CSV dataset loader

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

@@ -31,6 +31,7 @@
 #include "minddata/dataset/engine/datasetops/source/celeba_op.h"
 #include "minddata/dataset/engine/datasetops/source/cifar_op.h"
 #include "minddata/dataset/engine/datasetops/source/clue_op.h"
+#include "minddata/dataset/engine/datasetops/source/csv_op.h"
 #include "minddata/dataset/engine/datasetops/source/coco_op.h"
 #include "minddata/dataset/engine/datasetops/source/image_folder_op.h"
 #include "minddata/dataset/engine/datasetops/source/manifest_op.h"
@@ -88,6 +89,7 @@ static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {
   {kBuildVocab, &DEPipeline::ParseBuildVocabOp},
   {kClue, &DEPipeline::ParseClueOp},
   {kEpochCtrl, &DEPipeline::ParseEpochCtrlOp},
+  {kCsv, &DEPipeline::ParseCsvOp},
   {kSentencePieceVocab, &DEPipeline::ParseBuildSentencePieceVocabOp}};
 
 DEPipeline::DEPipeline() : iterator_(nullptr) {
@@ -1838,6 +1840,86 @@ Status DEPipeline::AddCacheOp(std::shared_ptr<CacheClient> cache_client, int num
   return Status::OK();
 }
 
+Status DEPipeline::ParseCsvOp(const py::dict &args, std::shared_ptr<DatasetOp> *top,
+                              std::shared_ptr<DatasetOp> *bottom) {
+  std::vector<std::string> files_list;
+  std::shared_ptr<CsvOp::Builder> builder = std::make_shared<CsvOp::Builder>();
+  if (!args["dataset_files"].is_none()) {
+    files_list = ToStringVector(args["dataset_files"]);
+    (void)builder->SetCsvFilesList(files_list);
+  } else {
+    RETURN_STATUS_UNEXPECTED("Error: dataset_files is missing");
+  }
+
+  // Optional arguments
+  bool shuffle_required = false;
+  int64_t num_devices = 0;
+  std::vector<std::string> col_names;
+  for (auto arg : args) {
+    std::string key = py::str(arg.first);
+    py::handle value = arg.second;
+    if (!value.is_none()) {
+      if (key == "num_parallel_workers") {
+        (void)builder->SetNumWorkers(ToInt(value));
+      } else if (key == "shuffle_files") {
+        (void)builder->SetShuffleFiles(ToBool(value));
+      } else if (key == "shuffle_global") {
+        shuffle_required = ToBool(value);
+      } else if (key == "num_samples") {
+        (void)builder->SetNumSamples(ToInt(value));
+      } else if (key == "num_shards") {
+        num_devices = ToInt(value);
+        (void)builder->SetNumDevices(num_devices);
+      } else if (key == "shard_id") {
+        (void)builder->SetDeviceId(ToInt(value));
+      } else if (key == "field_delim") {
+        (void)builder->SetFieldDelim(ToString(value)[0]);
+      } else if (key == "column_defaults") {
+        py::list py_object_list = py::reinterpret_borrow<py::list>(value);
+        std::vector<std::shared_ptr<CsvOp::BaseRecord>> column_default_list;
+        for (auto l : py_object_list) {
+          std::string type_s = (std::string)py::str(l.get_type().attr("__name__"));
+          if (type_s == "int") {
+            column_default_list.push_back(std::make_shared<CsvOp::Record<int>>(CsvOp::INT, ToInt(l)));
+          } else if (type_s == "float") {
+            column_default_list.push_back(std::make_shared<CsvOp::Record<float>>(CsvOp::FLOAT, ToFloat(l)));
+          } else if (type_s == "str") {
+            column_default_list.push_back(std::make_shared<CsvOp::Record<std::string>>(CsvOp::STRING, ToString(l)));
+          } else {
+            RETURN_STATUS_UNEXPECTED("Record type is not allowed");
+          }
+        }
+        (void)builder->SetColumDefault(column_default_list);
+      } else if (key == "column_names") {
+        col_names = ToStringVector(value);
+        (void)builder->SetColumName(col_names);
+      }
+    }
+  }
+
+  std::shared_ptr<CsvOp> csv_op;
+  RETURN_IF_NOT_OK(builder->Build(&csv_op));
+  RETURN_IF_NOT_OK(tree_->AssociateNode(csv_op));
+  *top = csv_op;
+
+  if (shuffle_required) {
+    std::shared_ptr<DatasetOp> shuffle_op = nullptr;
+    int64_t shuffle_size = 0;
+    int64_t num_rows = 0;
+
+    // First, get the number of rows in the dataset and then compute the shuffle size
+    RETURN_IF_NOT_OK(CsvOp::CountAllFileRows(files_list, col_names.empty(), &num_rows));
+    RETURN_IF_NOT_OK(ComputeShuffleSize(files_list.size(), num_devices, num_rows, 0, &shuffle_size));
+
+    // Add the shuffle op over top of this op and return the subtree (top/bottom) to caller
+    RETURN_IF_NOT_OK(AddShuffleOp(shuffle_size, csv_op, &shuffle_op));
+    *top = shuffle_op;
+    *bottom = csv_op;
+  }
+
+  return Status::OK();
+}
+
 // Helper function to inject a shuffle operator over top of the current operation being built.
 Status DEPipeline::AddShuffleOp(int64_t shuffle_size, std::shared_ptr<DatasetOp> input_op,
                                 std::shared_ptr<DatasetOp> *shuffle_op) {

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

@@ -73,6 +73,7 @@ enum OpName {
   kClue,
   kEpochCtrl,
   kSentencePieceVocab,
+  kCsv
 };
 
 // The C++ binder class that we expose to the python script.
@@ -201,6 +202,8 @@ class DEPipeline {
 
   Status ParseClueOp(const py::dict &args, std::shared_ptr<DatasetOp> *top, std::shared_ptr<DatasetOp> *bottom);
 
+  Status ParseCsvOp(const py::dict &args, std::shared_ptr<DatasetOp> *top, std::shared_ptr<DatasetOp> *bottom);
+
  private:
   // Execution tree that links the dataset operators.
   std::shared_ptr<ExecutionTree> tree_;

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

@@ -19,6 +19,7 @@
 #include "minddata/dataset/engine/cache/cache_client.h"
 #include "minddata/dataset/engine/datasetops/source/cifar_op.h"
 #include "minddata/dataset/engine/datasetops/source/clue_op.h"
+#include "minddata/dataset/engine/datasetops/source/csv_op.h"
 #include "minddata/dataset/engine/datasetops/source/coco_op.h"
 #include "minddata/dataset/engine/datasetops/source/image_folder_op.h"
 #include "minddata/dataset/engine/datasetops/source/io_block.h"
@@ -277,6 +278,17 @@ void bindDatasetOps(py::module *m) {
       return count;
     });
 
+  (void)py::class_<CsvOp, DatasetOp, std::shared_ptr<CsvOp>>(*m, "CsvOp")
+    .def_static("get_num_rows", [](const py::list &files, bool csv_header) {
+      int64_t count = 0;
+      std::vector<std::string> filenames;
+      for (auto file : files) {
+        file.is_none() ? (void)filenames.emplace_back("") : filenames.push_back(py::str(file));
+      }
+      THROW_IF_ERROR(CsvOp::CountAllFileRows(filenames, csv_header, &count));
+      return count;
+    });
+
   (void)py::class_<VOCOp, DatasetOp, std::shared_ptr<VOCOp>>(*m, "VOCOp")
     .def_static("get_num_rows",
                 [](const std::string &dir, const std::string &task_type, const std::string &task_mode,
@@ -1039,8 +1051,9 @@ PYBIND11_MODULE(_c_dataengine, m) {
     .value("SENTENCEPIECEVOCAB", OpName::kSentencePieceVocab)
     .value("CELEBA", OpName::kCelebA)
     .value("TEXTFILE", OpName::kTextFile)
-    .value("CLUE", OpName::kClue)
-    .value("EPOCHCTRL", OpName::kEpochCtrl);
+    .value("EPOCHCTRL", OpName::kEpochCtrl)
+    .value("CSV", OpName::kCsv)
+    .value("CLUE", OpName::kClue);
 
   (void)py::enum_<JiebaMode>(m, "JiebaMode", py::arithmetic())
     .value("DE_JIEBA_MIX", JiebaMode::kMix)

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

@@ -12,6 +12,7 @@ set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
     celeba_op.cc
     text_file_op.cc
     clue_op.cc
+    csv_op.cc
     )
 
 set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
@@ -29,4 +30,4 @@ if (ENABLE_PYTHON)
         )
 endif()
 
-add_library(engine-datasetops-source OBJECT ${DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES})
\ No newline at end of file
+add_library(engine-datasetops-source OBJECT ${DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES})

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

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef DATASET_ENGINE_DATASETOPS_SOURCE_CSV_OP_H_
+#define DATASET_ENGINE_DATASETOPS_SOURCE_CSV_OP_H_
+
+#include <string>
+#include <vector>
+#include <memory>
+#include <map>
+#include <utility>
+#include <limits>
+
+#include "minddata/dataset/util/auto_index.h"
+#include "minddata/dataset/engine/datasetops/parallel_op.h"
+#include "minddata/dataset/engine/datasetops/source/io_block.h"
+
+namespace mindspore {
+namespace dataset {
+
+const size_t CSV_BUFFER_SIZE = 4096;
+using StringIndex = AutoIndexObj<std::string>;
+class JaggedConnector;
+
+class CsvOp : public ParallelOp {
+ public:
+  enum RecordType : uint8_t { INT = 0, FLOAT, STRING };
+
+  struct BaseRecord {
+   public:
+    BaseRecord() = default;
+    explicit BaseRecord(RecordType t) : type(t) {}
+    virtual ~BaseRecord() {}
+    RecordType type;
+  };
+
+  template <typename T>
+  class Record : public BaseRecord {
+   public:
+    Record() = default;
+    Record(RecordType t, T v) : BaseRecord(t), value(v) {}
+    ~Record() {}
+    T value;
+  };
+
+  // CsvParser is a class that parsing CSV file.
+  // We design a state machine to implement CSV syntactic analysis. It contains two state diagram,'sd' and 'sdl'.
+  // The 'sd' is used for parsing CSV syntactic, it's complete and complicate.
+  // The 'sdl' is used for counting the record rows, it's concise and it runs fast.
+  struct CsvParser {
+   public:
+    CsvParser() = delete;
+
+    CsvParser(int32_t worker_id, std::shared_ptr<JaggedConnector> connector, int64_t rows_per_buffer, char field_delim,
+              std::vector<std::shared_ptr<CsvOp::BaseRecord>> column_default)
+        : worker_id_(worker_id),
+          buffer_connector_(connector),
+          csv_rows_per_buffer_(rows_per_buffer),
+          csv_field_delim_(field_delim),
+          column_default_(column_default),
+          cur_state_(START_OF_FILE),
+          pos_(0),
+          cur_row_(0),
+          cur_col_(0),
+          total_rows_(0),
+          start_offset_(0),
+          end_offset_(std::numeric_limits<int64_t>::max()) {
+      cur_buffer_ = std::make_unique<DataBuffer>(0, DataBuffer::BufferFlags::kDeBFlagNone);
+      initCsvParser();
+    }
+
+    ~CsvParser() = default;
+
+    void Reset() {
+      cur_state_ = START_OF_FILE;
+      pos_ = 0;
+      cur_row_ = 0;
+      cur_col_ = 0;
+    }
+
+    void setStartOffset(int64_t start_offset) { start_offset_ = start_offset; }
+
+    void setEndOffset(int64_t end_offset) { end_offset_ = end_offset; }
+
+    int processMessage(char c) {
+      Message m = getMessage(c);
+      StateDiagram::iterator it = sd.find({cur_state_, m});
+      if (it == sd.end()) {
+        return -1;
+      }
+      cur_state_ = it->second.first;
+      return it->second.second(*this, c);
+    }
+
+    int countRows(char c);
+
+    Status initCsvParser();
+
+    enum State : uint8_t {
+      START_OF_FILE = 0,
+      UNQUOTE,
+      DELIM,
+      QUOTE,
+      SECOND_QUOTE,
+      END_OF_LINE,
+      END_OF_FILE,
+      EXCEPTION
+    };
+
+    enum Message : uint8_t {
+      MS_NORMAL = 0,
+      MS_DELIM,
+      MS_QUOTE,
+      MS_END_OF_LINE,
+      MS_END_OF_FILE,
+    };
+
+    typedef std::pair<State, Message> StateMessagePair;
+    typedef std::pair<State, std::function<int(CsvParser &, char)>> StateActionPair;
+    typedef std::map<StateMessagePair, StateActionPair> StateDiagram;
+
+    Message getMessage(char c) {
+      if (c == csv_field_delim_) {
+        return Message::MS_DELIM;
+      } else if (c == '"') {
+        return Message::MS_QUOTE;
+      } else if (c == '\r' || c == '\n') {
+        return Message::MS_END_OF_LINE;
+      } else if (c == std::char_traits<char>::eof()) {
+        return Message::MS_END_OF_FILE;
+      } else {
+        return Message::MS_NORMAL;
+      }
+    }
+
+    int null_func(char c) { return 0; }
+
+    int put_char(char c) {
+      if (pos_ >= str_buf_.size()) {
+        str_buf_.resize(str_buf_.size() * 2);
+      }
+      str_buf_[pos_] = c;
+      pos_++;
+      return 0;
+    }
+
+    int put_record(char c);
+
+    int put_row(char c);
+
+    int end_file(char c);
+
+    int add_row(char c) {
+      total_rows_++;
+      return 0;
+    }
+
+    int catch_exception(char c) {
+      MS_LOG(ERROR) << "Invalid syntax!";
+      return -1;
+    }
+
+    int32_t worker_id_;
+    std::shared_ptr<JaggedConnector> buffer_connector_;
+    int64_t csv_rows_per_buffer_;
+    const char csv_field_delim_;
+    std::vector<std::shared_ptr<CsvOp::BaseRecord>> column_default_;
+    State cur_state_;
+    size_t pos_;
+    int cur_row_;
+    int cur_col_;
+    int64_t total_rows_;
+    int64_t start_offset_;
+    int64_t end_offset_;
+    StateDiagram sd;
+    StateDiagram sdl;
+    std::vector<char> str_buf_;
+    std::unique_ptr<TensorQTable> tensor_table_;
+    std::unique_ptr<DataBuffer> cur_buffer_;
+  };
+
+  class Builder {
+   public:
+    // Builder constructor. Creates the builder object.
+    // @note No default args
+    // @return This is a constructor.
+    Builder();
+
+    // Default destructor
+    ~Builder() = default;
+
+    // Checks if the inputs of the builder is valid.
+    // @return Status - the error code returned.
+    Status ValidateInputs() const;
+
+    // Create the final object.
+    // @param op - dataset op.
+    // @return - the error code return.
+    Status Build(std::shared_ptr<CsvOp> *op);
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetNumWorkers(int32_t num_workers) {
+      builder_num_workers_ = num_workers;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetOpConnectorSize(int32_t op_connector_size) {
+      builder_op_connector_size_ = op_connector_size;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetRowsPerBuffer(int64_t rows_per_buffer) {
+      builder_rows_per_buffer_ = rows_per_buffer;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetNumDevices(int64_t num_dev) {
+      builder_num_devices_ = num_dev;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetDeviceId(int64_t dev_id) {
+      builder_device_id_ = dev_id;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetCsvFilesList(const std::vector<std::string> &files_list) {
+      builder_csv_files_list_ = files_list;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetShuffleFiles(bool shuffle_files) {
+      builder_shuffle_files_ = shuffle_files;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetNumSamples(int64_t num_samples) {
+      builder_num_samples_ = num_samples;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetFieldDelim(char field_delim) {
+      builder_field_delim_ = field_delim;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetColumDefault(std::vector<std::shared_ptr<CsvOp::BaseRecord>> record_list) {
+      builder_column_default_list_ = record_list;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetColumName(std::vector<std::string> col_name_list) {
+      builder_column_name_list_ = col_name_list;
+      return *this;
+    }
+
+   private:
+    int32_t builder_device_id_;
+    int32_t builder_num_devices_;
+    int32_t builder_num_workers_;
+    int32_t builder_op_connector_size_;
+    int64_t builder_rows_per_buffer_;
+    int64_t builder_num_samples_;
+    int32_t builder_worker_connector_size_;
+    std::vector<std::string> builder_csv_files_list_;
+    bool builder_shuffle_files_;
+    char builder_field_delim_;
+    std::vector<std::shared_ptr<CsvOp::BaseRecord>> builder_column_default_list_;
+    std::vector<std::string> builder_column_name_list_;
+  };
+
+  // Constructor of CsvOp
+  CsvOp() = delete;
+
+  CsvOp(const std::vector<std::string> &csv_files_list, char field_delim,
+        const std::vector<std::shared_ptr<BaseRecord>> &column_default, const std::vector<std::string> &column_name,
+        int32_t num_workers, int64_t rows_per_buffer, int64_t num_samples, int32_t worker_connector_size,
+        int32_t op_connector_size, bool shuffle_files, int32_t num_devices, int32_t device_id);
+
+  // Default destructor
+  ~CsvOp() = default;
+
+  // A print method typically used for debugging
+  // @param out - The output stream to write output to
+  // @param show_all - A bool to control if you want to show all info or just a summary
+  void Print(std::ostream &out, bool show_all) const override;
+
+  // Instantiates the internal queues and connectors
+  // @return Status - the error code returned
+  Status Init();
+
+  // Class functor operator () override.
+  // All dataset operators operate by launching a thread (see ExecutionTree). This class functor will
+  // provide the master loop that drives the logic for performing the work
+  // @return Status - the error code returned.
+  Status operator()() override;
+
+  // Overrides base class reset method. Cleans up any state info from it's previous execution
+  // reinitializes itself so that it can be executed again, as if it was just created.
+  // @return Status - the error code returned.
+  Status Reset() override;
+
+  // Get total rows in files.
+  // @param files - all csv files.
+  // @param csv_header - a bool that indicates csv file include header line
+  // @param count - number of rows.
+  // @return Status - the error coed returned.
+  static Status CountAllFileRows(const std::vector<std::string> &files, bool csv_header, int64_t *count);
+
+  // File names getter
+  // @return Vector of the input file names
+  std::vector<std::string> FileNames() { return csv_files_list_; }
+
+ private:
+  // The entry point for when workers are launched.
+  // @param worker_id - the id of the worker that is executing this function.
+  // @return Status - the error code returned.
+  Status WorkerEntry(int32_t worker_id) override;
+
+  // Parses a single row and puts the data into a tensor table.
+  // @param line - the content of the row.
+  // @param tensor_table - the tensor table to put the parsed data in.
+  // @param row - the id of the row filled in the tensor table.
+  // @return Status - the error code returned.
+  Status LoadTensor(const std::string &line, std::unique_ptr<TensorQTable> *tensor_table, int64_t row);
+
+  // Reads a csv file and loads the data into multiple buffers.
+  // @param file - the file to read.
+  // @param start_offset - the start offset of file.
+  // @param end_offset - the end offset of file.
+  // @param worker_id - the id of the worker that is executing this function.
+  // @return Status - the error code returned.
+  Status LoadFile(const std::string &file, const int64_t start_offset, const int64_t end_offset,
+                  const int32_t worker_id);
+
+  // Pops an element from a queue in IOBlockQueue.
+  // @param index - the index of the queue to pop from.
+  // @param out_block - the popped element.
+  // @return Status - the error code returned.
+  Status PopIoBlockQueue(int32_t index, std::unique_ptr<FilenameBlock> *out_block);
+
+  // Pushes an element to a queue in IOBlockQueue.
+  // @param index - the index of the queue to push to.
+  // @param io_block - the element to push onto the queue.
+  // @return Status - the error code returned.
+  Status PushIoBlockQueue(int32_t index, std::unique_ptr<FilenameBlock> &&io_block);
+
+  // Called asynchronously by another thread. Will wait until notified to fill the IOBlockQueue.
+  // @return Status - the error code returned.
+  Status WaitToFillIOBlockQueue();
+
+  // Fill the IOBlockQueue.
+  // @para i_keys - keys of file to fill to the IOBlockQueue
+  // @return Status - the error code returned.
+  Status FillIOBlockQueue(const std::vector<int64_t> &i_keys);
+
+  // Notifies the thread which called FillIoBlockQueue to resume execution
+  void NotifyToFillIOBlockQueue();
+
+  // Select file and push it to the block queue.
+  // @param file_name - File name.
+  // @param start_file - If file contains the first sample of data.
+  // @param end_file - If file contains the end sample of data.
+  // @param pre_count - Total rows of previous files.
+  // @return Status - the error code returned.
+  bool NeedPushFileToBlockQueue(const std::string &file_name, int64_t *start_offset, int64_t *end_offset,
+                                const int64_t &pre_count);
+
+  // Pushes a control indicator onto the IOBlockQueue for each worker to consume. When the worker
+  // pops this control indicator, it will wait until the next epoch starts and then resume execution.
+  // @return Status - the error code returned.
+  Status PostEndOfEpoch(int32_t queue_index);
+
+  // Calculate number of rows in each shard.
+  // @return Status - the error code returned.
+  Status CalculateNumRowsPerShard();
+
+  // Count number of rows in each file.
+  // @param filename - csv file name.
+  // @return int64_t - the total number of rows in file.
+  int64_t CountTotalRows(const std::string &file);
+
+  // Pushes a control indicator onto the IOBlockQueue for each worker to consume.
+  // When the worker pops this control indicator, it will shut itself down gracefully.
+  // @return Status - the error code returned.
+  Status PostEndOfData();
+
+  // Private function for computing the assignment of the column name map.
+  // @return - Status
+  Status ComputeColMap() override;
+
+  // Split string based on a character delimiter
+  // @return - the a string vector
+  std::vector<std::string> split(const std::string &s, char delim);
+
+  int32_t device_id_;
+  bool shuffle_files_;
+  bool finished_reading_dataset_;
+  int32_t num_devices_;
+  int64_t rows_per_buffer_;
+  bool load_io_block_queue_;
+  int64_t num_rows_per_shard_;
+  int64_t all_num_rows_;
+  int64_t num_samples_;
+  std::map<std::string, int64_t> filename_numrows_;
+  std::unique_ptr<StringIndex> filename_index_;
+  std::vector<std::string> csv_files_list_;
+  WaitPost io_block_queue_wait_post_;
+  std::shared_ptr<JaggedConnector> jagged_buffer_connector_;
+  QueueList<std::unique_ptr<FilenameBlock>> io_block_queues_;
+  bool load_jagged_connector_;
+  char field_delim_;
+  std::vector<std::shared_ptr<CsvOp::BaseRecord>> column_default_list_;
+  std::vector<std::string> column_name_list_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // DATASET_ENGINE_DATASETOPS_SOURCE_CSV_OP_H_

mindspore/dataset/__init__.py

@@ -21,7 +21,7 @@ can also create samplers with this module to sample data.
 from .core import config
 from .engine.datasets import TFRecordDataset, ImageFolderDatasetV2, MnistDataset, MindDataset, NumpySlicesDataset, \
     GeneratorDataset, ManifestDataset, Cifar10Dataset, Cifar100Dataset, VOCDataset, CocoDataset, CelebADataset,\
-    TextFileDataset, CLUEDataset, Schema, Shuffle, zip, RandomDataset
+    TextFileDataset, CLUEDataset, CSVDataset, Schema, Shuffle, zip, RandomDataset
 from .engine.samplers import DistributedSampler, PKSampler, RandomSampler, SequentialSampler, SubsetRandomSampler, \
     WeightedRandomSampler, Sampler
 from .engine.cache_client import DatasetCache
@@ -31,5 +31,5 @@ from .engine.graphdata import GraphData
 __all__ = ["config", "ImageFolderDatasetV2", "MnistDataset",
            "MindDataset", "GeneratorDataset", "TFRecordDataset",
            "ManifestDataset", "Cifar10Dataset", "Cifar100Dataset", "CelebADataset", "NumpySlicesDataset", "VOCDataset",
-           "CocoDataset", "TextFileDataset", "CLUEDataset", "Schema", "DistributedSampler", "PKSampler",
+           "CocoDataset", "TextFileDataset", "CLUEDataset", "CSVDataset", "Schema", "DistributedSampler", "PKSampler",
            "RandomSampler", "SequentialSampler", "SubsetRandomSampler", "WeightedRandomSampler", "zip", "GraphData"]

mindspore/dataset/engine/__init__.py

@@ -29,7 +29,7 @@ from .samplers import *
 from ..core import config
 
 __all__ = ["config", "zip", "ImageFolderDatasetV2", "MnistDataset",
-           "MindDataset", "GeneratorDataset", "TFRecordDataset", "CLUEDataset",
+           "MindDataset", "GeneratorDataset", "TFRecordDataset", "CLUEDataset", "CSVDataset",
            "ManifestDataset", "Cifar10Dataset", "Cifar100Dataset", "CelebADataset",
            "VOCDataset", "CocoDataset", "TextFileDataset", "Schema", "DistributedSampler",
            "PKSampler", "RandomSampler", "SequentialSampler", "SubsetRandomSampler", "WeightedRandomSampler"]

mindspore/dataset/engine/datasets.py

@@ -33,7 +33,7 @@ import copy
 import numpy as np
 
 from mindspore._c_dataengine import DataType, TFReaderOp, ImageFolderOp, CifarOp, MnistOp, ManifestOp, \
-    MindRecordOp, TextFileOp, ClueOp, VOCOp, CocoOp, CBatchInfo
+    MindRecordOp, TextFileOp, ClueOp, CsvOp, VOCOp, CocoOp, CBatchInfo
 from mindspore._c_expression import typing
 
 from mindspore import log as logger
@@ -44,7 +44,7 @@ from .validators import check_batch, check_shuffle, check_map, check_filter, che
     check_take, check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_cocodataset, check_celebadataset, check_minddataset, \
     check_generatordataset, check_sync_wait, check_zip_dataset, check_add_column, check_textfiledataset, check_concat, \
-    check_random_dataset, check_split, check_bucket_batch_by_length, check_cluedataset, check_save
+    check_random_dataset, check_split, check_bucket_batch_by_length, check_cluedataset, check_save, check_csvdataset
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
 from ..text.utils import DE_C_INTER_SENTENCEPIECE_MODE
 
@@ -1012,7 +1012,7 @@ class Dataset:
                 if isinstance(sampler, samplers.DistributedSampler):
                     dev_id = sampler.shard_id
                 return "", dev_id
-            if isinstance(output_dataset, (TFRecordDataset, TextFileDataset, CLUEDataset)):
+            if isinstance(output_dataset, (TFRecordDataset, TextFileDataset, CLUEDataset, CSVDataset)):
                 if output_dataset.shard_id is not None:
                     dev_id = output_dataset.shard_id
                 return "", dev_id
@@ -4652,8 +4652,8 @@ class CLUEDataset(SourceDataset):
         }
 
     Args:
-        dataset_files (str or list[str]): String or list of files to be read or glob strings to search for a pattern of
-            files. The list will be sorted in a lexicographical order.
+        dataset_files (str or a list of strings): String or list of files to be read or glob strings to search for
+            a pattern of files. The list will be sorted in a lexicographical order.
         task (str, optional): The kind of task, one of 'AFQMC', 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' and 'CSL'.
             (default=AFQMC).
         usage (str, optional): Need train, test or eval data (default="train").
@@ -4860,6 +4860,108 @@ class CLUEDataset(SourceDataset):
         return False
 
 
+class CSVDataset(SourceDataset):
+    """
+    A source dataset that reads and parses CSV datasets.
+
+    Args:
+        dataset_files (str or a list of strings): String or list of files to be read or glob strings to search
+            for a pattern of files. The list will be sorted in a lexicographical order.
+        field_delim (str, optional): A string that indicates the char delimiter to separate fields (default=',').
+        column_defaults (list, optional): List of default values for the CSV field (default=None). Each item
+            in the list is either a valid type (float, int, or string). If this is not provided, treats all
+            columns as string type.
+        column_names (list of string, optional): List of column names of the dataset (default=None). If this
+            is not provided, infers the column_names from the first row of CSV file.
+        num_samples (int, optional): number of samples(rows) to read (default=None, reads the full dataset).
+        num_parallel_workers (int, optional): number of workers to read the data
+            (default=None, number set in the config).
+        shuffle (bool, Shuffle level, optional): perform reshuffling of the data every epoch (default=Shuffle.GLOBAL).
+            If shuffle is False, no shuffling will be performed;
+            If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
+            Otherwise, there are two levels of shuffling:
+
+            - Shuffle.GLOBAL: Shuffle both the files and samples.
+
+            - Shuffle.FILES: Shuffle files only.
+
+        num_shards (int, optional): Number of shards that the dataset should be divided into (default=None).
+        shard_id (int, optional): The shard ID within num_shards (default=None). This
+            argument should be specified only when num_shards is also specified.
+
+    Examples:
+        >>> import mindspore.dataset as ds
+        >>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
+        >>> dataset = ds.CSVDataset(dataset_files=dataset_files, column_names=['col1', 'col2', 'col3', 'col4'])
+    """
+
+    @check_csvdataset
+    def __init__(self, dataset_files, field_delim=',', column_defaults=None, column_names=None, num_samples=None,
+                 num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None):
+        super().__init__(num_parallel_workers)
+        self.dataset_files = self._find_files(dataset_files)
+        self.dataset_files.sort()
+        self.field_delim = field_delim
+        self.column_defaults = column_defaults
+        self.column_names = column_names
+        self.num_samples = num_samples
+
+        if not isinstance(shuffle, (bool, Shuffle)):
+            raise TypeError("shuffle should be of boolean or enum 'Shuffle'.")
+        if not isinstance(shuffle, Shuffle):
+            if shuffle:
+                self.shuffle_level = Shuffle.GLOBAL
+                self.shuffle_files = True
+            else:
+                self.shuffle_level = None
+                self.shuffle_files = False
+        else:
+            self.shuffle_level = shuffle
+            self.shuffle_files = True
+
+        self.num_shards = num_shards
+        self.shard_id = shard_id
+
+    def get_args(self):
+        args = super().get_args()
+        args["dataset_files"] = self.dataset_files
+        args['field_delim'] = self.field_delim
+        args['column_defaults'] = self.column_defaults
+        args['column_names'] = self.column_names
+        args["num_samples"] = self.num_samples
+        if self.shuffle_files is not None:
+            args["shuffle_files"] = self.shuffle_files
+        args["shuffle_global"] = (self.shuffle_level == Shuffle.GLOBAL)
+        args["shuffle"] = self.shuffle_level
+        args["num_shards"] = self.num_shards
+        args["shard_id"] = self.shard_id
+        return args
+
+    def get_dataset_size(self):
+        """
+        Get the number of batches in an epoch.
+
+        Return:
+            Number, number of batches.
+        """
+        if self._dataset_size is None:
+            num_rows = CsvOp.get_num_rows(self.dataset_files, self.column_names is None)
+            num_rows = get_num_rows(num_rows, self.num_shards)
+            if self.num_samples is None:
+                return num_rows
+            return min(self.num_samples, num_rows)
+        return self._dataset_size
+
+    def is_shuffled(self):
+        return self.shuffle_files
+
+    def is_sharded(self):
+        if self.num_shards is not None:
+            return self.num_shards > 1
+
+        return False
+
+
 class TextFileDataset(SourceDataset):
     """
     A source dataset that reads and parses datasets stored on disk in text format.

mindspore/dataset/engine/iterators.py

@@ -185,6 +185,8 @@ class Iterator:
             op_type = OpName.SENTENCEPIECEVOCAB
         elif isinstance(dataset, de.CLUEDataset):
             op_type = OpName.CLUE
+        elif isinstance(dataset, de.CSVDataset):
+            op_type = OpName.CSV
         else:
             raise ValueError("Unsupported DatasetOp")
 

mindspore/dataset/engine/validators.py

@@ -787,6 +787,49 @@ def check_cluedataset(method):
     return new_method
 
 
+def check_csvdataset(method):
+    """A wrapper that wrap a parameter checker to the original Dataset(CSVDataset)."""
+
+    @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']
+
+        # check dataset_files; required argument
+        dataset_files = param_dict.get('dataset_files')
+        type_check(dataset_files, (str, list), "dataset files")
+
+        # check field_delim
+        field_delim = param_dict.get('field_delim')
+        type_check(field_delim, (str,), 'field delim')
+        if field_delim in ['"', '\r', '\n'] or len(field_delim) > 1:
+            raise ValueError("field_delim is not legal.")
+
+        # check column_defaults
+        column_defaults = param_dict.get('column_defaults')
+        if column_defaults is not None:
+            if not isinstance(column_defaults, list):
+                raise TypeError("column_defaults should be type of list.")
+            for item in column_defaults:
+                if not isinstance(item, (str, int, float)):
+                    raise TypeError("column type is not legal.")
+
+        # check column_names: must be list of string.
+        column_names = param_dict.get("column_names")
+        if column_names is not None:
+            all_string = all(isinstance(item, str) for item in column_names)
+            if not all_string:
+                raise TypeError("column_names should be a list of str.")
+
+        validate_dataset_param_value(nreq_param_int, param_dict, int)
+        check_sampler_shuffle_shard_options(param_dict)
+
+        return method(self, *args, **kwargs)
+
+    return new_method
+
+
 def check_textfiledataset(method):
     """A wrapper that wraps a parameter checker to the original Dataset(TextFileDataset)."""
 

tests/ut/cpp/dataset/CMakeLists.txt

@@ -77,6 +77,7 @@ SET(DE_UT_SRCS
         celeba_op_test.cc
         take_op_test.cc
         clue_op_test.cc
+        csv_op_test.cc
         text_file_op_test.cc
         filter_op_test.cc
         concat_op_test.cc

tests/ut/cpp/dataset/csv_op_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 <iostream>
+#include <memory>
+#include <vector>
+
+#include "minddata/dataset/core/client.h"
+#include "common/common.h"
+#include "common/utils.h"
+#include "gtest/gtest.h"
+#include "utils/log_adapter.h"
+#include "minddata/dataset/engine/datasetops/source/csv_op.h"
+#include "minddata/dataset/util/status.h"
+
+
+namespace common = mindspore::common;
+
+using namespace mindspore::dataset;
+using mindspore::MsLogLevel::INFO;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+
+class MindDataTestCSVOp : public UT::DatasetOpTesting {
+
+};
+
+TEST_F(MindDataTestCSVOp, TestCSVBasic) {
+  // Start with an empty execution tree
+  auto tree = std::make_shared<ExecutionTree>();
+
+  std::string dataset_path;
+  dataset_path = datasets_root_path_ + "/testCSV/1.csv";
+
+  std::vector<std::shared_ptr<CsvOp::BaseRecord>> column_default_list;
+  column_default_list.push_back(std::make_shared<CsvOp::Record<int>>(CsvOp::INT, 0));
+  column_default_list.push_back(std::make_shared<CsvOp::Record<int>>(CsvOp::INT, 0));
+  column_default_list.push_back(std::make_shared<CsvOp::Record<int>>(CsvOp::INT, 0));
+  column_default_list.push_back(std::make_shared<CsvOp::Record<int>>(CsvOp::INT, 0));
+  std::shared_ptr<CsvOp> op;
+  CsvOp::Builder builder;
+  builder.SetCsvFilesList({dataset_path})
+      .SetRowsPerBuffer(16)
+      .SetNumWorkers(16)
+      .SetShuffleFiles(false)
+      .SetOpConnectorSize(2)
+      .SetFieldDelim(',')
+      .SetColumDefault(column_default_list)
+      .SetColumName({"col1", "col2", "col3", "col4"});
+
+  Status rc = builder.Build(&op);
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = tree->AssociateNode(op);
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = tree->AssignRoot(op);
+  ASSERT_TRUE(rc.IsOk());
+
+  MS_LOG(INFO) << "Launching tree and begin iteration.";
+  rc = tree->Prepare();
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = tree->Launch();
+  ASSERT_TRUE(rc.IsOk());
+
+  // Start the loop of reading tensors from our pipeline
+  DatasetIterator di(tree);
+  TensorRow tensor_list;
+  rc = di.FetchNextTensorRow(&tensor_list);
+  ASSERT_TRUE(rc.IsOk());
+
+  int row_count = 0;
+  while (!tensor_list.empty()) {
+    // Display the tensor by calling the printer on it
+    for (int i = 0; i < tensor_list.size(); i++) {
+      std::ostringstream ss;
+      ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
+      MS_LOG(INFO) << "Tensor print: " << ss.str() << ".";
+    }
+
+    rc = di.FetchNextTensorRow(&tensor_list);
+    ASSERT_TRUE(rc.IsOk());
+    row_count++;
+  }
+
+  ASSERT_EQ(row_count, 3);
+}
+
+TEST_F(MindDataTestCSVOp, TestTotalRows) {
+  std::string csv_file1 = datasets_root_path_ + "/testCSV/1.csv";
+  std::string csv_file2 = datasets_root_path_ + "/testCSV/size.csv";
+  std::vector<std::string> files;
+  files.push_back(csv_file1);
+  int64_t total_rows = 0;
+  CsvOp::CountAllFileRows(files, false, &total_rows);
+  ASSERT_EQ(total_rows, 3);
+  files.clear();
+
+  files.push_back(csv_file2);
+  CsvOp::CountAllFileRows(files, false, &total_rows);
+  ASSERT_EQ(total_rows, 5);
+  files.clear();
+
+  files.push_back(csv_file1);
+  files.push_back(csv_file2);
+  CsvOp::CountAllFileRows(files, false, &total_rows);
+  ASSERT_EQ(total_rows, 8);
+  files.clear();
+}

tests/ut/data/dataset/testCSV/1.csv

+1,2,3,4
+5,6,7,8
+9,10,11,12

tests/ut/data/dataset/testCSV/2.csv

+,"222",3,"4"""
+"5",6,,"8"
+9,10,"1""1",12
+,,"",
+,,,
+
+a,b,c,""
+a,b,c,d

tests/ut/data/dataset/testCSV/chinese.csv

+大家,早上好,中午好,下午好,晚上好

tests/ut/data/dataset/testCSV/embedded.csv

+"a,b","c""d","e
+f"," g "

tests/ut/data/dataset/testCSV/exception.csv

+1,2,3,4
+5,6,7,8
+a,"c",d,"e

tests/ut/data/dataset/testCSV/header.csv

+col1,col2,col3,col4
+a,b,c,d
\ No newline at end of file

tests/ut/data/dataset/testCSV/number.csv

+3,0.3,4,55.5

tests/ut/data/dataset/testCSV/quoted.csv

+"a","b","c","d"

tests/ut/data/dataset/testCSV/separated.csv

+a|b|c|d

tests/ut/data/dataset/testCSV/size.csv

+1,2,3,4
+"a","b","c
+","d
+e"
+5,6,7,8
+9,10,11,12
+a,"b
+",c,"d
+e"
+

tests/ut/python/dataset/test_datasets_csv.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import mindspore.dataset as ds
+import numpy as np
+import pytest
+
+DATA_FILE = '../data/dataset/testCSV/1.csv'
+
+
+def test_csv_dataset_basic():
+    """
+    Test CSV with repeat, skip and so on
+    """
+    TRAIN_FILE = '../data/dataset/testCSV/1.csv'
+
+    buffer = []
+    data = ds.CSVDataset(
+        TRAIN_FILE,
+        column_defaults=["0", 0, 0.0, "0"],
+        column_names=['1', '2', '3', '4'],
+        shuffle=False)
+    data = data.repeat(2)
+    data = data.skip(2)
+    for d in data.create_dict_iterator():
+        buffer.append(d)
+    assert len(buffer) == 4
+
+
+def test_csv_dataset_one_file():
+    data = ds.CSVDataset(
+        DATA_FILE,
+        column_defaults=["1", "2", "3", "4"],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.append(d)
+    assert len(buffer) == 3
+
+
+def test_csv_dataset_all_file():
+    APPEND_FILE = '../data/dataset/testCSV/2.csv'
+    data = ds.CSVDataset(
+        [DATA_FILE, APPEND_FILE],
+        column_defaults=["1", "2", "3", "4"],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.append(d)
+    assert len(buffer) == 10
+
+
+def test_csv_dataset_num_samples():
+    data = ds.CSVDataset(
+        DATA_FILE,
+        column_defaults=["1", "2", "3", "4"],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False, num_samples=2)
+    count = 0
+    for _ in data.create_dict_iterator():
+        count += 1
+    assert count == 2
+
+
+def test_csv_dataset_distribution():
+    TEST_FILE = '../data/dataset/testCSV/1.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=["1", "2", "3", "4"],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False, num_shards=2, shard_id=0)
+    count = 0
+    for _ in data.create_dict_iterator():
+        count += 1
+    assert count == 2
+
+
+def test_csv_dataset_quoted():
+    TEST_FILE = '../data/dataset/testCSV/quoted.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=["", "", "", ""],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.extend([d['col1'].item().decode("utf8"),
+                       d['col2'].item().decode("utf8"),
+                       d['col3'].item().decode("utf8"),
+                       d['col4'].item().decode("utf8")])
+    assert buffer == ['a', 'b', 'c', 'd']
+
+
+def test_csv_dataset_separated():
+    TEST_FILE = '../data/dataset/testCSV/separated.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        field_delim='|',
+        column_defaults=["", "", "", ""],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.extend([d['col1'].item().decode("utf8"),
+                       d['col2'].item().decode("utf8"),
+                       d['col3'].item().decode("utf8"),
+                       d['col4'].item().decode("utf8")])
+    assert buffer == ['a', 'b', 'c', 'd']
+
+
+def test_csv_dataset_embedded():
+    TEST_FILE = '../data/dataset/testCSV/embedded.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=["", "", "", ""],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.extend([d['col1'].item().decode("utf8"),
+                       d['col2'].item().decode("utf8"),
+                       d['col3'].item().decode("utf8"),
+                       d['col4'].item().decode("utf8")])
+    assert buffer == ['a,b', 'c"d', 'e\nf', ' g ']
+
+
+def test_csv_dataset_chinese():
+    TEST_FILE = '../data/dataset/testCSV/chinese.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=["", "", "", "", ""],
+        column_names=['col1', 'col2', 'col3', 'col4', 'col5'],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.extend([d['col1'].item().decode("utf8"),
+                       d['col2'].item().decode("utf8"),
+                       d['col3'].item().decode("utf8"),
+                       d['col4'].item().decode("utf8"),
+                       d['col5'].item().decode("utf8")])
+    assert buffer == ['大家', '早上好', '中午好', '下午好', '晚上好']
+
+
+def test_csv_dataset_header():
+    TEST_FILE = '../data/dataset/testCSV/header.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=["", "", "", ""],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.extend([d['col1'].item().decode("utf8"),
+                       d['col2'].item().decode("utf8"),
+                       d['col3'].item().decode("utf8"),
+                       d['col4'].item().decode("utf8")])
+    assert buffer == ['a', 'b', 'c', 'd']
+
+
+def test_csv_dataset_number():
+    TEST_FILE = '../data/dataset/testCSV/number.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=[0.0, 0.0, 0, 0.0],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    buffer = []
+    for d in data.create_dict_iterator():
+        buffer.extend([d['col1'].item(),
+                       d['col2'].item(),
+                       d['col3'].item(),
+                       d['col4'].item()])
+    assert np.allclose(buffer, [3.0, 0.3, 4, 55.5])
+
+
+def test_csv_dataset_size():
+    TEST_FILE = '../data/dataset/testCSV/size.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=[0.0, 0.0, 0, 0.0],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    assert data.get_dataset_size() == 5
+
+
+def test_csv_dataset_exception():
+    TEST_FILE = '../data/dataset/testCSV/exception.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=["", "", "", ""],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    with pytest.raises(Exception) as err:
+        for _ in data.create_dict_iterator():
+            pass
+    assert "Failed to parse CSV file" in str(err.value)
+
+
+def test_csv_dataset_type_error():
+    TEST_FILE = '../data/dataset/testCSV/exception.csv'
+    data = ds.CSVDataset(
+        TEST_FILE,
+        column_defaults=["", 0, "", ""],
+        column_names=['col1', 'col2', 'col3', 'col4'],
+        shuffle=False)
+    with pytest.raises(Exception) as err:
+        for _ in data.create_dict_iterator():
+            pass
+    assert "invalid argument of stoi" in str(err.value)
+
+
+if __name__ == "__main__":
+    test_csv_dataset_basic()
+    test_csv_dataset_one_file()
+    test_csv_dataset_all_file()
+    test_csv_dataset_num_samples()
+    test_csv_dataset_distribution()
+    test_csv_dataset_quoted()
+    test_csv_dataset_separated()
+    test_csv_dataset_embedded()
+    test_csv_dataset_chinese()
+    test_csv_dataset_header()
+    test_csv_dataset_number()
+    test_csv_dataset_size()
+    test_csv_dataset_exception()
+    test_csv_dataset_type_error()