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train_utils.py
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train_utils.py
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import torch
import os
import json
import logging
import time
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def accuracy_list(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = torch.tensor(output).cuda().topk(max(topk))
pred = pred.t()
correct = pred.eq(target)
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def init_logfile(filename: str, text: str):
f = open(filename, 'w')
f.write(text+"\n")
f.close()
def log(filename: str, text: str):
f = open(filename, 'a')
f.write(text+"\n")
f.close()
def makedirs(filename):
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
def get_logger(name, logpath, filepath, package_files=[],
displaying=True, saving=True):
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
log_path = logpath + name + time.strftime("-%Y%m%d-%H%M%S")
makedirs(log_path)
if saving:
info_file_handler = logging.FileHandler(log_path)
info_file_handler.setLevel(logging.INFO)
logger.addHandler(info_file_handler)
logger.info(filepath)
with open(filepath, 'r') as f:
logger.info(f.read())
for f in package_files:
logger.info(f)
with open(f, 'r') as package_f:
logger.info(package_f.read())
if displaying:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
logger.addHandler(console_handler)
return logger