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# Source code for torchvision.models.detection.fcos

    import math
    import warnings
    from collections import OrderedDict
    from functools import partial
    from typing import Any, Callable, Dict, List, Optional, Tuple

    import torch
    from torch import nn, Tensor

    from ...ops import boxes as box_ops, generalized_box_iou_loss, misc as misc_nn_ops, sigmoid_focal_loss
    from ...ops.feature_pyramid_network import LastLevelP6P7
    from ...transforms._presets import ObjectDetection
    from ...utils import _log_api_usage_once
    from .._api import register_model, Weights, WeightsEnum
    from .._meta import _COCO_CATEGORIES
    from .._utils import _ovewrite_value_param, handle_legacy_interface
    from ..resnet import resnet50, ResNet50_Weights
    from . import _utils as det_utils
    from .anchor_utils import AnchorGenerator
    from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
    from .transform import GeneralizedRCNNTransform

    __all__ = [
        "FCOS",
        "FCOS_ResNet50_FPN_Weights",
        "fcos_resnet50_fpn",
    ]

    class FCOSHead(nn.Module):
        """
        A regression and classification head for use in FCOS.

        Args:
            in_channels (int): number of channels of the input feature
            num_anchors (int): number of anchors to be predicted
            num_classes (int): number of classes to be predicted
            num_convs (Optional[int]): number of conv layer of head. Default: 4.
        """

        __annotations__ = {
            "box_coder": det_utils.BoxLinearCoder,
        }

        def __init__(self, in_channels: int, num_anchors: int, num_classes: int, num_convs: Optional[int] = 4) -> None:
            super().__init__()
            self.box_coder = det_utils.BoxLinearCoder(normalize_by_size=True)
            self.classification_head = FCOSClassificationHead(in_channels, num_anchors, num_classes, num_convs)
            self.regression_head = FCOSRegressionHead(in_channels, num_anchors, num_convs)

        def compute_loss(
            self,
            targets: List[Dict[str, Tensor]],
            head_outputs: Dict[str, Tensor],
            anchors: List[Tensor],
            matched_idxs: List[Tensor],
        ) -> Dict[str, Tensor]:

            cls_logits = head_outputs["cls_logits"]  # [N, HWA, C]
            bbox_regression = head_outputs["bbox_regression"]  # [N, HWA, 4]
            bbox_ctrness = head_outputs["bbox_ctrness"]  # [N, HWA, 1]

            all_gt_classes_targets = []
            all_gt_boxes_targets = []
            for targets_per_image, matched_idxs_per_image in zip(targets, matched_idxs):
                if len(targets_per_image["labels"]) == 0:
                    gt_classes_targets = targets_per_image["labels"].new_zeros((len(matched_idxs_per_image),))
                    gt_boxes_targets = targets_per_image["boxes"].new_zeros((len(matched_idxs_per_image), 4))
                else:
                    gt_classes_targets = targets_per_image["labels"][matched_idxs_per_image.clip(min=0)]
                    gt_boxes_targets = targets_per_image["boxes"][matched_idxs_per_image.clip(min=0)]
                gt_classes_targets[matched_idxs_per_image < 0] = -1  # background
                all_gt_classes_targets.append(gt_classes_targets)
                all_gt_boxes_targets.append(gt_boxes_targets)

            # List[Tensor] to Tensor conversion of  `all_gt_boxes_target`, `all_gt_classes_targets` and `anchors`
            all_gt_boxes_targets, all_gt_classes_targets, anchors = (
                torch.stack(all_gt_boxes_targets),
                torch.stack(all_gt_classes_targets),
                torch.stack(anchors),
            )

            # compute foregroud
            foregroud_mask = all_gt_classes_targets >= 0
            num_foreground = foregroud_mask.sum().item()

            # classification loss
            gt_classes_targets = torch.zeros_like(cls_logits)
            gt_classes_targets[foregroud_mask, all_gt_classes_targets[foregroud_mask]] = 1.0
            loss_cls = sigmoid_focal_loss(cls_logits, gt_classes_targets, reduction="sum")

            # amp issue: pred_boxes need to convert float
            pred_boxes = self.box_coder.decode(bbox_regression, anchors)

            # regression loss: GIoU loss
            loss_bbox_reg = generalized_box_iou_loss(
                pred_boxes[foregroud_mask],
                all_gt_boxes_targets[foregroud_mask],
                reduction="sum",
            )

            # ctrness loss

            bbox_reg_targets = self.box_coder.encode(anchors, all_gt_boxes_targets)

            if len(bbox_reg_targets) == 0:
                gt_ctrness_targets = bbox_reg_targets.new_zeros(bbox_reg_targets.size()[:-1])
            else:
                left_right = bbox_reg_targets[:, :, [0, 2]]
                top_bottom = bbox_reg_targets[:, :, [1, 3]]
                gt_ctrness_targets = torch.sqrt(
                    (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0])
                    * (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])
                )
            pred_centerness = bbox_ctrness.squeeze(dim=2)
            loss_bbox_ctrness = nn.functional.binary_cross_entropy_with_logits(
                pred_centerness[foregroud_mask], gt_ctrness_targets[foregroud_mask], reduction="sum"
            )

            return {
                "classification": loss_cls / max(1, num_foreground),
                "bbox_regression": loss_bbox_reg / max(1, num_foreground),
                "bbox_ctrness": loss_bbox_ctrness / max(1, num_foreground),
            }

        def forward(self, x: List[Tensor]) -> Dict[str, Tensor]:
            cls_logits = self.classification_head(x)
            bbox_regression, bbox_ctrness = self.regression_head(x)
            return {
                "cls_logits": cls_logits,
                "bbox_regression": bbox_regression,
                "bbox_ctrness": bbox_ctrness,
            }

    class FCOSClassificationHead(nn.Module):
        """
        A classification head for use in FCOS.

        Args:
            in_channels (int): number of channels of the input feature.
            num_anchors (int): number of anchors to be predicted.
            num_classes (int): number of classes to be predicted.
            num_convs (Optional[int]): number of conv layer. Default: 4.
            prior_probability (Optional[float]): probability of prior. Default: 0.01.
            norm_layer: Module specifying the normalization layer to use.
        """

        def __init__(
            self,
            in_channels: int,
            num_anchors: int,
            num_classes: int,
            num_convs: int = 4,
            prior_probability: float = 0.01,
            norm_layer: Optional[Callable[..., nn.Module]] = None,
        ) -> None:
            super().__init__()

            self.num_classes = num_classes
            self.num_anchors = num_anchors

            if norm_layer is None:
                norm_layer = partial(nn.GroupNorm, 32)

            conv = []
            for _ in range(num_convs):
                conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1))
                conv.append(norm_layer(in_channels))
                conv.append(nn.ReLU())
            self.conv = nn.Sequential(*conv)

            for layer in self.conv.children():
                if isinstance(layer, nn.Conv2d):
                    torch.nn.init.normal_(layer.weight, std=0.01)
                    torch.nn.init.constant_(layer.bias, 0)

            self.cls_logits = nn.Conv2d(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1)
            torch.nn.init.normal_(self.cls_logits.weight, std=0.01)
            torch.nn.init.constant_(self.cls_logits.bias, -math.log((1 - prior_probability) / prior_probability))

        def forward(self, x: List[Tensor]) -> Tensor:
            all_cls_logits = []

            for features in x:
                cls_logits = self.conv(features)
                cls_logits = self.cls_logits(cls_logits)

                # Permute classification output from (N, A * K, H, W) to (N, HWA, K).
                N, _, H, W = cls_logits.shape
                cls_logits = cls_logits.view(N, -1, self.num_classes, H, W)
                cls_logits = cls_logits.permute(0, 3, 4, 1, 2)
                cls_logits = cls_logits.reshape(N, -1, self.num_classes)  # Size=(N, HWA, 4)

                all_cls_logits.append(cls_logits)

            return torch.cat(all_cls_logits, dim=1)

    class FCOSRegressionHead(nn.Module):
        """
        A regression head for use in FCOS, which combines regression branch and center-ness branch.
        This can obtain better performance.

        Reference: `FCOS: A simple and strong anchor-free object detector <https://arxiv.org/abs/2006.09214>`_.

        Args:
            in_channels (int): number of channels of the input feature
            num_anchors (int): number of anchors to be predicted
            num_convs (Optional[int]): number of conv layer. Default: 4.
            norm_layer: Module specifying the normalization layer to use.
        """

        def __init__(
            self,
            in_channels: int,
            num_anchors: int,
            num_convs: int = 4,
            norm_layer: Optional[Callable[..., nn.Module]] = None,
        ):
            super().__init__()

            if norm_layer is None:
                norm_layer = partial(nn.GroupNorm, 32)

            conv = []
            for _ in range(num_convs):
                conv.append(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1))
                conv.append(norm_layer(in_channels))
                conv.append(nn.ReLU())
            self.conv = nn.Sequential(*conv)

            self.bbox_reg = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1)
            self.bbox_ctrness = nn.Conv2d(in_channels, num_anchors * 1, kernel_size=3, stride=1, padding=1)
            for layer in [self.bbox_reg, self.bbox_ctrness]:
                torch.nn.init.normal_(layer.weight, std=0.01)
                torch.nn.init.zeros_(layer.bias)

            for layer in self.conv.children():
                if isinstance(layer, nn.Conv2d):
                    torch.nn.init.normal_(layer.weight, std=0.01)
                    torch.nn.init.zeros_(layer.bias)

        def forward(self, x: List[Tensor]) -> Tuple[Tensor, Tensor]:
            all_bbox_regression = []
            all_bbox_ctrness = []

            for features in x:
                bbox_feature = self.conv(features)
                bbox_regression = nn.functional.relu(self.bbox_reg(bbox_feature))
                bbox_ctrness = self.bbox_ctrness(bbox_feature)

                # permute bbox regression output from (N, 4 * A, H, W) to (N, HWA, 4).
                N, _, H, W = bbox_regression.shape
                bbox_regression = bbox_regression.view(N, -1, 4, H, W)
                bbox_regression = bbox_regression.permute(0, 3, 4, 1, 2)
                bbox_regression = bbox_regression.reshape(N, -1, 4)  # Size=(N, HWA, 4)
                all_bbox_regression.append(bbox_regression)

                # permute bbox ctrness output from (N, 1 * A, H, W) to (N, HWA, 1).
                bbox_ctrness = bbox_ctrness.view(N, -1, 1, H, W)
                bbox_ctrness = bbox_ctrness.permute(0, 3, 4, 1, 2)
                bbox_ctrness = bbox_ctrness.reshape(N, -1, 1)
                all_bbox_ctrness.append(bbox_ctrness)

            return torch.cat(all_bbox_regression, dim=1), torch.cat(all_bbox_ctrness, dim=1)

    class FCOS(nn.Module):
        """
        Implements FCOS.

        The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
        image, and should be in 0-1 range. Different images can have different sizes.

        The behavior of the model changes depending on if it is in training or evaluation mode.

        During training, the model expects both the input tensors and targets (list of dictionary),
        containing:
            - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
              ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
            - labels (Int64Tensor[N]): the class label for each ground-truth box

        The model returns a Dict[Tensor] during training, containing the classification, regression
        and centerness losses.

        During inference, the model requires only the input tensors, and returns the post-processed
        predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
        follows:
            - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
              ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
            - labels (Int64Tensor[N]): the predicted labels for each image
            - scores (Tensor[N]): the scores for each prediction

        Args:
            backbone (nn.Module): the network used to compute the features for the model.
                It should contain an out_channels attribute, which indicates the number of output
                channels that each feature map has (and it should be the same for all feature maps).
                The backbone should return a single Tensor or an OrderedDict[Tensor].
            num_classes (int): number of output classes of the model (including the background).
            min_size (int): minimum size of the image to be rescaled before feeding it to the backbone
            max_size (int): maximum size of the image to be rescaled before feeding it to the backbone
            image_mean (Tuple[float, float, float]): mean values used for input normalization.
                They are generally the mean values of the dataset on which the backbone has been trained
                on
            image_std (Tuple[float, float, float]): std values used for input normalization.
                They are generally the std values of the dataset on which the backbone has been trained on
            anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
                maps. For FCOS, only set one anchor for per position of each level, the width and height equal to
                the stride of feature map, and set aspect ratio = 1.0, so the center of anchor is equivalent to the point
                in FCOS paper.
            head (nn.Module): Module run on top of the feature pyramid.
                Defaults to a module containing a classification and regression module.
            center_sampling_radius (int): radius of the "center" of a groundtruth box,
                within which all anchor points are labeled positive.
            score_thresh (float): Score threshold used for postprocessing the detections.
            nms_thresh (float): NMS threshold used for postprocessing the detections.
            detections_per_img (int): Number of best detections to keep after NMS.
            topk_candidates (int): Number of best detections to keep before NMS.

        Example:

            >>> import torch
            >>> import torchvision
            >>> from torchvision.models.detection import FCOS
            >>> from torchvision.models.detection.anchor_utils import AnchorGenerator
            >>> # load a pre-trained model for classification and return
            >>> # only the features
            >>> backbone = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).features
            >>> # FCOS needs to know the number of
            >>> # output channels in a backbone. For mobilenet_v2, it's 1280,
            >>> # so we need to add it here
            >>> backbone.out_channels = 1280
            >>>
            >>> # let's make the network generate 5 x 3 anchors per spatial
            >>> # location, with 5 different sizes and 3 different aspect
            >>> # ratios. We have a Tuple[Tuple[int]] because each feature
            >>> # map could potentially have different sizes and
            >>> # aspect ratios
            >>> anchor_generator = AnchorGenerator(
            >>>     sizes=((8,), (16,), (32,), (64,), (128,)),
            >>>     aspect_ratios=((1.0,),)
            >>> )
            >>>
            >>> # put the pieces together inside a FCOS model
            >>> model = FCOS(
            >>>     backbone,
            >>>     num_classes=80,
            >>>     anchor_generator=anchor_generator,
            >>> )
            >>> model.eval()
            >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
            >>> predictions = model(x)
        """

        __annotations__ = {
            "box_coder": det_utils.BoxLinearCoder,
        }

        def __init__(
            self,
            backbone: nn.Module,
            num_classes: int,
            # transform parameters
            min_size: int = 800,
            max_size: int = 1333,
            image_mean: Optional[List[float]] = None,
            image_std: Optional[List[float]] = None,
            # Anchor parameters
            anchor_generator: Optional[AnchorGenerator] = None,
            head: Optional[nn.Module] = None,
            center_sampling_radius: float = 1.5,
            score_thresh: float = 0.2,
            nms_thresh: float = 0.6,
            detections_per_img: int = 100,
            topk_candidates: int = 1000,
            **kwargs,
        ):
            super().__init__()
            _log_api_usage_once(self)

            if not hasattr(backbone, "out_channels"):
                raise ValueError(
                    "backbone should contain an attribute out_channels "
                    "specifying the number of output channels (assumed to be the "
                    "same for all the levels)"
                )
            self.backbone = backbone

            if not isinstance(anchor_generator, (AnchorGenerator, type(None))):
                raise TypeError(
                    f"anchor_generator should be of type AnchorGenerator or None, instead  got {type(anchor_generator)}"
                )

            if anchor_generator is None:
                anchor_sizes = ((8,), (16,), (32,), (64,), (128,))  # equal to strides of multi-level feature map
                aspect_ratios = ((1.0,),) * len(anchor_sizes)  # set only one anchor
                anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
            self.anchor_generator = anchor_generator
            if self.anchor_generator.num_anchors_per_location()[0] != 1:
                raise ValueError(
                    f"anchor_generator.num_anchors_per_location()[0] should be 1 instead of {anchor_generator.num_anchors_per_location()[0]}"
                )

            if head is None:
                head = FCOSHead(backbone.out_channels, anchor_generator.num_anchors_per_location()[0], num_classes)
            self.head = head

            self.box_coder = det_utils.BoxLinearCoder(normalize_by_size=True)

            if image_mean is None:
                image_mean = [0.485, 0.456, 0.406]
            if image_std is None:
                image_std = [0.229, 0.224, 0.225]
            self.transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs)

            self.center_sampling_radius = center_sampling_radius
            self.score_thresh = score_thresh
            self.nms_thresh = nms_thresh
            self.detections_per_img = detections_per_img
            self.topk_candidates = topk_candidates

            # used only on torchscript mode
            self._has_warned = False

        @torch.jit.unused
        def eager_outputs(
            self, losses: Dict[str, Tensor], detections: List[Dict[str, Tensor]]
        ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]:
            if self.training:
                return losses

            return detections

        def compute_loss(
            self,
            targets: List[Dict[str, Tensor]],
            head_outputs: Dict[str, Tensor],
            anchors: List[Tensor],
            num_anchors_per_level: List[int],
        ) -> Dict[str, Tensor]:
            matched_idxs = []
            for anchors_per_image, targets_per_image in zip(anchors, targets):
                if targets_per_image["boxes"].numel() == 0:
                    matched_idxs.append(
                        torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64, device=anchors_per_image.device)
                    )
                    continue

                gt_boxes = targets_per_image["boxes"]
                gt_centers = (gt_boxes[:, :2] + gt_boxes[:, 2:]) / 2  # Nx2
                anchor_centers = (anchors_per_image[:, :2] + anchors_per_image[:, 2:]) / 2  # N
                anchor_sizes = anchors_per_image[:, 2] - anchors_per_image[:, 0]
                # center sampling: anchor point must be close enough to gt center.
                pairwise_match = (anchor_centers[:, None, :] - gt_centers[None, :, :]).abs_().max(
                    dim=2
                ).values < self.center_sampling_radius * anchor_sizes[:, None]
                # compute pairwise distance between N points and M boxes
                x, y = anchor_centers.unsqueeze(dim=2).unbind(dim=1)  # (N, 1)
                x0, y0, x1, y1 = gt_boxes.unsqueeze(dim=0).unbind(dim=2)  # (1, M)
                pairwise_dist = torch.stack([x - x0, y - y0, x1 - x, y1 - y], dim=2)  # (N, M)

                # anchor point must be inside gt
                pairwise_match &= pairwise_dist.min(dim=2).values > 0

                # each anchor is only responsible for certain scale range.
                lower_bound = anchor_sizes * 4
                lower_bound[: num_anchors_per_level[0]] = 0
                upper_bound = anchor_sizes * 8
                upper_bound[-num_anchors_per_level[-1] :] = float("inf")
                pairwise_dist = pairwise_dist.max(dim=2).values
                pairwise_match &= (pairwise_dist > lower_bound[:, None]) & (pairwise_dist < upper_bound[:, None])

                # match the GT box with minimum area, if there are multiple GT matches
                gt_areas = (gt_boxes[:, 2] - gt_boxes[:, 0]) * (gt_boxes[:, 3] - gt_boxes[:, 1])  # N
                pairwise_match = pairwise_match.to(torch.float32) * (1e8 - gt_areas[None, :])
                min_values, matched_idx = pairwise_match.max(dim=1)  # R, per-anchor match
                matched_idx[min_values < 1e-5] = -1  # unmatched anchors are assigned -1

                matched_idxs.append(matched_idx)

            return self.head.compute_loss(targets, head_outputs, anchors, matched_idxs)

        def postprocess_detections(
            self, head_outputs: Dict[str, List[Tensor]], anchors: List[List[Tensor]], image_shapes: List[Tuple[int, int]]
        ) -> List[Dict[str, Tensor]]:
            class_logits = head_outputs["cls_logits"]
            box_regression = head_outputs["bbox_regression"]
            box_ctrness = head_outputs["bbox_ctrness"]

            num_images = len(image_shapes)

            detections: List[Dict[str, Tensor]] = []

            for index in range(num_images):
                box_regression_per_image = [br[index] for br in box_regression]
                logits_per_image = [cl[index] for cl in class_logits]
                box_ctrness_per_image = [bc[index] for bc in box_ctrness]
                anchors_per_image, image_shape = anchors[index], image_shapes[index]

                image_boxes = []
                image_scores = []
                image_labels = []

                for box_regression_per_level, logits_per_level, box_ctrness_per_level, anchors_per_level in zip(
                    box_regression_per_image, logits_per_image, box_ctrness_per_image, anchors_per_image
                ):
                    num_classes = logits_per_level.shape[-1]

                    # remove low scoring boxes
                    scores_per_level = torch.sqrt(
                        torch.sigmoid(logits_per_level) * torch.sigmoid(box_ctrness_per_level)
                    ).flatten()
                    keep_idxs = scores_per_level > self.score_thresh
                    scores_per_level = scores_per_level[keep_idxs]
                    topk_idxs = torch.where(keep_idxs)[0]

                    # keep only topk scoring predictions
                    num_topk = det_utils._topk_min(topk_idxs, self.topk_candidates, 0)
                    scores_per_level, idxs = scores_per_level.topk(num_topk)
                    topk_idxs = topk_idxs[idxs]

                    anchor_idxs = torch.div(topk_idxs, num_classes, rounding_mode="floor")
                    labels_per_level = topk_idxs % num_classes

                    boxes_per_level = self.box_coder.decode(
                        box_regression_per_level[anchor_idxs], anchors_per_level[anchor_idxs]
                    )
                    boxes_per_level = box_ops.clip_boxes_to_image(boxes_per_level, image_shape)

                    image_boxes.append(boxes_per_level)
                    image_scores.append(scores_per_level)
                    image_labels.append(labels_per_level)

                image_boxes = torch.cat(image_boxes, dim=0)
                image_scores = torch.cat(image_scores, dim=0)
                image_labels = torch.cat(image_labels, dim=0)

                # non-maximum suppression
                keep = box_ops.batched_nms(image_boxes, image_scores, image_labels, self.nms_thresh)
                keep = keep[: self.detections_per_img]

                detections.append(
                    {
                        "boxes": image_boxes[keep],
                        "scores": image_scores[keep],
                        "labels": image_labels[keep],
                    }
                )

            return detections

        def forward(
            self,
            images: List[Tensor],
            targets: Optional[List[Dict[str, Tensor]]] = None,
        ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]:
            """
            Args:
                images (list[Tensor]): images to be processed
                targets (list[Dict[Tensor]]): 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:

                if targets is None:
                    torch._assert(False, "targets should not be none when in training mode")
                else:
                    for target in targets:
                        boxes = target["boxes"]
                        torch._assert(isinstance(boxes, torch.Tensor), "Expected target boxes to be of type Tensor.")
                        torch._assert(
                            len(boxes.shape) == 2 and boxes.shape[-1] == 4,
                            f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.",
                        )

            original_image_sizes: List[Tuple[int, int]] = []
            for img in images:
                val = img.shape[-2:]
                torch._assert(
                    len(val) == 2,
                    f"expecting the last two dimensions of the Tensor to be H and W instead got {img.shape[-2:]}",
                )
                original_image_sizes.append((val[0], val[1]))

            # transform the input
            images, targets = self.transform(images, targets)

            # Check for degenerate boxes
            if targets is not None:
                for target_idx, target in enumerate(targets):
                    boxes = target["boxes"]
                    degenerate_boxes = boxes[:, 2:] <= boxes[:, :2]
                    if degenerate_boxes.any():
                        # print the first degenerate box
                        bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0]
                        degen_bb: List[float] = boxes[bb_idx].tolist()
                        torch._assert(
                            False,
                            f"All bounding boxes should have positive height and width. Found invalid box {degen_bb} for target at index {target_idx}.",
                        )

            # get the features from the backbone
            features = self.backbone(images.tensors)
            if isinstance(features, torch.Tensor):
                features = OrderedDict([("0", features)])

            features = list(features.values())

            # compute the fcos heads outputs using the features
            head_outputs = self.head(features)

            # create the set of anchors
            anchors = self.anchor_generator(images, features)
            # recover level sizes
            num_anchors_per_level = [x.size(2) * x.size(3) for x in features]

            losses = {}
            detections: List[Dict[str, Tensor]] = []
            if self.training:
                if targets is None:
                    torch._assert(False, "targets should not be none when in training mode")
                else:
                    # compute the losses
                    losses = self.compute_loss(targets, head_outputs, anchors, num_anchors_per_level)
            else:
                # split outputs per level
                split_head_outputs: Dict[str, List[Tensor]] = {}
                for k in head_outputs:
                    split_head_outputs[k] = list(head_outputs[k].split(num_anchors_per_level, dim=1))
                split_anchors = [list(a.split(num_anchors_per_level)) for a in anchors]

                # compute the detections
                detections = self.postprocess_detections(split_head_outputs, split_anchors, images.image_sizes)
                detections = self.transform.postprocess(detections, images.image_sizes, original_image_sizes)

            if torch.jit.is_scripting():
                if not self._has_warned:
                    warnings.warn("FCOS always returns a (Losses, Detections) tuple in scripting")
                    self._has_warned = True
                return losses, detections
            return self.eager_outputs(losses, detections)

    class FCOS_ResNet50_FPN_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/fcos_resnet50_fpn_coco-99b0c9b7.pth",
            transforms=ObjectDetection,
            meta={
                "num_params": 32269600,
                "categories": _COCO_CATEGORIES,
                "min_size": (1, 1),
                "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#fcos-resnet-50-fpn",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 39.2,
                    }
                },
                "_ops": 128.207,
                "_file_size": 123.608,
                "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
            },
        )
        DEFAULT = COCO_V1

    @register_model()
    @handle_legacy_interface(
        weights=("pretrained", FCOS_ResNet50_FPN_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
    )
    def fcos_resnet50_fpn(
        *,
        weights: Optional[FCOS_ResNet50_FPN_Weights] = None,
        progress: bool = True,
        num_classes: Optional[int] = None,
        weights_backbone: Optional[ResNet50_Weights] = ResNet50_Weights.IMAGENET1K_V1,
        trainable_backbone_layers: Optional[int] = None,
        **kwargs: Any,
    ) -> FCOS:
        """
        Constructs a FCOS model with a ResNet-50-FPN backbone.

        .. betastatus:: detection module

        Reference: `FCOS: Fully Convolutional One-Stage Object Detection <https://arxiv.org/abs/1904.01355>`_.
                   `FCOS: A simple and strong anchor-free object detector <https://arxiv.org/abs/2006.09214>`_.

        The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each
        image, and should be in ``0-1`` range. Different images can have different sizes.

        The behavior of the model changes depending on if it is in training or evaluation mode.

        During training, the model expects both the input tensors and targets (list of dictionary),
        containing:

            - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
              ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
            - labels (``Int64Tensor[N]``): the class label for each ground-truth box

        The model returns a ``Dict[Tensor]`` during training, containing the classification and regression
        losses.

        During inference, the model requires only the input tensors, and returns the post-processed
        predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as
        follows, where ``N`` is the number of detections:

            - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
              ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
            - labels (``Int64Tensor[N]``): the predicted labels for each detection
            - scores (``Tensor[N]``): the scores of each detection

        For more details on the output, you may refer to :ref:`instance_seg_output`.

        Example:

            >>> model = torchvision.models.detection.fcos_resnet50_fpn(weights=FCOS_ResNet50_FPN_Weights.DEFAULT)
            >>> model.eval()
            >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
            >>> predictions = model(x)

        Args:
            weights (:class:`~torchvision.models.detection.FCOS_ResNet50_FPN_Weights`, optional): The
                pretrained weights to use. See
                :class:`~torchvision.models.detection.FCOS_ResNet50_FPN_Weights`
                below for more details, and possible values. By default, no
                pre-trained weights are used.
            progress (bool): If True, displays a progress bar of the download to stderr
            num_classes (int, optional): number of output classes of the model (including the background)
            weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights for
                the backbone.
            trainable_backbone_layers (int, optional): number of trainable (not frozen) resnet layers starting
                from final block. Valid values are between 0 and 5, with 5 meaning all backbone layers are
                trainable. If ``None`` is passed (the default) this value is set to 3. Default: None
            **kwargs: parameters passed to the ``torchvision.models.detection.FCOS``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/fcos.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.FCOS_ResNet50_FPN_Weights
            :members:
        """
        weights = FCOS_ResNet50_FPN_Weights.verify(weights)
        weights_backbone = ResNet50_Weights.verify(weights_backbone)

        if weights is not None:
            weights_backbone = None
            num_classes = _ovewrite_value_param("num_classes", num_classes, len(weights.meta["categories"]))
        elif num_classes is None:
            num_classes = 91

        is_trained = weights is not None or weights_backbone is not None
        trainable_backbone_layers = _validate_trainable_layers(is_trained, trainable_backbone_layers, 5, 3)
        norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d

        backbone = resnet50(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
        backbone = _resnet_fpn_extractor(
            backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256)
        )
        model = FCOS(backbone, num_classes, **kwargs)

        if weights is not None:
            model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))

        return model

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