masks in the call function of modelling.rpn.loss.py

[just-eoghan]

In the call of modelling.rpn.loss.py a masks variable is generated

    def __call__(self, anchors, objectness, box_regression, targets):
        """
        Arguments:
            anchors (list[BoxList])
            objectness (list[Tensor])
            box_regression (list[Tensor])
            targets (list[BoxList])

        Returns:
            objectness_loss (Tensor)
            box_loss (Tensor
        """
        anchors = [cat_boxlist(anchors_per_image) for anchors_per_image in anchors]
        labels, regression_targets, masks = self.prepare_targets(anchors, targets)

        masks = torch.cat(masks, dim=0)

        sampled_pos_inds, sampled_neg_inds = self.fg_bg_sampler(labels)
        sampled_pos_inds = torch.nonzero(torch.cat(sampled_pos_inds, dim=0)).squeeze(1)
        sampled_neg_inds = torch.nonzero(torch.cat(sampled_neg_inds, dim=0)).squeeze(1)

        sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)

        objectness, box_regression = \
                concat_box_prediction_layers(objectness, box_regression)

        objectness = objectness.squeeze()

        labels = torch.cat(labels, dim=0)
        regression_targets = torch.cat(regression_targets, dim=0)

        box_loss = smooth_l1_loss(
            box_regression[sampled_pos_inds],
            regression_targets[sampled_pos_inds],
            beta=1.0 / 9,
            size_average=False,
        ) / (sampled_inds.numel())

        objectness_loss = F.binary_cross_entropy_with_logits(
            objectness[sampled_inds], labels[sampled_inds]
        )

        return objectness_loss, box_loss

It is returned from prepare_targets in line 2 and then the cat() function is called to on itself to generate a new masks var in line 3.

However, it is not used in the rest of the call function.

Should these masks have been used somewhere or are they a redundant return from prepare_targets() ?

Thanks