Custom initialization of weights in PyTorch

init_weights.py

# https://github.com/pytorch/examples/blob/master/dcgan/main.py#L95-L102
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)


# https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py#L46-L59
def _initialize_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
            if m.bias is not None:
                m.bias.data.zero_()
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()
        elif isinstance(m, nn.Linear):
            m.weight.data.normal_(0, 0.01)
            m.bias.data.zero_()