initialize.py

import torch.nn as nn
import numpy as np

def weights_init_kaimingUniform(module):
    for m in module.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.uniform_(m.weight, a=0, b=1)
            nn.init.constant_(m.bias, val=0.)
        elif isinstance(m, nn.Linear):
            nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')
            if m.bias is not None:
                nn.init.constant_(m.bias, val=0.)

def weights_init_kaimingNormal(module):
    for m in module.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.normal_(m.weight, 0, 0.01)
            nn.init.constant_(m.bias, val=0.)
        elif isinstance(m, nn.Linear):
            nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
            if m.bias is not None:
                nn.init.constant_(m.bias, val=0.)

def weights_init_xavierUniform(module):
    for m in module.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.xavier_uniform_(m.weight, gain=np.sqrt(2))
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.uniform_(m.weight, a=0, b=1)
            nn.init.constant_(m.bias, val=0.)
        elif isinstance(m, nn.Linear):
            nn.init.xavier_uniform_(m.weight, gain=np.sqrt(2))
            if m.bias is not None:
                nn.init.constant_(m.bias, val=0.)

def weights_init_xavierNormal(module):
    for m in module.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.xavier_normal_(m.weight, gain=np.sqrt(2))
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.normal_(m.weight, 0, 0.01)
            nn.init.constant_(m.bias, val=0.)
        elif isinstance(m, nn.Linear):
            nn.init.xavier_normal_(m.weight, gain=np.sqrt(2))
            if m.bias is not None:
                nn.init.constant_(m.bias, val=0.)