text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  import torch
  
  from models.backbone import Backbone, Joiner
  from models.detr import DETR, PostProcess
  from models.position_encoding import PositionEmbeddingSine
  from models.segmentation import DETRsegm, PostProcessPanoptic
  from models.transformer import Transformer
  
  dependencies = ["torch", "torchvision"]
  # 返回不同backbone的detr模型
  
  def _make_detr(backbone_name: str, dilation=False, num_classes=91, mask=False):
      hidden_dim = 256
      backbone = Backbone(backbone_name, train_backbone=True, return_interm_layers=mask, dilation=dilation)
      pos_enc = PositionEmbeddingSine(hidden_dim // 2, normalize=True)
      backbone_with_pos_enc = Joiner(backbone, pos_enc)
      backbone_with_pos_enc.num_channels = backbone.num_channels
      transformer = Transformer(d_model=hidden_dim, return_intermediate_dec=True)
      detr = DETR(backbone_with_pos_enc, transformer, num_classes=num_classes, num_queries=100)
      if mask:
          return DETRsegm(detr)
      return detr
  
  
  def detr_resnet50(pretrained=False, num_classes=91, return_postprocessor=False):
      """
      DETR R50 with 6 encoder and 6 decoder layers.
  
      Achieves 42/62.4 AP/AP50 on COCO val5k.
      """
      model = _make_detr("resnet50", dilation=False, num_classes=num_classes)
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth", map_location="cpu", check_hash=True
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcess()
      return model
  
  
  def detr_resnet50_dc5(pretrained=False, num_classes=91, return_postprocessor=False):
      """
      DETR-DC5 R50 with 6 encoder and 6 decoder layers.
  
      The last block of ResNet-50 has dilation to increase
      output resolution.
      Achieves 43.3/63.1 AP/AP50 on COCO val5k.
      """
      model = _make_detr("resnet50", dilation=True, num_classes=num_classes)
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-f0fb7ef5.pth", map_location="cpu", check_hash=True
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcess()
      return model
  
  
  def detr_resnet101(pretrained=False, num_classes=91, return_postprocessor=False):
      """
      DETR-DC5 R101 with 6 encoder and 6 decoder layers.
  
      Achieves 43.5/63.8 AP/AP50 on COCO val5k.
      """
      model = _make_detr("resnet101", dilation=False, num_classes=num_classes)
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth", map_location="cpu", check_hash=True
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcess()
      return model
  
  
  def detr_resnet101_dc5(pretrained=False, num_classes=91, return_postprocessor=False):
      """
      DETR-DC5 R101 with 6 encoder and 6 decoder layers.
  
      The last block of ResNet-101 has dilation to increase
      output resolution.
      Achieves 44.9/64.7 AP/AP50 on COCO val5k.
      """
      model = _make_detr("resnet101", dilation=True, num_classes=num_classes)
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r101-dc5-a2e86def.pth", map_location="cpu", check_hash=True
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcess()
      return model
  
  
  def detr_resnet50_panoptic(
      pretrained=False, num_classes=250, threshold=0.85, return_postprocessor=False
  ):
      """
      DETR R50 with 6 encoder and 6 decoder layers.
      Achieves 43.4 PQ on COCO val5k.
  
     threshold is the minimum confidence required for keeping segments in the prediction
      """
      model = _make_detr("resnet50", dilation=False, num_classes=num_classes, mask=True)
      is_thing_map = {i: i <= 90 for i in range(250)}
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r50-panoptic-00ce5173.pth",
              map_location="cpu",
              check_hash=True,
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcessPanoptic(is_thing_map, threshold=threshold)
      return model
  
  
  def detr_resnet50_dc5_panoptic(
      pretrained=False, num_classes=250, threshold=0.85, return_postprocessor=False
  ):
      """
      DETR-DC5 R50 with 6 encoder and 6 decoder layers.
  
      The last block of ResNet-50 has dilation to increase
      output resolution.
      Achieves 44.6 on COCO val5k.
  
     threshold is the minimum confidence required for keeping segments in the prediction
      """
      model = _make_detr("resnet50", dilation=True, num_classes=num_classes, mask=True)
      is_thing_map = {i: i <= 90 for i in range(250)}
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-panoptic-da08f1b1.pth",
              map_location="cpu",
              check_hash=True,
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcessPanoptic(is_thing_map, threshold=threshold)
      return model
  
  
  def detr_resnet101_panoptic(
      pretrained=False, num_classes=250, threshold=0.85, return_postprocessor=False
  ):
      """
      DETR-DC5 R101 with 6 encoder and 6 decoder layers.
  
      Achieves 45.1 PQ on COCO val5k.
  
     threshold is the minimum confidence required for keeping segments in the prediction
      """
      model = _make_detr("resnet101", dilation=False, num_classes=num_classes, mask=True)
      is_thing_map = {i: i <= 90 for i in range(250)}
      if pretrained:
          checkpoint = torch.hub.load_state_dict_from_url(
              url="https://dl.fbaipublicfiles.com/detr/detr-r101-panoptic-40021d53.pth",
              map_location="cpu",
              check_hash=True,
          )
          model.load_state_dict(checkpoint["model"])
      if return_postprocessor:
          return model, PostProcessPanoptic(is_thing_map, threshold=threshold)
      return model
  