| 124 | return x |
| 125 | |
| 126 | def init_params(self): |
| 127 | for m in self.modules(): |
| 128 | if isinstance(m, nn.Conv2d): |
| 129 | init.normal_(m.weight, std=0.01) |
| 130 | if m.bias is not None: |
| 131 | init.constant_(m.bias, 0) |
| 132 | elif isinstance(m, nn.ConvTranspose2d): |
| 133 | # init.kaiming_normal_(m.weight, mode='fan_out') |
| 134 | init.normal_(m.weight, std=0.01) |
| 135 | # init.xavier_normal_(m.weight) |
| 136 | if m.bias is not None: |
| 137 | init.constant_(m.bias, 0) |
| 138 | elif isinstance(m, nn.BatchNorm2d): # NN.BatchNorm2d |
| 139 | init.constant_(m.weight, 1) |
| 140 | init.constant_(m.bias, 0) |
| 141 | elif isinstance(m, nn.Linear): |
| 142 | init.normal_(m.weight, std=0.01) |
| 143 | if m.bias is not None: |
| 144 | init.constant_(m.bias, 0) |
| 145 | |
| 146 | |
| 147 | class ATA(nn.Module): |