(self, x)
| 137 | self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) |
| 138 | |
| 139 | def forward(self, x): |
| 140 | |
| 141 | hx = x |
| 142 | |
| 143 | hxin = self.rebnconvin(hx) |
| 144 | |
| 145 | hx1 = self.rebnconv1(hxin) |
| 146 | hx = self.pool1(hx1) |
| 147 | |
| 148 | hx2 = self.rebnconv2(hx) |
| 149 | hx = self.pool2(hx2) |
| 150 | |
| 151 | hx3 = self.rebnconv3(hx) |
| 152 | hx = self.pool3(hx3) |
| 153 | |
| 154 | hx4 = self.rebnconv4(hx) |
| 155 | hx = self.pool4(hx4) |
| 156 | |
| 157 | hx5 = self.rebnconv5(hx) |
| 158 | |
| 159 | hx6 = self.rebnconv6(hx5) |
| 160 | |
| 161 | hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) |
| 162 | hx5dup = _upsample_like(hx5d, hx4) |
| 163 | |
| 164 | hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) |
| 165 | hx4dup = _upsample_like(hx4d, hx3) |
| 166 | |
| 167 | hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) |
| 168 | hx3dup = _upsample_like(hx3d, hx2) |
| 169 | |
| 170 | hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) |
| 171 | hx2dup = _upsample_like(hx2d, hx1) |
| 172 | |
| 173 | hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) |
| 174 | |
| 175 | return hx1d + hxin |
| 176 | |
| 177 | |
| 178 | ### RSU-5 ### |
nothing calls this directly
no test coverage detected