(self, images, features, targets=None)
Arguments:
            images (ImageList): images for which we want to compute the predictions
            features (list[Tensor]): features computed from the images that are
                used for computing the predictions. Each tensor in the list
                correspond to different feature levels
            targets (list[BoxList): ground-truth boxes present in the image (optional)

        Returns:
            boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per
                image.
            losses (dict[Tensor]): the losses for the model during training. During
                testing, it is an empty dict.
Arguments:
            images (ImageList): images for which we want to compute the predictions
            features (list[Tensor]): features computed from the images that are
                used for computing the predictions. Each tensor in the list
                correspond to different feature levels
            targets (list[BoxList): ground-truth boxes present in the image (optional)
Arguments : images ( ImageList ) : images for which we want to compute the predictions features ( list [ Tensor ] ) : features computed from the images that are used for computing the predictions . Each tensor in the list correspond to different feature levels targets ( list [ BoxList ) : ground - truth boxes present in the image ( optional )
def forward(self, images, features, targets=None):
        """
        Arguments:
            images (ImageList): images for which we want to compute the predictions
            features (list[Tensor]): features computed from the images that are
                used for computing the predictions. Each tensor in the list
                correspond to different feature levels
            targets (list[BoxList): ground-truth boxes present in the image (optional)

        Returns:
            boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per
                image.
            losses (dict[Tensor]): the losses for the model during training. During
                testing, it is an empty dict.
        """
        box_cls, box_regression, centerness = self.head(features)
        locations = self.compute_locations(features)
 
        if self.training:
            return self._forward_train(
                locations, box_cls,
                box_regression,
                centerness, targets, images.image_sizes
            )
        else:
            return self._forward_test(
                locations, box_cls, box_regression,
                centerness, images.image_sizes
            )
def forward ( self , images , features , targets = None ) : box_cls , box_regression , centerness = self . head ( features ) locations = self . compute_locations ( features ) if self . training : return self . _forward_train ( locations , box_cls , box_regression , centerness , targets , images . image_sizes ) else : return self . _forward_test ( locations , box_cls , box_regression , centerness , images . image_sizes )