!32702 fix compile error on windows

Merge pull request !32702 from guozhijian/fix_compile_error

mindspore/ccsrc/minddata/dataset/api/CMakeLists.txt

@@ -26,6 +26,12 @@ if(ENABLE_PYTHON)
             python/pybind_register.cc
             )
     target_include_directories(APItoPython PRIVATE ${pybind11_INCLUDE_DIRS})
+
+    if(ENABLE_CACHE)
+        add_dependencies(APItoPython engine-cache-server)
+    else()
+        add_dependencies(APItoPython engine-cache-client)
+    endif()
 endif()
 
 add_library(cpp-API OBJECT

tests/ut/python/dataset/test_datasets_generator.py

@@ -1367,6 +1367,652 @@ def test_generator_single_input_6():
     assert_generator_single_input_6(SequentialAccessDatasetInner())
 
 
+def test_generator_with_single_numpy():
+    """
+    Feature: Test GeneratorDataset with single numpy and multi columns when use __getitem__
+    Description: single numpy, tuple numpy with single columns and multi columns
+    Expectation: success
+    """
+    class get_dataset_generator:
+        def __init__(self, value):
+            np.random.seed(58)
+            self.__value = value
+
+        def __getitem__(self, index):
+            return self.__value
+
+        def __len__(self):
+            return 20
+
+    def test_generator_one_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False, num_parallel_workers=number,
+                                      python_multiprocessing=process_flag)
+        count = 0
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            assert (data["data"] == value).all()
+            count += 1
+        assert count == 20
+
+    # test user define one column
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_one_column(numpy_1)
+    test_generator_one_column(numpy_2)
+    test_generator_one_column(numpy_3)
+    test_generator_one_column(numpy_4)
+    test_generator_one_column(numpy_5)
+    test_generator_one_column(numpy_6)
+    test_generator_one_column(numpy_7)
+    test_generator_one_column(numpy_8)
+    test_generator_one_column(numpy_9)
+    test_generator_one_column(numpy_10)
+
+    tuple_1 = (numpy_7,)
+    dataset_generator = get_dataset_generator(tuple_1)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == tuple_1[0]).all()
+        count += 1
+    assert count == 20
+
+    tuple_2 = (numpy_6, numpy_7)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_2)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_4 = (numpy_4, numpy_5, numpy_6, numpy_7)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_4)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:4" in str(info.value)
+
+    # test user define two column
+    def test_generator_two_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False, num_parallel_workers=number,
+                                      python_multiprocessing=process_flag)
+        count = 0
+        with pytest.raises(RuntimeError) as info:
+            for data in dataset.create_dict_iterator(output_numpy=True):
+                print(data)
+                count += 1
+            assert count == 20
+        assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+               "column_names," in str(info.value)
+        assert "the size of column_names is:2 and number of returned NumPy array is:1" in str(info.value)
+
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_two_column(numpy_1)
+    test_generator_two_column(numpy_2)
+    test_generator_two_column(numpy_3)
+    test_generator_two_column(numpy_4)
+    test_generator_two_column(numpy_5)
+    test_generator_two_column(numpy_6)
+    test_generator_two_column(numpy_7)
+    test_generator_two_column(numpy_8)
+    test_generator_two_column(numpy_9)
+    test_generator_two_column(numpy_10)
+    tuple_1 = (numpy_7,)
+    test_generator_two_column(tuple_1)
+
+    tuple_2 = (numpy_2, numpy_3)
+    dataset_generator = get_dataset_generator(tuple_2)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == numpy_2).all()
+        assert (data["label"] == numpy_3).all()
+        count += 1
+    assert count == 20
+
+    tuple_3 = (numpy_4, numpy_5, numpy_6)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_3)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:2 and number of returned NumPy array is:3" in str(info.value)
+
+    # test user define three column
+    def test_generator_three_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False,
+                                      num_parallel_workers=number, python_multiprocessing=process_flag)
+        count = 0
+        with pytest.raises(RuntimeError) as info:
+            for data in dataset.create_dict_iterator(output_numpy=True):
+                print(data)
+                count += 1
+            assert count == 20
+        assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+               "column_names," in str(info.value)
+        assert "the size of column_names is:3 and number of returned NumPy array is:1" in str(info.value)
+
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_three_column(numpy_1)
+    test_generator_three_column(numpy_2)
+    test_generator_three_column(numpy_3)
+    test_generator_three_column(numpy_4)
+    test_generator_three_column(numpy_5)
+    test_generator_three_column(numpy_6)
+    test_generator_three_column(numpy_7)
+    test_generator_three_column(numpy_8)
+    test_generator_three_column(numpy_9)
+    test_generator_three_column(numpy_10)
+    tuple_1 = (numpy_7,)
+    test_generator_three_column(tuple_1)
+
+    tuple_2 = (numpy_2, numpy_3)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_2)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:3 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_3 = (numpy_4, numpy_5, numpy_6)
+    dataset_generator = get_dataset_generator(tuple_3)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == numpy_4).all()
+        assert (data["label"] == numpy_5).all()
+        assert (data["label2"] == numpy_6).all()
+        count += 1
+    assert count == 20
+
+
+def test_generator_with_single_numpy_with_next():
+    """
+    Feature: Test GeneratorDataset with single numpy and multi columns when use __next__
+    Description: single numpy, tuple numpy with single columns and multi columns
+    Expectation: success
+    """
+    class get_dataset_generator:
+        def __init__(self, value):
+            np.random.seed(58)
+            self.__value = value
+            self.__index = 0
+
+        def __next__(self):
+            if self.__index >= 20:
+                raise StopIteration
+
+            self.__index += 1
+            return self.__value
+
+        def __iter__(self):
+            self.__index = 0
+            return self
+
+        def __len__(self):
+            return 20
+
+    def test_generator_one_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False, num_parallel_workers=number,
+                                      python_multiprocessing=process_flag)
+        count = 0
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            assert (data["data"] == value).all()
+            count += 1
+        assert count == 20
+
+    # test user define one column
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_one_column(numpy_1)
+    test_generator_one_column(numpy_2)
+    test_generator_one_column(numpy_3)
+    test_generator_one_column(numpy_4)
+    test_generator_one_column(numpy_5)
+    test_generator_one_column(numpy_6)
+    test_generator_one_column(numpy_7)
+    test_generator_one_column(numpy_8)
+    test_generator_one_column(numpy_9)
+    test_generator_one_column(numpy_10)
+
+    tuple_1 = (numpy_7,)
+    dataset_generator = get_dataset_generator(tuple_1)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == tuple_1[0]).all()
+        count += 1
+    assert count == 20
+
+    tuple_2 = (numpy_6, numpy_7)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_2)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_3 = (numpy_1, numpy_2)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_3)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_4 = (numpy_4, numpy_5, numpy_6, numpy_7)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_4)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:4" in str(info.value)
+
+    # test user define two column
+    def test_generator_two_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False, num_parallel_workers=number,
+                                      python_multiprocessing=process_flag)
+        count = 0
+        with pytest.raises(RuntimeError) as info:
+            for data in dataset.create_dict_iterator(output_numpy=True):
+                print(data)
+                count += 1
+            assert count == 20
+        assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+               "column_names," in str(info.value)
+        assert "the size of column_names is:2 and number of returned NumPy array is:1" in str(info.value)
+
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_two_column(numpy_1)
+    test_generator_two_column(numpy_2)
+    test_generator_two_column(numpy_3)
+    test_generator_two_column(numpy_4)
+    test_generator_two_column(numpy_5)
+    test_generator_two_column(numpy_6)
+    test_generator_two_column(numpy_7)
+    test_generator_two_column(numpy_8)
+    test_generator_two_column(numpy_9)
+    test_generator_two_column(numpy_10)
+    tuple_1 = (numpy_7,)
+    test_generator_two_column(tuple_1)
+
+    tuple_2 = (numpy_2, numpy_3)
+    dataset_generator = get_dataset_generator(tuple_2)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == numpy_2).all()
+        assert (data["label"] == numpy_3).all()
+        count += 1
+    assert count == 20
+
+    tuple_3 = (numpy_4, numpy_5, numpy_6)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_3)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:2 and number of returned NumPy array is:3" in str(info.value)
+
+    # test user define three column
+    def test_generator_three_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False,
+                                      num_parallel_workers=number, python_multiprocessing=process_flag)
+        count = 0
+        with pytest.raises(RuntimeError) as info:
+            for data in dataset.create_dict_iterator(output_numpy=True):
+                print(data)
+                count += 1
+            assert count == 20
+        assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+               "column_names," in str(info.value)
+        assert "the size of column_names is:3 and number of returned NumPy array is:1" in str(info.value)
+
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_three_column(numpy_1)
+    test_generator_three_column(numpy_2)
+    test_generator_three_column(numpy_3)
+    test_generator_three_column(numpy_4)
+    test_generator_three_column(numpy_5)
+    test_generator_three_column(numpy_6)
+    test_generator_three_column(numpy_7)
+    test_generator_three_column(numpy_8)
+    test_generator_three_column(numpy_9)
+    test_generator_three_column(numpy_10)
+    tuple_1 = (numpy_7,)
+    test_generator_three_column(tuple_1)
+
+    tuple_2 = (numpy_2, numpy_3)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_2)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:3 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_3 = (numpy_4, numpy_5, numpy_6)
+    dataset_generator = get_dataset_generator(tuple_3)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == numpy_4).all()
+        assert (data["label"] == numpy_5).all()
+        assert (data["label2"] == numpy_6).all()
+        count += 1
+    assert count == 20
+
+
+def test_generator_with_single_numpy_with_yield():
+    """
+    Feature: Test GeneratorDataset with single numpy and multi columns when use yield
+    Description: single numpy, tuple numpy with single columns and multi columns
+    Expectation: success
+    """
+    def get_dataset_generator(value):
+        for _ in range(20):
+            yield value
+
+    def test_generator_one_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False, num_parallel_workers=number,
+                                      python_multiprocessing=process_flag)
+        count = 0
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            assert (data["data"] == value).all()
+            count += 1
+        assert count == 20
+
+    # test user define one column
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_one_column(numpy_1)
+    test_generator_one_column(numpy_2)
+    test_generator_one_column(numpy_3)
+    test_generator_one_column(numpy_4)
+    test_generator_one_column(numpy_5)
+    test_generator_one_column(numpy_6)
+    test_generator_one_column(numpy_7)
+    test_generator_one_column(numpy_8)
+    test_generator_one_column(numpy_9)
+    test_generator_one_column(numpy_10)
+
+    tuple_1 = (numpy_7,)
+    dataset_generator = get_dataset_generator(tuple_1)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == tuple_1[0]).all()
+        count += 1
+    assert count == 20
+
+    tuple_2 = (numpy_6, numpy_7)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_2)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_3 = (numpy_1, numpy_2)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_3)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_4 = (numpy_4, numpy_5, numpy_6, numpy_7)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_4)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:1 and number of returned NumPy array is:4" in str(info.value)
+
+    # test user define two column
+    def test_generator_two_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False, num_parallel_workers=number,
+                                      python_multiprocessing=process_flag)
+        count = 0
+        with pytest.raises(RuntimeError) as info:
+            for data in dataset.create_dict_iterator(output_numpy=True):
+                print(data)
+                count += 1
+            assert count == 20
+        assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+               "column_names," in str(info.value)
+        assert "the size of column_names is:2 and number of returned NumPy array is:1" in str(info.value)
+
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_two_column(numpy_1)
+    test_generator_two_column(numpy_2)
+    test_generator_two_column(numpy_3)
+    test_generator_two_column(numpy_4)
+    test_generator_two_column(numpy_5)
+    test_generator_two_column(numpy_6)
+    test_generator_two_column(numpy_7)
+    test_generator_two_column(numpy_8)
+    test_generator_two_column(numpy_9)
+    test_generator_two_column(numpy_10)
+    tuple_1 = (numpy_7,)
+    test_generator_two_column(tuple_1)
+
+    tuple_2 = (numpy_2, numpy_3)
+    dataset_generator = get_dataset_generator(tuple_2)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == numpy_2).all()
+        assert (data["label"] == numpy_3).all()
+        count += 1
+    assert count == 20
+
+    tuple_3 = (numpy_4, numpy_5, numpy_6)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_3)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:2 and number of returned NumPy array is:3" in str(info.value)
+
+    # test user define three column
+    def test_generator_three_column(value):
+        number = np.random.randint(1, 4)
+        process_flag = False
+        if number > 1 and number % 2 == 0:
+            process_flag = True
+        dataset_generator = get_dataset_generator(value)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False,
+                                      num_parallel_workers=number, python_multiprocessing=process_flag)
+        count = 0
+        with pytest.raises(RuntimeError) as info:
+            for data in dataset.create_dict_iterator(output_numpy=True):
+                print(data)
+                count += 1
+            assert count == 20
+        assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+               "column_names," in str(info.value)
+        assert "the size of column_names is:3 and number of returned NumPy array is:1" in str(info.value)
+
+    numpy_1 = np.array(1)
+    numpy_2 = np.array([1])
+    numpy_3 = np.array([1, 2])
+    numpy_4 = np.array([1, 2, 3])
+    numpy_5 = np.array([[1], [2]])
+    numpy_6 = np.array([[1, 2], [2, 3]])
+    numpy_7 = np.array([[1, 2, 3], [2, 3, 4]])
+    numpy_8 = np.array([[1], [2], [3]])
+    numpy_9 = np.array([[1, 2], [2, 3], [3, 4]])
+    numpy_10 = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
+    test_generator_three_column(numpy_1)
+    test_generator_three_column(numpy_2)
+    test_generator_three_column(numpy_3)
+    test_generator_three_column(numpy_4)
+    test_generator_three_column(numpy_5)
+    test_generator_three_column(numpy_6)
+    test_generator_three_column(numpy_7)
+    test_generator_three_column(numpy_8)
+    test_generator_three_column(numpy_9)
+    test_generator_three_column(numpy_10)
+    tuple_1 = (numpy_7,)
+    test_generator_three_column(tuple_1)
+
+    tuple_2 = (numpy_2, numpy_3)
+    with pytest.raises(RuntimeError) as info:
+        dataset_generator = get_dataset_generator(tuple_2)
+        dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False)
+        for data in dataset.create_dict_iterator(output_numpy=True):
+            print(data["data"])
+    assert "the 'source' of 'GeneratorDataset' should return same number of NumPy arrays as specified in " \
+           "column_names," in str(info.value)
+    assert "the size of column_names is:3 and number of returned NumPy array is:2" in str(info.value)
+
+    tuple_3 = (numpy_4, numpy_5, numpy_6)
+    dataset_generator = get_dataset_generator(tuple_3)
+    dataset = ds.GeneratorDataset(dataset_generator, ["data", "label", "label2"], shuffle=False)
+    count = 0
+    for data in dataset.create_dict_iterator(output_numpy=True):
+        assert (data["data"] == numpy_4).all()
+        assert (data["label"] == numpy_5).all()
+        assert (data["label2"] == numpy_6).all()
+        count += 1
+    assert count == 20
+
+
 if __name__ == "__main__":
     test_generator_0()
     test_generator_1()
@@ -1415,3 +2061,6 @@ if __name__ == "__main__":
     test_generator_single_input_4()
     test_generator_single_input_5()
     test_generator_single_input_6()
+    test_generator_with_single_numpy()
+    test_generator_with_single_numpy_with_next()
+    test_generator_with_single_numpy_with_yield()