| 5 | |
| 6 | |
| 7 | def initialize_weights(net_l, scale=1): |
| 8 | if not isinstance(net_l, list): |
| 9 | net_l = [net_l] |
| 10 | for net in net_l: |
| 11 | for m in net.modules(): |
| 12 | if isinstance(m, nn.Conv2d): |
| 13 | init.kaiming_normal_(m.weight, a=0, mode='fan_in') |
| 14 | m.weight.data *= scale # for residual block |
| 15 | if m.bias is not None: |
| 16 | m.bias.data.zero_() |
| 17 | elif isinstance(m, nn.Linear): |
| 18 | init.kaiming_normal_(m.weight, a=0, mode='fan_in') |
| 19 | m.weight.data *= scale |
| 20 | if m.bias is not None: |
| 21 | m.bias.data.zero_() |
| 22 | elif isinstance(m, nn.BatchNorm2d): |
| 23 | init.constant_(m.weight, 1) |
| 24 | init.constant_(m.bias.data, 0.0) |
| 25 | |
| 26 | |
| 27 | def make_layer(block, n_layers): |