| 179 | return nn.ModuleList(branches) |
| 180 | |
| 181 | def _make_fuse_layers(self): |
| 182 | if self.num_branches == 1: |
| 183 | return None |
| 184 | |
| 185 | num_branches = self.num_branches |
| 186 | num_inchannels = self.num_inchannels |
| 187 | fuse_layers = [] |
| 188 | for i in range(num_branches if self.multi_scale_output else 1): |
| 189 | fuse_layer = [] |
| 190 | for j in range(num_branches): |
| 191 | if j > i: |
| 192 | fuse_layer.append(nn.Sequential( |
| 193 | nn.Conv2d(num_inchannels[j], |
| 194 | num_inchannels[i], |
| 195 | 1, |
| 196 | 1, |
| 197 | 0, |
| 198 | bias=False), |
| 199 | Norm2d(num_inchannels[i], momentum=BN_MOMENTUM))) |
| 200 | elif j == i: |
| 201 | fuse_layer.append(None) |
| 202 | else: |
| 203 | conv3x3s = [] |
| 204 | for k in range(i-j): |
| 205 | if k == i - j - 1: |
| 206 | num_outchannels_conv3x3 = num_inchannels[i] |
| 207 | conv3x3s.append(nn.Sequential( |
| 208 | nn.Conv2d(num_inchannels[j], |
| 209 | num_outchannels_conv3x3, |
| 210 | 3, 2, 1, bias=False), |
| 211 | Norm2d(num_outchannels_conv3x3, |
| 212 | momentum=BN_MOMENTUM))) |
| 213 | else: |
| 214 | num_outchannels_conv3x3 = num_inchannels[j] |
| 215 | conv3x3s.append(nn.Sequential( |
| 216 | nn.Conv2d(num_inchannels[j], |
| 217 | num_outchannels_conv3x3, |
| 218 | 3, 2, 1, bias=False), |
| 219 | Norm2d(num_outchannels_conv3x3, |
| 220 | momentum=BN_MOMENTUM), |
| 221 | nn.ReLU(inplace=relu_inplace))) |
| 222 | fuse_layer.append(nn.Sequential(*conv3x3s)) |
| 223 | fuse_layers.append(nn.ModuleList(fuse_layer)) |
| 224 | |
| 225 | return nn.ModuleList(fuse_layers) |
| 226 | |
| 227 | def get_num_inchannels(self): |
| 228 | return self.num_inchannels |