import torch.nn as nn

def init_weights(net, init_type='normal', gain=0.02):

    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)

            else:

                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)

            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)

    print('initialize network with %s' % init_type)

    net.apply(init_func)