def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            torch.nn.init.kaiming_normal_(m.weight)
        elif isinstance(m, SynchronizedBatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            torch.nn.init.kaiming_normal_(m.weight)
        elif isinstance(m, SynchronizedBatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()