Architectures/ControllabilityGAN/wgan/init_weights.py

import torch.nn as nn


def weights_init_D(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
        # nn.init.constant_(m.bias, 0)
    elif classname.find('BatchNorm') != -1:
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)


def weights_init_G(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='leaky_relu')
        # nn.init.constant_(m.bias, 0)
    elif classname.find('BatchNorm') != -1:
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)