!14639 Remove DataBuffer class

From: @hfarahat
Reviewed-by: @pandoublefeng,@robingrosman
Signed-off-by: @pandoublefeng

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

@@ -13,7 +13,6 @@ file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc"
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
 set(SRC_FILES_LIST
         execution_tree.cc
-        data_buffer.cc
         data_schema.cc
         dataset_iterator.cc
         tree_adapter.cc

mindspore/ccsrc/minddata/dataset/engine/cache/cache_client.h

@@ -34,7 +34,7 @@
 #else
 #include "minddata/dataset/engine/cache/stub/cache_grpc_client.h"
 #endif
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/util/lock.h"
 #include "minddata/dataset/util/cond_var.h"
 #include "minddata/dataset/util/queue_map.h"

mindspore/ccsrc/minddata/dataset/engine/cache/perf/cache_pipeline_run.cc

@@ -22,7 +22,7 @@
 #include <iomanip>
 #include <sstream>
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/util/random.h"
 #include "minddata/dataset/util/services.h"

mindspore/ccsrc/minddata/dataset/engine/consumers/tree_consumer.cc

@@ -115,7 +115,6 @@ Status IteratorConsumer::GetNextAsOrderedPair(std::vector<std::pair<std::string,
 Status ToDevice::Init(std::shared_ptr<DatasetNode> d) { return tree_adapter_->Compile(std::move(d), num_epochs_); }
 
 Status ToDevice::Send() {
-  std::unique_ptr<DataBuffer> db;
   RETURN_IF_NOT_OK(tree_adapter_->Launch());
   std::shared_ptr<DatasetOp> root = std::shared_ptr<DatasetOp>(tree_adapter_->GetRoot());
   CHECK_FAIL_RETURN_UNEXPECTED(root != nullptr, "Root is a nullptr.");

mindspore/ccsrc/minddata/dataset/engine/data_buffer.cc

@@ -1,89 +0,0 @@
-/**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "minddata/dataset/engine/data_buffer.h"
-#include "minddata/dataset/util/allocator.h"
-#include "minddata/dataset/core/global_context.h"
-#include "minddata/dataset/core/tensor.h"
-
-namespace mindspore {
-namespace dataset {
-// Name: Constructor #1
-// Description: This is the main constructor that is used for making a buffer
-DataBuffer::DataBuffer(int32_t id, BufferFlags flags) : buffer_id_(id), tensor_table_(nullptr), buffer_flags_(flags) {}
-
-// A method for debug printing of the buffer
-void DataBuffer::Print(std::ostream &out, bool show_all) const {
-  out << "bufferId: " << buffer_id_ << "\nflags: " << std::hex << buffer_flags_ << std::dec << "\n";
-
-  // If the column counts are set then it means that data has been set into
-  // the tensor table.  Display the tensor table here.
-  if (this->NumCols() > 0) {
-    out << "Tensor table:\n";
-    for (int32_t row = 0; row < DataBuffer::NumRows(); ++row) {
-      out << "Row #   : " << row << "\n";
-      TensorRow currRow = (*tensor_table_)[row];
-      for (int32_t col = 0; col < this->NumCols(); ++col) {
-        out << "Column #: " << col << "\n";  // Should add the column name here as well?
-        // Call the tensor display
-        out << *(currRow[col]) << "\n";
-      }
-    }
-  }
-}
-
-// Remove me!! Callers should fetch rows via pop
-Status DataBuffer::GetTensor(std::shared_ptr<Tensor> *ptr, int32_t row_id, int32_t col_id) const {
-  if (row_id < tensor_table_->size() && col_id < tensor_table_->at(row_id).size()) {
-    *ptr = (tensor_table_->at(row_id)).at(col_id);
-  } else {
-    std::string err_msg =
-      "indices for mTensorTable out of range: (" + std::to_string(row_id) + "," + std::to_string(col_id) + ").";
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
-  return Status::OK();
-}
-
-// Remove me!! Callers should fetch rows via pop
-Status DataBuffer::GetRow(int32_t row_id, TensorRow *ptr) const {
-  if (tensor_table_ && !tensor_table_->empty() && row_id < tensor_table_->size()) {
-    *ptr = tensor_table_->at(row_id);
-  } else {
-    std::string err_msg = "rowId for mTensorTable out of range: " + std::to_string(row_id);
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
-
-  return Status::OK();
-}
-
-Status DataBuffer::PopRow(TensorRow *ptr) {
-  if (tensor_table_ && !tensor_table_->empty()) {
-    *ptr = std::move(tensor_table_->front());
-    tensor_table_->pop_front();
-  }
-
-  return Status::OK();
-}
-
-Status DataBuffer::SliceOff(int64_t number_of_rows) {
-  while (number_of_rows > 0) {
-    tensor_table_->pop_back();
-    number_of_rows--;
-  }
-
-  return Status::OK();
-}
-}  // namespace dataset
-}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/data_buffer.h

@@ -1,114 +0,0 @@
-/**
- * Copyright 2019-2021 Huawei Technologies Co., Ltd
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATA_BUFFER_H_
-#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATA_BUFFER_H_
-
-#include <iostream>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-#include "minddata/dataset/util/allocator.h"
-#include "minddata/dataset/util/status.h"
-#include "minddata/dataset/include/constants.h"
-#include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/core/tensor_row.h"
-
-namespace mindspore {
-namespace dataset {
-/// \brief The DataBuffer class is a container of tensor data and is the unit of transmission between
-///     connectors of dataset operators.  Inside the buffer, tensors are organized into a table-like format
-///     where n TensorRows may consist of m tensors (columns).
-class DataBuffer {
- public:
-  // Buffer flags
-  enum BufferFlags : uint32_t {
-    kDeBFlagNone = 0,
-    kDeBFlagEOF = 1,         // The buffer is an eof end-of-data msg
-    kDeBFlagEOE = 1u << 1,   // The buffer is an eoe end-of-epoch msg
-    kDeBFlagWait = 1u << 2,  // The buffer is an control signal for workers to suspend operations
-    kDeBFlagQuit = 1u << 3   // The buffer is a control signal for workers to quit
-  };
-
-  // Name: Constructor #1
-  // Description: This is the main constructor that is used for making a buffer
-  DataBuffer(int32_t id, BufferFlags flags);
-
-  /// \brief default destructor
-  ~DataBuffer() = default;
-
-  /// \brief A method for debug printing of the buffer
-  /// \param[in/out] out The stream to write to
-  /// \param[in] show_all A boolean to toggle between details and summary printing
-  void Print(std::ostream &out, bool show_all) const;
-
-  // Provide stream operator for displaying it
-  friend std::ostream &operator<<(std::ostream &out, const DataBuffer &cb) {
-    cb.Print(out, false);
-    return out;
-  }
-
-  // Convenience getter functions for flag checking
-  bool eof() const { return (static_cast<uint32_t>(buffer_flags_) & static_cast<uint32_t>(kDeBFlagEOF)); }
-
-  bool eoe() const { return (static_cast<uint32_t>(buffer_flags_) & static_cast<uint32_t>(kDeBFlagEOE)); }
-
-  bool wait() const { return (static_cast<uint32_t>(buffer_flags_) & static_cast<uint32_t>(kDeBFlagWait)); }
-
-  bool quit() const { return (static_cast<uint32_t>(buffer_flags_) & static_cast<uint32_t>(kDeBFlagQuit)); }
-
-  // Simple getter funcs
-  int32_t id() const { return buffer_id_; }
-
-  void set_id(int32_t id) { buffer_id_ = id; }
-
-  int32_t NumRows() const { return ((tensor_table_) ? tensor_table_->size() : 0); }
-
-  int32_t NumCols() const {
-    return (tensor_table_ == nullptr || tensor_table_->empty()) ? 0 : tensor_table_->at(0).size();
-  }
-
-  BufferFlags buffer_flags() const { return buffer_flags_; }
-
-  // Remove me!! Callers should fetch rows via pop
-  Status GetTensor(std::shared_ptr<Tensor> *, int32_t row_id, int32_t col_id) const;
-
-  // Remove me!! Callers should drain rows via pop.
-  Status GetRow(int32_t row_id, TensorRow *) const;
-
-  // Get a row from the TensorTable
-  Status PopRow(TensorRow *);
-
-  Status SliceOff(int64_t number_of_rows);
-
-  // Replacing mTensorTable, the unique_ptr assignment will release the old TensorTable.
-  void set_tensor_table(std::unique_ptr<TensorQTable> new_table) { tensor_table_ = std::move(new_table); }
-
-  void set_flag(BufferFlags in_flag) {
-    buffer_flags_ = static_cast<BufferFlags>(static_cast<uint32_t>(buffer_flags_) | static_cast<uint32_t>(in_flag));
-  }
-
-  void Shuffle() {}  // does nothing right now.  possibly remove later
-
- protected:
-  int32_t buffer_id_;                           // An id for the buffer.
-  std::unique_ptr<TensorQTable> tensor_table_;  // A table (row major) of Tensors
-  BufferFlags buffer_flags_;                    // bit mask for various buffer properties
-};
-}  // namespace dataset
-}  // namespace mindspore
-
-#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATA_BUFFER_H_

mindspore/ccsrc/minddata/dataset/engine/dataset_iterator.cc

@@ -19,7 +19,7 @@
 #include "minddata/dataset/core/data_type.h"
 #include "minddata/dataset/core/tensor.h"
 #include "minddata/dataset/core/tensor_shape.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/execution_tree.h"
 #include "minddata/dataset/util/status.h"
 #include "minddata/dataset/engine/datasetops/dataset_op.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/barrier_op.cc

@@ -17,7 +17,7 @@
 #include <iomanip>
 #include <utility>
 #include "minddata/dataset/include/constants.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/db_connector.h"
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/core/global_context.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/barrier_op.h

@@ -28,7 +28,6 @@
 namespace mindspore {
 namespace dataset {
 // Forward declare
-class DataBuffer;
 class ExecutionTree;
 
 // BarrierOp class implements the Barrier operator. It will block sending of rows until a signal has

mindspore/ccsrc/minddata/dataset/engine/datasetops/batch_op.cc

@@ -21,7 +21,7 @@
 #ifdef ENABLE_PYTHON
 #include "minddata/dataset/core/pybind_support.h"
 #endif
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/db_connector.h"
 #include "minddata/dataset/kernels/data/data_utils.h"
 #include "minddata/dataset/util/status.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/batch_op.h

@@ -34,7 +34,6 @@
 
 namespace mindspore {
 namespace dataset {
-class DataBuffer;
 
 using PadInfo = std::map<std::string, std::pair<TensorShape, std::shared_ptr<Tensor>>>;
 

mindspore/ccsrc/minddata/dataset/engine/datasetops/bucket_batch_by_length_op.h

@@ -32,7 +32,6 @@
 
 namespace mindspore {
 namespace dataset {
-class DataBuffer;
 
 class BucketBatchByLengthOp : public PipelineOp {
  public:

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_base_op.cc

@@ -94,11 +94,9 @@ Status CacheBase::FetchSamplesToWorkers() {
     keys.reserve(1);
     std::vector<row_id_type> prefetch_keys;
     prefetch_keys.reserve(prefetch_size_);
-    std::unique_ptr<DataBuffer> sampler_buffer;
-    RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
-    while (!sampler_buffer->eoe()) {
-      TensorRow sample_row;
-      RETURN_IF_NOT_OK(sampler_buffer->PopRow(&sample_row));
+    TensorRow sample_row;
+    RETURN_IF_NOT_OK(sampler_->GetNextSample(&sample_row));
+    while (!sample_row.eoe()) {
       std::shared_ptr<Tensor> sample_ids = sample_row[0];
       for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); itr++) {
         ++row_cnt_;
@@ -115,7 +113,7 @@ Status CacheBase::FetchSamplesToWorkers() {
           prefetch_keys.clear();
         }
       }
-      RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
+      RETURN_IF_NOT_OK(sampler_->GetNextSample(&sample_row));
     }
     // Deal with any partial keys left.
     if (!prefetch_keys.empty()) {

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_lookup_op.cc

@@ -95,7 +95,7 @@ void CacheLookupOp::SamplerPrint(std::ostream &out, bool show_all) const {
     // Then add our own info if any
   }
 }
-Status CacheLookupOp::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
+Status CacheLookupOp::GetNextSample(TensorRow *out) {
   std::vector<row_id_type> cache_miss;
   RETURN_IF_NOT_OK(keys_miss_->Pop(0, &cache_miss));
   // Ignore the case we have no cache miss, we can't return empty samples.
@@ -104,19 +104,16 @@ Status CacheLookupOp::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
   }
   // Special code for eoe
   if (cache_miss.at(0) == eoe_row_id) {
-    *out_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    *out = std::move(TensorRow(TensorRow::kFlagEOE));
   } else {
     std::shared_ptr<Tensor> sample_ts;
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ts, cache_miss.size()));
-    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagNone);
     auto idPtr = sample_ts->begin<int64_t>();
     for (auto i = 0; i < cache_miss.size(); ++i) {
       *idPtr = cache_miss.at(i);
       ++idPtr;
     }
-    TensorRow row;
-    row.push_back(sample_ts);
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
+    *out = {sample_ts};
   }
   return Status::OK();
 }

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_lookup_op.h

@@ -96,7 +96,7 @@ class CacheLookupOp : public CacheBase, public SamplerRT {
   Status ResetSampler() override;
   Status HandshakeRandomAccessOp(const RandomAccessOp *op) override;
   Status InitSampler() override;
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
   void Print(std::ostream &out, bool show_all) const override;
   void SamplerPrint(std::ostream &out, bool show_all) const override;
   bool AllowCacheMiss() override { return true; }

mindspore/ccsrc/minddata/dataset/engine/datasetops/cache_op.cc

@@ -22,7 +22,7 @@
 #include "minddata/dataset/core/global_context.h"
 #include "minddata/dataset/engine/datasetops/repeat_op.h"
 #include "minddata/dataset/engine/dataset_iterator.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/execution_tree.h"
 #include "minddata/dataset/util/log_adapter.h"
 #include "minddata/dataset/util/task_manager.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/concat_op.cc

@@ -19,7 +19,7 @@
 #include <utility>
 
 #include "minddata/dataset/core/config_manager.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/db_connector.h"
 #include "utils/ms_utils.h"
 

mindspore/ccsrc/minddata/dataset/engine/datasetops/dataset_op.cc

@@ -26,7 +26,7 @@
 #include "minddata/dataset/engine/execution_tree.h"
 #include "minddata/dataset/engine/datasetops/device_queue_op.h"
 #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/db_connector.h"
 #ifndef ENABLE_ANDROID
 #include "utils/system/crc32c.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/dataset_op.h

@@ -59,8 +59,6 @@ constexpr char kZipOp[] = "ZipOp";
 // Forward declare
 class ExecutionTree;
 
-class DataBuffer;
-
 class NodePass;
 
 class SamplerRT;

mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.cc

@@ -19,7 +19,7 @@
 #include <algorithm>
 #include <iostream>
 #include <memory>
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/dataset_iterator.h"
 #include "minddata/dataset/util/status.h"
 #include "minddata/dataset/util/task_manager.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/epoch_ctrl_op.cc

@@ -18,7 +18,7 @@
 #include <utility>
 
 #include "minddata/dataset/engine/datasetops/epoch_ctrl_op.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/util/log_adapter.h"
 
 namespace mindspore {

mindspore/ccsrc/minddata/dataset/engine/datasetops/filter_op.cc

@@ -22,7 +22,7 @@
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/core/global_context.h"
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/kernels/tensor_op.h"
 #include "minddata/dataset/util/log_adapter.h"
 #include "minddata/dataset/util/task_manager.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/map_op/map_op.cc

@@ -23,7 +23,7 @@
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/core/global_context.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/datasetops/map_op/cpu_map_job.h"
 #include "minddata/dataset/engine/datasetops/map_op/gpu_map_job.h"
 #include "minddata/dataset/engine/execution_tree.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/map_op/map_op.h

@@ -34,7 +34,6 @@
 namespace mindspore {
 namespace dataset {
 // Forward declare
-class DataBuffer;
 class ExecutionTree;
 
 // MapOp class implements the Map operator. It will apply a list of operations to each record specified by column names.

mindspore/ccsrc/minddata/dataset/engine/datasetops/parallel_op.h

@@ -30,8 +30,6 @@ namespace dataset {
 constexpr int32_t kEndOfActions = -1;
 
 // Forward declares
-class DataBuffer;
-
 class DbConnector;
 
 // A ParallelOp provides a multi-threaded DatasetOp

mindspore/ccsrc/minddata/dataset/engine/datasetops/pipeline_op.h

@@ -26,8 +26,6 @@ namespace dataset {
 // forward declare
 class ExecutionTree;
 
-class DataBuffer;
-
 class PipelineOp : public DatasetOp {
  public:
   // Constructor

mindspore/ccsrc/minddata/dataset/engine/datasetops/project_op.cc

@@ -22,7 +22,7 @@
 #include <unordered_map>
 #include <utility>
 #include <vector>
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/execution_tree.h"
 #include "minddata/dataset/util/log_adapter.h"
 

mindspore/ccsrc/minddata/dataset/engine/datasetops/rename_op.cc

@@ -22,7 +22,7 @@
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/core/global_context.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/db_connector.h"
 #include "minddata/dataset/util/log_adapter.h"
 

mindspore/ccsrc/minddata/dataset/engine/datasetops/rename_op.h

@@ -27,9 +27,6 @@
 
 namespace mindspore {
 namespace dataset {
-// forward declare
-class DataBuffer;
-
 class RenameOp : public PipelineOp {
  public:
   //  The nested builder class inside of the RenameOp is used to help manage all of

mindspore/ccsrc/minddata/dataset/engine/datasetops/repeat_op.cc

@@ -19,7 +19,7 @@
 
 #include "minddata/dataset/engine/execution_tree.h"
 #include "minddata/dataset/engine/datasetops/repeat_op.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/util/log_adapter.h"
 
 namespace mindspore {

mindspore/ccsrc/minddata/dataset/engine/datasetops/shuffle_op.cc

@@ -25,7 +25,7 @@
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/engine/datasetops/shuffle_op.h"
 #include "minddata/dataset/engine/dataset_iterator.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/db_connector.h"
 #include "minddata/dataset/util/log_adapter.h"
 #include "minddata/dataset/util/random.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/shuffle_op.h

@@ -37,8 +37,6 @@ class ExecutionTree;
 
 class DbConnector;
 
-class DataBuffer;
-
 class ShuffleOp : public PipelineOp {
   // Shuffle buffer state flags
   //

mindspore/ccsrc/minddata/dataset/engine/datasetops/skip_op.cc

@@ -18,7 +18,7 @@
 #include <utility>
 
 #include "minddata/dataset/core/config_manager.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/dataset_iterator.h"
 #include "minddata/dataset/engine/datasetops/skip_op.h"
 #include "minddata/dataset/engine/db_connector.h"

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

@@ -487,10 +487,8 @@ Status AlbumOp::GetNextRowPullMode(TensorRow *row) {
   if (image_rows_.empty()) PrescanEntry();
   if (sample_ids_ == nullptr) {
     RETURN_IF_NOT_OK(this->InitSampler());
-    std::unique_ptr<DataBuffer> sample_buffer;
     TensorRow sample_row;
-    RETURN_IF_NOT_OK(sampler_->GetNextSample(&sample_buffer));
-    RETURN_IF_NOT_OK(sample_buffer->PopRow(&sample_row));
+    RETURN_IF_NOT_OK(sampler_->GetNextSample(&sample_row));
     sample_ids_ = sample_row[0];
   }
   if (curr_row_ + 1 > sample_ids_->Size()) {

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

@@ -27,7 +27,7 @@
 #include <utility>
 #include <vector>
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"

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

@@ -23,7 +23,7 @@
 #include <vector>
 
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"

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

@@ -24,7 +24,7 @@
 #include <vector>
 
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"

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

@@ -16,7 +16,7 @@
 #include "minddata/dataset/engine/datasetops/source/generator_op.h"
 #include <iomanip>
 #include "minddata/dataset/core/global_context.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/execution_tree.h"
 #include "minddata/dataset/util/task_manager.h"
 
@@ -219,12 +219,10 @@ Status GeneratorOp::operator()() {
     if (eoe) {
       // Push out EOE upon StopIteration exception from generator
       MS_LOG(DEBUG) << "Generator operator sends out EOE.";
-      std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
       RETURN_IF_NOT_OK(out_connector_->SendEOE());
       if (IsLastIteration()) {
         // If last repeat or not repeated, push out EOF and exit master loop
         MS_LOG(DEBUG) << "Generator operator sends out EOF.";
-        std::unique_ptr<DataBuffer> eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
         RETURN_IF_NOT_OK(out_connector_->SendEOF());
         MS_LOG(DEBUG) << "Generator operator main execution loop complete.";
         eof = true;

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

@@ -26,7 +26,7 @@
 #include <utility>
 #include <vector>
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"

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

@@ -23,7 +23,7 @@
 #include <vector>
 
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"

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

@@ -30,13 +30,11 @@ MappableLeafOp::MappableLeafOp(int32_t num_wkrs, int32_t queue_size, std::shared
 // Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work
 Status MappableLeafOp::operator()() {
   RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
-  std::unique_ptr<DataBuffer> sampler_buffer;
-  RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
+  TensorRow sample_row;
+  RETURN_IF_NOT_OK(sampler_->GetNextSample(&sample_row));
   int64_t row_cnt = 0;
   while (true) {  // each iteration is 1 epoch, breaks when IsLastIteration() is true
-    while (sampler_buffer->eoe() == false) {
-      TensorRow sample_row;
-      RETURN_IF_NOT_OK(sampler_buffer->PopRow(&sample_row));
+    while (sample_row.eoe() == false) {
       std::shared_ptr<Tensor> sample_ids = sample_row[0];
       for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
         if ((*itr) >= num_rows_) {
@@ -46,7 +44,7 @@ Status MappableLeafOp::operator()() {
         RETURN_IF_NOT_OK(
           io_block_queues_[row_cnt++ % num_workers_]->Add(std::make_unique<IOBlock>(*itr, IOBlock::kDeIoBlockNone)));
       }
-      RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
+      RETURN_IF_NOT_OK(sampler_->GetNextSample(&sample_row));
     }
     if (IsLastIteration()) {
       std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
@@ -71,7 +69,7 @@ Status MappableLeafOp::operator()() {
     // If not the last repeat, self-reset and go to loop again.
     if (!IsLastIteration()) {
       RETURN_IF_NOT_OK(Reset());
-      RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
+      RETURN_IF_NOT_OK(sampler_->GetNextSample(&sample_row));
     }
     UpdateRepeatAndEpochCounter();
   }
@@ -90,7 +88,7 @@ Status MappableLeafOp::InitSampler() {
   return Status::OK();
 }
 
-// contains the main logic of pulling a IOBlock from IOBlockQueue, load a buffer and push the buffer to out_connector_
+// contains the main logic of pulling a IOBlock from IOBlockQueue, load a row and push the row to out_connector_
 // IMPORTANT: 1 IOBlock produces 1 row
 Status MappableLeafOp::WorkerEntry(int32_t worker_id) {
   TaskManager::FindMe()->Post();

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

@@ -26,7 +26,7 @@
 #include <utility>
 #include <vector>
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"

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

@@ -25,7 +25,7 @@
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/core/global_context.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/datasetops/dataset_op.h"
 #include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
 #include "minddata/dataset/engine/db_connector.h"

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

@@ -42,7 +42,6 @@ namespace dataset {
 // Forward declares
 template <typename T>
 class Queue;
-class DataBuffer;
 
 using mindrecord::ShardOperator;
 using mindrecord::ShardReader;

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

@@ -24,7 +24,7 @@
 #include <utility>
 
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/distributed_sampler.cc

@@ -19,7 +19,6 @@
 #include <limits>
 #include <memory>
 
-#include "minddata/dataset/engine/data_buffer.h"
 #include "minddata/dataset/util/random.h"
 
 namespace mindspore {
@@ -63,15 +62,15 @@ Status DistributedSamplerRT::InitSampler() {
 
   if (offset_ != -1 || !even_dist_) {
     if (offset_ == -1) offset_ = 0;
-    samples_per_buffer_ = (num_rows_ + offset_) / num_devices_;
+    samples_per_tensor_ = (num_rows_ + offset_) / num_devices_;
     int64_t remainder = (num_rows_ + offset_) % num_devices_;
-    if (device_id_ < remainder) samples_per_buffer_++;
-    if (device_id_ < offset_) samples_per_buffer_--;
+    if (device_id_ < remainder) samples_per_tensor_++;
+    if (device_id_ < offset_) samples_per_tensor_--;
   } else {
     offset_ = 0;
-    samples_per_buffer_ = (num_rows_ + num_devices_ - 1) / num_devices_;  // equals to ceil(num_rows/num_devices)
+    samples_per_tensor_ = (num_rows_ + num_devices_ - 1) / num_devices_;  // equals to ceil(num_rows/num_devices)
   }
-  samples_per_buffer_ = num_samples_ < samples_per_buffer_ ? num_samples_ : samples_per_buffer_;
+  samples_per_tensor_ = num_samples_ < samples_per_tensor_ ? num_samples_ : samples_per_tensor_;
   if (shuffle_) {
     shuffle_vec_.reserve(num_rows_);
     for (int64_t i = 0; i < num_rows_; i++) {
@@ -79,51 +78,48 @@ Status DistributedSamplerRT::InitSampler() {
     }
     std::shuffle(shuffle_vec_.begin(), shuffle_vec_.end(), rnd_);
   }
-  if (!samples_per_buffer_) non_empty_ = false;
+  if (!samples_per_tensor_) non_empty_ = false;
 
   is_initialized = true;
   return Status::OK();
 }
 
-Status DistributedSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
-  if (cnt_ > samples_per_buffer_) {
+Status DistributedSamplerRT::GetNextSample(TensorRow *out) {
+  if (cnt_ > samples_per_tensor_) {
     RETURN_STATUS_UNEXPECTED(
       "Number of samples(cnt) that have already been filled in to buffer should be less than or "
       "equal to samples_per_buffer, but got cnt: " +
-      std::to_string(cnt_) + ", samples_per_buffer: " + std::to_string(samples_per_buffer_));
-  } else if (cnt_ == samples_per_buffer_ && (non_empty_ || !even_dist_)) {
-    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
-    if (!samples_per_buffer_) {
+      std::to_string(cnt_) + ", samples_per_buffer: " + std::to_string(samples_per_tensor_));
+  } else if (cnt_ == samples_per_tensor_ && (non_empty_ || !even_dist_)) {
+    (*out) = TensorRow(TensorRow::kFlagEOE);
+    if (!samples_per_tensor_) {
       non_empty_ = false;
     }
-  } else if (!samples_per_buffer_ && !non_empty_) {
+  } else if (!samples_per_tensor_ && !non_empty_) {
     // If the buffer is empty, we add samples with subscript 0 in the current dataset.
     // This step is to make up for the solution that the code default buffer is not empty before.
     // We will remove this value in the concat phase
     non_empty_ = true;
-    (*out_buffer) = std::make_unique<DataBuffer>(cnt_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sample_ids;
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ids, 1));
     auto id_ptr = sample_ids->begin<int64_t>();
     // add index 0
     *id_ptr = 0;
-    TensorRow row(1, sample_ids);
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
+    (*out) = {sample_ids};
   } else {
     if (HasChildSampler()) {
       RETURN_IF_NOT_OK(child_[0]->GetNextSample(&child_ids_));
     }
 
-    (*out_buffer) = std::make_unique<DataBuffer>(cnt_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sample_ids;
-    RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ids, samples_per_buffer_));
+    RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ids, samples_per_tensor_));
     auto id_ptr = sample_ids->begin<int64_t>();
     bool flag_add_1 = false;
-    while (cnt_ < samples_per_buffer_ && id_ptr != sample_ids->end<int64_t>()) {
+    while (cnt_ < samples_per_tensor_ && id_ptr != sample_ids->end<int64_t>()) {
       int64_t middle_value = num_devices_ * cnt_ + device_id_ - offset_;
       // if index < 0, we move back one place
       if (middle_value < 0) {
-        samples_per_buffer_++;
+        samples_per_tensor_++;
         cnt_++;
         flag_add_1 = true;
         middle_value = num_devices_ * cnt_ + device_id_ - offset_;
@@ -145,17 +141,16 @@ Status DistributedSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buff
 
     // If 1 was added before, we will cut off 1 here
     if (flag_add_1) {
-      samples_per_buffer_--;
+      samples_per_tensor_--;
       cnt_--;
     }
-    TensorRow row(1, sample_ids);
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
+    (*out) = {sample_ids};
   }
   return Status::OK();
 }
 
 Status DistributedSamplerRT::ResetSampler() {
-  CHECK_FAIL_RETURN_UNEXPECTED(cnt_ == samples_per_buffer_, "ERROR Reset() called early/late");
+  CHECK_FAIL_RETURN_UNEXPECTED(cnt_ == samples_per_tensor_, "ERROR Reset() called early/late");
   cnt_ = 0;
 
   if (shuffle_ == true) {

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/distributed_sampler.h

@@ -50,7 +50,7 @@ class DistributedSamplerRT : public SamplerRT {
   /// \param std::unique_ptr<DataBuffer> * pBuffer
   /// \param int32_t workerId
   /// \return Status code
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
 
   /// Init sampler, called by base class or python
   Status InitSampler() override;

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/pk_sampler.cc

@@ -52,7 +52,7 @@ Status PKSamplerRT::InitSampler() {
     num_samples_ = num_rows_;
   }
 
-  samples_per_buffer_ = (samples_per_buffer_ > num_samples_) ? num_samples_ : samples_per_buffer_;
+  samples_per_tensor_ = (samples_per_tensor_ > num_samples_) ? num_samples_ : samples_per_tensor_;
   if (shuffle_ == true) {
     std::shuffle(labels_.begin(), labels_.end(), rnd_);
   } else {
@@ -65,19 +65,18 @@ Status PKSamplerRT::InitSampler() {
   return Status::OK();
 }
 
-Status PKSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
+Status PKSamplerRT::GetNextSample(TensorRow *out) {
   if (next_id_ > num_samples_ || num_samples_ == 0) {
     RETURN_STATUS_UNEXPECTED("Index must be less than or equal to num_samples, but got: " + std::to_string(next_id_));
   } else if (next_id_ == num_samples_) {
-    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out) = TensorRow(TensorRow::kFlagEOE);
   } else {
     if (HasChildSampler()) {
       RETURN_IF_NOT_OK(child_[0]->GetNextSample(&child_ids_));
     }
 
-    (*out_buffer) = std::make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sample_ids;
-    int64_t last_id = (samples_per_buffer_ + next_id_ > num_samples_) ? num_samples_ : samples_per_buffer_ + next_id_;
+    int64_t last_id = (samples_per_tensor_ + next_id_ > num_samples_) ? num_samples_ : samples_per_tensor_ + next_id_;
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ids, last_id - next_id_));
     auto id_ptr = sample_ids->begin<int64_t>();
     CHECK_FAIL_RETURN_UNEXPECTED(samples_per_class_ != 0, "samples cannot be zero.");
@@ -95,8 +94,7 @@ Status PKSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
       id_ptr++;
     }
 
-    TensorRow row(1, sample_ids);
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
+    (*out) = {sample_ids};
   }
   return Status::OK();
 }

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/pk_sampler.h

@@ -41,7 +41,7 @@ class PKSamplerRT : public SamplerRT {  // NOT YET FINISHED
   // @param std::unique_ptr<DataBuffer pBuffer
   // @param int32_t workerId
   // @return Status The status code returned
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
 
   // first handshake between leaf source op and Sampler. This func will determine the amount of data
   // in the dataset that we can sample from.

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/python_sampler.cc

@@ -23,9 +23,9 @@ namespace dataset {
 PythonSamplerRT::PythonSamplerRT(int64_t num_samples, py::object py_sampler_instance, int64_t samples_per_buffer)
     : SamplerRT(num_samples, samples_per_buffer), py_sampler_instance(py_sampler_instance), need_to_reset_(false) {}
 
-Status PythonSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
+Status PythonSamplerRT::GetNextSample(TensorRow *out) {
   if (need_to_reset_) {
-    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out) = TensorRow(TensorRow::kFlagEOE);
   } else {
     if (HasChildSampler()) {
       RETURN_IF_NOT_OK(child_[0]->GetNextSample(&child_ids_));
@@ -34,7 +34,6 @@ Status PythonSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
     std::shared_ptr<Tensor> sample_ids;
     {
       py::gil_scoped_acquire gil_acquire;
-      (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagNone);
       if (Py_IsInitialized() == 0) {
         return Status(StatusCode::kMDPythonInterpreterFailure, "Python Interpreter is finalized");
       }
@@ -57,8 +56,7 @@ Status PythonSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
                       "Invalid data, python sampler iterator should return an integer index.");
       }
     }
-    TensorRow row(1, sample_ids);
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
+    (*out) = {sample_ids};
     need_to_reset_ = true;
   }
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/python_sampler.h

@@ -48,7 +48,7 @@ class PythonSamplerRT : public SamplerRT {
   // @param std::unique_ptr<DataBuffer> pBuffer - Buffer to be returned to corresponding Dataset Op
   // @param int32_t workerId - not meant to be used
   // @return Status The status code returned
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
 
   // Printer for debugging purposes.
   // @param out - output stream to write to

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/random_sampler.cc

@@ -31,19 +31,18 @@ RandomSamplerRT::RandomSamplerRT(int64_t num_samples, bool replacement, bool res
       dist(nullptr),
       reshuffle_each_epoch_(reshuffle_each_epoch) {}
 
-Status RandomSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
+Status RandomSamplerRT::GetNextSample(TensorRow *out) {
   if (next_id_ > num_samples_) {
     RETURN_STATUS_UNEXPECTED("RandomSampler Internal Error");
   } else if (next_id_ == num_samples_) {
-    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out) = TensorRow(TensorRow::kFlagEOE);
   } else {
     if (HasChildSampler()) {
       RETURN_IF_NOT_OK(child_[0]->GetNextSample(&child_ids_));
     }
-    (*out_buffer) = std::make_unique<DataBuffer>(next_id_, DataBuffer::kDeBFlagNone);
 
     std::shared_ptr<Tensor> sampleIds;
-    int64_t last_id = std::min(samples_per_buffer_ + next_id_, num_samples_);
+    int64_t last_id = std::min(samples_per_tensor_ + next_id_, num_samples_);
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, last_id - next_id_));
     auto id_ptr = sampleIds->begin<int64_t>();
 
@@ -62,8 +61,7 @@ Status RandomSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
       *(id_ptr + static_cast<ptrdiff_t>(i)) = sampled_id;
     }
     next_id_ = last_id;
-    TensorRow row(1, sampleIds);
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
+    (*out) = {sampleIds};
   }
   return Status::OK();
 }
@@ -81,7 +79,7 @@ Status RandomSamplerRT::InitSampler() {
     num_samples_ > 0 && num_rows_ > 0,
     "Invalid parameter, num_samples & num_rows must be greater than 0, but got num_samples: " +
       std::to_string(num_samples_) + ", num_rows: " + std::to_string(num_rows_));
-  samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_;
+  samples_per_tensor_ = samples_per_tensor_ > num_samples_ ? num_samples_ : samples_per_tensor_;
   rnd_.seed(seed_);
 
   if (!replacement_) {

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/random_sampler.h

@@ -41,7 +41,7 @@ class RandomSamplerRT : public SamplerRT {
   // @param std::unique_ptr<DataBuffer> pBuffer - Buffer to be returned to StorageOp
   // @param int32_t workerId - not meant to be used
   // @return Status The status code returned
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
 
   // meant to be called by base class or python
   Status InitSampler() override;

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

@@ -36,7 +36,7 @@ Status RandomAccessOp::GetNumRowsInDataset(int64_t *num) const {
 SamplerRT::SamplerRT(int64_t num_samples, int64_t samples_per_buffer)
     : num_rows_(0),
       num_samples_(num_samples),
-      samples_per_buffer_(samples_per_buffer),
+      samples_per_tensor_(samples_per_buffer),
       col_desc_(nullptr),
       is_initialized(false) {}
 
@@ -91,22 +91,19 @@ void SamplerRT::SamplerPrint(std::ostream &out, bool show_all) const {
 
 #ifdef ENABLE_PYTHON
 Status SamplerRT::GetAllIdsThenReset(py::array *data) {
-  std::unique_ptr<DataBuffer> db;
   std::shared_ptr<Tensor> sample_ids;
   TensorRow sample_row;
 
-  // A call to derived class to get sample ids wrapped inside a buffer
-  RETURN_IF_NOT_OK(GetNextSample(&db));
-  // Get the only tensor inside the buffer that contains the actual SampleIds for the entire epoch
-  RETURN_IF_NOT_OK(db->GetRow(0, &sample_row));
+  // Get the only tensor inside the row that contains the actual SampleIds for the entire epoch
+  RETURN_IF_NOT_OK(GetNextSample(&sample_row));
   sample_ids = sample_row[0];
 
   // check this buffer is not a ctrl buffer
-  CHECK_FAIL_RETURN_UNEXPECTED(db->buffer_flags() == DataBuffer::kDeBFlagNone, "ERROR ctrl buffer received");
+  CHECK_FAIL_RETURN_UNEXPECTED(sample_row.Flags() == TensorRow::kFlagNone, "ERROR ctrl row received");
 
   // perform error checking! Next buffer supposed to be EOE since last one already contains all ids for current epoch
-  RETURN_IF_NOT_OK(GetNextSample(&db));
-  CHECK_FAIL_RETURN_UNEXPECTED(db->eoe(), "ERROR Non EOE received");
+  RETURN_IF_NOT_OK(GetNextSample(&sample_row));
+  CHECK_FAIL_RETURN_UNEXPECTED(sample_row.eoe(), "ERROR Non EOE received");
   // Reset Sampler since this is the end of the epoch
   RETURN_IF_NOT_OK(ResetSampler());
 
@@ -178,13 +175,11 @@ Status SamplerRT::AddChild(std::shared_ptr<SamplerRT> child) {
 bool SamplerRT::HasChildSampler() { return !child_.empty(); }
 
 Status SamplerRT::GetAssociatedChildId(int64_t *out_associated_id, int64_t id) {
-  if (child_ids_ == nullptr) {
+  if (child_ids_.empty()) {
     RETURN_STATUS_UNEXPECTED("Trying to get associated child id, but there are no child ids!");
   }
 
-  TensorRow sample_row;
-  RETURN_IF_NOT_OK(child_ids_->GetRow(0, &sample_row));
-  std::shared_ptr<Tensor> sample_ids = sample_row[0];
+  std::shared_ptr<Tensor> sample_ids = child_ids_[0];
   RETURN_IF_NOT_OK(sample_ids->GetItemAt<int64_t>(out_associated_id, {id}));
   return Status::OK();
 }

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

@@ -23,7 +23,7 @@
 #include <vector>
 
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/dataset_op.h"
 
@@ -66,7 +66,7 @@ class SamplerRT {
   // @param int64_t samplesPerBuffer: Num of Sampler Ids to fetch via 1 GetNextBuffer call
   SamplerRT(int64_t num_samples, int64_t samples_per_buffer);
 
-  SamplerRT(const SamplerRT &s) : SamplerRT(s.num_samples_, s.samples_per_buffer_) {}
+  SamplerRT(const SamplerRT &s) : SamplerRT(s.num_samples_, s.samples_per_tensor_) {}
 
   // default destructor
   ~SamplerRT() = default;
@@ -76,7 +76,7 @@ class SamplerRT {
   // @param std::unique_ptr<DataBuffer> pBuffer - Buffer to be returned to StorageOp
   // @param int32_t workerId - not meant to be used
   // @return Status The status code returned
-  virtual Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) = 0;
+  virtual Status GetNextSample(TensorRow *out) = 0;
 
 // This function only called by python layer. Not needed by Android.
 #ifdef ENABLE_PYTHON
@@ -170,10 +170,10 @@ class SamplerRT {
   int64_t num_samples_;
 
   bool is_initialized;
-  int64_t samples_per_buffer_;
+  int64_t samples_per_tensor_;
   std::unique_ptr<ColDescriptor> col_desc_;
   std::vector<std::shared_ptr<SamplerRT>> child_;  // Child nodes
-  std::unique_ptr<DataBuffer> child_ids_;
+  TensorRow child_ids_;
 };
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.cc

@@ -24,23 +24,22 @@ namespace dataset {
 SequentialSamplerRT::SequentialSamplerRT(int64_t num_samples, int64_t start_index, int64_t samples_per_buffer)
     : SamplerRT(num_samples, samples_per_buffer), current_id_(start_index), start_index_(start_index), id_count_(0) {}
 
-Status SequentialSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
+Status SequentialSamplerRT::GetNextSample(TensorRow *out) {
   if (id_count_ > num_samples_) {
     RETURN_STATUS_UNEXPECTED("SequentialSampler Internal Error");
   } else if (id_count_ == num_samples_) {
-    (*out_buffer) = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    (*out) = TensorRow(TensorRow::kFlagEOE);
   } else {
     if (HasChildSampler()) {
       RETURN_IF_NOT_OK(child_[0]->GetNextSample(&child_ids_));
     }
 
-    (*out_buffer) = std::make_unique<DataBuffer>(current_id_, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> sampleIds;
 
     // Compute how many ids are left to pack, and pack this amount into a new buffer.  Respect the setting for
     // samples per buffer though.
     int64_t remaining_ids = num_samples_ - id_count_;
-    int64_t num_elements = std::min(remaining_ids, samples_per_buffer_);
+    int64_t num_elements = std::min(remaining_ids, samples_per_tensor_);
 
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, num_elements));
     auto idPtr = sampleIds->begin<int64_t>();
@@ -57,8 +56,7 @@ Status SequentialSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffe
 
     id_count_ += num_elements;  // Count the packed ids towards our overall sample count
 
-    TensorRow row(1, sampleIds);
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, row));
+    (*out) = {sampleIds};
   }
   return Status::OK();
 }
@@ -83,9 +81,9 @@ Status SequentialSamplerRT::InitSampler() {
     num_samples_ = available_row_count;
   }
   CHECK_FAIL_RETURN_UNEXPECTED(
-    (num_samples_ > 0 && samples_per_buffer_ > 0) || num_samples_ == 0,
-    "Invalid parameter, samples_per_buffer must be greater than 0, but got " + std::to_string(samples_per_buffer_));
-  samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_;
+    (num_samples_ > 0 && samples_per_tensor_ > 0) || num_samples_ == 0,
+    "Invalid parameter, samples_per_buffer must be greater than 0, but got " + std::to_string(samples_per_tensor_));
+  samples_per_tensor_ = samples_per_tensor_ > num_samples_ ? num_samples_ : samples_per_tensor_;
 
   is_initialized = true;
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h

@@ -47,7 +47,7 @@ class SequentialSamplerRT : public SamplerRT {
   // @param std::unique_ptr<DataBuffer> pBuffer - Buffer to be returned to corresponding Dataset Op
   // @param int32_t workerId - not meant to be used
   // @return Status The status code returned
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
 
   /// \brief Recursively calls this function on its children to get the actual number of samples on a tree of samplers
   /// \note This is not a getter for num_samples_. For example, if num_samples_ is 0 or if it's smaller than num_rows,

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/subset_sampler.cc

@@ -39,8 +39,8 @@ Status SubsetSamplerRT::InitSampler() {
     num_samples_ = static_cast<int64_t>(indices_.size());
   }
 
-  if (samples_per_buffer_ > num_samples_) {
-    samples_per_buffer_ = num_samples_;
+  if (samples_per_tensor_ > num_samples_) {
+    samples_per_tensor_ = num_samples_;
   }
 
   is_initialized = true;
@@ -61,19 +61,18 @@ Status SubsetSamplerRT::ResetSampler() {
 }
 
 // Get the sample ids.
-Status SubsetSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
+Status SubsetSamplerRT::GetNextSample(TensorRow *out) {
   // All samples have been drawn
   if (sample_id_ == num_samples_) {
-    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagEOE);
+    (*out) = TensorRow(TensorRow::kFlagEOE);
   } else {
     if (HasChildSampler()) {
       RETURN_IF_NOT_OK(child_[0]->GetNextSample(&child_ids_));
     }
 
-    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> outputIds;
 
-    int64_t last_id = sample_id_ + samples_per_buffer_;
+    int64_t last_id = sample_id_ + samples_per_tensor_;
     // Handling the return all samples at once, and when last draw is not a full batch.
     if (last_id > num_samples_) {
       last_id = num_samples_;
@@ -101,8 +100,7 @@ Status SubsetSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
       sample_id_++;
     }
 
-    // Create a TensorTable from that single tensor and push into DataBuffer
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, TensorRow(1, outputIds)));
+    (*out) = {outputIds};
   }
 
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/subset_sampler.h

@@ -47,9 +47,9 @@ class SubsetSamplerRT : public SamplerRT {
   Status ResetSampler() override;
 
   /// Get the sample ids.
-  /// \param[out] out_buffer The address of a unique_ptr to DataBuffer where the sample ids will be placed.
+  /// \param[out] out The address of a unique_ptr to DataBuffer where the sample ids will be placed.
   /// @note the sample ids (int64_t) will be placed in one Tensor and be placed into pBuffer.
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
 
   /// Printer for debugging purposes.
   /// \param out - output stream to write to

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/weighted_random_sampler.cc

@@ -49,9 +49,9 @@ Status WeightedRandomSamplerRT::InitSampler() {
     num_rows_ > 0 && num_samples_,
     "Invalid parameter, num_samples and num_rows must be greater than 0, but got num_rows: " +
       std::to_string(num_rows_) + ", num_samples: " + std::to_string(num_samples_));
-  CHECK_FAIL_RETURN_UNEXPECTED(samples_per_buffer_ > 0,
+  CHECK_FAIL_RETURN_UNEXPECTED(samples_per_tensor_ > 0,
                                "Invalid parameter, samples_per_buffer must be greater than 0, but got " +
-                                 std::to_string(samples_per_buffer_) + ".\n");
+                                 std::to_string(samples_per_tensor_) + ".\n");
 
   if (weights_.size() > static_cast<size_t>(num_rows_)) {
     return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
@@ -69,7 +69,7 @@ Status WeightedRandomSamplerRT::InitSampler() {
   // Initialize random generator with seed from config manager
   rand_gen_.seed(GetSeed());
 
-  samples_per_buffer_ = (samples_per_buffer_ > num_samples_) ? num_samples_ : samples_per_buffer_;
+  samples_per_tensor_ = (samples_per_tensor_ > num_samples_) ? num_samples_ : samples_per_tensor_;
 
   if (!replacement_) {
     exp_dist_ = std::make_unique<std::exponential_distribution<>>(1);
@@ -117,7 +117,7 @@ Status WeightedRandomSamplerRT::ResetSampler() {
 }
 
 // Get the sample ids.
-Status WeightedRandomSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
+Status WeightedRandomSamplerRT::GetNextSample(TensorRow *out) {
   if (weights_.size() > static_cast<size_t>(num_rows_)) {
     return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__,
                   "Invalid parameter, size of sample weights must be less than or equal to num of data, "
@@ -133,16 +133,15 @@ Status WeightedRandomSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_b
   }
 
   if (sample_id_ == num_samples_) {
-    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagEOE);
+    (*out) = TensorRow(TensorRow::kFlagEOE);
   } else {
     if (HasChildSampler()) {
       RETURN_IF_NOT_OK(child_[0]->GetNextSample(&child_ids_));
     }
 
-    (*out_buffer) = std::make_unique<DataBuffer>(buffer_id_++, DataBuffer::kDeBFlagNone);
     std::shared_ptr<Tensor> outputIds;
 
-    int64_t last_id = sample_id_ + samples_per_buffer_;
+    int64_t last_id = sample_id_ + samples_per_tensor_;
     // Handling the return all samples at once, and when last draw is not a full batch.
     if (last_id > num_samples_) {
       last_id = num_samples_;
@@ -178,8 +177,7 @@ Status WeightedRandomSamplerRT::GetNextSample(std::unique_ptr<DataBuffer> *out_b
       sample_id_++;
     }
 
-    // Create a TensorTable from that single tensor and push into DataBuffer
-    (*out_buffer)->set_tensor_table(std::make_unique<TensorQTable>(1, TensorRow(1, outputIds)));
+    (*out) = {outputIds};
   }
 
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h

@@ -51,7 +51,7 @@ class WeightedRandomSamplerRT : public SamplerRT {
   // Get the sample ids.
   // @param[out] out_buffer The address of a unique_ptr to DataBuffer where the sample ids will be placed.
   // @note the sample ids (int64_t) will be placed in one Tensor and be placed into pBuffer.
-  Status GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) override;
+  Status GetNextSample(TensorRow *out) override;
 
   // Printer for debugging purposes.
   // @param out - output stream to write to

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

@@ -24,7 +24,7 @@
 
 #include "./tinyxml2.h"
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/engine/datasetops/parallel_op.h"
 #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/take_op.cc

@@ -19,7 +19,7 @@
 #include <algorithm>
 #include "utils/ms_utils.h"
 #include "minddata/dataset/core/config_manager.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/dataset_iterator.h"
 #include "minddata/dataset/engine/datasetops/take_op.h"
 #include "minddata/dataset/engine/db_connector.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/zip_op.cc

@@ -18,7 +18,7 @@
 #include <utility>
 #include <iomanip>
 #include "minddata/dataset/include/constants.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/db_connector.h"
 #include "minddata/dataset/core/config_manager.h"
 #include "minddata/dataset/core/global_context.h"

mindspore/ccsrc/minddata/dataset/engine/datasetops/zip_op.h

@@ -29,8 +29,6 @@
 
 namespace mindspore {
 namespace dataset {
-// forward declare
-class DataBuffer;
 
 class ZipOp : public PipelineOp {
  public:

mindspore/ccsrc/minddata/dataset/engine/db_connector.h

@@ -18,8 +18,9 @@
 
 #include <memory>
 #include <utility>
+#include "minddata/dataset/core/tensor_row.h"
 #include "minddata/dataset/engine/connector.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/include/constants.h"
 
 namespace mindspore {

mindspore/ccsrc/minddata/dataset/engine/jagged_connector.h

@@ -21,7 +21,7 @@
 #include <utility>
 #include <vector>
 #include "minddata/dataset/engine/connector.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/util/status.h"
 #include "minddata/dataset/include/constants.h"
 

mindspore/lite/minddata/CMakeLists.txt

@@ -181,7 +181,6 @@ if(BUILD_MINDDATA STREQUAL "full")
         ${MINDDATA_DIR}/engine/datasetops/source/sampler/weighted_random_sampler.cc
         ${MINDDATA_DIR}/engine/runtime_context.cc
         ${MINDDATA_DIR}/engine/tree_adapter.cc
-        ${MINDDATA_DIR}/engine/data_buffer.cc
         ${MINDDATA_DIR}/engine/execution_tree.cc
         ${MINDDATA_DIR}/engine/dataset_iterator.cc
         ${MINDDATA_DIR}/core/tensor_row.cc

mindspore/lite/minddata/wrapper/album_op_android.h

@@ -27,7 +27,7 @@
 #include <vector>
 #include <unordered_map>
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/data_schema.h"
 #include "minddata/dataset/util/path.h"
 #include "minddata/dataset/util/status.h"
@@ -91,69 +91,69 @@ class AlbumOp {
   /// \brief Load image to tensor
   /// \param[in] image_file Image name of file
   /// \param[in] col_num Column num in schema
-  /// \param[inout] Tensor to push to
+  /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
   Status LoadImageTensor(const std::string &image_file, uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load vector of ints to tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing multi-dimensional label
   /// \param[in] col_num Column num in schema
-  /// \param[inout] Tensor to push to
+  /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
   Status LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load vector of floatss to tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing array data
   /// \param[in] col_num Column num in schema
-  /// \param[inout] Tensor to push to
+  /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
   Status LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load string array into a tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing string tensor
   /// \param[in] col_num Column num in schema
-  /// \param[inout] Tensor to push to
+  /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
   Status LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load string into a tensor, append tensor to tensor
   /// \param[in] json_obj Json object containing string tensor
   /// \param[in] col_num Column num in schema
-  /// \param[inout]  Tensor to push to
+  /// \param[in,out]  Tensor to push to
   /// \return Status The error code returned
   Status LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load float value to tensor
   /// \param[in] json_obj Json object containing float
   /// \param[in] col_num Column num in schema
-  /// \param[inout]  Tensor to push to
+  /// \param[in,out]  Tensor to push to
   /// \return Status The error code returned
   Status LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load int value to tensor
   /// \param[in] json_obj Json object containing int
   /// \param[in] col_num Column num in schema
-  /// \param[inout] Tensor to push to
+  /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
   Status LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num, TensorPtr *tensor);
 
-  /// \brief Load emtpy tensor to tensor
+  /// \brief Load empty tensor to tensor
   /// \param[in] col_num Column num in schema
-  /// \param[inout] Tensor to push to
+  /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
   Status LoadEmptyTensor(uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load id from file name to tensor
   /// \param[in] file The file name to get ID from
   /// \param[in] col_num Column num in schema
-  /// \param[inout] Tensor to push to
+  /// \param[in,out] Tensor to push to
   /// \return Status The error code returned
   Status LoadIDTensor(const std::string &file, uint32_t col_num, TensorPtr *tensor);
 
   /// \brief Load a tensor according to a json file
   /// \param[in] row_id_type row_id - id for this tensor row
   /// \param[in] ImageColumns file Json file location
-  /// \param[inout] TensorRow Json content stored into a tensor row
+  /// \param[in,out] TensorRow Json content stored into a tensor row
   /// \return Status The error code returned
   Status LoadTensorRow(row_id_type row_id, const std::string &file,
                        std::unordered_map<std::string, std::shared_ptr<Tensor>> *map_row);

tests/ut/cpp/dataset/distributed_sampler_test.cc

@@ -18,7 +18,7 @@
 
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
 #include "minddata/dataset/engine/datasetops/source/sampler/distributed_sampler.h"
 #include "utils/log_adapter.h"
@@ -52,11 +52,9 @@ TEST_F(MindDataTestDistributedSampler, TestTwoShardsOne) {
   DummyRandomAccessOp dummyRandomAccessOp(num_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -65,8 +63,8 @@ TEST_F(MindDataTestDistributedSampler, TestTwoShardsOne) {
 
   ASSERT_EQ(4, out.size());
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
 
 TEST_F(MindDataTestDistributedSampler, TestTwoShardsTwo) {
@@ -78,11 +76,10 @@ TEST_F(MindDataTestDistributedSampler, TestTwoShardsTwo) {
   DummyRandomAccessOp dummyRandomAccessOp(num_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -91,8 +88,8 @@ TEST_F(MindDataTestDistributedSampler, TestTwoShardsTwo) {
 
   ASSERT_EQ(3, out.size());
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
 
 TEST_F(MindDataTestDistributedSampler, TestThreeShards) {
@@ -104,11 +101,10 @@ TEST_F(MindDataTestDistributedSampler, TestThreeShards) {
   DummyRandomAccessOp dummyRandomAccessOp(num_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -117,7 +113,6 @@ TEST_F(MindDataTestDistributedSampler, TestThreeShards) {
 
   ASSERT_EQ(0, out.size());
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
-

tests/ut/cpp/dataset/rename_op_test.cc

@@ -22,7 +22,7 @@
 #include "minddata/dataset/engine/datasetops/rename_op.h"
 #include "common/common.h"
 #include "utils/ms_utils.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "gtest/gtest.h"
 #include "minddata/dataset/core/global_context.h"
 #include "utils/log_adapter.h"

tests/ut/cpp/dataset/stand_alone_samplers_test.cc

@@ -39,7 +39,7 @@ class MindDataTestStandAloneSampler : public UT::DatasetOpTesting {
  protected:
   class MockStorageOp : public RandomAccessOp {
    public:
-    MockStorageOp(int64_t val){
+    MockStorageOp(int64_t val) {
       // row count is in base class as protected member
       // GetNumRowsInDataset does not need an override, the default from base class is fine.
       num_rows_ = val;
@@ -57,17 +57,17 @@ TEST_F(MindDataTestStandAloneSampler, TestDistributedSampler) {
     row.push_back(t);
   }
   MockStorageOp mock(20);
-  std::unique_ptr<DataBuffer> db;
   std::shared_ptr<Tensor> tensor;
   int64_t num_samples = 0;
+  TensorRow sample_row;
   for (int i = 0; i < 6; i++) {
     std::shared_ptr<SamplerRT> sampler =
       std::make_shared<DistributedSamplerRT>(num_samples, 3, i % 3, (i < 3 ? false : true));
     sampler->HandshakeRandomAccessOp(&mock);
-    sampler->GetNextSample(&db);
-    db->GetTensor(&tensor, 0, 0);
+    sampler->GetNextSample(&sample_row);
+    tensor = sample_row[0];
     MS_LOG(DEBUG) << (*tensor);
-    if(i < 3) {  // This is added due to std::shuffle()
+    if (i < 3) {  // This is added due to std::shuffle()
       EXPECT_TRUE((*tensor) == (*row[i]));
     }
   }
@@ -83,20 +83,21 @@ TEST_F(MindDataTestStandAloneSampler, TestStandAoneSequentialSampler) {
   int64_t num_samples = 0;
   int64_t start_index = 0;
   std::shared_ptr<SamplerRT> sampler = std::make_shared<SequentialSamplerRT>(num_samples, start_index, 3);
-  std::unique_ptr<DataBuffer> db;
+
   std::shared_ptr<Tensor> tensor;
+  TensorRow sample_row;
   sampler->HandshakeRandomAccessOp(&mock);
-  sampler->GetNextSample(&db);
-  db->GetTensor(&tensor, 0, 0);
+  sampler->GetNextSample(&sample_row);
+  tensor = sample_row[0];
   EXPECT_TRUE((*tensor) == (*label1));
-  sampler->GetNextSample(&db);
-  db->GetTensor(&tensor, 0, 0);
+  sampler->GetNextSample(&sample_row);
+  tensor = sample_row[0];
   EXPECT_TRUE((*tensor) == (*label2));
   sampler->ResetSampler();
-  sampler->GetNextSample(&db);
-  db->GetTensor(&tensor, 0, 0);
+  sampler->GetNextSample(&sample_row);
+  tensor = sample_row[0];
   EXPECT_TRUE((*tensor) == (*label1));
-  sampler->GetNextSample(&db);
-  db->GetTensor(&tensor, 0, 0);
+  sampler->GetNextSample(&sample_row);
+  tensor = sample_row[0];
   EXPECT_TRUE((*tensor) == (*label2));
 }

tests/ut/cpp/dataset/subset_random_sampler_test.cc

@@ -18,7 +18,7 @@
 
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
 #include "minddata/dataset/engine/datasetops/source/sampler/subset_random_sampler.h"
 
@@ -46,11 +46,10 @@ TEST_F(MindDataTestSubsetRandomSampler, TestAllAtOnce) {
   DummyRandomAccessOp dummyRandomAccessOp(5);
   sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<int64_t> out;
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
       out.push_back(*it);
@@ -61,8 +60,8 @@ TEST_F(MindDataTestSubsetRandomSampler, TestAllAtOnce) {
     ASSERT_NE(in_set.find(out[i]), in_set.end());
   }
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
 
 TEST_F(MindDataTestSubsetRandomSampler, TestGetNextBuffer) {
@@ -75,23 +74,20 @@ TEST_F(MindDataTestSubsetRandomSampler, TestGetNextBuffer) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<int64_t> out;
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
   int epoch = 0;
-  while (!db->eoe()) {
+  while (!row.eoe()) {
     epoch++;
-    db->PopRow(&row);
     for (const auto &t : row) {
       for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
         out.push_back(*it);
       }
     }
-    db.reset();
 
-    ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
+    ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
   }
 
   ASSERT_EQ(epoch, (total_samples + samples_per_buffer - 1) / samples_per_buffer);
@@ -107,12 +103,10 @@ TEST_F(MindDataTestSubsetRandomSampler, TestReset) {
   DummyRandomAccessOp dummyRandomAccessOp(5);
   sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<int64_t> out;
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
   for (const auto &t : row) {
     for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
       out.push_back(*it);
@@ -125,9 +119,8 @@ TEST_F(MindDataTestSubsetRandomSampler, TestReset) {
 
   sampler.ResetSampler();
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), false);
-  db->PopRow(&row);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), false);
   out.clear();
   for (const auto &t : row) {
     for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
@@ -139,6 +132,6 @@ TEST_F(MindDataTestSubsetRandomSampler, TestReset) {
     ASSERT_NE(in_set.find(out[i]), in_set.end());
   }
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }

tests/ut/cpp/dataset/subset_sampler_test.cc

@@ -18,7 +18,7 @@
 
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
 #include "minddata/dataset/engine/datasetops/source/sampler/subset_sampler.h"
 
@@ -46,11 +46,10 @@ TEST_F(MindDataTestSubsetSampler, TestAllAtOnce) {
   DummyRandomAccessOp dummyRandomAccessOp(5);
   sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<int64_t> out;
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
       out.push_back(*it);
@@ -61,8 +60,8 @@ TEST_F(MindDataTestSubsetSampler, TestAllAtOnce) {
     ASSERT_EQ(in[i], out[i]);
   }
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
 
 TEST_F(MindDataTestSubsetSampler, TestGetNextBuffer) {
@@ -75,23 +74,21 @@ TEST_F(MindDataTestSubsetSampler, TestGetNextBuffer) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<int64_t> out;
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
   int epoch = 0;
-  while (!db->eoe()) {
+  while (!row.eoe()) {
     epoch++;
-    db->PopRow(&row);
+
     for (const auto &t : row) {
       for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
         out.push_back(*it);
       }
     }
-    db.reset();
 
-    ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
+    ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
   }
 
   ASSERT_EQ(epoch, (total_samples + samples_per_buffer - 1) / samples_per_buffer);
@@ -107,12 +104,11 @@ TEST_F(MindDataTestSubsetSampler, TestReset) {
   DummyRandomAccessOp dummyRandomAccessOp(5);
   sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<int64_t> out;
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
       out.push_back(*it);
@@ -125,9 +121,9 @@ TEST_F(MindDataTestSubsetSampler, TestReset) {
 
   sampler.ResetSampler();
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), false);
-  db->PopRow(&row);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), false);
+
   out.clear();
   for (const auto &t : row) {
     for (auto it = t->begin<int64_t>(); it != t->end<int64_t>(); it++) {
@@ -139,6 +135,6 @@ TEST_F(MindDataTestSubsetSampler, TestReset) {
     ASSERT_EQ(in[i], out[i]);
   }
 
-  ASSERT_EQ(sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }

tests/ut/cpp/dataset/weighted_random_sampler_test.cc

@@ -18,7 +18,7 @@
 
 #include "minddata/dataset/include/constants.h"
 #include "minddata/dataset/core/tensor.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
 #include "minddata/dataset/engine/datasetops/source/sampler/weighted_random_sampler.h"
 #include "utils/log_adapter.h"
@@ -55,11 +55,10 @@ TEST_F(MindDataTestWeightedRandomSampler, TestOneshotReplacement) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -69,8 +68,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestOneshotReplacement) {
 
   ASSERT_EQ(num_samples, out.size());
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
 
 TEST_F(MindDataTestWeightedRandomSampler, TestOneshotNoReplacement) {
@@ -85,11 +84,10 @@ TEST_F(MindDataTestWeightedRandomSampler, TestOneshotNoReplacement) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -105,8 +103,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestOneshotNoReplacement) {
     }
   }
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
 
 TEST_F(MindDataTestWeightedRandomSampler, TestGetNextBufferReplacement) {
@@ -121,21 +119,20 @@ TEST_F(MindDataTestWeightedRandomSampler, TestGetNextBufferReplacement) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
   int epoch = 0;
-  while (!db->eoe()) {
+  while (!row.eoe()) {
     epoch++;
-    db->PopRow(&row);
+
     for (const auto &t : row) {
       for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
         out.push_back(*it);
       }
     }
-    db.reset();
-    ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
+
+    ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
   }
 
   ASSERT_EQ(epoch, (num_samples + samples_per_buffer - 1) / samples_per_buffer);
@@ -157,22 +154,21 @@ TEST_F(MindDataTestWeightedRandomSampler, TestGetNextBufferNoReplacement) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
   int epoch = 0;
-  while (!db->eoe()) {
+  while (!row.eoe()) {
     epoch++;
-    db->PopRow(&row);
+
     for (const auto &t : row) {
       for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
         out.push_back(*it);
         freq[*it]++;
       }
     }
-    db.reset();
-    ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
+
+    ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
   }
 
   // Without replacement, each sample only drawn once.
@@ -198,11 +194,10 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetReplacement) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -211,14 +206,14 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetReplacement) {
   }
   ASSERT_EQ(num_samples, out.size());
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 
   m_sampler.ResetSampler();
   out.clear();
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -227,8 +222,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetReplacement) {
   }
   ASSERT_EQ(num_samples, out.size());
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }
 
 TEST_F(MindDataTestWeightedRandomSampler, TestResetNoReplacement) {
@@ -243,11 +238,10 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetNoReplacement) {
   DummyRandomAccessOp dummyRandomAccessOp(total_samples);
   m_sampler.HandshakeRandomAccessOp(&dummyRandomAccessOp);
 
-  std::unique_ptr<DataBuffer> db;
   TensorRow row;
   std::vector<uint64_t> out;
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -256,8 +250,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetNoReplacement) {
   }
   ASSERT_EQ(num_samples, out.size());
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 
   m_sampler.ResetSampler();
   out.clear();
@@ -265,8 +259,8 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetNoReplacement) {
   freq.resize(total_samples, 0);
   MS_LOG(INFO) << "Resetting sampler";
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  db->PopRow(&row);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+
   for (const auto &t : row) {
     for (auto it = t->begin<uint64_t>(); it != t->end<uint64_t>(); it++) {
       out.push_back(*it);
@@ -282,6 +276,6 @@ TEST_F(MindDataTestWeightedRandomSampler, TestResetNoReplacement) {
     }
   }
 
-  ASSERT_EQ(m_sampler.GetNextSample(&db), Status::OK());
-  ASSERT_EQ(db->eoe(), true);
+  ASSERT_EQ(m_sampler.GetNextSample(&row), Status::OK());
+  ASSERT_EQ(row.eoe(), true);
 }

tests/ut/cpp/dataset/zip_op_test.cc

@@ -28,7 +28,7 @@
 #include "minddata/dataset/core/config_manager.h"
 #include "common/common.h"
 #include "utils/ms_utils.h"
-#include "minddata/dataset/engine/data_buffer.h"
+
 #include "gtest/gtest.h"
 #include "minddata/dataset/core/global_context.h"
 #include "utils/log_adapter.h"

tests/ut/python/dataset/test_cache_map.py

@@ -760,11 +760,11 @@ def test_cache_map_parameter_check():
 
     with pytest.raises(TypeError) as info:
         ds.DatasetCache(session_id="1", size=0)
-    assert "Argument session_id with value 1 is not of type (<class 'int'>,)" in str(info.value)
+    assert "Argument session_id with value 1 is not of type [<class 'int'>]" in str(info.value)
 
     with pytest.raises(TypeError) as info:
         ds.DatasetCache(session_id=None, size=0)
-    assert "Argument session_id with value None is not of type (<class 'int'>,)" in str(info.value)
+    assert "Argument session_id with value None is not of type [<class 'int'>]" in str(info.value)
 
     with pytest.raises(ValueError) as info:
         ds.DatasetCache(session_id=1, size=-1)
@@ -772,19 +772,19 @@ def test_cache_map_parameter_check():
 
     with pytest.raises(TypeError) as info:
         ds.DatasetCache(session_id=1, size="1")
-    assert "Argument size with value 1 is not of type (<class 'int'>,)" in str(info.value)
+    assert "Argument size with value 1 is not of type [<class 'int'>]" in str(info.value)
 
     with pytest.raises(TypeError) as info:
         ds.DatasetCache(session_id=1, size=None)
-    assert "Argument size with value None is not of type (<class 'int'>,)" in str(info.value)
+    assert "Argument size with value None is not of type [<class 'int'>]" in str(info.value)
 
     with pytest.raises(TypeError) as info:
         ds.DatasetCache(session_id=1, size=0, spilling="illegal")
-    assert "Argument spilling with value illegal is not of type (<class 'bool'>,)" in str(info.value)
+    assert "Argument spilling with value illegal is not of type [<class 'bool'>]" in str(info.value)
 
     with pytest.raises(TypeError) as err:
         ds.DatasetCache(session_id=1, size=0, hostname=50052)
-    assert "Argument hostname with value 50052 is not of type (<class 'str'>,)" in str(err.value)
+    assert "Argument hostname with value 50052 is not of type [<class 'str'>]" in str(err.value)
 
     with pytest.raises(RuntimeError) as err:
         ds.DatasetCache(session_id=1, size=0, hostname="illegal")
@@ -796,11 +796,11 @@ def test_cache_map_parameter_check():
 
     with pytest.raises(TypeError) as info:
         ds.DatasetCache(session_id=1, size=0, port="illegal")
-    assert "Argument port with value illegal is not of type (<class 'int'>,)" in str(info.value)
+    assert "Argument port with value illegal is not of type [<class 'int'>]" in str(info.value)
 
     with pytest.raises(TypeError) as info:
         ds.DatasetCache(session_id=1, size=0, port="50052")
-    assert "Argument port with value 50052 is not of type (<class 'int'>,)" in str(info.value)
+    assert "Argument port with value 50052 is not of type [<class 'int'>]" in str(info.value)
 
     with pytest.raises(ValueError) as err:
         ds.DatasetCache(session_id=1, size=0, port=0)