Comments (2)
Wow, it is truly a bug. However, I am afraid such small masks deverse no shift at all. In fact, the information flow of convolution from the outside to the inside are propagating little by little. When the mask is very big, it is hard for the network to direclty predict the missing content. In this case, shift operation makes the inpainting adpots the information not only local context but also global one. However, when the mask is very small, the shift is not necessary at all. So you can direcly skip shift operaition when mask is missing in the reduction.
from shift-net_pytorch.
Indeed. So, I will code the fix and re-run some tests to make a pull request later. Thanks for the fast answer!
from shift-net_pytorch.
Related Issues (20)
- Why are the size of inpainting models so big? HOT 2
- On what image sizes can Face Shift-net work? HOT 2
- Question regarding neighbor searching. HOT 4
- test HOT 8
- Guidance loss HOT 1
- can't find the guidance loss in the code HOT 1
- Guide Loss: how is target passed to InnerCosFunction HOT 3
- Dataset
- About the parameter 'overlap' HOT 2
- about change mask size HOT 1
- Any help on custom dataset? HOT 4
- Discounting Loss
- random mask HOT 5
- the result is blurred HOT 3
- Error in training HOT 9
- test about face
- How to test with my own mask dataset? HOT 1
- the nearest neighbor searching can be computed as a convolutional layer HOT 2
- I have paired images and use one of them as a mask image, how can I modify the code?
- RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1] because the unspecified dimension size -1 can be any value and is ambiguous HOT 3
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