delete parameter block_reader for MindDataset

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

@@ -672,8 +672,6 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
     if (!value.is_none()) {
       if (key == "num_parallel_workers") {
         (void)builder->SetNumMindRecordWorkers(ToInt(value));
-      } else if (key == "block_reader" && ToBool(value) == true) {
-        (void)builder->SetBlockReader();
       } else if (key == "sampler") {
         int num_padded = 0;
         if (!args["num_padded"].is_none()) {

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

@@ -51,7 +51,6 @@ MindRecordOp::Builder::Builder() : build_dataset_file_({}) {
   build_num_mind_record_workers_ = kDefaultMindRecordWorkers;
   build_rows_per_buffer_ = cfg->rows_per_buffer();
   build_op_connector_queue_size_ = cfg->op_connector_size();
-  build_block_reader_ = false;
   builder_num_workers_ = 0;
   build_num_padded_ = 0;
   build_sample_ = nullptr;
@@ -69,10 +68,10 @@ Status MindRecordOp::Builder::Build(std::shared_ptr<MindRecordOp> *ptr) {
   if (build_num_padded_ > 0) {
     sample_json = ToJson(build_sample_);
   }
-  new_mind_record_op = std::make_shared<MindRecordOp>(
+  new_mind_record_op =
-    build_num_mind_record_workers_, build_rows_per_buffer_, build_dataset_file_, build_load_dataset_,
+    std::make_shared<MindRecordOp>(build_num_mind_record_workers_, build_rows_per_buffer_, build_dataset_file_,
-    build_op_connector_queue_size_, build_columns_to_load_, build_operators_, build_block_reader_, build_num_padded_,
+                                   build_load_dataset_, build_op_connector_queue_size_, build_columns_to_load_,
-    sample_json, build_sample_bytes_);
+                                   build_operators_, build_num_padded_, sample_json, build_sample_bytes_);
 
   RETURN_IF_NOT_OK(new_mind_record_op->Init());
   *ptr = std::move(new_mind_record_op);
@@ -113,9 +112,8 @@ mindrecord::json MindRecordOp::Builder::ToJson(const py::handle &obj) {
 MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buffer,
                            std::vector<std::string> dataset_file, bool load_dataset, int32_t op_connector_queue_size,
                            const std::vector<std::string> &columns_to_load,
-                           const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader,
+                           const std::vector<std::shared_ptr<ShardOperator>> &operators, int64_t num_padded,
-                           int64_t num_padded, const mindrecord::json &sample_json,
+                           const mindrecord::json &sample_json, const std::map<std::string, std::string> &sample_bytes)
-                           const std::map<std::string, std::string> &sample_bytes)
     : ParallelOp(num_mind_record_workers, op_connector_queue_size),
       rows_per_buffer_(rows_per_buffer),
       dataset_file_(dataset_file),
@@ -123,27 +121,21 @@ MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buf
       columns_to_load_(columns_to_load),
       operators_(operators),
       num_mind_record_workers_(num_mind_record_workers),
-      block_reader_(block_reader),
       num_rows_(0),
       buffers_needed_(0),
       buf_cnt_(0),
       ended_worker_(0),
-      buffer_water_mark_(0),
       num_padded_(num_padded),
       sample_json_(sample_json),
       sample_bytes_(sample_bytes) {
   io_blk_queues_.Init(num_workers_, op_connector_queue_size);
-  if (!block_reader_) return;
-  for (int32_t i = 0; i < num_workers_; ++i) {
-    block_buffer_.emplace_back(std::make_unique<std::vector<ShardTuple>>(std::vector<ShardTuple>{}));
-  }
 }
 
 // Private helper method to encapsulate some common construction/reset tasks
 Status MindRecordOp::Init() {
   shard_reader_ = std::make_unique<ShardReader>();
   auto rc = shard_reader_->Open(dataset_file_, load_dataset_, num_mind_record_workers_, columns_to_load_, operators_,
-                                block_reader_, num_padded_);
+                                num_padded_);
 
   CHECK_FAIL_RETURN_UNEXPECTED(rc == MSRStatus::SUCCESS,
                                "MindRecordOp init failed. Error message: " + ErrnoToMessage(rc));
@@ -264,23 +256,6 @@ Status MindRecordOp::WorkerEntry(int32_t worker_id) {
     }
     RETURN_IF_NOT_OK(GetBufferFromReader(&fetched_buffer, buffer_id, worker_id));
     RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(fetched_buffer)));
-    if (!block_reader_) {
-      RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block));
-      continue;
-    }
-
-    // update block-reader buffer
-    block_buffer_[buffer_id % num_workers_]->clear();
-    {
-      std::unique_lock<std::mutex> lck(mtx_block_reader_);
-      if (buffer_id == buffer_water_mark_) {
-        buffer_water_mark_++;
-        while (block_set_.count(buffer_water_mark_) > 0) (void)block_set_.erase(buffer_water_mark_++);
-      } else {
-        (void)block_set_.insert(buffer_id);
-      }
-    }
-    cv_reader_.notify_one();
     RETURN_IF_NOT_OK(io_blk_queues_[worker_id]->PopFront(&io_block));
   }
   RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker");
@@ -291,23 +266,16 @@ Status MindRecordOp::GetBufferFromReader(std::unique_ptr<DataBuffer> *fetched_bu
   *fetched_buffer = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
   std::unique_ptr<TensorQTable> tensor_table = std::make_unique<TensorQTable>();
   for (int32_t i = 0; i < rows_per_buffer_; ++i) {
-    ShardTuple tupled_buffer;
+    int32_t row_id = buffer_id * rows_per_buffer_ + i;
-    mindrecord::TaskType task_type = mindrecord::TaskType::kCommonTask;
+    auto rc = shard_reader_->GetNextById(row_id, worker_id);
-    if (block_reader_) {
+    auto task_type = rc.first;
-      if (i >= block_buffer_[buffer_id % num_workers_]->size()) break;
+    auto tupled_buffer = rc.second;
-      tupled_buffer = block_buffer_[buffer_id % num_workers_]->at(i);
+    if (task_type == mindrecord::TaskType::kPaddedTask) {
-    } else {
+      TensorRow tensor_row;
-      int32_t row_id = buffer_id * rows_per_buffer_ + i;
+      RETURN_IF_NOT_OK(LoadTensorRow(&tensor_row, {}, mindrecord::json(), task_type));
-      auto rc = shard_reader_->GetNextById(row_id, worker_id);
+      tensor_table->push_back(std::move(tensor_row));
-      task_type = rc.first;
-      tupled_buffer = rc.second;
-      if (task_type == mindrecord::TaskType::kPaddedTask) {
-        TensorRow tensor_row;
-        RETURN_IF_NOT_OK(LoadTensorRow(&tensor_row, {}, mindrecord::json(), task_type));
-        tensor_table->push_back(std::move(tensor_row));
-      }
-      if (tupled_buffer.empty()) break;
     }
+    if (tupled_buffer.empty()) break;
     if (task_type == mindrecord::TaskType::kCommonTask) {
       for (const auto &tupled_row : tupled_buffer) {
         std::vector<uint8_t> columns_blob = std::get<0>(tupled_row);
@@ -396,21 +364,6 @@ Status MindRecordOp::LoadTensorRow(TensorRow *tensor_row, const std::vector<uint
   return Status::OK();
 }
 
-Status MindRecordOp::FetchBlockBuffer(const int32_t &buffer_id) {
-  {
-    std::unique_lock<std::mutex> lck(mtx_block_reader_);
-    cv_reader_.wait(lck, [buffer_id, this] { return buffer_id < buffer_water_mark_ + num_workers_; });
-  }
-  for (int32_t i = 0; i < rows_per_buffer_; i++) {
-    // Block reader does NOT care about argument
-    auto rc = shard_reader_->GetNextById(i, i);
-    ShardTuple tuple_buffer = rc.second;
-    if (tuple_buffer.empty()) break;
-    block_buffer_[buffer_id % num_workers_]->push_back(std::move(tuple_buffer));
-  }
-  return Status::OK();
-}
-
 // Class functor operator () override.
 // All dataset ops operate by launching a thread (see ExecutionTree). This class functor will
 // provide the master loop that drives the logic for performing the work
@@ -423,7 +376,6 @@ Status MindRecordOp::operator()() {
 
   while (true) {  // each iterator is 1 epoch
     for (int32_t i = 0; i < buffers_needed_; ++i) {
-      if (block_reader_) RETURN_IF_NOT_OK(FetchBlockBuffer(i));
       std::vector<int64_t> keys(1, i);
       RETURN_IF_NOT_OK(io_blk_queues_[buf_cnt_++ % num_workers_]->Add(
         std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
@@ -455,12 +407,7 @@ Status MindRecordOp::operator()() {
 Status MindRecordOp::Reset() {
   RETURN_IF_NOT_OK(ParallelOp::Reset());  // Call our super class reset first.
 
-  if (block_reader_) {
+  shard_reader_->ShuffleTask();
-    shard_reader_->Reset();
-    buffer_water_mark_ = 0;
-  } else {
-    shard_reader_->ShuffleTask();
-  }
   shard_reader_wait_post_.Set();
 
   return Status::OK();
@@ -473,7 +420,7 @@ Status MindRecordOp::LaunchThreadAndInitOp() {
 
   RETURN_IF_NOT_OK(io_blk_queues_.Register(tree_->AllTasks()));
   RETURN_IF_NOT_OK(shard_reader_wait_post_.Register(tree_->AllTasks()));
-  if (shard_reader_->Launch(!block_reader_) == MSRStatus::FAILED) {
+  if (shard_reader_->Launch(true) == MSRStatus::FAILED) {
     RETURN_STATUS_UNEXPECTED("MindRecordOp launch failed.");
   }
   // Launch main workers that load DataBuffers by reading all images

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

@@ -94,11 +94,6 @@ class MindRecordOp : public ParallelOp {
       return *this;
     }
 
-    Builder &SetBlockReader() {
-      build_block_reader_ = true;
-      return *this;
-    }
-
     Builder &SetLoadDataset(bool load_dataset) {
       build_load_dataset_ = load_dataset;
       return *this;
@@ -132,7 +127,6 @@ class MindRecordOp : public ParallelOp {
     bool build_load_dataset_;
     std::vector<std::string> build_columns_to_load_;
     std::vector<std::shared_ptr<ShardOperator>> build_operators_;
-    bool build_block_reader_;
     int64_t build_num_padded_;
     py::handle build_sample_;
     std::map<std::string, std::string> build_sample_bytes_;
@@ -148,9 +142,8 @@ class MindRecordOp : public ParallelOp {
   // @param operators - ShardOperators for Shuffle, Category, Sample
   MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buffer, std::vector<std::string> dataset_file,
                bool load_dataset, int32_t op_connector_queue_size, const std::vector<std::string> &columns_to_load,
-               const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader,
+               const std::vector<std::shared_ptr<ShardOperator>> &operators, int64_t num_padded_,
-               int64_t num_padded_, const mindrecord::json &sample_json,
+               const mindrecord::json &sample_json, const std::map<std::string, std::string> &sample_bytes_);
-               const std::map<std::string, std::string> &sample_bytes_);
 
   // Destructor
   ~MindRecordOp() override;
@@ -206,8 +199,6 @@ class MindRecordOp : public ParallelOp {
   // Getter method
   std::vector<std::string> columns_to_load() const { return columns_to_load_; }
 
-  bool block_reader() const { return block_reader_; }
-
   bool load_dataset() const { return load_dataset_; }
 
   Status Init();
@@ -232,8 +223,6 @@ class MindRecordOp : public ParallelOp {
   Status LoadTensorRow(TensorRow *tensor_row, const std::vector<uint8_t> &columns_blob,
                        const mindrecord::json &columns_json, const mindrecord::TaskType task_type);
 
-  Status FetchBlockBuffer(const int32_t &buffer_id);
-
   // Private function for computing the assignment of the column name map.
   // @return - Status
   Status ComputeColMap() override;
@@ -244,12 +233,10 @@ class MindRecordOp : public ParallelOp {
   std::vector<std::string> columns_to_load_;               // Columns to load from dataset
   std::vector<std::shared_ptr<ShardOperator>> operators_;  // ShardOperators to use
   int32_t num_mind_record_workers_;                        // number of workers to be spawned by ShardReader
-  bool block_reader_;                                      // block reader switch
   int32_t buffers_needed_;                                 // Counter for the buffers that were fetched
   int64_t buf_cnt_;                                        // Buffer counter
   int32_t num_rows_;                                       // One more than the last row id in the range for this cache
   std::atomic<int32_t> ended_worker_;
-  std::atomic<int32_t> buffer_water_mark_;
 
   int64_t num_padded_;
   mindrecord::json sample_json_;
@@ -263,12 +250,6 @@ class MindRecordOp : public ParallelOp {
   WaitPost shard_reader_wait_post_;
   QueueList<std::unique_ptr<IOBlock>> io_blk_queues_;
 
-  // For block reader
-  std::mutex mtx_block_reader_;
-  std::condition_variable cv_reader_;
-  std::vector<std::unique_ptr<std::vector<ShardTuple>>> block_buffer_;
-  std::unordered_set<int32_t> block_set_;
-
   std::mutex ended_worker_mutex_;
 };
 }  // namespace dataset

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

@@ -63,7 +63,6 @@ using ROW_GROUP_BRIEF =
 using TASK_RETURN_CONTENT =
   std::pair<MSRStatus, std::pair<TaskType, std::vector<std::tuple<std::vector<uint8_t>, json>>>>;
 const int kNumBatchInMap = 1000;  // iterator buffer size in row-reader mode
-const int kNumPageInBuffer = 16;  // page buffer size in block-reader mode
 
 class ShardReader {
  public:
@@ -77,12 +76,10 @@ class ShardReader {
   /// \param[in] n_consumer number of threads when reading
   /// \param[in] selected_columns column list to be populated
   /// \param[in] operators operators applied to data, operator type is shuffle, sample or category
-  /// \param[in] block_reader block-reader mode if true, otherwise row-reader mode
   /// \return MSRStatus the status of MSRStatus
   MSRStatus Open(const std::vector<std::string> &file_paths, bool load_dataset, int n_consumer = 4,
                  const std::vector<std::string> &selected_columns = {},
-                 const std::vector<std::shared_ptr<ShardOperator>> &operators = {}, const bool &block_reader = false,
+                 const std::vector<std::shared_ptr<ShardOperator>> &operators = {}, const int num_padded = 0);
-                 const int num_padded = 0);
 
   /// \brief open files and initialize reader, python API
   /// \param[in] file_paths the path of ONE file, any file in dataset is fine or file list
@@ -189,10 +186,6 @@ class ShardReader {
   std::pair<TaskType, std::vector<std::tuple<std::vector<uint8_t>, json>>> GetNextById(const int64_t &task_id,
                                                                                        const int32_t &consumer_id);
 
-  /// \brief return a batch in block-reader mode, given that one is ready
-  /// \return a batch of images and image data
-  std::vector<std::tuple<std::vector<uint8_t>, json>> GetBlockNext();
-
   /// \brief return a batch, given that one is ready, python API
   /// \return a batch of images and image data
   std::vector<std::tuple<std::vector<std::vector<uint8_t>>, pybind11::object>> GetNextPy();
@@ -242,9 +235,6 @@ class ShardReader {
   /// \brief populate one row by task list in row-reader mode
   MSRStatus ConsumerByRow(int consumer_id);
 
-  /// \brief populate one row by task list in block-reader mode
-  MSRStatus ConsumerByBlock(int consumer_id);
-
   /// \brief get offset address of images within page
   std::vector<std::vector<uint64_t>> GetImageOffset(int group_id, int shard_id,
                                                     const std::pair<std::string, std::string> &criteria = {"", ""});
@@ -262,10 +252,6 @@ class ShardReader {
                                                             const std::pair<std::string, std::string> &criteria = {"",
                                                                                                                    ""});
 
-  /// \brief create task list in block-reader mode
-  MSRStatus CreateTasksByBlock(const std::vector<std::tuple<int, int, int, uint64_t>> &row_group_summary,
-                               const std::vector<std::shared_ptr<ShardOperator>> &operators);
-
   /// \brief create category-applied task list
   MSRStatus CreateTasksByCategory(const std::vector<std::tuple<int, int, int, uint64_t>> &row_group_summary,
                                   const std::shared_ptr<ShardOperator> &op);
@@ -290,15 +276,10 @@ class ShardReader {
   /// \brief read one row by one task
   TASK_RETURN_CONTENT ConsumerOneTask(int task_id, uint32_t consumer_id);
 
-  /// \brief get one row from buffer in block-reader mode
-  std::shared_ptr<std::vector<std::tuple<std::vector<uint8_t>, json>>> GetRowFromBuffer(int bufId, int rowId);
-
   /// \brief get labels from binary file
   std::pair<MSRStatus, std::vector<json>> GetLabelsFromBinaryFile(
     int shard_id, const std::vector<std::string> &columns, const std::vector<std::vector<std::string>> &label_offsets);
 
-  MSRStatus ReadBlob(const int &shard_id, const uint64_t &page_offset, const int &page_length, const int &buf_id);
-
   /// \brief get classes in one shard
   void GetClassesInShard(sqlite3 *db, int shard_id, const std::string sql, std::set<std::string> &categories);
 
@@ -349,16 +330,6 @@ class ShardReader {
   // map of delivery
   std::unordered_map<int, std::shared_ptr<std::vector<std::tuple<std::vector<uint8_t>, json>>>> delivery_map_;
   // Delivery/Iterator mode end
-
-  // Block reader mode begin
-  bool block_reader_;  // block-reader mode
-  int row_id_;         // row id in one page
-  int num_blocks_;     // number of pages
-  // raw data page
-  std::vector<std::shared_ptr<std::pair<std::vector<std::vector<uint64_t>>, std::vector<json>>>> delivery_block_;
-  std::unordered_set<int> delivery_block_set_;  // set of delivered pages
-  std::vector<std::vector<uint8_t>> buf_;       // page buffer
-  // Block reader mode end
 };
 }  // namespace mindrecord
 }  // namespace mindspore

mindspore/dataset/core/validator_helpers.py

@@ -300,7 +300,6 @@ def check_padding_options(param_dict):
     """
 
     columns_list = param_dict.get('columns_list')
-    block_reader = param_dict.get('block_reader')
     padded_sample, num_padded = param_dict.get('padded_sample'), param_dict.get('num_padded')
     if padded_sample is not None:
         if num_padded is None:
@@ -312,9 +311,6 @@ def check_padding_options(param_dict):
         for column in columns_list:
             if column not in padded_sample:
                 raise ValueError("padded_sample cannot match columns_list.")
-        if block_reader:
-            raise RuntimeError("block_reader and padded_sample cannot be specified at the same time.")
-
     if padded_sample is None and num_padded is not None:
         raise RuntimeError("num_padded is specified but padded_sample is not.")
 

mindspore/dataset/engine/datasets.py

@@ -2795,7 +2795,6 @@ class MindDataset(MappableDataset):
         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.
-        block_reader (bool, optional): Whether read data by block mode (default=False).
         sampler (Sampler, optional): Object used to choose samples from the
             dataset (default=None, sampler is exclusive
             with shuffle and block_reader). Support list: SubsetRandomSampler,
@@ -2810,13 +2809,12 @@ class MindDataset(MappableDataset):
     Raises:
         ValueError: If num_shards is specified but shard_id is None.
         ValueError: If shard_id is specified but num_shards is None.
-        ValueError: If block reader is true but partition is specified.
     """
 
     @check_minddataset
     def __init__(self, dataset_file, columns_list=None, num_parallel_workers=None,
                  shuffle=None, num_shards=None, shard_id=None,
-                 block_reader=False, sampler=None, padded_sample=None,
+                 sampler=None, padded_sample=None,
                  num_padded=None, num_samples=None):
         super().__init__(num_parallel_workers)
         if isinstance(dataset_file, list):
@@ -2828,14 +2826,7 @@ class MindDataset(MappableDataset):
         self.shuffle_option = shuffle
         self.num_shards = num_shards
         self.shard_id = shard_id
-
+        if shuffle is False:
-        if block_reader is True and num_shards is not None:
-            raise ValueError("block_reader not allowed true when use partitions")
-
-        if block_reader is True and shuffle is True:
-            raise ValueError("block_reader not allowed true when use shuffle")
-
-        if block_reader is True:
             logger.warning("WARN: global shuffle is not used.")
 
         if sampler is not None:
@@ -2846,15 +2837,9 @@ class MindDataset(MappableDataset):
 
         self.sampler = _select_sampler(num_samples, sampler, shuffle, num_shards, shard_id)
         self.num_samples = num_samples
-
-        # sampler exclusive
-        if block_reader is True and sampler is not None:
-            raise ValueError("block_reader not allowed true when use sampler")
-
         if num_padded is None:
             num_padded = 0
 
-        self.block_reader = block_reader
         self.padded_sample = padded_sample
         self.num_padded = num_padded
 
@@ -2873,7 +2858,6 @@ class MindDataset(MappableDataset):
         args["columns_list"] = self.columns_list
         args["shuffle_option"] = self.shuffle_option
         args["num_samples"] = self.num_samples
-        args["block_reader"] = self.block_reader
         args["num_padded"] = self.num_padded
         args["padded_sample"] = padded_sample
         args["sampler"] = self.sampler

mindspore/dataset/engine/serializer_deserializer.py

@@ -279,7 +279,7 @@ def create_node(node):
         sampler = construct_sampler(node.get('sampler'))
         pyobj = pyclass(node['dataset_file'], node.get('columns_list'),
                         node.get('num_parallel_workers'), node.get('seed'), node.get('num_shards'),
-                        node.get('shard_id'), node.get('block_reader'), sampler)
+                        node.get('shard_id'), sampler)
 
     elif dataset_op == 'TFRecordDataset':
         pyobj = pyclass(node['dataset_files'], node.get('schema'), node.get('column_list'),

mindspore/dataset/engine/validators.py

@@ -273,7 +273,6 @@ def check_minddataset(method):
 
         nreq_param_int = ['num_samples', 'num_parallel_workers', 'seed', 'num_shards', 'shard_id', 'num_padded']
         nreq_param_list = ['columns_list']
-        nreq_param_bool = ['block_reader']
         nreq_param_dict = ['padded_sample']
 
         dataset_file = param_dict.get('dataset_file')
@@ -287,7 +286,6 @@ def check_minddataset(method):
 
         validate_dataset_param_value(nreq_param_int, param_dict, int)
         validate_dataset_param_value(nreq_param_list, param_dict, list)
-        validate_dataset_param_value(nreq_param_bool, param_dict, bool)
         validate_dataset_param_value(nreq_param_dict, param_dict, dict)
 
         check_sampler_shuffle_shard_options(param_dict)

tests/ut/cpp/dataset/mind_record_op_test.cc

@@ -435,7 +435,6 @@ TEST_F(MindDataTestMindRecordOp, TestMindRecordBlockReaderRepeat) {
       .SetLoadDataset(true)
       .SetRowsPerBuffer(3)
       .SetNumMindRecordWorkers(4)
-      .SetBlockReader()
       .SetColumnsToLoad(column_list);
   rc = builder.Build(&my_mindrecord_op);
   ASSERT_TRUE(rc.IsOk());

tests/ut/cpp/mindrecord/ut_shard_reader_test.cc

@@ -94,31 +94,6 @@ TEST_F(TestShardReader, TestShardReaderSample) {
   dataset.Close();
 }
 
-TEST_F(TestShardReader, TestShardReaderBlock) {
-  MS_LOG(INFO) << FormatInfo("Test read imageNet with block way");
-  std::string file_name = "./imagenet.shard01";
-  auto column_list = std::vector<std::string>{"label"};
-
-  std::vector<std::shared_ptr<ShardOperator>> ops;
-  ops.push_back(std::make_shared<ShardSample>(3));
-  ShardReader dataset;
-  const bool kBlockReader = true;
-  dataset.Open({file_name}, true, 4, column_list, ops, kBlockReader);
-  dataset.Launch();
-
-  while (true) {
-    auto x = dataset.GetBlockNext();
-    if (x.empty()) break;
-    for (auto &j : x) {
-      for (auto &item : std::get<1>(j).items()) {
-        MS_LOG(INFO) << "key: " << item.key() << ", value: " << item.value().dump();
-      }
-    }
-  }
-  dataset.Finish();
-  dataset.Close();
-}
-
 TEST_F(TestShardReader, TestShardReaderEasy) {
   MS_LOG(INFO) << FormatInfo("Test read imageNet");
   std::string file_name = "./imagenet.shard01";

tests/ut/python/dataset/test_minddataset.py

@@ -591,49 +591,6 @@ def test_cv_minddataset_issue_888(add_and_remove_cv_file):
     assert num_iter == 18
 
 
-def test_cv_minddataset_blockreader_tutorial(add_and_remove_cv_file):
-    """tutorial for cv minddataset."""
-    columns_list = ["data", "label"]
-    num_readers = 4
-    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, block_reader=True)
-    assert data_set.get_dataset_size() == 10
-    repeat_num = 2
-    data_set = data_set.repeat(repeat_num)
-    num_iter = 0
-    for item in data_set.create_dict_iterator():
-        logger.info(
-            "-------------- block reader repeat tow {} -----------------".format(num_iter))
-        logger.info(
-            "-------------- item[label]: {} ----------------------------".format(item["label"]))
-        logger.info(
-            "-------------- item[data]: {} -----------------------------".format(item["data"]))
-        num_iter += 1
-    assert num_iter == 20
-
-
-def test_cv_minddataset_blockreader_some_field_not_in_index_tutorial(add_and_remove_cv_file):
-    """tutorial for cv minddataset."""
-    columns_list = ["id", "data", "label"]
-    num_readers = 4
-    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, shuffle=False,
-                              block_reader=True)
-    assert data_set.get_dataset_size() == 10
-    repeat_num = 2
-    data_set = data_set.repeat(repeat_num)
-    num_iter = 0
-    for item in data_set.create_dict_iterator():
-        logger.info(
-            "-------------- block reader repeat tow {} -----------------".format(num_iter))
-        logger.info(
-            "-------------- item[id]: {} ----------------------------".format(item["id"]))
-        logger.info(
-            "-------------- item[label]: {} ----------------------------".format(item["label"]))
-        logger.info(
-            "-------------- item[data]: {} -----------------------------".format(item["data"]))
-        num_iter += 1
-    assert num_iter == 20
-
-
 def test_cv_minddataset_reader_file_list(add_and_remove_cv_file):
     """tutorial for cv minderdataset."""
     columns_list = ["data", "file_name", "label"]