!3049 [dataset] add save operator in dataset

Merge pull request !3049 from liyong126/dataset_save_op

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

@@ -42,11 +42,17 @@
 #include "minddata/dataset/util/status.h"
 #include "minddata/mindrecord/include/shard_category.h"
 #include "minddata/mindrecord/include/shard_distributed_sample.h"
+#include "minddata/mindrecord/include/shard_header.h"
+#include "minddata/mindrecord/include/shard_index_generator.h"
+#include "minddata/mindrecord/include/shard_sample.h"
+#include "minddata/mindrecord/include/shard_shuffle.h"
+#include "minddata/mindrecord/include/shard_writer.h"
 #include "pybind11/stl.h"
 #include "utils/log_adapter.h"
 
 namespace mindspore {
 namespace dataset {
+using json = nlohmann::json;
 using pFunction = Status (DEPipeline::*)(const py::dict &, std::shared_ptr<DatasetOp> *, std::shared_ptr<DatasetOp> *);
 
 static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {
@@ -355,6 +361,226 @@ Status DEPipeline::ParseShuffleOp(const py::dict &args, std::shared_ptr<DatasetO
   return Status::OK();
 }
 
+Status DEPipeline::SaveDataset(const std::vector<std::string> &file_names, const std::string &file_type) {
+  Status s;
+  auto mr_header = std::make_shared<mindrecord::ShardHeader>();
+  auto mr_writer = std::make_unique<mindrecord::ShardWriter>();
+  std::vector<std::string> blob_fields;
+  uint64_t mr_schema_id = 0;
+  if (mindrecord::SUCCESS != mindrecord::ShardWriter::initialize(&mr_writer, file_names)) {
+    RETURN_STATUS_UNEXPECTED("Error: failed to initialize ShardWriter.");
+  }
+
+  TensorRow row;
+  std::unordered_map<std::string, int32_t> column_name_id_map =
+    iterator_->GetColumnNameMap();  // map of column name, id
+  bool first_loop = true;           // build schema in first loop
+  do {
+    json row_raw_data;
+    std::map<std::string, std::unique_ptr<std::vector<uint8_t>>> row_bin_data;
+    {
+      py::gil_scoped_release gil_release;
+      s = iterator_->FetchNextTensorRow(&row);
+    }
+    RETURN_IF_NOT_OK(s);
+    if (row.empty()) break;
+    if (first_loop) {
+      json mr_json;
+      std::vector<std::string> index_fields;
+      s = FetchMetaFromTensorRow(column_name_id_map, row, &mr_json, &index_fields);
+      RETURN_IF_NOT_OK(s);
+      mindrecord::ShardHeader::initialize(&mr_header, mr_json, index_fields, blob_fields, mr_schema_id);
+      mr_writer->SetShardHeader(mr_header);
+      first_loop = false;
+    }
+    // construct data
+    if (!row.empty()) {  // write data
+      s = FetchDataFromTensorRow(row, column_name_id_map, &row_raw_data, &row_bin_data);
+      RETURN_IF_NOT_OK(s);
+      std::shared_ptr<std::vector<uint8_t>> output_bin_data;
+      mr_writer->MergeBlobData(blob_fields, row_bin_data, &output_bin_data);
+      std::map<std::uint64_t, std::vector<json>> raw_data;
+      raw_data.insert(std::pair<uint64_t, std::vector<json>>(mr_schema_id, std::vector<json>{row_raw_data}));
+      std::vector<std::vector<uint8_t>> bin_data;
+      if (nullptr != output_bin_data) {
+        bin_data.emplace_back(*output_bin_data);
+      }
+      mr_writer->WriteRawData(raw_data, bin_data);
+    }
+  } while (!row.empty());
+  mr_writer->Commit();
+  mindrecord::ShardIndexGenerator::finalize(file_names);
+  return Status::OK();
+}
+
+Status DEPipeline::FetchDataFromTensorRow(const TensorRow &row,
+                                          const std::unordered_map<std::string, int32_t> &column_name_id_map,
+                                          json *row_raw_data,
+                                          std::map<std::string, std::unique_ptr<std::vector<uint8_t>>> *row_bin_data) {
+  if (row_raw_data == nullptr) {
+    RETURN_STATUS_UNEXPECTED("error: row raw data is NULL.");
+  }
+  if (row_bin_data == nullptr) {
+    RETURN_STATUS_UNEXPECTED("error: row bin data is NULL.");
+  }
+  if (column_name_id_map.empty()) {
+    RETURN_STATUS_UNEXPECTED("Error: column not found");
+  }
+  Status s;
+  for (auto &col : column_name_id_map) {
+    auto idx = col.second;
+    auto column_name = col.first;
+    auto &tensor = row[idx];
+    auto column_type = tensor->type();
+
+    std::unique_ptr<std::vector<uint8_t>> data_ptr;
+    if (column_type == DataType::DE_INT8) {
+      std::unique_ptr<int32_t> data;
+      std::unique_ptr<int8_t> dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy, true);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_INT16) {
+      std::unique_ptr<int32_t> data;
+      std::unique_ptr<int16_t> dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy, true);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_UINT16) {
+      std::unique_ptr<int32_t> data;
+      std::unique_ptr<uint16_t> dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy, true);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_UINT8) {
+      std::unique_ptr<uint8_t> data, dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_INT32) {
+      std::unique_ptr<int32_t> data, dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_UINT32) {
+      std::unique_ptr<int64_t> data;
+      std::unique_ptr<uint32_t> dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy, true);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_INT64) {
+      std::unique_ptr<int64_t> data, dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_FLOAT32) {
+      std::unique_ptr<float> data, dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_FLOAT64) {
+      std::unique_ptr<double> data, dummy;
+      s = TransfromTensor(tensor->GetBuffer(), tensor->shape(), tensor->Size(), &data, &data_ptr, &dummy);
+      RETURN_IF_NOT_OK(s);
+      if (data != nullptr) (*row_raw_data)[column_name] = std::move(*data);
+    } else if (column_type == DataType::DE_STRING) {
+      auto buffer = tensor->GetStringsBuffer();
+      std::string ss(reinterpret_cast<const char *>(buffer));  // assume scalar string tensor
+      (*row_raw_data)[column_name] = std::move(ss);
+      continue;
+    } else {
+      RETURN_STATUS_UNEXPECTED("Got unexpected type when casting data.");
+    }
+    RETURN_IF_NOT_OK(s);
+    if (data_ptr != nullptr) {
+      (*row_bin_data)[column_name] = std::move(data_ptr);
+    }
+  }
+  return Status::OK();
+}
+
+template <typename T, typename S>
+Status DEPipeline::TransfromTensor(const unsigned char *src, const TensorShape &shape, const int64_t num_of_elements,
+                                   std::unique_ptr<T> *data, std::unique_ptr<std::vector<uint8_t>> *data_ptr,
+                                   std::unique_ptr<S> *s, bool need_convert) {
+  if (nullptr == src) {
+    RETURN_STATUS_UNEXPECTED("Error: buffer of Tensor is NULL.");
+  }
+  *data_ptr = std::make_unique<std::vector<uint8_t>>(num_of_elements * sizeof(T));
+  if (need_convert) {
+    auto tmp_ptr = std::make_unique<std::vector<uint8_t>>(num_of_elements * sizeof(S));
+    std::copy(src, src + sizeof(S) * num_of_elements, tmp_ptr->begin());
+    auto s_ptr = reinterpret_cast<S *>(&(*(tmp_ptr->begin())));
+    auto el = std::make_unique<T>();
+    for (uint32_t i = 0; i < num_of_elements; ++i) {
+      *el = *(s_ptr + i);
+      auto t_ptr = reinterpret_cast<uint8_t *>(el.get());
+      for (uint32_t j = 0; j < sizeof(T); ++j) {
+        *((*data_ptr)->begin() + i * sizeof(T) + j) = *(t_ptr + j);
+      }
+    }
+  } else {
+    std::copy(src, src + sizeof(T) * num_of_elements, (*data_ptr)->begin());
+  }
+  if (shape.empty()) {
+    *data = std::make_unique<T>();
+    auto t_ptr = reinterpret_cast<uint8_t *>((*data).get());
+    for (uint32_t i = 0; i < sizeof(T); ++i) {
+      *(t_ptr + i) = *((*data_ptr)->begin() + i);
+    }
+  }
+  return Status::OK();
+}
+
+Status DEPipeline::FetchMetaFromTensorRow(const std::unordered_map<std::string, int32_t> &column_name_id_map,
+                                          const TensorRow &row, json *schema, std::vector<std::string> *index_fields) {
+  if (schema == nullptr) {
+    RETURN_STATUS_UNEXPECTED("error: schema is NULL.");
+  }
+  if (index_fields == nullptr) {
+    RETURN_STATUS_UNEXPECTED("error: index fields is NULL.");
+  }
+  if (column_name_id_map.empty()) {
+    RETURN_STATUS_UNEXPECTED("Error: column not found.");
+  }
+  for (auto &col : column_name_id_map) {
+    auto idx = col.second;
+    auto column_name = col.first;
+    auto &tensor = row[idx];
+    auto column_type = tensor->type();
+    auto column_shape = tensor->shape();
+
+    std::string mr_type;
+    auto shapes = column_shape.AsVector();
+    std::vector<int> mr_shape(shapes.begin(), shapes.end());
+    std::string el = column_type.ToString();
+    if (mindrecord::kTypesMap.find(el) == mindrecord::kTypesMap.end()) {
+      std::string err_msg("Error: can not support data type: " + el);
+      RETURN_STATUS_UNEXPECTED(err_msg);
+    } else {
+      mr_type = mindrecord::kTypesMap.at(el);
+    }
+    if (mr_shape.empty()) {
+      if (mr_type == "bytes") {  // map to int32 when bytes without shape.
+        mr_type == "int32";
+      }
+      (*schema)[column_name] = {{"type", mr_type}};
+    } else {
+      if (mr_type == "string") {  // mindrecord can not support string with shape.
+        std::string err_msg("Error: mindrecord can not support multi-dimensional string tensor.");
+        RETURN_STATUS_UNEXPECTED(err_msg);
+      }
+      if (mr_type == "bytes") {  // ignore shape of bytes in minrecord
+        (*schema)[column_name] = {{"type", mr_type}};
+      } else {
+        (*schema)[column_name] = {{"type", mr_type}, {"shape", mr_shape}};
+      }
+    }
+    if (mr_type == "bytes" || !mr_shape.empty()) continue;
+    index_fields->emplace_back(column_name);  // candidate of index fields
+  }
+  return Status::OK();
+}
 Status DEPipeline::BuildMindrecordSamplerChain(const py::handle &handle,
                                                std::vector<std::shared_ptr<mindrecord::ShardOperator>> *operators,
                                                int num_padded) {

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

@@ -17,6 +17,7 @@
 #define DATASET_API_DE_PIPELINE_H_
 
 #include <iostream>
+#include <map>
 #include <memory>
 #include <stack>
 #include <string>
@@ -33,6 +34,7 @@
 namespace py = pybind11;
 namespace mindspore {
 namespace dataset {
+using json = nlohmann::json;
 using DsOpPtr = std::shared_ptr<DatasetOp>;
 
 class CacheClient;
@@ -100,6 +102,8 @@ class DEPipeline {
 
   Status GetOutputTypes(py::list *output);
 
+  Status SaveDataset(const std::vector<std::string> &file_names, const std::string &file_type);
+
   int GetDatasetSize() const;
 
   int GetBatchSize() const;
@@ -110,6 +114,18 @@ class DEPipeline {
 
   Status ParseMindRecordOp(const py::dict &args, std::shared_ptr<DatasetOp> *top, std::shared_ptr<DatasetOp> *bottom);
 
+  template <typename T, typename S>
+  Status TransfromTensor(const unsigned char *src, const TensorShape &shape, const int64_t num_of_elements,
+                         std::unique_ptr<T> *data, std::unique_ptr<std::vector<uint8_t>> *data_ptr,
+                         std::unique_ptr<S> *s, bool need_convert = false);
+
+  Status FetchMetaFromTensorRow(const std::unordered_map<std::string, int32_t> &column_name_id_map,
+                                const TensorRow &row, json *schema, std::vector<std::string> *index_fields);
+
+  Status FetchDataFromTensorRow(const TensorRow &row,
+                                const std::unordered_map<std::string, int32_t> &column_name_id_map, json *row_raw_data,
+                                std::map<std::string, std::unique_ptr<std::vector<uint8_t>>> *row_bin_data);
+
   Status BuildMindrecordSamplerChain(const py::handle &handle,
                                      std::vector<std::shared_ptr<mindrecord::ShardOperator>> *operators,
                                      int num_padded);

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

@@ -184,7 +184,11 @@ void bindDEPipeline(py::module *m) {
     .def("GetDatasetSize", &DEPipeline::GetDatasetSize)
     .def("GetBatchSize", &DEPipeline::GetBatchSize)
     .def("GetNumClasses", &DEPipeline::GetNumClasses)
-    .def("GetRepeatCount", &DEPipeline::GetRepeatCount);
+    .def("GetRepeatCount", &DEPipeline::GetRepeatCount)
+    .def("SaveDataset", [](DEPipeline &de, const std::vector<std::string> &file_names, const std::string &file_type) {
+      THROW_IF_ERROR(de.SaveDataset(file_names, file_type));
+      return true;
+    });
 }
 void bindDatasetOps(py::module *m) {
   (void)py::class_<TFReaderOp, DatasetOp, std::shared_ptr<TFReaderOp>>(*m, "TFReaderOp")

mindspore/ccsrc/minddata/dataset/core/tensor.h

@@ -312,6 +312,11 @@ class Tensor {
   // @return const unsigned char*
   const unsigned char *GetBuffer() const;
 
+  // Skip the offsets and returns the start of the buffer where the real strings is stored. Caller needs to check if the
+  // tensor's type is a string, otherwise undefined address would be returned.
+  // @return address of the first string of the tensor.
+  uchar *GetStringsBuffer() const { return data_ + kOffsetSize * shape_.NumOfElements() + kOffsetSize; }
+
   // Getter of the type
   // @return
   DataType type() const { return type_; }
@@ -643,11 +648,6 @@ class Tensor {
   // @return length of the string
   Status GetStringAt(dsize_t index, uchar **string_start, offset_t *length) const;
 
-  // Skip the offsets and returns the start of the buffer where the real strings is stored. Caller needs to check if the
-  // tensor's type is a string, otherwise undefined address would be returned.
-  // @return address of the first string of the tensor.
-  uchar *GetStringsBuffer() const { return data_ + kOffsetSize * shape_.NumOfElements() + kOffsetSize; }
-
   // all access to shape_ should be via shape
   TensorShape shape_;
   // data type of tensor

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

@@ -215,7 +215,7 @@ void MindRecordOp::Print(std::ostream &out, bool show_all) const {
     // Call the super class for displaying any common detailed info
     ParallelOp::Print(out, show_all);
     // Then show any custom derived-internal stuff
-    out << "\n Dataset file : ";
+    out << "\nDataset file : ";
     for (auto &file : dataset_file_) {
       out << file << " ";
     }

mindspore/ccsrc/minddata/mindrecord/include/common/shard_utils.h

@@ -137,6 +137,10 @@ const std::set<std::string> kScalarFieldTypeSet = {"string", "int32", "int64", "
 // number field list
 const std::set<std::string> kNumberFieldTypeSet = {"int32", "int64", "float32", "float64"};
 
+const std::unordered_map<std::string, std::string> kTypesMap = {
+  {"bool", "int32"},      {"int8", "int32"},      {"uint8", "bytes"},     {"int16", "int32"},
+  {"uint16", "int32"},    {"int32", "int32"},     {"uint32", "int64"},    {"int64", "int64"},
+  {"float16", "float32"}, {"float32", "float32"}, {"float64", "float64"}, {"string", "string"}};
 /// \brief split a string using a character
 /// \param[in] field target string
 /// \param[in] separator a character for spliting

mindspore/ccsrc/minddata/mindrecord/include/shard_header.h

@@ -124,6 +124,10 @@ class ShardHeader {
 
   MSRStatus FileToPages(const std::string dump_file_name);
 
+  static MSRStatus initialize(const std::shared_ptr<ShardHeader> *header_ptr, const json &schema,
+                              const std::vector<std::string> &index_fields, std::vector<std::string> &blob_fields,
+                              uint64_t &schema_id);
+
  private:
   MSRStatus InitializeHeader(const std::vector<json> &headers, bool load_dataset);
 

mindspore/ccsrc/minddata/mindrecord/include/shard_index_generator.h

@@ -57,6 +57,8 @@ class ShardIndexGenerator {
   /// \brief create databases for indexes
   MSRStatus WriteToDatabase();
 
+  static MSRStatus finalize(const std::vector<std::string> file_names);
+
  private:
   static int Callback(void *not_used, int argc, char **argv, char **az_col_name);
 

mindspore/ccsrc/minddata/mindrecord/include/shard_writer.h

@@ -108,6 +108,13 @@ class ShardWriter {
                          std::map<uint64_t, std::vector<py::handle>> &blob_data, bool sign = true,
                          bool parallel_writer = false);
 
+  MSRStatus MergeBlobData(const std::vector<string> &blob_fields,
+                          const std::map<std::string, std::unique_ptr<std::vector<uint8_t>>> &row_bin_data,
+                          std::shared_ptr<std::vector<uint8_t>> *output);
+
+  static MSRStatus initialize(const std::unique_ptr<ShardWriter> *writer_ptr,
+                              const std::vector<std::string> &file_names);
+
  private:
   /// \brief write shard header data to disk
   MSRStatus WriteShardHeader();

mindspore/ccsrc/minddata/mindrecord/io/shard_index_generator.cc

@@ -622,5 +622,21 @@ void ShardIndexGenerator::DatabaseWriter() {
     shard_no = task_++;
   }
 }
+MSRStatus ShardIndexGenerator::finalize(const std::vector<std::string> file_names) {
+  if (file_names.empty()) {
+    MS_LOG(ERROR) << "Mindrecord files is empty.";
+    return FAILED;
+  }
+  ShardIndexGenerator sg{file_names[0]};
+  if (SUCCESS != sg.Build()) {
+    MS_LOG(ERROR) << "Failed to build index generator.";
+    return FAILED;
+  }
+  if (SUCCESS != sg.WriteToDatabase()) {
+    MS_LOG(ERROR) << "Failed to write to database.";
+    return FAILED;
+  }
+  return SUCCESS;
+}
 }  // namespace mindrecord
 }  // namespace mindspore

mindspore/ccsrc/minddata/mindrecord/io/shard_writer.cc

@@ -637,6 +637,42 @@ MSRStatus ShardWriter::WriteRawDataPreCheck(std::map<uint64_t, std::vector<json>
   *row_count = std::get<2>(v);
   return SUCCESS;
 }
+MSRStatus ShardWriter::MergeBlobData(const std::vector<string> &blob_fields,
+                                     const std::map<std::string, std::unique_ptr<std::vector<uint8_t>>> &row_bin_data,
+                                     std::shared_ptr<std::vector<uint8_t>> *output) {
+  if (blob_fields.empty()) {
+    return SUCCESS;
+  }
+  if (blob_fields.size() == 1) {
+    auto &blob = row_bin_data.at(blob_fields[0]);
+    auto blob_size = blob->size();
+    *output = std::make_shared<std::vector<uint8_t>>(blob_size);
+    std::copy(blob->begin(), blob->end(), (*output)->begin());
+  } else {
+    size_t output_size = 0;
+    for (auto &field : blob_fields) {
+      output_size += row_bin_data.at(field)->size();
+    }
+    output_size += blob_fields.size() * sizeof(uint64_t);
+    *output = std::make_shared<std::vector<uint8_t>>(output_size);
+    std::vector<uint8_t> buf(sizeof(uint64_t), 0);
+    size_t idx = 0;
+    for (auto &field : blob_fields) {
+      auto &blob = row_bin_data.at(field);
+      uint64_t blob_size = blob->size();
+      // big edian
+      for (size_t i = 0; i < buf.size(); ++i) {
+        buf[buf.size() - 1 - i] = std::numeric_limits<uint8_t>::max() & blob_size;
+        blob_size >>= 8u;
+      }
+      std::copy(buf.begin(), buf.end(), (*output)->begin() + idx);
+      idx += buf.size();
+      std::copy(blob->begin(), blob->end(), (*output)->begin() + idx);
+      idx += blob->size();
+    }
+  }
+  return SUCCESS;
+}
 
 MSRStatus ShardWriter::WriteRawData(std::map<uint64_t, std::vector<json>> &raw_data,
                                     std::vector<std::vector<uint8_t>> &blob_data, bool sign, bool parallel_writer) {
@@ -1250,5 +1286,21 @@ void ShardWriter::SetLastBlobPage(const int &shard_id, std::shared_ptr<Page> &la
     last_blob_page = page.first;
   }
 }
+
+MSRStatus ShardWriter::initialize(const std::unique_ptr<ShardWriter> *writer_ptr,
+                                  const std::vector<std::string> &file_names) {
+  if (nullptr == writer_ptr) {
+    MS_LOG(ERROR) << "ShardWriter pointer is NULL.";
+    return FAILED;
+  }
+  auto res = (*writer_ptr)->Open(file_names, false);
+  if (SUCCESS != res) {
+    MS_LOG(ERROR) << "Failed to open mindrecord files to writer.";
+    return FAILED;
+  }
+  (*writer_ptr)->SetHeaderSize(1 << 24);
+  (*writer_ptr)->SetPageSize(1 << 25);
+  return SUCCESS;
+}
 }  // namespace mindrecord
 }  // namespace mindspore

mindspore/ccsrc/minddata/mindrecord/meta/shard_header.cc

@@ -721,5 +721,35 @@ MSRStatus ShardHeader::FileToPages(const std::string dump_file_name) {
   page_in_handle.close();
   return SUCCESS;
 }
+
+MSRStatus ShardHeader::initialize(const std::shared_ptr<ShardHeader> *header_ptr, const json &schema,
+                                  const std::vector<std::string> &index_fields, std::vector<std::string> &blob_fields,
+                                  uint64_t &schema_id) {
+  if (nullptr == header_ptr) {
+    MS_LOG(ERROR) << "ShardHeader pointer is NULL.";
+    return FAILED;
+  }
+  auto schema_ptr = Schema::Build("mindrecord", schema);
+  if (nullptr == schema_ptr) {
+    MS_LOG(ERROR) << "Got unexpected error when building mindrecord schema.";
+    return FAILED;
+  }
+  schema_id = (*header_ptr)->AddSchema(schema_ptr);
+  // create index
+  std::vector<std::pair<uint64_t, std::string>> id_index_fields;
+  if (!index_fields.empty()) {
+    for (auto &el : index_fields) {
+      id_index_fields.emplace_back(schema_id, el);
+    }
+    if (SUCCESS != (*header_ptr)->AddIndexFields(id_index_fields)) {
+      MS_LOG(ERROR) << "Got unexpected error when adding mindrecord index.";
+      return FAILED;
+    }
+  }
+
+  auto build_schema_ptr = (*header_ptr)->GetSchemas()[0];
+  blob_fields = build_schema_ptr->GetBlobFields();
+  return SUCCESS;
+}
 }  // namespace mindrecord
 }  // namespace mindspore

mindspore/dataset/engine/datasets.py

@@ -38,13 +38,13 @@ from mindspore._c_expression import typing
 
 from mindspore import log as logger
 from . import samplers
-from .iterators import DictIterator, TupleIterator, DummyIterator
+from .iterators import DictIterator, TupleIterator, DummyIterator, SaveOp
 from .validators import check_batch, check_shuffle, check_map, check_filter, check_repeat, check_skip, check_zip, \
     check_rename, check_numpyslicesdataset, \
     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_positive_int32
+    check_random_dataset, check_split, check_bucket_batch_by_length, check_cluedataset, check_positive_int32, check_save
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
 
 try:
@@ -1044,6 +1044,34 @@ class Dataset:
 
         return TransferDataset(self, queue_name, device_id, device_type, num_batch)
 
+    @check_save
+    def save(self, file_name, num_files=1, file_type='mindrecord'):
+        """
+        Save the dynamic data processed by dataset pipeline as common dataset format, support: mindrecord.
+
+        Note:
+            1. To save the samples in order, should set dataset's shuffle false and num_files 1.
+            2. Before call the function, do not use batch, repeat operator or data augmentation operators
+               with random attribute in map operator.
+            3. Mindreocrd do not support np.uint64, multi-dimensional np.uint8(drop dimension) and
+               multi-dimensional string.
+
+        Args:
+            file_name (str): Path to dataset file.
+            num_files (int, optional): Number of dataset files.(default=1).
+            file_type (str, optional): dataset format.(default='mindrecord')
+
+        """
+
+        if num_files == 1:
+            file_names = [file_name]
+        else:
+            suffix = len(str(num_files - 1))
+            file_names = ["{}{}".format(file_name, str(x).rjust(suffix, '0'))
+                          for x in range(num_files)]
+
+        return SaveOp(self).save(file_names, file_type)
+
     def create_tuple_iterator(self, columns=None):
         """
         Create an Iterator over the dataset. The data retrieved will be a list of ndarray of data.

mindspore/dataset/engine/iterators.py

@@ -173,6 +173,7 @@ class Iterator:
 
     # Convert python node into C node and add to C layer execution tree in postorder traversal.
     def __convert_node_postorder(self, node):
+        self.check_node_type(node)
         op_type = self.__get_dataset_type(node)
         c_nodes = self.depipeline.AddNodeToTree(op_type, node.get_args())
 
@@ -224,6 +225,10 @@ class Iterator:
         self._index += 1
         return data
 
+    @abstractmethod
+    def check_node_type(self, node):
+        pass
+
     def get_output_shapes(self):
         return [t for t in self.depipeline.GetOutputShapes()]
 
@@ -245,11 +250,27 @@ class Iterator:
     def __deepcopy__(self, memo):
         return self
 
+class SaveOp(Iterator):
+    """
+    The derived class of Iterator with dict type.
+    """
+    def get_next(self):
+        pass
+
+    def check_node_type(self, node):
+        if isinstance(node, (de.ShuffleDataset, de.RepeatDataset, de.BatchDataset)):
+            logger.warning("Used shuffle, repeat, batch before save operator.")
+
+    def save(self, file_names, file_type):
+        return self.depipeline.SaveDataset(file_names, file_type)
+
 
 class DictIterator(Iterator):
     """
     The derived class of Iterator with dict type.
     """
+    def check_node_type(self, node):
+        pass
 
     def __iter__(self):
         return self
@@ -269,6 +290,8 @@ class TupleIterator(Iterator):
     """
     The derived class of Iterator with list type.
     """
+    def check_node_type(self, node):
+        pass
 
     def __init__(self, dataset, columns=None):
         if columns is not None:

mindspore/dataset/engine/validators.py

@@ -246,7 +246,24 @@ def check_celebadataset(method):
 
     return new_method
 
+def check_save(method):
+    """A wrapper that wrap a parameter checker to the save op."""
 
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        _, param_dict = parse_user_args(method, *args, **kwargs)
+
+        nreq_param_int = ['num_files']
+        nreq_param_str = ['file_name', 'file_type']
+        validate_dataset_param_value(nreq_param_int, param_dict, int)
+        if(param_dict.get('num_files') <= 0 or param_dict.get('num_files') > 1000):
+            raise ValueError("num_files should between {} and {}.".format(1, 1000))
+        validate_dataset_param_value(nreq_param_str, param_dict, str)
+        if param_dict.get('file_type') != 'mindrecord':
+            raise ValueError("{} dataset format is not supported.".format(param_dict.get('file_type')))
+        return method(self, *args, **kwargs)
+
+    return new_method
 def check_minddataset(method):
     """A wrapper that wraps a parameter checker to the original Dataset(MindDataset)."""
 

tests/ut/python/dataset/test_save_op.py

@@ -0,0 +1,390 @@
+# 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.
+# ==============================================================================
+"""
+This is the test module for saveOp.
+"""
+import os
+import mindspore.dataset as ds
+from mindspore import log as logger
+from mindspore.mindrecord import FileWriter
+import numpy as np
+import pytest
+
+CV_FILE_NAME1 = "../data/mindrecord/testMindDataSet/temp.mindrecord"
+CV_FILE_NAME2 = "../data/mindrecord/testMindDataSet/auto.mindrecord"
+
+FILES_NUM = 1
+num_readers = 1
+
+
+@pytest.fixture(name="add_and_remove_cv_file")
+def fixture_remove():
+    """add/remove cv file"""
+    if os.path.exists("{}".format(CV_FILE_NAME1)):
+        os.remove("{}".format(CV_FILE_NAME1))
+    if os.path.exists("{}.db".format(CV_FILE_NAME1)):
+        os.remove("{}.db".format(CV_FILE_NAME1))
+
+    if os.path.exists("{}".format(CV_FILE_NAME2)):
+        os.remove("{}".format(CV_FILE_NAME2))
+    if os.path.exists("{}.db".format(CV_FILE_NAME2)):
+        os.remove("{}.db".format(CV_FILE_NAME2))
+    yield "yield_cv_data"
+    if os.path.exists("{}".format(CV_FILE_NAME1)):
+        os.remove("{}".format(CV_FILE_NAME1))
+    if os.path.exists("{}.db".format(CV_FILE_NAME1)):
+        os.remove("{}.db".format(CV_FILE_NAME1))
+
+    if os.path.exists("{}".format(CV_FILE_NAME2)):
+        os.remove("{}".format(CV_FILE_NAME2))
+    if os.path.exists("{}.db".format(CV_FILE_NAME2)):
+        os.remove("{}.db".format(CV_FILE_NAME2))
+
+
+def test_case_00(add_and_remove_cv_file):  # only bin data
+    data = [{"image1": bytes("image1 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image1 bytes def", encoding='UTF-8'),
+             "image3": bytes("image1 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image1 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image1 bytes mno", encoding='UTF-8')},
+            {"image1": bytes("image2 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image2 bytes def", encoding='UTF-8'),
+             "image3": bytes("image2 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image2 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image2 bytes mno", encoding='UTF-8')},
+            {"image1": bytes("image3 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image3 bytes def", encoding='UTF-8'),
+             "image3": bytes("image3 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image3 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image3 bytes mno", encoding='UTF-8')},
+            {"image1": bytes("image5 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image5 bytes def", encoding='UTF-8'),
+             "image3": bytes("image5 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image5 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image5 bytes mno", encoding='UTF-8')},
+            {"image1": bytes("image6 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image6 bytes def", encoding='UTF-8'),
+             "image3": bytes("image6 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image6 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image6 bytes mno", encoding='UTF-8')}]
+    schema = {
+        "image1": {"type": "bytes"},
+        "image2": {"type": "bytes"},
+        "image3": {"type": "bytes"},
+        "image4": {"type": "bytes"},
+        "image5": {"type": "bytes"}}
+    writer = FileWriter(CV_FILE_NAME1, FILES_NUM)
+    writer.add_schema(schema, "schema")
+    writer.write_raw_data(data)
+    writer.commit()
+
+    d1 = ds.MindDataset(CV_FILE_NAME1, None, num_readers, shuffle=False)
+    d1.save(CV_FILE_NAME2, FILES_NUM)
+    data_value_to_list = []
+
+    for item in data:
+        new_data = {}
+        new_data['image1'] = np.asarray(list(item["image1"]), dtype=np.uint8)
+        new_data['image2'] = np.asarray(list(item["image2"]), dtype=np.uint8)
+        new_data['image3'] = np.asarray(list(item["image3"]), dtype=np.uint8)
+        new_data['image4'] = np.asarray(list(item["image4"]), dtype=np.uint8)
+        new_data['image5'] = np.asarray(list(item["image5"]), dtype=np.uint8)
+        data_value_to_list.append(new_data)
+
+    d2 = ds.MindDataset(dataset_file=CV_FILE_NAME2,
+                        num_parallel_workers=num_readers,
+                        shuffle=False)
+    assert d2.get_dataset_size() == 5
+    num_iter = 0
+    for item in d2.create_dict_iterator():
+        assert len(item) == 5
+        for field in item:
+            if isinstance(item[field], np.ndarray):
+                assert (item[field] ==
+                        data_value_to_list[num_iter][field]).all()
+            else:
+                assert item[field] == data_value_to_list[num_iter][field]
+        num_iter += 1
+    assert num_iter == 5
+
+
+def test_case_01(add_and_remove_cv_file):  # only raw data
+    data = [{"file_name": "001.jpg", "label": 43},
+            {"file_name": "002.jpg", "label": 91},
+            {"file_name": "003.jpg", "label": 61},
+            {"file_name": "004.jpg", "label": 29},
+            {"file_name": "005.jpg", "label": 78},
+            {"file_name": "006.jpg", "label": 37}]
+    schema = {"file_name": {"type": "string"},
+              "label": {"type": "int32"}
+              }
+
+    writer = FileWriter(CV_FILE_NAME1, FILES_NUM)
+    writer.add_schema(schema, "schema")
+    writer.write_raw_data(data)
+    writer.commit()
+
+    d1 = ds.MindDataset(CV_FILE_NAME1, None, num_readers, shuffle=False)
+    d1.save(CV_FILE_NAME2, FILES_NUM)
+
+    data_value_to_list = []
+    for item in data:
+        new_data = {}
+        new_data['file_name'] = np.asarray(item["file_name"], dtype='S')
+        new_data['label'] = np.asarray(list([item["label"]]), dtype=np.int32)
+        data_value_to_list.append(new_data)
+
+    d2 = ds.MindDataset(dataset_file=CV_FILE_NAME2,
+                        num_parallel_workers=num_readers,
+                        shuffle=False)
+    assert d2.get_dataset_size() == 6
+    num_iter = 0
+    for item in d2.create_dict_iterator():
+        logger.info(item)
+        assert len(item) == 2
+        for field in item:
+            if isinstance(item[field], np.ndarray):
+                assert (item[field] ==
+                        data_value_to_list[num_iter][field]).all()
+            else:
+                assert item[field] == data_value_to_list[num_iter][field]
+        num_iter += 1
+    assert num_iter == 6
+
+
+def test_case_02(add_and_remove_cv_file):  # muti-bytes
+    data = [{"file_name": "001.jpg", "label": 43,
+             "float32_array": np.array([1.2, 2.78, 3.1234, 4.9871, 5.12341], dtype=np.float32),
+             "float64_array": np.array([48.1234556789, 49.3251241431, 50.13514312414, 51.8971298471,
+                                        123414314.2141243, 87.1212122], dtype=np.float64),
+             "float32": 3456.12345,
+             "float64": 1987654321.123456785,
+             "source_sos_ids": np.array([1, 2, 3, 4, 5], dtype=np.int32),
+             "source_sos_mask": np.array([6, 7, 8, 9, 10, 11, 12], dtype=np.int64),
+             "image1": bytes("image1 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image1 bytes def", encoding='UTF-8'),
+             "image3": bytes("image1 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image1 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image1 bytes mno", encoding='UTF-8')},
+            {"file_name": "002.jpg", "label": 91,
+             "float32_array": np.array([1.2, 2.78, 4.1234, 4.9871, 5.12341], dtype=np.float32),
+             "float64_array": np.array([48.1234556789, 49.3251241431, 60.13514312414, 51.8971298471,
+                                        123414314.2141243, 87.1212122], dtype=np.float64),
+             "float32": 3456.12445,
+             "float64": 1987654321.123456786,
+             "source_sos_ids": np.array([11, 2, 3, 4, 5], dtype=np.int32),
+             "source_sos_mask": np.array([16, 7, 8, 9, 10, 11, 12], dtype=np.int64),
+             "image1": bytes("image2 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image2 bytes def", encoding='UTF-8'),
+             "image3": bytes("image2 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image2 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image2 bytes mno", encoding='UTF-8')},
+            {"file_name": "003.jpg", "label": 61,
+             "float32_array": np.array([1.2, 2.78, 5.1234, 4.9871, 5.12341], dtype=np.float32),
+             "float64_array": np.array([48.1234556789, 49.3251241431, 70.13514312414, 51.8971298471,
+                                        123414314.2141243, 87.1212122], dtype=np.float64),
+             "float32": 3456.12545,
+             "float64": 1987654321.123456787,
+             "source_sos_ids": np.array([21, 2, 3, 4, 5], dtype=np.int32),
+             "source_sos_mask": np.array([26, 7, 8, 9, 10, 11, 12], dtype=np.int64),
+             "image1": bytes("image3 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image3 bytes def", encoding='UTF-8'),
+             "image3": bytes("image3 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image3 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image3 bytes mno", encoding='UTF-8')},
+            {"file_name": "004.jpg", "label": 29,
+             "float32_array": np.array([1.2, 2.78, 6.1234, 4.9871, 5.12341], dtype=np.float32),
+             "float64_array": np.array([48.1234556789, 49.3251241431, 80.13514312414, 51.8971298471,
+                                        123414314.2141243, 87.1212122], dtype=np.float64),
+             "float32": 3456.12645,
+             "float64": 1987654321.123456788,
+             "source_sos_ids": np.array([31, 2, 3, 4, 5], dtype=np.int32),
+             "source_sos_mask": np.array([36, 7, 8, 9, 10, 11, 12], dtype=np.int64),
+             "image1": bytes("image4 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image4 bytes def", encoding='UTF-8'),
+             "image3": bytes("image4 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image4 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image4 bytes mno", encoding='UTF-8')},
+            {"file_name": "005.jpg", "label": 78,
+             "float32_array": np.array([1.2, 2.78, 7.1234, 4.9871, 5.12341], dtype=np.float32),
+             "float64_array": np.array([48.1234556789, 49.3251241431, 90.13514312414, 51.8971298471,
+                                        123414314.2141243, 87.1212122], dtype=np.float64),
+             "float32": 3456.12745,
+             "float64": 1987654321.123456789,
+             "source_sos_ids": np.array([41, 2, 3, 4, 5], dtype=np.int32),
+             "source_sos_mask": np.array([46, 7, 8, 9, 10, 11, 12], dtype=np.int64),
+             "image1": bytes("image5 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image5 bytes def", encoding='UTF-8'),
+             "image3": bytes("image5 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image5 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image5 bytes mno", encoding='UTF-8')},
+            {"file_name": "006.jpg", "label": 37,
+             "float32_array": np.array([1.2, 2.78, 7.1234, 4.9871, 5.12341], dtype=np.float32),
+             "float64_array": np.array([48.1234556789, 49.3251241431, 90.13514312414, 51.8971298471,
+                                        123414314.2141243, 87.1212122], dtype=np.float64),
+             "float32": 3456.12745,
+             "float64": 1987654321.123456789,
+             "source_sos_ids": np.array([51, 2, 3, 4, 5], dtype=np.int32),
+             "source_sos_mask": np.array([56, 7, 8, 9, 10, 11, 12], dtype=np.int64),
+             "image1": bytes("image6 bytes abc", encoding='UTF-8'),
+             "image2": bytes("image6 bytes def", encoding='UTF-8'),
+             "image3": bytes("image6 bytes ghi", encoding='UTF-8'),
+             "image4": bytes("image6 bytes jkl", encoding='UTF-8'),
+             "image5": bytes("image6 bytes mno", encoding='UTF-8')}
+            ]
+    schema = {"file_name": {"type": "string"},
+              "float32_array": {"type": "float32", "shape": [-1]},
+              "float64_array": {"type": "float64", "shape": [-1]},
+              "float32": {"type": "float32"},
+              "float64": {"type": "float64"},
+              "source_sos_ids": {"type": "int32", "shape": [-1]},
+              "source_sos_mask": {"type": "int64", "shape": [-1]},
+              "image1": {"type": "bytes"},
+              "image2": {"type": "bytes"},
+              "image3": {"type": "bytes"},
+              "label": {"type": "int32"},
+              "image4": {"type": "bytes"},
+              "image5": {"type": "bytes"}}
+    writer = FileWriter(CV_FILE_NAME1, FILES_NUM)
+    writer.add_schema(schema, "schema")
+    writer.write_raw_data(data)
+    writer.commit()
+
+    d1 = ds.MindDataset(CV_FILE_NAME1, None, num_readers, shuffle=False)
+    d1.save(CV_FILE_NAME2, FILES_NUM)
+    data_value_to_list = []
+
+    for item in data:
+        new_data = {}
+        new_data['file_name'] = np.asarray(item["file_name"], dtype='S')
+        new_data['float32_array'] = item["float32_array"]
+        new_data['float64_array'] = item["float64_array"]
+        new_data['float32'] = item["float32"]
+        new_data['float64'] = item["float64"]
+        new_data['source_sos_ids'] = item["source_sos_ids"]
+        new_data['source_sos_mask'] = item["source_sos_mask"]
+        new_data['label'] = np.asarray(list([item["label"]]), dtype=np.int32)
+        new_data['image1'] = np.asarray(list(item["image1"]), dtype=np.uint8)
+        new_data['image2'] = np.asarray(list(item["image2"]), dtype=np.uint8)
+        new_data['image3'] = np.asarray(list(item["image3"]), dtype=np.uint8)
+        new_data['image4'] = np.asarray(list(item["image4"]), dtype=np.uint8)
+        new_data['image5'] = np.asarray(list(item["image5"]), dtype=np.uint8)
+        data_value_to_list.append(new_data)
+
+    d2 = ds.MindDataset(dataset_file=CV_FILE_NAME2,
+                        num_parallel_workers=num_readers,
+                        shuffle=False)
+    assert d2.get_dataset_size() == 6
+    num_iter = 0
+    for item in d2.create_dict_iterator():
+        assert len(item) == 13
+        for field in item:
+            if isinstance(item[field], np.ndarray):
+                if item[field].dtype == np.float32:
+                    assert (item[field] ==
+                            np.array(data_value_to_list[num_iter][field], np.float32)).all()
+                else:
+                    assert (item[field] ==
+                            data_value_to_list[num_iter][field]).all()
+            else:
+                assert item[field] == data_value_to_list[num_iter][field]
+        num_iter += 1
+    assert num_iter == 6
+
+
+def generator_1d():
+    for i in range(10):
+        yield (np.array([i]),)
+
+
+def test_case_03(add_and_remove_cv_file):
+
+    # apply dataset operations
+    d1 = ds.GeneratorDataset(generator_1d, ["data"], shuffle=False)
+
+    d1.save(CV_FILE_NAME2)
+
+    d2 = ds.MindDataset(dataset_file=CV_FILE_NAME2,
+                        num_parallel_workers=num_readers,
+                        shuffle=False)
+
+    i = 0
+    for item in d2.create_dict_iterator():  # each data is a dictionary
+        golden = np.array([i])
+        assert np.array_equal(item["data"], golden)
+        i = i + 1
+
+
+def generator_with_type(t):
+    for i in range(64):
+        yield (np.array([i], dtype=t),)
+
+
+def type_tester(t):
+    logger.info("Test with Type {}".format(t.__name__))
+
+    # apply dataset operations
+    data1 = ds.GeneratorDataset((lambda: generator_with_type(t)), ["data"], shuffle=False)
+
+    data1 = data1.batch(4)
+
+    data1 = data1.repeat(3)
+
+    data1.save(CV_FILE_NAME2)
+
+    d2 = ds.MindDataset(dataset_file=CV_FILE_NAME2,
+                        num_parallel_workers=num_readers,
+                        shuffle=False)
+
+    i = 0
+    num_repeat = 0
+    for item in d2.create_dict_iterator():  # each data is a dictionary
+        golden = np.array([[i], [i + 1], [i + 2], [i + 3]], dtype=t)
+        logger.info(item)
+        assert np.array_equal(item["data"], golden)
+        i = i + 4
+        if i == 64:
+            i = 0
+            num_repeat += 1
+    assert num_repeat == 3
+    if os.path.exists("{}".format(CV_FILE_NAME2)):
+        os.remove("{}".format(CV_FILE_NAME2))
+    if os.path.exists("{}.db".format(CV_FILE_NAME2)):
+        os.remove("{}.db".format(CV_FILE_NAME2))
+
+
+def test_case_04():
+    # uint8 will drop shape as mindrecord store uint8 as bytes
+    types = [np.int8, np.int16, np.int32, np.int64,
+             np.uint16, np.uint32, np.float32, np.float64]
+
+    for t in types:
+        type_tester(t)
+
+
+def test_case_05(add_and_remove_cv_file):
+
+    d1 = ds.GeneratorDataset(generator_1d, ["data"], shuffle=False)
+
+    with pytest.raises(Exception, match="num_files should between 1 and 1000."):
+        d1.save(CV_FILE_NAME2, 0)
+
+
+def test_case_06(add_and_remove_cv_file):
+
+    d1 = ds.GeneratorDataset(generator_1d, ["data"], shuffle=False)
+
+    with pytest.raises(Exception, match="tfrecord dataset format is not supported."):
+        d1.save(CV_FILE_NAME2, 1, "tfrecord")