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# Source code for torchvision.models.detection.retinanet

    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, 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 ._utils import _box_loss, overwrite_eps
    from .anchor_utils import AnchorGenerator
    from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
    from .transform import GeneralizedRCNNTransform

    __all__ = [
        "RetinaNet",
        "RetinaNet_ResNet50_FPN_Weights",
        "RetinaNet_ResNet50_FPN_V2_Weights",
        "retinanet_resnet50_fpn",
        "retinanet_resnet50_fpn_v2",
    ]

    def _sum(x: List[Tensor]) -> Tensor:
        res = x[0]
        for i in x[1:]:
            res = res + i
        return res

    def _v1_to_v2_weights(state_dict, prefix):
        for i in range(4):
            for type in ["weight", "bias"]:
                old_key = f"{prefix}conv.{2*i}.{type}"
                new_key = f"{prefix}conv.{i}.0.{type}"
                if old_key in state_dict:
                    state_dict[new_key] = state_dict.pop(old_key)

    def _default_anchorgen():
        anchor_sizes = tuple((x, int(x * 2 ** (1.0 / 3)), int(x * 2 ** (2.0 / 3))) for x in [32, 64, 128, 256, 512])
        aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
        anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
        return anchor_generator

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

        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
            norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None
        """

        def __init__(self, in_channels, num_anchors, num_classes, norm_layer: Optional[Callable[..., nn.Module]] = None):
            super().__init__()
            self.classification_head = RetinaNetClassificationHead(
                in_channels, num_anchors, num_classes, norm_layer=norm_layer
            )
            self.regression_head = RetinaNetRegressionHead(in_channels, num_anchors, norm_layer=norm_layer)

        def compute_loss(self, targets, head_outputs, anchors, matched_idxs):
            # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor], List[Tensor]) -> Dict[str, Tensor]
            return {
                "classification": self.classification_head.compute_loss(targets, head_outputs, matched_idxs),
                "bbox_regression": self.regression_head.compute_loss(targets, head_outputs, anchors, matched_idxs),
            }

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

    class RetinaNetClassificationHead(nn.Module):
        """
        A classification head for use in RetinaNet.

        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
            norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None
        """

        _version = 2

        def __init__(
            self,
            in_channels,
            num_anchors,
            num_classes,
            prior_probability=0.01,
            norm_layer: Optional[Callable[..., nn.Module]] = None,
        ):
            super().__init__()

            conv = []
            for _ in range(4):
                conv.append(misc_nn_ops.Conv2dNormActivation(in_channels, in_channels, norm_layer=norm_layer))
            self.conv = nn.Sequential(*conv)

            for layer in self.conv.modules():
                if isinstance(layer, nn.Conv2d):
                    torch.nn.init.normal_(layer.weight, std=0.01)
                    if layer.bias is not None:
                        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))

            self.num_classes = num_classes
            self.num_anchors = num_anchors

            # This is to fix using det_utils.Matcher.BETWEEN_THRESHOLDS in TorchScript.
            # TorchScript doesn't support class attributes.
            # https://github.com/pytorch/vision/pull/1697#issuecomment-630255584
            self.BETWEEN_THRESHOLDS = det_utils.Matcher.BETWEEN_THRESHOLDS

        def _load_from_state_dict(
            self,
            state_dict,
            prefix,
            local_metadata,
            strict,
            missing_keys,
            unexpected_keys,
            error_msgs,
        ):
            version = local_metadata.get("version", None)

            if version is None or version < 2:
                _v1_to_v2_weights(state_dict, prefix)

            super()._load_from_state_dict(
                state_dict,
                prefix,
                local_metadata,
                strict,
                missing_keys,
                unexpected_keys,
                error_msgs,
            )

        def compute_loss(self, targets, head_outputs, matched_idxs):
            # type: (List[Dict[str, Tensor]], Dict[str, Tensor], List[Tensor]) -> Tensor
            losses = []

            cls_logits = head_outputs["cls_logits"]

            for targets_per_image, cls_logits_per_image, matched_idxs_per_image in zip(targets, cls_logits, matched_idxs):
                # determine only the foreground
                foreground_idxs_per_image = matched_idxs_per_image >= 0
                num_foreground = foreground_idxs_per_image.sum()

                # create the target classification
                gt_classes_target = torch.zeros_like(cls_logits_per_image)
                gt_classes_target[
                    foreground_idxs_per_image,
                    targets_per_image["labels"][matched_idxs_per_image[foreground_idxs_per_image]],
                ] = 1.0

                # find indices for which anchors should be ignored
                valid_idxs_per_image = matched_idxs_per_image != self.BETWEEN_THRESHOLDS

                # compute the classification loss
                losses.append(
                    sigmoid_focal_loss(
                        cls_logits_per_image[valid_idxs_per_image],
                        gt_classes_target[valid_idxs_per_image],
                        reduction="sum",
                    )
                    / max(1, num_foreground)
                )

            return _sum(losses) / len(targets)

        def forward(self, x):
            # type: (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 RetinaNetRegressionHead(nn.Module):
        """
        A regression head for use in RetinaNet.

        Args:
            in_channels (int): number of channels of the input feature
            num_anchors (int): number of anchors to be predicted
            norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None
        """

        _version = 2

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

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

            conv = []
            for _ in range(4):
                conv.append(misc_nn_ops.Conv2dNormActivation(in_channels, in_channels, norm_layer=norm_layer))
            self.conv = nn.Sequential(*conv)

            self.bbox_reg = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1)
            torch.nn.init.normal_(self.bbox_reg.weight, std=0.01)
            torch.nn.init.zeros_(self.bbox_reg.bias)

            for layer in self.conv.modules():
                if isinstance(layer, nn.Conv2d):
                    torch.nn.init.normal_(layer.weight, std=0.01)
                    if layer.bias is not None:
                        torch.nn.init.zeros_(layer.bias)

            self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
            self._loss_type = "l1"

        def _load_from_state_dict(
            self,
            state_dict,
            prefix,
            local_metadata,
            strict,
            missing_keys,
            unexpected_keys,
            error_msgs,
        ):
            version = local_metadata.get("version", None)

            if version is None or version < 2:
                _v1_to_v2_weights(state_dict, prefix)

            super()._load_from_state_dict(
                state_dict,
                prefix,
                local_metadata,
                strict,
                missing_keys,
                unexpected_keys,
                error_msgs,
            )

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

            bbox_regression = head_outputs["bbox_regression"]

            for targets_per_image, bbox_regression_per_image, anchors_per_image, matched_idxs_per_image in zip(
                targets, bbox_regression, anchors, matched_idxs
            ):
                # determine only the foreground indices, ignore the rest
                foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0]
                num_foreground = foreground_idxs_per_image.numel()

                # select only the foreground boxes
                matched_gt_boxes_per_image = targets_per_image["boxes"][matched_idxs_per_image[foreground_idxs_per_image]]
                bbox_regression_per_image = bbox_regression_per_image[foreground_idxs_per_image, :]
                anchors_per_image = anchors_per_image[foreground_idxs_per_image, :]

                # compute the loss
                losses.append(
                    _box_loss(
                        self._loss_type,
                        self.box_coder,
                        anchors_per_image,
                        matched_gt_boxes_per_image,
                        bbox_regression_per_image,
                    )
                    / max(1, num_foreground)
                )

            return _sum(losses) / max(1, len(targets))

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

            for features in x:
                bbox_regression = self.conv(features)
                bbox_regression = self.bbox_reg(bbox_regression)

                # 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)

            return torch.cat(all_bbox_regression, dim=1)

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

        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:
            - 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.
            head (nn.Module): Module run on top of the feature pyramid.
                Defaults to a module containing a classification and regression module.
            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.
            fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
                considered as positive during training.
            bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
                considered as negative during training.
            topk_candidates (int): Number of best detections to keep before NMS.

        Example:

            >>> import torch
            >>> import torchvision
            >>> from torchvision.models.detection import RetinaNet
            >>> 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
            >>> # RetinaNet 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=((32, 64, 128, 256, 512),),
            >>>     aspect_ratios=((0.5, 1.0, 2.0),)
            >>> )
            >>>
            >>> # put the pieces together inside a RetinaNet model
            >>> model = RetinaNet(backbone,
            >>>                   num_classes=2,
            >>>                   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.BoxCoder,
            "proposal_matcher": det_utils.Matcher,
        }

        def __init__(
            self,
            backbone,
            num_classes,
            # transform parameters
            min_size=800,
            max_size=1333,
            image_mean=None,
            image_std=None,
            # Anchor parameters
            anchor_generator=None,
            head=None,
            proposal_matcher=None,
            score_thresh=0.05,
            nms_thresh=0.5,
            detections_per_img=300,
            fg_iou_thresh=0.5,
            bg_iou_thresh=0.4,
            topk_candidates=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 of {type(anchor_generator)}"
                )

            if anchor_generator is None:
                anchor_generator = _default_anchorgen()
            self.anchor_generator = anchor_generator

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

            if proposal_matcher is None:
                proposal_matcher = det_utils.Matcher(
                    fg_iou_thresh,
                    bg_iou_thresh,
                    allow_low_quality_matches=True,
                )
            self.proposal_matcher = proposal_matcher

            self.box_coder = det_utils.BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))

            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.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, detections):
            # type: (Dict[str, Tensor], List[Dict[str, Tensor]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]
            if self.training:
                return losses

            return detections

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

                match_quality_matrix = box_ops.box_iou(targets_per_image["boxes"], anchors_per_image)
                matched_idxs.append(self.proposal_matcher(match_quality_matrix))

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

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

            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]
                anchors_per_image, image_shape = anchors[index], image_shapes[index]

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

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

                    # remove low scoring boxes
                    scores_per_level = torch.sigmoid(logits_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_single(
                        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, targets=None):
            # type: (List[Tensor], Optional[List[Dict[str, Tensor]]]) -> 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,
                            "Expected target boxes to be a tensor of shape [N, 4].",
                        )

            # get the original image sizes
            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
            # TODO: Move this to a function
            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,
                            "All bounding boxes should have positive height and width."
                            f" 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)])

            # TODO: Do we want a list or a dict?
            features = list(features.values())

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

            # create the set of anchors
            anchors = self.anchor_generator(images, 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)
            else:
                # recover level sizes
                num_anchors_per_level = [x.size(2) * x.size(3) for x in features]
                HW = 0
                for v in num_anchors_per_level:
                    HW += v
                HWA = head_outputs["cls_logits"].size(1)
                A = HWA // HW
                num_anchors_per_level = [hw * A for hw in num_anchors_per_level]

                # 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("RetinaNet always returns a (Losses, Detections) tuple in scripting")
                    self._has_warned = True
                return losses, detections
            return self.eager_outputs(losses, detections)

    _COMMON_META = {
        "categories": _COCO_CATEGORIES,
        "min_size": (1, 1),
    }

    class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/retinanet_resnet50_fpn_coco-eeacb38b.pth",
            transforms=ObjectDetection,
            meta={
                **_COMMON_META,
                "num_params": 34014999,
                "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#retinanet",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 36.4,
                    }
                },
                "_ops": 151.54,
                "_file_size": 130.267,
                "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
            },
        )
        DEFAULT = COCO_V1

    class RetinaNet_ResNet50_FPN_V2_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/retinanet_resnet50_fpn_v2_coco-5905b1c5.pth",
            transforms=ObjectDetection,
            meta={
                **_COMMON_META,
                "num_params": 38198935,
                "recipe": "https://github.com/pytorch/vision/pull/5756",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 41.5,
                    }
                },
                "_ops": 152.238,
                "_file_size": 146.037,
                "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
            },
        )
        DEFAULT = COCO_V1

    @register_model()
    @handle_legacy_interface(
        weights=("pretrained", RetinaNet_ResNet50_FPN_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
    )
    def retinanet_resnet50_fpn(
        *,
        weights: Optional[RetinaNet_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,
    ) -> RetinaNet:
        """
        Constructs a RetinaNet model with a ResNet-50-FPN backbone.

        .. betastatus:: detection module

        Reference: `Focal Loss for Dense Object Detection <https://arxiv.org/abs/1708.02002>`_.

        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.retinanet_resnet50_fpn(weights=RetinaNet_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.RetinaNet_ResNet50_FPN_Weights`, optional): The
                pretrained weights to use. See
                :class:`~torchvision.models.detection.RetinaNet_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. Default is True.
            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) 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.
            **kwargs: parameters passed to the ``torchvision.models.detection.RetinaNet``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/retinanet.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights
            :members:
        """
        weights = RetinaNet_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)
        # skip P2 because it generates too many anchors (according to their paper)
        backbone = _resnet_fpn_extractor(
            backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(256, 256)
        )
        model = RetinaNet(backbone, num_classes, **kwargs)

        if weights is not None:
            model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
            if weights == RetinaNet_ResNet50_FPN_Weights.COCO_V1:
                overwrite_eps(model, 0.0)

        return model

    @register_model()
    @handle_legacy_interface(
        weights=("pretrained", RetinaNet_ResNet50_FPN_V2_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
    )
    def retinanet_resnet50_fpn_v2(
        *,
        weights: Optional[RetinaNet_ResNet50_FPN_V2_Weights] = None,
        progress: bool = True,
        num_classes: Optional[int] = None,
        weights_backbone: Optional[ResNet50_Weights] = None,
        trainable_backbone_layers: Optional[int] = None,
        **kwargs: Any,
    ) -> RetinaNet:
        """
        Constructs an improved RetinaNet model with a ResNet-50-FPN backbone.

        .. betastatus:: detection module

        Reference: `Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
        <https://arxiv.org/abs/1912.02424>`_.

        :func:`~torchvision.models.detection.retinanet_resnet50_fpn` for more details.

        Args:
            weights (:class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_Weights`, optional): The
                pretrained weights to use. See
                :class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_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. Default is True.
            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) 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.
            **kwargs: parameters passed to the ``torchvision.models.detection.RetinaNet``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/retinanet.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_Weights
            :members:
        """
        weights = RetinaNet_ResNet50_FPN_V2_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)

        backbone = resnet50(weights=weights_backbone, progress=progress)
        backbone = _resnet_fpn_extractor(
            backbone, trainable_backbone_layers, returned_layers=[2, 3, 4], extra_blocks=LastLevelP6P7(2048, 256)
        )
        anchor_generator = _default_anchorgen()
        head = RetinaNetHead(
            backbone.out_channels,
            anchor_generator.num_anchors_per_location()[0],
            num_classes,
            norm_layer=partial(nn.GroupNorm, 32),
        )
        head.regression_head._loss_type = "giou"
        model = RetinaNet(backbone, num_classes, anchor_generator=anchor_generator, head=head, **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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