# Modified based on the HRNet repo.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import torch


def accuracy(output, target, topk=(1,)):
    """Computes the precision@k 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))
        if len(topk) == 1:
            res.append(torch.zeros_like(res[-1]))
        return res
