diff --git a/mindspore/dataset/transforms/vision/py_transforms.py b/mindspore/dataset/transforms/vision/py_transforms.py
index 8d81f8f3b0..51bea80b21 100644
--- a/mindspore/dataset/transforms/vision/py_transforms.py
+++ b/mindspore/dataset/transforms/vision/py_transforms.py
@@ -1312,3 +1312,177 @@ class HsvToRgb:
             rgb_imgs (numpy.ndarray), Numpy RGB image with same shape of hsv_imgs.
         """
         return util.hsv_to_rgbs(hsv_imgs, self.is_hwc)
+
+
+class RandomColor:
+    """
+    Adjust the color of the input PIL image by a random degree.
+
+    Args:
+        degrees (sequence): Range of random color adjustment degrees.
+            It should be in (min, max) format (default=(0.1,1.9)).
+
+    Examples:
+        >>> py_transforms.ComposeOp([py_transforms.Decode(),
+        >>>                          py_transforms.RandomColor(0.5,1.5),
+        >>>                          py_transforms.ToTensor()])
+    """
+
+    def __init__(self, degrees=(0.1, 1.9)):
+        self.degrees = degrees
+
+    def __call__(self, img):
+        """
+        Call method.
+
+        Args:
+            img (PIL Image): Image to be color adjusted.
+
+        Returns:
+            img (PIL Image), Color adjusted image.
+        """
+
+        return util.random_color(img, self.degrees)
+
+class RandomSharpness:
+    """
+    Adjust the sharpness of the input PIL image by a random degree.
+
+    Args:
+        degrees (sequence): Range of random sharpness adjustment degrees.
+            It should be in (min, max) format (default=(0.1,1.9)).
+
+    Examples:
+        >>> py_transforms.ComposeOp([py_transforms.Decode(),
+        >>>                          py_transforms.RandomColor(0.5,1.5),
+        >>>                          py_transforms.ToTensor()])
+
+    """
+
+    def __init__(self, degrees=(0.1, 1.9)):
+        self.degrees = degrees
+
+    def __call__(self, img):
+        """
+        Call method.
+
+        Args:
+            img (PIL Image): Image to be sharpness adjusted.
+
+        Returns:
+            img (PIL Image), Color adjusted image.
+        """
+
+        return util.random_sharpness(img, self.degrees)
+
+
+class AutoContrast:
+    """
+    Automatically maximize the contrast of the input PIL image.
+
+    Examples:
+        >>> py_transforms.ComposeOp([py_transforms.Decode(),
+        >>>                          py_transforms.AutoContrast(),
+        >>>                          py_transforms.ToTensor()])
+
+    """
+
+    def __call__(self, img):
+        """
+        Call method.
+
+        Args:
+            img (PIL Image): Image to be augmented with AutoContrast.
+
+        Returns:
+            img (PIL Image), Augmented image.
+        """
+
+        return util.auto_contrast(img)
+
+
+class Invert:
+    """
+    Invert colors of input PIL image.
+
+    Examples:
+        >>> py_transforms.ComposeOp([py_transforms.Decode(),
+        >>>                          py_transforms.Invert(),
+        >>>                          py_transforms.ToTensor()])
+
+    """
+
+    def __call__(self, img):
+        """
+        Call method.
+
+        Args:
+            img (PIL Image): Image to be color Inverted.
+
+        Returns:
+            img (PIL Image), Color inverted image.
+        """
+
+        return util.invert_color(img)
+
+
+class Equalize:
+    """
+    Equalize the histogram of input PIL image.
+
+    Examples:
+        >>> py_transforms.ComposeOp([py_transforms.Decode(),
+        >>>                          py_transforms.Equalize(),
+        >>>                          py_transforms.ToTensor()])
+
+    """
+
+    def __call__(self, img):
+        """
+        Call method.
+
+        Args:
+            img (PIL Image): Image to be equalized.
+
+        Returns:
+            img (PIL Image), Equalized image.
+        """
+
+        return util.equalize(img)
+
+
+class UniformAugment:
+    """
+    Uniformly select and apply a number of transforms sequentially from
+    a list of transforms. Randomly assigns a probability to each transform for
+    each image to decide whether apply it or not.
+
+    Args:
+         transforms (list): List of transformations to be chosen from to apply.
+         num_ops (int, optional): number of transforms to sequentially apply (default=2).
+
+    Examples:
+        >>> transforms_list = [py_transforms.CenterCrop(64),
+        >>>                    py_transforms.RandomColor(),
+        >>>                    py_transforms.RandomSharpness(),
+        >>>                    py_transforms.RandomRotation(30)]
+        >>> py_transforms.ComposeOp([py_transforms.Decode(),
+        >>>                          py_transforms.UniformAugment(transforms_list),
+        >>>                          py_transforms.ToTensor()])
+    """
+
+    def __init__(self, transforms, num_ops=2):
+        self.transforms = transforms
+        self.num_ops = num_ops
+
+    def __call__(self, img):
+        """
+        Call method.
+
+        Args:
+            img (PIL Image): Image to be applied transformation.
+
+        Returns:
+            img (PIL Image), Transformed image.
+        """
+        return util.uniform_augment(img, self.transforms, self.num_ops)
diff --git a/mindspore/dataset/transforms/vision/py_transforms_util.py b/mindspore/dataset/transforms/vision/py_transforms_util.py
index 10c71bbe38..54fb4c8274 100644
--- a/mindspore/dataset/transforms/vision/py_transforms_util.py
+++ b/mindspore/dataset/transforms/vision/py_transforms_util.py
@@ -1408,3 +1408,160 @@ def hsv_to_rgbs(np_hsv_imgs, is_hwc):
     if batch_size == 0:
         return hsv_to_rgb(np_hsv_imgs, is_hwc)
     return np.array([hsv_to_rgb(img, is_hwc) for img in np_hsv_imgs])
+
+
+def random_color(img, degrees):
+
+    """
+    Adjust the color of the input PIL image by a random degree.
+
+    Args:
+        img (PIL Image): Image to be color adjusted.
+        degrees (sequence): Range of random color adjustment degrees.
+            It should be in (min, max) format (default=(0.1,1.9)).
+
+    Returns:
+        img (PIL Image), Color adjusted image.
+    """
+
+    if not is_pil(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    if isinstance(degrees, (list, tuple)):
+        if len(degrees) != 2:
+            raise ValueError("Degrees must be a sequence length 2.")
+        if degrees[0] < 0:
+            raise ValueError("Degree value must be non-negative.")
+        if degrees[0] > degrees[1]:
+            raise ValueError("Degrees should be in (min,max) format. Got (max,min).")
+
+    else:
+        raise TypeError("Degrees must be a sequence in (min,max) format.")
+
+    v = (degrees[1] - degrees[0]) * random.random() + degrees[0]
+    return ImageEnhance.Color(img).enhance(v)
+
+
+def random_sharpness(img, degrees):
+
+    """
+    Adjust the sharpness of the input PIL image by a random degree.
+
+    Args:
+        img (PIL Image): Image to be sharpness adjusted.
+        degrees (sequence): Range of random sharpness adjustment degrees.
+            It should be in (min, max) format (default=(0.1,1.9)).
+
+    Returns:
+        img (PIL Image), Sharpness adjusted image.
+    """
+
+    if not is_pil(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    if isinstance(degrees, (list, tuple)):
+        if len(degrees) != 2:
+            raise ValueError("Degrees must be a sequence length 2.")
+        if degrees[0] < 0:
+            raise ValueError("Degree value must be non-negative.")
+        if degrees[0] > degrees[1]:
+            raise ValueError("Degrees should be in (min,max) format. Got (max,min).")
+
+    else:
+        raise TypeError("Degrees must be a sequence in (min,max) format.")
+
+    v = (degrees[1] - degrees[0]) * random.random() + degrees[0]
+    return ImageEnhance.Sharpness(img).enhance(v)
+
+
+def auto_contrast(img):
+
+    """
+    Automatically maximize the contrast of the input PIL image.
+
+    Args:
+        img (PIL Image): Image to be augmented with AutoContrast.
+
+    Returns:
+        img (PIL Image), Augmented image.
+
+    """
+
+    if not is_pil(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    return ImageOps.autocontrast(img)
+
+
+def invert_color(img):
+
+    """
+    Invert colors of input PIL image.
+
+    Args:
+        img (PIL Image): Image to be color inverted.
+
+    Returns:
+        img (PIL Image), Color inverted image.
+
+    """
+
+    if not is_pil(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    return ImageOps.invert(img)
+
+
+def equalize(img):
+
+    """
+    Equalize the histogram of input PIL image.
+
+    Args:
+        img (PIL Image): Image to be equalized
+
+    Returns:
+        img (PIL Image), Equalized image.
+
+    """
+
+    if not is_pil(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    return ImageOps.equalize(img)
+
+
+def uniform_augment(img, transforms, num_ops):
+
+    """
+    Uniformly select and apply a number of transforms sequentially from
+    a list of transforms. Randomly assigns a probability to each transform for
+    each image to decide whether apply it or not.
+
+    Args:
+        img: Image to be applied transformation.
+        transforms (list): List of transformations to be chosen from to apply.
+        num_ops (int): number of transforms to sequentially aaply.
+
+    Returns:
+        img, Transformed image.
+    """
+
+    if transforms is None:
+        raise ValueError("transforms is not provided.")
+    if not isinstance(transforms, list):
+        raise ValueError("The transforms needs to be a list.")
+
+    if not isinstance(num_ops, int):
+        raise ValueError("Number of operations should be a positive integer.")
+    if num_ops < 1:
+        raise ValueError("Number of operators should equal or greater than one.")
+
+    for _ in range(num_ops):
+        AugmentOp = random.choice(transforms)
+        pr = random.random()
+        if random.random() < pr:
+            img = AugmentOp(img.copy())
+        transforms.remove(AugmentOp)
+
+    return img
diff --git a/tests/ut/python/dataset/test_autocontrast.py b/tests/ut/python/dataset/test_autocontrast.py
new file mode 100644
index 0000000000..7dba2f21f6
--- /dev/null
+++ b/tests/ut/python/dataset/test_autocontrast.py
@@ -0,0 +1,101 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import matplotlib.pyplot as plt
+from mindspore import log as logger
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.py_transforms as F
+
+DATA_DIR = "../data/dataset/testImageNetData/train/"
+
+
+def visualize(image_original, image_auto_contrast):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    num = len(image_auto_contrast)
+    for i in range(num):
+        plt.subplot(2, num, i + 1)
+        plt.imshow(image_original[i])
+        plt.title("Original image")
+
+        plt.subplot(2, num, i + num + 1)
+        plt.imshow(image_auto_contrast[i])
+        plt.title("DE AutoContrast image")
+
+    plt.show()
+    
+
+def test_auto_contrast(plot=False):
+    """
+    Test AutoContrast
+    """
+    logger.info("Test AutoContrast")
+    
+    # Original Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_original = F.ComposeOp([F.Decode(),
+                                       F.Resize((224,224)),
+                                       F.ToTensor()])    
+    
+    ds_original = ds.map(input_columns="image",
+                         operations=transforms_original())
+    
+    ds_original = ds_original.batch(512)
+            
+    for idx, (image,label) in enumerate(ds_original):
+        if idx == 0:
+            images_original = np.transpose(image, (0, 2,3,1))
+        else:
+            images_original = np.append(images_original,
+                                        np.transpose(image, (0, 2,3,1)),
+                                        axis=0)    
+
+    # AutoContrast Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_auto_contrast = F.ComposeOp([F.Decode(),
+                                            F.Resize((224,224)),
+                                            F.AutoContrast(),
+                                            F.ToTensor()])    
+    
+    ds_auto_contrast = ds.map(input_columns="image",
+                                 operations=transforms_auto_contrast())
+    
+    ds_auto_contrast = ds_auto_contrast.batch(512)    
+      
+    for idx, (image,label) in enumerate(ds_auto_contrast):
+        if idx == 0:
+            images_auto_contrast = np.transpose(image, (0, 2,3,1))
+        else:
+            images_auto_contrast = np.append(images_auto_contrast,
+                                      np.transpose(image, (0, 2,3,1)),
+                                      axis=0)
+    
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = np.mean((images_auto_contrast[i]-images_original[i])**2)
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+    
+    if plot:
+        visualize(images_original, images_auto_contrast)
+        
+
+if __name__ == "__main__":
+    test_auto_contrast(plot=True)
+    
diff --git a/tests/ut/python/dataset/test_equalize.py b/tests/ut/python/dataset/test_equalize.py
new file mode 100644
index 0000000000..077c316d67
--- /dev/null
+++ b/tests/ut/python/dataset/test_equalize.py
@@ -0,0 +1,101 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import matplotlib.pyplot as plt
+from mindspore import log as logger
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.py_transforms as F
+
+DATA_DIR = "../data/dataset/testImageNetData/train/"
+
+
+def visualize(image_original, image_equalize):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    num = len(image_equalize)
+    for i in range(num):
+        plt.subplot(2, num, i + 1)
+        plt.imshow(image_original[i])
+        plt.title("Original image")
+
+        plt.subplot(2, num, i + num + 1)
+        plt.imshow(image_equalize[i])
+        plt.title("DE Color Equalized image")
+
+    plt.show()
+    
+
+def test_equalize(plot=False):
+    """
+    Test Equalize
+    """
+    logger.info("Test Equalize")
+    
+    # Original Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_original = F.ComposeOp([F.Decode(),
+                                       F.Resize((224,224)),
+                                       F.ToTensor()])    
+    
+    ds_original = ds.map(input_columns="image",
+                         operations=transforms_original())
+    
+    ds_original = ds_original.batch(512)
+            
+    for idx, (image,label) in enumerate(ds_original):
+        if idx == 0:
+            images_original = np.transpose(image, (0, 2,3,1))
+        else:
+            images_original = np.append(images_original,
+                                        np.transpose(image, (0, 2,3,1)),
+                                        axis=0)    
+
+    # Color Equalized Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_equalize = F.ComposeOp([F.Decode(),
+                                       F.Resize((224,224)),
+                                       F.Equalize(),
+                                       F.ToTensor()])    
+    
+    ds_equalize = ds.map(input_columns="image",
+                                 operations=transforms_equalize())
+    
+    ds_equalize = ds_equalize.batch(512)    
+      
+    for idx, (image,label) in enumerate(ds_equalize):
+        if idx == 0:
+            images_equalize = np.transpose(image, (0, 2,3,1))
+        else:
+            images_equalize = np.append(images_equalize,
+                                      np.transpose(image, (0, 2,3,1)),
+                                      axis=0)
+    
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = np.mean((images_equalize[i]-images_original[i])**2)
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+    
+    if plot:
+        visualize(images_original, images_equalize)
+        
+
+if __name__ == "__main__":
+    test_equalize(plot=True)
+    
diff --git a/tests/ut/python/dataset/test_invert.py b/tests/ut/python/dataset/test_invert.py
new file mode 100644
index 0000000000..a1bfd63431
--- /dev/null
+++ b/tests/ut/python/dataset/test_invert.py
@@ -0,0 +1,100 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import matplotlib.pyplot as plt
+from mindspore import log as logger
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.py_transforms as F
+
+DATA_DIR = "../data/dataset/testImageNetData/train/"
+
+def visualize(image_original, image_invert):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    num = len(image_invert)
+    for i in range(num):
+        plt.subplot(2, num, i + 1)
+        plt.imshow(image_original[i])
+        plt.title("Original image")
+
+        plt.subplot(2, num, i + num + 1)
+        plt.imshow(image_invert[i])
+        plt.title("DE Color Inverted image")
+
+    plt.show()
+    
+
+def test_invert(plot=False):
+    """
+    Test Invert
+    """
+    logger.info("Test Invert")
+    
+    # Original Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_original = F.ComposeOp([F.Decode(),
+                                       F.Resize((224,224)),
+                                       F.ToTensor()])    
+    
+    ds_original = ds.map(input_columns="image",
+                         operations=transforms_original())
+    
+    ds_original = ds_original.batch(512)
+            
+    for idx, (image,label) in enumerate(ds_original):
+        if idx == 0:
+            images_original = np.transpose(image, (0, 2,3,1))
+        else:
+            images_original = np.append(images_original,
+                                        np.transpose(image, (0, 2,3,1)),
+                                        axis=0)    
+
+    # Color Inverted Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_invert = F.ComposeOp([F.Decode(),
+                                     F.Resize((224,224)),
+                                     F.Invert(),
+                                     F.ToTensor()])    
+    
+    ds_invert = ds.map(input_columns="image",
+                                 operations=transforms_invert())
+    
+    ds_invert = ds_invert.batch(512)    
+      
+    for idx, (image,label) in enumerate(ds_invert):
+        if idx == 0:
+            images_invert = np.transpose(image, (0, 2,3,1))
+        else:
+            images_invert = np.append(images_invert,
+                                      np.transpose(image, (0, 2,3,1)),
+                                      axis=0)
+    
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = np.mean((images_invert[i]-images_original[i])**2)
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+    
+    if plot:
+        visualize(images_original, images_invert)
+        
+
+if __name__ == "__main__":
+    test_invert(plot=True)
+    
diff --git a/tests/ut/python/dataset/test_random_color.py b/tests/ut/python/dataset/test_random_color.py
new file mode 100644
index 0000000000..9472b7e35a
--- /dev/null
+++ b/tests/ut/python/dataset/test_random_color.py
@@ -0,0 +1,102 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import matplotlib.pyplot as plt
+from mindspore import log as logger
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.py_transforms as F
+
+DATA_DIR = "../data/dataset/testImageNetData/train/"
+
+
+def visualize(image_original, image_random_color):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    num = len(image_random_color)
+    for i in range(num):
+        plt.subplot(2, num, i + 1)
+        plt.imshow(image_original[i])
+        plt.title("Original image")
+
+        plt.subplot(2, num, i + num + 1)
+        plt.imshow(image_random_color[i])
+        plt.title("DE Random Color image")
+
+    plt.show()
+    
+
+def test_random_color(degrees=(0.1,1.9), plot=False):
+    """
+    Test RandomColor
+    """
+    logger.info("Test RandomColor")
+    
+    # Original Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_original = F.ComposeOp([F.Decode(),
+                                       F.Resize((224,224)),
+                                       F.ToTensor()])    
+    
+    ds_original = ds.map(input_columns="image",
+                         operations=transforms_original())
+    
+    ds_original = ds_original.batch(512)
+          
+    for idx, (image,label) in enumerate(ds_original):
+        if idx == 0:
+            images_original = np.transpose(image, (0, 2,3,1))
+        else:
+            images_original = np.append(images_original,
+                                        np.transpose(image, (0, 2,3,1)),
+                                        axis=0)    
+
+    # Random Color Adjusted Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_random_color = F.ComposeOp([F.Decode(),
+                                           F.Resize((224,224)),
+                                           F.RandomColor(degrees=degrees),
+                                           F.ToTensor()])    
+    
+    ds_random_color = ds.map(input_columns="image",
+                             operations=transforms_random_color())
+    
+    ds_random_color = ds_random_color.batch(512)    
+       
+    for idx, (image,label) in enumerate(ds_random_color):
+        if idx == 0:
+            images_random_color = np.transpose(image, (0, 2,3,1))
+        else:
+            images_random_color = np.append(images_random_color,
+                                            np.transpose(image, (0, 2,3,1)),
+                                            axis=0)
+
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = np.mean((images_random_color[i]-images_original[i])**2)
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+    
+    if plot:
+        visualize(images_original, images_random_color)
+    
+
+if __name__ == "__main__":
+    test_random_color()
+    test_random_color(plot=True)
+    test_random_color(degrees=(0.5,1.5), plot=True)    
diff --git a/tests/ut/python/dataset/test_random_sharpness.py b/tests/ut/python/dataset/test_random_sharpness.py
new file mode 100644
index 0000000000..949a658597
--- /dev/null
+++ b/tests/ut/python/dataset/test_random_sharpness.py
@@ -0,0 +1,102 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import matplotlib.pyplot as plt
+from mindspore import log as logger
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.py_transforms as F
+
+DATA_DIR = "../data/dataset/testImageNetData/train/"
+
+
+def visualize(image_original, image_random_sharpness):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    num = len(image_random_sharpness)
+    for i in range(num):
+        plt.subplot(2, num, i + 1)
+        plt.imshow(image_original[i])
+        plt.title("Original image")
+
+        plt.subplot(2, num, i + num + 1)
+        plt.imshow(image_random_sharpness[i])
+        plt.title("DE Random Sharpness image")
+
+    plt.show()
+    
+
+def test_random_sharpness(degrees=(0.1,1.9), plot=False):
+    """
+    Test RandomSharpness
+    """
+    logger.info("Test RandomSharpness")
+    
+    # Original Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_original = F.ComposeOp([F.Decode(),
+                                       F.Resize((224,224)),
+                                       F.ToTensor()])    
+    
+    ds_original = ds.map(input_columns="image",
+                         operations=transforms_original())
+    
+    ds_original = ds_original.batch(512)
+           
+    for idx, (image,label) in enumerate(ds_original):
+        if idx == 0:
+            images_original = np.transpose(image, (0, 2,3,1))
+        else:
+            images_original = np.append(images_original,
+                                        np.transpose(image, (0, 2,3,1)),
+                                        axis=0)    
+
+    # Random Sharpness Adjusted Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_random_sharpness = F.ComposeOp([F.Decode(),
+                                               F.Resize((224,224)),
+                                               F.RandomSharpness(degrees=degrees),
+                                               F.ToTensor()])    
+    
+    ds_random_sharpness = ds.map(input_columns="image",
+                                 operations=transforms_random_sharpness())
+    
+    ds_random_sharpness = ds_random_sharpness.batch(512)    
+      
+    for idx, (image,label) in enumerate(ds_random_sharpness):
+        if idx == 0:
+            images_random_sharpness = np.transpose(image, (0, 2,3,1))
+        else:
+            images_random_sharpness = np.append(images_random_sharpness,
+                                                np.transpose(image, (0, 2,3,1)),
+                                                axis=0)
+    
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = np.mean((images_random_sharpness[i]-images_original[i])**2)
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+
+    if plot:
+        visualize(images_original, images_random_sharpness)
+        
+
+if __name__ == "__main__":
+    test_random_sharpness()
+    test_random_sharpness(plot=True)
+    test_random_sharpness(degrees=(0.5,1.5), plot=True)      
diff --git a/tests/ut/python/dataset/test_uniform_augment.py b/tests/ut/python/dataset/test_uniform_augment.py
new file mode 100644
index 0000000000..ce0490336e
--- /dev/null
+++ b/tests/ut/python/dataset/test_uniform_augment.py
@@ -0,0 +1,107 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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 numpy as np
+import matplotlib.pyplot as plt
+from mindspore import log as logger
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.vision.py_transforms as F
+
+DATA_DIR = "../data/dataset/testImageNetData/train/"
+
+def visualize(image_original, image_ua):
+    """
+    visualizes the image using DE op and Numpy op
+    """
+    num = len(image_ua)
+    for i in range(num):
+        plt.subplot(2, num, i + 1)
+        plt.imshow(image_original[i])
+        plt.title("Original image")
+
+        plt.subplot(2, num, i + num + 1)
+        plt.imshow(image_ua[i])
+        plt.title("DE UniformAugment image")
+
+    plt.show()
+    
+
+def test_uniform_augment(plot=False, num_ops=2):
+    """
+    Test UniformAugment
+    """
+    logger.info("Test UniformAugment")
+    
+    # Original Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transforms_original = F.ComposeOp([F.Decode(),
+                                       F.Resize((224,224)),
+                                       F.ToTensor()])    
+    
+    ds_original = ds.map(input_columns="image",
+                         operations=transforms_original())
+    
+    ds_original = ds_original.batch(512)
+            
+    for idx, (image,label) in enumerate(ds_original):
+        if idx == 0:
+            images_original = np.transpose(image, (0, 2,3,1))
+        else:
+            images_original = np.append(images_original,
+                                        np.transpose(image, (0, 2,3,1)),
+                                        axis=0)    
+
+    # UniformAugment Images
+    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)    
+    
+    transform_list = [F.RandomRotation(45),
+                      F.RandomColor(),
+                      F.RandomSharpness(),
+                      F.Invert(),
+                      F.AutoContrast(),
+                      F.Equalize()]
+    
+    transforms_ua = F.ComposeOp([F.Decode(),
+                                 F.Resize((224,224)),
+                                 F.UniformAugment(transforms=transform_list, num_ops=num_ops),
+                                 F.ToTensor()])    
+    
+    ds_ua = ds.map(input_columns="image",
+                   operations=transforms_ua())
+    
+    ds_ua = ds_ua.batch(512)    
+      
+    for idx, (image,label) in enumerate(ds_ua):
+        if idx == 0:
+            images_ua = np.transpose(image, (0, 2,3,1))
+        else:
+            images_ua = np.append(images_ua,
+                                  np.transpose(image, (0, 2,3,1)),
+                                  axis=0)
+    
+    num_samples = images_original.shape[0]
+    mse = np.zeros(num_samples)
+    for i in range(num_samples):
+        mse[i] = np.mean((images_ua[i]-images_original[i])**2)
+    logger.info("MSE= {}".format(str(np.mean(mse))))
+    
+    if plot:
+        visualize(images_original, images_ua)
+        
+
+if __name__ == "__main__":
+    test_uniform_augment(num_ops=1)
+