Comments (3)
1zb
commented on July 17, 2024
Hi, @ran337287. You can say there is only one kernel with shape (4, 3, kH, kW), or four kernels with shape (1, 3, kH, kW). Both are acceptable. So yes, in every spatial location (i, j) ( 0 < i < H, 0 < j < W), four kernels share the same offsets.
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bkvie
commented on July 17, 2024
One could probably change that by modifying conv2d( groups=1) although that would not be what the original paper intended.
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bkvie
commented on July 17, 2024
@1zb would it make sense to have individual offsets? So 4 kernels of shape (1,3, kH, kW) with 4 offsets?
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Related Issues (20)
- 梯度测试不通过 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 ！ 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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