!3010 mindspore.dataset c_transform support for RandomApply RandomChoice, Compose and RandomSelectSubpolicy

Merge pull request !3010 from ZiruiWu/random_tensor_ops

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

@@ -42,8 +42,6 @@
 #include "minddata/dataset/util/status.h"
 #include "minddata/mindrecord/include/shard_category.h"
 #include "minddata/mindrecord/include/shard_distributed_sample.h"
-#include "minddata/mindrecord/include/shard_sample.h"
-#include "minddata/mindrecord/include/shard_shuffle.h"
 #include "pybind11/stl.h"
 #include "utils/log_adapter.h"
 

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

@@ -16,6 +16,7 @@
 #include <exception>
 
 #include "minddata/dataset/api/de_pipeline.h"
+#include "minddata/dataset/engine/cache/cache_client.h"
 #include "minddata/dataset/engine/datasetops/source/cifar_op.h"
 #include "minddata/dataset/engine/datasetops/source/clue_op.h"
 #include "minddata/dataset/engine/datasetops/source/coco_op.h"
@@ -35,9 +36,9 @@
 #include "minddata/dataset/engine/datasetops/source/text_file_op.h"
 #include "minddata/dataset/engine/datasetops/source/tf_reader_op.h"
 #include "minddata/dataset/engine/datasetops/source/voc_op.h"
-#include "minddata/dataset/engine/cache/cache_client.h"
 #include "minddata/dataset/engine/gnn/graph.h"
 #include "minddata/dataset/engine/jagged_connector.h"
+#include "minddata/dataset/kernels/compose_op.h"
 #include "minddata/dataset/kernels/data/concatenate_op.h"
 #include "minddata/dataset/kernels/data/duplicate_op.h"
 #include "minddata/dataset/kernels/data/fill_op.h"
@@ -61,11 +62,12 @@
 #include "minddata/dataset/kernels/image/random_crop_decode_resize_op.h"
 #include "minddata/dataset/kernels/image/random_crop_op.h"
 #include "minddata/dataset/kernels/image/random_crop_with_bbox_op.h"
-#include "minddata/dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_horizontal_flip_op.h"
+#include "minddata/dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_resize_op.h"
 #include "minddata/dataset/kernels/image/random_resize_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/random_rotation_op.h"
+#include "minddata/dataset/kernels/image/random_select_subpolicy_op.h"
 #include "minddata/dataset/kernels/image/random_vertical_flip_op.h"
 #include "minddata/dataset/kernels/image/random_vertical_flip_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/rescale_op.h"
@@ -74,6 +76,9 @@
 #include "minddata/dataset/kernels/image/resize_with_bbox_op.h"
 #include "minddata/dataset/kernels/image/uniform_aug_op.h"
 #include "minddata/dataset/kernels/no_op.h"
+#include "minddata/dataset/kernels/py_func_op.h"
+#include "minddata/dataset/kernels/random_apply_op.h"
+#include "minddata/dataset/kernels/random_choice_op.h"
 #include "minddata/dataset/text/kernels/jieba_tokenizer_op.h"
 #include "minddata/dataset/text/kernels/lookup_op.h"
 #include "minddata/dataset/text/kernels/ngram_op.h"
@@ -88,6 +93,7 @@
 #include "minddata/mindrecord/include/shard_sample.h"
 #include "minddata/mindrecord/include/shard_sequential_sample.h"
 #include "mindspore/ccsrc/minddata/dataset/text/kernels/truncate_sequence_pair_op.h"
+
 #include "pybind11/pybind11.h"
 #include "pybind11/stl.h"
 #include "pybind11/stl_bind.h"
@@ -113,6 +119,24 @@ namespace dataset {
     if (rc.IsError()) throw std::runtime_error(rc.ToString()); \
   } while (false)
 
+Status PyListToTensorOps(const py::list &py_ops, std::vector<std::shared_ptr<TensorOp>> *ops) {
+  RETURN_UNEXPECTED_IF_NULL(ops);
+  for (auto op : py_ops) {
+    if (py::isinstance<TensorOp>(op)) {
+      ops->emplace_back(op.cast<std::shared_ptr<TensorOp>>());
+    } else if (py::isinstance<py::function>(op)) {
+      ops->emplace_back(std::make_shared<PyFuncOp>(op.cast<py::function>()));
+    } else {
+      RETURN_STATUS_UNEXPECTED("element is neither a TensorOp nor a pyfunc.");
+    }
+  }
+  CHECK_FAIL_RETURN_UNEXPECTED(!ops->empty(), "TensorOp list is empty.");
+  for (auto const &op : *ops) {
+    RETURN_UNEXPECTED_IF_NULL(op);
+  }
+  return Status::OK();
+}
+
 void bindDEPipeline(py::module *m) {
   (void)py::class_<DEPipeline>(*m, "DEPipeline")
     .def(py::init<>())
@@ -623,7 +647,7 @@ void bindTokenizerOps(py::module *m) {
       WordIdType default_id = vocab->Lookup(word);
       if (default_id == Vocab::kNoTokenExists) {
         THROW_IF_ERROR(
-          Status(StatusCode::kUnexpectedError, "default unknown token:" + word + " doesn't exist in vocab."));
+          Status(StatusCode::kUnexpectedError, "default unknown token: " + word + " doesn't exist in vocab."));
       }
       return std::make_shared<LookupOp>(vocab, default_id);
     }));
@@ -868,6 +892,58 @@ void bindGraphData(py::module *m) {
     });
 }
 
+void bindRandomTransformTensorOps(py::module *m) {
+  (void)py::class_<ComposeOp, TensorOp, std::shared_ptr<ComposeOp>>(*m, "ComposeOp")
+    .def(py::init([](const py::list &ops) {
+      std::vector<std::shared_ptr<TensorOp>> t_ops;
+      THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
+      return std::make_shared<ComposeOp>(t_ops);
+    }));
+  (void)py::class_<RandomChoiceOp, TensorOp, std::shared_ptr<RandomChoiceOp>>(*m, "RandomChoiceOp")
+    .def(py::init([](const py::list &ops) {
+      std::vector<std::shared_ptr<TensorOp>> t_ops;
+      THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
+      return std::make_shared<RandomChoiceOp>(t_ops);
+    }));
+  (void)py::class_<RandomApplyOp, TensorOp, std::shared_ptr<RandomApplyOp>>(*m, "RandomApplyOp")
+    .def(py::init([](double prob, const py::list &ops) {
+      std::vector<std::shared_ptr<TensorOp>> t_ops;
+      THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
+      if (prob < 0 || prob > 1) {
+        THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "prob needs to be within [0,1]."));
+      }
+      return std::make_shared<RandomApplyOp>(prob, t_ops);
+    }));
+  (void)py::class_<RandomSelectSubpolicyOp, TensorOp, std::shared_ptr<RandomSelectSubpolicyOp>>(
+    *m, "RandomSelectSubpolicyOp")
+    .def(py::init([](const py::list &py_policy) {
+      std::vector<Subpolicy> cpp_policy;
+      for (auto &py_sub : py_policy) {
+        cpp_policy.push_back({});
+        for (auto handle : py_sub.cast<py::list>()) {
+          py::tuple tp = handle.cast<py::tuple>();
+          if (tp.is_none() || tp.size() != 2) {
+            THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "Each tuple in subpolicy should be (op, prob)."));
+          }
+          std::shared_ptr<TensorOp> t_op;
+          if (py::isinstance<TensorOp>(tp[0])) {
+            t_op = (tp[0]).cast<std::shared_ptr<TensorOp>>();
+          } else if (py::isinstance<py::function>(tp[0])) {
+            t_op = std::make_shared<PyFuncOp>((tp[0]).cast<py::function>());
+          } else {
+            THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "op is neither a tensorOp nor a pyfunc."));
+          }
+          double prob = (tp[1]).cast<py::float_>();
+          if (prob < 0 || prob > 1) {
+            THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "prob needs to be with [0,1]."));
+          }
+          cpp_policy.back().emplace_back(std::make_pair(t_op, prob));
+        }
+      }
+      return std::make_shared<RandomSelectSubpolicyOp>(cpp_policy);
+    }));
+}
+
 // This is where we externalize the C logic as python modules
 PYBIND11_MODULE(_c_dataengine, m) {
   m.doc() = "pybind11 for _c_dataengine";
@@ -949,6 +1025,7 @@ PYBIND11_MODULE(_c_dataengine, m) {
   bindVocabObjects(&m);
   bindGraphData(&m);
   bindDependIcuTokenizerOps(&m);
+  bindRandomTransformTensorOps(&m);
 }
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/CMakeLists.txt

@@ -4,11 +4,16 @@ file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc"
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
 if (ENABLE_PYTHON)
     add_library(kernels OBJECT
+        compose_op.cc
+        random_apply_op.cc
+        random_choice_op.cc
         py_func_op.cc
         tensor_op.cc)
     target_include_directories(kernels PRIVATE ${pybind11_INCLUDE_DIRS})
 else()
     add_library(kernels OBJECT
+        compose_op.cc
+        random_apply_op.cc
+        random_choice_op.cc
         tensor_op.cc)
 endif()
-

mindspore/ccsrc/minddata/dataset/kernels/compose_op.cc

@@ -0,0 +1,66 @@
+/**
+ * Copyright 2019 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.
+ */
+#include "minddata/dataset/kernels/compose_op.h"
+
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/py_func_op.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+Status ComposeOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  std::vector<TensorShape> in_shapes = inputs;
+  for (auto &op : ops_) {
+    RETURN_IF_NOT_OK(op->OutputShape(in_shapes, outputs));
+    in_shapes = std::move(outputs);  // outputs become empty after move
+  }
+  outputs = std::move(in_shapes);
+  return Status::OK();
+}
+Status ComposeOp::OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) {
+  std::vector<DataType> in_types = inputs;
+  for (auto &op : ops_) {
+    RETURN_IF_NOT_OK(op->OutputType(in_types, outputs));
+    in_types = std::move(outputs);  // outputs become empty after move
+  }
+  outputs = std::move(in_types);
+  return Status::OK();
+}
+Status ComposeOp::Compute(const TensorRow &inputs, TensorRow *outputs) {
+  IO_CHECK_VECTOR(inputs, outputs);
+  TensorRow in_rows = inputs;
+  for (auto &op : ops_) {
+    RETURN_IF_NOT_OK(op->Compute(in_rows, outputs));
+    in_rows = std::move(*outputs);  // after move, *outputs become empty
+  }
+  (*outputs) = std::move(in_rows);
+  return Status::OK();
+}
+
+ComposeOp::ComposeOp(const std::vector<std::shared_ptr<TensorOp>> &ops) : ops_(ops) {
+  if (ops_.empty()) {
+    MS_LOG(ERROR) << "op_list is empty this might lead to Segmentation Fault.";
+  } else if (ops_.size() == 1) {
+    MS_LOG(WARNING) << "op_list has only 1 op. Compose is probably not needed.";
+  }
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/compose_op.h

@@ -0,0 +1,70 @@
+/**
+ * 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.
+ */
+
+#ifndef DATASET_KERNELS_COMPOSE_OP_
+#define DATASET_KERNELS_COMPOSE_OP_
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+
+namespace mindspore {
+namespace dataset {
+class ComposeOp : public TensorOp {
+ public:
+  /// constructor
+  /// \param[in] ops list of TensorOps to compose into 1 TensorOp
+  explicit ComposeOp(const std::vector<std::shared_ptr<TensorOp>> &ops);
+
+  /// default destructor
+  ~ComposeOp() override = default;
+
+  /// return the number of inputs the first tensorOp in compose takes
+  /// \return number of input tensors
+  uint32_t NumInput() override { return ops_.front()->NumInput(); }
+
+  /// return the number of outputs the last tensorOp in compose produces
+  /// \return number of output tensors
+  uint32_t NumOutput() override { return ops_.back()->NumOutput(); }
+
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return  Status code
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return Status code
+  Status OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) override;
+
+  /// \param[in] input
+  /// \param[out] output
+  /// \return Status code
+  Status Compute(const TensorRow &input, TensorRow *output) override;
+
+  std::string Name() const override { return kComposeOp; }
+
+ private:
+  std::vector<std::shared_ptr<TensorOp>> ops_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_COMPOSE_OP_

mindspore/ccsrc/minddata/dataset/kernels/image/CMakeLists.txt

@@ -19,6 +19,7 @@ add_library(kernels-image OBJECT
     bounding_box_augment_op.cc
     random_resize_op.cc
     random_rotation_op.cc
+    random_select_subpolicy_op.cc
     random_vertical_flip_op.cc
     random_vertical_flip_with_bbox_op.cc
     rescale_op.cc

mindspore/ccsrc/minddata/dataset/kernels/image/random_select_subpolicy_op.cc

@@ -0,0 +1,96 @@
+/**
+ * Copyright 2019 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.
+ */
+#include "minddata/dataset/kernels/image/random_select_subpolicy_op.h"
+
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+Status RandomSelectSubpolicyOp::Compute(const TensorRow &input, TensorRow *output) {
+  TensorRow in_row = input;
+  size_t rand_num = rand_int_(gen_);
+  CHECK_FAIL_RETURN_UNEXPECTED(rand_num < policy_.size(), "invalid rand_num:" + std::to_string(rand_num));
+  for (auto &sub : policy_[rand_num]) {
+    if (rand_double_(gen_) <= sub.second) {
+      RETURN_IF_NOT_OK(sub.first->Compute(in_row, output));
+      in_row = std::move(*output);
+    }
+  }
+  *output = std::move(in_row);
+  return Status::OK();
+}
+
+uint32_t RandomSelectSubpolicyOp::NumInput() {
+  uint32_t num_in = policy_.front().front().first->NumInput();
+  for (auto &sub : policy_) {
+    for (auto p : sub) {
+      if (num_in != p.first->NumInput()) {
+        MS_LOG(WARNING) << "Unable to determine numInput.";
+        return 0;
+      }
+    }
+  }
+  return num_in;
+}
+
+uint32_t RandomSelectSubpolicyOp::NumOutput() {
+  uint32_t num_out = policy_.front().front().first->NumOutput();
+  for (auto &sub : policy_) {
+    for (auto p : sub) {
+      if (num_out != p.first->NumOutput()) {
+        MS_LOG(WARNING) << "Unable to determine numInput.";
+        return 0;
+      }
+    }
+  }
+  return num_out;
+}
+
+Status RandomSelectSubpolicyOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  outputs.clear();
+  outputs.resize(NumOutput(), TensorShape::CreateUnknownRankShape());
+  return Status::OK();
+}
+
+Status RandomSelectSubpolicyOp::OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) {
+  RETURN_IF_NOT_OK(policy_.front().front().first->OutputType(inputs, outputs));
+  for (auto &sub : policy_) {
+    for (auto p : sub) {
+      std::vector<DataType> tmp_types;
+      RETURN_IF_NOT_OK(p.first->OutputType(inputs, tmp_types));
+      if (outputs != tmp_types) {
+        outputs.clear();
+        outputs.resize(NumOutput(), DataType(DataType::DE_UNKNOWN));
+        return Status::OK();
+      }
+    }
+  }
+  return Status::OK();
+}
+RandomSelectSubpolicyOp::RandomSelectSubpolicyOp(const std::vector<Subpolicy> &policy)
+    : gen_(GetSeed()), policy_(policy), rand_int_(0, policy.size() - 1), rand_double_(0, 1) {
+  if (policy_.empty()) {
+    MS_LOG(ERROR) << "policy in RandomSelectSubpolicyOp is empty.";
+  }
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/image/random_select_subpolicy_op.h

@@ -0,0 +1,79 @@
+/**
+ * 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.
+ */
+
+#ifndef DATASET_KERNELS_IMAGE_RANDOM_SELECT_SUBPOLICY_OP_
+#define DATASET_KERNELS_IMAGE_RANDOM_SELECT_SUBPOLICY_OP_
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+
+using Subpolicy = std::vector<std::pair<std::shared_ptr<TensorOp>, double>>;
+
+class RandomSelectSubpolicyOp : public TensorOp {
+ public:
+  /// constructor
+  /// \param[in] policy policy to choose subpolicy from
+  explicit RandomSelectSubpolicyOp(const std::vector<Subpolicy> &policy);
+
+  /// destructor
+  ~RandomSelectSubpolicyOp() override = default;
+
+  /// return number of input tensors
+  /// \return number of inputs if all ops in policy have the same NumInput, otherwise return 0
+  uint32_t NumInput() override;
+
+  /// return number of output tensors
+  /// \return number of outputs if all ops in policy have the same NumOutput, otherwise return 0
+  uint32_t NumOutput() override;
+
+  /// return unknown shapes
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return Status Code
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  /// return output type if all ops in policy return the same type, otherwise return unknown type
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return Status Code
+  Status OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) override;
+
+  /// \param[in] input
+  /// \param[out] output
+  /// \return Status code
+  Status Compute(const TensorRow &input, TensorRow *output) override;
+
+  std::string Name() const override { return kRandomSelectSubpolicyOp; }
+
+ private:
+  std::vector<Subpolicy> policy_;
+  std::mt19937 gen_;  // mersenne_twister_engine
+  std::uniform_int_distribution<size_t> rand_int_;
+  std::uniform_real_distribution<double> rand_double_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_IMAGE_RANDOM_SELECT_SUBPOLICY_OP_

mindspore/ccsrc/minddata/dataset/kernels/random_apply_op.cc

@@ -0,0 +1,68 @@
+/**
+ * Copyright 2019 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.
+ */
+#include "minddata/dataset/kernels/random_apply_op.h"
+
+#include <memory>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+uint32_t RandomApplyOp::NumOutput() {
+  if (compose_->NumOutput() != NumInput()) {
+    MS_LOG(WARNING) << "NumOutput!=NumInput (randomApply would randomly affect number of outputs).";
+    return 0;
+  }
+  return compose_->NumOutput();
+}
+
+Status RandomApplyOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  RETURN_IF_NOT_OK(compose_->OutputShape(inputs, outputs));
+  // randomApply either runs all ops or do nothing. If the two methods don't give the same result. return unknown shape.
+  if (inputs != outputs) {  // when RandomApply is not applied, input should be the same as output
+    outputs.clear();
+    outputs.resize(NumOutput(), TensorShape::CreateUnknownRankShape());
+  }
+  return Status::OK();
+}
+Status RandomApplyOp::OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) {
+  RETURN_IF_NOT_OK(compose_->OutputType(inputs, outputs));
+  if (inputs != outputs) {  // when RandomApply is not applied, input should be the same as output
+    outputs.clear();
+    outputs.resize(NumOutput(), DataType(DataType::DE_UNKNOWN));
+  }
+  return Status::OK();
+}
+Status RandomApplyOp::Compute(const TensorRow &input, TensorRow *output) {
+  if (rand_double_(gen_) <= prob_) {
+    RETURN_IF_NOT_OK(compose_->Compute(input, output));
+  } else {
+    IO_CHECK_VECTOR(input, output);
+    *output = input;  // copy over the tensors
+  }
+  return Status::OK();
+}
+RandomApplyOp::RandomApplyOp(double prob, const std::vector<std::shared_ptr<TensorOp>> &ops)
+    : prob_(prob), gen_(GetSeed()), rand_double_(0, 1) {
+  compose_ = std::make_unique<ComposeOp>(ops);
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/random_apply_op.h

@@ -0,0 +1,79 @@
+/**
+ * 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.
+ */
+
+#ifndef DATASET_KERNELS_RANDOM_APPLY_OP_
+#define DATASET_KERNELS_RANDOM_APPLY_OP_
+
+#include <memory>
+#include <random>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/compose_op.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+class RandomApplyOp : public TensorOp {
+ public:
+  /// constructor
+  /// \param[in] prob probability whether the list of TensorOps will be applied
+  /// \param[in] ops the list of TensorOps to apply with prob likelihood
+  explicit RandomApplyOp(double prob, const std::vector<std::shared_ptr<TensorOp>> &ops);
+
+  /// default destructor
+  ~RandomApplyOp() = default;
+
+  /// return the number of inputs the first tensorOp in compose takes
+  /// \return number of input tensors
+  uint32_t NumInput() override { return compose_->NumInput(); }
+
+  /// return the number of outputs
+  /// \return number of output tensors
+  uint32_t NumOutput() override;
+
+  /// return output shape if randomApply won't affect the output shape, otherwise return unknown shape
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return  Status code
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  /// return output type if randomApply won't affect the output type, otherwise return unknown type
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return Status code
+  Status OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) override;
+
+  /// \param[in] input
+  /// \param[out] output
+  /// \return Status code
+  Status Compute(const TensorRow &input, TensorRow *output) override;
+
+  std::string Name() const override { return kRandomApplyOp; }
+
+ private:
+  double prob_;
+  std::shared_ptr<TensorOp> compose_;
+  std::mt19937 gen_;  // mersenne_twister_engine
+  std::uniform_real_distribution<double> rand_double_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_RANDOM_APPLY_OP_

mindspore/ccsrc/minddata/dataset/kernels/random_choice_op.cc

@@ -0,0 +1,97 @@
+/**
+ * Copyright 2019 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.
+ */
+#include "minddata/dataset/kernels/random_choice_op.h"
+
+#include <memory>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+uint32_t RandomChoiceOp::NumInput() {
+  uint32_t num_input = ops_.front()->NumInput();
+  for (auto &op : ops_) {
+    uint32_t cur_num = op->NumInput();
+    if (num_input != cur_num && cur_num > 0) {
+      MS_LOG(WARNING) << "Unable to determine NumInput, ops in RandomChoice don't take the same number of input.";
+      return 0;
+    }
+  }
+  return num_input;
+}
+
+uint32_t RandomChoiceOp::NumOutput() {
+  uint32_t num_output = ops_.front()->NumOutput();
+  for (auto &op : ops_) {
+    uint32_t cur_num = op->NumOutput();
+    if (num_output != cur_num) {
+      MS_LOG(WARNING) << "Unable to determine NumInput, ops in RandomChoice don't have the same number of input.";
+      return 0;
+    }
+  }
+  return num_output;
+}
+
+Status RandomChoiceOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
+  RETURN_IF_NOT_OK(ops_.front()->OutputShape(inputs, outputs));
+  for (auto &op : ops_) {
+    std::vector<TensorShape> out_shapes;
+    RETURN_IF_NOT_OK(op->OutputShape(inputs, out_shapes));
+    if (outputs != out_shapes) {
+      MS_LOG(WARNING) << "TensorOp in RandomChoice don't return the same tensorShape.";
+      outputs.clear();
+      outputs.resize(NumOutput(), TensorShape::CreateUnknownRankShape());
+      return Status::OK();
+    }
+  }
+  return Status::OK();
+}
+
+Status RandomChoiceOp::OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) {
+  RETURN_IF_NOT_OK(ops_.front()->OutputType(inputs, outputs));
+  for (auto &op : ops_) {
+    std::vector<DataType> out_types;
+    RETURN_IF_NOT_OK(op->OutputType(inputs, out_types));
+    if (outputs != out_types) {
+      MS_LOG(WARNING) << "TensorOp in RandomChoice don't return the same tensorType.";
+      outputs.clear();
+      outputs.resize(NumOutput(), DataType(DataType::DE_UNKNOWN));
+      return Status::OK();
+    }
+  }
+  return Status::OK();
+}
+
+Status RandomChoiceOp::Compute(const TensorRow &input, TensorRow *output) {
+  size_t rand_num = rand_int_(gen_);
+  CHECK_FAIL_RETURN_UNEXPECTED(rand_num < ops_.size(), "invalid rand_num:" + std::to_string(rand_num));
+  RETURN_IF_NOT_OK(ops_[rand_num]->Compute(input, output));
+  return Status::OK();
+}
+RandomChoiceOp::RandomChoiceOp(const std::vector<std::shared_ptr<TensorOp>> &ops)
+    : ops_(ops), gen_(GetSeed()), rand_int_(0, ops.size() - 1) {
+  if (ops_.empty()) {
+    MS_LOG(ERROR) << "op_list in RandomChoiceOp is empty.";
+  } else if (ops_.size() == 1) {
+    MS_LOG(WARNING) << "op_list has only 1 op, this op would be picked every time.";
+  }
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/random_choice_op.h

@@ -0,0 +1,77 @@
+/**
+ * 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.
+ */
+
+#ifndef DATASET_KERNELS_RANDOM_CHOICE_OP_
+#define DATASET_KERNELS_RANDOM_CHOICE_OP_
+
+#include <memory>
+#include <random>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/kernels/tensor_op.h"
+#include "minddata/dataset/kernels/compose_op.h"
+#include "minddata/dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+class RandomChoiceOp : public TensorOp {
+ public:
+  /// constructor
+  /// \param[in] ops list of TensorOps to randomly choose 1 from
+  explicit RandomChoiceOp(const std::vector<std::shared_ptr<TensorOp>> &ops);
+
+  /// default destructor
+  ~RandomChoiceOp() = default;
+
+  /// return the number of inputs. All op in ops_ should have the same number of inputs
+  /// \return number of input tensors
+  uint32_t NumInput() override;
+
+  /// return the number of outputs. All op in ops_ should have the same number of outputs
+  /// \return number of input tensors
+  uint32_t NumOutput() override;
+
+  /// return output shape if all ops in ops_ return the same shape, otherwise return unknown shape
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return  Status code
+  Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
+
+  /// return output type if all ops in ops_ return the same type, otherwise return unknown type
+  /// \param[in] inputs
+  /// \param[out] outputs
+  /// \return Status code
+  Status OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) override;
+
+  /// \param[in] input
+  /// \param[out] output
+  /// \return Status code
+  Status Compute(const TensorRow &input, TensorRow *output) override;
+
+  std::string Name() const override { return kRandomChoiceOp; }
+
+ private:
+  std::vector<std::shared_ptr<TensorOp>> ops_;
+  std::mt19937 gen_;  // mersenne_twister_engine
+  std::uniform_int_distribution<size_t> rand_int_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_KERNELS_RANDOM_CHOICE_OP_

mindspore/ccsrc/minddata/dataset/kernels/tensor_op.h

@@ -129,6 +129,10 @@ constexpr char kUnicodeCharTokenizerOp[] = "UnicodeCharTokenizerOp";
 constexpr char kUnicodeScriptTokenizerOp[] = "UnicodeScriptTokenizerOp";
 constexpr char kWhitespaceTokenizerOp[] = "WhitespaceTokenizerOp";
 constexpr char kWordpieceTokenizerOp[] = "WordpieceTokenizerOp";
+constexpr char kRandomChoiceOp[] = "RandomChoiceOp";
+constexpr char kRandomApplyOp[] = "RandomApplyOp";
+constexpr char kComposeOp[] = "ComposeOp";
+constexpr char kRandomSelectSubpolicyOp[] = "RandomSelectSubpolicyOp";
 
 // data
 constexpr char kConcatenateOp[] = "kConcatenateOp";

mindspore/dataset/core/validator_helpers.py

@@ -19,6 +19,8 @@ import inspect
 from multiprocessing import cpu_count
 import os
 import numpy as np
+
+import mindspore._c_dataengine as cde
 from ..engine import samplers
 
 # POS_INT_MIN is used to limit values from starting from 0
@@ -358,3 +360,9 @@ def check_gnn_list_or_ndarray(param, param_name):
         if not param.dtype == np.int32:
             raise TypeError("Each member in {0} should be of type int32. Got {1}.".format(
                 param_name, param.dtype))
+
+
+def check_tensor_op(param, param_name):
+    """check whether param is a tensor op or a callable python function"""
+    if not isinstance(param, cde.TensorOp) and not callable(param):
+        raise TypeError("{0} is not a c_transform op (TensorOp) nor a callable pyfunc.".format(param_name))

mindspore/dataset/transforms/c_transforms.py

@@ -22,7 +22,7 @@ import mindspore.common.dtype as mstype
 import mindspore._c_dataengine as cde
 
 from .validators import check_num_classes, check_de_type, check_fill_value, check_slice_op, check_mask_op, \
-    check_pad_end, check_concat_type
+    check_pad_end, check_concat_type, check_random_transform_ops
 from ..core.datatypes import mstype_to_detype
 
 
@@ -82,7 +82,7 @@ class Slice(cde.SliceOp):
             Maximum `n` number of arguments to slice a tensor of rank `n`.
             One object in slices can be one of:
             1.  :py:obj:`int`: Slice this index only. Negative index is supported.
-            2.  :py:obj:`list(int)`: Slice these indices ion the list only. Negative indices are supdeported.
+            2.  :py:obj:`list(int)`: Slice these indices ion the list only. Negative indices are supported.
             3.  :py:obj:`slice`: Slice the generated indices from the slice object. Similar to `start:stop:step`.
             4.  :py:obj:`None`: Slice the whole dimension. Similar to `:` in python indexing.
             5.  :py:obj:`Ellipses`: Slice all dimensions between the two slices. Similar to `...` in python indexing.
@@ -232,3 +232,50 @@ class Duplicate(cde.DuplicateOp):
         >>> # | [1,2,3] | [1,2,3] |
         >>> # +---------+---------+
     """
+
+
+class Compose(cde.ComposeOp):
+    """
+    Compose a list of transforms into a single transform.
+
+    Args:
+        transforms (list): List of transformations to be applied.
+    Example:
+        >>> compose = Compose([vision.Decode(), vision.RandomCrop()])
+        >>> dataset = ds.map(operations=compose)
+    """
+
+    @check_random_transform_ops
+    def __init__(self, op_list):
+        super().__init__(op_list)
+
+
+class RandomApply(cde.RandomApplyOp):
+    """
+    Randomly performs a series of transforms with a given probability.
+    Args:
+        transforms (list): List of transformations to be applied.
+        prob (float, optional): The probability to apply the transformation list (default=0.5)
+    Example:
+        >>> rand_apply = RandomApply([vision.RandomCrop()])
+        >>> dataset = ds.map(operations=rand_apply)
+    """
+
+    @check_random_transform_ops
+    def __init__(self, op_list, prob=0.5):
+        super().__init__(prob, op_list)
+
+
+class RandomChoice(cde.RandomChoiceOp):
+    """
+    Randomly selects one transform from a list of transforms to perform operation.
+    Args:
+        transforms (list): List of transformations to be chosen from to apply.
+    Example:
+        >>> rand_choice = RandomChoice([vision.CenterCrop(), vision.RandomCrop()])
+        >>> dataset = ds.map(operations=rand_choice)
+    """
+
+    @check_random_transform_ops
+    def __init__(self, op_list):
+        super().__init__(op_list)

mindspore/dataset/transforms/validators.py

@@ -18,7 +18,8 @@ from functools import wraps
 import numpy as np
 
 from mindspore._c_expression import typing
-from ..core.validator_helpers import parse_user_args, type_check, check_pos_int64, check_value, check_positive
+from ..core.validator_helpers import parse_user_args, type_check, check_pos_int64, check_value, check_positive, \
+    check_tensor_op
 
 # POS_INT_MIN is used to limit values from starting from 0
 POS_INT_MIN = 1
@@ -180,3 +181,22 @@ def check_concat_type(method):
         return method(self, *args, **kwargs)
 
     return new_method
+
+
+def check_random_transform_ops(method):
+    """Wrapper method to check the parameters of RandomChoice, RandomApply and Compose."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        arg_list, _ = parse_user_args(method, *args, **kwargs)
+        type_check(arg_list[0], (list,), "op_list")
+        if not arg_list[0]:
+            raise ValueError("op_list can not be empty.")
+        for ind, op in enumerate(arg_list[0]):
+            check_tensor_op(op, "op_list[{0}]".format(ind))
+        if len(arg_list) == 2:  # random apply takes an additional arg
+            type_check(arg_list[1], (float, int), "prob")
+            check_value(arg_list[1], (0, 1), "prob")
+        return method(self, *args, **kwargs)
+
+    return new_method

mindspore/dataset/transforms/vision/c_transforms.py

@@ -47,7 +47,7 @@ from .utils import Inter, Border
 from .validators import check_prob, check_crop, check_resize_interpolation, check_random_resize_crop, \
     check_normalize_c, check_random_crop, check_random_color_adjust, check_random_rotation, check_range, \
     check_resize, check_rescale, check_pad, check_cutout, check_uniform_augment_cpp, check_bounding_box_augment_cpp, \
-    FLOAT_MAX_INTEGER
+    check_random_select_subpolicy_op, FLOAT_MAX_INTEGER
 
 DE_C_INTER_MODE = {Inter.NEAREST: cde.InterpolationMode.DE_INTER_NEAREST_NEIGHBOUR,
                    Inter.LINEAR: cde.InterpolationMode.DE_INTER_LINEAR,
@@ -712,3 +712,9 @@ class UniformAugment(cde.UniformAugOp):
         self.operations = operations
         self.num_ops = num_ops
         super().__init__(operations, num_ops)
+
+
+class RandomSelectSubpolicy(cde.RandomSelectSubpolicyOp):
+    @check_random_select_subpolicy_op
+    def __init__(self, policy):
+        super().__init__(policy)

mindspore/dataset/transforms/vision/validators.py

@@ -21,7 +21,7 @@ from mindspore._c_dataengine import TensorOp
 
 from .utils import Inter, Border
 from ...core.validator_helpers import check_value, check_uint8, FLOAT_MAX_INTEGER, check_pos_float32, \
-    check_2tuple, check_range, check_positive, INT32_MAX, parse_user_args, type_check, type_check_list
+    check_2tuple, check_range, check_positive, INT32_MAX, parse_user_args, type_check, type_check_list, check_tensor_op
 
 
 def check_crop_size(size):
@@ -588,3 +588,26 @@ def check_compose_list(method):
         return method(self, *args, **kwargs)
 
     return new_method
+
+
+def check_random_select_subpolicy_op(method):
+    """Wrapper method to check the parameters of RandomSelectSubpolicyOp."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        [policy], _ = parse_user_args(method, *args, **kwargs)
+        type_check(policy, (list,), "policy")
+        if not policy:
+            raise ValueError("policy can not be empty.")
+        for sub_ind, sub in enumerate(policy):
+            type_check(sub, (list,), "policy[{0}]".format([sub_ind]))
+            if not sub:
+                raise ValueError("policy[{0}] can not be empty.".format(sub_ind))
+            for op_ind, tp in enumerate(sub):
+                check_2tuple(tp, "policy[{0}][{1}]".format(sub_ind, op_ind))
+                check_tensor_op(tp[0], "op of (op, prob) in policy[{0}][{1}]".format(sub_ind, op_ind))
+                check_value(tp[1], (0, 1), "prob of (op, prob) policy[{0}][{1}]".format(sub_ind, op_ind))
+
+        return method(self, *args, **kwargs)
+
+    return new_method

tests/ut/python/dataset/test_c_compose.py

@@ -0,0 +1,50 @@
+# 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 mindspore.common.dtype as mstype
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as ops
+import mindspore.dataset.transforms.py_transforms as py_ops
+
+
+def test_compose():
+    ds.config.set_seed(0)
+
+    def test_config(arr, op_list):
+        try:
+            data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
+            data = data.map(input_columns=["col"], operations=ops.Compose(op_list))
+            res = []
+            for i in data.create_dict_iterator():
+                res.append(i["col"].tolist())
+            return res
+        except (TypeError, ValueError) as e:
+            return str(e)
+
+    # test simple compose with only 1 op, this would generate a warning
+    assert test_config([[1, 0], [3, 4]], [ops.Fill(2)]) == [[2, 2], [2, 2]]
+    # test 1 column -> 2columns -> 1 -> 2 -> 1
+    assert test_config([[1, 0]], [ops.Duplicate(), ops.Concatenate(), ops.Duplicate(), ops.Concatenate()]) == [
+        [1, 0] * 4]
+    # test one python transform followed by a C transform. type after oneHot is float (mixed use-case)
+    assert test_config([1, 0], [py_ops.OneHotOp(2), ops.TypeCast(mstype.int32)]) == [[[0, 1]], [[1, 0]]]
+    # test exceptions. compose, randomApply randomChoice use the same validator
+    assert "op_list[0] is not a c_transform op" in test_config([1, 0], [1, ops.TypeCast(mstype.int32)])
+    # test empty op list
+    assert "op_list can not be empty." in test_config([1, 0], [])
+
+
+if __name__ == "__main__":
+    test_compose()

tests/ut/python/dataset/test_c_random_apply.py

@@ -0,0 +1,48 @@
+# 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 mindspore.common.dtype as mstype
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as ops
+
+
+def test_random_apply():
+    ds.config.set_seed(0)
+
+    def test_config(arr, op_list, prob=0.5):
+        try:
+            data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
+            data = data.map(input_columns=["col"], operations=ops.RandomApply(op_list, prob))
+            res = []
+            for i in data.create_dict_iterator():
+                res.append(i["col"].tolist())
+            return res
+        except (TypeError, ValueError) as e:
+            return str(e)
+
+    res1 = test_config([[0, 1]], [ops.Duplicate(), ops.Concatenate()])
+    assert res1 in [[[0, 1]], [[0, 1, 0, 1]]]
+    # test single nested compose
+    assert test_config([[0, 1, 2]], [ops.Compose([ops.Duplicate(), ops.Concatenate(), ops.Slice([0, 1, 2])])]) == [
+        [0, 1, 2]]
+    # test exception
+    assert "is not of type (<class 'list'>" in test_config([1, 0], ops.TypeCast(mstype.int32))
+    assert "Input prob is not within the required interval" in test_config([0, 1], [ops.Slice([0, 1])], 1.1)
+    assert "is not of type (<class 'float'>" in test_config([1, 0], [ops.TypeCast(mstype.int32)], None)
+    assert "op_list with value None is not of type (<class 'list'>" in test_config([1, 0], None)
+
+
+if __name__ == "__main__":
+    test_random_apply()

tests/ut/python/dataset/test_c_random_choice.py

@@ -0,0 +1,48 @@
+# 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 mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as ops
+
+
+def test_random_choice():
+    ds.config.set_seed(0)
+
+    def test_config(arr, op_list):
+        try:
+            data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
+            data = data.map(input_columns=["col"], operations=ops.RandomChoice(op_list))
+            res = []
+            for i in data.create_dict_iterator():
+                res.append(i["col"].tolist())
+            return res
+        except (TypeError, ValueError) as e:
+            return str(e)
+
+    # test whether a op would be randomly chosen. In order to prevent random failure, both results need to be checked
+    res1 = test_config([[0, 1, 2]], [ops.PadEnd([4], 0), ops.Slice([0, 2])])
+    assert res1 in [[[0, 1, 2, 0]], [[0, 2]]]
+
+    # test nested structure
+    res2 = test_config([[0, 1, 2]], [ops.Compose([ops.Duplicate(), ops.Concatenate()]),
+                                     ops.Compose([ops.Slice([0, 1]), ops.OneHot(2)])])
+    assert res2 in [[[[1, 0], [0, 1]]], [[0, 1, 2, 0, 1, 2]]]
+    # test random_choice where there is only 1 op
+    assert test_config([[4, 3], [2, 1]], [ops.Slice([0])]) == [[4], [2]]
+
+
+if __name__ == "__main__":
+    test_random_choice()

tests/ut/python/dataset/test_random_select_subpolicy.py

@@ -0,0 +1,51 @@
+# 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 mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as ops
+import mindspore.dataset.transforms.vision.c_transforms as visions
+
+
+def test_random_select_subpolicy():
+    ds.config.set_seed(0)
+
+    def test_config(arr, policy):
+        try:
+            data = ds.NumpySlicesDataset(arr, column_names="col", shuffle=False)
+            data = data.map(input_columns=["col"], operations=visions.RandomSelectSubpolicy(policy))
+            res = []
+            for i in data.create_dict_iterator():
+                res.append(i["col"].tolist())
+            return res
+        except (TypeError, ValueError) as e:
+            return str(e)
+
+    # 3 possible outcomes
+    policy1 = [[(ops.PadEnd([4], 0), 0.5), (ops.Compose([ops.Duplicate(), ops.Concatenate()]), 1)],
+               [(ops.Slice([0, 1]), 0.5), (ops.Duplicate(), 1), (ops.Concatenate(), 1)]]
+    res1 = test_config([[1, 2, 3]], policy1)
+    assert res1 in [[[1, 2, 1, 2]], [[1, 2, 3, 1, 2, 3]], [[1, 2, 3, 0, 1, 2, 3, 0]]]
+
+    # test exceptions
+    assert "policy can not be empty." in test_config([[1, 2, 3]], [])
+    assert "policy[0] can not be empty." in test_config([[1, 2, 3]], [[]])
+    assert "op of (op, prob) in policy[1][0] is not a c_transform op (TensorOp) nor a callable pyfunc" in test_config(
+        [[1, 2, 3]], [[(ops.PadEnd([4], 0), 0.5)], [(1, 0.4)]])
+    assert "prob of (op, prob) policy[1][0] is not within the required interval of (0 to 1)" in test_config([[1]], [
+        [(ops.Duplicate(), 0)], [(ops.Duplicate(), -0.1)]])
+
+
+if __name__ == "__main__":
+    test_random_select_subpolicy()