# Copyright (c) Alibaba, Inc. and its affiliates.
import torch.nn as nn
[docs]def accuracy(pred, target, topk=1):
"""
Args:
pred: [N x num_classes]
target: [num_classes]
"""
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
_, pred_label = pred.topk(maxk, dim=1)
pred_label = pred_label.t()
correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / pred.size(0)))
return res[0] if return_single else res
[docs]class Accuracy(nn.Module):
[docs] def __init__(self, topk=(1, )):
super().__init__()
self.topk = topk
[docs] def forward(self, pred, target):
return accuracy(pred, target, self.topk)