original_docstring: Evaluate fitted parameters through loss computation. This function
          serves two purposes: 1) internally, for loss backpropagation 2)
          externally, for fitting quality evaluation.
  
          Notes:
              B: batch size
              K: number of keypoints
              D: shape dimension
  
          Args:
              betas: shape (B, D)
              body_pose: shape (B, 69)
              global_orient: shape (B, 3)
              transl: shape (B, 3)
              keypoints2d: 2D keypoints of shape (B, K, 2)
              keypoints2d_conf: 2D keypoint confidence of shape (B, K)
              keypoints2d_weight: weight of 2D keypoint loss
              keypoints3d: 3D keypoints of shape (B, K, 3).
              keypoints3d_conf: 3D keypoint confidence of shape (B, K)
              keypoints3d_weight: weight of 3D keypoint loss
              shape_prior_weight: weight of shape prior loss
              joint_prior_weight: weight of joint prior loss
              smooth_loss_weight: weight of smooth loss
              pose_prior_weight: weight of pose prior loss
              joint_weights: per joint weight of shape (K, )
              return_verts: whether to return vertices
              return_joints: whether to return joints
              return_full_pose: whether to return full pose
              reduction_override: reduction method, e.g., 'none', 'sum', 'mean'
  
          Returns:
              ret: a dictionary that includes body model parameters,
                  and optional attributes such as vertices and joints
          
docstring: Evaluate fitted parameters through loss computation. This function
  serves two purposes: 1) internally, for loss backpropagation 2)
  externally, for fitting quality evaluation.
  
  batch size
  K: number of keypoints
  D: shape dimension
docstring_tokens: Evaluate fitted parameters through loss computation . This function serves two purposes : 1 ) internally for loss backpropagation 2 ) externally for fitting quality evaluation . batch size K : number of keypoints D : shape dimension
code: def evaluate(
          self,
          betas: torch.Tensor = None,
          body_pose: torch.Tensor = None,
          global_orient: torch.Tensor = None,
          transl: torch.Tensor = None,
          keypoints2d: torch.Tensor = None,
          keypoints2d_conf: torch.Tensor = None,
          keypoints2d_weight: float = None,
          keypoints3d: torch.Tensor = None,
          keypoints3d_conf: torch.Tensor = None,
          keypoints3d_weight: float = None,
          shape_prior_weight: float = None,
          joint_prior_weight: float = None,
          smooth_loss_weight: float = None,
          pose_prior_weight: float = None,
          joint_weights: dict = {},
          return_verts: bool = False,
          return_full_pose: bool = False,
          return_joints: bool = False,
          reduction_override: str = None,
      ) -> dict:
          """Evaluate fitted parameters through loss computation. This function
          serves two purposes: 1) internally, for loss backpropagation 2)
          externally, for fitting quality evaluation.
  
          Notes:
              B: batch size
              K: number of keypoints
              D: shape dimension
  
          Args:
              betas: shape (B, D)
              body_pose: shape (B, 69)
              global_orient: shape (B, 3)
              transl: shape (B, 3)
              keypoints2d: 2D keypoints of shape (B, K, 2)
              keypoints2d_conf: 2D keypoint confidence of shape (B, K)
              keypoints2d_weight: weight of 2D keypoint loss
              keypoints3d: 3D keypoints of shape (B, K, 3).
              keypoints3d_conf: 3D keypoint confidence of shape (B, K)
              keypoints3d_weight: weight of 3D keypoint loss
              shape_prior_weight: weight of shape prior loss
              joint_prior_weight: weight of joint prior loss
              smooth_loss_weight: weight of smooth loss
              pose_prior_weight: weight of pose prior loss
              joint_weights: per joint weight of shape (K, )
              return_verts: whether to return vertices
              return_joints: whether to return joints
              return_full_pose: whether to return full pose
              reduction_override: reduction method, e.g., 'none', 'sum', 'mean'
  
          Returns:
              ret: a dictionary that includes body model parameters,
                  and optional attributes such as vertices and joints
          """
  
          ret = {}
  
          body_model_output = self.body_model(
              global_orient=global_orient,
              body_pose=body_pose,
              betas=betas,
              transl=transl,
              return_verts=return_verts,
              return_full_pose=return_full_pose)
  
          model_joints = body_model_output['joints']
  
          loss_dict = self._compute_loss(
              model_joints,
              keypoints2d=keypoints2d,
              keypoints2d_conf=keypoints2d_conf,
              keypoints2d_weight=keypoints2d_weight,
              keypoints3d=keypoints3d,
              keypoints3d_conf=keypoints3d_conf,
              keypoints3d_weight=keypoints3d_weight,
              joint_prior_weight=joint_prior_weight,
              shape_prior_weight=shape_prior_weight,
              smooth_loss_weight=smooth_loss_weight,
              pose_prior_weight=pose_prior_weight,
              joint_weights=joint_weights,
              reduction_override=reduction_override,
              body_pose=body_pose,
              betas=betas)
          ret.update(loss_dict)
  
          if return_verts:
              ret['vertices'] = body_model_output['vertices']
          if return_full_pose:
              ret['full_pose'] = body_model_output['full_pose']
          if return_joints:
              ret['joints'] = model_joints
  
          return ret
code_tokens: def evaluate ( self , betas : torch . Tensor = None , body_pose : torch . Tensor = None , global_orient : torch . Tensor = None , transl : torch . Tensor = None , keypoints2d : torch . Tensor = None , keypoints2d_conf : torch . Tensor = None , keypoints2d_weight : float = None , keypoints3d : torch . Tensor = None , keypoints3d_conf : torch . Tensor = None , keypoints3d_weight : float = None , shape_prior_weight : float = None , joint_prior_weight : float = None , smooth_loss_weight : float = None , pose_prior_weight : float = None , joint_weights : dict = { } , return_verts : bool = False , return_full_pose : bool = False , return_joints : bool = False , reduction_override : str = None , ) -> dict : ret = { } body_model_output = self . body_model ( global_orient = global_orient , body_pose = body_pose , betas = betas , transl = transl , return_verts = return_verts , return_full_pose = return_full_pose ) model_joints = body_model_output [ 'joints' ] loss_dict = self . _compute_loss ( model_joints , keypoints2d = keypoints2d , keypoints2d_conf = keypoints2d_conf , keypoints2d_weight = keypoints2d_weight , keypoints3d = keypoints3d , keypoints3d_conf = keypoints3d_conf , keypoints3d_weight = keypoints3d_weight , joint_prior_weight = joint_prior_weight , shape_prior_weight = shape_prior_weight , smooth_loss_weight = smooth_loss_weight , pose_prior_weight = pose_prior_weight , joint_weights = joint_weights , reduction_override = reduction_override , body_pose = body_pose , betas = betas ) ret . update ( loss_dict ) if return_verts : ret [ 'vertices' ] = body_model_output [ 'vertices' ] if return_full_pose : ret [ 'full_pose' ] = body_model_output [ 'full_pose' ] if return_joints : ret [ 'joints' ] = model_joints return ret
short_docstring: Evaluate fitted parameters through loss computation.
short_docstring_tokens: Evaluate fitted parameters through loss computation .
comment: """Evaluate fitted parameters through loss computation. This function
          serves two purposes: 1) internally, for loss backpropagation 2)
          externally, for fitting quality evaluation.
  
          Notes:
              B: batch size
              K: number of keypoints
              D: shape dimension
  
          Args:
              betas: shape (B, D)
              body_pose: shape (B, 69)
              global_orient: shape (B, 3)
              transl: shape (B, 3)
              keypoints2d: 2D keypoints of shape (B, K, 2)
              keypoints2d_conf: 2D keypoint confidence of shape (B, K)
              keypoints2d_weight: weight of 2D keypoint loss
              keypoints3d: 3D keypoints of shape (B, K, 3).
              keypoints3d_conf: 3D keypoint confidence of shape (B, K)
              keypoints3d_weight: weight of 3D keypoint loss
              shape_prior_weight: weight of shape prior loss
              joint_prior_weight: weight of joint prior loss
              smooth_loss_weight: weight of smooth loss
              pose_prior_weight: weight of pose prior loss
              joint_weights: per joint weight of shape (K, )
              return_verts: whether to return vertices
              return_joints: whether to return joints
              return_full_pose: whether to return full pose
              reduction_override: reduction method, e.g., 'none', 'sum', 'mean'
  
          Returns:
              ret: a dictionary that includes body model parameters,
                  and optional attributes such as vertices and joints
          """