docstr: Evaluate semantic segmentation on a test dataset based on "torchmetrics".
  
  Parameters
  ----------
  data
      A dataframe, containing the same columns as the training data.
      Or a str, that is a path of the annotation file for detection.
  metrics
      Metrics used for evaluation.
  return_pred
      Whether to return the prediction result of each row.
  realtime
      Whether to do realtime inference, which is efficient for small data (default None).
      If provided None, we would infer it on based on the data modalities
      and sample number.
code: def evaluate_semantic_segmentation(
      self,
      data: Union[pd.DataFrame, dict, list, str],
      metrics: Optional[Union[str, List[str]]] = None,
      return_pred: Optional[bool] = False,
      realtime: Optional[bool] = False,
  ):
      """
      Evaluate semantic segmentation on a test dataset based on "torchmetrics".
  
      Parameters
      ----------
      data
          A dataframe, containing the same columns as the training data.
          Or a str, that is a path of the annotation file for detection.
      metrics
          Metrics used for evaluation.
      return_pred
          Whether to return the prediction result of each row.
      realtime
          Whether to do realtime inference, which is efficient for small data (default None).
          If provided None, we would infer it on based on the data modalities
          and sample number.
      """
  
      def get_metric_predict(
          metric_name: str,
          num_classes: Optional[int] = None,
      ):
          """
          Obtain a torchmerics.Metric from its name.
          Define a customized metric function in case that torchmetrics doesn't support some metric.
  
          Parameters
          ----------
          metric_name
              Name of metric.
          num_classes
              Number of classes.
          is_matching
              Whether is matching.
          problem_type
              Type of problem, e.g., binary and multiclass.
  
          Returns
          -------
          torchmetrics.Metric
              A torchmetrics.Metric object.
          custom_metric_func
              A customized metric function.
          """
          if metric_name == BER:
              return Balanced_Error_Rate()
          elif metric_name in [SM, EM, FM, MAE]:
              return COD_METRICS_NAMES[metric_name]
          elif metric_name == IOU:
              if num_classes == 1:
                  return Binary_IoU()
              else:
                  return Multiclass_IoU(num_classes=num_classes)
          else:
              raise ValueError(f"Unknown metric {metric_name}")
  
      outputs = self.predict_per_run(
          data=data,
          realtime=realtime,
          requires_label=False,
      )
  
      if self._output_shape == 1:
          logits = extract_from_output(
              ret_type=LOGITS, outputs=outputs, as_ndarray=False
          )
      else:
          logits = extract_from_output(
              ret_type=SEMANTIC_MASK, outputs=outputs, as_ndarray=False
          )
      y_pred = logits.float()
      y_true = [ele[LABEL] for ele in outputs]
      y_true = torch.cat(y_true)
  
      assert len(y_true) == len(y_pred)
  
      results = {}
      if isinstance(metrics, str):
          metrics = [metrics]
      for per_metric_name in metrics:
          per_metric = get_metric_predict(
              metric_name=per_metric_name.lower(),
              num_classes=self._output_shape,
          )
          for y_p, y_t in zip(y_pred, y_true):
              per_metric.update(y_p.unsqueeze(0), y_t.unsqueeze(0))
          score = per_metric.compute()
          results[per_metric_name] = score.item()
  
      if return_pred:
          return results, outputs
      else:
          return results
  
imports: import logging
  import os
  from typing import Dict, Iterable, List, Optional, Union
  import matplotlib.pyplot
  import numpy
  import pandas
  import torch
  import torch.nn.functional
  from omegaconf import OmegaConf
  from PIL import Image
  from scipy.special import softmax
  from autogluon.core.metrics import Scorer
  from ..constants import LABEL, LOGITS, SEMANTIC_MASK, SEMANTIC_SEGMENTATION, SEMANTIC_SEGMENTATION_IMG
  from ..optimization.lit_semantic_seg import SemanticSegmentationLitModule
  from ..optimization.semantic_seg_metrics import Balanced_Error_Rate_Pred
  from ..optimization.semantic_seg_metrics import Binary_IoU_Pred
  from ..optimization.semantic_seg_metrics import COD_METRICS_NAMES_Pred
  from ..optimization.semantic_seg_metrics import Multiclass_IoU_Pred
  from ..optimization.utils import get_loss_func, get_norm_layer_param_names, get_trainable_params_efficient_finetune
  from ..utils import extract_from_output, setup_save_path
  from .base import BaseLearner
  from ..constants import BER, EM, FM, IOU, MAE, SEMANTIC_SEGMENTATION, SM
label_desc: Use image node_id 6 for calling the SemanticSegmentationLearner obj's underlying member method code with example usage: obj.evaluate_semantic_segmentation(data, metrics, return_pred, realtime) and returns: results,  outputs, results, Balanced_Error_Rate, COD_METRICS_NAMES, Binary_IoU, Multiclass_IoU