Method add_ops

caffe2/python/layers/fc_with_bootstrap.py:290–321

Both the predict net and the eval net will call this function. For bootstrapping approach, the goal is to pass the cur_layer feature inputs through all the bootstrapped FCs that are stored under self.bootstrapped_FCs. Return the preds in the same out

(self, net)

            )

    def add_ops(self, net):
        """
            Both the predict net and the eval net will call this function.

            For bootstrapping approach, the goal is to pass the cur_layer feature
            inputs through all the bootstrapped FCs that are stored under
            self.bootstrapped_FCs. Return the preds in the same output_schema
            with dummy indices (because they are not needed).
        """

        version_info = get_current_scope().get(
            get_fc_predictor_version.__name__, {"fc_version": "fp32"}
        )
        predictor_fc_fp_version = version_info["fc_version"]

        for i in range(self.num_bootstrap):
            # these are dummy indices, not to be used anywhere
            indices = self._generate_bootstrapped_indices(
                net=net,
                copied_cur_layer=self.input_record.field_blobs()[0],
                iteration=i,
            )

            params = self.bootstrapped_FCs[i * 2 : (i * 2) + 2]

            self._add_ops(
                net=net,
                features=self.input_record,
                params=params,
                iteration=i,
                version=predictor_fc_fp_version,
            )

    def add_train_ops(self, net):
        # use the train_param_blobs to be consistent with the SamplingTrain unittest