Comments (3)
In the fine-gained, torch.roll is used to expand the key and value set. In the coarse-gained, torch.unfold is used. I was confused that why torch.roll and torch.unfold are needed? see isuse6 in detail. Thanks very much. So sorry for my poor English.
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Hi, @liyiersan
Focal window size means on which feature map size the window pooling is performed, while focal region size means the size of region the query in a local window will attend to. For example, at level 0, we use the most fine-grained tokens and thus do not use any window pooling, so sw=1, but the focal region becomes 7+2*3 = 13, where 7 is the size of each window, 3 is the extension to its all sides so that the window tokens can attend to their surroundings outside the local window. Hope this clarifies the idea of focal attention, but please let me know if it is still confusing. thanks!
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I believe this issue has been addressed.
from focal-transformer.
Related Issues (20)
- Some problems with the reproduction process about focal block
- How the load the pretrained model on classification when trained on segmentation?
- about pool_method HOT 1
- num_heads value
- num_heads value HOT 1
- how can i extract features ? HOT 1
- Some question about focal_transformer_v2.py
- about detection and segmentation HOT 1
- Inference of Instance Segmentation Model
- Confusion about window size at different focal level HOT 2
- Link error
- Welcome update to OpenMMLab 2.0
- Inference on CPU
- FocalTransformerV2 not using expand_size
- Can't download ImageNet-1k pretrained weights
- How to get q,k,v? HOT 2
- Can you give me the segmentation model,thanks
- Link expires? HOT 4
- Your computational complexity is much higher than swin transformer HOT 1
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