Deformable Convolutional Networks V2 with Pytorch 1.0

Build

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

An Example

• deformable conv

    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)

• deformable roi pooling

    from dcn_v2 import DCNPooling
    input = torch.randn(2, 32, 64, 64).cuda()
    batch_inds = torch.randint(2, (20, 1)).cuda().float()
    x = torch.randint(256, (20, 1)).cuda().float()
    y = torch.randint(256, (20, 1)).cuda().float()
    w = torch.randint(64, (20, 1)).cuda().float()
    h = torch.randint(64, (20, 1)).cuda().float()
    rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1)

    # mdformable pooling (V2)
    # wrap all things (offset and mask) in DCNPooling
    dpooling = DCNPooling(spatial_scale=1.0 / 4,
                         pooled_size=7,
                         output_dim=32,
                         no_trans=False,
                         group_size=1,
                         trans_std=0.1).cuda()

    dout = dpooling(input, rois)

Note

Now the master branch is for pytorch 1.0 (new ATen API), you can switch back to pytorch 0.4 with,

git checkout pytorch_0.4

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.

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