(self, images, features, targets=None, prefix='')
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, prefix=''):
        """
        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.
        """
        objectness, rpn_box_regression = self.head(features)
        anchors = self.anchor_generator(images, features)

        if self.training:
            return self._forward_train(anchors, objectness,
                                       rpn_box_regression, targets, prefix)
        else:
            return self._forward_test(anchors, objectness, rpn_box_regression)
def forward ( self , images , features , targets = None , prefix = '' ) : objectness , rpn_box_regression = self . head ( features ) anchors = self . anchor_generator ( images , features ) if self . training : return self . _forward_train ( anchors , objectness , rpn_box_regression , targets , prefix ) else : return self . _forward_test ( anchors , objectness , rpn_box_regression )