(self, features, proposals, targets=None)
return x, all_proposals, dict(loss_mask=loss_mask)
Arguments:
            features (list[Tensor]): feature-maps from possibly several levels
            proposals (list[BoxList]): proposal boxes
            targets (list[BoxList], optional): the ground-truth targets.

        Returns:
            x (Tensor): the result of the feature extractor
            proposals (list[BoxList]): during training, the original proposals
                are returned. During testing, the predicted boxlists are returned
                with the `mask` field set
            losses (dict[Tensor]): During training, returns the losses for the
                head. During testing, returns an empty dict.
Arguments:
            features (list[Tensor]): feature-maps from possibly several levels
            proposals (list[BoxList]): proposal boxes
            targets (list[BoxList], optional): the ground-truth targets.
Arguments : features ( list [ Tensor ] ) : feature - maps from possibly several levels proposals ( list [ BoxList ] ) : proposal boxes targets ( list [ BoxList ] optional ) : the ground - truth targets .
def forward(self, features, proposals, targets=None):
        """
        Arguments:
            features (list[Tensor]): feature-maps from possibly several levels
            proposals (list[BoxList]): proposal boxes
            targets (list[BoxList], optional): the ground-truth targets.

        Returns:
            x (Tensor): the result of the feature extractor
            proposals (list[BoxList]): during training, the original proposals
                are returned. During testing, the predicted boxlists are returned
                with the `mask` field set
            losses (dict[Tensor]): During training, returns the losses for the
                head. During testing, returns an empty dict.
        """

        if self.training:
            # during training, only focus on positive boxes
            all_proposals = proposals
            proposals, positive_inds = keep_only_positive_boxes(proposals)
        if self.training and self.cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR:
            x = features
            x = x[torch.cat(positive_inds, dim=0)]
        else:
            x = self.feature_extractor(features, proposals)
        mask_logits = self.predictor(x)

        if not self.training:
            result = self.post_processor(mask_logits, proposals)
            return x, result, {}

        loss_mask = self.loss_evaluator(proposals, mask_logits, targets)

        return x, all_proposals, dict(loss_mask=loss_mask)
def forward ( self , features , proposals , targets = None ) : if self . training : # during training, only focus on positive boxes all_proposals = proposals proposals , positive_inds = keep_only_positive_boxes ( proposals ) if self . training and self . cfg . MODEL . ROI_MASK_HEAD . SHARE_BOX_FEATURE_EXTRACTOR : x = features x = x [ torch . cat ( positive_inds , dim = 0 ) ] else : x = self . feature_extractor ( features , proposals ) mask_logits = self . predictor ( x ) if not self . training : result = self . post_processor ( mask_logits , proposals ) return x , result , { } loss_mask = self . loss_evaluator ( proposals , mask_logits , targets ) return x , all_proposals , dict ( loss_mask = loss_mask )