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weight_init.py
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weight_init.py
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#https://gist.github.com/jeasinema/ed9236ce743c8efaf30fa2ff732749f5
import torch
import torch.nn as nn
import torch.nn.init as init
def weight_init(m):
'''
Usage:
model = Model()
model.apply(weight_init)
'''
if isinstance(m, nn.Conv1d):
init.normal_(m.weight.data)
if m.bias is not None:
init.normal_(m.bias.data)
elif isinstance(m, nn.Conv2d):
init.xavier_normal_(m.weight.data)
if m.bias is not None:
init.normal_(m.bias.data)
elif isinstance(m, nn.Conv3d):
init.kaiming_normal_(m.weight.data,a=1e-2,nonlinearity='leaky_relu')
#init.xavier_normal_(m.weight.data)
if m.bias is not None:
#init.zeros_(m.bias.data)
init.normal_(m.bias.data)
elif isinstance(m, nn.ConvTranspose1d):
init.normal_(m.weight.data)
if m.bias is not None:
init.normal_(m.bias.data)
elif isinstance(m, nn.ConvTranspose2d):
init.xavier_normal_(m.weight.data)
if m.bias is not None:
init.normal_(m.bias.data)
elif isinstance(m, nn.ConvTranspose3d):
init.xavier_normal_(m.weight.data)
if m.bias is not None:
init.normal_(m.bias.data)
elif isinstance(m, nn.BatchNorm1d):
init.normal_(m.weight.data, mean=1, std=0.02)
init.constant_(m.bias.data, 0)
elif isinstance(m, nn.BatchNorm2d):
init.normal_(m.weight.data, mean=1, std=0.02)
init.constant_(m.bias.data, 0)
elif isinstance(m, nn.BatchNorm3d):
init.normal_(m.weight.data, mean=1, std=0.02)
init.constant_(m.bias.data, 0)
elif isinstance(m, nn.Linear):
init.xavier_normal_(m.weight.data)
if m.bias is not None:
init.normal_(m.bias.data)
elif isinstance(m, nn.LSTM):
for param in m.parameters():
if len(param.shape) >= 2:
init.orthogonal_(param.data)
else:
init.normal_(param.data)
elif isinstance(m, nn.LSTMCell):
for param in m.parameters():
if len(param.shape) >= 2:
init.orthogonal_(param.data)
else:
init.normal_(param.data)
elif isinstance(m, nn.GRU):
for param in m.parameters():
if len(param.shape) >= 2:
init.orthogonal_(param.data)
else:
init.normal_(param.data)
elif isinstance(m, nn.GRUCell):
for param in m.parameters():
if len(param.shape) >= 2:
init.orthogonal_(param.data)
else:
init.normal_(param.data)
elif isinstance(m, nn.ModuleList):
for l in m:
weight_init(l)
if __name__ == '__main__':
pass