retinanet.py

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

import torch
from torch import nn, Tensor

from ...ops import sigmoid_focal_loss
from ...ops import boxes as box_ops
from ...ops import misc as misc_nn_ops
from ...ops.feature_pyramid_network import LastLevelP6P7
from ...transforms._presets import ObjectDetection
from ...utils import _log_api_usage_once
from .._api import WeightsEnum, Weights
from .._meta import _COCO_CATEGORIES
from .._utils import handle_legacy_interface, _ovewrite_value_param
from ..resnet import ResNet50_Weights, resnet50
from . import _utils as det_utils
from ._utils import overwrite_eps, _box_loss
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 if it is in training or evaluation mode.

    During training, the model expects both the input tensors, as well as a 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 = {
    "task": "image_object_detection",
    "architecture": "RetinaNet",
    "categories": _COCO_CATEGORIES,
}


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",
            "map": 36.4,
        },
    )
    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",
            "map": 41.5,
        },
    )
    DEFAULT = COCO_V1


@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.

    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 if it is in training or evaluation mode.

    During training, the model expects both the input tensors, as well as a 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 (RetinaNet_ResNet50_FPN_Weights, optional): The pretrained weights for the model
        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 (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.
    """
    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, 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))
        if weights == RetinaNet_ResNet50_FPN_Weights.COCO_V1:
            overwrite_eps(model, 0.0)

    return model


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.

    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 (RetinaNet_ResNet50_FPN_V2_Weights, optional): The pretrained weights for the model
        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 (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.
    """
    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, 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))

    return model