2021-04-25 15:02:43 +02:00
|
|
|
#####################################################
|
|
|
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.04 #
|
|
|
|
|
#####################################################
|
|
|
|
|
import abc
|
|
|
|
|
|
2021-04-11 15:45:20 +02:00
|
|
|
def obtain_accuracy(output, target, topk=(1,)):
|
|
|
|
|
"""Computes the precision@k for the specified values of k"""
|
|
|
|
|
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))
|
|
|
|
|
return res
|
2021-04-25 15:02:43 +02:00
|
|
|
|
|
|
|
|
|
|
|
|
|
class EvaluationMetric(abc.ABC):
|
|
|
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
|
self._total_metrics = 0
|
|
|
|
|
|
|
|
|
|
def __len__(self):
|
|
|
|
|
return self._total_metrics
|
