TO_SHAPE = 87,
OH_NN_INSTANCE_NORM_EPSILON = 88, OH_NN_EXP_BASE = 89, OH_NN_EXP_SCALE = 90, OH_NN_EXP_SHIFT = 91,
OH_NN_LEAKY_RELU_NEGATIVE_SLOPE = 92, OH_NN_LSTM_BIDIRECTIONAL = 93, OH_NN_LSTM_HAS_BIAS = 94, OH_NN_LSTM_INPUT_SIZE = 95,
OH_NN_LSTM_HIDDEN_SIZE = 96, OH_NN_LSTM_NUM_LAYERS = 97, OH_NN_LSTM_NUM_DIRECTIONS = 98, OH_NN_LSTM_DROPOUT = 99,
OH_NN_LSTM_ZONEOUT_CELL = 100, OH_NN_LSTM_ZONEOUT_HIDDEN = 101, OH_NN_LSTM_PROJ_SIZE = 102, OH_NN_CLIP_MAX = 103,
OH_NN_CLIP_MIN = 104, OH_NN_ALL_KEEP_DIMS = 105, OH_NN_ASSERT_SUMMARIZE = 106, OH_NN_POW_SCALE = 107,
OH_NN_POW_SHIFT = 108, OH_NN_AVG_POOL_ROUND_MODE = 109, OH_NN_AVG_POOL_GLOBAL = 110, OH_NN_FULL_CONNECTION_HAS_BIAS = 111,
OH_NN_FULL_CONNECTION_USE_AXIS = 112, OH_NN_GELU_APPROXIMATE = 113, OH_NN_MAX_POOL_ROUND_MODE = 114, OH_NN_MAX_POOL_GLOBAL = 115,
OH_NN_PAD_PADDING_MODE = 116, OH_NN_REDUCE_MEAN_REDUCE_TO_END = 117, OH_NN_REDUCE_MEAN_COEFF = 118, OH_NN_REDUCE_PROD_REDUCE_TO_END = 119,
OH_NN_REDUCE_PROD_COEFF = 120, OH_NN_REDUCE_ALL_REDUCE_TO_END = 121, OH_NN_REDUCE_ALL_COEFF = 122, OH_NN_TOP_K_AXIS = 123,
OH_NN_ARG_MAX_TOP_K = 124, OH_NN_ARG_MAX_OUT_MAX_VALUE = 125, OH_NN_QUANT_DTYPE_CAST_AXIS = 126, OH_NN_SLICE_AXES = 127,
OH_NN_TILE_DIMS = 128, OH_NN_CROP_AXIS = 129, OH_NN_CROP_OFFSET = 130, OH_NN_DETECTION_POST_PROCESS_INPUT_SIZE = 131,
OH_NN_DETECTION_POST_PROCESS_SCALE = 132, OH_NN_DETECTION_POST_PROCESS_NMS_IOU_THRESHOLD = 133, OH_NN_DETECTION_POST_PROCESS_NMS_SCORE_THRESHOLD = 134, OH_NN_DETECTION_POST_PROCESS_MAX_DETECTIONS = 135,
OH_NN_DETECTION_POST_PROCESS_DETECTIONS_PER_CLASS = 136, OH_NN_DETECTION_POST_PROCESS_MAX_CLASSES_PER_DETECTION = 137, OH_NN_DETECTION_POST_PROCESS_NUM_CLASSES = 138, OH_NN_DETECTION_POST_PROCESS_USE_REGULAR_NMS = 139,
OH_NN_DETECTION_POST_PROCESS_OUT_QUANTIZED = 140, OH_NN_L2_NORMALIZE_AXIS = 141, OH_NN_L2_NORMALIZE_EPSILON = 142, OH_NN_L2_NORMALIZE_ACTIVATION_TYPE = 143,
OH_NN_LOG_SOFTMAX_AXIS = 144, OH_NN_LRN_DEPTH_RADIUS = 145, OH_NN_LRN_BIAS = 146, OH_NN_LRN_ALPHA = 147,
OH_NN_LRN_BETA = 148, OH_NN_LRN_NORM_REGION = 149, OH_NN_SPACE_TO_DEPTH_BLOCK_SIZE = 150, OH_NN_REDUCE_MAX_KEEP_DIMS = 151,
OH_NN_REDUCE_MAX_REDUCE_TO_END = 152, OH_NN_REDUCE_MAX_COEFF = 153, OH_NN_REDUCE_MIN_KEEP_DIMS = 154, OH_NN_REDUCE_MIN_REDUCE_TO_END = 155,
OH_NN_REDUCE_MIN_COEFF = 156, OH_NN_REDUCE_SUM_KEEP_DIMS = 157, OH_NN_REDUCE_SUM_REDUCE_TO_END = 158, OH_NN_REDUCE_SUM_COEFF = 159,
OH_NN_REDUCE_L2_KEEP_DIMS = 160, OH_NN_REDUCE_L2_REDUCE_TO_END = 161, OH_NN_REDUCE_L2_COEFF = 162
}		 张量的类型。

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