Open windyrobin opened 5 years ago
Thanks for your report. However the dla_up in your script is not from our codebase. I could not understand your 24
channels. When you are running test using src/test.py
folllowing readme, our script will print the detailed time in each stage (e.g., network feedforward, decoding). You can start debugging from here. I don't have a GP100 but I can basically reproduce similar testing performance (~3ms slower) on a GTX 1080Ti.
Thanks for your report. However the dla_up in your script is not from our codebase. I could not understand your
24
channels. When you are running test usingsrc/test.py
folllowing readme, our script will print the detailed time in each stage (e.g., network feedforward, decoding). You can start debugging from here. I don't have a GP100 but I can basically reproduce similar testing performance (~3ms slower) on a GTX 1080Ti.
tks ,i'll check it
btw, tks for your great work
Does fps in the article only calculate the network inference time, or does it include pre-processing and post-processing?
I test 512*512 resolution and get this result(2080ti)
DLA-34 | 37.4 / 52 | 39.2 / 28 | 41.7 / 4
the paper says use DLA-34 in resulotion 512x512 , could achieve 52fps, (Titian XP) this means <20ms for one frame,
but I test the origin dla34up in pytorch it costs about 30+ ms, (GP100) and paper says CenterNet also add deformable component to the origin, and it should add some extra cost...
i think there is no big difference in xp and gp100...
so , how 52-fps comes from ?
result: