(self, images, targets=None)
Arguments:
            images (list[Tensor] or ImageList): images to be processed
            targets (list[BoxList]): ground-truth boxes present in the image (optional)

        Returns:
            result (list[BoxList] or dict[Tensor]): the output from the model.
                During training, it returns a dict[Tensor] which contains the losses.
                During testing, it returns list[BoxList] contains additional fields
                like `scores`, `labels` and `mask` (for Mask R-CNN models).
Arguments:
            images (list[Tensor] or ImageList): images to be processed
            targets (list[BoxList]): ground-truth boxes present in the image (optional)
Arguments : images ( list [ Tensor ] or ImageList ) : images to be processed targets ( list [ BoxList ] ) : ground - truth boxes present in the image ( optional )
def forward(self, images, targets=None):
        """
        Arguments:
            images (list[Tensor] or ImageList): images to be processed
            targets (list[BoxList]): ground-truth boxes present in the image (optional)

        Returns:
            result (list[BoxList] or dict[Tensor]): the output from the model.
                During training, it returns a dict[Tensor] which contains the losses.
                During testing, it returns list[BoxList] contains additional fields
                like `scores`, `labels` and `mask` (for Mask R-CNN models).

        """
        if self.training and targets is None:
            raise ValueError("In training mode, targets should be passed")


        images = to_image_list(images)
        features = self.backbone(images.tensors)
        proposals, proposal_losses = self.rpn(images, features, targets)
        if self.roi_heads:
            x, result, detector_losses = self.roi_heads(features, proposals, targets)
        else:
            #self.warm_start -= 1
            # RPN-only models don't have roi_heads
            x = features
            result = proposals
            detector_losses = {}

        if self.training:
            losses = {}
            losses.update(detector_losses)
            losses.update(proposal_losses)

            return losses
        else:
            return result
def forward ( self , images , targets = None ) : if self . training and targets is None : raise ValueError ( "In training mode, targets should be passed" ) images = to_image_list ( images ) features = self . backbone ( images . tensors ) proposals , proposal_losses = self . rpn ( images , features , targets ) if self . roi_heads : x , result , detector_losses = self . roi_heads ( features , proposals , targets ) else : #self.warm_start -= 1 # RPN-only models don't have roi_heads x = features result = proposals detector_losses = { } if self . training : losses = { } losses . update ( detector_losses ) losses . update ( proposal_losses ) return losses else : return result