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utils.py
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utils.py
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import argparse
import os
import torch
import torch.nn as nn
def set_lambda(networks, lambda_):
for n, l in zip(networks, lambda_):
if n is None:
continue
n.set_lambda(l)
def get_optim_and_scheduler(networks, lrs, epochs, lr_steps, gamma):
if not isinstance(networks, list):
networks = [networks]
params = []
for network, lr in zip(networks, lrs):
if network is not None:
params += network.get_params(lr)
if not isinstance(lr_steps, list):
lr_steps = [lr_steps,]
optimizer = torch.optim.SGD(params, weight_decay=.0005, momentum=.9)
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=lr_steps, gamma=gamma)
return optimizer, scheduler
def set_requires_grad(nets, requires_grad=False):
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
def set_mode(model, mode="train"):
if model is not None:
if mode == "train":
model.train()
elif mode == "eval":
model.eva()
def save_options(opt, save_folder):
message = ''
message += '----------------- Options ---------------\n'
for k, v in sorted(vars(opt).items()):
message += '{:>25}: {:<30}\n'.format(str(k), str(v))
message += '----------------- End -------------------'
print(message)
# save to the disk
file_name = os.path.join(save_folder, 'opt.txt')
with open(file_name, 'wt') as opt_file:
opt_file.write(message)
opt_file.write('\n')