remove unnassacary error raising, filter sample by inference labels

fix pylint issues

mindinsight/backend/explainer/explainer_api.py

@@ -25,7 +25,6 @@ from flask import request
 from mindinsight.conf import settings
 from mindinsight.utils.exceptions import ParamMissError
 from mindinsight.utils.exceptions import ParamValueError
-from mindinsight.datavisual.common.exceptions import ImageNotExistError
 from mindinsight.datavisual.common.validation import Validation
 from mindinsight.datavisual.data_transform.summary_watcher import SummaryWatcher
 from mindinsight.datavisual.utils.tools import get_train_id
@@ -168,8 +167,6 @@ def query_image():
 
     encapsulator = DatafileEncap(EXPLAIN_MANAGER)
     image = encapsulator.query_image_binary(train_id, image_path, image_type)
-    if image is None:
-        raise ImageNotExistError(f"{image_path}")
 
     return image
 

mindinsight/explainer/encapsulator/saliency_encap.py

@@ -18,6 +18,7 @@ import copy
 
 from mindinsight.utils.exceptions import ParamValueError
 from mindinsight.explainer.encapsulator.explain_data_encap import ExplainDataEncap
+from mindinsight.datavisual.common.exceptions import TrainJobNotExistError
 
 
 def _sort_key_min_confidence(sample):
@@ -90,19 +91,17 @@ class SaliencyEncap(ExplainDataEncap):
         """
         job = self.job_manager.get_job(train_id)
         if job is None:
-            return 0, None
+            raise TrainJobNotExistError(train_id)
 
         samples = copy.deepcopy(job.get_all_samples())
         if labels:
             filtered = []
             for sample in samples:
-                has_label = False
-                for label in sample["labels"]:
-                    if label in labels:
-                        has_label = True
+                infer_labels = [inference["label"] for inference in sample["inferences"]]
+                for infer_label in infer_labels:
+                    if infer_label in labels:
+                        filtered.append(sample)
                         break
-                if has_label:
-                    filtered.append(sample)
             samples = filtered
 
         reverse = sorted_type == "descending"