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import torch.nn as nn
def initialize_decoder(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.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def initialize_head(module):
for m in module.modules():
if isinstance(m, (nn.Linear, nn.Conv2d)):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
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