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# Source code for torchvision.models.detection.faster_rcnn

    from typing import Any, Callable, List, Optional, Tuple, Union

    import torch
    import torch.nn.functional as F
    from torch import nn
    from torchvision.ops import MultiScaleRoIAlign

    from ...ops import misc as misc_nn_ops
    from ...transforms._presets import ObjectDetection
    from .._api import register_model, Weights, WeightsEnum
    from .._meta import _COCO_CATEGORIES
    from .._utils import _ovewrite_value_param, handle_legacy_interface
    from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights
    from ..resnet import resnet50, ResNet50_Weights
    from ._utils import overwrite_eps
    from .anchor_utils import AnchorGenerator
    from .backbone_utils import _mobilenet_extractor, _resnet_fpn_extractor, _validate_trainable_layers
    from .generalized_rcnn import GeneralizedRCNN
    from .roi_heads import RoIHeads
    from .rpn import RegionProposalNetwork, RPNHead
    from .transform import GeneralizedRCNNTransform

    __all__ = [
        "FasterRCNN",
        "FasterRCNN_ResNet50_FPN_Weights",
        "FasterRCNN_ResNet50_FPN_V2_Weights",
        "FasterRCNN_MobileNet_V3_Large_FPN_Weights",
        "FasterRCNN_MobileNet_V3_Large_320_FPN_Weights",
        "fasterrcnn_resnet50_fpn",
        "fasterrcnn_resnet50_fpn_v2",
        "fasterrcnn_mobilenet_v3_large_fpn",
        "fasterrcnn_mobilenet_v3_large_320_fpn",
    ]

    def _default_anchorgen():
        anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
        aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
        return AnchorGenerator(anchor_sizes, aspect_ratios)

    class FasterRCNN(GeneralizedRCNN):
        """
        Implements Faster R-CNN.

        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 for both the RPN and the R-CNN.

        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 or 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 and OrderedDict[Tensor].
            num_classes (int): number of output classes of the model (including the background).
                If box_predictor is specified, num_classes should be None.
            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
            rpn_anchor_generator (AnchorGenerator): module that generates the anchors for a set of feature
                maps.
            rpn_head (nn.Module): module that computes the objectness and regression deltas from the RPN
            rpn_pre_nms_top_n_train (int): number of proposals to keep before applying NMS during training
            rpn_pre_nms_top_n_test (int): number of proposals to keep before applying NMS during testing
            rpn_post_nms_top_n_train (int): number of proposals to keep after applying NMS during training
            rpn_post_nms_top_n_test (int): number of proposals to keep after applying NMS during testing
            rpn_nms_thresh (float): NMS threshold used for postprocessing the RPN proposals
            rpn_fg_iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
                considered as positive during training of the RPN.
            rpn_bg_iou_thresh (float): maximum IoU between the anchor and the GT box so that they can be
                considered as negative during training of the RPN.
            rpn_batch_size_per_image (int): number of anchors that are sampled during training of the RPN
                for computing the loss
            rpn_positive_fraction (float): proportion of positive anchors in a mini-batch during training
                of the RPN
            rpn_score_thresh (float): during inference, only return proposals with a classification score
                greater than rpn_score_thresh
            box_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in
                the locations indicated by the bounding boxes
            box_head (nn.Module): module that takes the cropped feature maps as input
            box_predictor (nn.Module): module that takes the output of box_head and returns the
                classification logits and box regression deltas.
            box_score_thresh (float): during inference, only return proposals with a classification score
                greater than box_score_thresh
            box_nms_thresh (float): NMS threshold for the prediction head. Used during inference
            box_detections_per_img (int): maximum number of detections per image, for all classes.
            box_fg_iou_thresh (float): minimum IoU between the proposals and the GT box so that they can be
                considered as positive during training of the classification head
            box_bg_iou_thresh (float): maximum IoU between the proposals and the GT box so that they can be
                considered as negative during training of the classification head
            box_batch_size_per_image (int): number of proposals that are sampled during training of the
                classification head
            box_positive_fraction (float): proportion of positive proposals in a mini-batch during training
                of the classification head
            bbox_reg_weights (Tuple[float, float, float, float]): weights for the encoding/decoding of the
                bounding boxes

        Example::

            >>> import torch
            >>> import torchvision
            >>> from torchvision.models.detection import FasterRCNN
            >>> from torchvision.models.detection.rpn 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
            >>> # FasterRCNN 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 RPN 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),))
            >>>
            >>> # let's define what are the feature maps that we will
            >>> # use to perform the region of interest cropping, as well as
            >>> # the size of the crop after rescaling.
            >>> # if your backbone returns a Tensor, featmap_names is expected to
            >>> # be ['0']. More generally, the backbone should return an
            >>> # OrderedDict[Tensor], and in featmap_names you can choose which
            >>> # feature maps to use.
            >>> roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
            >>>                                                 output_size=7,
            >>>                                                 sampling_ratio=2)
            >>>
            >>> # put the pieces together inside a FasterRCNN model
            >>> model = FasterRCNN(backbone,
            >>>                    num_classes=2,
            >>>                    rpn_anchor_generator=anchor_generator,
            >>>                    box_roi_pool=roi_pooler)
            >>> model.eval()
            >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
            >>> predictions = model(x)
        """

        def __init__(
            self,
            backbone,
            num_classes=None,
            # transform parameters
            min_size=800,
            max_size=1333,
            image_mean=None,
            image_std=None,
            # RPN parameters
            rpn_anchor_generator=None,
            rpn_head=None,
            rpn_pre_nms_top_n_train=2000,
            rpn_pre_nms_top_n_test=1000,
            rpn_post_nms_top_n_train=2000,
            rpn_post_nms_top_n_test=1000,
            rpn_nms_thresh=0.7,
            rpn_fg_iou_thresh=0.7,
            rpn_bg_iou_thresh=0.3,
            rpn_batch_size_per_image=256,
            rpn_positive_fraction=0.5,
            rpn_score_thresh=0.0,
            # Box parameters
            box_roi_pool=None,
            box_head=None,
            box_predictor=None,
            box_score_thresh=0.05,
            box_nms_thresh=0.5,
            box_detections_per_img=100,
            box_fg_iou_thresh=0.5,
            box_bg_iou_thresh=0.5,
            box_batch_size_per_image=512,
            box_positive_fraction=0.25,
            bbox_reg_weights=None,
            **kwargs,
        ):

            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)"
                )

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

            if num_classes is not None:
                if box_predictor is not None:
                    raise ValueError("num_classes should be None when box_predictor is specified")
            else:
                if box_predictor is None:
                    raise ValueError("num_classes should not be None when box_predictor is not specified")

            out_channels = backbone.out_channels

            if rpn_anchor_generator is None:
                rpn_anchor_generator = _default_anchorgen()
            if rpn_head is None:
                rpn_head = RPNHead(out_channels, rpn_anchor_generator.num_anchors_per_location()[0])

            rpn_pre_nms_top_n = dict(training=rpn_pre_nms_top_n_train, testing=rpn_pre_nms_top_n_test)
            rpn_post_nms_top_n = dict(training=rpn_post_nms_top_n_train, testing=rpn_post_nms_top_n_test)

            rpn = RegionProposalNetwork(
                rpn_anchor_generator,
                rpn_head,
                rpn_fg_iou_thresh,
                rpn_bg_iou_thresh,
                rpn_batch_size_per_image,
                rpn_positive_fraction,
                rpn_pre_nms_top_n,
                rpn_post_nms_top_n,
                rpn_nms_thresh,
                score_thresh=rpn_score_thresh,
            )

            if box_roi_pool is None:
                box_roi_pool = MultiScaleRoIAlign(featmap_names=["0", "1", "2", "3"], output_size=7, sampling_ratio=2)

            if box_head is None:
                resolution = box_roi_pool.output_size[0]
                representation_size = 1024
                box_head = TwoMLPHead(out_channels * resolution**2, representation_size)

            if box_predictor is None:
                representation_size = 1024
                box_predictor = FastRCNNPredictor(representation_size, num_classes)

            roi_heads = RoIHeads(
                # Box
                box_roi_pool,
                box_head,
                box_predictor,
                box_fg_iou_thresh,
                box_bg_iou_thresh,
                box_batch_size_per_image,
                box_positive_fraction,
                bbox_reg_weights,
                box_score_thresh,
                box_nms_thresh,
                box_detections_per_img,
            )

            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]
            transform = GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, **kwargs)

            super().__init__(backbone, rpn, roi_heads, transform)

    class TwoMLPHead(nn.Module):
        """
        Standard heads for FPN-based models

        Args:
            in_channels (int): number of input channels
            representation_size (int): size of the intermediate representation
        """

        def __init__(self, in_channels, representation_size):
            super().__init__()

            self.fc6 = nn.Linear(in_channels, representation_size)
            self.fc7 = nn.Linear(representation_size, representation_size)

        def forward(self, x):
            x = x.flatten(start_dim=1)

            x = F.relu(self.fc6(x))
            x = F.relu(self.fc7(x))

            return x

    class FastRCNNConvFCHead(nn.Sequential):
        def __init__(
            self,
            input_size: Tuple[int, int, int],
            conv_layers: List[int],
            fc_layers: List[int],
            norm_layer: Optional[Callable[..., nn.Module]] = None,
        ):
            """
            Args:
                input_size (Tuple[int, int, int]): the input size in CHW format.
                conv_layers (list): feature dimensions of each Convolution layer
                fc_layers (list): feature dimensions of each FCN layer
                norm_layer (callable, optional): Module specifying the normalization layer to use. Default: None
            """
            in_channels, in_height, in_width = input_size

            blocks = []
            previous_channels = in_channels
            for current_channels in conv_layers:
                blocks.append(misc_nn_ops.Conv2dNormActivation(previous_channels, current_channels, norm_layer=norm_layer))
                previous_channels = current_channels
            blocks.append(nn.Flatten())
            previous_channels = previous_channels * in_height * in_width
            for current_channels in fc_layers:
                blocks.append(nn.Linear(previous_channels, current_channels))
                blocks.append(nn.ReLU(inplace=True))
                previous_channels = current_channels

            super().__init__(*blocks)
            for layer in self.modules():
                if isinstance(layer, nn.Conv2d):
                    nn.init.kaiming_normal_(layer.weight, mode="fan_out", nonlinearity="relu")
                    if layer.bias is not None:
                        nn.init.zeros_(layer.bias)

    class FastRCNNPredictor(nn.Module):
        """
        Standard classification + bounding box regression layers
        for Fast R-CNN.

        Args:
            in_channels (int): number of input channels
            num_classes (int): number of output classes (including background)
        """

        def __init__(self, in_channels, num_classes):
            super().__init__()
            self.cls_score = nn.Linear(in_channels, num_classes)
            self.bbox_pred = nn.Linear(in_channels, num_classes * 4)

        def forward(self, x):
            if x.dim() == 4:
                torch._assert(
                    list(x.shape[2:]) == [1, 1],
                    f"x has the wrong shape, expecting the last two dimensions to be [1,1] instead of {list(x.shape[2:])}",
                )
            x = x.flatten(start_dim=1)
            scores = self.cls_score(x)
            bbox_deltas = self.bbox_pred(x)

            return scores, bbox_deltas

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

    class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth",
            transforms=ObjectDetection,
            meta={
                **_COMMON_META,
                "num_params": 41755286,
                "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-resnet-50-fpn",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 37.0,
                    }
                },
                "_ops": 134.38,
                "_file_size": 159.743,
                "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
            },
        )
        DEFAULT = COCO_V1

    class FasterRCNN_ResNet50_FPN_V2_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_v2_coco-dd69338a.pth",
            transforms=ObjectDetection,
            meta={
                **_COMMON_META,
                "num_params": 43712278,
                "recipe": "https://github.com/pytorch/vision/pull/5763",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 46.7,
                    }
                },
                "_ops": 280.371,
                "_file_size": 167.104,
                "_docs": """These weights were produced using an enhanced training recipe to boost the model accuracy.""",
            },
        )
        DEFAULT = COCO_V1

    class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth",
            transforms=ObjectDetection,
            meta={
                **_COMMON_META,
                "num_params": 19386354,
                "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-fpn",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 32.8,
                    }
                },
                "_ops": 4.494,
                "_file_size": 74.239,
                "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
            },
        )
        DEFAULT = COCO_V1

    class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth",
            transforms=ObjectDetection,
            meta={
                **_COMMON_META,
                "num_params": 19386354,
                "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#faster-r-cnn-mobilenetv3-large-320-fpn",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 22.8,
                    }
                },
                "_ops": 0.719,
                "_file_size": 74.239,
                "_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", FasterRCNN_ResNet50_FPN_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
    )
    def fasterrcnn_resnet50_fpn(
        *,
        weights: Optional[FasterRCNN_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,
    ) -> FasterRCNN:
        """
        Faster R-CNN model with a ResNet-50-FPN backbone from the `Faster R-CNN: Towards Real-Time Object
        Detection with Region Proposal Networks <https://arxiv.org/abs/1506.01497>`__
        paper.

        .. betastatus:: detection module

        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 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 for both the RPN and the R-CNN.

        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`.

        Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

        Example::

            >>> model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)
            >>> # For training
            >>> images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4)
            >>> boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4]
            >>> labels = torch.randint(1, 91, (4, 11))
            >>> images = list(image for image in images)
            >>> targets = []
            >>> for i in range(len(images)):
            >>>     d = {}
            >>>     d['boxes'] = boxes[i]
            >>>     d['labels'] = labels[i]
            >>>     targets.append(d)
            >>> output = model(images, targets)
            >>> # For inference
            >>> model.eval()
            >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
            >>> predictions = model(x)
            >>>
            >>> # optionally, if you want to export the model to ONNX:
            >>> torch.onnx.export(model, x, "faster_rcnn.onnx", opset_version = 11)

        Args:
            weights (:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights`, optional): The
                pretrained weights to use. See
                :class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): 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.faster_rcnn.FasterRCNN``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights
            :members:
        """
        weights = FasterRCNN_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)
        model = FasterRCNN(backbone, num_classes=num_classes, **kwargs)

        if weights is not None:
            model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
            if weights == FasterRCNN_ResNet50_FPN_Weights.COCO_V1:
                overwrite_eps(model, 0.0)

        return model

    @register_model()
    @handle_legacy_interface(
        weights=("pretrained", FasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", ResNet50_Weights.IMAGENET1K_V1),
    )
    def fasterrcnn_resnet50_fpn_v2(
        *,
        weights: Optional[FasterRCNN_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,
    ) -> FasterRCNN:
        """
        Constructs an improved Faster R-CNN model with a ResNet-50-FPN backbone from `Benchmarking Detection
        Transfer Learning with Vision Transformers <https://arxiv.org/abs/2111.11429>`__ paper.

        .. betastatus:: detection module

        It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See
        :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more
        details.

        Args:
            weights (:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights`, optional): The
                pretrained weights to use. See
                :class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): 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.faster_rcnn.FasterRCNN``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights
            :members:
        """
        weights = FasterRCNN_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, norm_layer=nn.BatchNorm2d)
        rpn_anchor_generator = _default_anchorgen()
        rpn_head = RPNHead(backbone.out_channels, rpn_anchor_generator.num_anchors_per_location()[0], conv_depth=2)
        box_head = FastRCNNConvFCHead(
            (backbone.out_channels, 7, 7), [256, 256, 256, 256], [1024], norm_layer=nn.BatchNorm2d
        )
        model = FasterRCNN(
            backbone,
            num_classes=num_classes,
            rpn_anchor_generator=rpn_anchor_generator,
            rpn_head=rpn_head,
            box_head=box_head,
            **kwargs,
        )

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

        return model

    def _fasterrcnn_mobilenet_v3_large_fpn(
        *,
        weights: Optional[Union[FasterRCNN_MobileNet_V3_Large_FPN_Weights, FasterRCNN_MobileNet_V3_Large_320_FPN_Weights]],
        progress: bool,
        num_classes: Optional[int],
        weights_backbone: Optional[MobileNet_V3_Large_Weights],
        trainable_backbone_layers: Optional[int],
        **kwargs: Any,
    ) -> FasterRCNN:
        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, 6, 3)
        norm_layer = misc_nn_ops.FrozenBatchNorm2d if is_trained else nn.BatchNorm2d

        backbone = mobilenet_v3_large(weights=weights_backbone, progress=progress, norm_layer=norm_layer)
        backbone = _mobilenet_extractor(backbone, True, trainable_backbone_layers)
        anchor_sizes = (
            (
                32,
                64,
                128,
                256,
                512,
            ),
        ) * 3
        aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
        model = FasterRCNN(
            backbone, num_classes, rpn_anchor_generator=AnchorGenerator(anchor_sizes, aspect_ratios), **kwargs
        )

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

        return model

    @register_model()
    @handle_legacy_interface(
        weights=("pretrained", FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
    )
    def fasterrcnn_mobilenet_v3_large_320_fpn(
        *,
        weights: Optional[FasterRCNN_MobileNet_V3_Large_320_FPN_Weights] = None,
        progress: bool = True,
        num_classes: Optional[int] = None,
        weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1,
        trainable_backbone_layers: Optional[int] = None,
        **kwargs: Any,
    ) -> FasterRCNN:
        """
        Low resolution Faster R-CNN model with a MobileNetV3-Large backbone tuned for mobile use cases.

        .. betastatus:: detection module

        It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See
        :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more
        details.

        Example::

            >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_320_fpn(weights=FasterRCNN_MobileNet_V3_Large_320_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.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights`, optional): The
                pretrained weights to use. See
                :class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): 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.MobileNet_V3_Large_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 6, with 6 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.faster_rcnn.FasterRCNN``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_320_FPN_Weights
            :members:
        """
        weights = FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.verify(weights)
        weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone)

        defaults = {
            "min_size": 320,
            "max_size": 640,
            "rpn_pre_nms_top_n_test": 150,
            "rpn_post_nms_top_n_test": 150,
            "rpn_score_thresh": 0.05,
        }

        kwargs = {**defaults, **kwargs}
        return _fasterrcnn_mobilenet_v3_large_fpn(
            weights=weights,
            progress=progress,
            num_classes=num_classes,
            weights_backbone=weights_backbone,
            trainable_backbone_layers=trainable_backbone_layers,
            **kwargs,
        )

    @register_model()
    @handle_legacy_interface(
        weights=("pretrained", FasterRCNN_MobileNet_V3_Large_FPN_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", MobileNet_V3_Large_Weights.IMAGENET1K_V1),
    )
    def fasterrcnn_mobilenet_v3_large_fpn(
        *,
        weights: Optional[FasterRCNN_MobileNet_V3_Large_FPN_Weights] = None,
        progress: bool = True,
        num_classes: Optional[int] = None,
        weights_backbone: Optional[MobileNet_V3_Large_Weights] = MobileNet_V3_Large_Weights.IMAGENET1K_V1,
        trainable_backbone_layers: Optional[int] = None,
        **kwargs: Any,
    ) -> FasterRCNN:
        """
        Constructs a high resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone.

        .. betastatus:: detection module

        It works similarly to Faster R-CNN with ResNet-50 FPN backbone. See
        :func:`~torchvision.models.detection.fasterrcnn_resnet50_fpn` for more
        details.

        Example::

            >>> model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(weights=FasterRCNN_MobileNet_V3_Large_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.FasterRCNN_MobileNet_V3_Large_FPN_Weights`, optional): The
                pretrained weights to use. See
                :class:`~torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights` below for
                more details, and possible values. By default, no pre-trained
                weights are used.
            progress (bool, optional): 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.MobileNet_V3_Large_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 6, with 6 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.faster_rcnn.FasterRCNN``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.FasterRCNN_MobileNet_V3_Large_FPN_Weights
            :members:
        """
        weights = FasterRCNN_MobileNet_V3_Large_FPN_Weights.verify(weights)
        weights_backbone = MobileNet_V3_Large_Weights.verify(weights_backbone)

        defaults = {
            "rpn_score_thresh": 0.05,
        }

        kwargs = {**defaults, **kwargs}
        return _fasterrcnn_mobilenet_v3_large_fpn(
            weights=weights,
            progress=progress,
            num_classes=num_classes,
            weights_backbone=weights_backbone,
            trainable_backbone_layers=trainable_backbone_layers,
            **kwargs,
        )

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