I have a code that converts an image with 32 output layers, from an AI segmentation model output, into a single layer where each pixel in each layer has a probability proportional to its score to make to this single layer. In order to do that, I need to generate a random float number to figure out each of the 32 layers is going to be the winner.
When I run this code in a single thread, it generates the same output every single time. However, when I use it with OMP (to make it faster), it generates a different output every time, even when I make the random number generator private to each thread and initialize it with the same seed (for each row). I also tried to hardcode the seed to 0 and it did not solve the problem. It is like one thread is interfering with the sequence of the numbers in the other one.
I need this code to generate consistently the same result every time in order to make it easier to test the output. Any idea?
cv::Mat prediction_map(aiPanoHeight, aiPanoWidth, CV_8UC1);
#pragma omp parallel for schedule(dynamic, aiPanoHeight/32)
for (int y=0;y<aiPanoHeight;++y){
static std::minstd_rand0 rng(y);
std::uniform_real_distribution<float> dist(0, 1);
for (int x=0;x< aiPanoWidth;++x){
float values[NUM_CLASSES];
// populate values with the normalized score for each class, so that the total is 1
float r = dist(rng);
for (int c = 0; c < NUM_CLASSES; ++c)
{
r -= values[c];
if(r<=0) {
prediction_map.at<uchar>(y, correctedX) = int(aiClassesLUT[c]); // paint prediction map with the corresponding color of the winning layer
break;
}
}
}
}
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