Fix label saving format in ExplainLoader and remove useless methods

mindinsight/explainer/manager/explain_loader.py

@@ -301,9 +301,10 @@ class ExplainLoader:
                 'id': sample_id,
                 'name': str(sample_id),
                 'image': sample_info['image'],
-                'labels': sample_info['ground_truth_label'],
+                'labels': [self._metadata['labels'][i] for i in sample_info['ground_truth_label']],
             }
 
+            # Check whether the sample has valid label-prob pairs.
             if not ExplainLoader._is_inference_valid(sample_info):
                 continue
 
@@ -348,7 +349,7 @@ class ExplainLoader:
             elif tag == ExplainFieldsEnum.SAMPLE_ID.value:
                 self._import_sample_from_event(event)
             else:
-                logger.info('Unknown ExplainField: %s', tag)
+                logger.info('Unknown ExplainField: %s.', tag)
 
     def _is_metadata_empty(self):
         """Check whether metadata is completely loaded first."""
@@ -455,13 +456,11 @@ class ExplainLoader:
 
         for tag in _SAMPLE_FIELD_NAMES:
             try:
-                if ExplainLoader._is_attr_empty(sample, tag.value):
-                    continue
                 if tag == ExplainFieldsEnum.GROUND_TRUTH_LABEL:
                     self._samples[sample_id]['ground_truth_label'].extend(list(sample.ground_truth_label))
                 elif tag == ExplainFieldsEnum.INFERENCE:
                     self._import_inference_from_event(sample, sample_id)
-                elif tag == ExplainFieldsEnum.EXPLANATION:
+                else:
                     self._import_explanation_from_event(sample, sample_id)
             except UnknownError as ex:
                 logger.warning("Parse %s data failed within image related data, detail: %r", tag, str(ex))
@@ -518,59 +517,35 @@ class ExplainLoader:
         Returns:
             list[str], filename list.
         """
-        return list(filter(lambda filename: (re.search(r'summary\.\d+', filename) and filename.endswith("_explain")),
-                           filenames))
-
-    @staticmethod
-    def _is_attr_empty(event, attr_name) -> bool:
-        if not getattr(event, attr_name):
-            return True
-        for item in getattr(event, attr_name):
-            if not isinstance(item, list) or item:
-                return False
-        return True
-
-    @staticmethod
-    def _is_ground_truth_label_valid(sample_id: str, sample_info: Dict) -> bool:
-        if len(sample_info['ground_truth_label']) != len(sample_info['ground_truth_prob']):
-            logger.info('length of ground_truth_prob does not match the length of ground_truth_label'
-                        'length of ground_turth_label is: %s but length of ground_truth_prob is: %s.'
-                        'sample_id is : %s.',
-                        len(sample_info['ground_truth_label']), len(sample_info['ground_truth_prob']), sample_id)
-            return False
-        return True
+        return list(filter(
+            lambda filename: (re.search(r'summary\.\d+', filename) and filename.endswith("_explain")), filenames))
 
     @staticmethod
     def _is_inference_valid(sample):
         """
         Check whether the inference data is empty or have the same length.
 
-        If the probs have different length with the labels, it can be confusing when assigning each prob to label.
-        'is_inference_valid' return True only when the data size of match to each other. Note that prob data could be
+        If probs have different length with the labels, it can be confusing when assigning each prob to label.
+        '_is_inference_valid' returns True only when the data size of match to each other. Note that prob data could be
         empty, so empty prob will pass the check.
         """
         ground_truth_len = len(sample['ground_truth_label'])
         for name in ['ground_truth_prob', 'ground_truth_prob_sd',
                      'ground_truth_prob_itl95_low', 'ground_truth_prob_itl95_hi']:
             if sample[name] and len(sample[name]) != ground_truth_len:
+                logger.info('Length of %s not match the ground_truth_label. Length of ground_truth_label: %d,'
+                            'length of %s: %d', name, ground_truth_len, name, len(sample[name]))
                 return False
 
         predicted_len = len(sample['predicted_label'])
         for name in ['predicted_prob', 'predicted_prob_sd',
                      'predicted_prob_itl95_low', 'predicted_prob_itl95_hi']:
             if sample[name] and len(sample[name]) != predicted_len:
+                logger.info('Length of %s not match the predicted_labels. Length of predicted_label: %d,'
+                            'length of %s: %d', name, predicted_len, name, len(sample[name]))
                 return False
         return True
 
-    @staticmethod
-    def _is_predicted_label_valid(sample_id: str, sample_info: Dict) -> bool:
-        if len(sample_info['predicted_label']) != len(sample_info['predicted_prob']):
-            logger.info('length of predicted_probs does not match the length of predicted_labels'
-                        'length of predicted_probs: %s but receive length of predicted_label: %s, sample_id: %s.',
-                        len(sample_info['predicted_prob']), len(sample_info['predicted_label']), sample_id)
-            return False
-        return True
-
     @staticmethod
     def _score_event_to_dict(label_score_event, metric) -> Dict:
         """Transfer metric scores per label to pre-defined structure."""

mindinsight/explainer/manager/explain_manager.py

@@ -133,7 +133,6 @@ class ExplainManager:
                     return
                 time.sleep(repeat_interval)
             except UnknownError as ex:
-                logger.exception(ex)
                 logger.error('Unexpected error happens when loading data. Loading status: %s, loading pool size: %d'
                              'Detail: %s', self._loading_status, len(self._loader_pool), str(ex))