text: from typing import Any, Optional, Union
  
  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, InterpolationMode
  from .._api import WeightsEnum, Weights
  from .._meta import _COCO_CATEGORIES
  from .._utils import handle_legacy_interface, _ovewrite_value_param
  from ..mobilenetv3 import MobileNet_V3_Large_Weights, mobilenet_v3_large
  from ..resnet import ResNet50_Weights, resnet50
  from ._utils import overwrite_eps
  from .anchor_utils import AnchorGenerator
  from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers, _mobilenet_extractor
  from .generalized_rcnn import GeneralizedRCNN
  from .roi_heads import RoIHeads
  from .rpn import RPNHead, RegionProposalNetwork
  from .transform import GeneralizedRCNNTransform
  
  
  __all__ = [
      "FasterRCNN",
      "FasterRCNN_ResNet50_FPN_Weights",
      "FasterRCNN_MobileNet_V3_Large_FPN_Weights",
      "FasterRCNN_MobileNet_V3_Large_320_FPN_Weights",
      "fasterrcnn_resnet50_fpn",
      "fasterrcnn_mobilenet_v3_large_fpn",
      "fasterrcnn_mobilenet_v3_large_320_fpn",
  ]
  
  
  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 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 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 a 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,
      ):
  
          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:
              anchor_sizes = ((32,), (64,), (128,), (256,), (512,))
              aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
              rpn_anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios)
          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)
  
          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 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:
              if list(x.shape[2:]) != [1, 1]:
                  raise ValueError(
                      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 = {
      "task": "image_object_detection",
      "architecture": "FasterRCNN",
      "publication_year": 2015,
      "categories": _COCO_CATEGORIES,
      "interpolation": InterpolationMode.BILINEAR,
  }
  
  
  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",
              "map": 37.0,
          },
      )
      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",
              "map": 32.8,
          },
      )
      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",
              "map": 22.8,
          },
      )
      DEFAULT = COCO_V1
  
  
  @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:
      """
      Constructs a Faster R-CNN model with a ResNet-50-FPN backbone.
  
      Reference: `"Faster R-CNN: Towards Real-Time Object Detection with
      Region Proposal Networks" <https://arxiv.org/abs/1506.01497>`_.
  
      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 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 (FasterRCNN_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 = 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, 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))
          if weights == FasterRCNN_ResNet50_FPN_Weights.COCO_V1:
              overwrite_eps(model, 0.0)
  
      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, 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))
  
      return 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:
      """
      Constructs a low resolution Faster R-CNN model with a MobileNetV3-Large FPN backbone tunned for mobile use-cases.
      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 (FasterRCNN_MobileNet_V3_Large_320_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 (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.
      """
      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,
      )
  
  
  @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.
      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 (FasterRCNN_MobileNet_V3_Large_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 (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.
      """
      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,
      )
  