code: import torch
  from torch import nn
  
  from torchvision.ops import MultiScaleRoIAlign
  
  from ..utils import load_state_dict_from_url
  
  from .faster_rcnn import FasterRCNN
  from .backbone_utils import resnet_fpn_backbone
  
  
  __all__ = [
      "KeypointRCNN", "keypointrcnn_resnet50_fpn"
  ]
  
  
  class KeypointRCNN(FasterRCNN):
      """
      Implements Keypoint 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 values of x
            between 0 and W and values of y between 0 and H
          - labels (Int64Tensor[N]): the class label for each ground-truth box
          - keypoints (FloatTensor[N, K, 3]): the K keypoints location for each of the N instances, in the
            format [x, y, visibility], where visibility=0 means that the keypoint is not visible.
      The model returns a Dict[Tensor] during training, containing the classification and regression
      losses for both the RPN and the R-CNN, and the keypoint loss.
      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 values of x
            between 0 and W and values of y between 0 and H
          - labels (Int64Tensor[N]): the predicted labels for each image
          - scores (Tensor[N]): the scores or each prediction
          - keypoints (FloatTensor[N, K, 3]): the locations of the predicted keypoints, in [x, y, v] format.
      Arguments:
          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
          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
          keypoint_roi_pool (MultiScaleRoIAlign): the module which crops and resizes the feature maps in
               the locations indicated by the bounding boxes, which will be used for the keypoint head.
          keypoint_head (nn.Module): module that takes the cropped feature maps as input
          keypoint_predictor (nn.Module): module that takes the output of the keypoint_head and returns the
              heatmap logits
      Example::
          >>> import torch
          >>> import torchvision
          >>> from torchvision.models.detection import KeypointRCNN
          >>> from torchvision.models.detection.rpn import AnchorGenerator
          >>>
          >>> # load a pre-trained model for classification and return
          >>> # only the features
          >>> backbone = torchvision.models.mobilenet_v2(pretrained=True).features
          >>> # KeypointRCNN 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)
          >>>
          >>> keypoint_roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
          >>>                                                          output_size=14,
          >>>                                                          sampling_ratio=2)
          >>> # put the pieces together inside a KeypointRCNN model
          >>> model = KeypointRCNN(backbone,
          >>>                      num_classes=2,
          >>>                      rpn_anchor_generator=anchor_generator,
          >>>                      box_roi_pool=roi_pooler,
          >>>                      keypoint_roi_pool=keypoint_roi_pooler)
          >>> model.eval()
          >>> 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=None, 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,
                   # 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,
                   # keypoint parameters
                   keypoint_roi_pool=None, keypoint_head=None, keypoint_predictor=None,
                   num_keypoints=17):
  
          assert isinstance(keypoint_roi_pool, (MultiScaleRoIAlign, type(None)))
          if min_size is None:
              min_size = (640, 672, 704, 736, 768, 800)
  
          if num_classes is not None:
              if keypoint_predictor is not None:
                  raise ValueError("num_classes should be None when keypoint_predictor is specified")
  
          out_channels = backbone.out_channels
  
          if keypoint_roi_pool is None:
              keypoint_roi_pool = MultiScaleRoIAlign(
                  featmap_names=['0', '1', '2', '3'],
                  output_size=14,
                  sampling_ratio=2)
  
          if keypoint_head is None:
              keypoint_layers = tuple(512 for _ in range(8))
              keypoint_head = KeypointRCNNHeads(out_channels, keypoint_layers)
  
          if keypoint_predictor is None:
              keypoint_dim_reduced = 512  # == keypoint_layers[-1]
              keypoint_predictor = KeypointRCNNPredictor(keypoint_dim_reduced, num_keypoints)
  
          super(KeypointRCNN, self).__init__(
              backbone, num_classes,
              # transform parameters
              min_size, max_size,
              image_mean, image_std,
              # RPN-specific parameters
              rpn_anchor_generator, rpn_head,
              rpn_pre_nms_top_n_train, rpn_pre_nms_top_n_test,
              rpn_post_nms_top_n_train, rpn_post_nms_top_n_test,
              rpn_nms_thresh,
              rpn_fg_iou_thresh, rpn_bg_iou_thresh,
              rpn_batch_size_per_image, rpn_positive_fraction,
              # Box parameters
              box_roi_pool, box_head, box_predictor,
              box_score_thresh, box_nms_thresh, box_detections_per_img,
              box_fg_iou_thresh, box_bg_iou_thresh,
              box_batch_size_per_image, box_positive_fraction,
              bbox_reg_weights)
  
          self.roi_heads.keypoint_roi_pool = keypoint_roi_pool
          self.roi_heads.keypoint_head = keypoint_head
          self.roi_heads.keypoint_predictor = keypoint_predictor
  
  
  class KeypointRCNNHeads(nn.Sequential):
      def __init__(self, in_channels, layers):
          d = []
          next_feature = in_channels
          for out_channels in layers:
              d.append(nn.Conv2d(next_feature, out_channels, 3, stride=1, padding=1))
              d.append(nn.ReLU(inplace=True))
              next_feature = out_channels
          super(KeypointRCNNHeads, self).__init__(*d)
          for m in self.children():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                  nn.init.constant_(m.bias, 0)
  
  
  class KeypointRCNNPredictor(nn.Module):
      def __init__(self, in_channels, num_keypoints):
          super(KeypointRCNNPredictor, self).__init__()
          input_features = in_channels
          deconv_kernel = 4
          self.kps_score_lowres = nn.ConvTranspose2d(
              input_features,
              num_keypoints,
              deconv_kernel,
              stride=2,
              padding=deconv_kernel // 2 - 1,
          )
          nn.init.kaiming_normal_(
              self.kps_score_lowres.weight, mode="fan_out", nonlinearity="relu"
          )
          nn.init.constant_(self.kps_score_lowres.bias, 0)
          self.up_scale = 2
          self.out_channels = num_keypoints
  
      def forward(self, x):
          x = self.kps_score_lowres(x)
          return torch.nn.functional.interpolate(
              x, scale_factor=float(self.up_scale), mode="bilinear", align_corners=False, recompute_scale_factor=False
          )
  
  
  model_urls = {
      # legacy model for BC reasons, see https://github.com/pytorch/vision/issues/1606
      'keypointrcnn_resnet50_fpn_coco_legacy':
          'https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth',
      'keypointrcnn_resnet50_fpn_coco':
          'https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth',
  }
  
  
  def keypointrcnn_resnet50_fpn(pretrained=False, progress=True,
                                num_classes=2, num_keypoints=17,
                                pretrained_backbone=True, trainable_backbone_layers=3, **kwargs):
      """
      Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone.
      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 values of ``x``
            between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
          - labels (``Int64Tensor[N]``): the class label for each ground-truth box
          - keypoints (``FloatTensor[N, K, 3]``): the ``K`` keypoints location for each of the ``N`` instances, in the
            format ``[x, y, visibility]``, where ``visibility=0`` means that the keypoint is not visible.
      The model returns a ``Dict[Tensor]`` during training, containing the classification and regression
      losses for both the RPN and the R-CNN, and the keypoint loss.
      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 values of ``x``
            between ``0`` and ``W`` and values of ``y`` between ``0`` and ``H``
          - labels (``Int64Tensor[N]``): the predicted labels for each image
          - scores (``Tensor[N]``): the scores or each prediction
          - keypoints (``FloatTensor[N, K, 3]``): the locations of the predicted keypoints, in ``[x, y, v]`` format.
      Keypoint R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.
      Example::
          >>> model = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained=True)
          >>> 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, "keypoint_rcnn.onnx", opset_version = 11)
      Arguments:
          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
          pretrained_backbone (bool): If True, returns a model with backbone pre-trained on Imagenet
          num_classes (int): number of output classes of the model (including the background)
          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.
      """
      assert trainable_backbone_layers <= 5 and trainable_backbone_layers >= 0
      # dont freeze any layers if pretrained model or backbone is not used
      if not (pretrained or pretrained_backbone):
          trainable_backbone_layers = 5
      if pretrained:
          # no need to download the backbone if pretrained is set
          pretrained_backbone = False
      backbone = resnet_fpn_backbone('resnet50', pretrained_backbone, trainable_layers=trainable_backbone_layers)
      model = KeypointRCNN(backbone, num_classes, num_keypoints=num_keypoints, **kwargs)
      if pretrained:
          key = 'keypointrcnn_resnet50_fpn_coco'
          if pretrained == 'legacy':
              key += '_legacy'
          state_dict = load_state_dict_from_url(model_urls[key],
                                                progress=progress)
          model.load_state_dict(state_dict)
      return model
apis: torch.nn.init.kaiming_normal_ torch.nn.init.constant_ torch.nn.Conv2d torch.nn.ReLU torch.nn.ConvTranspose2d