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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

  • Memory effeicent implementation.
  • 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.

Requirements