ncnn学习总结

作者：DayInAI 日期：20190124

ncnn学习总结

1、支持的网络

Support most commonly used CNN network
支持大部分常用的 CNN 网络
Classical CNN Network: VGG AlexNet GoogleNet Inception …
Practical CNN Network: ResNet DenseNet SENet FPN …
Light-weight CNN Network: SqueezeNet MobileNetV1/V2 ShuffleNetV1/V2 MNasNet …
Detection Network: MTCNN facedetection …
Detection Network: VGG-SSD MobileNet-SSD SqueezeNet-SSD MobileNetV2-SSDLite …
Detection Network: Faster-RCNN R-FCN …
Detection Network: YOLOV2 YOLOV3 MobileNet-YOLOV3 …
Segmentation Network: FCN PSPNet …

2、加速技巧

1）使用低精度

半精度浮点数类型, 浮点数转化8bit量化

// convert float to half precision floating point
static unsigned short float2half(float value)
{
    // 1 : 8 : 23
    union
    {
        unsigned int u;
        float f;
    } tmp;

    tmp.f = value;

    // 1 : 8 : 23
    unsigned short sign = (tmp.u & 0x80000000) >> 31;
    unsigned short exponent = (tmp.u & 0x7F800000) >> 23;
    unsigned int significand = tmp.u & 0x7FFFFF;

//     fprintf(stderr, "%d %d %d\n", sign, exponent, significand);

    // 1 : 5 : 10
    unsigned short fp16;
    if (exponent == 0)
    {
        // zero or denormal, always underflow
        fp16 = (sign << 15) | (0x00 << 10) | 0x00;
    }
    else if (exponent == 0xFF)
    {
        // infinity or NaN
        fp16 = (sign << 15) | (0x1F << 10) | (significand ? 0x200 : 0x00);
    }
    else
    {
        // normalized
        short newexp = exponent + (- 127 + 15);
        if (newexp >= 31)
        {
            // overflow, return infinity
            fp16 = (sign << 15) | (0x1F << 10) | 0x00;
        }
        else if (newexp <= 0)
        {
            // underflow
            if (newexp >= -10)
            {
                // denormal half-precision
                unsigned short sig = (significand | 0x800000) >> (14 - newexp);
                fp16 = (sign << 15) | (0x00 << 10) | sig;
            }
            else
            {
                // underflow
                fp16 = (sign << 15) | (0x00 << 10) | 0x00;
            }
        }
        else
        {
            fp16 = (sign << 15) | (newexp << 10) | (significand >> 13);
        }
    }

    return fp16;
}

2）openmp多核多线程加速

  #pragma omp parallel for num_threads(opt.num_threads)
    for (int q=0; q<channels; q++)
    {
        float* ptr = bottom_top_blob.channel(q);

        for (int i=0; i<size; i++)
        {
            if (ptr[i] < 0)
                ptr[i] = -ptr[i];
        }
    }