| 187 | return nn.ModuleList(branches) |
| 188 | |
| 189 | def _make_fuse_layers(self): |
| 190 | if self.num_branches == 1: |
| 191 | return None |
| 192 | |
| 193 | num_branches = self.num_branches |
| 194 | num_inchannels = self.num_inchannels |
| 195 | fuse_layers = [] |
| 196 | for i in range(num_branches if self.multi_scale_output else 1): |
| 197 | fuse_layer = [] |
| 198 | for j in range(num_branches): |
| 199 | if j > i: |
| 200 | fuse_layer.append( |
| 201 | nn.Sequential( |
| 202 | nn.Conv2d( |
| 203 | num_inchannels[j], |
| 204 | num_inchannels[i], |
| 205 | 1, 1, 0, bias=False |
| 206 | ), |
| 207 | nn.BatchNorm2d(num_inchannels[i]), |
| 208 | nn.Upsample(scale_factor=2**(j - i), mode='nearest') |
| 209 | ) |
| 210 | ) |
| 211 | elif j == i: |
| 212 | fuse_layer.append(None) |
| 213 | else: |
| 214 | conv3x3s = [] |
| 215 | for k in range(i - j): |
| 216 | if k == i - j - 1: |
| 217 | num_outchannels_conv3x3 = num_inchannels[i] |
| 218 | conv3x3s.append( |
| 219 | nn.Sequential( |
| 220 | nn.Conv2d( |
| 221 | num_inchannels[j], |
| 222 | num_outchannels_conv3x3, |
| 223 | 3, 2, 1, bias=False |
| 224 | ), |
| 225 | nn.BatchNorm2d(num_outchannels_conv3x3) |
| 226 | ) |
| 227 | ) |
| 228 | else: |
| 229 | num_outchannels_conv3x3 = num_inchannels[j] |
| 230 | conv3x3s.append( |
| 231 | nn.Sequential( |
| 232 | nn.Conv2d( |
| 233 | num_inchannels[j], |
| 234 | num_outchannels_conv3x3, |
| 235 | 3, 2, 1, bias=False |
| 236 | ), |
| 237 | nn.BatchNorm2d(num_outchannels_conv3x3), |
| 238 | nn.ReLU(True) |
| 239 | ) |
| 240 | ) |
| 241 | fuse_layer.append(nn.Sequential(*conv3x3s)) |
| 242 | fuse_layers.append(nn.ModuleList(fuse_layer)) |
| 243 | |
| 244 | return nn.ModuleList(fuse_layers) |
| 245 | |
| 246 | def get_num_inchannels(self): |