| 252 | |
| 253 | class DeConv2dFuse(nn.Module): |
| 254 | def __init__(self, in_channels, out_channels, kernel_size, relu=True, bn=True, |
| 255 | bn_momentum=0.1): |
| 256 | super(DeConv2dFuse, self).__init__() |
| 257 | |
| 258 | self.deconv = Deconv2d(in_channels, out_channels, kernel_size, stride=2, padding=1, output_padding=1, |
| 259 | bn=True, relu=relu, bn_momentum=bn_momentum) |
| 260 | |
| 261 | self.conv = Conv2d(2 * out_channels, out_channels, kernel_size, stride=1, padding=1, |
| 262 | bn=bn, relu=relu, bn_momentum=bn_momentum) |
| 263 | |
| 264 | # assert init_method in ["kaiming", "xavier"] |
| 265 | # self.init_weights(init_method) |
| 266 | |
| 267 | def forward(self, x_pre, x): |
| 268 | x = self.deconv(x) |