(self)
return ap
Returns the model's average precision for each class
        Return:
            ap (FloatTensor): 1xK tensor, with avg precision for each class k
Returns the model's average precision for each class
        Return:
            ap (FloatTensor): 1xK tensor, with avg precision for each class k
Returns the model s average precision for each class Return : ap ( FloatTensor ) : 1xK tensor with avg precision for each class k
def value(self):
        """Returns the model's average precision for each class
        Return:
            ap (FloatTensor): 1xK tensor, with avg precision for each class k
        """

        if self.scores.numel() == 0:
            return 0
        ap = torch.zeros(self.scores.size(1))
        rg = torch.arange(1, self.scores.size(0)).float()

        # compute average precision for each class
        for k in range(self.scores.size(1)):
            # sort scores
            scores = self.scores[:, k]
            targets = self.targets[:, k]

            # compute average precision
            ap[k] = AveragePrecisionMeter.average_precision(scores, targets, self.difficult_examples)
        return ap
def value ( self ) : if self . scores . numel ( ) == 0 : return 0 ap = torch . zeros ( self . scores . size ( 1 ) ) rg = torch . arange ( 1 , self . scores . size ( 0 ) ) . float ( ) # compute average precision for each class for k in range ( self . scores . size ( 1 ) ) : # sort scores scores = self . scores [ : , k ] targets = self . targets [ : , k ] # compute average precision ap [ k ] = AveragePrecisionMeter . average_precision ( scores , targets , self . difficult_examples ) return ap