Function main

tools/latency.py:38–72

()

def main():
    # init arguments
    args = get_parser()
    torch.cuda.set_device(0)
    # create model
    model, _ = build_segmenter(args)
    model = model.cuda()
    model.eval()
    # set cudnn state
    cudnn.benchmark = True
    cudnn.deterministic = False
    cudnn.enabled = True
    # init dummy tensor
    image = torch.randn(1, 3, 416, 416).cuda()
    text = torch.randint(4096, size=(1, args.word_len)).long().cuda()
    # init time & memory
    avg_time = 0
    avg_mem = 0
    # record initial gpu memory
    mem = torch.cuda.max_memory_allocated()

    with torch.no_grad():
        for i in range(500):
            start_time = time.time()
            _ = model(image, text)
            torch.cuda.synchronize()
            if (i+1) >= 100:
                avg_time += (time.time() - start_time)
                avg_mem += (torch.cuda.max_memory_allocated() - mem) / 1.073742e9
    params = count_parameters(model) * 1e-6
    print('#########################################')
    print("Average Parameters : {:.2f} M".format(params))
    print("Average FPS: {:.2f}".format(400/avg_time))
    print("Average GPU Memory: {:.2f} GB".format(avg_mem/400))
    print('#########################################')


if __name__ == '__main__':