ONNXRUNTUIME实例分割网络说明

• ONNXRUNTUIME c++使用（分割网络）与相关资料（暂记）

• initiate a env with an id name(使用id名称启动env)

• create session (创建会话 )

• onnx+env -> session
• inputname = [“x”] ,outputname = [“t”]
• inputnodedim = [[1,1,192,192]] , outputnodedim = [[1,192,192,2]]
  

推理 Mat dstimg = mynet.inference(frame);

预处理 (srcimg序列化到this->input_image_)

void pphuman_seg::preprocess(Mat srcimg)// srcimg cv::Mat 3,800,1920
{
    Mat dstimg;
    resize(srcimg, dstimg, Size(this->inpWidth, this->inpHeight), INTER_LINEAR);// resize

    int row = dstimg.rows;
    int col = dstimg.cols;
    this->input_image_.resize(row * col * dstimg.channels());// vector<float> 变成row * col * 3大小
    for (int c = 0; c < 3; c++)//  序列化
    {
        for (int i = 0; i < row; i++)
        {
            for (int j = 0; j < col; j++)
            {
                float pix = dstimg.ptr<uchar>(i)[j * 3 + c];// uchar* data = image.ptr<uchar>(i);
                this->input_image_[c * row * col + i * col + j] = (pix / 255.0 - 0.5) / 0.5;
            }
        }
    }
}

前向传播

• Value input_tensor_ = Value::CreateTensor<float>(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size());

• vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, input_names.data(), &input_tensor_, 1, output_names.data(), output_names.size());

后处理

Value对象 Value &mask_pred = ort_outputs.at(0);

struct Value : Base<OrtValue> {
  // This structure is used to feed  sparse tensor values
  // information for use with FillSparseTensor<Format>() API
  // if the data type for the sparse tensor values is numeric
  // use data.p_data, otherwise, use data.str pointer to feed
  // values. data.str is an array of const char* that are zero terminated.
  // number of strings in the array must match shape size.
  // For fully sparse tensors use shape {0} and set p_data/str
  // to nullptr.
  struct OrtSparseValuesParam {
    const int64_t* values_shape;
    size_t values_shape_len;
    union {
      const void* p_data;
      const char** str;
    } data;
  };

  // Provides a way to pass shape in a single
  // argument
  struct Shape {
    const int64_t* shape;
    size_t shape_len;
  };

转为 Mat mask_out(out_h, out_w, CV_32FC2, mask_ptr);

CV_32FC2 应该是32Float 2 通道

    /** @overload
    @param rows Number of rows in a 2D array.
    @param cols Number of columns in a 2D array.
    @param type Array type. Use CV_8UC1, ..., CV_64FC4 to create 1-4 channel matrices, or
    CV_8UC(n), ..., CV_64FC(n) to create multi-channel (up to CV_CN_MAX channels) matrices.
    @param s An optional value to initialize each matrix element with. To set all the matrix elements to
    the particular value after the construction, use the assignment operator
    Mat::operator=(const Scalar& value) .
    */
    Mat(int rows, int cols, int type, const Scalar& s);

结果展示

 for (int h = 0; h < srcimg.rows; h++)
    {
        for (int w = 0; w < srcimg.cols; w++)
        {
            float pix = segmentation_map.ptr<float>(h)[w * 2];
            if (pix > this->conf_threshold)
            {
                float b = (float)srcimg.at<Vec3b>(h, w)[0];
                dstimg.at<Vec3b>(h, w)[0] = uchar(b * 0.5 + 1);
                float g = (float)srcimg.at<Vec3b>(h, w)[1];
                dstimg.at<Vec3b>(h, w)[1] = uchar(g * 0.5 + 1);
                float r = (float)srcimg.at<Vec3b>(h, w)[2];
                dstimg.at<Vec3b>(h, w)[2] = uchar(r * 0.5 + 1);
            }
        }
    }

    for (int h = 0; h < srcimg.rows; h++)
    {
        for (int w = 0; w < srcimg.cols; w++)
        {
            float pix = segmentation_map.ptr<float>(h)[w * 2 + 1];
            if (pix > this->conf_threshold)
            {
                float b = (float)dstimg.at<Vec3b>(h, w)[0];
                dstimg.at<Vec3b>(h, w)[0] = uchar(b * 0.5 + 1);
                float g = (float)dstimg.at<Vec3b>(h, w)[1] + 255.0;
                dstimg.at<Vec3b>(h, w)[1] = uchar(g * 0.5 + 1);
                float r = (float)dstimg.at<Vec3b>(h, w)[2];
                dstimg.at<Vec3b>(h, w)[2] = uchar(r * 0.5 + 1);
            }
        }
    }

OrtSessionOptionsAppendExecutionProvider_CUDA

/*
 * This is the old way to add the CUDA provider to the session, please use SessionOptionsAppendExecutionProvider_CUDA above to access the latest functionality
 * This function always exists, but will only succeed if Onnxruntime was built with CUDA support and the CUDA provider shared library exists
 *
 * \param device_id CUDA device id, starts from zero.
*/
ORT_API_STATUS(OrtSessionOptionsAppendExecutionProvider_CUDA, _In_ OrtSessionOptions* options, int device_id);

SessionOptionsAppendExecutionProvider_CUDA

• https://github.com/microsoft/onnxruntime/blob/0fceb33288ce35472d1cbab24fd7d95d5d3c9b07/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/CXX_Api_Sample.cpp#L22

• https://github.com/microsoft/onnxruntime/issues/3218

CG

ONNXRUNTUIME c++ on web

• 官方教程

• 镜像服务器设置：npm config set registry= https://registry.npm.taobao.org/

• https://github.com/microsoft/onnxruntime-inference-examples