38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
from __future__ import division, print_function, absolute_import
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def accuracy(output, target, topk=(1, )):
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"""Computes the accuracy over the k top predictions for
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the specified values of k.
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Args:
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output (torch.Tensor): prediction matrix with shape (batch_size, num_classes).
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target (torch.LongTensor): ground truth labels with shape (batch_size).
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topk (tuple, optional): accuracy at top-k will be computed. For example,
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topk=(1, 5) means accuracy at top-1 and top-5 will be computed.
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Returns:
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list: accuracy at top-k.
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Examples::
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>>> from torchreid import metrics
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>>> metrics.accuracy(output, target)
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"""
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maxk = max(topk)
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batch_size = target.size(0)
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if isinstance(output, (tuple, list)):
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output = output[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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acc = correct_k.mul_(100.0 / batch_size)
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res.append(acc)
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return res
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