Comments (3)
I found the same problem. It seems the implementation in the code is equivalent to having #attention heads = #embed dimensions.
from stand-alone-self-attention.
@XiaLiPKU How would that modify line 49 and 50?
from stand-alone-self-attention.
@20171130 That was also my first opinion, but then there is an inconsistency with "groups" definition (to replicate the "attention heads") throughout the paper & the code.
Anyway, your alternative implementation helped me to understand the general concepts:
https://github.com/20171130/AttentionLite/blob/master/model.py
from stand-alone-self-attention.
Related Issues (20)
- problem with unfold HOT 1
- Does not work when out_channels is not even
- Question about einsum.
- how about replacing einsum with normal multiplication
- Train with IMAGENET HOT 3
- matrix multiplication instead of scalar dot product HOT 4
- Problems about groups HOT 4
- Error loading pretrained model HOT 1
- Excessive Memory Usage HOT 14
- Can anyone train resnet50 successfully without NaN HOT 3
- Has anyone tried changing the batch size HOT 1
- Add `sum(dim=2)` for dot-product HOT 1
- A question about relative position embeddings HOT 1
- Stand alone self attention combine with CycleGAN
- Loss is NaN HOT 2
- Large memory consumption HOT 2
- v_out = torch.cat((v_out_h + self.rel_h, v_out_w + self.rel_w), dim=1) HOT 2
- How to calculate the relative positional embeddings from a row offset and column offset? HOT 3
- Is the 89% acc reported in the readme consisitent with the paper? HOT 1
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from stand-alone-self-attention.