Inference Speed Test? about shufflenet HOT 9 OPEN

The entire script takes about 400ms for me to run, and the actual inference step y = net(x) takes about 70ms. The infer.py script never calls .cuda() so everything is running on CPU. I tried moving it to the GPU, but that just makes the single inference slower (takes longer to move the single image on and off the GPU); ends up being 16 seconds, with 5 seconds on inference.

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For completeness, I was running with pytorch version 0.4.0, and pip freeze gave this. I installed the conda env following the instructions to build pytorch from source.

Also, here are the CPU details:

Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                32
On-line CPU(s) list:   0-31
Thread(s) per core:    2
Core(s) per socket:    8
Socket(s):             2
NUMA node(s):          2
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 79
Model name:            Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
Stepping:              1
CPU MHz:               1200.281

With an old conda env on pytorch version 0.2.0, it took 150ms for inference and 350ms for the whole script.

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Hmm okay. I guess there's no need to improve speed if it works well enough. I'll figure out what the problem is on my end.

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Hi @ildoonet, I'll take a look when I get time :)

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I finally got around to doing some inference on ShuffleNet today. And it is definitely far too slow. Any ideas on how to speed it up? I suspect the snail-like speed is due to the frequent channel shuffling.

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@gngdb if you have any ideas on how to speed it up in PyTorch, would love to know. I can't imagine doing a full training run at this speed. Speeding up would drastically help you with training too

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What version of PyTorch are you running? The speed of grouped convolutions increased a lot in the most recent versions.

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I'm running PyTorch 0.3.0 with CUDA. How long does it take for you to do one inference on the cat image? It takes probably 30 seconds for me.

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@jaxony HI，have you solved it?

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