inconsistency with the original paper

[duducheng]

Hello, thanks for your nice code!

I found there were 2 inconsistencies with the original paper, and they are very easy to fix indeed:

1. the gamma: in the original paper, all the block_mask are complete squares (or cubes), sinces its mask are only sampled on the central parts.
2. in the paper, it said the channels use different masks, while in your implement they use the same.

I just figure them out, actually I do not know whether they are effective tricks, there are insufficient details discussed in the paper :)

[miguelvr]

The gamma issue is a minor thing but I can have a look at it.

The channels share the same mask in the paper.

[duducheng]

“We experimented with a shared DropBlock mask across different feature channels or each feature
channel has its DropBlock mask. Algorithm 1 corresponds to the latter, which tends to work better in
our experiments.” (page 2 bottom line)

[miguelvr]

Sure, that is easily fixable

Expect it soon

Edit: you can also do a PR if you want

[huyvnphan]

Hi,
Any updates on this?
Best

[miguelvr]

Hi,
Any updates on this?
Best

I haven't had much free time to deal with this, but I will review and accept merge requests

[JarvisKevin]

I also found some difference between paper and code.

[Eliza-and-black]

To solve this issue, you could have a look at this folk(only for DropBlock2D)

[miguelvr]

To solve this issue, you could have a look at this folk(only for DropBlock2D)

I would encourage you to do a pull request

[JohnDLee]

If you do look at the code linked above, note that mask_center is not initialized on the device, so the part where nn.ZeroPad2d is called will by default run on the CPU. For me, since I was training on a GPU, this slowed down a single forward call (of my model which uses many Dropblocks) from .15 seconds to 3 seconds.