Method losses

det/detectron2/modeling/meta_arch/fcos.py:193–238

This method is almost identical to :meth:`RetinaNet.losses`, with an extra "loss_centerness" in the returned dict.

(
        self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness
    )

        return gt_labels, matched_gt_boxes

    def losses(
        self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness
    ):
        """
        This method is almost identical to :meth:`RetinaNet.losses`, with an extra
        "loss_centerness" in the returned dict.
        """
        num_images = len(gt_labels)
        gt_labels = torch.stack(gt_labels)  # (M, R)

        pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
        num_pos_anchors = pos_mask.sum().item()
        get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
        normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 300)

        # classification and regression loss
        gt_labels_target = F.one_hot(gt_labels, num_classes=self.num_classes + 1)[
            :, :, :-1
        ]  # no loss for the last (background) class
        loss_cls = sigmoid_focal_loss_jit(
            torch.cat(pred_logits, dim=1),
            gt_labels_target.to(pred_logits[0].dtype),
            alpha=self.focal_loss_alpha,
            gamma=self.focal_loss_gamma,
            reduction="sum",
        )

        loss_box_reg = _dense_box_regression_loss(
            anchors,
            self.box2box_transform,
            pred_anchor_deltas,
            gt_boxes,
            pos_mask,
            box_reg_loss_type="giou",
        )

        ctrness_targets = self.compute_ctrness_targets(anchors, gt_boxes)  # (M, R)
        pred_centerness = torch.cat(pred_centerness, dim=1).squeeze(dim=2)  # (M, R)
        ctrness_loss = F.binary_cross_entropy_with_logits(
            pred_centerness[pos_mask], ctrness_targets[pos_mask], reduction="sum"
        )
        return {
            "loss_fcos_cls": loss_cls / normalizer,
            "loss_fcos_loc": loss_box_reg / normalizer,
            "loss_fcos_ctr": ctrness_loss / normalizer,
        }

    def compute_ctrness_targets(self, anchors: List[Boxes], gt_boxes: List[torch.Tensor]):
        anchors = Boxes.cat(anchors).tensor  # Rx4