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util.py
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util.py
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from __future__ import print_function
import torch
import numpy as np
def adjust_learning_rate(epoch, opt, optimizer):
"""Sets the learning rate to the initial LR decayed by 0.2 every steep step"""
steps = np.sum(epoch > np.asarray(opt.lr_decay_epochs))
if steps > 0:
new_lr = opt.learning_rate * (opt.lr_decay_rate ** steps)
print(new_lr)
for param_group in optimizer.param_groups:
param_group['lr'] = new_lr
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
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
if __name__ == '__main__':
meter = AverageMeter()