original_docstring: Returns AUC values of all trackers for all sequences as tensor
docstring: Returns AUC values of all trackers for all sequences as tensor
docstring_tokens: Returns AUC values of all trackers for all sequences as tensor
code: def _getAUCAllTrackers(self, default=False, border=False, nfov=False):
          """Returns AUC values of all trackers for all sequences as tensor"""
          dataset = self.DATASET
  
          plot_bin_gap = 0.05
          threshold_set_overlap = torch.arange(0.0, 1.0 + plot_bin_gap, plot_bin_gap, dtype=torch.float64)
          ave_success_rate_plot_overlap = torch.zeros((len(dataset), len(self.TRACKERS), threshold_set_overlap.numel()), dtype=torch.float32)
  
          if default:
              path = self.PATH_IOU_DEFAULT
          elif border:
              path = self.PATH_IOU_BORDER
          elif nfov:
              path = self.PATH_IOU_NFOV
  
          for i in range(len(dataset)):
              for j in range(len(self.TRACKERS)):
                  # load and parse data
                  current_path = path.replace("<TRACKER>", self.TRACKERS[j]).replace("<NUMBER>", dataset[i])
                  iou = self._parseGivenDataFile(current_path)
  
                  # transform python lists to tensors
                  iou_tensor = torch.Tensor(iou)
  
                  # success computing
                  ave_success_rate_plot_overlap[i,j,:] = (iou_tensor.view(-1, 1) > threshold_set_overlap.view(1, -1)).sum(0).float() / len(iou)
  
          # auc_curve as mean of ave_success_rate_plot_overlap tensors
          auc_curve = ave_success_rate_plot_overlap.mean(0) * 100.0
          auc = auc_curve.mean(-1)
  
          return auc
code_tokens: def _getAUCAllTrackers ( self , default = False , border = False , nfov = False ) : dataset = self . DATASET plot_bin_gap = 0.05 threshold_set_overlap = torch . arange ( 0.0 , 1.0 + plot_bin_gap , plot_bin_gap , dtype = torch . float64 ) ave_success_rate_plot_overlap = torch . zeros ( ( len ( dataset ) , len ( self . TRACKERS ) , threshold_set_overlap . numel ( ) ) , dtype = torch . float32 ) if default : path = self . PATH_IOU_DEFAULT elif border : path = self . PATH_IOU_BORDER elif nfov : path = self . PATH_IOU_NFOV for i in range ( len ( dataset ) ) : for j in range ( len ( self . TRACKERS ) ) : current_path = path . replace ( "<TRACKER>" , self . TRACKERS [ j ] ) . replace ( "<NUMBER>" , dataset [ i ] ) iou = self . _parseGivenDataFile ( current_path ) iou_tensor = torch . Tensor ( iou ) ave_success_rate_plot_overlap [ i , j , : ] = ( iou_tensor . view ( - 1 , 1 ) > threshold_set_overlap . view ( 1 , - 1 ) ) . sum ( 0 ) . float ( ) / len ( iou ) auc_curve = ave_success_rate_plot_overlap . mean ( 0 ) * 100.0 auc = auc_curve . mean ( - 1 ) return auc
short_docstring: Returns AUC values of all trackers for all sequences as tensor
short_docstring_tokens: Returns AUC values of all trackers for all sequences as tensor
comment: """Returns AUC values of all trackers for all sequences as tensor"""$SEP$# load and parse data$SEP$# transform python lists to tensors$SEP$# success computing$SEP$# auc_curve as mean of ave_success_rate_plot_overlap tensors