class_docstr: The roi embed head.
  
  This module is used in multi-object tracking methods, such as MaskTrack
  R-CNN.
  
  Args:
      num_convs (int): The number of convoluational layers to embed roi
          features. Defaults to 0.
      num_fcs (int): The number of fully connection layers to embed roi
          features. Defaults to 0.
      roi_feat_size (int|tuple(int)): The spatial size of roi features.
          Defaults to 7.
      in_channels (int): The input channel of roi features. Defaults to 256.
      conv_out_channels (int): The output channel of roi features after
          forwarding convoluational layers. Defaults to 256.
      with_avg_pool (bool): Whether use average pooling before passing roi
          features into fully connection layers. Defaults to False.
      fc_out_channels (int): The output channel of roi features after
          forwarding fully connection layers. Defaults to 1024.
      conv_cfg (dict): Config dict for convolution layer. Defaults to None,
          which means using conv2d.
      norm_cfg (dict): Config dict for normalization layer. Defaults to None.
      loss_match (dict): The loss function. Defaults to
          dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
      init_cfg (dict): Configuration of initialization. Defaults to None.
class_docstr_tok: ["The","roi","embed","head",".","This","module","is","used","in","multi-object","tracking","methods",",","such","as","MaskTrack","R-CNN",".","Args",":","num_convs","(","int",")",":","The","number","of","convoluational","layers","to","embed","roi","features",".","Defaults","to","0.","num_fcs","(","int",")",":","The","number","of","fully","connection","layers","to","embed","roi","features",".","Defaults","to","0.","roi_feat_size","(","int|tuple","(","int",")",")",":","The","spatial","size","of","roi","features",".","Defaults","to","7.","in_channels","(","int",")",":","The","input","channel","of","roi","features",".","Defaults","to","256.","conv_out_channels","(","int",")",":","The","output","channel","of","roi","features","after","forwarding","convoluational","layers",".","Defaults","to","256.","with_avg_pool","(","bool",")",":","Whether","use","average","pooling","before","passing","roi","features","into","fully","connection","layers",".","Defaults","to","False",".","fc_out_channels","(","int",")",":","The","output","channel","of","roi","features","after","forwarding","fully","connection","layers",".","Defaults","to","1024.","conv_cfg","(","dict",")",":","Config","dict","for","convolution","layer",".","Defaults","to","None",",","which","means","using","conv2d",".","norm_cfg","(","dict",")",":","Config","dict","for","normalization","layer",".","Defaults","to","None",".","loss_match","(","dict",")",":","The","loss","function",".","Defaults","to","dict","(","type='CrossEntropyLoss","'",",","use_sigmoid=False",",","loss_weight=1.0",")","init_cfg","(","dict",")",":","Configuration","of","initialization",".","Defaults","to","None","."]
docstr: Perform forward propagation of the tracking head and predict
  tracking results on the features of the upstream network.
  
  Args:
      roi_feats (Tensor): Feature map of current images rois.
      prev_roi_feats (Tensor): Feature map of previous images rois.
  
  Returns:
      list[Tensor]: The predicted similarity_logits of each pair of key
      image and reference image.
docstr_tok: ["Perform","forward","propagation","of","the","tracking","head","and","predict","tracking","results","on","the","features","of","the","upstream","network",".","Args",":","roi_feats","(","Tensor",")",":","Feature","map","of","current","images","rois",".","prev_roi_feats","(","Tensor",")",":","Feature","map","of","previous","images","rois",".","Returns",":","list","[","Tensor","]",":","The","predicted","similarity_logits","of","each","pair","of","key","image","and","reference","image","."]
code: def predict(
      self, roi_feats: Tensor, prev_roi_feats: Tensor
  ) -> List[Tensor]:
      """Perform forward propagation of the tracking head and predict
      tracking results on the features of the upstream network.
  
      Args:
          roi_feats (Tensor): Feature map of current images rois.
          prev_roi_feats (Tensor): Feature map of previous images rois.
  
      Returns:
          list[Tensor]: The predicted similarity_logits of each pair of key
          image and reference image.
      """
      x_split, ref_x_split = self(
          roi_feats,
          prev_roi_feats,
          [roi_feats.shape[0]],
          [prev_roi_feats.shape[0]],
      )
  
      similarity_logits = self.predict_by_feat(x_split, ref_x_split)
  
      return similarity_logits
  
imports: from collections import defaultdict
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule
  from mmengine.model import BaseModule
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.models.losses import accuracy
  from mmdet.models.task_modules import SamplingResult
  from mmdet.models.task_modules.tracking import embed_similarity
  from mmdet.registry import MODELS
label_desc: Use image node_id 9 for calling the RoIEmbedHead obj's underlying member method code with example usage: obj.predict(roi_feats, prev_roi_feats) and returns: similarity_logits