Comments (6)
Yes. You are right.
inC
must be divisible by num_deformable_groups
. Then each group has their own offset.
from deformable-convolution-pytorch.
Thank you for your attention! π
from deformable-convolution-pytorch.
Sorry for bothering you again with this, @1zb, I have a follow up question. In the mxnet repository, the authors provide two options, num_group
and num_deformable_group
. According to their definition, num_group
splits the input data, while num_deformable_group
splits the output tensor and applies the i-th offset group to the i-th output group.
Thus, here in this repository, is num_deformable_group
actually equivalent to num_group
in the original implementation, as it is splitting the input data instead of output?
from deformable-convolution-pytorch.
Thanks for your response.
According to these lines and these lines, the parameter num_deformable_group
splits the input tensor along channel axis, which contradicts the definition. I will take a look at the code again.
The parameter num_group
or groups
exists in vanilla CNN. You might find useful discussions here.
from deformable-convolution-pytorch.
I think you're right, it does seem to be splitting the input tensor. I should check their model to see how they are being used, to make sure.
Thank you for the num_group
links, by the way!
from deformable-convolution-pytorch.
I was checking their code, it seems they really got the definitions reversed. In this line, they divide the number of output channels by the number of groups, while here they convolve the i-th output with the i-th weight.
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Related Issues (20)
- ζ’―εΊ¦ζ΅θ―δΈιθΏ HOT 21
- Extend this to 1-D HOT 8
- performance issue HOT 4
- How do you embed this module into a structure? HOT 3
- Slow speed HOT 2
- Get the offset_grad
- Build error HOT 4
- Not working on pytorch 0.4.1 HOT 3
- Error occurred
- Different interpretation of def_conv: HOT 1
- offset size
- Problem in test.py
- offset number? HOT 1
- License
- make.sh error
- Is it useful to use dcn on 1x1conv?
- test error οΌ HOT 4
- Is it possible that this version can be realized by Tensorflow? HOT 1
- Is it possible to support 3D convolution? HOT 8
- Evaluation of deformable conv nets on real-world problems HOT 3
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