diff --git a/mindspore/dataset/text/transforms.py b/mindspore/dataset/text/transforms.py
index 8b34ff280e..15682db22a 100644
--- a/mindspore/dataset/text/transforms.py
+++ b/mindspore/dataset/text/transforms.py
@@ -51,7 +51,8 @@ from .utils import JiebaMode, NormalizeForm, to_str, SPieceTokenizerOutType, SPi
 from .validators import check_lookup, check_jieba_add_dict, \
     check_jieba_add_word, check_jieba_init, check_with_offsets, check_unicode_script_tokenizer, \
     check_wordpiece_tokenizer, check_regex_replace, check_regex_tokenizer, check_basic_tokenizer, check_ngram, \
-    check_pair_truncate, check_to_number, check_bert_tokenizer, check_python_tokenizer, check_slidingwindow
+    check_pair_truncate, check_to_number, check_bert_tokenizer, check_python_tokenizer, check_slidingwindow, \
+    check_sentence_piece_tokenizer
 from ..core.datatypes import mstype_to_detype
 from ..core.validator_helpers import replace_none
 from ..transforms.c_transforms import TensorOperation
@@ -325,7 +326,7 @@ class SentencePieceTokenizer(TextTensorOperation):
     Args:
         mode (Union[str, SentencePieceVocab]): If the input parameter is a file, then it is of type string.
             If the input parameter is a SentencePieceVocab object, then it is of type SentencePieceVocab.
-        out_type (Union[str, int]): The type of output.
+        out_type (SPieceTokenizerOutType): The type of output, the type is int or string
 
     Examples:
         >>> from mindspore.dataset.text import SentencePieceModel, SPieceTokenizerOutType
@@ -335,7 +336,7 @@ class SentencePieceTokenizer(TextTensorOperation):
         >>> tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
         >>> text_file_dataset = text_file_dataset.map(operations=tokenizer)
     """
-
+    @check_sentence_piece_tokenizer
     def __init__(self, mode, out_type):
         self.mode = mode
         self.out_type = out_type
diff --git a/mindspore/dataset/text/validators.py b/mindspore/dataset/text/validators.py
index 7cd191c054..cf96b74643 100644
--- a/mindspore/dataset/text/validators.py
+++ b/mindspore/dataset/text/validators.py
@@ -515,3 +515,19 @@ def check_save_model(method):
         return method(self, *args, **kwargs)
 
     return new_method
+
+def check_sentence_piece_tokenizer(method):
+
+    """A wrapper that wraps a parameter checker to the original function."""
+
+    from .utils import SPieceTokenizerOutType
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        [mode, out_type], _ = parse_user_args(method, *args, **kwargs)
+
+        type_check(mode, (str, cde.SentencePieceVocab), "mode is not an instance of str or cde.SentencePieceVocab.")
+        type_check(out_type, (SPieceTokenizerOutType,), "out_type is not an instance of SPieceTokenizerOutType")
+
+        return method(self, *args, **kwargs)
+
+    return new_method
diff --git a/mindspore/dataset/vision/validators.py b/mindspore/dataset/vision/validators.py
index 10459ceb12..126a7d545b 100644
--- a/mindspore/dataset/vision/validators.py
+++ b/mindspore/dataset/vision/validators.py
@@ -31,7 +31,8 @@ def check_crop_size(size):
     if isinstance(size, int):
         check_value(size, (1, FLOAT_MAX_INTEGER))
     elif isinstance(size, (tuple, list)) and len(size) == 2:
-        for value in size:
+        for index, value in enumerate(size):
+            type_check(value, (int,), "size[{}]".format(index))
             check_value(value, (1, FLOAT_MAX_INTEGER))
     else:
         raise TypeError("Size should be a single integer or a list/tuple (h, w) of length 2.")
@@ -93,6 +94,8 @@ def check_normalize_c_param(mean, std):
 
 
 def check_normalize_py_param(mean, std):
+    type_check(mean, (list, tuple), "mean")
+    type_check(std, (list, tuple), "std")
     if len(mean) != len(std):
         raise ValueError("Length of mean and std must be equal.")
     for mean_value in mean: