score_model.py 文件源码

def _loss_proto(self, xPr, xLb, xSemPr, xSemLb, lCW):
        ns = self.netspec

        # Classification loss
        if self.sem_coeff < 1:
            name = 'SCoRe/objLoss'
            ns[name] = L.SoftmaxWithLoss(*[xPr, xLb], name=name, loss_weight=1.0 - self.sem_coeff, include=dict(phase=caffe.TRAIN))

        # Semantic regularization
        if self.sem_coeff > 0:
            self._semantic_regularization(xSemPr, xSemLb, self.sem_coeff)

        # Codeword regularization
        if 0 < self.code_coeff < np.inf:
            self._code_regularization(lCW)