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| from .utils.network_blocks_2d import * | |
| class BaseNetwork(nn.Module): | |
| def __init__(self, conv_type): | |
| super(BaseNetwork, self).__init__() | |
| self.conv_type = conv_type | |
| if conv_type == 'gated': | |
| self.ConvBlock = GatedConv | |
| self.DeconvBlock = GatedDeconv | |
| if conv_type == 'partial': | |
| self.ConvBlock = PartialConv | |
| self.DeconvBlock = PartialDeconv | |
| if conv_type == 'vanilla': | |
| self.ConvBlock = VanillaConv | |
| self.DeconvBlock = VanillaDeconv | |
| self.ConvBlock2d = self.ConvBlock | |
| self.DeconvBlock2d = self.DeconvBlock | |
| def init_weights(self, init_type='normal', gain=0.02): | |
| ''' | |
| initialize network's weights | |
| init_type: normal | xavier | kaiming | orthogonal | |
| https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39 | |
| ''' | |
| def init_func(m): | |
| classname = m.__class__.__name__ | |
| if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): | |
| if init_type == 'normal': | |
| nn.init.normal_(m.weight.data, 0.0, gain) | |
| elif init_type == 'xavier': | |
| nn.init.xavier_normal_(m.weight.data, gain=gain) | |
| elif init_type == 'kaiming': | |
| nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') | |
| elif init_type == 'orthogonal': | |
| nn.init.orthogonal_(m.weight.data, gain=gain) | |
| if hasattr(m, 'bias') and m.bias is not None: | |
| nn.init.constant_(m.bias.data, 0.0) | |
| elif classname.find('BatchNorm2d') != -1: | |
| nn.init.normal_(m.weight.data, 1.0, gain) | |
| nn.init.constant_(m.bias.data, 0.0) | |
| self.apply(init_func) | |