job->get() takes a long time(~200ms with default settings on a small image

[danielj-genesis]

Describe the problem I wrote a c++ file to read video frames(854x480) from an input video and am calling a siftjob on each frame. The code is below

// main.cpp
#include <opencv2/opencv.hpp>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgcodecs.hpp>
#include <popsift/popsift.h>
#include <popsift/features.h>
#include <stdio.h>
#include <string>
#include <iostream>

using namespace cv;
using namespace std;
using std::string;

int main()
{
    cudaDeviceReset();
    std::clock_t start;
    popsift::Config config;
    PopSift PopSift(
        config, 
        popsift::Config::ExtractingMode,
        false ? PopSift::FloatImages : PopSift::ByteImages
    );

    string filename = "input.mp4";
    VideoCapture cap(filename); 
    
    Mat frame;
    if (!cap.isOpened())
	{
	       std::cerr << "Couldn't open capture." << std::endl;
       	       return -1;
	}
	for (;;) 
	{
	       cap >> frame;
	       if (frame.empty()) break;
               unsigned char* dataMat = frame.data;
          
               SiftJob* job = PopSift.enqueue(frame.cols, frame.rows, dataMat);
               start = std::clock();
               popsift::Features* feature_list = job->get();
               std::cout << "Time: " << (std::clock() - start) / (double)(CLOCKS_PER_SEC / 1000) << " ms" << std::endl;
	       char c = cv::waitKey(10);
	       if (c == 27) break;
	}
	cap.release();
	return 0;
}

Here's my printed device information

Device information:
    Name: NVIDIA GeForce GTX 1660
    Compute Capability:    7.5
    Total device mem:      6441992192 B 6291008 kB 6143 MB
    Per-block shared mem:  49152
    Warp size:             32
    Max threads per block: 1024
    Max threads per SM(X): 1024
    Max block sizes:       {1024,1024,64}
    Max grid sizes:        {2147483647,65535,65535}
    Number of SM(x)s:      22
    Concurrent kernels:    yes
    Mapping host memory:   yes
    Unified addressing:    yes

On my gtx1660 the time for job->get() takes ~200ms on the 854x480px frame.

If I add:

    config.setDownsampling(0);
    config.setFilterMaxExtrema(false);

I can bring down the time to ~65ms Which is still slower than the python version of this code using

sift = cv2.SIFT_create()
sift.detect(frame,None)

Which only takes ~50ms

PopSift should be running much much faster according the paper? Is there something I'm missing?

[Azhng]

Is this the latency of a single frame? Or it's an average latency?

My guess is that CUDA lazily allocate and initializes a lot of internal objects, and transferring data from RAM to VRAM also has high latency.

You would have to "warm-up" the popsift queue before it can achieve it's maximum throughput.

This is how the neighbouring VulkanSIFT measured the performance too as well