Deformable Convolutional Networks V2 with Pytorch

Build

    ./make.sh         # build
    python test.py    # run examples and gradient check 

An Example

    from dcn_v2 import DCN
    input = torch.randn(2, 64, 128, 128).cuda()
    # wrap all things (offset and mask) in DCN
    dcn = DCN(64, 64, kernel_size=(3,3), stride=1, padding=1, deformable_groups=2).cuda()
    output = dcn(input)
    print(output.shape)

Known Issues:

• Gradient check w.r.t offset (solved)
• Backward is not reentrant (minor)

This is an adaption of the official Deformable-ConvNets.

I have ran the gradient check for many times with DOUBLE type. Every tensor except offset passes. However, when I set the offset to 0.5, it passes. I'm still wondering what cause this problem. Is it because some non-differential points?

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-7 for float <1e-15 for double), so it may not be a serious problem (?)

Please post an issue or PR if you have any comments.