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# Source code for torchvision.models.detection.ssd

    import warnings
    from collections import OrderedDict
    from typing import Any, Dict, List, Optional, Tuple

    import torch
    import torch.nn.functional as F
    from torch import nn, Tensor

    from ...ops import boxes as box_ops
    from ...transforms._presets import ObjectDetection
    from ...utils import _log_api_usage_once
    from .._api import register_model, Weights, WeightsEnum
    from .._meta import _COCO_CATEGORIES
    from .._utils import _ovewrite_value_param, handle_legacy_interface
    from ..vgg import VGG, vgg16, VGG16_Weights
    from . import _utils as det_utils
    from .anchor_utils import DefaultBoxGenerator
    from .backbone_utils import _validate_trainable_layers
    from .transform import GeneralizedRCNNTransform

    __all__ = [
        "SSD300_VGG16_Weights",
        "ssd300_vgg16",
    ]

    class SSD300_VGG16_Weights(WeightsEnum):
        COCO_V1 = Weights(
            url="https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth",
            transforms=ObjectDetection,
            meta={
                "num_params": 35641826,
                "categories": _COCO_CATEGORIES,
                "min_size": (1, 1),
                "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16",
                "_metrics": {
                    "COCO-val2017": {
                        "box_map": 25.1,
                    }
                },
                "_ops": 34.858,
                "_file_size": 135.988,
                "_docs": """These weights were produced by following a similar training recipe as on the paper.""",
            },
        )
        DEFAULT = COCO_V1

    def _xavier_init(conv: nn.Module):
        for layer in conv.modules():
            if isinstance(layer, nn.Conv2d):
                torch.nn.init.xavier_uniform_(layer.weight)
                if layer.bias is not None:
                    torch.nn.init.constant_(layer.bias, 0.0)

    class SSDHead(nn.Module):
        def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int):
            super().__init__()
            self.classification_head = SSDClassificationHead(in_channels, num_anchors, num_classes)
            self.regression_head = SSDRegressionHead(in_channels, num_anchors)

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

    class SSDScoringHead(nn.Module):
        def __init__(self, module_list: nn.ModuleList, num_columns: int):
            super().__init__()
            self.module_list = module_list
            self.num_columns = num_columns

        def _get_result_from_module_list(self, x: Tensor, idx: int) -> Tensor:
            """
            This is equivalent to self.module_list[idx](x),
            but torchscript doesn't support this yet
            """
            num_blocks = len(self.module_list)
            if idx < 0:
                idx += num_blocks
            out = x
            for i, module in enumerate(self.module_list):
                if i == idx:
                    out = module(x)
            return out

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

            for i, features in enumerate(x):
                results = self._get_result_from_module_list(features, i)

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

                all_results.append(results)

            return torch.cat(all_results, dim=1)

    class SSDClassificationHead(SSDScoringHead):
        def __init__(self, in_channels: List[int], num_anchors: List[int], num_classes: int):
            cls_logits = nn.ModuleList()
            for channels, anchors in zip(in_channels, num_anchors):
                cls_logits.append(nn.Conv2d(channels, num_classes * anchors, kernel_size=3, padding=1))
            _xavier_init(cls_logits)
            super().__init__(cls_logits, num_classes)

    class SSDRegressionHead(SSDScoringHead):
        def __init__(self, in_channels: List[int], num_anchors: List[int]):
            bbox_reg = nn.ModuleList()
            for channels, anchors in zip(in_channels, num_anchors):
                bbox_reg.append(nn.Conv2d(channels, 4 * anchors, kernel_size=3, padding=1))
            _xavier_init(bbox_reg)
            super().__init__(bbox_reg, 4)

    class SSD(nn.Module):
        """
        Implements SSD architecture from `"SSD: Single Shot MultiBox Detector" <https://arxiv.org/abs/1512.02325>`_.

        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, but they will be resized
        to a fixed size before passing it to the backbone.

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

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

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

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

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

        Args:
            backbone (nn.Module): the network used to compute the features for the model.
                It should contain an out_channels attribute with the list of the output channels of
                each feature map. The backbone should return a single Tensor or an OrderedDict[Tensor].
            anchor_generator (DefaultBoxGenerator): module that generates the default boxes for a
                set of feature maps.
            size (Tuple[int, int]): the width and height to which images will be rescaled before feeding them
                to the backbone.
            num_classes (int): number of output classes of the model (including the background).
            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
            head (nn.Module, optional): Module run on top of the backbone features. 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.
            iou_thresh (float): minimum IoU between the anchor and the GT box so that they can be
                considered as positive during training.
            topk_candidates (int): Number of best detections to keep before NMS.
            positive_fraction (float): a number between 0 and 1 which indicates the proportion of positive
                proposals used during the training of the classification head. It is used to estimate the negative to
                positive ratio.
        """

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

        def __init__(
            self,
            backbone: nn.Module,
            anchor_generator: DefaultBoxGenerator,
            size: Tuple[int, int],
            num_classes: int,
            image_mean: Optional[List[float]] = None,
            image_std: Optional[List[float]] = None,
            head: Optional[nn.Module] = None,
            score_thresh: float = 0.01,
            nms_thresh: float = 0.45,
            detections_per_img: int = 200,
            iou_thresh: float = 0.5,
            topk_candidates: int = 400,
            positive_fraction: float = 0.25,
            **kwargs: Any,
        ):
            super().__init__()
            _log_api_usage_once(self)

            self.backbone = backbone

            self.anchor_generator = anchor_generator

            self.box_coder = det_utils.BoxCoder(weights=(10.0, 10.0, 5.0, 5.0))

            if head is None:
                if hasattr(backbone, "out_channels"):
                    out_channels = backbone.out_channels
                else:
                    out_channels = det_utils.retrieve_out_channels(backbone, size)

                if len(out_channels) != len(anchor_generator.aspect_ratios):
                    raise ValueError(
                        f"The length of the output channels from the backbone ({len(out_channels)}) do not match the length of the anchor generator aspect ratios ({len(anchor_generator.aspect_ratios)})"
                    )

                num_anchors = self.anchor_generator.num_anchors_per_location()
                head = SSDHead(out_channels, num_anchors, num_classes)
            self.head = head

            self.proposal_matcher = det_utils.SSDMatcher(iou_thresh)

            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, size_divisible=1, fixed_size=size, **kwargs
            )

            self.score_thresh = score_thresh
            self.nms_thresh = nms_thresh
            self.detections_per_img = detections_per_img
            self.topk_candidates = topk_candidates
            self.neg_to_pos_ratio = (1.0 - positive_fraction) / positive_fraction

            # used only on torchscript mode
            self._has_warned = False

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

            return detections

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

            # Match original targets with default boxes
            num_foreground = 0
            bbox_loss = []
            cls_targets = []
            for (
                targets_per_image,
                bbox_regression_per_image,
                cls_logits_per_image,
                anchors_per_image,
                matched_idxs_per_image,
            ) in zip(targets, bbox_regression, cls_logits, anchors, matched_idxs):
                # produce the matching between boxes and targets
                foreground_idxs_per_image = torch.where(matched_idxs_per_image >= 0)[0]
                foreground_matched_idxs_per_image = matched_idxs_per_image[foreground_idxs_per_image]
                num_foreground += foreground_matched_idxs_per_image.numel()

                # Calculate regression loss
                matched_gt_boxes_per_image = targets_per_image["boxes"][foreground_matched_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, :]
                target_regression = self.box_coder.encode_single(matched_gt_boxes_per_image, anchors_per_image)
                bbox_loss.append(
                    torch.nn.functional.smooth_l1_loss(bbox_regression_per_image, target_regression, reduction="sum")
                )

                # Estimate ground truth for class targets
                gt_classes_target = torch.zeros(
                    (cls_logits_per_image.size(0),),
                    dtype=targets_per_image["labels"].dtype,
                    device=targets_per_image["labels"].device,
                )
                gt_classes_target[foreground_idxs_per_image] = targets_per_image["labels"][
                    foreground_matched_idxs_per_image
                ]
                cls_targets.append(gt_classes_target)

            bbox_loss = torch.stack(bbox_loss)
            cls_targets = torch.stack(cls_targets)

            # Calculate classification loss
            num_classes = cls_logits.size(-1)
            cls_loss = F.cross_entropy(cls_logits.view(-1, num_classes), cls_targets.view(-1), reduction="none").view(
                cls_targets.size()
            )

            # Hard Negative Sampling
            foreground_idxs = cls_targets > 0
            num_negative = self.neg_to_pos_ratio * foreground_idxs.sum(1, keepdim=True)
            # num_negative[num_negative < self.neg_to_pos_ratio] = self.neg_to_pos_ratio
            negative_loss = cls_loss.clone()
            negative_loss[foreground_idxs] = -float("inf")  # use -inf to detect positive values that creeped in the sample
            values, idx = negative_loss.sort(1, descending=True)
            # background_idxs = torch.logical_and(idx.sort(1)[1] < num_negative, torch.isfinite(values))
            background_idxs = idx.sort(1)[1] < num_negative

            N = max(1, num_foreground)
            return {
                "bbox_regression": bbox_loss.sum() / N,
                "classification": (cls_loss[foreground_idxs].sum() + cls_loss[background_idxs].sum()) / N,
            }

        def forward(
            self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None
        ) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]]:
            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"]
                        if isinstance(boxes, torch.Tensor):
                            torch._assert(
                                len(boxes.shape) == 2 and boxes.shape[-1] == 4,
                                f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.",
                            )
                        else:
                            torch._assert(False, f"Expected target boxes to be of type Tensor, got {type(boxes)}.")

            # 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
            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():
                        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)])

            features = list(features.values())

            # compute the ssd 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:
                matched_idxs = []
                if targets is None:
                    torch._assert(False, "targets should not be none when in training mode")
                else:
                    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))

                    losses = self.compute_loss(targets, head_outputs, anchors, matched_idxs)
            else:
                detections = self.postprocess_detections(head_outputs, 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("SSD always returns a (Losses, Detections) tuple in scripting")
                    self._has_warned = True
                return losses, detections
            return self.eager_outputs(losses, detections)

        def postprocess_detections(
            self, head_outputs: Dict[str, Tensor], image_anchors: List[Tensor], image_shapes: List[Tuple[int, int]]
        ) -> List[Dict[str, Tensor]]:
            bbox_regression = head_outputs["bbox_regression"]
            pred_scores = F.softmax(head_outputs["cls_logits"], dim=-1)

            num_classes = pred_scores.size(-1)
            device = pred_scores.device

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

            for boxes, scores, anchors, image_shape in zip(bbox_regression, pred_scores, image_anchors, image_shapes):
                boxes = self.box_coder.decode_single(boxes, anchors)
                boxes = box_ops.clip_boxes_to_image(boxes, image_shape)

                image_boxes = []
                image_scores = []
                image_labels = []
                for label in range(1, num_classes):
                    score = scores[:, label]

                    keep_idxs = score > self.score_thresh
                    score = score[keep_idxs]
                    box = boxes[keep_idxs]

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

                    image_boxes.append(box)
                    image_scores.append(score)
                    image_labels.append(torch.full_like(score, fill_value=label, dtype=torch.int64, device=device))

                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

    class SSDFeatureExtractorVGG(nn.Module):
        def __init__(self, backbone: nn.Module, highres: bool):
            super().__init__()

            _, _, maxpool3_pos, maxpool4_pos, _ = (i for i, layer in enumerate(backbone) if isinstance(layer, nn.MaxPool2d))

            # Patch ceil_mode for maxpool3 to get the same WxH output sizes as the paper
            backbone[maxpool3_pos].ceil_mode = True

            # parameters used for L2 regularization + rescaling
            self.scale_weight = nn.Parameter(torch.ones(512) * 20)

            # Multiple Feature maps - page 4, Fig 2 of SSD paper
            self.features = nn.Sequential(*backbone[:maxpool4_pos])  # until conv4_3

            # SSD300 case - page 4, Fig 2 of SSD paper
            extra = nn.ModuleList(
                [
                    nn.Sequential(
                        nn.Conv2d(1024, 256, kernel_size=1),
                        nn.ReLU(inplace=True),
                        nn.Conv2d(256, 512, kernel_size=3, padding=1, stride=2),  # conv8_2
                        nn.ReLU(inplace=True),
                    ),
                    nn.Sequential(
                        nn.Conv2d(512, 128, kernel_size=1),
                        nn.ReLU(inplace=True),
                        nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2),  # conv9_2
                        nn.ReLU(inplace=True),
                    ),
                    nn.Sequential(
                        nn.Conv2d(256, 128, kernel_size=1),
                        nn.ReLU(inplace=True),
                        nn.Conv2d(128, 256, kernel_size=3),  # conv10_2
                        nn.ReLU(inplace=True),
                    ),
                    nn.Sequential(
                        nn.Conv2d(256, 128, kernel_size=1),
                        nn.ReLU(inplace=True),
                        nn.Conv2d(128, 256, kernel_size=3),  # conv11_2
                        nn.ReLU(inplace=True),
                    ),
                ]
            )
            if highres:
                # Additional layers for the SSD512 case. See page 11, footernote 5.
                extra.append(
                    nn.Sequential(
                        nn.Conv2d(256, 128, kernel_size=1),
                        nn.ReLU(inplace=True),
                        nn.Conv2d(128, 256, kernel_size=4),  # conv12_2
                        nn.ReLU(inplace=True),
                    )
                )
            _xavier_init(extra)

            fc = nn.Sequential(
                nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=False),  # add modified maxpool5
                nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=6, dilation=6),  # FC6 with atrous
                nn.ReLU(inplace=True),
                nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1),  # FC7
                nn.ReLU(inplace=True),
            )
            _xavier_init(fc)
            extra.insert(
                0,
                nn.Sequential(
                    *backbone[maxpool4_pos:-1],  # until conv5_3, skip maxpool5
                    fc,
                ),
            )
            self.extra = extra

        def forward(self, x: Tensor) -> Dict[str, Tensor]:
            # L2 regularization + Rescaling of 1st block's feature map
            x = self.features(x)
            rescaled = self.scale_weight.view(1, -1, 1, 1) * F.normalize(x)
            output = [rescaled]

            # Calculating Feature maps for the rest blocks
            for block in self.extra:
                x = block(x)
                output.append(x)

            return OrderedDict([(str(i), v) for i, v in enumerate(output)])

    def _vgg_extractor(backbone: VGG, highres: bool, trainable_layers: int):
        backbone = backbone.features
        # Gather the indices of maxpools. These are the locations of output blocks.
        stage_indices = [0] + [i for i, b in enumerate(backbone) if isinstance(b, nn.MaxPool2d)][:-1]
        num_stages = len(stage_indices)

        # find the index of the layer from which we won't freeze
        torch._assert(
            0 <= trainable_layers <= num_stages,
            f"trainable_layers should be in the range [0, {num_stages}]. Instead got {trainable_layers}",
        )
        freeze_before = len(backbone) if trainable_layers == 0 else stage_indices[num_stages - trainable_layers]

        for b in backbone[:freeze_before]:
            for parameter in b.parameters():
                parameter.requires_grad_(False)

        return SSDFeatureExtractorVGG(backbone, highres)

    @register_model()
    @handle_legacy_interface(
        weights=("pretrained", SSD300_VGG16_Weights.COCO_V1),
        weights_backbone=("pretrained_backbone", VGG16_Weights.IMAGENET1K_FEATURES),
    )
    def ssd300_vgg16(
        *,
        weights: Optional[SSD300_VGG16_Weights] = None,
        progress: bool = True,
        num_classes: Optional[int] = None,
        weights_backbone: Optional[VGG16_Weights] = VGG16_Weights.IMAGENET1K_FEATURES,
        trainable_backbone_layers: Optional[int] = None,
        **kwargs: Any,
    ) -> SSD:
        """The SSD300 model is based on the `SSD: Single Shot MultiBox Detector
        <https://arxiv.org/abs/1512.02325>`_ 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, but they will be resized
        to a fixed size before passing it to the backbone.

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

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

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

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

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

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

        Example:

            >>> model = torchvision.models.detection.ssd300_vgg16(weights=SSD300_VGG16_Weights.DEFAULT)
            >>> model.eval()
            >>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)]
            >>> predictions = model(x)

        Args:
            weights (:class:`~torchvision.models.detection.SSD300_VGG16_Weights`, optional): The pretrained
                    weights to use. See
                    :class:`~torchvision.models.detection.SSD300_VGG16_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.VGG16_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 4.
            **kwargs: parameters passed to the ``torchvision.models.detection.SSD``
                base class. Please refer to the `source code
                <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py>`_
                for more details about this class.

        .. autoclass:: torchvision.models.detection.SSD300_VGG16_Weights
            :members:
        """
        weights = SSD300_VGG16_Weights.verify(weights)
        weights_backbone = VGG16_Weights.verify(weights_backbone)

        if "size" in kwargs:
            warnings.warn("The size of the model is already fixed; ignoring the parameter.")

        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

        trainable_backbone_layers = _validate_trainable_layers(
            weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 4
        )

        # Use custom backbones more appropriate for SSD
        backbone = vgg16(weights=weights_backbone, progress=progress)
        backbone = _vgg_extractor(backbone, False, trainable_backbone_layers)
        anchor_generator = DefaultBoxGenerator(
            [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
            scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05],
            steps=[8, 16, 32, 64, 100, 300],
        )

        defaults = {
            # Rescale the input in a way compatible to the backbone
            "image_mean": [0.48235, 0.45882, 0.40784],
            "image_std": [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0],  # undo the 0-1 scaling of toTensor
        }
        kwargs: Any = {**defaults, **kwargs}
        model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs)

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

        return model

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