split c_api_test

split into datasets

review fix

fix merge issues

fixed minddata lite build issues

tests/ut/cpp/dataset/CMakeLists.txt

@@ -88,7 +88,13 @@ SET(DE_UT_SRCS
         concatenate_op_test.cc
         cyclic_array_test.cc
         perf_data_test.cc
-        c_api_test.cc
+        c_api_samplers_test.cc
+        c_api_transforms_test.cc
+        c_api_dataset_ops_test.cc
+        c_api_dataset_cifar_test.cc
+        c_api_dataset_coco_test.cc
+        c_api_dataset_voc_test.cc
+        c_api_datasets_test.cc
         tensor_op_fusion_pass_test.cc
         sliding_window_op_test.cc
         epoch_ctrl_op_test.cc

tests/ut/cpp/dataset/c_api_dataset_cifar_test.cc

+/**
+ * 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.
+ */
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <string>
+
+#include "utils/log_adapter.h"
+#include "utils/ms_utils.h"
+#include "common/common.h"
+#include "gtest/gtest.h"
+#include "securec.h"
+#include "minddata/dataset/include/datasets.h"
+#include "minddata/dataset/include/status.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/iterator.h"
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/include/samplers.h"
+
+using namespace mindspore::dataset::api;
+using mindspore::MsLogLevel::ERROR;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::TensorShape;
+using mindspore::dataset::TensorImpl;
+using mindspore::dataset::DataType;
+using mindspore::dataset::Status;
+using mindspore::dataset::BorderType;
+using mindspore::dataset::dsize_t;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+TEST_F(MindDataTestPipeline, TestCifar10Dataset) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar10Dataset.";
+
+  // Create a Cifar10 Dataset
+  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
+  std::shared_ptr<Dataset> ds = Cifar10(folder_path, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  EXPECT_NE(row.find("image"), row.end());
+  EXPECT_NE(row.find("label"), row.end());
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+  i++;
+  auto image = row["image"];
+  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+  iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 10);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCifar10DatasetFail1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar10DatasetFail1.";
+
+  // Create a Cifar10 Dataset
+  std::shared_ptr<Dataset> ds = Cifar10("", RandomSampler(false, 10));
+  EXPECT_EQ(ds, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestCifar100Dataset) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar100Dataset.";
+
+  // Create a Cifar100 Dataset
+  std::string folder_path = datasets_root_path_ + "/testCifar100Data/";
+  std::shared_ptr<Dataset> ds = Cifar100(folder_path, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  EXPECT_NE(row.find("image"), row.end());
+  EXPECT_NE(row.find("coarse_label"), row.end());
+  EXPECT_NE(row.find("fine_label"), row.end());
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 10);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCifar100DatasetFail1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar100DatasetFail1.";
+
+  // Create a Cifar100 Dataset
+  std::shared_ptr<Dataset> ds = Cifar100("", RandomSampler(false, 10));
+  EXPECT_EQ(ds, nullptr);
+}

tests/ut/cpp/dataset/c_api_dataset_coco_test.cc

+/**
+ * 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.
+ */
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <string>
+
+#include "utils/log_adapter.h"
+#include "utils/ms_utils.h"
+#include "common/common.h"
+#include "gtest/gtest.h"
+#include "securec.h"
+#include "minddata/dataset/include/datasets.h"
+#include "minddata/dataset/include/status.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/iterator.h"
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/include/samplers.h"
+
+using namespace mindspore::dataset::api;
+using mindspore::MsLogLevel::ERROR;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::TensorShape;
+using mindspore::dataset::TensorImpl;
+using mindspore::dataset::DataType;
+using mindspore::dataset::Status;
+using mindspore::dataset::BorderType;
+using mindspore::dataset::dsize_t;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+TEST_F(MindDataTestPipeline, TestCocoDetection) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoDetection.";
+  // Create a Coco Dataset
+  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
+  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";
+
+  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Detection", false, SequentialSampler(0, 6));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  std::string expect_file[] = {"000000391895", "000000318219", "000000554625", "000000574769", "000000060623",
+                               "000000309022"};
+  std::vector<std::vector<float>> expect_bbox_vector = {{10.0, 10.0, 10.0, 10.0, 70.0, 70.0, 70.0, 70.0},
+                                                        {20.0, 20.0, 20.0, 20.0, 80.0, 80.0, 80.0, 80.0},
+                                                        {30.0, 30.0, 30.0, 30.0}, {40.0, 40.0, 40.0, 40.0},
+                                                        {50.0, 50.0, 50.0, 50.0}, {60.0, 60.0, 60.0, 60.0}};
+  std::vector<std::vector<uint32_t>> expect_catagoryid_list = {{1, 7}, {2, 8}, {3}, {4}, {5}, {6}};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto bbox = row["bbox"];
+    auto category_id = row["category_id"];
+    std::shared_ptr<Tensor> expect_image;
+    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
+    EXPECT_EQ(*image, *expect_image);
+    std::shared_ptr<Tensor> expect_bbox;
+    dsize_t bbox_num = static_cast<dsize_t>(expect_bbox_vector[i].size() / 4);
+    Tensor::CreateFromVector(expect_bbox_vector[i], TensorShape({bbox_num, 4}), &expect_bbox);
+    EXPECT_EQ(*bbox, *expect_bbox);
+    std::shared_ptr<Tensor> expect_categoryid;
+    Tensor::CreateFromVector(expect_catagoryid_list[i], TensorShape({bbox_num, 1}), &expect_categoryid);
+    EXPECT_EQ(*category_id, *expect_categoryid);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 6);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCocoStuff) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoStuff.";
+  // Create a Coco Dataset
+  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
+  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";
+
+  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Stuff", false, SequentialSampler(0, 6));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  std::string expect_file[] = {"000000391895", "000000318219", "000000554625", "000000574769", "000000060623",
+                               "000000309022"};
+  std::vector<std::vector<float>> expect_segmentation_vector =
+    {{10.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0,
+      70.0, 72.0, 73.0, 74.0, 75.0, -1.0, -1.0, -1.0, -1.0, -1.0},
+     {20.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0,
+      10.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, -1.0},
+     {40.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 40.0, 41.0, 42.0},
+     {50.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0},
+     {60.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0},
+     {60.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0}};
+  std::vector<std::vector<dsize_t>> expect_size = {{2, 10}, {2, 11}, {1, 12}, {1, 13}, {1, 14}, {2, 7}};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto segmentation = row["segmentation"];
+    auto iscrowd = row["iscrowd"];
+    std::shared_ptr<Tensor> expect_image;
+    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
+    EXPECT_EQ(*image, *expect_image);
+    std::shared_ptr<Tensor> expect_segmentation;
+    Tensor::CreateFromVector(expect_segmentation_vector[i], TensorShape(expect_size[i]), &expect_segmentation);
+    EXPECT_EQ(*segmentation, *expect_segmentation);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 6);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCocoKeypoint) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoKeypoint.";
+  // Create a Coco Dataset
+  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
+  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/key_point.json";
+
+  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Keypoint", false, SequentialSampler(0, 2));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  std::string expect_file[] = {"000000391895", "000000318219"};
+  std::vector<std::vector<float>> expect_keypoint_vector =
+    {{368.0, 61.0, 1.0, 369.0, 52.0, 2.0, 0.0, 0.0, 0.0, 382.0, 48.0, 2.0, 0.0, 0.0, 0.0, 368.0, 84.0, 2.0, 435.0,
+       81.0, 2.0, 362.0, 125.0, 2.0, 446.0, 125.0, 2.0, 360.0, 153.0, 2.0, 0.0, 0.0, 0.0, 397.0, 167.0, 1.0, 439.0,
+       166.0, 1.0, 369.0, 193.0, 2.0, 461.0, 234.0, 2.0, 361.0, 246.0, 2.0, 474.0, 287.0, 2.0},
+     {244.0, 139.0, 2.0, 0.0, 0.0, 0.0, 226.0, 118.0, 2.0, 0.0, 0.0, 0.0, 154.0, 159.0, 2.0, 143.0, 261.0, 2.0, 135.0,
+       312.0, 2.0, 271.0, 423.0, 2.0, 184.0, 530.0, 2.0, 261.0, 280.0, 2.0, 347.0, 592.0, 2.0, 0.0, 0.0, 0.0, 123.0,
+       596.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}};
+  std::vector<std::vector<dsize_t>> expect_size = {{1, 51}, {1, 51}};
+  std::vector<std::vector<uint32_t>> expect_num_keypoints_list = {{14}, {10}};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto keypoints = row["keypoints"];
+    auto num_keypoints = row["num_keypoints"];
+    std::shared_ptr<Tensor> expect_image;
+    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
+    EXPECT_EQ(*image, *expect_image);
+    std::shared_ptr<Tensor> expect_keypoints;
+    dsize_t keypoints_size = expect_size[i][0];
+    Tensor::CreateFromVector(expect_keypoint_vector[i], TensorShape(expect_size[i]), &expect_keypoints);
+    EXPECT_EQ(*keypoints, *expect_keypoints);
+    std::shared_ptr<Tensor> expect_num_keypoints;
+    Tensor::CreateFromVector(expect_num_keypoints_list[i], TensorShape({keypoints_size, 1}), &expect_num_keypoints);
+    EXPECT_EQ(*num_keypoints, *expect_num_keypoints);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 2);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCocoPanoptic) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoPanoptic.";
+  // Create a Coco Dataset
+  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
+  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/panoptic.json";
+
+  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file, "Panoptic", false, SequentialSampler(0, 2));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  std::string expect_file[] = {"000000391895", "000000574769"};
+  std::vector<std::vector<float>> expect_bbox_vector = {{472, 173, 36, 48, 340, 22, 154, 301, 486, 183, 30, 35},
+                                                        {103, 133, 229, 422, 243, 175, 93, 164}};
+  std::vector<std::vector<uint32_t>> expect_categoryid_vector = {{1, 1, 2}, {1, 3}};
+  std::vector<std::vector<uint32_t>> expect_iscrowd_vector = {{0, 0, 0}, {0, 0}};
+  std::vector<std::vector<uint32_t>> expect_area_vector = {{705, 14062, 626}, {43102, 6079}};
+  std::vector<std::vector<dsize_t>> expect_size = {{3, 4}, {2, 4}};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto bbox = row["bbox"];
+    auto category_id = row["category_id"];
+    auto iscrowd = row["iscrowd"];
+    auto area = row["area"];
+    std::shared_ptr<Tensor> expect_image;
+    Tensor::CreateFromFile(folder_path + "/" + expect_file[i] + ".jpg", &expect_image);
+    EXPECT_EQ(*image, *expect_image);
+    std::shared_ptr<Tensor> expect_bbox;
+    dsize_t bbox_size = expect_size[i][0];
+    Tensor::CreateFromVector(expect_bbox_vector[i], TensorShape(expect_size[i]), &expect_bbox);
+    EXPECT_EQ(*bbox, *expect_bbox);
+    std::shared_ptr<Tensor> expect_categoryid;
+    Tensor::CreateFromVector(expect_categoryid_vector[i], TensorShape({bbox_size, 1}), &expect_categoryid);
+    EXPECT_EQ(*category_id, *expect_categoryid);
+    std::shared_ptr<Tensor> expect_iscrowd;
+    Tensor::CreateFromVector(expect_iscrowd_vector[i], TensorShape({bbox_size, 1}), &expect_iscrowd);
+    EXPECT_EQ(*iscrowd, *expect_iscrowd);
+    std::shared_ptr<Tensor> expect_area;
+    Tensor::CreateFromVector(expect_area_vector[i], TensorShape({bbox_size, 1}), &expect_area);
+    EXPECT_EQ(*area, *expect_area);
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 2);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCocoDefault) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoDetection.";
+  // Create a Coco Dataset
+  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
+  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";
+
+  std::shared_ptr<Dataset> ds = Coco(folder_path, annotation_file);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto bbox = row["bbox"];
+    auto category_id = row["category_id"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Tensor bbox shape: " << bbox->shape();
+    MS_LOG(INFO) << "Tensor category_id shape: " << category_id->shape();
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 6);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCocoException) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCocoDetection.";
+  // Create a Coco Dataset
+  std::string folder_path = datasets_root_path_ + "/testCOCO/train";
+  std::string annotation_file = datasets_root_path_ + "/testCOCO/annotations/train.json";
+  std::string invalid_folder_path = "./NotExist";
+  std::string invalid_annotation_file = "./NotExistFile";
+
+  std::shared_ptr<Dataset> ds = Coco(invalid_folder_path, annotation_file);
+  EXPECT_EQ(ds, nullptr);
+
+  std::shared_ptr<Dataset> ds1 = Coco(folder_path, invalid_annotation_file);
+  EXPECT_EQ(ds1, nullptr);
+
+  std::shared_ptr<Dataset> ds2 = Coco(folder_path, annotation_file, "valid_mode");
+  EXPECT_EQ(ds2, nullptr);
+}

tests/ut/cpp/dataset/c_api_dataset_voc_test.cc

+/**
+ * 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.
+ */
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <string>
+
+#include "utils/log_adapter.h"
+#include "utils/ms_utils.h"
+#include "common/common.h"
+#include "gtest/gtest.h"
+#include "securec.h"
+#include "minddata/dataset/include/datasets.h"
+#include "minddata/dataset/include/status.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/iterator.h"
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/include/samplers.h"
+#include "minddata/dataset/engine/datasetops/source/voc_op.h"
+
+using namespace mindspore::dataset::api;
+using mindspore::MsLogLevel::ERROR;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::TensorShape;
+using mindspore::dataset::TensorImpl;
+using mindspore::dataset::DataType;
+using mindspore::dataset::Status;
+using mindspore::dataset::BorderType;
+using mindspore::dataset::dsize_t;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+TEST_F(MindDataTestPipeline, TestVOCSegmentation) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCSegmentation.";
+
+  // Create a VOC Dataset
+  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
+  std::shared_ptr<Dataset> ds = VOC(folder_path, "Segmentation", "train", {}, false, SequentialSampler(0, 3));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  // Check if VOCOp read correct images/targets
+  using Tensor = mindspore::dataset::Tensor;
+  std::string expect_file[] = {"32", "33", "39", "32", "33", "39"};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto target = row["target"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Tensor target shape: " << target->shape();
+
+    std::shared_ptr<Tensor> expect_image;
+    Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);
+    EXPECT_EQ(*image, *expect_image);
+
+    std::shared_ptr<Tensor> expect_target;
+    Tensor::CreateFromFile(folder_path + "/SegmentationClass/" + expect_file[i] + ".png", &expect_target);
+    EXPECT_EQ(*target, *expect_target);
+
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 6);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestVOCSegmentationError1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCSegmentationError1.";
+
+  // Create a VOC Dataset
+  std::map<std::string, int32_t> class_index;
+  class_index["car"] = 0;
+  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
+  std::shared_ptr<Dataset> ds = VOC(folder_path, "Segmentation", "train", class_index, false, RandomSampler(false, 6));
+
+  // Expect nullptr for segmentation task with class_index
+  EXPECT_EQ(ds, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestVOCInvalidTaskOrMode) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCInvalidTaskOrMode.";
+
+  // Create a VOC Dataset
+  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
+  std::shared_ptr<Dataset> ds_1 = VOC(folder_path, "Classification", "train", {}, false, SequentialSampler(0, 3));
+  // Expect nullptr for invalid task
+  EXPECT_EQ(ds_1, nullptr);
+
+  std::shared_ptr<Dataset> ds_2 = VOC(folder_path, "Segmentation", "validation", {}, false, RandomSampler(false, 4));
+  // Expect nullptr for invalid mode
+  EXPECT_EQ(ds_2, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestVOCDetection) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCDetection.";
+
+  // Create a VOC Dataset
+  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
+  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, false, SequentialSampler(0, 4));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  // Check if VOCOp read correct images/labels
+  std::string expect_file[] = {"15", "32", "33", "39"};
+  uint32_t expect_num[] = {5, 5, 4, 3};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Tensor label shape: " << label->shape();
+
+    std::shared_ptr<Tensor> expect_image;
+    Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);
+    EXPECT_EQ(*image, *expect_image);
+
+    std::shared_ptr<Tensor> expect_label;
+    Tensor::CreateFromMemory(TensorShape({1, 1}), DataType(DataType::DE_UINT32), nullptr, &expect_label);
+    expect_label->SetItemAt({0, 0}, expect_num[i]);
+    EXPECT_EQ(*label, *expect_label);
+
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 4);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestVOCClassIndex) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCClassIndex.";
+
+  // Create a VOC Dataset
+  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
+  std::map<std::string, int32_t> class_index;
+  class_index["car"] = 0;
+  class_index["cat"] = 1;
+  class_index["train"] = 9;
+
+  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", class_index, false, SequentialSampler(0, 6));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  // Check if VOCOp read correct labels
+  // When we provide class_index, label of ["car","cat","train"] become [0,1,9]
+  std::shared_ptr<Tensor> expect_label;
+  Tensor::CreateFromMemory(TensorShape({1, 1}), DataType(DataType::DE_UINT32), nullptr, &expect_label);
+
+  uint32_t expect[] = {9, 9, 9, 1, 1, 0};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto label = row["label"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Tensor label shape: " << label->shape();
+    expect_label->SetItemAt({0, 0}, expect[i]);
+    EXPECT_EQ(*label, *expect_label);
+
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 6);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}

tests/ut/cpp/dataset/c_api_datasets_test.cc

+/**
+ * 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.
+ */
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <string>
+
+#include "utils/log_adapter.h"
+#include "utils/ms_utils.h"
+#include "common/common.h"
+#include "gtest/gtest.h"
+#include "securec.h"
+#include "minddata/dataset/include/datasets.h"
+#include "minddata/dataset/include/status.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/iterator.h"
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/include/samplers.h"
+
+using namespace mindspore::dataset::api;
+using mindspore::MsLogLevel::ERROR;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::TensorShape;
+using mindspore::dataset::TensorImpl;
+using mindspore::dataset::DataType;
+using mindspore::dataset::Status;
+using mindspore::dataset::BorderType;
+using mindspore::dataset::dsize_t;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+TEST_F(MindDataTestPipeline, TestMnistFail1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMnistFail1.";
+
+  // Create a Mnist Dataset
+  std::shared_ptr<Dataset> ds = Mnist("", RandomSampler(false, 10));
+  EXPECT_EQ(ds, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestImageFolderFail1) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestImageFolderFail1.";
+
+  // Create an ImageFolder Dataset
+  std::shared_ptr<Dataset> ds = ImageFolder("", true, nullptr);
+  EXPECT_EQ(ds, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestCelebADataset) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCelebADataset.";
+
+  // Create a CelebA Dataset
+  std::string folder_path = datasets_root_path_ + "/testCelebAData/";
+  std::shared_ptr<Dataset> ds = CelebA(folder_path, "all", SequentialSampler(0, 2), false, {});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  // Check if CelebAOp read correct images/attr
+  std::string expect_file[] = {"1.JPEG", "2.jpg"};
+  std::vector<std::vector<uint32_t>> expect_attr_vector =
+    {{0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,
+      1, 0, 0, 1}, {0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
+      1, 0, 0, 0, 0, 0, 0, 0, 1}};
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto attr = row["attr"];
+
+    std::shared_ptr<Tensor> expect_image;
+    Tensor::CreateFromFile(folder_path + expect_file[i], &expect_image);
+    EXPECT_EQ(*image, *expect_image);
+
+    std::shared_ptr<Tensor> expect_attr;
+    Tensor::CreateFromVector(expect_attr_vector[i], TensorShape({40}), &expect_attr);
+    EXPECT_EQ(*attr, *expect_attr);
+
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 2);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCelebADefault) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCelebADefault.";
+
+  // Create a CelebA Dataset
+  std::string folder_path = datasets_root_path_ + "/testCelebAData/";
+  std::shared_ptr<Dataset> ds = CelebA(folder_path);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  // Check if CelebAOp read correct images/attr
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    auto image = row["image"];
+    auto attr = row["attr"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    MS_LOG(INFO) << "Tensor attr shape: " << attr->shape();
+
+    iter->GetNextRow(&row);
+    i++;
+  }
+
+  EXPECT_EQ(i, 2);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCelebAException) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCelebAException.";
+
+  // Create a CelebA Dataset
+  std::string folder_path = datasets_root_path_ + "/testCelebAData/";
+  std::string invalid_folder_path = "./testNotExist";
+  std::string invalid_dataset_type = "invalid_type";
+  std::shared_ptr<Dataset> ds = CelebA(invalid_folder_path);
+  EXPECT_EQ(ds, nullptr);
+  std::shared_ptr<Dataset> ds1 = CelebA(folder_path, invalid_dataset_type);
+  EXPECT_EQ(ds1, nullptr);
+}

tests/ut/cpp/dataset/c_api_samplers_test.cc

+/**
+ * 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.
+ */
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <string>
+
+#include "utils/log_adapter.h"
+#include "utils/ms_utils.h"
+#include "common/common.h"
+#include "gtest/gtest.h"
+#include "securec.h"
+#include "minddata/dataset/include/datasets.h"
+#include "minddata/dataset/include/status.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/iterator.h"
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/include/samplers.h"
+
+using namespace mindspore::dataset::api;
+using mindspore::MsLogLevel::ERROR;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::Status;
+using mindspore::dataset::BorderType;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+TEST_F(MindDataTestPipeline, TestImageFolderWithSamplers) {
+  std::shared_ptr<SamplerObj> sampl = DistributedSampler(2, 1);
+  EXPECT_NE(sampl, nullptr);
+
+  sampl = PKSampler(3);
+  EXPECT_NE(sampl, nullptr);
+
+  sampl = RandomSampler(false, 12);
+  EXPECT_NE(sampl, nullptr);
+
+  sampl = SequentialSampler(0, 12);
+  EXPECT_NE(sampl, nullptr);
+
+  std::vector<double> weights = {0.9, 0.8, 0.68, 0.7, 0.71, 0.6, 0.5, 0.4, 0.3, 0.5, 0.2, 0.1};
+  sampl = WeightedRandomSampler(weights, 12);
+  EXPECT_NE(sampl, nullptr);
+
+  std::vector<int64_t> indices = {1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23};
+  sampl = SubsetRandomSampler(indices);
+  EXPECT_NE(sampl, nullptr);
+
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, sampl);
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 2;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 12);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestSamplersMoveParameters) {
+  std::vector<int64_t> indices = {1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23};
+  std::shared_ptr<SamplerObj> sampl1 = SubsetRandomSampler(indices);
+  EXPECT_FALSE(indices.empty());
+  EXPECT_NE(sampl1->Build(), nullptr);
+  std::shared_ptr<SamplerObj> sampl2 = SubsetRandomSampler(std::move(indices));
+  EXPECT_TRUE(indices.empty());
+  EXPECT_NE(sampl2->Build(), nullptr);
+}

tests/ut/cpp/dataset/c_api_transforms_test.cc

+/**
+ * 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.
+ */
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <string>
+
+#include "utils/log_adapter.h"
+#include "utils/ms_utils.h"
+#include "common/common.h"
+#include "gtest/gtest.h"
+#include "securec.h"
+#include "minddata/dataset/include/datasets.h"
+#include "minddata/dataset/include/status.h"
+#include "minddata/dataset/include/transforms.h"
+#include "minddata/dataset/include/iterator.h"
+#include "minddata/dataset/core/constants.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/include/samplers.h"
+
+using namespace mindspore::dataset::api;
+using mindspore::MsLogLevel::ERROR;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::Status;
+using mindspore::dataset::BorderType;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+TEST_F(MindDataTestPipeline, TestUniformAugWithOps) {
+  // Create a Mnist Dataset
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+  std::shared_ptr<Dataset> ds = Mnist(folder_path, RandomSampler(false, 20));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 1;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> resize_op = vision::Resize({30, 30});
+  EXPECT_NE(resize_op, nullptr);
+
+  std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
+  EXPECT_NE(random_crop_op, nullptr);
+
+  std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
+  EXPECT_NE(center_crop_op, nullptr);
+
+  std::shared_ptr<TensorOperation> uniform_aug_op = vision::UniformAugment({random_crop_op, center_crop_op}, 2);
+  EXPECT_NE(uniform_aug_op, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({resize_op, uniform_aug_op});
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomFlip) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.5);
+  EXPECT_NE(random_vertical_flip_op, nullptr);
+
+  std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlip(0.5);
+  EXPECT_NE(random_horizontal_flip_op, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_vertical_flip_op, random_horizontal_flip_op});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestPad) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> pad_op1 = vision::Pad({1, 2, 3, 4}, {0}, BorderType::kSymmetric);
+  EXPECT_NE(pad_op1, nullptr);
+
+  std::shared_ptr<TensorOperation> pad_op2 = vision::Pad({1}, {1, 1, 1}, BorderType::kEdge);
+  EXPECT_NE(pad_op2, nullptr);
+
+  std::shared_ptr<TensorOperation> pad_op3 = vision::Pad({1, 4});
+  EXPECT_NE(pad_op3, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({pad_op1, pad_op2, pad_op3});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+  i++;
+  auto image = row["image"];
+  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+  iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestCutOut) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> cut_out1 = vision::CutOut(30, 5);
+  EXPECT_NE(cut_out1, nullptr);
+
+  std::shared_ptr<TensorOperation> cut_out2 = vision::CutOut(30);
+  EXPECT_NE(cut_out2, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({cut_out1, cut_out2});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+  i++;
+  auto image = row["image"];
+  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+  iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestNormalize) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> normalize = vision::Normalize({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0});
+  EXPECT_NE(normalize, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({normalize});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestDecode) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> decode = vision::Decode(true);
+  EXPECT_NE(decode, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({decode});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomColorAdjust) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_color_adjust1 = vision::RandomColorAdjust({1.0}, {0.0}, {0.5}, {0.5});
+  EXPECT_NE(random_color_adjust1, nullptr);
+
+  std::shared_ptr<TensorOperation> random_color_adjust2 = vision::RandomColorAdjust({1.0, 1.0}, {0.0, 0.0}, {0.5, 0.5},
+                                                                                    {0.5, 0.5});
+  EXPECT_NE(random_color_adjust2, nullptr);
+
+  std::shared_ptr<TensorOperation> random_color_adjust3 = vision::RandomColorAdjust({0.5, 1.0}, {0.0, 0.5}, {0.25, 0.5},
+                                                                             {0.25, 0.5});
+  EXPECT_NE(random_color_adjust3, nullptr);
+
+  std::shared_ptr<TensorOperation> random_color_adjust4 = vision::RandomColorAdjust();
+  EXPECT_NE(random_color_adjust4, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_color_adjust1, random_color_adjust2, random_color_adjust3, random_color_adjust4});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+  i++;
+  auto image = row["image"];
+  MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+  iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestRandomRotation) {
+  // Create an ImageFolder Dataset
+  std::string folder_path = datasets_root_path_ + "/testPK/data/";
+  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 2;
+  ds = ds->Repeat(repeat_num);
+  EXPECT_NE(ds, nullptr);
+
+  // Create objects for the tensor ops
+  std::shared_ptr<TensorOperation> random_rotation_op = vision::RandomRotation({-180, 180});
+  EXPECT_NE(random_rotation_op, nullptr);
+
+  // Create a Map operation on ds
+  ds = ds->Map({random_rotation_op});
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 1;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  iter->GetNextRow(&row);
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
+    iter->GetNextRow(&row);
+  }
+
+  EXPECT_EQ(i, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}