Fusion Branch of MODNet
| 168 | |
| 169 | |
| 170 | class FusionBranch(nn.Module): |
| 171 | """ Fusion Branch of MODNet |
| 172 | """ |
| 173 | |
| 174 | def __init__(self, hr_channels, enc_channels): |
| 175 | super(FusionBranch, self).__init__() |
| 176 | self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2) |
| 177 | |
| 178 | self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1) |
| 179 | self.conv_f = nn.Sequential( |
| 180 | Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1), |
| 181 | Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False), |
| 182 | ) |
| 183 | |
| 184 | def forward(self, img, lr8x, hr2x): |
| 185 | lr4x = F.interpolate(lr8x, scale_factor=2.0, mode='bilinear', align_corners=False) |
| 186 | lr4x = self.conv_lr4x(lr4x) |
| 187 | lr2x = F.interpolate(lr4x, scale_factor=2.0, mode='bilinear', align_corners=False) |
| 188 | |
| 189 | f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1)) |
| 190 | f = F.interpolate(f2x, scale_factor=2.0, mode='bilinear', align_corners=False) |
| 191 | f = self.conv_f(torch.cat((f, img), dim=1)) |
| 192 | pred_matte = torch.sigmoid(f) |
| 193 | |
| 194 | return pred_matte |
| 195 | |
| 196 | |
| 197 | #------------------------------------------------------------------------------ |