Cleanup dataset UT: remove num_parallel_workers=1 in test_exception

tests/ut/python/dataset/test_datasets_sharding.py

@@ -35,9 +35,9 @@ def test_imagefolder_shardings(print_res=False):
     assert (sharding_config(4, 0, 5, False, dict()) == [0, 0, 0, 1, 1])  # 5 rows
     assert (sharding_config(4, 0, 12, False, dict()) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3])  # 11 rows
     assert (sharding_config(4, 3, None, False, dict()) == [0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3])  # 11 rows
-    assert (sharding_config(1, 0, 55, False, dict()) == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]  )  # 44 rows
-    assert (sharding_config(2, 0, 55, False, dict()) == [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3]  )  # 22 rows
-    assert (sharding_config(2, 1, 55, False, dict()) == [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]  )  # 22 rows
+    assert (sharding_config(1, 0, 55, False, dict()) == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])  # 44 rows
+    assert (sharding_config(2, 0, 55, False, dict()) == [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])  # 22 rows
+    assert (sharding_config(2, 1, 55, False, dict()) == [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3])  # 22 rows
     # total 22 in dataset rows because of class indexing which takes only 2 folders
     assert (len(sharding_config(4, 0, None, True, {"class1": 111, "class2": 999})) == 6)
     assert (len(sharding_config(4, 2, 3, True, {"class1": 111, "class2": 999})) == 3)

tests/ut/python/dataset/test_exceptions.py

@@ -27,7 +27,6 @@ def test_exception_01():
     Test single exception with invalid input
     """
     logger.info("test_exception_01")
-    ds.config.set_num_parallel_workers(1)
     data = ds.TFRecordDataset(DATA_DIR, columns_list=["image"])
     with pytest.raises(ValueError) as info:
         data = data.map(input_columns=["image"], operations=vision.Resize(100, 100))