[feat][assistant][I3J6VH] add new data operator SBU

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

@@ -102,6 +102,7 @@
 #include "minddata/dataset/engine/ir/datasetops/source/text_file_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/manifest_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/minddata_node.h"
+#include "minddata/dataset/engine/ir/datasetops/source/sbu_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/tf_record_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/usps_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/voc_node.h"
@@ -1277,6 +1278,27 @@ RandomDataDataset::RandomDataDataset(const int32_t &total_rows, const std::vecto
   ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
 }
 
+SBUDataset::SBUDataset(const std::vector<char> &dataset_dir, bool decode, const std::shared_ptr<Sampler> &sampler,
+                       const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler ? sampler->Parse() : nullptr;
+  auto ds = std::make_shared<SBUNode>(CharToString(dataset_dir), decode, sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
+SBUDataset::SBUDataset(const std::vector<char> &dataset_dir, bool decode, const Sampler *sampler,
+                       const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler ? sampler->Parse() : nullptr;
+  auto ds = std::make_shared<SBUNode>(CharToString(dataset_dir), decode, sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
+SBUDataset::SBUDataset(const std::vector<char> &dataset_dir, bool decode, const std::reference_wrapper<Sampler> sampler,
+                       const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler.get().Parse();
+  auto ds = std::make_shared<SBUNode>(CharToString(dataset_dir), decode, sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
 TFRecordDataset::TFRecordDataset(const std::vector<std::vector<char>> &dataset_files, const std::vector<char> &schema,
                                  const std::vector<std::vector<char>> &columns_list, int64_t num_samples,
                                  ShuffleMode shuffle, int32_t num_shards, int32_t shard_id, bool shard_equal_rows,

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/ir/datasetops/source/bindings.cc

@@ -44,6 +44,7 @@
 #ifndef ENABLE_ANDROID
 #include "minddata/dataset/engine/ir/datasetops/source/manifest_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/minddata_node.h"
+#include "minddata/dataset/engine/ir/datasetops/source/sbu_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/tf_record_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/usps_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/voc_node.h"
@@ -264,6 +265,16 @@ PYBIND_REGISTER(RandomNode, 2, ([](const py::module *m) {
                     }));
                 }));
 
+PYBIND_REGISTER(SBUNode, 2, ([](const py::module *m) {
+                  (void)py::class_<SBUNode, DatasetNode, std::shared_ptr<SBUNode>>(*m, "SBUNode",
+                                                                                   "to create an SBUNode")
+                    .def(py::init([](std::string dataset_dir, bool decode, py::handle sampler) {
+                      auto sbu = std::make_shared<SBUNode>(dataset_dir, decode, toSamplerObj(sampler), nullptr);
+                      THROW_IF_ERROR(sbu->ValidateParams());
+                      return sbu;
+                    }));
+                }));
+
 PYBIND_REGISTER(TextFileNode, 2, ([](const py::module *m) {
                   (void)py::class_<TextFileNode, DatasetNode, std::shared_ptr<TextFileNode>>(*m, "TextFileNode",
                                                                                              "to create a TextFileNode")

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/CMakeLists.txt

@@ -10,6 +10,7 @@ set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
     cifar_op.cc
     random_data_op.cc
     celeba_op.cc
+    sbu_op.cc
     text_file_op.cc
     clue_op.cc
     csv_op.cc

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sbu_op.cc

@@ -0,0 +1,234 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "minddata/dataset/engine/datasetops/source/sbu_op.h"
+
+#include <algorithm>
+#include <fstream>
+#include <iomanip>
+#include <set>
+#include <utility>
+
+#include "debug/common.h"
+#include "minddata/dataset/core/config_manager.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
+#include "minddata/dataset/engine/db_connector.h"
+#include "minddata/dataset/engine/execution_tree.h"
+#include "utils/ms_utils.h"
+
+namespace mindspore {
+namespace dataset {
+SBUOp::SBUOp(const std::string &folder_path, bool decode, std::unique_ptr<DataSchema> data_schema,
+             std::shared_ptr<SamplerRT> sampler, int32_t num_workers, int32_t queue_size)
+    : MappableLeafOp(num_workers, queue_size, std::move(sampler)),
+      folder_path_(folder_path),
+      decode_(decode),
+      url_path_(""),
+      caption_path_(""),
+      image_folder_(""),
+      data_schema_(std::move(data_schema)) {
+  io_block_queues_.Init(num_workers, queue_size);
+}
+
+void SBUOp::Print(std::ostream &out, bool show_all) const {
+  if (!show_all) {
+    // Call the super class for displaying any common 1-liner info
+    ParallelOp::Print(out, show_all);
+    // Then show any custom derived-internal 1-liner info for this op
+    out << "\n";
+  } else {
+    // Call the super class for displaying any common detailed info
+    ParallelOp::Print(out, show_all);
+    // Then show any custom derived-internal stuff
+    out << "\nNumber of rows: " << num_rows_ << "\nSBU directory: " << folder_path_
+        << "\nDecode: " << (decode_ ? "yes" : "no") << "\n\n";
+  }
+}
+
+// Load 1 TensorRow (image, caption) using 1 SBUImageCaptionPair.
+Status SBUOp::LoadTensorRow(row_id_type row_id, TensorRow *trow) {
+  RETURN_UNEXPECTED_IF_NULL(trow);
+
+  SBUImageCaptionPair image_caption_pair = image_caption_pairs_[row_id];
+  Path path = image_caption_pair.first;
+
+  std::shared_ptr<Tensor> image, caption;
+  RETURN_IF_NOT_OK(ReadImageToTensor(path.ToString(), &image));
+  RETURN_IF_NOT_OK(Tensor::CreateScalar(image_caption_pair.second, &caption));
+
+  (*trow) = TensorRow(row_id, {std::move(image), std::move(caption)});
+  trow->setPath({path.ToString()});
+  return Status::OK();
+}
+
+Status SBUOp::ReadImageToTensor(const std::string &path, std::shared_ptr<Tensor> *tensor) {
+  RETURN_IF_NOT_OK(Tensor::CreateFromFile(path, tensor));
+  if (decode_ == true) {
+    Status rc = Decode(*tensor, tensor);
+    if (rc.IsError()) {
+      RETURN_STATUS_UNEXPECTED("Invalid data, failed to decode image: " + path);
+    }
+  }
+  return Status::OK();
+}
+
+Status SBUOp::ComputeColMap() {
+  // set the column name map (base class field)
+  if (column_name_id_map_.empty()) {
+    for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) {
+      column_name_id_map_[data_schema_->Column(i).Name()] = i;
+    }
+  } else {
+    MS_LOG(WARNING) << "Column name map is already set!";
+  }
+  return Status::OK();
+}
+
+Status SBUOp::CountTotalRows(const std::string &dir, int64_t *count) {
+  RETURN_UNEXPECTED_IF_NULL(count);
+  *count = 0;
+
+  auto schema = std::make_unique<DataSchema>();
+  RETURN_IF_NOT_OK(schema->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
+  RETURN_IF_NOT_OK(schema->AddColumn(ColDescriptor("caption", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
+
+  const int64_t num_samples = 0;
+  const int64_t start_index = 0;
+  auto sampler = std::make_shared<SequentialSamplerRT>(start_index, num_samples);
+
+  std::shared_ptr<ConfigManager> cfg = GlobalContext::config_manager();
+  int32_t num_workers = cfg->num_parallel_workers();
+  int32_t op_connector_size = cfg->op_connector_size();
+
+  // compat does not affect the count result, so set it to true default.
+  auto op = std::make_shared<SBUOp>(dir, true, std::move(schema), std::move(sampler), num_workers, op_connector_size);
+
+  // the logic of counting the number of samples
+  RETURN_IF_NOT_OK(op->ParseSBUData());
+  *count = op->image_caption_pairs_.size();
+
+  return Status::OK();
+}
+
+Status SBUOp::LaunchThreadsAndInitOp() {
+  if (tree_ == nullptr) {
+    RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set.");
+  }
+  RETURN_IF_NOT_OK(io_block_queues_.Register(tree_->AllTasks()));
+  RETURN_IF_NOT_OK(wait_for_workers_post_.Register(tree_->AllTasks()));
+  RETURN_IF_NOT_OK(
+    tree_->LaunchWorkers(num_workers_, std::bind(&SBUOp::WorkerEntry, this, std::placeholders::_1), "", id()));
+  TaskManager::FindMe()->Post();
+
+  RETURN_IF_NOT_OK(this->ParseSBUData());
+  RETURN_IF_NOT_OK(this->InitSampler());  // handle shake with sampler
+  return Status::OK();
+}
+
+Status SBUOp::ParseSBUData() {
+  const Path url_file_name("SBU_captioned_photo_dataset_urls.txt");
+  const Path caption_file_name("SBU_captioned_photo_dataset_captions.txt");
+  const Path image_folder_name("sbu_images");
+  auto real_folder_path = Common::GetRealPath(folder_path_);
+  CHECK_FAIL_RETURN_UNEXPECTED(real_folder_path.has_value(), "Get real path failed: " + folder_path_);
+  Path root_dir(real_folder_path.value());
+
+  url_path_ = root_dir / url_file_name;
+  CHECK_FAIL_RETURN_UNEXPECTED(url_path_.Exists() && !url_path_.IsDirectory(),
+                               "Invalid file, failed to find SBU url file: " + url_path_.ToString());
+  MS_LOG(INFO) << "SBU operator found url file " << url_path_.ToString() << ".";
+
+  caption_path_ = root_dir / caption_file_name;
+  CHECK_FAIL_RETURN_UNEXPECTED(caption_path_.Exists() && !caption_path_.IsDirectory(),
+                               "Invalid file, failed to find SBU caption file: " + caption_path_.ToString());
+  MS_LOG(INFO) << "SBU operator found caption file " << caption_path_.ToString() << ".";
+
+  image_folder_ = root_dir / image_folder_name;
+  CHECK_FAIL_RETURN_UNEXPECTED(image_folder_.Exists() && image_folder_.IsDirectory(),
+                               "Invalid folder, failed to find SBU image folder: " + image_folder_.ToString());
+  MS_LOG(INFO) << "SBU operator found image folder " << image_folder_.ToString() << ".";
+
+  std::ifstream url_file_reader;
+  std::ifstream caption_file_reader;
+
+  url_file_reader.open(url_path_.ToString(), std::ios::in);
+  caption_file_reader.open(caption_path_.ToString(), std::ios::in);
+
+  CHECK_FAIL_RETURN_UNEXPECTED(url_file_reader.is_open(),
+                               "Invalid file, failed to open SBU url file: " + url_path_.ToString());
+  CHECK_FAIL_RETURN_UNEXPECTED(caption_file_reader.is_open(),
+                               "Invalid file, failed to open SBU caption file: " + caption_path_.ToString());
+
+  Status rc = GetAvailablePairs(url_file_reader, caption_file_reader);
+  url_file_reader.close();
+  caption_file_reader.close();
+  if (rc.IsError()) {
+    return rc;
+  }
+
+  return Status::OK();
+}
+
+Status SBUOp::GetAvailablePairs(std::ifstream &url_file_reader, std::ifstream &caption_file_reader) {
+  std::string url_line;
+  std::string caption_line;
+  int64_t line_num = 0;
+
+  while (std::getline(url_file_reader, url_line) && std::getline(caption_file_reader, caption_line)) {
+    CHECK_FAIL_RETURN_UNEXPECTED(
+      (url_line.empty() && caption_line.empty()) || (!url_line.empty() && !caption_line.empty()),
+      "Invalid data, SBU url and caption file are mismatched: " + url_path_.ToString() + " and " +
+        caption_path_.ToString());
+    if (!url_line.empty() && !caption_line.empty()) {
+      line_num++;
+      RETURN_IF_NOT_OK(this->ParsePair(url_line, caption_line));
+    }
+  }
+
+  image_caption_pairs_.shrink_to_fit();
+
+  CHECK_FAIL_RETURN_UNEXPECTED(image_caption_pairs_.size() > 0, "No valid images in " + image_folder_.ToString());
+
+  // base field of RandomAccessOp
+  num_rows_ = image_caption_pairs_.size();
+
+  return Status::OK();
+}
+
+Status SBUOp::ParsePair(const std::string &url, const std::string &caption) {
+  std::string image_name = url.substr(23, std::string::npos);
+  RETURN_IF_NOT_OK(this->ReplaceAll(&image_name, "/", "_"));
+
+  Path image_path = image_folder_ / Path(image_name);
+  if (image_path.Exists() && !image_path.IsDirectory()) {
+    // rstrip caption
+    image_caption_pairs_.emplace_back(std::make_pair(image_path, caption.substr(0, caption.find_last_not_of(" ") + 1)));
+  }
+
+  return Status::OK();
+}
+
+Status SBUOp::ReplaceAll(std::string *str, const std::string &from, const std::string &to) {
+  size_t pos = 0;
+  while ((pos = str->find(from, pos)) != std::string::npos) {
+    str->replace(pos, from.length(), to);
+    pos += to.length();
+  }
+  return Status::OK();
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sbu_op.h

@@ -0,0 +1,125 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_SBU_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_SBU_OP_H_
+
+#include <algorithm>
+#include <map>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/core/tensor.h"
+#include "minddata/dataset/engine/data_schema.h"
+#include "minddata/dataset/engine/datasetops/parallel_op.h"
+#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
+#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
+#include "minddata/dataset/util/path.h"
+#include "minddata/dataset/util/queue.h"
+#include "minddata/dataset/util/status.h"
+#include "minddata/dataset/util/wait_post.h"
+
+namespace mindspore {
+namespace dataset {
+
+using SBUImageCaptionPair = std::pair<Path, std::string>;
+
+class SBUOp : public MappableLeafOp {
+ public:
+  // Constructor.
+  // @param const std::string &folder_path - dir directory of SBU data file.
+  // @param bool decode - whether to decode images.
+  // @param std::unique_ptr<DataSchema> data_schema - the schema of the SBU dataset.
+  // @param std::unique_ptr<Sampler> sampler - sampler tells SBUOp what to read.
+  // @param int32_t num_workers - number of workers reading images in parallel.
+  // @param int32_t queue_size - connector queue size.
+  SBUOp(const std::string &folder_path, bool decode, std::unique_ptr<DataSchema> data_schema,
+        std::shared_ptr<SamplerRT> sampler, int32_t num_workers, int32_t queue_size);
+
+  // Destructor.
+  ~SBUOp() = default;
+
+  // Op name getter.
+  // @return std::string - Name of the current Op.
+  std::string Name() const override { return "SBUOp"; }
+
+  // A print method typically used for debugging.
+  // @param std::ostream &out - out stream.
+  // @param bool show_all - whether to show all information.
+  void Print(std::ostream &out, bool show_all) const override;
+
+  // Function to count the number of samples in the SBU dataset.
+  // @param const std::string &dir - path to the SBU directory.
+  // @param int64_t *count - output arg that will hold the minimum of the actual dataset size and numSamples.
+  // @return Status - The status code returned.
+  static Status CountTotalRows(const std::string &dir, int64_t *count);
+
+ private:
+  // Load a tensor row according to a pair.
+  // @param row_id_type row_id - id for this tensor row.
+  // @param TensorRow row - image & label read into this tensor row.
+  // @return Status - The status code returned.
+  Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
+
+  // Private function for computing the assignment of the column name map.
+  // @return Status - The status code returned.
+  Status ComputeColMap() override;
+
+  // Called first when function is called.
+  // @return Status - The status code returned.
+  Status LaunchThreadsAndInitOp() override;
+
+  // @param const std::string &path - path to the image file.
+  // @param std::shared_ptr<Tensor> tensor - tensor to store image.
+  // @return Status - The status code returned.
+  Status ReadImageToTensor(const std::string &path, std::shared_ptr<Tensor> *tensor);
+
+  // Parse SBU data file.
+  // @return Status - The status code returned.
+  Status ParseSBUData();
+
+  // Get available image-caption pairs.
+  // @param std::ifstream &url_file_reader - url file reader.
+  // @param std::ifstream &caption_file_reader - caption file reader.
+  // @return Status - The status code returned.
+  Status GetAvailablePairs(std::ifstream &url_file_reader, std::ifstream &caption_file_reader);
+
+  // Parse path-caption pair.
+  // @param const std::string &url - image url.
+  // @param const std::string &caption - caption.
+  // @return Status - The status code returned.
+  Status ParsePair(const std::string &url, const std::string &caption);
+
+  // A util for string replace.
+  // @param std::string *str - string to be replaces.
+  // @param const std::string &from - string from.
+  // @param const std::string &to - string to.
+  // @return Status - The status code returned.
+  Status ReplaceAll(std::string *str, const std::string &from, const std::string &to);
+
+  std::string folder_path_;  // directory of data files
+  const bool decode_;
+  std::unique_ptr<DataSchema> data_schema_;
+
+  Path url_path_;
+  Path caption_path_;
+  Path image_folder_;
+  std::vector<SBUImageCaptionPair> image_caption_pairs_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_SBU_OP_H_

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/dataset_node.h

@@ -90,6 +90,7 @@ constexpr char kManifestNode[] = "ManifestDataset";
 constexpr char kMindDataNode[] = "MindDataDataset";
 constexpr char kMnistNode[] = "MnistDataset";
 constexpr char kRandomNode[] = "RandomDataset";
+constexpr char kSBUNode[] = "SBUDataset";
 constexpr char kTextFileNode[] = "TextFileDataset";
 constexpr char kTFRecordNode[] = "TFRecordDataset";
 constexpr char kUSPSNode[] = "USPSDataset";

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/CMakeLists.txt

@@ -18,6 +18,7 @@ set(DATASET_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES
         minddata_node.cc
         mnist_node.cc
         random_node.cc
+        sbu_node.cc
         text_file_node.cc
         tf_record_node.cc
         usps_node.cc

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/sbu_node.cc

@@ -0,0 +1,123 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "minddata/dataset/engine/ir/datasetops/source/sbu_node.h"
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/engine/datasetops/source/sbu_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+
+SBUNode::SBUNode(const std::string &dataset_dir, bool decode, const std::shared_ptr<SamplerObj> &sampler,
+                 const std::shared_ptr<DatasetCache> &cache)
+    : MappableSourceNode(std::move(cache)), dataset_dir_(dataset_dir), decode_(decode), sampler_(sampler) {}
+
+std::shared_ptr<DatasetNode> SBUNode::Copy() {
+  std::shared_ptr<SamplerObj> sampler = (sampler_ == nullptr) ? nullptr : sampler_->SamplerCopy();
+  auto node = std::make_shared<SBUNode>(dataset_dir_, decode_, sampler, cache_);
+  return node;
+}
+
+void SBUNode::Print(std::ostream &out) const {
+  out << (Name() + "(dataset dir: " + dataset_dir_ + ", decode: " + (decode_ ? "true" : "false") +
+          ", cache: " + ((cache_ != nullptr) ? "true" : "false") + ")");
+}
+
+Status SBUNode::ValidateParams() {
+  RETURN_IF_NOT_OK(DatasetNode::ValidateParams());
+  RETURN_IF_NOT_OK(ValidateDatasetDirParam("SBUNode", dataset_dir_));
+  RETURN_IF_NOT_OK(ValidateDatasetSampler("SBUNode", sampler_));
+
+  Path root_dir(dataset_dir_);
+
+  Path url_path = root_dir / Path("SBU_captioned_photo_dataset_urls.txt");
+  Path caption_path = root_dir / Path("SBU_captioned_photo_dataset_captions.txt");
+  Path image_path = root_dir / Path("sbu_images");
+
+  RETURN_IF_NOT_OK(ValidateDatasetFilesParam("SBUNode", {url_path.ToString()}));
+  RETURN_IF_NOT_OK(ValidateDatasetFilesParam("SBUNode", {caption_path.ToString()}));
+  RETURN_IF_NOT_OK(ValidateDatasetDirParam("SBUNode", {image_path.ToString()}));
+
+  return Status::OK();
+}
+
+Status SBUNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops) {
+  // Do internal Schema generation.
+  auto schema = std::make_unique<DataSchema>();
+  RETURN_IF_NOT_OK(schema->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
+  RETURN_IF_NOT_OK(schema->AddColumn(ColDescriptor("caption", DataType(DataType::DE_UINT8), TensorImpl::kFlexible, 1)));
+  std::shared_ptr<SamplerRT> sampler_rt = nullptr;
+  RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt));
+
+  auto op = std::make_shared<SBUOp>(dataset_dir_, decode_, std::move(schema), std::move(sampler_rt), num_workers_,
+                                    connector_que_size_);
+  op->set_total_repeats(GetTotalRepeats());
+  op->set_num_repeats_per_epoch(GetNumRepeatsPerEpoch());
+  node_ops->push_back(op);
+
+  return Status::OK();
+}
+
+// Get the shard id of node
+Status SBUNode::GetShardId(int32_t *shard_id) {
+  *shard_id = sampler_->ShardId();
+
+  return Status::OK();
+}
+
+// Get Dataset size
+Status SBUNode::GetDatasetSize(const std::shared_ptr<DatasetSizeGetter> &size_getter, bool estimate,
+                               int64_t *dataset_size) {
+  if (dataset_size_ > 0) {
+    *dataset_size = dataset_size_;
+    return Status::OK();
+  }
+  int64_t num_rows, sample_size;
+  RETURN_IF_NOT_OK(SBUOp::CountTotalRows(dataset_dir_, &num_rows));
+  std::shared_ptr<SamplerRT> sampler_rt = nullptr;
+  RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt));
+  sample_size = sampler_rt->CalculateNumSamples(num_rows);
+  if (sample_size == -1) {
+    RETURN_IF_NOT_OK(size_getter->DryRun(shared_from_this(), &sample_size));
+  }
+  *dataset_size = sample_size;
+  dataset_size_ = *dataset_size;
+  return Status::OK();
+}
+
+Status SBUNode::to_json(nlohmann::json *out_json) {
+  nlohmann::json args, sampler_args;
+  RETURN_IF_NOT_OK(sampler_->to_json(&sampler_args));
+  args["sampler"] = sampler_args;
+  args["num_parallel_workers"] = num_workers_;
+  args["dataset_dir"] = dataset_dir_;
+  args["decode"] = decode_;
+  if (cache_ != nullptr) {
+    nlohmann::json cache_args;
+    RETURN_IF_NOT_OK(cache_->to_json(&cache_args));
+    args["cache"] = cache_args;
+  }
+  *out_json = args;
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/sbu_node.h

@@ -0,0 +1,97 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_SBU_NODE_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_SBU_NODE_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "minddata/dataset/engine/ir/datasetops/dataset_node.h"
+
+namespace mindspore {
+namespace dataset {
+
+class SBUNode : public MappableSourceNode {
+ public:
+  /// \brief Constructor.
+  SBUNode(const std::string &dataset_dir, bool decode, const std::shared_ptr<SamplerObj> &sampler,
+          const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Destructor.
+  ~SBUNode() = default;
+
+  /// \brief Node name getter.
+  /// \return Name of the current node.
+  std::string Name() const override { return kSBUNode; }
+
+  /// \brief Print the description.
+  /// \param out - The output stream to write output to.
+  void Print(std::ostream &out) const override;
+
+  /// \brief Copy the node to a new object.
+  /// \return A shared pointer to the new copy.
+  std::shared_ptr<DatasetNode> Copy() override;
+
+  /// \brief a base class override function to create the required runtime dataset op objects for this class.
+  /// \param node_ops - A vector containing shared pointer to the Dataset Ops that this object will create.
+  /// \return Status Status::OK() if build successfully.
+  Status Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops) override;
+
+  /// \brief Parameters validation.
+  /// \return Status Status::OK() if all the parameters are valid.
+  Status ValidateParams() override;
+
+  /// \brief Get the shard id of node.
+  /// \param[in] shard_id The shard id.
+  /// \return Status Status::OK() if get shard id successfully.
+  Status GetShardId(int32_t *shard_id) override;
+
+  /// \brief Base-class override for GetDatasetSize.
+  /// \param[in] size_getter Shared pointer to DatasetSizeGetter.
+  /// \param[in] estimate This is only supported by some of the ops and it's used to speed up the process of getting
+  ///     dataset size at the expense of accuracy.
+  /// \param[out] dataset_size the size of the dataset.
+  /// \return Status of the function.
+  Status GetDatasetSize(const std::shared_ptr<DatasetSizeGetter> &size_getter, bool estimate,
+                        int64_t *dataset_size) override;
+
+  /// \brief Getter functions.
+  const std::string &DatasetDir() const { return dataset_dir_; }
+  bool Decode() const { return decode_; }
+
+  /// \brief Get the arguments of node.
+  /// \param[out] out_json JSON string of all attributes.
+  /// \return Status of the function.
+  Status to_json(nlohmann::json *out_json) override;
+
+  /// \brief Sampler getter.
+  /// \return SamplerObj of the current node.
+  std::shared_ptr<SamplerObj> Sampler() override { return sampler_; }
+
+  /// \brief Sampler setter.
+  void SetSampler(std::shared_ptr<SamplerObj> sampler) override { sampler_ = sampler; }
+
+ private:
+  std::string dataset_dir_;
+  bool decode_;
+  std::shared_ptr<SamplerObj> sampler_;
+};
+
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_SBU_NODE_H_

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

@@ -2332,6 +2332,78 @@ std::shared_ptr<RandomDataDataset> RandomData(const int32_t &total_rows = 0, con
   return ds;
 }
 
+/// \class SBUDataset
+/// \brief A source dataset that reads and parses SBU dataset.
+class SBUDataset : public Dataset {
+ public:
+  /// \brief Constructor of SBUDataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] decode Decode the images after reading.
+  /// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset. If sampler is not
+  ///     given, a `RandomSampler` will be used to randomly iterate the entire dataset.
+  /// \param[in] cache Tensor cache to use.
+  explicit SBUDataset(const std::vector<char> &dataset_dir, bool decode, const std::shared_ptr<Sampler> &sampler,
+                      const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Constructor of SBUDataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] decode Decode the images after reading.
+  /// \param[in] sampler Raw pointer to a sampler object used to choose samples from the dataset.
+  /// \param[in] cache Tensor cache to use.
+  explicit SBUDataset(const std::vector<char> &dataset_dir, bool decode, const Sampler *sampler,
+                      const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Constructor of SBUDataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] decode Decode the images after reading.
+  /// \param[in] sampler Sampler object used to choose samples from the dataset.
+  /// \param[in] cache Tensor cache to use.
+  explicit SBUDataset(const std::vector<char> &dataset_dir, bool decode, const std::reference_wrapper<Sampler> sampler,
+                      const std::shared_ptr<DatasetCache> &cache);
+
+  /// Destructor of SBUDataset.
+  ~SBUDataset() = default;
+};
+
+/// \brief Function to create a SBUDataset.
+/// \notes The generated dataset has two columns ["image", "caption"].
+/// \param[in] dataset_dir Path to the root directory that contains the dataset.
+/// \param[in] decode Decode the images after reading (default=false).
+/// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset. If sampler is not
+///     given, a `RandomSampler` will be used to randomly iterate the entire dataset (default = RandomSampler()).
+/// \param[in] cache Tensor cache to use (default=nullptr which means no cache is used).
+/// \return Shared pointer to the current SBUDataset.
+inline std::shared_ptr<SBUDataset> SBU(const std::string &dataset_dir, bool decode = false,
+                                       const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
+                                       const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<SBUDataset>(StringToChar(dataset_dir), decode, sampler, cache);
+}
+
+/// \brief Function to create a SBUDataset.
+/// \notes The generated dataset has two columns ["image", "caption"].
+/// \param[in] dataset_dir Path to the root directory that contains the dataset.
+/// \param[in] decode Decode the images after reading.
+/// \param[in] sampler Raw pointer to a sampler object used to choose samples from the dataset.
+/// \param[in] cache Tensor cache to use (default=nullptr which means no cache is used).
+/// \return Shared pointer to the current SBUDataset.
+inline std::shared_ptr<SBUDataset> SBU(const std::string &dataset_dir, bool decode, const Sampler *sampler,
+                                       const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<SBUDataset>(StringToChar(dataset_dir), decode, sampler, cache);
+}
+
+/// \brief Function to create a SBUDataset.
+/// \notes The generated dataset has two columns ["image", "caption"].
+/// \param[in] dataset_dir Path to the root directory that contains the dataset.
+/// \param[in] decode Decode the images after reading.
+/// \param[in] sampler Sampler object used to choose samples from the dataset.
+/// \param[in] cache Tensor cache to use (default=nullptr which means no cache is used).
+/// \return Shared pointer to the current SBUDataset.
+inline std::shared_ptr<SBUDataset> SBU(const std::string &dataset_dir, bool decode,
+                                       const std::reference_wrapper<Sampler> sampler,
+                                       const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<SBUDataset>(StringToChar(dataset_dir), decode, sampler, cache);
+}
+
 /// \class TextFileDataset
 /// \brief A source dataset that reads and parses datasets stored on disk in text format.
 class TextFileDataset : public Dataset {

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

@@ -45,6 +45,7 @@ class Sampler : std::enable_shared_from_this<Sampler> {
   friend class MindDataDataset;
   friend class MnistDataset;
   friend class RandomDataDataset;
+  friend class SBUDataset;
   friend class TextFileDataset;
   friend class TFRecordDataset;
   friend class USPSDataset;

mindspore/dataset/engine/datasets.py

@@ -64,7 +64,8 @@ from .validators import check_batch, check_shuffle, check_map, check_filter, che
     check_add_column, check_textfiledataset, check_concat, check_random_dataset, check_split, \
     check_bucket_batch_by_length, check_cluedataset, check_save, check_csvdataset, check_paddeddataset, \
     check_tuple_iterator, check_dict_iterator, check_schema, check_to_device_send, check_flickr_dataset, \
-    check_sb_dataset, check_flowers102dataset, check_cityscapes_dataset, check_usps_dataset, check_div2k_dataset
+    check_sb_dataset, check_flowers102dataset, check_cityscapes_dataset, check_usps_dataset, check_div2k_dataset, \
+    check_sbu_dataset
 from ..core.config import get_callback_timeout, _init_device_info, get_enable_shared_mem, get_num_parallel_workers, \
     get_prefetch_size
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
@@ -5612,6 +5613,120 @@ class CSVDataset(SourceDataset):
                            self.num_samples, self.shuffle_flag, self.num_shards, self.shard_id)
 
 
+class SBUDataset(MappableDataset):
+    """
+    A source dataset for reading and parsing the SBU dataset.
+
+    The generated dataset has two columns :py:obj:`[image, caption]`.
+    The tensor of column :py:obj:`image` is of the uint8 type.
+    The tensor of column :py:obj:`caption` is of the string type.
+
+    Args:
+        dataset_dir (str): Path to the root directory that contains the dataset.
+        decode (bool, optional): Decode the images after reading (default=False).
+        num_samples (int, optional): The number of images to be included in the dataset
+            (default=None, will read all images).
+        num_parallel_workers (int, optional): Number of workers to read the data
+            (default=None, will use value set in the config).
+        shuffle (bool, optional): Whether or not to perform shuffle on the dataset
+            (default=None, expected order behavior shown in the table).
+        sampler (Sampler, optional): Object used to choose samples from the
+            dataset (default=None, expected order behavior shown in the table).
+        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
+            When this argument is specified, `num_samples` reflects the max sample number of per shard.
+        shard_id (int, optional): The shard ID within `num_shards` (default=None). This
+            argument can only be specified when `num_shards` is also specified.
+        cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
+            (default=None, which means no cache is used).
+
+    Raises:
+        RuntimeError: If dataset_dir does not contain data files.
+        RuntimeError: If num_parallel_workers exceeds the max thread numbers.
+        RuntimeError: If sampler and shuffle are specified at the same time.
+        RuntimeError: If sampler and sharding are specified at the same time.
+        RuntimeError: If num_shards is specified but shard_id is None.
+        RuntimeError: If shard_id is specified but num_shards is None.
+        ValueError: If shard_id is invalid (< 0 or >= num_shards).
+
+    Note:
+        - This dataset can take in a sampler. 'sampler' and 'shuffle' are mutually exclusive.
+          The table below shows what input arguments are allowed and their expected behavior.
+
+    .. list-table:: Expected Order Behavior of Using 'sampler' and 'shuffle'
+       :widths: 25 25 50
+       :header-rows: 1
+
+       * - Parameter 'sampler'
+         - Parameter 'shuffle'
+         - Expected Order Behavior
+       * - None
+         - None
+         - random order
+       * - None
+         - True
+         - random order
+       * - None
+         - False
+         - sequential order
+       * - Sampler object
+         - None
+         - order defined by sampler
+       * - Sampler object
+         - True
+         - not allowed
+       * - Sampler object
+         - False
+         - not allowed
+
+    Examples:
+        >>> sbu_dataset_dir = "/path/to/sbu_dataset_directory"
+        >>> # Read 3 samples from SBU dataset
+        >>> dataset = ds.SBUDataset(dataset_dir=sbu_dataset_dir, num_samples=3)
+
+    About SBU dataset:
+
+    SBU dataset is a large captioned photo collection.
+    It contains one million images with associated visually relevant captions.
+
+    You should manually download the images using official download.m by replacing 'urls{i}(24, end)' with
+    'urls{i}(24:1:end)' and keep the directory as below.
+
+    .. code-block::
+
+        .
+        └─ dataset_dir
+           ├── SBU_captioned_photo_dataset_captions.txt
+           ├── SBU_captioned_photo_dataset_urls.txt
+           └── sbu_images
+               ├── m_3326_3596303505_3ce4c20529.jpg
+               ├── ......
+               └── m_2522_4182181099_c3c23ab1cc.jpg
+
+    Citation:
+
+    .. code-block::
+
+        @inproceedings{Ordonez:2011:im2text,
+          Author    = {Vicente Ordonez and Girish Kulkarni and Tamara L. Berg},
+          Title     = {Im2Text: Describing Images Using 1 Million Captioned Photographs},
+          Booktitle = {Neural Information Processing Systems ({NIPS})},
+          Year      = {2011},
+        }
+    """
+
+    @check_sbu_dataset
+    def __init__(self, dataset_dir, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False,
+                 sampler=None, num_shards=None, shard_id=None, cache=None):
+        super().__init__(num_parallel_workers=num_parallel_workers, sampler=sampler, num_samples=num_samples,
+                         shuffle=shuffle, num_shards=num_shards, shard_id=shard_id, cache=cache)
+
+        self.dataset_dir = dataset_dir
+        self.decode = replace_none(decode, False)
+
+    def parse(self, children=None):
+        return cde.SBUNode(self.dataset_dir, self.decode, self.sampler)
+
+
 class _Flowers102Dataset:
     """
     Mainly for loading Flowers102 Dataset, and return one row each time.

mindspore/dataset/engine/validators.py

@@ -122,6 +122,36 @@ def check_manifestdataset(method):
     return new_method
 
 
+def check_sbu_dataset(method):
+    """A wrapper that wraps a parameter checker around the original Dataset(SBUDataset)."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        _, param_dict = parse_user_args(method, *args, **kwargs)
+
+        nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
+        nreq_param_bool = ['shuffle', 'decode']
+
+        dataset_dir = param_dict.get('dataset_dir')
+        check_dir(dataset_dir)
+
+        check_file(os.path.join(dataset_dir, "SBU_captioned_photo_dataset_urls.txt"))
+        check_file(os.path.join(dataset_dir, "SBU_captioned_photo_dataset_captions.txt"))
+        check_dir(os.path.join(dataset_dir, "sbu_images"))
+
+        validate_dataset_param_value(nreq_param_int, param_dict, int)
+        validate_dataset_param_value(nreq_param_bool, param_dict, bool)
+
+        check_sampler_shuffle_shard_options(param_dict)
+
+        cache = param_dict.get('cache')
+        check_cache_option(cache)
+
+        return method(self, *args, **kwargs)
+
+    return new_method
+
+
 def check_tfrecorddataset(method):
     """A wrapper that wraps a parameter checker around the original Dataset(TFRecordDataset)."""
 

tests/ut/cpp/dataset/CMakeLists.txt

@@ -30,6 +30,7 @@ SET(DE_UT_SRCS
         c_api_dataset_ops_test.cc
         c_api_dataset_randomdata_test.cc
         c_api_dataset_save.cc
+        c_api_dataset_sbu_test.cc
         c_api_dataset_textfile_test.cc
         c_api_dataset_tfrecord_test.cc
         c_api_dataset_usps_test.cc

tests/ut/cpp/dataset/c_api_dataset_sbu_test.cc

@@ -0,0 +1,188 @@
+/**
+ * Copyright 2021 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/dataset/datasets.h"
+
+using namespace mindspore::dataset;
+using mindspore::dataset::DataType;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::TensorShape;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+TEST_F(MindDataTestPipeline, TestSBUDataset) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSBUDataset.";
+
+  // Create a SBU Dataset
+  std::string folder_path = datasets_root_path_ + "/testSBUDataset/";
+  std::shared_ptr<Dataset> ds = SBU(folder_path, true, std::make_shared<RandomSampler>(false, 5));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+
+  EXPECT_NE(row.find("image"), row.end());
+  EXPECT_NE(row.find("caption"), row.end());
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    ASSERT_OK(iter->GetNextRow(&row));
+  }
+
+  EXPECT_EQ(i, 5);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestSBUDatasetWithPipeline) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSBUDatasetWithPipeline.";
+
+  // Create two SBU Dataset
+  std::string folder_path = datasets_root_path_ + "/testSBUDataset/";
+  std::shared_ptr<Dataset> ds1 = SBU(folder_path, true, std::make_shared<RandomSampler>(false, 5));
+  std::shared_ptr<Dataset> ds2 = SBU(folder_path, true, std::make_shared<RandomSampler>(false, 5));
+  EXPECT_NE(ds1, nullptr);
+  EXPECT_NE(ds2, nullptr);
+
+  // Create two Repeat operation on ds
+  int32_t repeat_num = 1;
+  ds1 = ds1->Repeat(repeat_num);
+  EXPECT_NE(ds1, nullptr);
+  repeat_num = 1;
+  ds2 = ds2->Repeat(repeat_num);
+  EXPECT_NE(ds2, nullptr);
+
+  // Create two Project operation on ds
+  std::vector<std::string> column_project = {"image", "caption"};
+  ds1 = ds1->Project(column_project);
+  EXPECT_NE(ds1, nullptr);
+  ds2 = ds2->Project(column_project);
+  EXPECT_NE(ds2, nullptr);
+
+  // Create a Concat operation on the ds
+  ds1 = ds1->Concat({ds2});
+  EXPECT_NE(ds1, 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 = ds1->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+
+  EXPECT_NE(row.find("image"), row.end());
+  EXPECT_NE(row.find("caption"), row.end());
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    ASSERT_OK(iter->GetNextRow(&row));
+  }
+
+  EXPECT_EQ(i, 10);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+TEST_F(MindDataTestPipeline, TestGetSBUDatasetSize) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestGetSBUDatasetSize.";
+
+  // Create a SBU Dataset
+  std::string folder_path = datasets_root_path_ + "/testSBUDataset/";
+  std::shared_ptr<Dataset> ds = SBU(folder_path, true);
+  EXPECT_NE(ds, nullptr);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 5);
+}
+
+TEST_F(MindDataTestPipeline, TestSBUDatasetGetters) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSBUDatasetGetters.";
+
+  // Create a SBU Dataset
+  std::string folder_path = datasets_root_path_ + "/testSBUDataset/";
+  std::shared_ptr<Dataset> ds = SBU(folder_path, true);
+  EXPECT_NE(ds, nullptr);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 5);
+  std::vector<DataType> types = ToDETypes(ds->GetOutputTypes());
+  std::vector<TensorShape> shapes = ToTensorShapeVec(ds->GetOutputShapes());
+  std::vector<std::string> column_names = {"image", "caption"};
+  EXPECT_EQ(types.size(), 2);
+  EXPECT_EQ(types[0].ToString(), "uint8");
+  EXPECT_EQ(types[1].ToString(), "string");
+
+  EXPECT_EQ(ds->GetBatchSize(), 1);
+  EXPECT_EQ(ds->GetRepeatCount(), 1);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 5);
+  EXPECT_EQ(ToDETypes(ds->GetOutputTypes()), types);
+  EXPECT_EQ(ToTensorShapeVec(ds->GetOutputShapes()), shapes);
+  EXPECT_EQ(ds->GetNumClasses(), -1);
+
+  EXPECT_EQ(ds->GetColumnNames(), column_names);
+  EXPECT_EQ(ds->GetDatasetSize(), 5);
+  EXPECT_EQ(ToDETypes(ds->GetOutputTypes()), types);
+  EXPECT_EQ(ToTensorShapeVec(ds->GetOutputShapes()), shapes);
+  EXPECT_EQ(ds->GetBatchSize(), 1);
+  EXPECT_EQ(ds->GetRepeatCount(), 1);
+  EXPECT_EQ(ds->GetNumClasses(), -1);
+  EXPECT_EQ(ds->GetDatasetSize(), 5);
+}
+
+TEST_F(MindDataTestPipeline, TestSBUDatasetFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSBUDatasetFail.";
+
+  // Create a SBU Dataset
+  std::shared_ptr<Dataset> ds = SBU("", true, std::make_shared<RandomSampler>(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  // Expect failure: invalid SBU input
+  EXPECT_EQ(iter, nullptr);
+}
+
+TEST_F(MindDataTestPipeline, TestSBUDatasetWithNullSamplerFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestSBUDatasetWithNullSamplerFail.";
+
+  // Create a SBU Dataset
+  std::string folder_path = datasets_root_path_ + "/testSBUDataset/";
+  std::shared_ptr<Dataset> ds = SBU(folder_path, true, nullptr);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  // Expect failure: invalid SBU input, sampler cannot be nullptr
+  EXPECT_EQ(iter, nullptr);
+}

tests/ut/data/dataset/testSBUDataset/SBU_captioned_photo_dataset_captions.txt

@@ -0,0 +1,10 @@
+ This is 0
+ This is 1.
+ This is 2
+ This is 3.
+ This is 4
+ This is 5.
+ This is 6
+ This is 7.
+ This is 8
+ This is 9.

tests/ut/data/dataset/testSBUDataset/SBU_captioned_photo_dataset_urls.txt

@@ -0,0 +1,10 @@
+http://123456.123456.123/123/123456789_123456780.jpg
+http://123456.123456.123/123/123456789_123456781.jpg
+http://123456.123456.123/123/123456789_123456782.jpg
+http://123456.123456.123/123/123456789_123456783.jpg
+http://123456.123456.123/123/123456789_123456784.jpg
+http://123456.123456.123/123/123456789_123456785.jpg
+http://123456.123456.123/123/123456789_123456786.jpg
+http://123456.123456.123/123/123456789_123456787.jpg
+http://123456.123456.123/123/123456789_123456788.jpg
+http://123456.123456.123/123/123456789_123456789.jpg

tests/ut/python/dataset/test_datasets_sbu.py

@@ -0,0 +1,303 @@
+# Copyright 2021 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.
+# ==============================================================================
+"""
+Test USPS dataset operators
+"""
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+from PIL import Image
+
+import mindspore.dataset as ds
+import mindspore.dataset.vision.c_transforms as vision
+from mindspore import log as logger
+
+DATA_DIR = "../data/dataset/testSBUDataset"
+WRONG_DIR = "../data/dataset/testMnistData"
+
+
+def load_sbu(path):
+    """
+    load SBU data
+    """
+    images = []
+    captions = []
+
+    file1 = os.path.realpath(os.path.join(path, 'SBU_captioned_photo_dataset_urls.txt'))
+    file2 = os.path.realpath(os.path.join(path, 'SBU_captioned_photo_dataset_captions.txt'))
+
+    for line1, line2 in zip(open(file1), open(file2)):
+        url = line1.rstrip()
+        image = url[23:].replace("/", "_")
+        filename = os.path.join(path, 'sbu_images', image)
+        if os.path.exists(filename):
+            caption = line2.rstrip()
+            images.append(np.asarray(Image.open(filename).convert('RGB')).astype(np.uint8))
+            captions.append(caption)
+    return images, captions
+
+
+def visualize_dataset(images, captions):
+    """
+    Helper function to visualize the dataset samples
+    """
+    num_samples = len(images)
+    for i in range(num_samples):
+        plt.subplot(1, num_samples, i + 1)
+        plt.imshow(images[i].squeeze())
+        plt.title(captions[i])
+    plt.show()
+
+
+def test_sbu_content_check():
+    """
+    Validate SBUDataset image readings
+    """
+    logger.info("Test SBUDataset Op with content check")
+    dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=50, shuffle=False)
+    images, captions = load_sbu(DATA_DIR)
+    num_iter = 0
+    # in this example, each dictionary has keys "image" and "caption"
+    for i, data in enumerate(dataset.create_dict_iterator(num_epochs=1, output_numpy=True)):
+        assert data["image"].shape == images[i].shape
+        assert data["caption"].item().decode("utf8") == captions[i]
+        num_iter += 1
+    assert num_iter == 5
+
+
+def test_sbu_case():
+    """
+    Validate SBUDataset cases
+    """
+    dataset = ds.SBUDataset(DATA_DIR, decode=True)
+
+    dataset = dataset.map(operations=[vision.Resize((224, 224))], input_columns=["image"])
+    repeat_num = 4
+    dataset = dataset.repeat(repeat_num)
+    batch_size = 2
+    dataset = dataset.batch(batch_size, drop_remainder=True, pad_info={})
+
+    num = 0
+    for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num += 1
+    # 4 x 5 / 2
+    assert num == 10
+
+    dataset = ds.SBUDataset(DATA_DIR, decode=False)
+
+    dataset = dataset.map(operations=[vision.Decode(rgb=True), vision.Resize((224, 224))], input_columns=["image"])
+    repeat_num = 4
+    dataset = dataset.repeat(repeat_num)
+    batch_size = 2
+    dataset = dataset.batch(batch_size, drop_remainder=True, pad_info={})
+
+    num = 0
+    for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num += 1
+    # 4 x 5 / 2
+    assert num == 10
+
+
+def test_sbu_basic():
+    """
+    Validate SBUDataset
+    """
+    logger.info("Test SBUDataset Op")
+
+    # case 1: test loading whole dataset
+    dataset = ds.SBUDataset(DATA_DIR, decode=True)
+    num_iter = 0
+    for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter += 1
+    assert num_iter == 5
+
+
+    # case 2: test num_samples
+    dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
+    num_iter = 0
+    for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter += 1
+    assert num_iter == 5
+
+    # case 3: test repeat
+    dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
+    dataset = dataset.repeat(5)
+    num_iter = 0
+    for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter += 1
+    assert num_iter == 25
+
+    # case 4: test batch
+    dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
+    assert dataset.get_dataset_size() == 5
+    assert dataset.get_batch_size() == 1
+
+    num_iter = 0
+    for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter += 1
+    assert num_iter == 5
+
+    # case 5: test get_class_indexing
+    dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
+    assert dataset.get_class_indexing() == {}
+
+    # case 6: test get_col_names
+    dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
+    assert dataset.get_col_names() == ["image", "caption"]
+
+
+def test_sbu_sequential_sampler():
+    """
+    Test SBUDataset with SequentialSampler
+    """
+    logger.info("Test SBUDataset Op with SequentialSampler")
+    num_samples = 5
+    sampler = ds.SequentialSampler(num_samples=num_samples)
+    dataset_1 = ds.SBUDataset(DATA_DIR, decode=True, sampler=sampler)
+    dataset_2 = ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_samples=num_samples)
+
+    num_iter = 0
+    for item1, item2 in zip(dataset_1.create_dict_iterator(num_epochs=1, output_numpy=True),
+                            dataset_2.create_dict_iterator(num_epochs=1, output_numpy=True)):
+        np.testing.assert_array_equal(item1["caption"], item2["caption"])
+        num_iter += 1
+    assert num_iter == num_samples
+
+
+def test_sbu_exception():
+    """
+    Test error cases for SBUDataset
+    """
+    logger.info("Test error cases for SBUDataset")
+    error_msg_1 = "sampler and shuffle cannot be specified at the same time"
+    with pytest.raises(RuntimeError, match=error_msg_1):
+        ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, sampler=ds.SequentialSampler())
+
+    error_msg_2 = "sampler and sharding cannot be specified at the same time"
+    with pytest.raises(RuntimeError, match=error_msg_2):
+        ds.SBUDataset(DATA_DIR, decode=True, sampler=ds.SequentialSampler(), num_shards=2, shard_id=0)
+
+    error_msg_3 = "num_shards is specified and currently requires shard_id as well"
+    with pytest.raises(RuntimeError, match=error_msg_3):
+        ds.SBUDataset(DATA_DIR, decode=True, num_shards=10)
+
+    error_msg_4 = "shard_id is specified but num_shards is not"
+    with pytest.raises(RuntimeError, match=error_msg_4):
+        ds.SBUDataset(DATA_DIR, decode=True, shard_id=0)
+
+    error_msg_5 = "Input shard_id is not within the required interval"
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.SBUDataset(DATA_DIR, decode=True, num_shards=5, shard_id=-1)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.SBUDataset(DATA_DIR, decode=True, num_shards=5, shard_id=5)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.SBUDataset(DATA_DIR, decode=True, num_shards=2, shard_id=5)
+
+    error_msg_6 = "num_parallel_workers exceeds"
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=0)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=256)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=-2)
+
+    error_msg_7 = "Argument shard_id"
+    with pytest.raises(TypeError, match=error_msg_7):
+        ds.SBUDataset(DATA_DIR, decode=True, num_shards=2, shard_id="0")
+
+    def exception_func(item):
+        raise Exception("Error occur!")
+
+    error_msg_8 = "The corresponding data files"
+    with pytest.raises(RuntimeError, match=error_msg_8):
+        dataset = ds.SBUDataset(DATA_DIR, decode=True)
+        dataset = dataset.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
+        for _ in dataset.__iter__():
+            pass
+
+    with pytest.raises(RuntimeError, match=error_msg_8):
+        dataset = ds.SBUDataset(DATA_DIR, decode=True)
+        dataset = dataset.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
+        for _ in dataset.__iter__():
+            pass
+
+    error_msg_9 = "does not exist or permission denied"
+    with pytest.raises(ValueError, match=error_msg_9):
+        dataset = ds.SBUDataset(WRONG_DIR, decode=True)
+        for _ in dataset.__iter__():
+            pass
+
+    error_msg_10 = "Argument decode with value"
+    with pytest.raises(TypeError, match=error_msg_10):
+        dataset = ds.SBUDataset(DATA_DIR, decode="not_bool")
+        for _ in dataset.__iter__():
+            pass
+
+
+def test_sbu_visualize(plot=False):
+    """
+    Visualize SBUDataset results
+    """
+    logger.info("Test SBUDataset visualization")
+
+    dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=10, shuffle=False)
+    num_iter = 0
+    image_list, caption_list = [], []
+    for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        image = item["image"]
+        caption = item["caption"].item().decode("utf8")
+        image_list.append(image)
+        caption_list.append("caption {}".format(caption))
+        assert isinstance(image, np.ndarray)
+
+        assert image.dtype == np.uint8
+        assert isinstance(caption, str)
+        num_iter += 1
+    assert num_iter == 5
+    if plot:
+        visualize_dataset(image_list, caption_list)
+
+
+def test_sbu_decode():
+    """
+    Validate SBUDataset image readings
+    """
+    logger.info("Test SBUDataset decode flag")
+
+    sampler = ds.SequentialSampler(num_samples=50)
+    dataset = ds.SBUDataset(dataset_dir=DATA_DIR, decode=False, sampler=sampler)
+    dataset_1 = dataset.map(operations=[vision.Decode(rgb=True)], input_columns=["image"])
+
+    dataset_2 = ds.SBUDataset(dataset_dir=DATA_DIR, decode=True, sampler=sampler)
+
+    num_iter = 0
+    for item1, item2 in zip(dataset_1.create_dict_iterator(num_epochs=1, output_numpy=True),
+                            dataset_2.create_dict_iterator(num_epochs=1, output_numpy=True)):
+        np.testing.assert_array_equal(item1["caption"], item2["caption"])
+        num_iter += 1
+
+    assert num_iter == 5
+
+
+if __name__ == '__main__':
+    test_sbu_content_check()
+    test_sbu_basic()
+    test_sbu_case()
+    test_sbu_sequential_sampler()
+    test_sbu_exception()
+    test_sbu_visualize(plot=True)
+    test_sbu_decode()