check for param in choice if it is bool

If the values for batch_size is 'choice: [1, 2, True]', it will raise
validation exception.

Additionally, use 'type(value) is int' will not pass pylint. so use
'isinstance(x, bool) or not isinstance(x, int)' to filter invalid
value(s).

mindinsight/optimizer/common/validator/optimizer_config.py

@@ -219,7 +219,8 @@ class OptimizerConfig(Schema):
             if list(filter(lambda x: not isinstance(x, float), choice)):
                 err_msg = "The value(s) should be float number."
                 raise ValidationError(_generate_err_msg_for_nested_keys(err_msg, name, HyperParamKey.CHOICE.value))
-        elif list(filter(lambda x: not isinstance(x, int), choice)):
+        elif list(filter(lambda x: isinstance(x, bool) or not isinstance(x, int), choice)):
+            # isinstance(x, int) will return True if x is bool. use 'type(x)' will not pass lint.
             err_msg = "The value(s) should be integer."
             raise ValidationError(_generate_err_msg_for_nested_keys(err_msg, name, HyperParamKey.CHOICE.value))
 

tests/ut/optimizer/common/validator/test_optimizer_config.py

@@ -231,6 +231,12 @@ class TestOptimizerConfig:
         err = OptimizerConfig().validate(config_dict)
         assert expected_err == err
 
+        # test bool
+        expected_err['parameters'][param_name]['choice'] = 'The value(s) should be integer.'
+        config_dict['parameters'][param_name]['choice'] = [1, True]
+        err = OptimizerConfig().validate(config_dict)
+        assert expected_err == err
+
         config_dict['parameters'][param_name] = {'choice': [0.1, 0.2]}
         err = OptimizerConfig().validate(config_dict)
         assert expected_err == err