C++ncnn模型验证精度实现代码

    model = init_model(model, data_cfg, device=device, mode='eval')
    ###.pth转.onnx模型
    # #---
    # input_names = ["x"]
    # output_names = ["y"]
    # inp = torch.randn(1, 3, 256, 128) ##错误示例
    inp = np.full((1, 3, 160, 320), 0.5).astype(np.float) #(160,320) = (h,w)
    inp = torch.FloatTensor(inp)
    out = model(inp)
    print(out)

   sess = onnxruntime.InferenceSession("G:\\PyCharm_PyTorch171\\pytorch_classification\\main\\sim.onnx", providers=["CUDAExecutionProvider"])  # use gpu
    input_name = sess.get_inputs()[0].name
    print("input_name: ", input_name)
    output_name = sess.get_outputs()[0].name
    print("output_name: ", output_name)
    # test_images = torch.rand([1, 3, 256, 128])
    test_images = np.full((1, 3, 160, 320), 0.5).astype(np.float) #(160,320) = (h,w)
    test_images = torch.FloatTensor(test_images)
    print("test_image", test_images)
    prediction = sess.run([output_name], {input_name: test_images.numpy()})
    print(prediction)

 ```cpp
     constexpr int w = 320;
     constexpr int h = 160;
     float cbuf[h][w];
     cv::Mat img(h, w, CV_8UC3,(float *)cbuf);
     //BYTE* iPtr = new BYTE[128 * 256 * 3];
     BYTE* iPtr = new BYTE[h * w * 3];
     for (int i = 0; i < h; i++)
     {
         for (int j = 0; j < w; j++)
         {
             for (int k = 0; k < 3; k++)
             {
                 //iPtr[i * 256 * 3 + j * 3 + k] = img.at<cv::Vec3f>(i, j)[k];
                 img.at<cv::Vec3b>(i, j)[k] = 1;
             }
         }
     }
 ```
 - 经过上面的赋值，通过了mean、norm计算后，得到的结果进行查看，值为0.5则正确转换。得到的结果送入下面的代码进行输出。
 ncnn结果为mat，因此采用该方法进行遍历查看。
 ```cpp
 //输出ncnn mat
 void ncnn_mat_print(const ncnn::Mat& m)
 {
     for (int q = 0; q < m.c; q++)
     {
         const float* ptr = m.channel(q);
         for (int y = 0; y < m.h; y++)
         {
             for (int x = 0; x < m.w; x++)
             {
                 printf("%f ", ptr[x]);
             }
             ptr += m.w;
             printf("\n");
         }
         printf("------------------------\n");
     }
 }
 ```
 将mat给到模型进行推理得到结果。