Fix CI warning and test team issues

Signed-off-by: alex-yuyue <yue.yu1@huawei.com>

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/ir/image/bindings.cc

@@ -492,6 +492,5 @@ PYBIND_REGISTER(
         return uniform_aug;
       }));
   }));
-
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/text/kernels/ir/bindings.cc

@@ -25,7 +25,6 @@
 
 namespace mindspore {
 namespace dataset {
-
 #ifdef ENABLE_ICU4C
 
 PYBIND_REGISTER(
@@ -262,6 +261,5 @@ PYBIND_REGISTER(SPieceTokenizerOutType, 0, ([](const py::module *m) {
                     .value("DE_SPIECE_TOKENIZER_OUTTYPE_KINT", SPieceTokenizerOutType::kInt)
                     .export_values();
                 }));
-
 }  // namespace dataset
 }  // namespace mindspore

mindspore/dataset/engine/samplers.py

@@ -605,7 +605,7 @@ class SubsetSampler(BuiltinSampler):
     Samples the elements from a sequence of indices.
 
     Args:
-        indices (list[int]): A sequence of indices.
+        indices (Any iterable python object but string): A sequence of indices.
         num_samples (int, optional): Number of elements to sample (default=None, all elements).
 
     Examples:
@@ -633,6 +633,13 @@ class SubsetSampler(BuiltinSampler):
 
             return [sample_id for sample_id, _ in zip(sampler, range(number_of_samples))]
 
+        if num_samples is not None:
+            if not isinstance(num_samples, int):
+                raise TypeError("num_samples must be integer but was: {}.".format(num_samples))
+            if num_samples < 0 or num_samples > validator.INT64_MAX:
+                raise ValueError("num_samples exceeds the boundary between {} and {}(INT64_MAX)!"
+                                 .format(0, validator.INT64_MAX))
+
         if not isinstance(indices, str) and validator.is_iterable(indices):
             indices = _get_sample_ids_as_list(indices, num_samples)
         elif isinstance(indices, int):
@@ -645,13 +652,6 @@ class SubsetSampler(BuiltinSampler):
                 raise TypeError("SubsetSampler: Type of indices element must be int, "
                                 "but got list[{}]: {}, type: {}.".format(i, item, type(item)))
 
-        if num_samples is not None:
-            if not isinstance(num_samples, int):
-                raise TypeError("num_samples must be integer but was: {}.".format(num_samples))
-            if num_samples < 0 or num_samples > validator.INT64_MAX:
-                raise ValueError("num_samples exceeds the boundary between {} and {}(INT64_MAX)!"
-                                 .format(0, validator.INT64_MAX))
-
         self.indices = indices
         super().__init__(num_samples)
 

mindspore/dataset/transforms/c_transforms.py

@@ -31,7 +31,13 @@ class TensorOperation:
     Base class Tensor Ops
     """
     def __call__(self, *input_tensor_list):
-        tensor_row = [cde.Tensor(np.asarray(tensor)) for tensor in input_tensor_list]
+        tensor_row = []
+        for tensor in input_tensor_list:
+            try:
+                tensor_row.append(cde.Tensor(np.asarray(tensor)))
+            except RuntimeError:
+                raise TypeError("Invalid user input. Got {}: {}, cannot be converted into tensor." \
+                      .format(type(tensor), tensor))
         callable_op = cde.Execute(self.parse())
         output_tensor_list = callable_op(tensor_row)
         for i, element in enumerate(output_tensor_list):

mindspore/dataset/vision/c_transforms.py

@@ -1197,12 +1197,13 @@ class RandomSharpness(ImageTensorOperation):
 
 class RandomSolarize(ImageTensorOperation):
     """
-    Invert all pixel values with given range.
+    Randomly invert the pixel values of input image within given range.
 
     Args:
-        threshold (tuple, optional): Range of random solarize threshold. Threshold values should always be
+        threshold (tuple, optional): Range of random solarize threshold (default=(0, 255)).
-            in the range (0, 255), include at least one integer value in the given range and be in
+            Threshold values should always be in (min, max) format,
-            (min, max) format. If min=max, then invert all pixel values above min(max) (default=(0, 255)).
+            where min <= max, min and max are integers in the range (0, 255).
+            If min=max, then invert all pixel values above min(max).
 
     Examples:
         >>> transforms_list = [c_vision.Decode(), c_vision.RandomSolarize(threshold=(10,100))]

tests/ut/python/dataset/test_sentencepiece_tokenizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 import copy
+import numpy as np
 import mindspore.dataset.text as text
 import mindspore.dataset as ds
 from mindspore.dataset.text import SentencePieceModel, to_str, SPieceTokenizerOutType
@@ -21,6 +22,13 @@ VOCAB_FILE = "../data/dataset/test_sentencepiece/botchan.txt"
 DATA_FILE = "../data/dataset/testTokenizerData/sentencepiece_tokenizer.txt"
 
 
+def test_sentence_piece_tokenizer_callable():
+    vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
+    tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
+    data = '123'
+    assert np.array_equal(tokenizer(data), ['▁', '12', '3'])
+
+
 def test_from_vocab_to_str_UNIGRAM():
     vocab = text.SentencePieceVocab.from_file([VOCAB_FILE], 5000, 0.9995, SentencePieceModel.UNIGRAM, {})
     tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
@@ -160,6 +168,7 @@ def test_with_zip_concat():
 
 
 if __name__ == "__main__":
+    test_sentence_piece_tokenizer_callable()
     test_from_vocab_to_str_UNIGRAM()
     test_from_vocab_to_str_BPE()
     test_from_vocab_to_str_CHAR()

tests/ut/python/dataset/test_text_bert_tokenizer.py

@@ -16,6 +16,7 @@
 Testing BertTokenizer op in DE
 """
 import numpy as np
+import pytest
 import mindspore.dataset as ds
 from mindspore import log as logger
 import mindspore.dataset.text as text
@@ -127,7 +128,7 @@ test_paras = [
         preserve_unused_token=True,
         vocab_list=vocab_bert
     ),
-    # test non-default parms
+    # test non-default params
     dict(
         first=8,
         last=8,
@@ -242,6 +243,19 @@ def test_bert_tokenizer_with_offsets():
         check_bert_tokenizer_with_offsets(**paras)
 
 
+def test_bert_tokenizer_callable_invalid_input():
+    """
+    Test WordpieceTokenizer in eager mode with invalid input
+    """
+    data = {'张三': 18, '王五': 20}
+    vocab = text.Vocab.from_list(vocab_bert)
+    tokenizer_op = text.BertTokenizer(vocab=vocab)
+
+    with pytest.raises(TypeError) as info:
+        _ = tokenizer_op(data)
+    assert "Invalid user input. Got <class 'dict'>: {'张三': 18, '王五': 20}, cannot be converted into tensor." in str(info)
+
 if __name__ == '__main__':
+    test_bert_tokenizer_callable_invalid_input()
     test_bert_tokenizer_default()
     test_bert_tokenizer_with_offsets()

tests/ut/python/dataset/test_to_number_op.py

@@ -52,9 +52,10 @@ def test_to_number_eager():
 
     # test input invalid tensor
     invalid_input = [["1", "2", "3"], ["4", "5"]]
-    with pytest.raises(RuntimeError) as info:
+    with pytest.raises(TypeError) as info:
         _ = op(invalid_input)
-    assert "Invalid data type." in str(info.value)
+    assert "Invalid user input. Got <class 'list'>: [['1', '2', '3'], ['4', '5']], cannot be converted into tensor" in \
+           str(info.value)
 
 
 def test_to_number_typical_case_integral():