Comments (3)
Thanks for your comments !
The paper is implemented referring to the following paper.
Attention Augmented Convolutional Networks Link
Thank you.
from stand-alone-self-attention.
@leaderj1001
Hello, I wonder whether you implement the relative position embedding in the selfattention.
I notice that you just exploit two random tensors to represent position in h and w direction while the paper said "The row and column offsets are associated with an embedding r_{a−i} and r_{b−j}".
I am confused about whether the so called "embedding" should be implemented as the nn.Embedding operation in pytorch.
from stand-alone-self-attention.
@leaderj1001
Hello, I wonder whether you implement the relative position embedding in the selfattention.
I notice that you just exploit two random tensors to represent position in h and w direction while the paper said "The row and column offsets are associated with an embedding r_{a−i} and r_{b−j}".
I am confused about whether the so called "embedding" should be implemented as the nn.Embedding operation in pytorch.
Hi, I am having the same question here. Did you figure out how to compute the relative position embedding?Thanks
from stand-alone-self-attention.
Related Issues (20)
- The wrong imp of the inner-product operation HOT 3
- 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
- Is the 89% acc reported in the readme consisitent with the paper? HOT 1
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