| class StatValue: | |
| def __init__(self): | |
| self.clear() | |
| def reset(self): | |
| self.val = 0 | |
| def clear(self): | |
| self.reset() | |
| self.history = [] | |
| def update(self, val): | |
| self.val = val | |
| self.history.append(self.val) | |
| class AverageMeter(object): | |
| """Computes and stores the average and current value""" | |
| def __init__(self): | |
| self.clear() | |
| self.has_new_data = False | |
| def reset(self): | |
| self.avg = 0 | |
| self.val = 0 | |
| self.sum = 0 | |
| self.count = 0 | |
| def clear(self): | |
| self.reset() | |
| self.history = [] | |
| def update(self, val, n=1): | |
| self.val = val | |
| self.sum += val * n | |
| self.count += n | |
| self.avg = self.sum / self.count | |
| def new_epoch(self): | |
| if self.count > 0: | |
| self.history.append(self.avg) | |
| self.reset() | |
| self.has_new_data = True | |
| else: | |
| self.has_new_data = False | |
| def topk_accuracy(output, target, topk=(1,)): | |
| """Computes the precision@k for the specified values of k""" | |
| single_input = not isinstance(topk, (tuple, list)) | |
| if single_input: | |
| topk = (topk,) | |
| 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)[0] | |
| res.append(correct_k * 100.0 / batch_size) | |
| if single_input: | |
| return res[0] | |
| return res | |