Method init_weight

caffe2/python/layers/batch_lr_loss.py:119–166

(self, jsd_weight, homotopy_weighting)

            )

    def init_weight(self, jsd_weight, homotopy_weighting):
        if homotopy_weighting:
            self.mutex = self.create_param(
                param_name=('%s_mutex' % self.name),
                shape=None,
                initializer=('CreateMutex', ),
                optimizer=self.model.NoOptim,
            )
            self.counter = self.create_param(
                param_name=('%s_counter' % self.name),
                shape=[1],
                initializer=(
                    'ConstantFill', {
                        'value': 0,
                        'dtype': core.DataType.INT64
                    }
                ),
                optimizer=self.model.NoOptim,
            )
            self.xent_weight = self.create_param(
                param_name=('%s_xent_weight' % self.name),
                shape=[1],
                initializer=(
                    'ConstantFill', {
                        'value': 1.,
                        'dtype': core.DataType.FLOAT
                    }
                ),
                optimizer=self.model.NoOptim,
            )
            self.jsd_weight = self.create_param(
                param_name=('%s_jsd_weight' % self.name),
                shape=[1],
                initializer=(
                    'ConstantFill', {
                        'value': 0.,
                        'dtype': core.DataType.FLOAT
                    }
                ),
                optimizer=self.model.NoOptim,
            )
        else:
            self.jsd_weight = self.model.add_global_constant(
                '%s_jsd_weight' % self.name, jsd_weight
            )
            self.xent_weight = self.model.add_global_constant(
                '%s_xent_weight' % self.name, 1. - jsd_weight
            )

    def update_weight(self, net):
        net.AtomicIter([self.mutex, self.counter], [self.counter])