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utils.py
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utils.py
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from contextlib import contextmanager
from torch.nn.modules.batchnorm import _BatchNorm
import torch
import torch.nn as nn
def initialize_weights(module: nn.Module):
"""Initialize the weights of a module."""
if isinstance(module, nn.Sequential):
for m in module:
initialize_weights(m)
if isinstance(module, nn.Linear):
nn.init.kaiming_normal_(module.weight, nonlinearity="relu")
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, nonlinearity="relu")
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.BatchNorm2d):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
@contextmanager
def eval_context(net: torch.nn.Module):
"""Temporarily switch to evaluation mode."""
istrain = net.training
try:
if istrain:
net.eval()
yield net
finally:
if istrain:
net.train()
@contextmanager
def train_context(net: torch.nn.Module):
"""Temporarily switch to training mode."""
istrain = net.training
try:
if not istrain:
net.train()
yield net
finally:
if not istrain:
net.eval()
@contextmanager
def batchnorm_no_update_context(net: torch.nn.Module):
"""Temporarily disable batchnorm update."""
istrain = net.training
try:
if istrain:
for module in net.modules():
if isinstance(module, _BatchNorm):
module.track_running_stats = False
yield net
finally:
if istrain:
for module in net.modules():
if isinstance(module, _BatchNorm):
module.track_running_stats = True