!14579 TensorOp decoupling stage 3 (transform ops)

From: @alexyuyue
Reviewed-by: 
Signed-off-by:

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/data/bindings.cc

@@ -18,91 +18,22 @@
 #include "pybind11/stl_bind.h"
 
 #include "minddata/dataset/api/python/pybind_register.h"
-#include "minddata/dataset/core/tensor_helpers.h"
-#include "minddata/dataset/kernels/data/concatenate_op.h"
 #include "minddata/dataset/kernels/data/fill_op.h"
-#include "minddata/dataset/kernels/data/mask_op.h"
-#include "minddata/dataset/kernels/data/pad_end_op.h"
-#include "minddata/dataset/kernels/data/slice_op.h"
 #include "minddata/dataset/kernels/data/to_float16_op.h"
 
 namespace mindspore {
 namespace dataset {
 
-PYBIND_REGISTER(ConcatenateOp, 1, ([](const py::module *m) {
-                  (void)py::class_<ConcatenateOp, TensorOp, std::shared_ptr<ConcatenateOp>>(*m, "ConcatenateOp")
-                    .def(py::init<int8_t, std::shared_ptr<Tensor>, std::shared_ptr<Tensor>>());
-                }));
-
 PYBIND_REGISTER(
   FillOp, 1, ([](const py::module *m) {
     (void)py::class_<FillOp, TensorOp, std::shared_ptr<FillOp>>(*m, "FillOp").def(py::init<std::shared_ptr<Tensor>>());
   }));
 
-PYBIND_REGISTER(MaskOp, 1, ([](const py::module *m) {
-                  (void)py::class_<MaskOp, TensorOp, std::shared_ptr<MaskOp>>(*m, "MaskOp")
-                    .def(py::init<RelationalOp, std::shared_ptr<Tensor>, DataType>());
-                }));
-
-PYBIND_REGISTER(PadEndOp, 1, ([](const py::module *m) {
-                  (void)py::class_<PadEndOp, TensorOp, std::shared_ptr<PadEndOp>>(*m, "PadEndOp")
-                    .def(py::init<TensorShape, std::shared_ptr<Tensor>>());
-                }));
-
-PYBIND_REGISTER(SliceOption, 0, ([](const py::module *m) {
-                  (void)py::class_<SliceOption>(*m, "SliceOption")
-                    .def(py::init([](const py::slice &py_slice) {
-                      Slice c_slice;
-                      if (!py_slice.attr("start").is_none() && !py_slice.attr("stop").is_none() &&
-                          !py_slice.attr("step").is_none()) {
-                        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice.attr("start")),
-                                        py::reinterpret_borrow<py::int_>(py_slice.attr("stop")),
-                                        py::reinterpret_borrow<py::int_>(py_slice.attr("step")));
-                      } else if (py_slice.attr("start").is_none() && py_slice.attr("step").is_none()) {
-                        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice.attr("stop")));
-                      } else if (!py_slice.attr("start").is_none() && !py_slice.attr("stop").is_none()) {
-                        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice.attr("start")),
-                                        py::reinterpret_borrow<py::int_>(py_slice.attr("stop")));
-                      }
-
-                      if (!c_slice.valid()) {
-                        THROW_IF_ERROR(
-                          Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
-                      }
-                      return SliceOption(c_slice);
-                    }))
-                    .def(py::init([](const py::list &py_list) {
-                      std::vector<dsize_t> indices;
-                      for (auto l : py_list) {
-                        indices.push_back(py::reinterpret_borrow<py::int_>(l));
-                      }
-                      return SliceOption(indices);
-                    }))
-                    .def(py::init<bool>())
-                    .def(py::init<SliceOption>());
-                }));
-
-PYBIND_REGISTER(SliceOp, 1, ([](const py::module *m) {
-                  (void)py::class_<SliceOp, TensorOp, std::shared_ptr<SliceOp>>(*m, "SliceOp")
-                    .def(py::init<std::vector<SliceOption>>());
-                }));
-
 PYBIND_REGISTER(ToFloat16Op, 1, ([](const py::module *m) {
                   (void)py::class_<ToFloat16Op, TensorOp, std::shared_ptr<ToFloat16Op>>(*m, "ToFloat16Op",
                                                                                         py::dynamic_attr())
                     .def(py::init<>());
                 }));
 
-PYBIND_REGISTER(RelationalOp, 0, ([](const py::module *m) {
-                  (void)py::enum_<RelationalOp>(*m, "RelationalOp", py::arithmetic())
-                    .value("EQ", RelationalOp::kEqual)
-                    .value("NE", RelationalOp::kNotEqual)
-                    .value("LT", RelationalOp::kLess)
-                    .value("LE", RelationalOp::kLessEqual)
-                    .value("GT", RelationalOp::kGreater)
-                    .value("GE", RelationalOp::kGreaterEqual)
-                    .export_values();
-                }));
-
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/ir/bindings.cc

@@ -15,6 +15,7 @@
  */
 #include "pybind11/pybind11.h"
 
+#include "mindspore/ccsrc/minddata/dataset/include/transforms.h"
 #include "minddata/dataset/api/python/pybind_register.h"
 #include "minddata/dataset/core/global_context.h"
 
@@ -64,6 +65,16 @@ PYBIND_REGISTER(
       }));
   }));
 
+PYBIND_REGISTER(ConcatenateOperation, 1, ([](const py::module *m) {
+                  (void)py::class_<transforms::ConcatenateOperation, TensorOperation,
+                                   std::shared_ptr<transforms::ConcatenateOperation>>(*m, "ConcatenateOperation")
+                    .def(py::init([](int8_t axis, std::shared_ptr<Tensor> prepend, std::shared_ptr<Tensor> append) {
+                      auto concatenate = std::make_shared<transforms::ConcatenateOperation>(axis, prepend, append);
+                      THROW_IF_ERROR(concatenate->ValidateParams());
+                      return concatenate;
+                    }));
+                }));
+
 PYBIND_REGISTER(
   DuplicateOperation, 1, ([](const py::module *m) {
     (void)py::class_<transforms::DuplicateOperation, TensorOperation, std::shared_ptr<transforms::DuplicateOperation>>(
@@ -75,6 +86,17 @@ PYBIND_REGISTER(
       }));
   }));
 
+PYBIND_REGISTER(MaskOperation, 1, ([](const py::module *m) {
+                  (void)
+                    py::class_<transforms::MaskOperation, TensorOperation, std::shared_ptr<transforms::MaskOperation>>(
+                      *m, "MaskOperation")
+                      .def(py::init([](RelationalOp op, std::shared_ptr<Tensor> constant, DataType dtype) {
+                        auto mask = std::make_shared<transforms::MaskOperation>(op, constant, dtype);
+                        THROW_IF_ERROR(mask->ValidateParams());
+                        return mask;
+                      }));
+                }));
+
 PYBIND_REGISTER(
   OneHotOperation, 1, ([](const py::module *m) {
     (void)py::class_<transforms::OneHotOperation, TensorOperation, std::shared_ptr<transforms::OneHotOperation>>(
@@ -86,6 +108,17 @@ PYBIND_REGISTER(
       }));
   }));
 
+PYBIND_REGISTER(
+  PadEndOperation, 1, ([](const py::module *m) {
+    (void)py::class_<transforms::PadEndOperation, TensorOperation, std::shared_ptr<transforms::PadEndOperation>>(
+      *m, "PadEndOperation")
+      .def(py::init([](TensorShape pad_shape, std::shared_ptr<Tensor> pad_value) {
+        auto pad_end = std::make_shared<transforms::PadEndOperation>(pad_shape, pad_value);
+        THROW_IF_ERROR(pad_end->ValidateParams());
+        return pad_end;
+      }));
+  }));
+
 PYBIND_REGISTER(RandomChoiceOperation, 1, ([](const py::module *m) {
                   (void)py::class_<transforms::RandomChoiceOperation, TensorOperation,
                                    std::shared_ptr<transforms::RandomChoiceOperation>>(*m, "RandomChoiceOperation")
@@ -110,6 +143,50 @@ PYBIND_REGISTER(RandomApplyOperation, 1, ([](const py::module *m) {
                     }));
                 }));
 
+PYBIND_REGISTER(
+  SliceOperation, 1, ([](const py::module *m) {
+    (void)py::class_<transforms::SliceOperation, TensorOperation, std::shared_ptr<transforms::SliceOperation>>(
+      *m, "SliceOperation")
+      .def(py::init([](std::vector<SliceOption> slice_input) {
+        auto slice = std::make_shared<transforms::SliceOperation>(slice_input);
+        THROW_IF_ERROR(slice->ValidateParams());
+        return slice;
+      }));
+  }));
+
+PYBIND_REGISTER(SliceOption, 0, ([](const py::module *m) {
+                  (void)py::class_<SliceOption>(*m, "SliceOption")
+                    .def(py::init([](const py::slice &py_slice) {
+                      Slice c_slice;
+                      if (!py_slice.attr("start").is_none() && !py_slice.attr("stop").is_none() &&
+                          !py_slice.attr("step").is_none()) {
+                        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice.attr("start")),
+                                        py::reinterpret_borrow<py::int_>(py_slice.attr("stop")),
+                                        py::reinterpret_borrow<py::int_>(py_slice.attr("step")));
+                      } else if (py_slice.attr("start").is_none() && py_slice.attr("step").is_none()) {
+                        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice.attr("stop")));
+                      } else if (!py_slice.attr("start").is_none() && !py_slice.attr("stop").is_none()) {
+                        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice.attr("start")),
+                                        py::reinterpret_borrow<py::int_>(py_slice.attr("stop")));
+                      }
+
+                      if (!c_slice.valid()) {
+                        THROW_IF_ERROR(
+                          Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
+                      }
+                      return SliceOption(c_slice);
+                    }))
+                    .def(py::init([](const py::list &py_list) {
+                      std::vector<dsize_t> indices;
+                      for (auto l : py_list) {
+                        indices.push_back(py::reinterpret_borrow<py::int_>(l));
+                      }
+                      return SliceOption(indices);
+                    }))
+                    .def(py::init<bool>())
+                    .def(py::init<SliceOption>());
+                }));
+
 PYBIND_REGISTER(
   TypeCastOperation, 1, ([](const py::module *m) {
     (void)py::class_<transforms::TypeCastOperation, TensorOperation, std::shared_ptr<transforms::TypeCastOperation>>(
@@ -132,5 +209,16 @@ PYBIND_REGISTER(
       }));
   }));
 
+PYBIND_REGISTER(RelationalOp, 0, ([](const py::module *m) {
+                  (void)py::enum_<RelationalOp>(*m, "RelationalOp", py::arithmetic())
+                    .value("EQ", RelationalOp::kEqual)
+                    .value("NE", RelationalOp::kNotEqual)
+                    .value("LT", RelationalOp::kLess)
+                    .value("LE", RelationalOp::kLessEqual)
+                    .value("GT", RelationalOp::kGreater)
+                    .value("GE", RelationalOp::kGreaterEqual)
+                    .export_values();
+                }));
+
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/api/transforms.cc

@@ -18,6 +18,7 @@
 
 #include <algorithm>
 
+#include "mindspore/ccsrc/minddata/dataset/core/type_id.h"
 #include "minddata/dataset/kernels/ir/data/transforms_ir.h"
 
 namespace mindspore {
@@ -56,11 +57,52 @@ Compose::Compose(const std::vector<std::reference_wrapper<TensorTransform>> &tra
 
 std::shared_ptr<TensorOperation> Compose::Parse() { return std::make_shared<ComposeOperation>(data_->transforms_); }
 
+#ifndef ENABLE_ANDROID
+// Constructor to Concatenate
+struct Concatenate::Data {
+  explicit Data(int8_t axis, MSTensor prepend, MSTensor append) : axis_(axis), prepend_(prepend), append_(append) {}
+  int8_t axis_;
+  MSTensor prepend_;
+  MSTensor append_;
+};
+
+Concatenate::Concatenate(int8_t axis, MSTensor prepend, MSTensor append)
+    : data_(std::make_shared<Data>(axis, prepend, append)) {}
+
+std::shared_ptr<TensorOperation> Concatenate::Parse() {
+  std::shared_ptr<Tensor> out_prepend, out_append;
+  Tensor::CreateFromMSTensor(data_->prepend_, &out_prepend);
+  Tensor::CreateFromMSTensor(data_->append_, &out_append);
+  return std::make_shared<ConcatenateOperation>(data_->axis_, out_prepend, out_append);
+}
+#endif  // not ENABLE_ANDROID
+
 // Constructor to Duplicate
 Duplicate::Duplicate() {}
 
 std::shared_ptr<TensorOperation> Duplicate::Parse() { return std::make_shared<DuplicateOperation>(); }
 
+#ifndef ENABLE_ANDROID
+// Constructor to Mask
+struct Mask::Data {
+  explicit Data(RelationalOp op, MSTensor constant, mindspore::DataType ms_type)
+      : op_(op), constant_(constant), ms_type_(ms_type) {}
+  RelationalOp op_;
+  MSTensor constant_;
+  mindspore::DataType ms_type_;
+};
+
+Mask::Mask(RelationalOp op, MSTensor constant, mindspore::DataType ms_type)
+    : data_(std::make_shared<Data>(op, constant, ms_type)) {}
+
+std::shared_ptr<TensorOperation> Mask::Parse() {
+  std::shared_ptr<Tensor> out_constant;
+  Tensor::CreateFromMSTensor(data_->constant_, &out_constant);
+  DataType de_type = dataset::MSTypeToDEType(static_cast<TypeId>(data_->ms_type_));
+  return std::make_shared<MaskOperation>(data_->op_, out_constant, de_type);
+}
+#endif  // not ENABLE_ANDROID
+
 // Constructor to OneHot
 struct OneHot::Data {
   explicit Data(int32_t num_classes) : num_classes_(num_classes) {}
@@ -71,6 +113,25 @@ OneHot::OneHot(int32_t num_classes) : data_(std::make_shared<Data>(num_classes))
 
 std::shared_ptr<TensorOperation> OneHot::Parse() { return std::make_shared<OneHotOperation>(data_->num_classes_); }
 
+#ifndef ENABLE_ANDROID
+// Constructor to PadEnd
+struct PadEnd::Data {
+  explicit Data(const std::vector<dsize_t> &pad_shape, MSTensor pad_value)
+      : pad_shape_(pad_shape), pad_value_(pad_value) {}
+  std::vector<dsize_t> pad_shape_;
+  MSTensor pad_value_;
+};
+
+PadEnd::PadEnd(const std::vector<dsize_t> &pad_shape, MSTensor pad_value)
+    : data_(std::make_shared<Data>(pad_shape, pad_value)) {}
+
+std::shared_ptr<TensorOperation> PadEnd::Parse() {
+  std::shared_ptr<Tensor> pad_value;
+  Tensor::CreateFromMSTensor(data_->pad_value_, &pad_value);
+  return std::make_shared<PadEndOperation>(TensorShape(data_->pad_shape_), pad_value);
+}
+#endif  // not ENABLE_ANDROID
+
 // Constructor to RandomApply.
 struct RandomApply::Data {
   std::vector<std::shared_ptr<TensorOperation>> transforms_;
@@ -136,6 +197,18 @@ std::shared_ptr<TensorOperation> RandomChoice::Parse() {
   return std::make_shared<RandomChoiceOperation>(data_->transforms_);
 }
 
+#ifndef ENABLE_ANDROID
+// Constructor to Slice
+struct Slice::Data {
+  explicit Data(const std::vector<SliceOption> &slice_input) : slice_input_(slice_input) {}
+  std::vector<SliceOption> slice_input_;
+};
+
+Slice::Slice(const std::vector<SliceOption> &slice_input) : data_(std::make_shared<Data>(slice_input)) {}
+
+std::shared_ptr<TensorOperation> Slice::Parse() { return std::make_shared<SliceOperation>(data_->slice_input_); }
+#endif  // not ENABLE_ANDROID
+
 // Constructor to TypeCast
 struct TypeCast::Data {
   explicit Data(const std::vector<char> &data_type) : data_type_(CharToString(data_type)) {}

mindspore/ccsrc/minddata/dataset/core/tensor_helpers.h

@@ -19,43 +19,11 @@
 #include <memory>
 #include <vector>
 
+#include "mindspore/ccsrc/minddata/dataset/include/transforms.h"
 #include "minddata/dataset/include/constants.h"
 
 namespace mindspore {
 namespace dataset {
-class Slice {
- public:
-  Slice() : start_(0), stop_(0), step_(0) {}
-  Slice(dsize_t start, dsize_t stop, dsize_t step) : start_(start), stop_(stop), step_(step) {}
-  Slice(dsize_t start, dsize_t stop) : start_(start), stop_(stop), step_(1) {}
-  explicit Slice(dsize_t stop) : start_(0), stop_(stop), step_(1) {}
-  Slice(Slice const &slice) = default;
-
-  ~Slice() = default;
-
-  bool valid() const { return step_ != 0; }
-  dsize_t start_;
-  dsize_t stop_;
-  dsize_t step_;
-};
-
-class SliceOption {
- public:
-  explicit SliceOption(bool all) : all_(all) {}
-  explicit SliceOption(std::vector<dsize_t> indices) : indices_(indices) {}
-  explicit SliceOption(Slice slice) : slice_(slice) {}
-  SliceOption(SliceOption const &slice) = default;
-
-  ~SliceOption() = default;
-
-  // only one of the following will be valid
-  // given indices to slice the Tensor.
-  std::vector<dsize_t> indices_ = {};
-  // Slice object. All start, stop and step are 0 if invalid.
-  Slice slice_;
-  bool all_ = false;
-};
-
 /// Recursive helper function to generate indices based on vector of SliceOptions. It recursively iterates through each
 /// range represented by slice_options to generate a list of indices to be sliced.
 /// \param[out] matrix Generated nested vector of indices

mindspore/ccsrc/minddata/dataset/include/constants.h

@@ -71,6 +71,16 @@ enum class NormalizeForm {
   kNfkd,
 };
 
+// Possible values for Mask
+enum class RelationalOp {
+  kEqual = 0,     // ==
+  kNotEqual,      // !=
+  kLess,          // <
+  kLessEqual,     // <=
+  kGreater,       // >
+  kGreaterEqual,  // >=
+};
+
 // Possible values for SamplingStrategy
 enum class SamplingStrategy { kRandom = 0, kEdgeWeight = 1 };
 

mindspore/ccsrc/minddata/dataset/include/transforms.h

@@ -75,6 +75,54 @@ class TensorTransform : public std::enable_shared_from_this<TensorTransform> {
   virtual std::shared_ptr<TensorOperation> Parse(const MapTargetDevice &env) { return nullptr; }
 };
 
+/// \brief Slice object used in SliceOption.
+class Slice {
+ public:
+  /// \brief Constructor, with start, stop and step default to 0.
+  Slice() : start_(0), stop_(0), step_(0) {}
+  /// \brief Constructor.
+  /// \param[in] start Starting integer specifying where to start the slicing.
+  /// \param[in] stop Ending integer specifying where to stop the slicing.
+  /// \param[in] step An integer specifying the step of the slicing.
+  Slice(dsize_t start, dsize_t stop, dsize_t step) : start_(start), stop_(stop), step_(step) {}
+  /// \brief Constructor, with step=1
+  /// \param[in] start Starting integer specifying where to start the slicing.
+  /// \param[in] stop Ending integer specifying where to stop the slicing.
+  Slice(dsize_t start, dsize_t stop) : start_(start), stop_(stop), step_(1) {}
+  /// \brief Constructor, with start=0 and step=1
+  /// \param[in] stop Ending integer specifying where to stop the slicing.
+  explicit Slice(dsize_t stop) : start_(0), stop_(stop), step_(1) {}
+  Slice(Slice const &slice) = default;
+
+  ~Slice() = default;
+
+  bool valid() const { return step_ != 0; }
+  dsize_t start_;
+  dsize_t stop_;
+  dsize_t step_;
+};
+
+/// \brief SliceOption used in Slice Op.
+class SliceOption {
+ public:
+  /// \param[in] all Slice the whole dimension
+  explicit SliceOption(bool all) : all_(all) {}
+  /// \param[in] indices Slice these indices along the dimension. Negative indices are supported.
+  explicit SliceOption(std::vector<dsize_t> indices) : indices_(indices) {}
+  /// \param[in] slice Slice the generated indices from the slice object along the dimension.
+  explicit SliceOption(Slice slice) : slice_(slice) {}
+  SliceOption(SliceOption const &slice) = default;
+
+  ~SliceOption() = default;
+
+  // only one of the following will be valid
+  // given indices to slice the Tensor.
+  std::vector<dsize_t> indices_ = {};
+  // Slice object. All start, stop and step are 0 if invalid.
+  Slice slice_;
+  bool all_ = false;
+};
+
 // Transform operations for performing data transformation.
 namespace transforms {
 
@@ -105,6 +153,29 @@ class Compose final : public TensorTransform {
   std::shared_ptr<Data> data_;
 };
 
+/// \brief Concatenate Op.
+/// \notes Tensor operation that concatenates all columns into a single tensor.
+class Concatenate final : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] axis Concatenate the tensors along given axis (Default=0).
+  /// \param[in] prepend MSTensor to be prepended to the already concatenated tensors (Default={}).
+  /// \param[in] append MSTensor to be appended to the already concatenated tensors (Default={}).
+  explicit Concatenate(int8_t axis = 0, MSTensor prepend = {}, MSTensor append = {});
+
+  /// \brief Destructor
+  ~Concatenate() = default;
+
+ protected:
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  struct Data;
+  std::shared_ptr<Data> data_;
+};
+
 /// \brief Duplicate Op.
 /// \notes Duplicate the input tensor to a new output tensor.
 ///     The input tensor is carried over to the output list.
@@ -122,6 +193,32 @@ class Duplicate final : public TensorTransform {
   std::shared_ptr<TensorOperation> Parse() override;
 };
 
+/// \brief Mask Op.
+/// \notes Mask content of the input tensor with the given predicate.
+///     Any element of the tensor that matches the predicate will be evaluated to True, otherwise False.
+class Mask final : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] op One of the relational operators EQ, NE LT, GT, LE or GE.
+  /// \param[in] constant Constant to be compared to.
+  ///               Can only be MSTensor of str, int, float, bool.
+  /// \param[in] de_type Type of the generated mask (Default to be mindspore::DataType::kNumberTypeBool).
+  explicit Mask(RelationalOp op, MSTensor constant,
+                mindspore::DataType ms_type = mindspore::DataType(mindspore::DataType::kNumberTypeBool));
+
+  /// \brief Destructor
+  ~Mask() = default;
+
+ protected:
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  struct Data;
+  std::shared_ptr<Data> data_;
+};
+
 /// \brief OneHot Op.
 /// \notes Convert the labels into OneHot format.
 class OneHot final : public TensorTransform {
@@ -143,6 +240,30 @@ class OneHot final : public TensorTransform {
   std::shared_ptr<Data> data_;
 };
 
+/// \brief PadEnd Op.
+/// \notes Pad input tensor according to pad_shape, need to have same rank.
+class PadEnd final : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] pad_shape List of integers representing the shape needed.
+  ///               Dimensions that set to `None` will not be padded (i.e., original dim will be used).
+  ///               Shorter dimensions will truncate the values.
+  /// \param[in] pad_value Value used to pad. Default to be {}.
+  explicit PadEnd(const std::vector<dsize_t> &pad_shape, MSTensor pad_value = {});
+
+  /// \brief Destructor
+  ~PadEnd() = default;
+
+ protected:
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  struct Data;
+  std::shared_ptr<Data> data_;
+};
+
 /// \brief RandomApply Op.
 /// \notes Randomly perform a series of transforms with a given probability.
 class RandomApply final : public TensorTransform {
@@ -200,6 +321,29 @@ class RandomChoice final : public TensorTransform {
   std::shared_ptr<Data> data_;
 };
 
+/// \brief Slice Op.
+/// \notes Slice operation to extract a tensor out using the given n slices.
+///     The functionality of Slice is similar to NumPy's indexing feature.
+///     (Currently only rank-1 tensors are supported).
+class Slice final : public TensorTransform {
+ public:
+  /// \brief Constructor.
+  /// \param[in] slice_input Vector of SliceOption
+  explicit Slice(const std::vector<SliceOption> &slice_input);
+
+  /// \brief Destructor
+  ~Slice() = default;
+
+ protected:
+  /// \brief Function to convert TensorTransform object into a TensorOperation object.
+  /// \return Shared pointer to TensorOperation object.
+  std::shared_ptr<TensorOperation> Parse() override;
+
+ private:
+  struct Data;
+  std::shared_ptr<Data> data_;
+};
+
 /// \brief TypeCast Op.
 /// \notes Tensor operation to cast to a given MindSpore data type.
 class TypeCast final : public TensorTransform {

mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.h

@@ -124,15 +124,6 @@ Status PadEndStringHelper(const std::shared_ptr<Tensor> &src, std::vector<std::s
                           const TensorShape &dst_shape, std::vector<dsize_t> cur_ind, size_t cur_dim,
                           const std::string &pad_value);
 
-enum class RelationalOp {
-  kEqual = 0,     // ==
-  kNotEqual,      // !=
-  kLess,          // <
-  kLessEqual,     // <=
-  kGreater,       // >
-  kGreaterEqual,  // >=
-};
-
 /// Helper method that masks the input tensor
 /// @tparam T type of the tensor
 /// @param input[in] input tensor

mindspore/ccsrc/minddata/dataset/kernels/ir/data/transforms_ir.cc

@@ -20,10 +20,22 @@
 
 // Kernel data headers (in alphabetical order)
 #include "minddata/dataset/kernels/data/compose_op.h"
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/data/concatenate_op.h"
+#endif
 #include "minddata/dataset/kernels/data/duplicate_op.h"
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/data/mask_op.h"
+#endif
 #include "minddata/dataset/kernels/data/one_hot_op.h"
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/data/pad_end_op.h"
+#endif
 #include "minddata/dataset/kernels/data/random_apply_op.h"
 #include "minddata/dataset/kernels/data/random_choice_op.h"
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/kernels/data/slice_op.h"
+#endif
 #include "minddata/dataset/kernels/data/type_cast_op.h"
 #ifndef ENABLE_ANDROID
 #include "minddata/dataset/kernels/data/unique_op.h"
@@ -58,11 +70,55 @@ std::shared_ptr<TensorOp> ComposeOperation::Build() {
   return std::make_shared<ComposeOp>(tensor_ops);
 }
 
+#ifndef ENABLE_ANDROID
+// ConcatenateOperation
+ConcatenateOperation::ConcatenateOperation(int8_t axis, const std::shared_ptr<Tensor> &prepend,
+                                           const std::shared_ptr<Tensor> &append)
+    : axis_(axis), prepend_(prepend), append_(append) {}
+
+Status ConcatenateOperation::ValidateParams() {
+  if (axis_ != 0 && axis_ != -1) {
+    std::string err_msg = "Concatenate: Only 1D concatenation supported.";
+    MS_LOG(ERROR) << err_msg;
+    RETURN_STATUS_SYNTAX_ERROR(err_msg);
+  }
+  if (prepend_) {
+    if (prepend_->shape().Size() != 1) {
+      std::string err_msg = "Concatenate: Can only prepend 1D arrays.";
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+  if (append_) {
+    if (append_->shape().Size() != 1) {
+      std::string err_msg = "Concatenate: Can only append 1D arrays.";
+      MS_LOG(ERROR) << err_msg;
+      RETURN_STATUS_SYNTAX_ERROR(err_msg);
+    }
+  }
+  return Status::OK();
+}
+
+std::shared_ptr<TensorOp> ConcatenateOperation::Build() {
+  return std::make_shared<ConcatenateOp>(axis_, prepend_, append_);
+}
+#endif
+
 // DuplicateOperation
 Status DuplicateOperation::ValidateParams() { return Status::OK(); }
 
 std::shared_ptr<TensorOp> DuplicateOperation::Build() { return std::make_shared<DuplicateOp>(); }
 
+#ifndef ENABLE_ANDROID
+// MaskOperation
+MaskOperation::MaskOperation(RelationalOp op, const std::shared_ptr<Tensor> &constant, DataType dtype)
+    : op_(op), constant_(constant), dtype_(dtype) {}
+
+Status MaskOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> MaskOperation::Build() { return std::make_shared<MaskOp>(op_, constant_, dtype_); }
+#endif
+
 // OneHotOperation
 OneHotOperation::OneHotOperation(int32_t num_classes) : num_classes_(num_classes) {}
 
@@ -85,6 +141,16 @@ Status OneHotOperation::to_json(nlohmann::json *out_json) {
   return Status::OK();
 }
 
+#ifndef ENABLE_ANDROID
+// PadEndOperation
+PadEndOperation::PadEndOperation(const TensorShape &pad_shape, const std::shared_ptr<Tensor> &pad_value)
+    : pad_shape_(pad_shape), pad_value_(pad_value) {}
+
+Status PadEndOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> PadEndOperation::Build() { return std::make_shared<PadEndOp>(pad_shape_, pad_value_); }
+#endif
+
 // PreBuiltOperation
 PreBuiltOperation::PreBuiltOperation(std::shared_ptr<TensorOp> tensor_op) : op_(tensor_op) {
 #ifdef ENABLE_PYTHON
@@ -137,6 +203,15 @@ std::shared_ptr<TensorOp> RandomChoiceOperation::Build() {
   return std::make_shared<RandomChoiceOp>(tensor_ops);
 }
 
+#ifndef ENABLE_ANDROID
+// SliceOperation
+SliceOperation::SliceOperation(const std::vector<SliceOption> &slice_input) : slice_input_(slice_input) {}
+
+Status SliceOperation::ValidateParams() { return Status::OK(); }
+
+std::shared_ptr<TensorOp> SliceOperation::Build() { return std::make_shared<SliceOp>(slice_input_); }
+#endif
+
 // TypeCastOperation
 TypeCastOperation::TypeCastOperation(std::string data_type) : data_type_(data_type) {}
 

mindspore/ccsrc/minddata/dataset/kernels/ir/data/transforms_ir.h

@@ -28,9 +28,13 @@ namespace mindspore {
 namespace dataset {
 // Char arrays storing name of corresponding classes (in alphabetical order)
 constexpr char kComposeOperation[] = "Compose";
+constexpr char kConcatenateOperation[] = "Concatenate";
 constexpr char kDuplicateOperation[] = "Duplicate";
+constexpr char kMaskOperation[] = "Mask";
 constexpr char kOneHotOperation[] = "OneHot";
+constexpr char kPadEndOperation[] = "PadEnd";
 constexpr char kPreBuiltOperation[] = "PreBuilt";
+constexpr char kSliceOperation[] = "Slice";
 constexpr char kRandomApplyOperation[] = "RandomApply";
 constexpr char kRandomChoiceOperation[] = "RandomChoice";
 constexpr char kTypeCastOperation[] = "TypeCast";
@@ -56,6 +60,25 @@ class ComposeOperation : public TensorOperation {
   std::vector<std::shared_ptr<TensorOperation>> transforms_;
 };
 
+class ConcatenateOperation : public TensorOperation {
+ public:
+  explicit ConcatenateOperation(int8_t axis, const std::shared_ptr<Tensor> &prepend,
+                                const std::shared_ptr<Tensor> &append);
+
+  ~ConcatenateOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kConcatenateOperation; }
+
+ private:
+  int8_t axis_;
+  std::shared_ptr<Tensor> prepend_;
+  std::shared_ptr<Tensor> append_;
+};
+
 class DuplicateOperation : public TensorOperation {
  public:
   DuplicateOperation() = default;
@@ -69,6 +92,24 @@ class DuplicateOperation : public TensorOperation {
   std::string Name() const override { return kDuplicateOperation; }
 };
 
+class MaskOperation : public TensorOperation {
+ public:
+  explicit MaskOperation(RelationalOp op, const std::shared_ptr<Tensor> &constant, DataType dtype);
+
+  ~MaskOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kMaskOperation; }
+
+ private:
+  RelationalOp op_;
+  std::shared_ptr<Tensor> constant_;
+  DataType dtype_;
+};
+
 class OneHotOperation : public TensorOperation {
  public:
   explicit OneHotOperation(int32_t num_classes);
@@ -87,6 +128,23 @@ class OneHotOperation : public TensorOperation {
   int32_t num_classes_;
 };
 
+class PadEndOperation : public TensorOperation {
+ public:
+  explicit PadEndOperation(const TensorShape &pad_shape, const std::shared_ptr<Tensor> &pad_value);
+
+  ~PadEndOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kPadEndOperation; }
+
+ private:
+  TensorShape pad_shape_;
+  std::shared_ptr<Tensor> pad_value_;
+};
+
 class PreBuiltOperation : public TensorOperation {
  public:
   explicit PreBuiltOperation(std::shared_ptr<TensorOp> tensor_op);
@@ -137,6 +195,23 @@ class RandomChoiceOperation : public TensorOperation {
  private:
   std::vector<std::shared_ptr<TensorOperation>> transforms_;
 };
+
+class SliceOperation : public TensorOperation {
+ public:
+  explicit SliceOperation(const std::vector<SliceOption> &slice_input);
+
+  ~SliceOperation() = default;
+
+  std::shared_ptr<TensorOp> Build() override;
+
+  Status ValidateParams() override;
+
+  std::string Name() const override { return kSliceOperation; }
+
+ private:
+  std::vector<SliceOption> slice_input_;
+};
+
 class TypeCastOperation : public TensorOperation {
  public:
   explicit TypeCastOperation(std::string data_type);

mindspore/dataset/transforms/c_transforms.py

@@ -160,7 +160,7 @@ class _SliceOption(cde.SliceOption):
         super().__init__(slice_option)
 
 
-class Slice(cde.SliceOp):
+class Slice():
     """
     Slice operation to extract a tensor out using the given n slices.
 
@@ -200,7 +200,10 @@ class Slice(cde.SliceOp):
     def __init__(self, *slices):
         slice_input_ = list(slices)
         slice_input_ = [_SliceOption(slice_dim) for slice_dim in slice_input_]
-        super().__init__(slice_input_)
+        self.slice_input_ = slice_input_
+
+    def parse(self):
+        return cde.SliceOperation(self.slice_input_)
 
 
 class Relational(IntEnum):
@@ -220,7 +223,7 @@ DE_C_RELATIONAL = {Relational.EQ: cde.RelationalOp.EQ,
                    Relational.LE: cde.RelationalOp.LE}
 
 
-class Mask(cde.MaskOp):
+class Mask():
     """
     Mask content of the input tensor with the given predicate.
     Any element of the tensor that matches the predicate will be evaluated to True, otherwise False.
@@ -250,12 +253,15 @@ class Mask(cde.MaskOp):
 
     @check_mask_op
     def __init__(self, operator, constant, dtype=mstype.bool_):
-        dtype = mstype_to_detype(dtype)
-        constant = cde.Tensor(np.array(constant))
-        super().__init__(DE_C_RELATIONAL[operator], constant, dtype)
+        self.operator = operator
+        self.dtype = mstype_to_detype(dtype)
+        self.constant = cde.Tensor(np.array(constant))
+
+    def parse(self):
+        return cde.MaskOperation(DE_C_RELATIONAL[self.operator], self.constant, self.dtype)
 
 
-class PadEnd(cde.PadEndOp):
+class PadEnd():
     """
     Pad input tensor according to pad_shape, need to have same rank.
 
@@ -284,12 +290,14 @@ class PadEnd(cde.PadEndOp):
 
     @check_pad_end
     def __init__(self, pad_shape, pad_value=None):
-        if pad_value is not None:
-            pad_value = cde.Tensor(np.array(pad_value))
-        super().__init__(cde.TensorShape(pad_shape), pad_value)
+        self.pad_shape = cde.TensorShape(pad_shape)
+        self.pad_value = cde.Tensor(np.array(pad_value)) if pad_value is not None else pad_value
+
+    def parse(self):
+        return cde.PadEndOperation(self.pad_shape, self.pad_value)
 
 
-class Concatenate(cde.ConcatenateOp):
+class Concatenate():
     """
     Tensor operation that concatenates all columns into a single tensor.
 
@@ -311,11 +319,12 @@ class Concatenate(cde.ConcatenateOp):
 
     @check_concat_type
     def __init__(self, axis=0, prepend=None, append=None):
-        if prepend is not None:
-            prepend = cde.Tensor(np.array(prepend))
-        if append is not None:
-            append = cde.Tensor(np.array(append))
-        super().__init__(axis, prepend, append)
+        self.axis = axis
+        self.prepend = cde.Tensor(np.array(prepend)) if prepend is not None else prepend
+        self.append = cde.Tensor(np.array(append)) if append is not None else append
+
+    def parse(self):
+        return cde.ConcatenateOperation(self.axis, self.prepend, self.append)
 
 
 class Duplicate(TensorOperation):

tests/ut/cpp/dataset/c_api_transforms_test.cc

@@ -137,6 +137,70 @@ TEST_F(MindDataTestPipeline, TestComposeFail3) {
   EXPECT_EQ(iter, nullptr);
 }
 
+TEST_F(MindDataTestPipeline, TestConcatenateSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestConcatenateSuccess.";
+
+  // Create a RandomDataset
+  u_int32_t curr_seed = GlobalContext::config_manager()->seed();
+  GlobalContext::config_manager()->set_seed(246);
+  std::shared_ptr<SchemaObj> schema = Schema();
+  schema->add_column("col1", mindspore::DataType::kNumberTypeInt16, {1});
+  std::shared_ptr<Dataset> ds = RandomData(4, schema);
+  EXPECT_NE(ds, nullptr);
+  ds = ds->SetNumWorkers(2);
+  EXPECT_NE(ds, nullptr);
+
+  // Create Concatenate op
+  std::vector<std::int16_t> prepend_vector = {1, 2};
+  std::shared_ptr<Tensor> prepend_tensor;
+  ASSERT_OK(Tensor::CreateFromVector(prepend_vector, &prepend_tensor));
+  mindspore::MSTensor prepend_MSTensor =
+    mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(prepend_tensor));
+
+  std::vector<std::int16_t> append_vector = {3};
+  std::shared_ptr<Tensor> append_tensor;
+  ASSERT_OK(Tensor::CreateFromVector(append_vector, &append_tensor));
+  mindspore::MSTensor append_MSTensor =
+    mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(append_tensor));
+
+  transforms::Concatenate concatenate = transforms::Concatenate(0, prepend_MSTensor, append_MSTensor);
+
+  // Create a Map operation on ds
+  ds = ds->Map({concatenate}, {"col1"});
+  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, mindspore::MSTensor> row;
+  iter->GetNextRow(&row);
+
+  std::vector<std::vector<std::int16_t>> expected = {
+    {1, 2, 31354, 3}, {1, 2, -5655, 3}, {1, 2, -17734, 3}, {1, 2, -17220, 3}};
+
+  // Check concatnate results
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto ind = row["col1"];
+    std::shared_ptr<Tensor> de_expected_tensor;
+    ASSERT_OK(Tensor::CreateFromVector(expected[i], &de_expected_tensor));
+    mindspore::MSTensor expected_tensor =
+      mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_expected_tensor));
+    EXPECT_MSTENSOR_EQ(ind, expected_tensor);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 4);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+  GlobalContext::config_manager()->set_seed(curr_seed);
+}
+
 TEST_F(MindDataTestPipeline, TestDuplicateSuccess) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestDuplicateSuccess.";
 
@@ -177,6 +241,59 @@ TEST_F(MindDataTestPipeline, TestDuplicateSuccess) {
   iter->Stop();
 }
 
+TEST_F(MindDataTestPipeline, TestMaskSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMaskSuccess.";
+
+  // Create a RandomDataset
+  u_int32_t curr_seed = GlobalContext::config_manager()->seed();
+  GlobalContext::config_manager()->set_seed(246);
+  std::shared_ptr<SchemaObj> schema = Schema();
+  schema->add_column("col1", mindspore::DataType::kNumberTypeInt16, {4});
+  std::shared_ptr<Dataset> ds = RandomData(4, schema);
+  EXPECT_NE(ds, nullptr);
+  ds = ds->SetNumWorkers(2);
+  EXPECT_NE(ds, nullptr);
+
+  // Create Mask op
+  std::shared_ptr<Tensor> constant_tensor;
+  ASSERT_OK(Tensor::CreateScalar(0, &constant_tensor));
+  mindspore::MSTensor constant_MSTensor =
+    mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(constant_tensor));
+  transforms::Mask mask = transforms::Mask(RelationalOp::kGreater, constant_MSTensor);
+  ds = ds->Map({mask}, {"col1"});
+  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, mindspore::MSTensor> row;
+  iter->GetNextRow(&row);
+
+  std::vector<std::vector<bool>> expected = {
+    {true, true, true, true}, {false, false, false, false}, {false, false, false, false}, {false, false, false, false}};
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto ind = row["col1"];
+    std::shared_ptr<Tensor> de_expected_tensor;
+    ASSERT_OK(Tensor::CreateFromVector(expected[i], &de_expected_tensor));
+    mindspore::MSTensor expected_tensor =
+      mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_expected_tensor));
+    EXPECT_MSTENSOR_EQ(ind, expected_tensor);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 4);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+  GlobalContext::config_manager()->set_seed(curr_seed);
+}
+
 TEST_F(MindDataTestPipeline, TestOneHotSuccess1) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestOneHotSuccess1.";
   // Testing CutMixBatch on a batch of CHW images
@@ -330,6 +447,59 @@ TEST_F(MindDataTestPipeline, TestOneHotFail2) {
   EXPECT_EQ(iter, nullptr);
 }
 
+TEST_F(MindDataTestPipeline, TestPadEndSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestPadEndSuccess.";
+
+  // Create a RandomDataset
+  u_int32_t curr_seed = GlobalContext::config_manager()->seed();
+  GlobalContext::config_manager()->set_seed(246);
+  std::shared_ptr<SchemaObj> schema = Schema();
+  schema->add_column("col1", mindspore::DataType::kNumberTypeInt16, {1});
+  std::shared_ptr<Dataset> ds = RandomData(4, schema);
+  EXPECT_NE(ds, nullptr);
+  ds = ds->SetNumWorkers(2);
+  EXPECT_NE(ds, nullptr);
+
+  // Create PadEnd op
+  std::shared_ptr<Tensor> pad_value;
+  ASSERT_OK(Tensor::CreateScalar(0, &pad_value));
+  mindspore::MSTensor pad_value_MSTensor =
+    mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(pad_value));
+
+  transforms::PadEnd pad_end = transforms::PadEnd({3}, pad_value_MSTensor);
+  ds = ds->Map({pad_end}, {"col1"});
+  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, mindspore::MSTensor> row;
+  iter->GetNextRow(&row);
+
+  std::vector<std::vector<std::int16_t>> expected = {{31354, 0, 0}, {-5655, 0, 0}, {-17734, 0, 0}, {-17220, 0, 0}};
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto ind = row["col1"];
+    std::shared_ptr<Tensor> de_expected_tensor;
+    ASSERT_OK(Tensor::CreateFromVector(expected[i], &de_expected_tensor));
+    mindspore::MSTensor expected_tensor =
+      mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_expected_tensor));
+    EXPECT_MSTENSOR_EQ(ind, expected_tensor);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 4);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+  GlobalContext::config_manager()->set_seed(curr_seed);
+}
+
 TEST_F(MindDataTestPipeline, TestRandomApplySuccess) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomApplySuccess.";
 
@@ -565,6 +735,69 @@ TEST_F(MindDataTestPipeline, TestRandomChoiceFail3) {
   EXPECT_EQ(iter, nullptr);
 }
 
+TEST_F(MindDataTestPipeline, TestSliceSuccess) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSliceSuccess.";
+
+  // Create a RandomDataset
+  u_int32_t curr_seed = GlobalContext::config_manager()->seed();
+  GlobalContext::config_manager()->set_seed(246);
+  std::shared_ptr<SchemaObj> schema = Schema();
+  schema->add_column("col1", mindspore::DataType::kNumberTypeInt16, {1});
+  std::shared_ptr<Dataset> ds = RandomData(4, schema);
+  EXPECT_NE(ds, nullptr);
+  ds = ds->SetNumWorkers(2);
+  EXPECT_NE(ds, nullptr);
+
+  // Create concatenate op
+  std::vector<std::int16_t> prepend_vector = {1, 2, 3};
+  std::shared_ptr<Tensor> prepend_tensor;
+  ASSERT_OK(Tensor::CreateFromVector(prepend_vector, &prepend_tensor));
+  mindspore::MSTensor prepend_MSTensor =
+    mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(prepend_tensor));
+
+  transforms::Concatenate concatenate = transforms::Concatenate(0, prepend_MSTensor);
+
+  // Create a Map operation on ds
+  ds = ds->Map({concatenate}, {"col1"});
+  EXPECT_NE(ds, nullptr);
+
+  // Apply Slice op on ds, get the first and third elements in each row.
+  SliceOption slice_option = SliceOption(Slice(0, 3, 2));
+  transforms::Slice slice = transforms::Slice({slice_option});
+  ds = ds->Map({slice}, {"col1"});
+  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, mindspore::MSTensor> row;
+  iter->GetNextRow(&row);
+
+  std::vector<std::vector<std::int16_t>> expected = {{1, 3}, {1, 3}, {1, 3}, {1, 3}};
+
+  // Check slice results
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto ind = row["col1"];
+    std::shared_ptr<Tensor> de_expected_tensor;
+    ASSERT_OK(Tensor::CreateFromVector(expected[i], &de_expected_tensor));
+    mindspore::MSTensor expected_tensor =
+      mindspore::MSTensor(std::make_shared<mindspore::dataset::DETensor>(de_expected_tensor));
+    EXPECT_MSTENSOR_EQ(ind, expected_tensor);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 4);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+  GlobalContext::config_manager()->set_seed(curr_seed);
+}
+
 TEST_F(MindDataTestPipeline, TestTypeCastSuccess) {
   MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTypeCastSuccess.";