Network forward pass speed

[mrgloom]

I have benchmarked models with build-in caffe time util, does it look sane? Also I'm not sure why I get so large CPU time difference between mac and ubuntu builds and build with CUDNN is slower for some reason.

Mac OS

./build/tools/caffe time --model=./models/WFLW/WFLW_final/rel.prototxt
Average Forward pass: 2493.78 ms.
Average Backward pass: 375.324 ms.
Average Forward-Backward: 2870.7 ms

./build/tools/caffe time --model=./models/WFLW/WFLW_wo_mp/rel.prototxt
Average Forward pass: 694.657 ms.
Average Backward pass: 196.416 ms.
Average Forward-Backward: 891.54 ms

Ubuntu

CPU
./build/tools/caffe time --model=./models/WFLW/WFLW_final/rel.prototxt
Average Forward pass: 4468.37 ms.
Average Backward pass: 393.096 ms.
Average Forward-Backward: 4863.64 ms.

GeForce GTX TITAN X
./build/tools/caffe time --model=./models/WFLW/WFLW_final/rel.prototxt -gpu 0
Average Forward pass: 178.025 ms.
Average Backward pass: 133.243 ms.
Average Forward-Backward: 312.231 ms.

CPU
./build/tools/caffe time --model=./models/WFLW/WFLW_wo_mp/rel.prototxt
Average Forward pass: 806.207 ms.
Average Backward pass: 193.004 ms.
Average Forward-Backward: 1001.08 ms.

GeForce GTX TITAN X
./build/tools/caffe time --model=./models/WFLW/WFLW_wo_mp/rel.prototxt -gpu 0
Average Forward pass: 60.9703 ms.
Average Backward pass: 46.9149 ms.
Average Forward-Backward: 108.198 ms.

With CUDNN build:

 ./build/tools/caffe time --model=./models/WFLW/WFLW_final/rel.prototxt -gpu 0
I1124 18:21:10.306239 14327 caffe.cpp:408] Average Forward pass: 207.585 ms.
I1124 18:21:10.306246 14327 caffe.cpp:410] Average Backward pass: 137.963 ms.
I1124 18:21:10.306253 14327 caffe.cpp:412] Average Forward-Backward: 346.198 ms.

./build/tools/caffe time --model=./models/WFLW/WFLW_wo_mp/rel.prototxt -gpu 0
I1124 18:19:52.215446 14266 caffe.cpp:408] Average Forward pass: 74.862 ms.
I1124 18:19:52.215452 14266 caffe.cpp:410] Average Backward pass: 47.7963 ms.
I1124 18:19:52.215461 14266 caffe.cpp:412] Average Forward-Backward: 122.95 ms

[deshmukhavd]

I tried this model with openCv dnn on CPU, and its taking average 300 ms per face call