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# -*- coding: utf-8 -*-
# Copyright (c) XiMing Xing. All rights reserved.
# Author: XiMing Xing
# Description:
def accuracy(output, target, topk=(1,)):
"""
Computes the accuracy over the k top predictions for the specified values of k.
Args
output: logits or probs (num of batch, num of classes)
target: (num of batch, 1) or (num of batch, )
topk: list of returned k
refer: https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
maxK = max(topk) # get k in top-k
batch_size = target.size(0)
_, pred = output.topk(k=maxK, dim=1, largest=True, sorted=True) # pred: [num of batch, k]
pred = pred.t() # pred: [k, num of batch]
# [1, num of batch] -> [k, num_of_batch] : bool
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res # np.shape(res): [k, 1]
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