utills.py

import torch


def adjust_learning_rate(optimizer, cur_epoch, max_epoch):
    if cur_epoch == (max_epoch*0.5) or cur_epoch == (max_epoch*0.7) or cur_epoch==(max_epoch*0.9):
        for param_group in optimizer.param_groups:
            param_group['lr'] /= 10


def accuracy(outp, 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 = outp.topk(maxk, 1, True, True)
        pred = pred.t()
        correct = pred.eq(target.view(1, -1).expand_as(pred))
        # import pdb; pdb.set_trace()

        res = []
        for k in topk:
            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) #FIXME:
            res.append(correct_k.mul_(100.0 / batch_size))
        return res
## can only get top1

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