Understanding time difference in finetunning vs freeze and train

[miguelgfierro]

Understanding time difference in finetunning vs freeze and train

[miguelgfierro]

More info about finetuning: https://discuss.pytorch.org/t/how-to-perform-finetuning-in-pytorch/419

https://github.com/mortezamg63/Accessing-and-modifying-different-layers-of-a-pretrained-model-in-pytorch

[miguelgfierro]

Using CNTK with simpsons dataset:
freeze:
Test accuracy is 0.763938618926
Process time 621.0402624607086 seconds

finetune:
Test accuracy is 0.959335038363
Process time 1635.1398813724518 seconds

[miguelgfierro]

Using Simpsons dataset (20k images)

ResNet18 on 1GPU finetune:
Training complete in 21m 37s
Best val Acc: 0.953708

ResNet18 on 1GPU freeze:
Training complete in 8m 30s
Best val Acc: 0.692327

ResNet18 on 4GPU finetune:
Training complete in 10m 11s
Best val Acc: 0.954220

ResNet18 on 4GPU freeze:
Training complete in 9m 42s
Best val Acc: 0.686957

ResNet152 on 4GPU finetune:
Training complete in 64m 55s
Best val Acc: 0.987468

ResNet152 on 4GPU freeze:
Training complete in 59m 18s
Best val Acc: 0.727110

[miguelgfierro]

There is a big overload when doing Dataparallel.

In words of one of the developers:

At the moment, DataParallel broadcasts parameters that are not modified, and has some other overhead around replicate and broadcast_coalesce. We are in the process to improve it. See some progress here: pytorch/pytorch#4216.