Closed fengluodb closed 1 year ago
Sorry for long time passed and I may have forgotten some details. Due to some equipment differences, there can be huge differences in speed tests.
For your question, there may be the following points to note:
This is the original data I counted at that time, the code may have been lost. You will find that the KNN is fast because I miscalculated the NLA time at that time........
Thank you for your reply. If I remove the auxiliary segmentation header, the speed may be faster. I will try when my gpu is free.
Hi @fengluodb @huixiancheng ,
Thanks for the work! A minor question about the FPS. I can see in the log it shows 32.75 it/s, but the Mean CNN inference time
is 0.01585113, which means ~63 FPS. Why do they not match? Is there some other latency besides the CNN inference time?
Hi~! @shawnding KNN time should add into all time. Since CNN infer label can't gather the 3d results. Moreover, this value may not be very reliable, since it depends on your hardware and related computing code.
Thanks for your kind reply~ However, adding CNN time and KNN time together would be 0.018s (~55 FPS), which still doesn't match with 32.75 it/s. Is there any other overhead such as data processing, or am I missing something here...
Hi~ @shawnding
You mean the tqdm value and the FPS results from Feng?
I guess part diff come from data processing & torch.cuda.synchronize() & results save.
Here is the code maybe he used.
https://github.com/huixiancheng/CENet/blob/9a84103d186a1f93637cae3d96426760deb04140/modules/user.py#L126-L220
By the way, do you think the iteration time of tqdm is meaningful? It only depends on the complexity of your for
loop code.
That makes sense now. Thanks for the elaboration!
I use your model in my project. But the fps is different with that your paper show. With size being
512x64
,The fps is 67, lower than 84.9 in your paper.
I infer the valid dataset on 3090.