Inconsistency in memory consumption of Resnet-101 libraries about pytorch-deeplab-resnet

[isht7]

I just opened the implementation that you have referred above, and I noticed that it is an edited version of my repository. It is unprofessional of @speedinghzl to use my code without citing my repo even though I have MIT license. If you are talking about this file, then it seems that this version does not implement loss over multiple scales. This allows it to have a higher batch size. @speedinghzl might have done some other modification to my code, which I do not know of.

[isht7]

I guess, you cannot run my code with 11.1 GB of GPU memory. If you modify this line, you can make it fit to lower memory. Change random.uniform(0.5, 1.3) to random.uniform(0.5, 1.1) or some even lower number. Lower it in steps till it fits in your GPU. Also, in the original code which is in caffe, the caffe parameter in the solver file iter_size is 10, which means that gradient is acummulated over 10 iterations and then applied. This is what I have done in my code. It is kind of equivalent to setting the batch_size as 10 (see iter_size at this link). The library that you mentioned might be doing this.

[omkar13]

But the time taken is not affected by using iter_size. I wanted to increase the batch size in order to decrease the time taken. I am running on a different dataset with 480x853 size images. Maybe that is why I am able to run within 7-8 GB. What image size did you test with? Also, the library mentioned is not using the iter_size trick. It is passing the batch_size parameter (value=10) to the Pytorch DataLoader (I tried using the mentioned library with images of size 321x321 and could run with a batch size of 10).

[4F2E4A2E]

A lot of copy paste ai these days...
@isht7 thanks for this repo!

[omkar13]

Thank you for the responses.