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Fix accuracy when topk > num_classes

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Yohann Lereclus 2021-08-19 11:58:59 +02:00
parent a16a753852
commit 35c9740826
1 changed files with 6 additions and 2 deletions
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8
timm/utils/metrics.py
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@ -2,6 +2,7 @@
Hacked together by / Copyright 2020 Ross Wightman Hacked together by / Copyright 2020 Ross Wightman
""" """
import torch
class AverageMeter: class AverageMeter:
@ -24,9 +25,12 @@ class AverageMeter:
def accuracy(output, target, topk=(1,)): def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k""" """Computes the accuracy over the k top predictions for the specified values of k"""
maxk = max(topk) maxk = min(max(topk), output.size()[1])
batch_size = target.size(0) batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True) _, pred = output.topk(maxk, 1, True, True)
pred = pred.t() pred = pred.t()
correct = pred.eq(target.reshape(1, -1).expand_as(pred)) correct = pred.eq(target.reshape(1, -1).expand_as(pred))
return [correct[:k].reshape(-1).float().sum(0) * 100. / batch_size for k in topk] return [
correct[:k].reshape(-1).float().sum(0) * 100. / batch_size
if k <= maxk else torch.tensor(100.) for k in topk
]