Repair that split can be called normally when the GeneratorDataset input is an iterator and synchronize to r2.0.0-alpha

.jenkins/check/config/filter_pylint.txt

@@ -75,6 +75,7 @@
 "mindspore/mindspore/python/mindspore/dataset/engine/__init__.py"                                                        "redefined-builtin"
 "mindspore/mindspore/python/mindspore/dataset/engine/datasets.py"                                                        "redefined-builtin"
 "mindspore/mindspore/python/mindspore/dataset/engine/datasets.py"                                                        "broad-except"
+"mindspore/mindspore/python/mindspore/dataset/engine/datasets_user_defined.py"                                           "bad-super-call"
 "mindspore/mindspore/python/mindspore/dataset/engine/graphdata.py"                                                       "super-init-not-called"
 "mindspore/mindspore/python/mindspore/dataset/transforms/py_transforms_util.py"                                          "broad-except"
 

mindspore/python/mindspore/dataset/engine/datasets_user_defined.py

@@ -643,7 +643,7 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset):
         super().__init__(num_parallel_workers=num_parallel_workers, sampler=sampler, num_samples=num_samples,
                          shuffle=shuffle, num_shards=num_shards, shard_id=shard_id)
         if isinstance(source, builtins.zip):
-            # Although zip is iteratable, it does not have the feature of repeated iteration, so pass it to the array.
+            # Although zip is iterable, it does not have the feature of repeated iteration, so pass it to the array.
             self.source = [item for item in source]
         else:
             self.source = source
@@ -735,6 +735,13 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset):
             return self.sampler.is_sharded()
         return False
 
+    def split(self, sizes, randomize=True):
+        if hasattr(self.source, "__getitem__"):
+            # If the source has __getitem__ attribute, call the split method of MappableDataset.
+            # Otherwise, call the split method of Dataset.
+            return super().split(sizes, randomize)
+        return super(MappableDataset, self).split(sizes, randomize)
+
     def parse(self, children=None):
         if self.schema is None:
             return cde.GeneratorNode(self.prepared_source, self.column_names, self.column_types, self.source_len,

tests/ut/python/dataset/test_datasets_generator.py

@@ -2208,6 +2208,65 @@ def test_generator_with_single_numpy_with_yield():
     assert count == 20
 
 
+def test_generator_split_with_yield():
+    """
+    Feature: GeneratorDataset
+    Description: When GeneratorDataset calls split, it can be split if the input is in yield mode
+    Expectation: The dataset is processed as expected
+    """
+    dataset = ds.GeneratorDataset(generator_1d, ["data"], shuffle=False)
+    dataset_train, dataset_val = dataset.split([0.8, 0.2])
+    assert dataset_train.get_dataset_size() == 51
+    assert dataset_val.get_dataset_size() == 13
+
+
+def test_generator_split_with_getitem():
+    """
+    Feature: GeneratorDataset
+    Description: When GeneratorDataset calls split, it can be split if the input is in getitem mode
+    Expectation: The dataset is processed as expected
+    """
+    dataset_generator = DatasetGenerator()
+    dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+    dataset_train, dataset_val = dataset.split([0.8, 0.2])
+    assert dataset_train.get_dataset_size() == 8
+    assert dataset_val.get_dataset_size() == 2
+
+
+def test_generator_split_with_next():
+    """
+    Feature: GeneratorDataset
+    Description: When GeneratorDataset calls split, it can be split if the input is in next mode
+    Expectation: The dataset is processed as expected
+    """
+
+    class GetDatasetGenerator:
+        def __init__(self, data):
+            self.__data = data
+            self.__count = 0
+
+        def __next__(self):
+            if self.__count >= 10:
+                raise StopIteration
+
+            self.__count += 1
+            return self.__data
+
+        def __iter__(self):
+            self.__count = 0
+            return self
+
+        def __len__(self):
+            return 10
+
+    data_tuple = (np.array([[1, 2, 3], [2, 3, 4]]),)
+    dataset_generator = GetDatasetGenerator(data_tuple)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+    dataset_train, dataset_val = dataset.split([0.8, 0.2])
+    assert dataset_train.get_dataset_size() == 8
+    assert dataset_val.get_dataset_size() == 2
+
+
 if __name__ == "__main__":
     test_generator_0()
     test_generator_1()
@@ -2261,3 +2320,6 @@ if __name__ == "__main__":
     test_generator_with_single_numpy_with_yield()
     test_generator_with_seed_5489_when_dist()
     test_generator_with_set_seed_when_dist()
+    test_generator_split_with_yield()
+    test_generator_split_with_getitem()
+    test_generator_split_with_next()