support non tensor inputs in model predict

mindspore/_checkparam.py

@@ -635,7 +635,7 @@ def check_input_data(*data, data_class):
                                  f' either a single'
                                  f' or a list of {data_class.__name__},'
                                  f' but got part data type is {str(type(item))}.')
-            if item.size() == 0:
+            if hasattr(item, "size") and item.size() == 0:
                 msg = "Please provide non-empty data."
                 logger.error(msg)
                 raise ValueError(msg)

mindspore/train/model.py

@@ -724,7 +724,7 @@ class Model:
             Batch data should be put together in one tensor.
 
         Args:
-           predict_data (Tensor): Tensor of predict data. can be array, list or tuple.
+           predict_data: The predict data, can be array, number, str, dict, list or tuple.
 
         Returns:
             Tensor, array(s) of predictions.
@@ -735,7 +735,7 @@ class Model:
             >>> result = model.predict(input_data)
         """
         self._predict_network.set_train(False)
-        check_input_data(*predict_data, data_class=Tensor)
+        check_input_data(*predict_data, data_class=(int, float, str, tuple, list, dict, Tensor))
         _parallel_predict_check()
         result = self._predict_network(*predict_data)
 

tests/ut/python/train/test_training.py

@@ -214,8 +214,8 @@ def test_model_build_abnormal_string():
     err = False
     try:
         model.predict('aaa')
-    except ValueError as e:
-        log.error("Find value error: %r ", e)
+    except TypeError as e:
+        log.error("Find type error: %r ", e)
         err = True
     finally:
         assert err