diff --git a/monai/data/__init__.py b/monai/data/__init__.py
index fca170335b..edfcc02996 100644
--- a/monai/data/__init__.py
+++ b/monai/data/__init__.py
@@ -58,6 +58,7 @@
     pickle_hashing,
     rectify_header_sform_qform,
     rep_scalar_to_batch,
+    resample_datalist,
     select_cross_validation_folds,
     set_rnd,
     sorted_dict,
diff --git a/monai/data/utils.py b/monai/data/utils.py
index 880ceed7b8..4b29004ad9 100644
--- a/monai/data/utils.py
+++ b/monai/data/utils.py
@@ -66,6 +66,7 @@
     "is_supported_format",
     "partition_dataset",
     "partition_dataset_classes",
+    "resample_datalist",
     "select_cross_validation_folds",
     "json_hashing",
     "pickle_hashing",
@@ -991,6 +992,31 @@ def partition_dataset_classes(
     return datasets
 
 
+def resample_datalist(data: Sequence, factor: float, random_pick: bool = False, seed: int = 0):
+    """
+    Utility function to resample the loaded datalist for training, for example:
+    If factor < 1.0, randomly pick part of the datalist and set to Dataset, useful to quickly test the program.
+    If factor > 1.0, repeat the datalist to enhance the Dataset.
+
+    Args:
+        data: original datalist to scale.
+        factor: scale factor for the datalist, for example, factor=4.5, repeat the datalist 4 times and plus
+            50% of the original datalist.
+        random_pick: whether to randomly pick data if scale factor has decimal part.
+        seed: random seed to randomly pick data.
+
+    """
+    scale, repeats = math.modf(factor)
+    ret: List = list()
+
+    for _ in range(int(repeats)):
+        ret.extend(list(deepcopy(data)))
+    if scale > 1e-6:
+        ret.extend(partition_dataset(data=data, ratios=[scale, 1 - scale], shuffle=random_pick, seed=seed)[0])
+
+    return ret
+
+
 def select_cross_validation_folds(partitions: Sequence[Iterable], folds: Union[Sequence[int], int]) -> List:
     """
     Select cross validation data based on data partitions and specified fold index.
diff --git a/tests/test_inverse.py b/tests/test_inverse.py
index d547fe7595..d662441494 100644
--- a/tests/test_inverse.py
+++ b/tests/test_inverse.py
@@ -450,7 +450,7 @@ def test_inverse_inferred_seg(self, extra_transform):
 
         batch_size = 10
         # num workers = 0 for mac
-        num_workers = 2 if sys.platform != "darwin" else 0
+        num_workers = 2 if sys.platform == "linux" else 0
         transforms = Compose([AddChanneld(KEYS), SpatialPadd(KEYS, (150, 153)), extra_transform])
         num_invertible_transforms = sum(1 for i in transforms.transforms if isinstance(i, InvertibleTransform))
 
diff --git a/tests/test_resample_datalist.py b/tests/test_resample_datalist.py
new file mode 100644
index 0000000000..1d92e431cd
--- /dev/null
+++ b/tests/test_resample_datalist.py
@@ -0,0 +1,40 @@
+# Copyright 2020 - 2021 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from parameterized import parameterized
+
+from monai.data import resample_datalist
+
+TEST_CASE_1 = [
+    {"data": [1, 2, 3, 4, 5], "factor": 2.5, "random_pick": True, "seed": 123},
+    [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 2, 4, 5],
+]
+
+TEST_CASE_2 = [
+    {"data": [1, 2, 3, 4, 5], "factor": 2.5, "random_pick": False, "seed": 0},
+    [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3],
+]
+
+TEST_CASE_3 = [{"data": [1, 2, 3, 4, 5], "factor": 0.6, "random_pick": True, "seed": 123}, [2, 4, 5]]
+
+
+class TestResampleDatalist(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
+    def test_value_shape(self, input_param, expected):
+        result = resample_datalist(**input_param)
+        np.testing.assert_allclose(result, expected)
+
+
+if __name__ == "__main__":
+    unittest.main()