def forward(self, outputs, targets):
        """Performs the matching
        Params:
            outputs: This is a dict that contains at least these entries:
                "pred_logits":  Tensor of dim [batch_size, num_queries, num_classes]
                                with the classification logits
                "pred_boxes":   Tensor of dim [batch_size, num_queries, 4]
                                with the predicted box coordinates
            targets: This is a list of targets (len(targets) = batch_size),
                where each target is a dict containing:
                "labels":   Tensor of dim [num_target_boxes] (where num_target_boxes
                            is the number of ground-truth
                            objects in the target) containing the class labels
                "boxes":    Tensor of dim [num_target_boxes, 4] containing the target box coordinates
        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        """
        from scipy.optimize import linear_sum_assignment

        batch_size, num_queries = outputs["pred_logits"].shape[:2]

        # We flatten to compute the cost matrices in a batch
        out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1)  # [batch_size * num_queries, num_classes]
        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]

        # Also concat the target labels and boxes
        tgt_ids = torch.cat([v["labels"] for v in targets])
        tgt_bbox = torch.cat([v["boxes"] for v in targets])

        # Compute the classification cost. Contrary to the loss, we don't use the NLL,
        # but approximate it in 1 - proba[target class].
        # The 1 is a constant that doesn't change the matching, it can be omitted.
        cost_class = -out_prob[:, tgt_ids]

        # Compute the L1 cost between boxes
        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)

        # Compute the giou cost between boxes
        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))

        # Final cost matrix
        cost_matrix = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
        cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu()

        sizes = [len(v["boxes"]) for v in targets]
        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]
Performs the matching
        Params:
            outputs: This is a dict that contains at least these entries:
                "pred_logits":  Tensor of dim [batch_size, num_queries, num_classes]
                                with the classification logits
                "pred_boxes":   Tensor of dim [batch_size, num_queries, 4]
                                with the predicted box coordinates
            targets: This is a list of targets (len(targets) = batch_size),
                where each target is a dict containing:
                "labels":   Tensor of dim [num_target_boxes] (where num_target_boxes
                            is the number of ground-truth
                            objects in the target) containing the class labels
                "boxes":    Tensor of dim [num_target_boxes, 4] containing the target box coordinates
        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        
art/estimators/object_detection/detr.py