主题
加载明文模型 - YoloLoadModelEx
函数简介
从磁盘加载明文模型(ONNX / TensorRT Engine / NCNN)。
接口名称
YoloLoadModelExDLL 调用
long YoloLoadModelEx(long ola, string modelPath, string ncnnParamPath, string namesLabel, int modelType, int inferenceType, int inferenceDevice);参数说明
| 参数名 | 类型 | 说明 |
|---|---|---|
| ola | 长整数型 | OLAPlug 对象指针,由 CreateCOLAPlugInterFace 生成。 |
| modelPath | 字符串 | 主模型路径:ONNX/Engine 单文件;NCNN 为 .bin |
| ncnnParamPath | 字符串 | NCNN 的 .param 路径;非 NCNN 传空字符串 |
| namesLabel | 字符串 | 类别名文件(一行一类),可选空 |
| modelType | 整数型 | 模型后端类型,见下表 |
| inferenceType | 整数型 | 推理任务类型,见下表 |
| inferenceDevice | 整数型 | inferenceDevice:-1 表示 CPU;0 及以上表示 GPU 索引。GPU 不可用时可能自动回退 CPU(以实际 ExecutionProvider 为准)。 |
示例
SDK 调用
cpp
#include "OLAPlugServer.h"
OLAPlugServer ola;
long handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0);csharp
using OLAPlug;
var ola = new OLAPlugServer();
long handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0);python
from OLAPlugServer import OLAPlugServer
ola = OLAPlugServer()
handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0)java
import com.olaplug.OLAPlugServer;
OLAPlugServer ola = new OLAPlugServer();
long handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0);cpp
var ola = com("OlaPlug.OlaSoft")
var handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0)vbscript
Set ola = CreateObject("OlaPlug.OlaSoft")
handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0)text
.局部变量 ola, OLAPlug
ola.创建 ()
handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0)aardio
import OLAPlugServer;
var ola = OLAPlugServer();
var handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0);text
变量 ola <类型 = OLAPlugServer>
ola = 新建 OLAPlugServer
长整数 handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0)cpp
#include "OLAPlugServer.h"
OLAPlugServer ola;
long handle = ola.YoloLoadModelEx("models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0);原生 DLL 调用
cpp
long instance = CreateCOLAPlugInterFace();
YoloLoadModelEx(instance, "models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0);csharp
using System.Runtime.InteropServices;
using System.Text;
[DllImport("OLAPlug_x64.dll", CallingConvention = CallingConvention.StdCall)]
static extern long CreateCOLAPlugInterFace();
[DllImport("OLAPlug_x64.dll", CallingConvention = CallingConvention.StdCall)]
static extern long YoloLoadModelEx(long ola, string modelPath, string ncnnParamPath, string namesLabel, int modelType, int inferenceType, int inferenceDevice);
long instance = CreateCOLAPlugInterFace();
YoloLoadModelEx(instance, "models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0);python
from ctypes import CDLL, c_int, c_int64, create_string_buffer
ola = CDLL("OLAPlug_x64.dll")
ola.CreateCOLAPlugInterFace.restype = c_int64
instance = ola.CreateCOLAPlugInterFace()
ola.YoloLoadModelEx(instance, "models/yolov8n.olam", "models/yolov8n.olam", "value", 0, 0, 0)返回值
长整数型:模型句柄,失败返回 0。
注意事项
- 需要插件已开通 YOLO 模块权限(Reg、Login的FeatureList中包含YOLO特性)。
| 值 | 含义 |
|---|---|
| 0 | TensorRT Engine(.engine) |
| 1 | ONNX(.onnx) |
| 2 | NCNN(.bin + .param 双文件) |
| 值 | 含义 |
|---|---|
| 0 | Detect 目标检测 |
| 1 | Classify 图像分类 |
| 2 | Segment 实例分割 |
| 3 | Pose 姿态估计 |
| 4 | Obb 旋转框检测 |
inferenceDevice:-1表示 CPU;0及以上表示 GPU 索引。GPU 不可用时可能自动回退 CPU(以实际 ExecutionProvider 为准)。- 勿传入加密包路径。
