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| # -------------------------------------------------------- | |
| # Based on timm and MAE-priv code bases | |
| # https://github.com/rwightman/pytorch-image-models/tree/master/timm | |
| # https://github.com/BUPT-PRIV/MAE-priv | |
| # -------------------------------------------------------- | |
| """ Eval metrics and related | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| class AverageMeter: | |
| """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 | |
| def accuracy(output, target, topk=(1,)): | |
| """Computes the accuracy over the k top predictions for the specified values of k""" | |
| maxk = min(max(topk), output.size()[1]) | |
| batch_size = target.size(0) | |
| _, pred = output.topk(maxk, 1, True, True) | |
| pred = pred.t() | |
| correct = pred.eq(target.reshape(1, -1).expand_as(pred)) | |
| return [correct[:min(k, maxk)].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] | |
| def cls_map(output, target): | |
| # batch_size = target.size(0) | |
| # idx_axes = torch.arange(batch_size) | |
| scores, preds = output.softmax(dim=-1).topk(1, 1, True, True) | |
| return scores, preds | |