!9128 [Fixbug]explainer]NaiveFaithfulness NaN warning and system errors when benchmark num_labels larger than num_labels in dataset

From: @lixiaohui33
Reviewed-by: @ouwenchang
Signed-off-by:

mindspore/explainer/benchmark/_attribution/faithfulness.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Faithfulness."""
+from decimal import Decimal
 from typing import Callable, Optional, Union
 
 import numpy as np
@@ -147,8 +148,8 @@ class NaiveFaithfulness(_FaithfulnessHelper):
             - faithfulness (np.ndarray): faithfulness score
 
         """
-        if not np.count_nonzero(saliency):
-            log.warning("The saliency map is zero everywhere. The correlation will be set to zero.")
+        if Decimal(str(saliency.max())) == Decimal(str(saliency.min())):
+            log.warning("The saliency map is uniform everywhere. The correlation will be set to zero.")
             correlation = 0
             return np.array([correlation], np.float)
 
@@ -163,6 +164,11 @@ class NaiveFaithfulness(_FaithfulnessHelper):
         predictions = model(perturbations)[:, targets].asnumpy()
         predictions = predictions.reshape(*feature_importance.shape)
 
+        if Decimal(str(predictions.max())) == Decimal(str(predictions.min())):
+            log.warning("The perturbations do not affect the predictions. The correlation will be set to zero.")
+            correlation = 0
+            return np.array([correlation], np.float)
+
         faithfulness = -np.corrcoef(feature_importance, predictions)
         faithfulness = np.diag(faithfulness[:batch_size, batch_size:])
         return faithfulness

mindspore/explainer/benchmark/_attribution/metric.py

@@ -203,8 +203,8 @@ class LabelSensitiveMetric(AttributionMetric):
         output = explainer.model(inputs)
         check_value_type("output of explainer model", output, Tensor)
         output_dim = explainer.model(inputs).shape[1]
-        if output_dim > self._num_labels:
-            raise ValueError("The output dimension of of black-box model in explainer should not exceed the dimension "
-                             "of num_labels set in the __init__, please set num_labels larger.")
+        if output_dim != self._num_labels:
+            raise ValueError("The output dimension of of black-box model in explainer does not match the dimension "
+                             "of num_labels set in the __init__, please check explainer and num_labels again.")
         verify_targets(targets, self._num_labels)
         check_value_type('saliency', saliency, (Tensor, type(None)))