mindspore2022/tests/ut/cpp/dataset/c_api_vision_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 "common/common.h"
#include "minddata/dataset/include/datasets.h"
#include "minddata/dataset/include/transforms.h"
#include "minddata/dataset/include/vision.h"

using namespace mindspore::dataset;
using mindspore::dataset::BorderType;
using mindspore::dataset::InterpolationMode;
using mindspore::dataset::Tensor;

class MindDataTestPipeline : public UT::DatasetOpTesting {
 protected:
};

// Tests for vision ops (in alphabetical order)

TEST_F(MindDataTestPipeline, TestAutoContrastSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAutoContrastSuccess1.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 3;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create auto contrast object with default values
  std::shared_ptr<TensorOperation> auto_contrast = vision::AutoContrast();
  EXPECT_NE(auto_contrast, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({auto_contrast});
  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, 15);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestAutoContrastSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAutoContrastSuccess2.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 3;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create auto contrast object
  std::shared_ptr<TensorOperation> auto_contrast = vision::AutoContrast(10, {10, 20});
  EXPECT_NE(auto_contrast, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({auto_contrast});
  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, 15);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestAutoContrastFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestAutoContrastFail with invalid params.";
  // Testing invalid cutoff < 0
  std::shared_ptr<TensorOperation> auto_contrast1 = vision::AutoContrast(-1.0);
  EXPECT_EQ(auto_contrast1, nullptr);
  // Testing invalid cutoff > 100
  std::shared_ptr<TensorOperation> auto_contrast2 = vision::AutoContrast(110.0, {10, 20});
  EXPECT_EQ(auto_contrast2, nullptr);
}

TEST_F(MindDataTestPipeline, TestBoundingBoxAugmentSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestBoundingBoxAugmentSuccess.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> bound_box_augment = vision::BoundingBoxAugment(vision::RandomRotation({90.0}), 1.0);
  EXPECT_NE(bound_box_augment, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({bound_box_augment}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
  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, 3);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestBoundingBoxAugmentFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestBoundingBoxAugmentFail with invalid params.";
  // Testing invalid ratio < 0.0
  std::shared_ptr<TensorOperation> bound_box_augment = vision::BoundingBoxAugment(vision::RandomRotation({90.0}), -1.0);
  EXPECT_EQ(bound_box_augment, nullptr);
  // Testing invalid ratio > 1.0
  std::shared_ptr<TensorOperation> bound_box_augment1 = vision::BoundingBoxAugment(vision::RandomRotation({90.0}), 2.0);
  EXPECT_EQ(bound_box_augment1, nullptr);
  // Testing invalid transform
  std::shared_ptr<TensorOperation> bound_box_augment2 = vision::BoundingBoxAugment(nullptr, 0.5);
  EXPECT_EQ(bound_box_augment2, nullptr);
}

TEST_F(MindDataTestPipeline, TestCenterCrop) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCenterCrop with single integer input.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 3;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create centre crop object with square crop
  std::shared_ptr<TensorOperation> centre_out1 = vision::CenterCrop({30});
  EXPECT_NE(centre_out1, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({centre_out1});
  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, 15);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCenterCropFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCenterCrop with invalid parameters.";

  // center crop height value negative
  std::shared_ptr<TensorOperation> center_crop = mindspore::dataset::vision::CenterCrop({-32, 32});
  EXPECT_EQ(center_crop, nullptr);
  // center crop width value negative
  center_crop = mindspore::dataset::vision::CenterCrop({32, -32});
  EXPECT_EQ(center_crop, nullptr);
  // 0 value would result in nullptr
  center_crop = mindspore::dataset::vision::CenterCrop({0, 32});
  EXPECT_EQ(center_crop, nullptr);
  // center crop with 3 values
  center_crop = mindspore::dataset::vision::CenterCrop({10, 20, 30});
  EXPECT_EQ(center_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestCropFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCrop with invalid parameters.";

  // wrong width
  std::shared_ptr<TensorOperation> crop = mindspore::dataset::vision::Crop({0, 0}, {32, -32});
  EXPECT_EQ(crop, nullptr);
  // wrong height
  crop = mindspore::dataset::vision::Crop({0, 0}, {-32, -32});
  EXPECT_EQ(crop, nullptr);
  // zero height
  crop = mindspore::dataset::vision::Crop({0, 0}, {0, 32});
  EXPECT_EQ(crop, nullptr);
  // negative coordinates
  crop = mindspore::dataset::vision::Crop({-1, 0}, {32, 32});
  EXPECT_EQ(crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestCutMixBatchSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchSuccess1.";
  // Testing CutMixBatch on a batch of CHW images

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  int number_of_classes = 10;
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> hwc_to_chw = vision::HWC2CHW();
  EXPECT_NE(hwc_to_chw, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({hwc_to_chw}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(number_of_classes);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> cutmix_batch_op =
    vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNCHW, 1.0, 1.0);
  EXPECT_NE(cutmix_batch_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({cutmix_batch_op}, {"image", "label"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    auto label = row["label"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    MS_LOG(INFO) << "Label shape: " << label->shape();
    EXPECT_EQ(image->shape().AsVector().size() == 4 && batch_size == image->shape()[0] && 3 == image->shape()[1] &&
                32 == image->shape()[2] && 32 == image->shape()[3],
              true);
    EXPECT_EQ(label->shape().AsVector().size() == 2 && batch_size == label->shape()[0] &&
                number_of_classes == label->shape()[1],
              true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 2);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCutMixBatchSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchSuccess2.";
  // Calling CutMixBatch on a batch of HWC images with default values of alpha and prob

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  int number_of_classes = 10;
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(number_of_classes);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> cutmix_batch_op = vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC);
  EXPECT_NE(cutmix_batch_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({cutmix_batch_op}, {"image", "label"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    auto label = row["label"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    MS_LOG(INFO) << "Label shape: " << label->shape();
    EXPECT_EQ(image->shape().AsVector().size() == 4 && batch_size == image->shape()[0] && 32 == image->shape()[1] &&
                32 == image->shape()[2] && 3 == image->shape()[3],
              true);
    EXPECT_EQ(label->shape().AsVector().size() == 2 && batch_size == label->shape()[0] &&
                number_of_classes == label->shape()[1],
              true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 2);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestCutMixBatchFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail1 with invalid negative alpha parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> cutmix_batch_op =
    vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, -1, 0.5);
  EXPECT_EQ(cutmix_batch_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestCutMixBatchFail2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail2 with invalid negative prob parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> cutmix_batch_op =
    vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 1, -0.5);
  EXPECT_EQ(cutmix_batch_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestCutMixBatchFail3) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail3 with invalid zero alpha parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> cutmix_batch_op =
    vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 0.0, 0.5);
  EXPECT_EQ(cutmix_batch_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestCutMixBatchFail4) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutMixBatchFail4 with invalid greater than 1 prob parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 10;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> cutmix_batch_op =
    vision::CutMixBatch(mindspore::dataset::ImageBatchFormat::kNHWC, 1, 1.5);
  EXPECT_EQ(cutmix_batch_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestCutOutFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutOutFail1 with invalid parameters.";

  // Create object for the tensor op
  // Invalid negative length
  std::shared_ptr<TensorOperation> cutout_op = vision::CutOut(-10);
  EXPECT_EQ(cutout_op, nullptr);
  // Invalid negative number of patches
  cutout_op = vision::CutOut(10, -1);
  EXPECT_EQ(cutout_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestCutOutFail2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCutOutFail2 with invalid params, boundary cases.";

  // Create object for the tensor op
  // Invalid zero length
  std::shared_ptr<TensorOperation> cutout_op = vision::CutOut(0);
  EXPECT_EQ(cutout_op, nullptr);
  // Invalid zero number of patches
  cutout_op = vision::CutOut(10, 0);
  EXPECT_EQ(cutout_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestCutOut) {
  MS_LOG(INFO) << "Doing 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, TestDecode) {
  MS_LOG(INFO) << "Doing 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, TestHwcToChw) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestHwcToChw.";

  // 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> channel_swap = vision::HWC2CHW();
  EXPECT_NE(channel_swap, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({channel_swap});
  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();
    // check if the image is in NCHW
    EXPECT_EQ(batch_size == image->shape()[0] && 3 == image->shape()[1] && 2268 == image->shape()[2] &&
                4032 == image->shape()[3],
              true);
    iter->GetNextRow(&row);
  }
  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestInvert) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestInvert.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 20));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> invert_op = vision::Invert();
  EXPECT_NE(invert_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({invert_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, TestMixUpBatchFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchFail1 with negative alpha parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(-1);
  EXPECT_EQ(mixup_batch_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestMixUpBatchFail2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchFail2 with zero alpha parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(0.0);
  EXPECT_EQ(mixup_batch_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchSuccess1 with explicit alpha parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch(2.0);
  EXPECT_NE(mixup_batch_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({mixup_batch_op}, {"image", "label"});
  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, 2);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestMixUpBatchSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestMixUpBatchSuccess1 with default alpha parameter.";

  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create a Batch operation on ds
  int32_t batch_size = 5;
  ds = ds->Batch(batch_size);
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> one_hot_op = transforms::OneHot(10);
  EXPECT_NE(one_hot_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({one_hot_op}, {"label"});
  EXPECT_NE(ds, nullptr);

  std::shared_ptr<TensorOperation> mixup_batch_op = vision::MixUpBatch();
  EXPECT_NE(mixup_batch_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({mixup_batch_op}, {"image", "label"});
  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, 2);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestNormalize) {
  MS_LOG(INFO) << "Doing 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, 0.0}, {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, TestNormalizeFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestNormalizeFail with invalid parameters.";

  // std value at 0.0
  std::shared_ptr<TensorOperation> normalize =
    mindspore::dataset::vision::Normalize({121.0, 115.0, 100.0}, {0.0, 68.0, 71.0});
  EXPECT_EQ(normalize, nullptr);
  // mean out of range
  normalize = mindspore::dataset::vision::Normalize({121.0, 0.0, 100.0}, {256.0, 68.0, 71.0});
  EXPECT_EQ(normalize, nullptr);
  // mean out of range
  normalize = mindspore::dataset::vision::Normalize({256.0, 0.0, 100.0}, {70.0, 68.0, 71.0});
  EXPECT_EQ(normalize, nullptr);
  // mean out of range
  normalize = mindspore::dataset::vision::Normalize({-1.0, 0.0, 100.0}, {70.0, 68.0, 71.0});
  EXPECT_EQ(normalize, nullptr);
  // normalize with 2 values (not 3 values) for mean
  normalize = mindspore::dataset::vision::Normalize({121.0, 115.0}, {70.0, 68.0, 71.0});
  EXPECT_EQ(normalize, nullptr);
  // normalize with 2 values (not 3 values) for standard deviation
  normalize = mindspore::dataset::vision::Normalize({121.0, 115.0, 100.0}, {68.0, 71.0});
  EXPECT_EQ(normalize, nullptr);
}

TEST_F(MindDataTestPipeline, TestNormalizePad) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestNormalizePad.";

  // 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> normalizepad = vision::NormalizePad({121.0, 115.0, 100.0}, {70.0, 68.0, 71.0},
                                                                       "float32");
  EXPECT_NE(normalizepad, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({normalizepad});
  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"];
    EXPECT_EQ(image->shape()[2], 4);
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 20);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestNormalizePadFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestNormalizePadFail with invalid parameters.";

  // std value at 0.0
  std::shared_ptr<TensorOperation> normalizepad =
    mindspore::dataset::vision::NormalizePad({121.0, 115.0, 100.0}, {0.0, 68.0, 71.0});
  EXPECT_EQ(normalizepad, nullptr);
  // normalizepad with 2 values (not 3 values) for mean
  normalizepad = mindspore::dataset::vision::NormalizePad({121.0, 115.0}, {70.0, 68.0, 71.0});
  EXPECT_EQ(normalizepad, nullptr);
  // normalizepad with 2 values (not 3 values) for standard deviation
  normalizepad = mindspore::dataset::vision::NormalizePad({121.0, 115.0, 100.0}, {68.0, 71.0});
  EXPECT_EQ(normalizepad, nullptr);
  // normalizepad with invalid dtype
  normalizepad = mindspore::dataset::vision::NormalizePad({121.0, 115.0, 100.0}, {68.0, 71.0, 71.0}, "123");
  EXPECT_EQ(normalizepad, nullptr);
}

TEST_F(MindDataTestPipeline, TestPad) {
  MS_LOG(INFO) << "Doing 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, TestRandomAffineFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAffineFail with invalid parameters.";

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> affine = vision::RandomAffine({0.0, 0.0}, {});
  EXPECT_EQ(affine, nullptr);
  // Invalid number of values for translate
  affine = vision::RandomAffine({0.0, 0.0}, {1, 1, 1, 1, 1});
  EXPECT_EQ(affine, nullptr);
  // Invalid number of values for shear
  affine = vision::RandomAffine({30.0, 30.0}, {0.0, 0.0}, {2.0, 2.0}, {10.0});
  EXPECT_EQ(affine, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomAffineSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAffineSuccess1 with non-default parameters.";

  // 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> affine =
    vision::RandomAffine({30.0, 30.0}, {-1.0, 1.0, -1.0, 1.0}, {2.0, 2.0}, {10.0, 10.0, 20.0, 20.0});
  EXPECT_NE(affine, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({affine});
  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, TestRandomAffineSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomAffineSuccess2 with default parameters.";

  // 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> affine = vision::RandomAffine({0.0, 0.0});
  EXPECT_NE(affine, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({affine});
  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, TestRandomColor) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomColor with non-default parameters.";

  // 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
  // Valid case: Set lower bound and upper bound to be the same value zero
  std::shared_ptr<TensorOperation> random_color_op_1 = vision::RandomColor(0.0, 0.0);
  EXPECT_NE(random_color_op_1, nullptr);

  // Failure case: Set invalid lower bound greater than upper bound
  std::shared_ptr<TensorOperation> random_color_op_2 = vision::RandomColor(1.0, 0.1);
  EXPECT_EQ(random_color_op_2, nullptr);

  // Valid case: Set lower bound as zero and less than upper bound
  std::shared_ptr<TensorOperation> random_color_op_3 = vision::RandomColor(0.0, 1.1);
  EXPECT_NE(random_color_op_3, nullptr);

  // Failure case: Set invalid negative lower bound
  std::shared_ptr<TensorOperation> random_color_op_4 = vision::RandomColor(-0.5, 0.5);
  EXPECT_EQ(random_color_op_4, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_color_op_1, random_color_op_3});
  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) {
  MS_LOG(INFO) << "Doing 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
  // Use single value for vectors
  std::shared_ptr<TensorOperation> random_color_adjust1 = vision::RandomColorAdjust({1.0}, {0.0}, {0.5}, {0.5});
  EXPECT_NE(random_color_adjust1, nullptr);

  // Use same 2 values for vectors
  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);

  // Use different 2 value for vectors
  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);

  // Use default input values
  std::shared_ptr<TensorOperation> random_color_adjust4 = vision::RandomColorAdjust();
  EXPECT_NE(random_color_adjust4, nullptr);

  // Use subset of explicitly set parameters
  std::shared_ptr<TensorOperation> random_color_adjust5 = vision::RandomColorAdjust({0.0, 0.5}, {0.25});
  EXPECT_NE(random_color_adjust5, nullptr);

  // Create a Map operation on ds
  ds = ds->Map(
    {random_color_adjust1, random_color_adjust2, random_color_adjust3, random_color_adjust4, random_color_adjust5});
  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, TestRandomColorAdjustFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomColorAdjustFail.";
  // brightness out of range
  std::shared_ptr<TensorOperation> random_color_adjust1 = vision::RandomColorAdjust({-1.0});
  EXPECT_EQ(random_color_adjust1, nullptr);

  // contrast out of range
  std::shared_ptr<TensorOperation> random_color_adjust2 = vision::RandomColorAdjust({1.0}, {-0.1});
  EXPECT_EQ(random_color_adjust2, nullptr);

  // saturation out of range
  std::shared_ptr<TensorOperation> random_color_adjust3 = vision::RandomColorAdjust({0.0}, {0.0}, {-0.2});
  EXPECT_EQ(random_color_adjust3, nullptr);

  // hue out of range
  std::shared_ptr<TensorOperation> random_color_adjust4 = vision::RandomColorAdjust({0.0}, {0.0}, {0.0}, {-0.6});
  EXPECT_EQ(random_color_adjust4, nullptr);

  std::shared_ptr<TensorOperation> random_color_adjust5 = vision::RandomColorAdjust({0.0}, {0.0}, {0.0}, {-0.5, 0.6});
  EXPECT_EQ(random_color_adjust5, nullptr);

  std::shared_ptr<TensorOperation> random_color_adjust6 = vision::RandomColorAdjust({0.0}, {0.0}, {0.0}, {0.51});
  EXPECT_EQ(random_color_adjust6, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomCropSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropSuccess.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  // Testing siez of size vector is 1
  std::shared_ptr<TensorOperation> random_crop = vision::RandomCrop({20});
  EXPECT_NE(random_crop, nullptr);

  // Testing siez of size vector is 2
  std::shared_ptr<TensorOperation> random_crop1 = vision::RandomCrop({20, 20});
  EXPECT_NE(random_crop1, nullptr);

  // Testing siez of paddiing vector is 1
  std::shared_ptr<TensorOperation> random_crop2 = vision::RandomCrop({20, 20}, {10});
  EXPECT_NE(random_crop2, nullptr);

  // Testing siez of paddiing vector is 2
  std::shared_ptr<TensorOperation> random_crop3 = vision::RandomCrop({20, 20}, {10, 20});
  EXPECT_NE(random_crop3, nullptr);

  // Testing siez of paddiing vector is 2
  std::shared_ptr<TensorOperation> random_crop4 = vision::RandomCrop({20, 20}, {10, 10, 10, 10});
  EXPECT_NE(random_crop4, nullptr);

  // Testing siez of fill_value vector is 1
  std::shared_ptr<TensorOperation> random_crop5 = vision::RandomCrop({20, 20}, {10, 10, 10, 10}, false, {5});
  EXPECT_NE(random_crop5, nullptr);

  // Testing siez of fill_value vector is 3
  std::shared_ptr<TensorOperation> random_crop6 = vision::RandomCrop({20, 20}, {10, 10, 10, 10}, false, {4, 4, 4});
  EXPECT_NE(random_crop6, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_crop, random_crop1, random_crop2, random_crop3, random_crop4, random_crop5, random_crop6});
  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, 10);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomCropFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropFail with invalid parameters.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  // Testing the size parameter is negative.
  std::shared_ptr<TensorOperation> random_crop = vision::RandomCrop({-28, 28});
  EXPECT_EQ(random_crop, nullptr);
  // Testing the size parameter is None.
  std::shared_ptr<TensorOperation> random_crop1 = vision::RandomCrop({});
  EXPECT_EQ(random_crop1, nullptr);
  // Testing the size of size vector is 3.
  std::shared_ptr<TensorOperation> random_crop2 = vision::RandomCrop({28, 28, 28});
  EXPECT_EQ(random_crop2, nullptr);
  // Testing the padding parameter is negative.
  std::shared_ptr<TensorOperation> random_crop3 = vision::RandomCrop({28, 28}, {-5});
  EXPECT_EQ(random_crop3, nullptr);
  // Testing the size of padding vector is empty.
  std::shared_ptr<TensorOperation> random_crop4 = vision::RandomCrop({28, 28}, {});
  EXPECT_EQ(random_crop4, nullptr);
  // Testing the size of padding vector is 3.
  std::shared_ptr<TensorOperation> random_crop5 = vision::RandomCrop({28, 28}, {5, 5, 5});
  EXPECT_EQ(random_crop5, nullptr);
  // Testing the size of padding vector is 5.
  std::shared_ptr<TensorOperation> random_crop6 = vision::RandomCrop({28, 28}, {5, 5, 5, 5, 5});
  EXPECT_EQ(random_crop6, nullptr);
  // Testing the size of fill_value vector is empty.
  std::shared_ptr<TensorOperation> random_crop7 = vision::RandomCrop({28, 28}, {0, 0, 0, 0}, false, {});
  EXPECT_EQ(random_crop7, nullptr);
  // Testing the size of fill_value vector is 2.
  std::shared_ptr<TensorOperation> random_crop8 = vision::RandomCrop({28, 28}, {0, 0, 0, 0}, false, {0, 0});
  EXPECT_EQ(random_crop8, nullptr);
  // Testing the size of fill_value vector is 4.
  std::shared_ptr<TensorOperation> random_crop9 = vision::RandomCrop({28, 28}, {0, 0, 0, 0}, false, {0, 0, 0, 0});
  EXPECT_EQ(random_crop9, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomCropWithBboxSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropWithBboxSuccess.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_crop = mindspore::dataset::vision::RandomCropWithBBox({128, 128});
  EXPECT_NE(random_crop, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_crop}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
  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();
    EXPECT_EQ(image->shape()[0], 128);
    EXPECT_EQ(image->shape()[1], 128);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 3);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomCropWithBboxFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomCropWithBboxFail with invalid parameters.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  // The size parameter is negative.
  std::shared_ptr<TensorOperation> random_crop = vision::RandomCropWithBBox({-10});
  EXPECT_EQ(random_crop, nullptr);
  // The parameter in the padding vector is negative.
  std::shared_ptr<TensorOperation> random_crop1 = vision::RandomCropWithBBox({10, 10}, {-2, 2, 2, 2});
  EXPECT_EQ(random_crop1, nullptr);
  // The size container is empty.
  std::shared_ptr<TensorOperation> random_crop2 = vision::RandomCropWithBBox({});
  EXPECT_EQ(random_crop2, nullptr);
  // The size of the size container is too large.
  std::shared_ptr<TensorOperation> random_crop3 = vision::RandomCropWithBBox({10, 10, 10});
  EXPECT_EQ(random_crop3, nullptr);
  // The padding container is empty.
  std::shared_ptr<TensorOperation> random_crop4 = vision::RandomCropWithBBox({10, 10}, {});
  EXPECT_EQ(random_crop4, nullptr);
  // The size of the padding container is too large.
  std::shared_ptr<TensorOperation> random_crop5 = vision::RandomCropWithBBox({10, 10}, {5, 5, 5, 5, 5});
  EXPECT_EQ(random_crop5, nullptr);
  // The fill_value container is empty.
  std::shared_ptr<TensorOperation> random_crop6 = vision::RandomCropWithBBox({10, 10}, {5, 5, 5, 5}, false, {});
  EXPECT_EQ(random_crop6, nullptr);
  // The size of the fill_value container is too large.
  std::shared_ptr<TensorOperation> random_crop7 =
    vision::RandomCropWithBBox({10, 10}, {5, 5, 5, 5}, false, {3, 3, 3, 3});
  EXPECT_EQ(random_crop7, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomHorizontalFlipFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalFlipFail with invalid parameters.";

  // Create object for the tensor op
  // Invalid negative input
  std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlip(-0.5);
  EXPECT_EQ(random_horizontal_flip_op, nullptr);
  // Invalid >1 input
  random_horizontal_flip_op = vision::RandomHorizontalFlip(2);
  EXPECT_EQ(random_horizontal_flip_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomHorizontalFlipWithBBoxSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalFlipWithBBoxSuccess.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlipWithBBox(0.5);
  EXPECT_NE(random_horizontal_flip_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_horizontal_flip_op}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
  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, 3);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomHorizontalFlipWithBBoxFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalFlipWithBBoxFail with invalid parameters.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  // Incorrect prob parameter.
  std::shared_ptr<TensorOperation> random_horizontal_flip_op = vision::RandomHorizontalFlipWithBBox(-1.0);
  EXPECT_EQ(random_horizontal_flip_op, nullptr);
  // Incorrect prob parameter.
  std::shared_ptr<TensorOperation> random_horizontal_flip_op1 = vision::RandomHorizontalFlipWithBBox(2.0);
  EXPECT_EQ(random_horizontal_flip_op1, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomHorizontalAndVerticalFlip) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomHorizontalAndVerticalFlip for horizontal and vertical flips.";

  // 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(0.75);
  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, TestRandomPosterizeFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeFail with invalid parameters.";

  // Create objects for the tensor ops
  // Invalid max > 8
  std::shared_ptr<TensorOperation> posterize = vision::RandomPosterize({1, 9});
  EXPECT_EQ(posterize, nullptr);
  // Invalid min < 1
  posterize = vision::RandomPosterize({0, 8});
  EXPECT_EQ(posterize, nullptr);
  // min > max
  posterize = vision::RandomPosterize({8, 1});
  EXPECT_EQ(posterize, nullptr);
  // empty
  posterize = vision::RandomPosterize({});
  EXPECT_EQ(posterize, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomPosterizeSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeSuccess1 with non-default parameters.";

  // 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> posterize = vision::RandomPosterize({1, 4});
  EXPECT_NE(posterize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({posterize});
  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, TestRandomPosterizeSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomPosterizeSuccess2 with default parameters.";

  // 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> posterize = vision::RandomPosterize();
  EXPECT_NE(posterize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({posterize});
  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, TestRandomResizeSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeSuccess1 with single integer input.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 5));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resize = vision::RandomResize({66});
  EXPECT_NE(random_resize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resize}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    EXPECT_EQ(image->shape()[0] == 66, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 5);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizeSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeSuccess2 with (height, width) input.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 3));
  EXPECT_NE(ds, nullptr);

  // Create 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_resize = vision::RandomResize({66, 77});
  EXPECT_NE(random_resize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resize}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    EXPECT_EQ(image->shape()[0] == 66 && image->shape()[1] == 77, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 6);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizeFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeFail incorrect size.";

  // RandomResize : size must only contain positive integers
  std::shared_ptr<TensorOperation> random_resize1 = vision::RandomResize({-66, 77});
  EXPECT_EQ(random_resize1, nullptr);

  // RandomResize : size must only contain positive integers
  std::shared_ptr<TensorOperation> random_resize2 = vision::RandomResize({0, 77});
  EXPECT_EQ(random_resize2, nullptr);

  // RandomResize : size must be a vector of one or two values
  std::shared_ptr<TensorOperation> random_resize3 = vision::RandomResize({1, 2, 3});
  EXPECT_EQ(random_resize3, nullptr);

  // RandomResize : size must be a vector of one or two values
  std::shared_ptr<TensorOperation> random_resize4 = vision::RandomResize({});
  EXPECT_EQ(random_resize4, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizeWithBBoxSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeWithBBoxSuccess1 with single integer input.";

  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resize = vision::RandomResizeWithBBox({88});
  EXPECT_NE(random_resize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resize}, {"image", "bbox"});
  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();
    EXPECT_EQ(image->shape()[0] == 88, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 3);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizeWithBBoxSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeWithBBoxSuccess2 with (height, width) input.";

  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 4));
  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_resize = vision::RandomResizeWithBBox({88, 99});
  EXPECT_NE(random_resize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resize}, {"image", "bbox"});
  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();
    EXPECT_EQ(image->shape()[0] == 88 && image->shape()[1] == 99, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 8);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizeWithBBoxFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomResizeWithBBoxFail incorrect size.";

  // RandomResizeWithBBox : size must only contain positive integers
  std::shared_ptr<TensorOperation> random_resize_with_bbox1 = vision::RandomResizeWithBBox({-66, 77});
  EXPECT_EQ(random_resize_with_bbox1, nullptr);

  // RandomResizeWithBBox : size must be a vector of one or two values
  std::shared_ptr<TensorOperation> random_resize_with_bbox2 = vision::RandomResizeWithBBox({1, 2, 3});
  EXPECT_EQ(random_resize_with_bbox2, nullptr);

  // RandomResizeWithBBox : size must be a vector of one or two values
  std::shared_ptr<TensorOperation> random_resize_with_bbox3 = vision::RandomResizeWithBBox({});
  EXPECT_EQ(random_resize_with_bbox3, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropSuccess1) {
  // Testing RandomResizedCrop with default values
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5});
  EXPECT_NE(random_resized_crop, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resized_crop}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    EXPECT_EQ(image->shape()[0] == 5 && image->shape()[1] == 5, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropSuccess2) {
  // Testing RandomResizedCrop with non-default values
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop =
    vision::RandomResizedCrop({5, 10}, {0.25, 0.75}, {0.5, 1.25}, mindspore::dataset::InterpolationMode::kArea, 20);
  EXPECT_NE(random_resized_crop, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resized_crop}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    EXPECT_EQ(image->shape()[0] == 5 && image->shape()[1] == 10, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropFail1) {
  // This should fail because size has negative value
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, -10});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropFail2) {
  // This should fail because scale isn't in {min, max} format
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, 10}, {4, 3});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropFail3) {
  // This should fail because ratio isn't in {min, max} format
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, 10}, {4, 5}, {7, 6});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropFail4) {
  // This should fail because scale has a size of more than 2
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCrop({5, 10, 20}, {4, 5}, {7, 6});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropWithBBoxSuccess1) {
  // Testing RandomResizedCropWithBBox with default values
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 4));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCropWithBBox({5});
  EXPECT_NE(random_resized_crop, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resized_crop}, {"image", "bbox"});
  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();
    EXPECT_EQ(image->shape()[0] == 5 && image->shape()[1] == 5, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 4);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropWithBBoxSuccess2) {
  // Testing RandomResizedCropWithBBox with non-default values
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 4));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCropWithBBox(
    {5, 10}, {0.25, 0.75}, {0.5, 1.25}, mindspore::dataset::InterpolationMode::kArea, 20);
  EXPECT_NE(random_resized_crop, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_resized_crop}, {"image", "bbox"});
  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();
    EXPECT_EQ(image->shape()[0] == 5 && image->shape()[1] == 10, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 4);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropWithBBoxFail1) {
  // This should fail because size has negative value
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCropWithBBox({5, -10});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropWithBBoxFail2) {
  // This should fail because scale isn't in {min, max} format
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCropWithBBox({5, 10}, {4, 3});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropWithBBoxFail3) {
  // This should fail because ratio isn't in {min, max} format
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCropWithBBox({5, 10}, {4, 5}, {7, 6});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomResizedCropWithBBoxFail4) {
  // This should fail because scale has a size of more than 2
  // Create a Cifar10 Dataset
  std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
  std::shared_ptr<Dataset> ds = Cifar10(folder_path, "all", RandomSampler(false, 10));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_resized_crop = vision::RandomResizedCropWithBBox({5, 10, 20}, {4, 5}, {7, 6});
  EXPECT_EQ(random_resized_crop, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomRotation) {
  MS_LOG(INFO) << "Doing 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
  // Testing the size of degrees is 1
  std::shared_ptr<TensorOperation> random_rotation_op = vision::RandomRotation({180});
  EXPECT_NE(random_rotation_op, nullptr);
  // Testing the size of degrees is 2
  std::shared_ptr<TensorOperation> random_rotation_op1 = vision::RandomRotation({-180, 180});
  EXPECT_NE(random_rotation_op1, nullptr);
  // Testing the size of fill_value is 1
  std::shared_ptr<TensorOperation> random_rotation_op2 =
    vision::RandomRotation({180}, InterpolationMode::kNearestNeighbour, false, {-1, -1}, {2});
  EXPECT_NE(random_rotation_op2, nullptr);
  // Testing the size of fill_value is 3
  std::shared_ptr<TensorOperation> random_rotation_op3 =
    vision::RandomRotation({180}, InterpolationMode::kNearestNeighbour, false, {-1, -1}, {2, 2, 2});
  EXPECT_NE(random_rotation_op3, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_rotation_op, random_rotation_op1, random_rotation_op2, random_rotation_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, TestRandomRotationFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomRotationFail with invalid parameters.";

  // 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 objects for the tensor ops
  // Testing the size of degrees vector is 0
  std::shared_ptr<TensorOperation> random_rotation_op = vision::RandomRotation({});
  EXPECT_EQ(random_rotation_op, nullptr);
  // Testing the size of degrees vector is 3
  std::shared_ptr<TensorOperation> random_rotation_op1 = vision::RandomRotation({-50.0, 50.0, 100.0});
  EXPECT_EQ(random_rotation_op1, nullptr);
  // Test the case where the first column value of degrees is greater than the second column value
  std::shared_ptr<TensorOperation> random_rotation_op2 = vision::RandomRotation({50.0, -50.0});
  EXPECT_EQ(random_rotation_op2, nullptr);
  // Testing the size of center vector is 1
  std::shared_ptr<TensorOperation> random_rotation_op3 =
    vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0});
  EXPECT_EQ(random_rotation_op3, nullptr);
  // Testing the size of center vector is 3
  std::shared_ptr<TensorOperation> random_rotation_op4 =
    vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0, -1.0, -1.0});
  EXPECT_EQ(random_rotation_op4, nullptr);
  // Testing the size of fill_value vector is 2
  std::shared_ptr<TensorOperation> random_rotation_op5 =
    vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0, -1.0}, {2, 2});
  EXPECT_EQ(random_rotation_op5, nullptr);
  // Testing the size of fill_value vector is 4
  std::shared_ptr<TensorOperation> random_rotation_op6 =
    vision::RandomRotation({-50.0, 50.0}, InterpolationMode::kNearestNeighbour, false, {-1.0, -1.0}, {2, 2, 2, 2});
  EXPECT_EQ(random_rotation_op6, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomSelectSubpolicySuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSelectSubpolicySuccess.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 7));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  // Valid case: TensorOperation is not null and probability is between (0,1)
  std::shared_ptr<TensorOperation> random_select_subpolicy = vision::RandomSelectSubpolicy(
    {{{vision::Invert(), 0.5}, {vision::Equalize(), 0.5}}, {{vision::Resize({15, 15}), 1}}});
  EXPECT_NE(random_select_subpolicy, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_select_subpolicy});
  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, 7);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomSelectSubpolicyFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSelectSubpolicyFail.";

  // RandomSelectSubpolicy : probability of transform must be between 0.0 and 1.0
  std::shared_ptr<TensorOperation> random_select_subpolicy1 = vision::RandomSelectSubpolicy(
    {{{vision::Invert(), 1.5}, {vision::Equalize(), 0.5}}, {{vision::Resize({15, 15}), 1}}});
  EXPECT_EQ(random_select_subpolicy1, nullptr);

  // RandomSelectSubpolicy: policy must not be empty
  std::shared_ptr<TensorOperation> random_select_subpolicy2 =
    vision::RandomSelectSubpolicy({{{vision::Invert(), 0.5}, {vision::Equalize(), 0.5}}, {{nullptr, 1}}});
  EXPECT_EQ(random_select_subpolicy2, nullptr);

  // RandomSelectSubpolicy: policy must not be empty
  std::shared_ptr<TensorOperation> random_select_subpolicy3 = vision::RandomSelectSubpolicy({});
  EXPECT_EQ(random_select_subpolicy3, nullptr);

  // RandomSelectSubpolicy: policy must not be empty
  std::shared_ptr<TensorOperation> random_select_subpolicy4 =
    vision::RandomSelectSubpolicy({{{vision::Invert(), 0.5}, {vision::Equalize(), 0.5}}, {}});
  EXPECT_EQ(random_select_subpolicy4, nullptr);

  // RandomSelectSubpolicy: policy must not be empty
  std::shared_ptr<TensorOperation> random_select_subpolicy5 =
    vision::RandomSelectSubpolicy({{{}, {vision::Equalize(), 0.5}}, {{vision::Resize({15, 15}), 1}}});
  EXPECT_EQ(random_select_subpolicy5, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomSharpness) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSharpness.";

  // 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
  // Valid case: Input start degree and end degree
  std::shared_ptr<TensorOperation> random_sharpness_op_1 = vision::RandomSharpness({0.4, 2.3});
  EXPECT_NE(random_sharpness_op_1, nullptr);

  // Failure case: Empty degrees vector
  std::shared_ptr<TensorOperation> random_sharpness_op_2 = vision::RandomSharpness({});
  EXPECT_EQ(random_sharpness_op_2, nullptr);

  // Valid case: Use default input values
  std::shared_ptr<TensorOperation> random_sharpness_op_3 = vision::RandomSharpness();
  EXPECT_NE(random_sharpness_op_3, nullptr);

  // Failure case: Single degree value
  std::shared_ptr<TensorOperation> random_sharpness_op_4 = vision::RandomSharpness({0.1});
  EXPECT_EQ(random_sharpness_op_4, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_sharpness_op_1, random_sharpness_op_3});
  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, TestRandomSolarizeSucess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSolarizeSucess1.";

  // 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 objects for the tensor ops
  std::vector<uint8_t> threshold = {10, 100};
  std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
  EXPECT_NE(random_solarize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_solarize});
  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, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomSolarizeSucess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSolarizeSuccess2 with default parameters.";

  // 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 objects for the tensor ops
  std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::vision::RandomSolarize();
  EXPECT_NE(random_solarize, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_solarize});
  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, 10);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomSolarizeFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomSolarizeFail with invalid parameters.";

  std::vector<uint8_t> threshold = {13, 1};
  std::shared_ptr<TensorOperation> random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
  EXPECT_EQ(random_solarize, nullptr);

  threshold = {1, 2, 3};
  random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
  EXPECT_EQ(random_solarize, nullptr);

  threshold = {1};
  random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
  EXPECT_EQ(random_solarize, nullptr);

  threshold = {};
  random_solarize = mindspore::dataset::vision::RandomSolarize(threshold);
  EXPECT_EQ(random_solarize, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomVerticalFlipFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomVerticalFlipFail with invalid parameters.";

  // Create object for the tensor op
  // Invalid negative input
  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(-0.5);
  EXPECT_EQ(random_vertical_flip_op, nullptr);
  // Invalid >1 input
  random_vertical_flip_op = vision::RandomVerticalFlip(1.1);
  EXPECT_EQ(random_vertical_flip_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestResizeFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestResize with invalid parameters.";
  // negative resize value
  std::shared_ptr<TensorOperation> resize_op = mindspore::dataset::vision::Resize({30, -30});
  EXPECT_EQ(resize_op, nullptr);
  // zero resize value
  resize_op = mindspore::dataset::vision::Resize({0, 30});
  EXPECT_EQ(resize_op, nullptr);
  // resize with 3 values
  resize_op = mindspore::dataset::vision::Resize({30, 20, 10});
  EXPECT_EQ(resize_op, nullptr);
}

TEST_F(MindDataTestPipeline, TestResizeWithBBoxSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestResizeWithBBoxSuccess.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> resize_with_bbox_op = vision::ResizeWithBBox({30});
  EXPECT_NE(resize_with_bbox_op, nullptr);

  std::shared_ptr<TensorOperation> resize_with_bbox_op1 = vision::ResizeWithBBox({30, 30});
  EXPECT_NE(resize_with_bbox_op1, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({resize_with_bbox_op, resize_with_bbox_op1}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
  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, 3);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestResizeWithBBoxFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestResizeWithBBoxFail with invalid parameters.";
  // Testing negative resize value
  std::shared_ptr<TensorOperation> resize_with_bbox_op = vision::ResizeWithBBox({10, -10});
  EXPECT_EQ(resize_with_bbox_op, nullptr);
  // Testing negative resize value
  std::shared_ptr<TensorOperation> resize_with_bbox_op1 = vision::ResizeWithBBox({-10});
  EXPECT_EQ(resize_with_bbox_op1, nullptr);
  // Testinig zero resize value
  std::shared_ptr<TensorOperation> resize_with_bbox_op2 = vision::ResizeWithBBox({0, 10});
  EXPECT_EQ(resize_with_bbox_op2, nullptr);
  // Testing resize with 3 values
  std::shared_ptr<TensorOperation> resize_with_bbox_op3 = vision::ResizeWithBBox({10, 10, 10});
  EXPECT_EQ(resize_with_bbox_op3, nullptr);
}

TEST_F(MindDataTestPipeline, TestRandomVerticalFlipWithBBoxSuccess) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomVerticalFlipWithBBoxSuccess.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlipWithBBox(0.4);
  EXPECT_NE(random_vertical_flip_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({random_vertical_flip_op}, {"image", "bbox"}, {"image", "bbox"}, {"image", "bbox"});
  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, 3);
  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRandomVerticalFlipWithBBoxFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRandomVerticalFlipWithBBoxFail with invalid parameters.";
  // Create an VOC Dataset
  std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
  std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, true, SequentialSampler(0, 3));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  // Incorrect prob parameter.
  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlipWithBBox(-0.5);
  EXPECT_EQ(random_vertical_flip_op, nullptr);
  // Incorrect prob parameter.
  std::shared_ptr<TensorOperation> random_vertical_flip_op1 = vision::RandomVerticalFlipWithBBox(3.0);
  EXPECT_EQ(random_vertical_flip_op1, nullptr);
}

TEST_F(MindDataTestPipeline, TestResize1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestResize1 with single integer input.";
  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 6));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 4;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create resize object with single integer input
  std::shared_ptr<TensorOperation> resize_op = vision::Resize({30});
  EXPECT_NE(resize_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({resize_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, 24);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRescaleSucess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRescaleSucess1.";
  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, SequentialSampler(0, 1));
  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);

  auto image = row["image"];

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> rescale = mindspore::dataset::vision::Rescale(1.0, 0.0);
  EXPECT_NE(rescale, nullptr);

  // Convert to the same type
  std::shared_ptr<TensorOperation> type_cast = transforms::TypeCast("uint8");
  EXPECT_NE(type_cast, nullptr);

  ds = ds->Map({rescale, type_cast}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter1 = ds->CreateIterator();
  EXPECT_NE(iter1, nullptr);

  // Iterate the dataset and get each row1
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row1;
  iter1->GetNextRow(&row1);

  auto image1 = row1["image"];

  EXPECT_EQ(*image, *image1);

  // Manually terminate the pipeline
  iter1->Stop();
}

TEST_F(MindDataTestPipeline, TestRescaleSucess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRescaleSucess2 with different params.";
  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 1));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> rescale = mindspore::dataset::vision::Rescale(1.0 / 255, 1.0);
  EXPECT_NE(rescale, nullptr);

  ds = ds->Map({rescale}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 1);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestRescaleFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestRescaleFail with invalid params.";
  // incorrect negative rescale parameter
  std::shared_ptr<TensorOperation> rescale = mindspore::dataset::vision::Rescale(-1.0, 0.0);
  EXPECT_EQ(rescale, nullptr);
}

TEST_F(MindDataTestPipeline, TestSoftDvppDecodeRandomCropResizeJpegSuccess1) {
  MS_LOG(INFO)
    << "Doing MindDataTestPipeline-TestSoftDvppDecodeRandomCropResizeJpegSuccess1 with single integer input.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 4));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> soft_dvpp_decode_random_crop_resize_jpeg =
    vision::SoftDvppDecodeRandomCropResizeJpeg({500});
  EXPECT_NE(soft_dvpp_decode_random_crop_resize_jpeg, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({soft_dvpp_decode_random_crop_resize_jpeg}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    EXPECT_EQ(image->shape()[0] == 500 && image->shape()[1] == 500, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 4);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestSoftDvppDecodeRandomCropResizeJpegSuccess2) {
  MS_LOG(INFO)
    << "Doing MindDataTestPipeline-TestSoftDvppDecodeRandomCropResizeJpegSuccess2 with (height, width) input.";

  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 6));
  EXPECT_NE(ds, nullptr);

  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> soft_dvpp_decode_random_crop_resize_jpeg =
    vision::SoftDvppDecodeRandomCropResizeJpeg({500, 600}, {0.25, 0.75}, {0.5, 1.25}, 20);
  EXPECT_NE(soft_dvpp_decode_random_crop_resize_jpeg, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({soft_dvpp_decode_random_crop_resize_jpeg}, {"image"});
  EXPECT_NE(ds, nullptr);

  // Create an iterator over the result of the above dataset
  // This will trigger the creation of the Execution Tree and launch it.
  std::shared_ptr<Iterator> iter = ds->CreateIterator();
  EXPECT_NE(iter, nullptr);

  // Iterate the dataset and get each row
  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
  iter->GetNextRow(&row);

  uint64_t i = 0;
  while (row.size() != 0) {
    i++;
    auto image = row["image"];
    MS_LOG(INFO) << "Tensor image shape: " << image->shape();
    EXPECT_EQ(image->shape()[0] == 500 && image->shape()[1] == 600, true);
    iter->GetNextRow(&row);
  }

  EXPECT_EQ(i, 6);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestSoftDvppDecodeRandomCropResizeJpegFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeRandomCropResizeJpegFail with incorrect parameters.";

  // SoftDvppDecodeRandomCropResizeJpeg: size must only contain positive integers
  auto soft_dvpp_decode_random_crop_resize_jpeg1 = vision::SoftDvppDecodeRandomCropResizeJpeg({-500, 600});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg1, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: size must only contain positive integers
  auto soft_dvpp_decode_random_crop_resize_jpeg2 = vision::SoftDvppDecodeRandomCropResizeJpeg({-500});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg2, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: size must be a vector of one or two values
  auto soft_dvpp_decode_random_crop_resize_jpeg3 = vision::SoftDvppDecodeRandomCropResizeJpeg({500, 600, 700});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg3, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: scale must be greater than or equal to 0
  auto soft_dvpp_decode_random_crop_resize_jpeg4 = vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {-0.1, 0.9});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg4, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: scale must be in the format of (min, max)
  auto soft_dvpp_decode_random_crop_resize_jpeg5 = vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.6, 0.2});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg5, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: scale must be a vector of two values
  auto soft_dvpp_decode_random_crop_resize_jpeg6 = vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.6, 0.7});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg6, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: ratio must be greater than or equal to 0
  auto soft_dvpp_decode_random_crop_resize_jpeg7 =
    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {-0.2, 0.4});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg7, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: ratio must be in the format of (min, max)
  auto soft_dvpp_decode_random_crop_resize_jpeg8 =
    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {0.4, 0.2});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg8, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: ratio must be a vector of two values
  auto soft_dvpp_decode_random_crop_resize_jpeg9 =
    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {0.1, 0.2, 0.3});
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg9, nullptr);

  // SoftDvppDecodeRandomCropResizeJpeg: max_attempts must be greater than or equal to 1
  auto soft_dvpp_decode_random_crop_resize_jpeg10 =
    vision::SoftDvppDecodeRandomCropResizeJpeg({500}, {0.5, 0.9}, {0.1, 0.2}, 0);
  EXPECT_EQ(soft_dvpp_decode_random_crop_resize_jpeg10, nullptr);
}

TEST_F(MindDataTestPipeline, TestSoftDvppDecodeResizeJpegSuccess1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeResizeJpegSuccess1 with single integer input.";
  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 4));
  EXPECT_NE(ds, nullptr);

  // Create a Repeat operation on ds
  int32_t repeat_num = 3;
  ds = ds->Repeat(repeat_num);
  EXPECT_NE(ds, nullptr);

  // Create SoftDvppDecodeResizeJpeg object with single integer input
  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op = vision::SoftDvppDecodeResizeJpeg({1134});
  EXPECT_NE(soft_dvpp_decode_resize_jpeg_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({soft_dvpp_decode_resize_jpeg_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, 12);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestSoftDvppDecodeResizeJpegSuccess2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeResizeJpegSuccess2 with (height, width) input.";
  // Create an ImageFolder Dataset
  std::string folder_path = datasets_root_path_ + "/testPK/data/";
  std::shared_ptr<Dataset> ds = ImageFolder(folder_path, false, RandomSampler(false, 2));
  EXPECT_NE(ds, nullptr);

  // Create SoftDvppDecodeResizeJpeg object with single integer input
  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op = vision::SoftDvppDecodeResizeJpeg({100, 200});
  EXPECT_NE(soft_dvpp_decode_resize_jpeg_op, nullptr);

  // Create a Map operation on ds
  ds = ds->Map({soft_dvpp_decode_resize_jpeg_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, 2);

  // Manually terminate the pipeline
  iter->Stop();
}

TEST_F(MindDataTestPipeline, TestSoftDvppDecodeResizeJpegFail) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSoftDvppDecodeResizeJpegFail with incorrect size.";

  // CSoftDvppDecodeResizeJpeg: size must be a vector of one or two values
  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op1 = vision::SoftDvppDecodeResizeJpeg({});
  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op1, nullptr);

  // SoftDvppDecodeResizeJpeg: size must be a vector of one or two values
  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op2 = vision::SoftDvppDecodeResizeJpeg({1, 2, 3});
  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op2, nullptr);

  // SoftDvppDecodeResizeJpeg: size must only contain positive integers
  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op3 = vision::SoftDvppDecodeResizeJpeg({20, -20});
  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op3, nullptr);

  // SoftDvppDecodeResizeJpeg: size must only contain positive integers
  std::shared_ptr<TensorOperation> soft_dvpp_decode_resize_jpeg_op4 = vision::SoftDvppDecodeResizeJpeg({0});
  EXPECT_EQ(soft_dvpp_decode_resize_jpeg_op4, nullptr);
}

TEST_F(MindDataTestPipeline, TestUniformAugmentFail1) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail1 with invalid num_ops parameter.";
  // Create objects for the tensor ops
  std::shared_ptr<TensorOperation> random_crop_op = vision::RandomCrop({28, 28});
  EXPECT_NE(random_crop_op, nullptr);

  std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop({16, 16});
  EXPECT_NE(center_crop_op, nullptr);

  // UniformAug: num_ops must be greater than 0
  std::shared_ptr<TensorOperation> uniform_aug_op1 = vision::UniformAugment({random_crop_op, center_crop_op}, 0);
  EXPECT_EQ(uniform_aug_op1, nullptr);

  // UniformAug: num_ops must be greater than 0
  std::shared_ptr<TensorOperation> uniform_aug_op2 = vision::UniformAugment({random_crop_op, center_crop_op}, -1);
  EXPECT_EQ(uniform_aug_op2, nullptr);

  // UniformAug: num_ops is greater than transforms size
  std::shared_ptr<TensorOperation> uniform_aug_op3 = vision::UniformAugment({random_crop_op, center_crop_op}, 3);
  EXPECT_EQ(uniform_aug_op3, nullptr);
}

TEST_F(MindDataTestPipeline, TestUniformAugmentFail2) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugmentFail2 with invalid transform.";

  // UniformAug: transform ops must not be null
  std::shared_ptr<TensorOperation> uniform_aug_op1 = vision::UniformAugment({vision::RandomCrop({-28})}, 1);
  EXPECT_EQ(uniform_aug_op1, nullptr);

  // UniformAug: transform ops must not be null
  std::shared_ptr<TensorOperation> uniform_aug_op2 = vision::UniformAugment({vision::RandomCrop({28}), nullptr}, 2);
  EXPECT_EQ(uniform_aug_op2, nullptr);

  // UniformAug: transform list must not be empty
  std::shared_ptr<TensorOperation> uniform_aug_op3 = vision::UniformAugment({}, 1);
  EXPECT_EQ(uniform_aug_op3, nullptr);
}

TEST_F(MindDataTestPipeline, TestUniformAugWithOps) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestUniformAugWithOps.";

  // Create a Mnist Dataset
  std::string folder_path = datasets_root_path_ + "/testMnistData/";
  std::shared_ptr<Dataset> ds = Mnist(folder_path, "all", RandomSampler(false, 20));
  EXPECT_NE(ds, nullptr);

  // 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, TestVisionOperationName) {
  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVisionOperationName.";

  std::string correct_name;

  // Create object for the tensor op, and check the name
  std::shared_ptr<TensorOperation> random_vertical_flip_op = vision::RandomVerticalFlip(0.5);
  correct_name = "RandomVerticalFlip";
  EXPECT_EQ(correct_name, random_vertical_flip_op->Name());

  // Create object for the tensor op, and check the name
  std::shared_ptr<TensorOperation> softDvpp_decode_resize_jpeg_op = vision::SoftDvppDecodeResizeJpeg({1, 1});
  correct_name = "SoftDvppDecodeResizeJpeg";
  EXPECT_EQ(correct_name, softDvpp_decode_resize_jpeg_op->Name());
}