(m)
| 17 | ''' |
| 18 | |
| 19 | def init_func(m): |
| 20 | classname = m.__class__.__name__ |
| 21 | if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): |
| 22 | if init_type == 'normal': |
| 23 | nn.init.normal_(m.weight.data, 0.0, gain) |
| 24 | elif init_type == 'xavier': |
| 25 | nn.init.xavier_normal_(m.weight.data, gain=gain) |
| 26 | elif init_type == 'kaiming': |
| 27 | nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') |
| 28 | elif init_type == 'orthogonal': |
| 29 | nn.init.orthogonal_(m.weight.data, gain=gain) |
| 30 | |
| 31 | if hasattr(m, 'bias') and m.bias is not None: |
| 32 | nn.init.constant_(m.bias.data, 0.0) |
| 33 | |
| 34 | elif classname.find('BatchNorm2d') != -1: |
| 35 | nn.init.normal_(m.weight.data, 1.0, gain) |
| 36 | nn.init.constant_(m.bias.data, 0.0) |
| 37 | |
| 38 | self.apply(init_func) |
| 39 |
nothing calls this directly
no outgoing calls
no test coverage detected