why use slimmable_training instead of universally_slimmable_training: True in Autoslim

[GYxiaOH]

just like title,in my opinion, if you want slim you should know error of model in any channels

[JiahuiYu]

Please look at branch v3.0.0

The master version code for AutoSlim is for inference only. Training code is under branch v3.0.0

[GYxiaOH]

@JiahuiYu yes,i see branch v3.0.0 ,in your autoslim config ,you use slimmable_training but not slimmable_training,and use SwitchableBatchNorm2d SlimmableConv2d, SlimmableLinear,and in my opinion, although calibrate_bn = true

[JiahuiYu]

@GYxiaOH Ah, you are right. The released config is for validation of model performance (inference ). To train a weight-shared model for NAS, you will need to enable universally_slimmable_training.

[GYxiaOH]

thank you for your reply, in my opinion ,i can use your us config to train a model and use slimming to find a best model(after prunning)? by the way ,i see not getattr(m, 'linked', False) in slimming function，it's mean residual_connection in us_mobilenet_v2.py?

[JiahuiYu]

Yes.

[lyn0102]

Thank you for your work, could you share the configuration of aotoslim training?

[HaloTrouvaille]

thank you for your reply, in my opinion ,i can use your us config to train a model and use slimming to find a best model(after prunning)? by the way ,i see not getattr(m, 'linked', False) in slimming function，it's mean residual_connection in us_mobilenet_v2.py?

Hello, can I add your wechat to ask some question? Thank you!