| import torch |
| import torch.nn as nn |
| from torch.nn import init as init |
| from torch.nn.modules.utils import _pair, _single |
| import math |
|
|
| class ModulatedDeformConv2d(nn.Module): |
| def __init__(self, |
| in_channels, |
| out_channels, |
| kernel_size, |
| stride=1, |
| padding=0, |
| dilation=1, |
| groups=1, |
| deform_groups=1, |
| bias=True): |
| super(ModulatedDeformConv2d, self).__init__() |
| |
| self.in_channels = in_channels |
| self.out_channels = out_channels |
| self.kernel_size = _pair(kernel_size) |
| self.stride = stride |
| self.padding = padding |
| self.dilation = dilation |
| self.groups = groups |
| self.deform_groups = deform_groups |
| self.with_bias = bias |
| |
| self.transposed = False |
| self.output_padding = _single(0) |
|
|
| self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) |
| if bias: |
| self.bias = nn.Parameter(torch.Tensor(out_channels)) |
| else: |
| self.register_parameter('bias', None) |
| self.init_weights() |
|
|
| def init_weights(self): |
| n = self.in_channels |
| for k in self.kernel_size: |
| n *= k |
| stdv = 1. / math.sqrt(n) |
| self.weight.data.uniform_(-stdv, stdv) |
| if self.bias is not None: |
| self.bias.data.zero_() |
|
|
| if hasattr(self, 'conv_offset'): |
| self.conv_offset.weight.data.zero_() |
| self.conv_offset.bias.data.zero_() |
|
|
| def forward(self, x, offset, mask): |
| pass |
