(self, features, proposals, targets=None)
return (
            x,
            proposals,
            dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg),
        )
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 subsampled proposals
                are returned. During testing, the predicted boxlists are returned
            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 subsampled proposals
                are returned. During testing, the predicted boxlists are returned
            losses (dict[Tensor]): During training, returns the losses for the
                head. During testing, returns an empty dict.
        """

        if self.training:
            # Faster R-CNN subsamples during training the proposals with a fixed
            # positive / negative ratio
            with torch.no_grad():
                proposals = self.loss_evaluator.subsample(proposals, targets)

        # extract features that will be fed to the final classifier. The
        # feature_extractor generally corresponds to the pooler + heads
        x = self.feature_extractor(features, proposals)
        # final classifier that converts the features into predictions
        class_logits, box_regression = self.predictor(x)

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

        loss_classifier, loss_box_reg = self.loss_evaluator(
            [class_logits], [box_regression]
        )
        return (
            x,
            proposals,
            dict(loss_classifier=loss_classifier, loss_box_reg=loss_box_reg),
        )
def forward ( self , features , proposals , targets = None ) : if self . training : # Faster R-CNN subsamples during training the proposals with a fixed # positive / negative ratio with torch . no_grad ( ) : proposals = self . loss_evaluator . subsample ( proposals , targets ) # extract features that will be fed to the final classifier. The # feature_extractor generally corresponds to the pooler + heads x = self . feature_extractor ( features , proposals ) # final classifier that converts the features into predictions class_logits , box_regression = self . predictor ( x ) if not self . training : result = self . post_processor ( ( class_logits , box_regression ) , proposals ) return x , result , { } loss_classifier , loss_box_reg = self . loss_evaluator ( [ class_logits ] , [ box_regression ] ) return ( x , proposals , dict ( loss_classifier = loss_classifier , loss_box_reg = loss_box_reg ) , )