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#!/usr/bin/env python3
"""Initialize modules for espnet2 neural networks."""
import torch
from typeguard import check_argument_types
def initialize(model: torch.nn.Module, init: str):
"""Initialize weights of a neural network module.
Parameters are initialized using the given method or distribution.
Custom initialization routines can be implemented into submodules
as function `espnet_initialization_fn` within the custom module.
Args:
model: Target.
init: Method of initialization.
"""
assert check_argument_types()
print("init with", init)
# weight init
for p in model.parameters():
if p.dim() > 1:
if init == "xavier_uniform":
torch.nn.init.xavier_uniform_(p.data)
elif init == "xavier_normal":
torch.nn.init.xavier_normal_(p.data)
elif init == "kaiming_uniform":
torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
elif init == "kaiming_normal":
torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
else:
raise ValueError("Unknown initialization: " + init)
# bias init
for name, p in model.named_parameters():
if ".bias" in name and p.dim() == 1:
p.data.zero_()
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