| import math |
|
|
| import torch |
| import torch.nn as nn |
| from torch.nn.modules.module import Module |
| from torch.nn.modules.utils import _pair |
| from lib.extensions.dcn.functions import deform_conv_function |
|
|
|
|
| class DeformConv(Module): |
| def __init__(self, |
| in_channels, |
| out_channels, |
| kernel_size, |
| stride=1, |
| padding=0, |
| dilation=1, |
| num_deformable_groups=1): |
| super(DeformConv, self).__init__() |
| self.in_channels = in_channels |
| self.out_channels = out_channels |
| self.kernel_size = _pair(kernel_size) |
| self.stride = _pair(stride) |
| self.padding = _pair(padding) |
| self.dilation = _pair(dilation) |
| self.num_deformable_groups = num_deformable_groups |
|
|
| self.weight = nn.Parameter( |
| torch.Tensor(out_channels, in_channels, *self.kernel_size)) |
|
|
| self.reset_parameters() |
|
|
| def reset_parameters(self): |
| n = self.in_channels |
| for k in self.kernel_size: |
| n *= k |
| stdv = 1. / math.sqrt(n) |
| self.weight.data.uniform_(-stdv, stdv) |
|
|
| def forward(self, input, offset): |
| return deform_conv_function(input, offset, self.weight, self.stride, |
| self.padding, self.dilation, |
| self.num_deformable_groups) |
|
|
