diff --git a/mindspore/ccsrc/dataset/kernels/image/uniform_aug_op.cc b/mindspore/ccsrc/dataset/kernels/image/uniform_aug_op.cc
index 5725c10908031e3f550caaf49c822a890d806107..1214345c37014572b00e8a6848b2688d8ed1853c 100644
--- a/mindspore/ccsrc/dataset/kernels/image/uniform_aug_op.cc
+++ b/mindspore/ccsrc/dataset/kernels/image/uniform_aug_op.cc
@@ -42,34 +42,28 @@ Status UniformAugOp::Compute(const std::vector<std::shared_ptr<Tensor>> &input,
                              std::vector<std::shared_ptr<Tensor>> *output) {
   IO_CHECK_VECTOR(input, output);
 
-  // variables to generate random number to select ops from the list
-  std::vector<int> random_indexes;
-
   // variables to copy the result to output if it is not already
   std::vector<std::shared_ptr<Tensor>> even_out;
   std::vector<std::shared_ptr<Tensor>> *even_out_ptr = &even_out;
   int count = 1;
 
-  // select random indexes for candidates to be applied
-  for (int i = 0; i < num_ops_; ++i) {
-    random_indexes.insert(random_indexes.end(),
-                          std::uniform_int_distribution<int>(0, tensor_op_list_.size() - 1)(rnd_));
-  }
+  // randomly select ops to be applied
+  std::vector<std::shared_ptr<TensorOp>> selected_tensor_ops;
+  std::sample(tensor_op_list_.begin(), tensor_op_list_.end(), std::back_inserter(selected_tensor_ops), num_ops_, rnd_);
 
-  for (auto it = random_indexes.begin(); it != random_indexes.end(); ++it) {
+  for (auto tensor_op = selected_tensor_ops.begin(); tensor_op != selected_tensor_ops.end(); ++tensor_op) {
     // Do NOT apply the op, if second random generator returned zero
     if (std::uniform_int_distribution<int>(0, 1)(rnd_)) {
       continue;
     }
-    std::shared_ptr<TensorOp> tensor_op = tensor_op_list_[*it];
 
     // apply python/C++ op
     if (count == 1) {
-      (*tensor_op).Compute(input, output);
+      (**tensor_op).Compute(input, output);
     } else if (count % 2 == 0) {
-      (*tensor_op).Compute(*output, even_out_ptr);
+      (**tensor_op).Compute(*output, even_out_ptr);
     } else {
-      (*tensor_op).Compute(even_out, output);
+      (**tensor_op).Compute(even_out, output);
     }
     count++;
   }
diff --git a/mindspore/dataset/transforms/vision/validators.py b/mindspore/dataset/transforms/vision/validators.py
index 713d9c5714111e1dbe7dad27ed409bfc398e03d0..2c299b077bdd25ca7d36585a71e3c362beb242da 100644
--- a/mindspore/dataset/transforms/vision/validators.py
+++ b/mindspore/dataset/transforms/vision/validators.py
@@ -17,11 +17,12 @@
 import numbers
 from functools import wraps
 
+from mindspore._c_dataengine import TensorOp
+
 from .utils import Inter, Border
 from ...transforms.validators import check_pos_int32, check_pos_float32, check_value, check_uint8, FLOAT_MAX_INTEGER, \
     check_bool, check_2tuple, check_range, check_list, check_type, check_positive, INT32_MAX
 
-
 def check_inter_mode(mode):
     if not isinstance(mode, Inter):
         raise ValueError("Invalid interpolation mode.")
@@ -836,7 +837,7 @@ def check_uniform_augmentation(method):
         if not isinstance(operations, list):
             raise ValueError("operations is not a python list")
         for op in operations:
-            if not callable(op):
+            if not callable(op) and not isinstance(op, TensorOp):
                 raise ValueError("non-callable op in operations list")
 
         kwargs["num_ops"] = num_ops