vggface2_pytorch/utils.py
def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    output_sorted, 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
Computes the precision@k for the specified values of k
Computes the precision@k for the specified values of k
Computes the precision@k for the specified values of k
def accuracy(output, target, topk=(1,)):
    maxk = max(topk)
    batch_size = target.size(0)
    output_sorted, 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
def accuracy ( output , target , topk = ( 1 , ) ) : maxk = max ( topk ) batch_size = target . size ( 0 ) output_sorted , 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
Computes the precision@k for the specified values of k
Computes the precision@k for the specified values of k
"""Computes the precision@k for the specified values of k"""
output
target
topk