Deformable Convolutional Networks V2 with Pytorch 1.X
./make.sh # build python testcpu.py # run examples and gradient check on cpu python testcuda.py # run examples and gradient check on gpu
Now the master branch is for pytorch 1.x, you can switch back to pytorch 0.4 with,
git checkout pytorch_0.4
- Gradient check w.r.t offset (solved)
- Backward is not reentrant (minor)
This is an adaption of the official Deformable-ConvNets.
Update: all gradient check passes with double precision.
Another issue is that it raises
RuntimeError: Backward is not reentrant. However, the error is very small (
<1e-15 for double),
so it may not be a serious problem (?)
Please post an issue or PR if you have any comments.