import torch
import shutil
from os.path import join
[docs]class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
[docs] def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
[docs] def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
[docs]def save_checkpoint(state, is_best, path, filename="checkpoint.pth"):
torch.save(state, join(path, filename))
if is_best:
shutil.copyfile(join(path, filename), join(path, 'model_best.pth'))
[docs]def accuracy(output, target, topk=(1,)):
"""Computes the precision@k 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