!8699 Add the option no need of training the epistemic model

From: @zhangxinfeng3
Reviewed-by: @zichun_ye,@wang_zi_dong
Signed-off-by: @zichun_ye

mindspore/nn/probability/toolbox/uncertainty_evaluation.py

@@ -47,6 +47,7 @@ class UncertaintyEvaluation:
                       Default: None.
         epochs (int): Total number of iterations on the data. Default: 1.
         epi_uncer_model_path (str): The save or read path of the epistemic uncertainty model. Default: None.
+                        If the epi_uncer_model_path is 'Untrain', the epistemic model need not to be trained.
         ale_uncer_model_path (str): The save or read path of the aleatoric uncertainty model. Default: None.
         save_model (bool): Whether to save the uncertainty model or not, if true, the epi_uncer_model_path
                         and ale_uncer_model_path must not be None. If false, the model to evaluate will be loaded from
@@ -111,7 +112,7 @@ class UncertaintyEvaluation:
         """
         if self.epi_uncer_model is None:
             self.epi_uncer_model = EpistemicUncertaintyModel(self.epi_model)
-            if self.epi_uncer_model.drop_count == 0:
+            if self.epi_uncer_model.drop_count == 0 and self.epi_uncer_model_path != 'Untrain':
                 if self.task_type == 'classification':
                     net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
                     net_opt = Adam(self.epi_uncer_model.trainable_params())