ChunhuanLin / Deform_conv_pytorch
PyTorch Implementation of Deformable Convolution
Stars: ✭ 217
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PyTorch Implementation of Deformable Convolution
This repository implements the defromable convolution architecture proposed in this paper:
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu and Yichen Wei. Deformable Convolutional Networks. arXiv preprint arXiv:1703.06211, 2017.
Usage
- The defromable convolution module, i.e., DeformConv2D, is defined in
deform_conv.py. - A simple demo is shown in
demo.py, it's easy to interpolate the DeformConv2D module into your own networks.
TODO
- [x] Memory effeicent implementation.
- [x] Test against MXNet's official implementation.
- [ ] Visualize offsets
- [ ] Demo for RFCN implemantation
Notes
- Although there has already been some implementations, such as PyTorch/TensorFlow, they seem to have some problems as discussed here.
- In my opinion, the DeformConv2D module is better added to top of higher-level features for the sake of better learning the offsets. More experiments are needed to validate this conjecture.
- This repo has been verified by comparing with the official MXNet implementation, as showed in
test_against_mxnet.ipynb.
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