diff --git a/mindspore/ccsrc/minddata/dataset/api/transforms.cc b/mindspore/ccsrc/minddata/dataset/api/transforms.cc
index 46822e1323..a21bf34724 100644
--- a/mindspore/ccsrc/minddata/dataset/api/transforms.cc
+++ b/mindspore/ccsrc/minddata/dataset/api/transforms.cc
@@ -17,39 +17,168 @@
 #include "minddata/dataset/include/transforms.h"
 
 // Kernel data headers (in alphabetical order)
+#include "minddata/dataset/kernels/data/compose_op.h"
+#include "minddata/dataset/kernels/data/duplicate_op.h"
 #include "minddata/dataset/kernels/data/one_hot_op.h"
+#include "minddata/dataset/kernels/data/random_apply_op.h"
+#include "minddata/dataset/kernels/data/random_choice_op.h"
 #include "minddata/dataset/kernels/data/type_cast_op.h"
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/data/unique_op.h"
+#endif
 
 namespace mindspore {
 namespace dataset {
 
 TensorOperation::TensorOperation() {}
 
+/* ####################################### Validator Functions ############################################ */
+Status ValidateVectorFillvalue(const std::string &transform_name, const std::vector<uint8_t> &fill_value) {
+  if (fill_value.empty() || (fill_value.size() != 1 && fill_value.size() != 3)) {
+    std::string err_msg =
+      transform_name + ": fill_value vector has incorrect size: " + std::to_string(fill_value.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  for (uint8_t single_fill_value : fill_value) {
+    if (single_fill_value > 255) {
+      std::string err_msg =
+        transform_name + ": fill_value has to be between 0 and 255, got:" + std::to_string(single_fill_value);
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+
+  return Status::OK();
+}
+
+Status ValidateProbability(const std::string &transform_name, const float &probability) {
+  if (probability < 0.0 || probability > 1.0) {
+    std::string err_msg =
+      transform_name + ": probability must be between 0.0 and 1.0, got: " + std::to_string(probability);
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+
+  return Status::OK();
+}
+
+Status ValidateVectorPadding(const std::string &transform_name, const std::vector<int32_t> &padding) {
+  if (padding.empty() || padding.size() == 3 || padding.size() > 4) {
+    std::string err_msg = transform_name + ": padding vector has incorrect size: " + std::to_string(padding.size());
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  for (int32_t i = 0; i < padding.size(); ++i) {
+    if (padding[i] < 0) {
+      std::string err_msg =
+        transform_name +
+        ": invalid padding, padding value must be greater than or equal to 0, got: " + std::to_string(padding[i]);
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+    if (padding[i] == INT_MAX) {
+      std::string err_msg =
+        transform_name + ": invalid padding, padding value too large, got: " + std::to_string(padding[i]);
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+
+  return Status::OK();
+}
+
+Status ValidateVectorPositive(const std::string &transform_name, const std::vector<int32_t> &size) {
+  for (int32_t i = 0; i < size.size(); ++i) {
+    if (size[i] <= 0) {
+      std::string err_msg =
+        transform_name + ": Non-positive size value: " + std::to_string(size[i]) + " at element: " + std::to_string(i);
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+
+  return Status::OK();
+}
+
+Status ValidateVectorTransforms(const std::string &transform_name,
+                                const std::vector<std::shared_ptr<TensorOperation>> &transforms) {
+  if (transforms.empty()) {
+    std::string err_msg = transform_name + ": transform list must not be empty.";
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  for (int32_t i = 0; i < transforms.size(); ++i) {
+    if (transforms[i] == nullptr) {
+      std::string err_msg =
+        transform_name + ": transform ops must not be null, got transform[" + std::to_string(i) + "] == nullptr.";
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+
+  return Status::OK();
+}
+
+bool CmpFloat(const float &a, const float &b, float epsilon) { return (std::fabs(a - b) < epsilon); }
+
 // Transform operations for data.
 namespace transforms {
 
 // FUNCTIONS TO CREATE DATA TRANSFORM OPERATIONS
 // (In alphabetical order)
 
+// Function to create ComposeOperation.
+std::shared_ptr<ComposeOperation> Compose(const std::vector<std::shared_ptr<TensorOperation>> &transforms) {
+  auto op = std::make_shared<ComposeOperation>(transforms);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create DuplicateOperation.
+std::shared_ptr<DuplicateOperation> Duplicate() {
+  auto op = std::make_shared<DuplicateOperation>();
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
 // Function to create OneHotOperation.
 std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes) {
   auto op = std::make_shared<OneHotOperation>(num_classes);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create RandomApplyOperation.
+std::shared_ptr<RandomApplyOperation> RandomApply(const std::vector<std::shared_ptr<TensorOperation>> &transforms,
+                                                  double prob) {
+  auto op = std::make_shared<RandomApplyOperation>(transforms, prob);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
+}
+
+// Function to create RandomChoiceOperation.
+std::shared_ptr<RandomChoiceOperation> RandomChoice(const std::vector<std::shared_ptr<TensorOperation>> &transforms) {
+  auto op = std::make_shared<RandomChoiceOperation>(transforms);
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
 }
 
 // Function to create TypeCastOperation.
 std::shared_ptr<TypeCastOperation> TypeCast(std::string data_type) {
   auto op = std::make_shared<TypeCastOperation>(data_type);
   // Input validation
-  if (!op->ValidateParams()) {
-    return nullptr;
-  }
-  return op;
+  return op->ValidateParams() ? op : nullptr;
+}
+
+#ifndef ENABLE_ANDROID
+// Function to create UniqueOperation.
+std::shared_ptr<UniqueOperation> Unique() {
+  auto op = std::make_shared<UniqueOperation>();
+  // Input validation
+  return op->ValidateParams() ? op : nullptr;
 }
+#endif
 
 /* ####################################### Validator Functions ############################################ */
 
@@ -57,13 +186,33 @@ std::shared_ptr<TypeCastOperation> TypeCast(std::string data_type) {
 
 // (In alphabetical order)
 
+// ComposeOperation
+ComposeOperation::ComposeOperation(const std::vector<std::shared_ptr<TensorOperation>> &transforms)
+    : transforms_(transforms) {}
+
+Status ComposeOperation::ValidateParams() {
+  RETURN_IF_NOT_OK(ValidateVectorTransforms("Compose", transforms_));
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> ComposeOperation::Build() {
+  std::vector<std::shared_ptr<TensorOp>> tensor_ops;
+  (void)std::transform(transforms_.begin(), transforms_.end(), std::back_inserter(tensor_ops),
+                       [](std::shared_ptr<TensorOperation> op) -> std::shared_ptr<TensorOp> { return op->Build(); });
+  return std::make_shared<ComposeOp>(tensor_ops);
+}
+
+// DuplicateOperation
+Status DuplicateOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> DuplicateOperation::Build() { return std::make_shared<DuplicateOp>(); }
+
 // OneHotOperation
 OneHotOperation::OneHotOperation(int32_t num_classes) : num_classes_(num_classes) {}
 
 Status OneHotOperation::ValidateParams() {
   if (num_classes_ <= 0) {
-    std::string err_msg =
-      "OneHot: Number of classes must be greater than 0. num_classes: " + std::to_string(num_classes_);
+    std::string err_msg = "OneHot: Number of classes must be greater than 0, but got: " + std::to_string(num_classes_);
     MS_LOG(ERROR) << err_msg;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
@@ -73,6 +222,39 @@ Status OneHotOperation::ValidateParams() {
 
 std::shared_ptr<TensorOp> OneHotOperation::Build() { return std::make_shared<OneHotOp>(num_classes_); }
 
+// RandomApplyOperation
+RandomApplyOperation::RandomApplyOperation(const std::vector<std::shared_ptr<TensorOperation>> &transforms, double prob)
+    : transforms_(transforms), prob_(prob) {}
+
+Status RandomApplyOperation::ValidateParams() {
+  RETURN_IF_NOT_OK(ValidateVectorTransforms("RandomApply", transforms_));
+  RETURN_IF_NOT_OK(ValidateProbability("RandomApply", prob_));
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> RandomApplyOperation::Build() {
+  std::vector<std::shared_ptr<TensorOp>> tensor_ops;
+  (void)std::transform(transforms_.begin(), transforms_.end(), std::back_inserter(tensor_ops),
+                       [](std::shared_ptr<TensorOperation> op) -> std::shared_ptr<TensorOp> { return op->Build(); });
+  return std::make_shared<RandomApplyOp>(prob_, tensor_ops);
+}
+
+// RandomChoiceOperation
+RandomChoiceOperation::RandomChoiceOperation(const std::vector<std::shared_ptr<TensorOperation>> &transforms)
+    : transforms_(transforms) {}
+
+Status RandomChoiceOperation::ValidateParams() {
+  RETURN_IF_NOT_OK(ValidateVectorTransforms("RandomChoice", transforms_));
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> RandomChoiceOperation::Build() {
+  std::vector<std::shared_ptr<TensorOp>> tensor_ops;
+  (void)std::transform(transforms_.begin(), transforms_.end(), std::back_inserter(tensor_ops),
+                       [](std::shared_ptr<TensorOperation> op) -> std::shared_ptr<TensorOp> { return op->Build(); });
+  return std::make_shared<RandomChoiceOp>(tensor_ops);
+}
+
 // TypeCastOperation
 TypeCastOperation::TypeCastOperation(std::string data_type) : data_type_(data_type) {}
 
@@ -83,7 +265,7 @@ Status TypeCastOperation::ValidateParams() {
   if (itr == predefine_type.end()) {
     std::string err_msg = "TypeCast: Invalid data type: " + data_type_;
     MS_LOG(ERROR) << "TypeCast: Only supports data type bool, int8, uint8, int16, uint16, int32, uint32, "
-                  << "int64, uint64, float16, float32, float64, string, but got " << data_type_;
+                  << "int64, uint64, float16, float32, float64, string, but got: " << data_type_;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
 
@@ -92,6 +274,13 @@ Status TypeCastOperation::ValidateParams() {
 
 std::shared_ptr<TensorOp> TypeCastOperation::Build() { return std::make_shared<TypeCastOp>(data_type_); }
 
+#ifndef ENABLE_ANDROID
+// UniqueOperation
+Status UniqueOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> UniqueOperation::Build() { return std::make_shared<UniqueOp>(); }
+#endif
+
 }  // namespace transforms
 }  // namespace dataset
 }  // namespace mindspore
diff --git a/mindspore/ccsrc/minddata/dataset/api/vision.cc b/mindspore/ccsrc/minddata/dataset/api/vision.cc
index d7d354df43..3a9685cf87 100644
--- a/mindspore/ccsrc/minddata/dataset/api/vision.cc
+++ b/mindspore/ccsrc/minddata/dataset/api/vision.cc
@@ -394,77 +394,7 @@ std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<
   // Input validation
   return op->ValidateParams() ? op : nullptr;
 }
-
 #endif
-/* ####################################### Validator Functions ############################################ */
-Status ValidateVectorFillvalue(const std::string &dataset_name, const std::vector<uint8_t> &fill_value) {
-  if (fill_value.empty() || (fill_value.size() != 1 && fill_value.size() != 3)) {
-    std::string err_msg = dataset_name + ": fill_value vector has incorrect size: " + std::to_string(fill_value.size());
-    MS_LOG(ERROR) << err_msg;
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-  for (uint8_t single_fill_value : fill_value) {
-    if (single_fill_value > 255) {
-      std::string err_msg =
-        dataset_name + ": fill_value has to be between 0 and 255, got:" + std::to_string(single_fill_value);
-      MS_LOG(ERROR) << err_msg;
-      RETURN_STATUS_SYNTAX_ERROR(err_msg);
-    }
-  }
-
-  return Status::OK();
-}
-
-Status ValidateVectorProbability(const std::string &dataset_name, const float &probability) {
-  if (probability < 0.0 || probability > 1.0) {
-    std::string err_msg =
-      dataset_name + ": probability must be between 0.0 and 1.0, got: " + std::to_string(probability);
-    MS_LOG(ERROR) << err_msg;
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-
-  return Status::OK();
-}
-
-Status ValidateVectorPadding(const std::string &dataset_name, const std::vector<int32_t> &padding) {
-  if (padding.empty() || padding.size() == 3 || padding.size() > 4) {
-    std::string err_msg = dataset_name + ": padding vector has incorrect size: " + std::to_string(padding.size());
-    MS_LOG(ERROR) << err_msg;
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-  for (int32_t i = 0; i < padding.size(); ++i) {
-    if (padding[i] < 0) {
-      std::string err_msg =
-        dataset_name +
-        ": invalid padding, padding value must be greater than or equal to 0, got: " + std::to_string(padding[i]);
-      MS_LOG(ERROR) << err_msg;
-      RETURN_STATUS_SYNTAX_ERROR(err_msg);
-    }
-    if (padding[i] == INT_MAX) {
-      std::string err_msg =
-        dataset_name + ": invalid padding, padding value too large, got: " + std::to_string(padding[i]);
-      MS_LOG(ERROR) << err_msg;
-      RETURN_STATUS_SYNTAX_ERROR(err_msg);
-    }
-  }
-
-  return Status::OK();
-}
-
-Status ValidateVectorPositive(const std::string &dataset_name, const std::vector<int32_t> &size) {
-  for (int32_t i = 0; i < size.size(); ++i) {
-    if (size[i] <= 0) {
-      std::string err_msg =
-        dataset_name + ": Non-positive size value: " + std::to_string(size[i]) + " at element: " + std::to_string(i);
-      MS_LOG(ERROR) << err_msg;
-      RETURN_STATUS_SYNTAX_ERROR(err_msg);
-    }
-  }
-
-  return Status::OK();
-}
-
-bool CmpFloat(const float &a, const float &b, float epsilon = 0.0000000001f) { return (std::fabs(a - b) < epsilon); }
 
 /* ####################################### Derived TensorOperation classes ################################# */
 
@@ -503,17 +433,8 @@ BoundingBoxAugmentOperation::BoundingBoxAugmentOperation(std::shared_ptr<TensorO
     : transform_(transform), ratio_(ratio) {}
 
 Status BoundingBoxAugmentOperation::ValidateParams() {
-  if (transform_ == nullptr) {
-    std::string err_msg = "BoundingBoxAugment: transform must not be null.";
-    MS_LOG(ERROR) << err_msg;
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
-
-  if (ratio_ < 0.0 || ratio_ > 1.0) {
-    std::string err_msg = "BoundingBoxAugment: ratio has to be between 0.0 and 1.0, got: " + std::to_string(ratio_);
-    MS_LOG(ERROR) << err_msg;
-    RETURN_STATUS_SYNTAX_ERROR(err_msg);
-  }
+  RETURN_IF_NOT_OK(ValidateVectorTransforms("BoundingBoxAugment", {transform_}));
+  RETURN_IF_NOT_OK(ValidateProbability("BoundingBoxAugment", ratio_));
   return Status::OK();
 }
 
@@ -1315,7 +1236,7 @@ std::shared_ptr<TensorOp> RandomCropWithBBoxOperation::Build() {
 RandomHorizontalFlipOperation::RandomHorizontalFlipOperation(float probability) : probability_(probability) {}
 
 Status RandomHorizontalFlipOperation::ValidateParams() {
-  RETURN_IF_NOT_OK(ValidateVectorProbability("RandomHorizontalFlip", probability_));
+  RETURN_IF_NOT_OK(ValidateProbability("RandomHorizontalFlip", probability_));
 
   return Status::OK();
 }
@@ -1330,7 +1251,7 @@ RandomHorizontalFlipWithBBoxOperation::RandomHorizontalFlipWithBBoxOperation(flo
     : probability_(probability) {}
 
 Status RandomHorizontalFlipWithBBoxOperation::ValidateParams() {
-  RETURN_IF_NOT_OK(ValidateVectorProbability("RandomHorizontalFlipWithBBox", probability_));
+  RETURN_IF_NOT_OK(ValidateProbability("RandomHorizontalFlipWithBBox", probability_));
 
   return Status::OK();
 }
@@ -1696,7 +1617,7 @@ std::shared_ptr<TensorOp> RandomSolarizeOperation::Build() {
 RandomVerticalFlipOperation::RandomVerticalFlipOperation(float probability) : probability_(probability) {}
 
 Status RandomVerticalFlipOperation::ValidateParams() {
-  RETURN_IF_NOT_OK(ValidateVectorProbability("RandomVerticalFlip", probability_));
+  RETURN_IF_NOT_OK(ValidateProbability("RandomVerticalFlip", probability_));
 
   return Status::OK();
 }
@@ -1711,7 +1632,7 @@ RandomVerticalFlipWithBBoxOperation::RandomVerticalFlipWithBBoxOperation(float p
     : probability_(probability) {}
 
 Status RandomVerticalFlipWithBBoxOperation::ValidateParams() {
-  RETURN_IF_NOT_OK(ValidateVectorProbability("RandomVerticalFlipWithBBox", probability_));
+  RETURN_IF_NOT_OK(ValidateProbability("RandomVerticalFlipWithBBox", probability_));
 
   return Status::OK();
 }
@@ -1945,21 +1866,15 @@ UniformAugOperation::UniformAugOperation(std::vector<std::shared_ptr<TensorOpera
 
 Status UniformAugOperation::ValidateParams() {
   // transforms
+  RETURN_IF_NOT_OK(ValidateVectorTransforms("UniformAug", transforms_));
   if (num_ops_ > transforms_.size()) {
-    std::string err_msg = "UniformAug: num_ops is greater than transforms size, num_ops: " + std::to_string(num_ops_);
+    std::string err_msg = "UniformAug: num_ops is greater than transforms size, but got: " + std::to_string(num_ops_);
     MS_LOG(ERROR) << err_msg;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
-  for (int32_t i = 0; i < transforms_.size(); ++i) {
-    if (transforms_[i] == nullptr) {
-      std::string err_msg = "UniformAug: transform ops must not be null.";
-      MS_LOG(ERROR) << err_msg;
-      RETURN_STATUS_SYNTAX_ERROR(err_msg);
-    }
-  }
   // num_ops
   if (num_ops_ <= 0) {
-    std::string err_msg = "UniformAug: num_ops must be greater than 0, num_ops: " + std::to_string(num_ops_);
+    std::string err_msg = "UniformAug: num_ops must be greater than 0, but got: " + std::to_string(num_ops_);
     MS_LOG(ERROR) << err_msg;
     RETURN_STATUS_SYNTAX_ERROR(err_msg);
   }
diff --git a/mindspore/ccsrc/minddata/dataset/include/transforms.h b/mindspore/ccsrc/minddata/dataset/include/transforms.h
index 2df71e40be..39977ded67 100644
--- a/mindspore/ccsrc/minddata/dataset/include/transforms.h
+++ b/mindspore/ccsrc/minddata/dataset/include/transforms.h
@@ -44,12 +44,50 @@ class TensorOperation : public std::enable_shared_from_this<TensorOperation> {
   virtual Status ValidateParams() = 0;
 };
 
+// Helper function to validate fill value
+Status ValidateVectorFillvalue(const std::string &transform_name, const std::vector<uint8_t> &fill_value);
+
+// Helper function to validate probability
+Status ValidateProbability(const std::string &transform_name, const float &probability);
+
+// Helper function to validate padding
+Status ValidateVectorPadding(const std::string &transform_name, const std::vector<int32_t> &padding);
+
+// Helper function to validate size
+Status ValidateVectorPositive(const std::string &transform_name, const std::vector<int32_t> &size);
+
+// Helper function to validate transforms
+Status ValidateVectorTransforms(const std::string &transform_name,
+                                const std::vector<std::shared_ptr<TensorOperation>> &transforms);
+
+// Helper function to compare float value
+bool CmpFloat(const float &a, const float &b, float epsilon = 0.0000000001f);
+
 // Transform operations for performing data transformation.
 namespace transforms {
 
 // Transform Op classes (in alphabetical order)
+class ComposeOperation;
+class DuplicateOperation;
 class OneHotOperation;
+class RandomApplyOperation;
+class RandomChoiceOperation;
 class TypeCastOperation;
+#ifndef ENABLE_ANDROID
+class UniqueOperation;
+#endif
+
+/// \brief Function to create a Compose TensorOperation.
+/// \notes Compose a list of transforms into a single transform.
+/// \param[in] transforms A vector of transformations to be applied.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<ComposeOperation> Compose(const std::vector<std::shared_ptr<TensorOperation>> &transforms);
+
+/// \brief Function to create a Duplicate TensorOperation.
+/// \notes Duplicate the input tensor to a new output tensor.
+///     The input tensor is carried over to the output list.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<DuplicateOperation> Duplicate();
 
 /// \brief Function to create a OneHot TensorOperation.
 /// \notes Convert the labels into OneHot format.
@@ -57,14 +95,61 @@ class TypeCastOperation;
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<OneHotOperation> OneHot(int32_t num_classes);
 
+/// \brief Function to create a RandomApply TensorOperation.
+/// \notes Randomly perform a series of transforms with a given probability.
+/// \param[in] transforms A vector of transformations to be applied.
+/// \param[in] prob The probability to apply the transformation list (default=0.5)
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<RandomApplyOperation> RandomApply(const std::vector<std::shared_ptr<TensorOperation>> &transforms,
+                                                  double prob = 0.5);
+
+/// \brief Function to create a RandomChoice TensorOperation.
+/// \notes Randomly selects one transform from a list of transforms to perform operation.
+/// \param[in] transforms A vector of transformations to be chosen from to apply.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<RandomChoiceOperation> RandomChoice(const std::vector<std::shared_ptr<TensorOperation>> &transforms);
+
 /// \brief Function to create a TypeCast TensorOperation.
 /// \notes Tensor operation to cast to a given MindSpore data type.
 /// \param[in] data_type mindspore.dtype to be cast to.
 /// \return Shared pointer to the current TensorOperation.
 std::shared_ptr<TypeCastOperation> TypeCast(std::string data_type);
 
+#ifndef ENABLE_ANDROID
+/// \brief Function to create a Unique TensorOperation.
+/// \notes Return an output tensor containing all the unique elements of the input tensor in
+///     the same order that they occur in the input tensor.
+/// \return Shared pointer to the current TensorOperation.
+std::shared_ptr<UniqueOperation> Unique();
+#endif
+
 /* ####################################### Derived TensorOperation classes ################################# */
 
+class ComposeOperation : public TensorOperation {
+ public:
+  explicit ComposeOperation(const std::vector<std::shared_ptr<TensorOperation>> &transforms);
+
+  ~ComposeOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<std::shared_ptr<TensorOperation>> transforms_;
+};
+
+class DuplicateOperation : public TensorOperation {
+ public:
+  DuplicateOperation() = default;
+
+  ~DuplicateOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+};
+
 class OneHotOperation : public TensorOperation {
  public:
   explicit OneHotOperation(int32_t num_classes_);
@@ -79,6 +164,35 @@ class OneHotOperation : public TensorOperation {
   float num_classes_;
 };
 
+class RandomApplyOperation : public TensorOperation {
+ public:
+  explicit RandomApplyOperation(const std::vector<std::shared_ptr<TensorOperation>> &transforms, double prob);
+
+  ~RandomApplyOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<std::shared_ptr<TensorOperation>> transforms_;
+  double prob_;
+};
+
+class RandomChoiceOperation : public TensorOperation {
+ public:
+  explicit RandomChoiceOperation(const std::vector<std::shared_ptr<TensorOperation>> &transforms);
+
+  ~RandomChoiceOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+ private:
+  std::vector<std::shared_ptr<TensorOperation>> transforms_;
+};
+
 class TypeCastOperation : public TensorOperation {
  public:
   explicit TypeCastOperation(std::string data_type);
@@ -92,6 +206,19 @@ class TypeCastOperation : public TensorOperation {
  private:
   std::string data_type_;
 };
+
+#ifndef ENABLE_ANDROID
+class UniqueOperation : public TensorOperation {
+ public:
+  UniqueOperation() = default;
+
+  ~UniqueOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+};
+#endif
 }  // namespace transforms
 }  // namespace dataset
 }  // namespace mindspore
diff --git a/tests/ut/cpp/dataset/c_api_transforms_test.cc b/tests/ut/cpp/dataset/c_api_transforms_test.cc
index c0e96eeca6..0719d386e5 100644
--- a/tests/ut/cpp/dataset/c_api_transforms_test.cc
+++ b/tests/ut/cpp/dataset/c_api_transforms_test.cc
@@ -28,6 +28,107 @@ class MindDataTestPipeline : public UT::DatasetOpTesting {
 
 // Tests for data transforms ops (in alphabetical order)
 
+TEST_F(MindDataTestPipeline, TestComposeSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestComposeSuccess.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 3));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> compose = transforms::Compose({vision::Decode(), vision::Resize({777, 777})});
+  EXPECT_NE(compose, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({compose}, {"image"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Label shape: " << label->shape();
+    EXPECT_EQ(image->shape()[0], 777);
+    EXPECT_EQ(image->shape()[1], 777);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 3);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestComposeFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestComposeFail with invalid transform.";
+
+  // Resize: Non-positive size value: -1 at element: 0
+  // Compose: transform ops must not be null
+  std::shared_ptr<TensorOperation> compose1 = transforms::Compose({vision::Decode(), vision::Resize({-1})});
+  EXPECT_EQ(compose1, nullptr);
+
+  // Compose: transform ops must not be null
+  std::shared_ptr<TensorOperation> compose2 = transforms::Compose({vision::Decode(), nullptr});
+  EXPECT_EQ(compose2, nullptr);
+
+  // Compose: transform list must not be empty
+  std::shared_ptr<TensorOperation> compose3 = transforms::Compose({});
+  EXPECT_EQ(compose3, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestDuplicateSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestDuplicateSuccess.";
+
+  // Create a Cifar10 Dataset
+  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
+  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> duplicate = transforms::Duplicate();
+  EXPECT_NE(duplicate, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({duplicate}, {"image"}, {"image", "image_copy"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    auto image_copy = row["image_copy"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    EXPECT_EQ(*image, *image_copy);
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 10);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
 TEST_F(MindDataTestPipeline, TestOneHotSuccess1) {
   // Testing CutMixBatch on a batch of CHW images
   // Create a Cifar10 Dataset
@@ -157,6 +258,127 @@ TEST_F(MindDataTestPipeline, TestOneHotFail) {
   EXPECT_EQ(one_hot_op2, nullptr);
 }
 
+TEST_F(MindDataTestPipeline, TestRandomApplySuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomApplySuccess.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_apply = transforms::RandomApply({vision::Resize({777, 777})}, 0.8);
+  EXPECT_NE(random_apply, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_apply}, {"image"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Label shape: " << label->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 5);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomApplyFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomApplyFail with invalid transform.";
+
+  // Resize: Non-positive size value: -1 at element: 0
+  // RandomApply: transform ops must not be null
+  std::shared_ptr<TensorOperation> random_apply1 = transforms::RandomApply({vision::Decode(), vision::Resize({-1})});
+  EXPECT_EQ(random_apply1, nullptr);
+
+  // RandomApply: transform ops must not be null
+  std::shared_ptr<TensorOperation> random_apply2 = transforms::RandomApply({vision::Decode(), nullptr});
+  EXPECT_EQ(random_apply2, nullptr);
+
+  // RandomApply: transform list must not be empty
+  std::shared_ptr<TensorOperation> random_apply3 = transforms::RandomApply({});
+  EXPECT_EQ(random_apply3, nullptr);
+
+  // RandomApply: Probability has to be between 0 and 1
+  std::shared_ptr<TensorOperation> random_apply4 = transforms::RandomApply({vision::Resize({100})}, -1);
+  EXPECT_EQ(random_apply4, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestRandomChoiceSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomChoiceSuccess.";
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 3));
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_choice =
+    transforms::RandomChoice({vision::Resize({777, 777}), vision::Resize({888, 888})});
+  EXPECT_NE(random_choice, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_choice}, {"image"});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Label shape: " << label->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 3);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomChoiceFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomChoiceFail with invalid transform.";
+
+  // Resize: Non-positive size value: -1 at element: 0
+  // RandomChoice: transform ops must not be null
+  std::shared_ptr<TensorOperation> random_choice1 = transforms::RandomChoice({vision::Decode(), vision::Resize({-1})});
+  EXPECT_EQ(random_choice1, nullptr);
+
+  // RandomChoice: transform ops must not be null
+  std::shared_ptr<TensorOperation> random_choice2 = transforms::RandomChoice({vision::Decode(), nullptr});
+  EXPECT_EQ(random_choice2, nullptr);
+
+  // RandomChoice: transform list must not be empty
+  std::shared_ptr<TensorOperation> random_choice3 = transforms::RandomChoice({});
+  EXPECT_EQ(random_choice3, nullptr);
+}
+
 TEST_F(MindDataTestPipeline, TestTypeCastSuccess) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTypeCastSuccess.";
 
diff --git a/tests/ut/cpp/dataset/c_api_vision_test.cc b/tests/ut/cpp/dataset/c_api_vision_test.cc
index d13b3236fa..a5306e4aea 100644
--- a/tests/ut/cpp/dataset/c_api_vision_test.cc
+++ b/tests/ut/cpp/dataset/c_api_vision_test.cc
@@ -2799,14 +2799,8 @@ TEST_F(MindDataTestPipeline, TestSoftDvppDecodeResizeJpegFail) {
   EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op4, nullptr);
 }
 
-TEST_F(MindDataTestPipeline, DISABLED_TestUniformAugmentFail1) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail1 with invalid zero num_ops parameter.";
-
-  // Create a Mnist Dataset
-  std::string folder_path = datasets_root_path_ + "/testMnistData/";
-  std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", RandomSampler(false, 20));
-  EXPECT_NE(ds, nullptr);
-
+TEST_F(MindDataTestPipeline, TestUniformAugmentFail1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail1 with invalid num_ops parameter.";
   // Create objects for the tensor ops
   std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
   EXPECT_NE(random_crop_op, nullptr);
@@ -2814,29 +2808,33 @@ TEST_F(MindDataTestPipeline, DISABLED_TestUniformAugmentFail1) {
   std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
   EXPECT_NE(center_crop_op, nullptr);
 
-  // Try UniformAugment with invalid zero num_ops value
-  std::shared_ptr<TensorOperation> uniform_aug_op = vision::UniformAugment({random_crop_op, center_crop_op}, 0);
-  EXPECT_EQ(uniform_aug_op, nullptr);
-}
+  // UniformAug: num_ops must be greater than 0
+  std::shared_ptr<TensorOperation> uniform_aug_op1 = vision::UniformAugment({random_crop_op, center_crop_op}, 0);
+  EXPECT_EQ(uniform_aug_op1, nullptr);
 
-TEST_F(MindDataTestPipeline, DISABLED_TestUniformAugmentFail2) {
-  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail2 with invalid negative num_ops parameter.";
+  // UniformAug: num_ops must be greater than 0
+  std::shared_ptr<TensorOperation> uniform_aug_op2 = vision::UniformAugment({random_crop_op, center_crop_op}, -1);
+  EXPECT_EQ(uniform_aug_op2, nullptr);
 
-  // Create a Mnist Dataset
-  std::string folder_path = datasets_root_path_ + "/testMnistData/";
-  std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", RandomSampler(false, 20));
-  EXPECT_NE(ds, nullptr);
+  // UniformAug: num_ops is greater than transforms size
+  std::shared_ptr<TensorOperation> uniform_aug_op3 = vision::UniformAugment({random_crop_op, center_crop_op}, 3);
+  EXPECT_EQ(uniform_aug_op3, nullptr);
+}
 
-  // Create objects for the tensor ops
-  std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
-  EXPECT_NE(random_crop_op, nullptr);
+TEST_F(MindDataTestPipeline, TestUniformAugmentFail2) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail2 with invalid transform.";
 
-  std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
-  EXPECT_NE(center_crop_op, nullptr);
+  // UniformAug: transform ops must not be null
+  std::shared_ptr<TensorOperation> uniform_aug_op1 = vision::UniformAugment({vision::RandomCrop({-28})}, 1);
+  EXPECT_EQ(uniform_aug_op1, nullptr);
+
+  // UniformAug: transform ops must not be null
+  std::shared_ptr<TensorOperation> uniform_aug_op2 = vision::UniformAugment({vision::RandomCrop({28}), nullptr}, 2);
+  EXPECT_EQ(uniform_aug_op2, nullptr);
 
-  // Try UniformAugment with invalid negative num_ops value
-  std::shared_ptr<TensorOperation> uniform_aug_op = vision::UniformAugment({random_crop_op, center_crop_op}, -1);
-  EXPECT_EQ(uniform_aug_op, nullptr);
+  // UniformAug: transform list must not be empty
+  std::shared_ptr<TensorOperation> uniform_aug_op3 = vision::UniformAugment({}, 1);
+  EXPECT_EQ(uniform_aug_op3, nullptr);
 }
 
 TEST_F(MindDataTestPipeline, TestUniformAugWithOps) {