score_model.py 文件源码

def _code_regularization(self, lCW):
        ns = self.netspec

        # Semantic codes. Needs to be initialized.
        code_shape = [sum(self.code_dim), len(self.train_classes) if self.semantics == ATTRIBUTES else sum(self.num_states)]

        name = 'SCoRe/cwReg/codewords'
        sem_cw = ns[name] = L.DummyData(name=name, shape=dict(dim=code_shape), include=dict(phase=caffe.TRAIN))

        # Classification codes.
        name = 'SCoRe/cwReg/eye'
        x = ns[name] = L.DummyData(name=name, shape=dict(dim=[code_shape[0], code_shape[0]]), include=dict(phase=caffe.TRAIN))

        name = 'SCoRe/cwReg/cls_codewords'
        clf_cw = ns[name] = L.InnerProduct(x, name=name, num_output=code_shape[1], bias_term=False,
                                           param=[{'name': lCW}], include=dict(phase=caffe.TRAIN))

        # Compute \sum |S-C|^2
        name = 'SCoRe/cwReg/diff'
        x_diff = ns[name] = L.Eltwise(*[sem_cw, clf_cw], name=name,
                                      operation=P.Eltwise.SUM, coeff=[1., -1.], include=dict(phase=caffe.TRAIN))

        name = 'SCoRe/cwReg'
        ns[name] = L.Reduction(x_diff, name=name,
                               operation=P.Reduction.SUMSQ, axis=0,
                               loss_weight=self.code_coeff, include=dict(phase=caffe.TRAIN))