| import math | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn.modules.utils import _pair | |
| from mmcv.ops import modulated_deform_conv2d | |
| class DCN_layer(nn.Module): | |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, | |
| groups=1, deformable_groups=1, bias=True, extra_offset_mask=True): | |
| super(DCN_layer, 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.deformable_groups = deformable_groups | |
| self.with_bias = bias | |
| self.weight = nn.Parameter( | |
| torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) | |
| self.extra_offset_mask = extra_offset_mask | |
| self.conv_offset_mask = nn.Conv2d( | |
| self.in_channels * 2, | |
| self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1], | |
| kernel_size=self.kernel_size, stride=_pair(self.stride), padding=_pair(self.padding), | |
| bias=True | |
| ) | |
| if bias: | |
| self.bias = nn.Parameter(torch.Tensor(out_channels)) | |
| else: | |
| self.register_parameter('bias', None) | |
| self.init_offset() | |
| 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) | |
| if self.bias is not None: | |
| self.bias.data.zero_() | |
| def init_offset(self): | |
| self.conv_offset_mask.weight.data.zero_() | |
| self.conv_offset_mask.bias.data.zero_() | |
| def forward(self, input_feat, inter): | |
| feat_degradation = torch.cat([input_feat, inter], dim=1) | |
| out = self.conv_offset_mask(feat_degradation) | |
| o1, o2, mask = torch.chunk(out, 3, dim=1) | |
| offset = torch.cat((o1, o2), dim=1) | |
| mask = torch.sigmoid(mask) | |
| return modulated_deform_conv2d(input_feat.contiguous(), offset, mask, self.weight, self.bias, self.stride, | |
| self.padding, self.dilation, self.groups, self.deformable_groups) | |