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utils.py
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import logging
import os
import random
import numpy as np
import torch
# 固定随即种子
def random_seed(seed=0):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
class Logger:
def __init__(self, logfile="output.log"):
self.logfile = logfile
self.logger = logging.getLogger(__name__)
logging.basicConfig(
format="[%(asctime)s] - %(message)s",
datefmt="%Y/%m/%d %H:%M:%S",
level=logging.INFO,
filename=self.logfile,
)
def info(self, msg, *args):
msg = str(msg)
if args:
print(msg % args)
self.logger.info(msg, *args)
else:
print(msg)
self.logger.info(msg)
def save_checkpoint(
state,
epoch,
is_best,
which_best,
save_path,
save_freq=10,
):
filename = os.path.join(save_path, "checkpoint_" + str(epoch) + ".tar")
if epoch % save_freq == 0:
if not os.path.exists(filename):
torch.save(state, filename)
if is_best:
best_filename = os.path.join(
save_path, "best_" + str(which_best) + "_checkpoint.tar"
)
torch.save(state, best_filename)
def squared_l2_norm(x):
flattened = x.view(x.unsqueeze(0).shape[0], -1)
return (flattened**2).sum(1)
def l2_norm(x):
return squared_l2_norm(x).sqrt()
import logging
import os
import random
import numpy as np
import torch
# 固定随即种子
def random_seed(seed=0):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
class Logger:
def __init__(self, logfile="output.log"):
self.logfile = logfile
self.logger = logging.getLogger(__name__)
logging.basicConfig(
format="[%(asctime)s] - %(message)s",
datefmt="%Y/%m/%d %H:%M:%S",
level=logging.INFO,
filename=self.logfile,
)
def info(self, msg, *args):
msg = str(msg)
if args:
print(msg % args)
self.logger.info(msg, *args)
else:
print(msg)
self.logger.info(msg)
def save_checkpoint(
state,
epoch,
is_best,
which_best,
save_path,
save_freq=10,
):
filename = os.path.join(save_path, "checkpoint_" + str(epoch) + ".tar")
if epoch % save_freq == 0:
if not os.path.exists(filename):
torch.save(state, filename)
if is_best:
best_filename = os.path.join(
save_path, "best_" + str(which_best) + "_checkpoint.tar"
)
torch.save(state, best_filename)
def squared_l2_norm(x):
flattened = x.view(x.unsqueeze(0).shape[0], -1)
return (flattened**2).sum(1)
def l2_norm(x):
return squared_l2_norm(x).sqrt()