original_docstring: Returns Precision values of all trackers for all sequences as tensor
docstring: Returns Precision values of all trackers for all sequences as tensor
docstring_tokens: Returns Precision values of all trackers for all sequences as tensor
code: def _getCerrorAllTrackers(self, default=False, border=False, nfov=False):
          """Returns Precision values of all trackers for all sequences as tensor"""
          dataset = self.DATASET
          
          threshold_set_center = torch.arange(0, 51, dtype=torch.float64)
          ave_success_rate_plot_center = torch.zeros((len(dataset), len(self.TRACKERS), threshold_set_center.numel()), dtype=torch.float32)
  
          path = ""
          if default:
              path = self.PATH_CENTER_ERROR_DEFAULT
          elif border:
              path = self.PATH_CENTER_ERROR_BORDER
          elif nfov:
              path = self.PATH_CENTER_ERROR_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])
                  cerror = self._parseGivenDataFile(current_path)
  
                  # transform python lists to tensors
                  cerror_tensor = torch.Tensor(cerror)
  
                  # success computing
                  ave_success_rate_plot_center[i,j,:] = (cerror_tensor.view(-1, 1) <= threshold_set_center.view(1, -1)).sum(0).float() / len(cerror)
  
          # create curves
          prec_curve = ave_success_rate_plot_center.mean(0) * 100.0
          # score should be counted for max 20 pixel error
          prec_score = prec_curve[:, 20]
  
          return prec_score
code_tokens: def _getCerrorAllTrackers ( self , default = False , border = False , nfov = False ) : dataset = self . DATASET threshold_set_center = torch . arange ( 0 , 51 , dtype = torch . float64 ) ave_success_rate_plot_center = torch . zeros ( ( len ( dataset ) , len ( self . TRACKERS ) , threshold_set_center . numel ( ) ) , dtype = torch . float32 ) path = "" if default : path = self . PATH_CENTER_ERROR_DEFAULT elif border : path = self . PATH_CENTER_ERROR_BORDER elif nfov : path = self . PATH_CENTER_ERROR_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 ] ) cerror = self . _parseGivenDataFile ( current_path ) cerror_tensor = torch . Tensor ( cerror ) ave_success_rate_plot_center [ i , j , : ] = ( cerror_tensor . view ( - 1 , 1 ) <= threshold_set_center . view ( 1 , - 1 ) ) . sum ( 0 ) . float ( ) / len ( cerror ) prec_curve = ave_success_rate_plot_center . mean ( 0 ) * 100.0 prec_score = prec_curve [ : , 20 ] return prec_score
short_docstring: Returns Precision values of all trackers for all sequences as tensor
short_docstring_tokens: Returns Precision values of all trackers for all sequences as tensor
comment: """Returns Precision values of all trackers for all sequences as tensor"""$SEP$# load and parse data$SEP$# transform python lists to tensors$SEP$# success computing$SEP$# create curves$SEP$# score should be counted for max 20 pixel error