56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
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from __future__ import print_function
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import torch
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import numpy as np
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def adjust_learning_rate(epoch, opt, optimizer):
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"""Sets the learning rate to the initial LR decayed by 0.2 every steep step"""
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steps = np.sum(epoch > np.asarray(opt.lr_decay_epochs))
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if steps > 0:
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new_lr = opt.learning_rate * (opt.lr_decay_rate ** steps)
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for param_group in optimizer.param_groups:
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param_group['lr'] = new_lr
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class AverageMeter(object):
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"""Computes and stores the average and current value"""
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def __init__(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def accuracy(output, target, topk=(1,)):
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"""Computes the accuracy over the k top predictions for the specified values of k"""
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with torch.no_grad():
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maxk = max(topk)
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batch_size = target.size(0)
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_, pred = output.topk(maxk, 1, True, True)
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pred = pred.t()
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correct = pred.eq(target.view(1, -1).expand_as(pred))
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res = []
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for k in topk:
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correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
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res.append(correct_k.mul_(100.0 / batch_size))
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return res
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if __name__ == '__main__':
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meter = AverageMeter()
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