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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)
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