def deform_sampling(self, feat, offset):
"""Sampling the feature x according to offset.
Args:
feat (Tensor): Feature
offset (Tensor): Spatial offset for for feature sampliing
"""
# it is an equivalent implementation of bilinear interpolation
b, c, h, w = feat.shape
weight = feat.new_ones(c, 1, 1, 1)
y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
return y
def forward(self, i, x):
if i < self.start_level:
return torch.nn.functional.conv2d(x,
self.weight,
bias=self.bias,
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
groups=self.groups)
offset = self.conv_offset(x)
return deform_conv2d(x, offset, self.weight, self.stride, self.padding,
self.dilation, self.groups,
self.deform_groups) + self.bias.unsqueeze(
0).unsqueeze(-1).unsqueeze(-1)
def forward(self, x, extra_feat):
offset = self.conv_offset(extra_feat)
return deform_conv2d(x, offset, self.weight, self.stride, self.padding,
self.dilation, self.groups, self.deform_groups)