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