text: 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 ..._internally_replaced_utils import load_state_dict_from_url
  from ...ops import boxes as box_ops
  from ...utils import _log_api_usage_once
  from .. import vgg
  from . import _utils as det_utils
  from .anchor_utils import DefaultBoxGenerator
  from .backbone_utils import _validate_trainable_layers
  from .transform import GeneralizedRCNNTransform
  
  __all__ = ["SSD", "ssd300_vgg16"]
  
  model_urls = {
      "ssd300_vgg16_coco": "https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth",
  }
  
  backbone_urls = {
      # We port the features of a VGG16 backbone trained by amdegroot because unlike the one on TorchVision, it uses the
      # same input standardization method as the paper. Ref: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
      # Only the `features` weights have proper values, those on the `classifier` module are filled with nans.
      "vgg16_features": "https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth"
  }
  
  
  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 if it is in training or evaluation mode.
  
      During training, the model expects both the input tensors, as well as a targets (list of dictionary),
      containing:
          - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (Int64Tensor[N]): the class label for each ground-truth box
  
      The model returns a Dict[Tensor] during training, containing the classification and regression
      losses.
  
      During inference, the model requires only the input tensors, and returns the post-processed
      predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
      follows, where ``N`` is the number of detections:
  
          - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (Int64Tensor[N]): the predicted labels for each detection
          - scores (Tensor[N]): the scores 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,
      ):
          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)
  
              assert len(out_channels) == 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
          )
  
          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 and targets is None:
              raise ValueError("In training mode, targets should be passed")
  
          if self.training:
              assert targets is not None
              for target in targets:
                  boxes = target["boxes"]
                  if isinstance(boxes, torch.Tensor):
                      if len(boxes.shape) != 2 or boxes.shape[-1] != 4:
                          raise ValueError(f"Expected target boxes to be a tensor of shape [N, 4], got {boxes.shape}.")
                  else:
                      raise ValueError(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:]
              assert len(val) == 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()
                      raise ValueError(
                          "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:
              assert targets is not None
  
              matched_idxs = []
              for anchors_per_image, targets_per_image in zip(anchors, targets):
                  if targets_per_image["boxes"].numel() == 0:
                      matched_idxs.append(
                          torch.full((anchors_per_image.size(0),), -1, dtype=torch.int64, device=anchors_per_image.device)
                      )
                      continue
  
                  match_quality_matrix = box_ops.box_iou(targets_per_image["boxes"], anchors_per_image)
                  matched_idxs.append(self.proposal_matcher(match_quality_matrix))
  
              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 = min(self.topk_candidates, score.size(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.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 wont freeze
      assert 0 <= trainable_layers <= num_stages
      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)
  
  
  def ssd300_vgg16(
      pretrained: bool = False,
      progress: bool = True,
      num_classes: int = 91,
      pretrained_backbone: bool = True,
      trainable_backbone_layers: Optional[int] = None,
      **kwargs: Any,
  ):
      """Constructs an SSD model with input size 300x300 and a VGG16 backbone.
  
      Reference: `"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 if it is in training or evaluation mode.
  
      During training, the model expects both the input tensors, as well as a targets (list of dictionary),
      containing:
  
          - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (Int64Tensor[N]): the class label for each ground-truth box
  
      The model returns a Dict[Tensor] during training, containing the classification and regression
      losses.
  
      During inference, the model requires only the input tensors, and returns the post-processed
      predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
      follows, where ``N`` is the number of detections:
  
          - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (Int64Tensor[N]): the predicted labels for each detection
          - scores (Tensor[N]): the scores for each detection
  
      Example:
  
          >>> model = torchvision.models.detection.ssd300_vgg16(pretrained=True)
          >>> model.eval()
          >>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)]
          >>> predictions = model(x)
  
      Args:
          pretrained (bool): If True, returns a model pre-trained on COCO train2017
          progress (bool): If True, displays a progress bar of the download to stderr
          num_classes (int): number of output classes of the model (including the background)
          pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
          trainable_backbone_layers (int): number of trainable (not frozen) resnet 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.
      """
      if "size" in kwargs:
          warnings.warn("The size of the model is already fixed; ignoring the argument.")
  
      trainable_backbone_layers = _validate_trainable_layers(
          pretrained or pretrained_backbone, trainable_backbone_layers, 5, 4
      )
  
      if pretrained:
          # no need to download the backbone if pretrained is set
          pretrained_backbone = False
  
      # Use custom backbones more appropriate for SSD
      backbone = vgg.vgg16(pretrained=False, progress=progress)
      if pretrained_backbone:
          state_dict = load_state_dict_from_url(backbone_urls["vgg16_features"], progress=progress)
          backbone.load_state_dict(state_dict)
  
      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 = {**defaults, **kwargs}
      model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs)
      if pretrained:
          weights_name = "ssd300_vgg16_coco"
          if model_urls.get(weights_name, None) is None:
              raise ValueError(f"No checkpoint is available for model {weights_name}")
          state_dict = load_state_dict_from_url(model_urls[weights_name], progress=progress)
          model.load_state_dict(state_dict)
      return model
  