diff --git a/mindspore/python/mindspore/dataset/engine/offload.py b/mindspore/python/mindspore/dataset/engine/offload.py
index f67785ddc34..6fe4846be39 100644
--- a/mindspore/python/mindspore/dataset/engine/offload.py
+++ b/mindspore/python/mindspore/dataset/engine/offload.py
@@ -277,17 +277,20 @@ class RandomSharpness(nn.Cell):
 
         self.cast = P.Cast()
         self.shape = P.Shape()
+        self.ones = P.Ones()
         self.reshape = P.Reshape()
         self.expand_dims = P.ExpandDims()
         self.mul = P.Mul()
         self.transpose = P.Transpose()
 
+        self.check_rand = Tensor(self.degree_min == self.degree_max)
+
         self.weight = np.array([[1, 1, 1], [1, 5, 1], [1, 1, 1]])/13.0
         self.weight = np.repeat(self.weight[np.newaxis, :, :], 3, axis=0)
         self.weight = np.repeat(self.weight[np.newaxis, :, :], 3, axis=0)
         self.weight = Tensor(self.weight, mstype.float32)
 
-        self.filter = P.Conv2D(out_channel=3, kernel_size=(3, 3), pad_mode='same')
+        self.filter = P.Conv2D(out_channel=3, kernel_size=(3, 3), pad_mode='pad', pad=1)
 
     def construct(self, x):
 
@@ -298,6 +301,8 @@ class RandomSharpness(nn.Cell):
 
         degree_rand_factor = Tensor(np.random.uniform(size=(bs, 1)), dtype=mstype.float32)
         degree_rand_factor = self.degree_min + (self.degree_max - self.degree_min)*degree_rand_factor
+        degree_factor = self.degree_min * self.ones((bs, 1), mstype.float32)
+        degree_rand_factor = (self.check_rand * degree_factor) + (~self.check_rand * degree_rand_factor)
         degree_rand_factor = self.reshape(C.repeat_elements(degree_rand_factor, rep=(h*w*c)), (bs, h, w, c))
 
         x_sharp = self.filter(self.transpose(x, (0, 3, 1, 2)), self.weight)
diff --git a/tests/ut/python/dataset/test_map_offload.py b/tests/ut/python/dataset/test_map_offload.py
index 111f723c06f..92a69afc7ea 100644
--- a/tests/ut/python/dataset/test_map_offload.py
+++ b/tests/ut/python/dataset/test_map_offload.py
@@ -339,6 +339,32 @@ def test_offload_dim_check():
             continue
 
 
+def test_offload_random_sharpness_op():
+    """
+    Feature: test map offload RandomSharpness op.
+    Description: Input is image dataset.
+    Expectation: Output should be same with activated or deactivated offload for RandomSharpness op.
+    """
+
+    # Dataset with offload activated.
+    dataset_0 = ds.ImageFolderDataset(DATA_DIR)
+    dataset_0 = dataset_0.map(operations=[C.Decode()], input_columns="image")
+    dataset_0 = dataset_0.map(operations=[C.RandomSharpness(degrees=[1.0, 1.0])], input_columns="image", offload=True)
+    dataset_0 = dataset_0.map(operations=[C.HWC2CHW()], input_columns="image", offload=True)
+    dataset_0 = dataset_0.batch(8, drop_remainder=True)
+
+    # Dataset with offload not activated.
+    dataset_1 = ds.ImageFolderDataset(DATA_DIR)
+    dataset_1 = dataset_1.map(operations=[C.Decode()], input_columns="image")
+    dataset_1 = dataset_1.map(operations=[C.RandomSharpness(degrees=[1.0, 1.0])], input_columns="image")
+    dataset_1 = dataset_1.map(operations=[C.HWC2CHW()], input_columns="image")
+    dataset_1 = dataset_1.batch(8, drop_remainder=True)
+
+    for (img_0, _), (img_1, _) in zip(dataset_0.create_tuple_iterator(num_epochs=1, output_numpy=True),
+                                      dataset_1.create_tuple_iterator(num_epochs=1, output_numpy=True)):
+        np.testing.assert_almost_equal(img_0, img_1, decimal=6)
+
+
 if __name__ == "__main__":
     test_offload()
     test_auto_offload()
@@ -353,3 +379,4 @@ if __name__ == "__main__":
     test_offload_different_column_end_of_pipeline()
     test_offload_not_end_of_pipeline()
     test_offload_dim_check()
+    test_offload_random_sharpness_op()