- Move MutableBuffer to be protected

- Add new CreateTensors

mindspore/ccsrc/dataset/core/data_type.h

@@ -128,7 +128,9 @@ class DataType {
   // @tparam T
   // @return true or false
   template <typename T>
-  bool IsCompatible() const;
+  bool IsCompatible() const {
+    return type_ == FromCType<T>();
+  }
 
   // returns true if the template type is the same as the Tensor type_
   // @tparam T
@@ -146,6 +148,9 @@ class DataType {
     return out;
   }
 
+  template <typename T>
+  static DataType FromCType();
+
   // Convert from DataType to Pybind type
   // @return
   py::dtype AsNumpyType() const;
@@ -191,68 +196,68 @@ class DataType {
 };
 
 template <>
-inline bool DataType::IsCompatible<bool>() const {
-  return type_ == DataType::DE_BOOL;
+inline DataType DataType::FromCType<bool>() {
+  return DataType(DataType::DE_BOOL);
 }
 
 template <>
-inline bool DataType::IsCompatible<double>() const {
-  return type_ == DataType::DE_FLOAT64;
+inline DataType DataType::FromCType<double>() {
+  return DataType(DataType::DE_FLOAT64);
 }
 
 template <>
-inline bool DataType::IsCompatible<float>() const {
-  return type_ == DataType::DE_FLOAT32;
+inline DataType DataType::FromCType<float>() {
+  return DataType(DataType::DE_FLOAT32);
 }
 
 template <>
-inline bool DataType::IsCompatible<float16>() const {
-  return type_ == DataType::DE_FLOAT16;
+inline DataType DataType::FromCType<float16>() {
+  return DataType(DataType::DE_FLOAT16);
 }
 
 template <>
-inline bool DataType::IsCompatible<int64_t>() const {
-  return type_ == DataType::DE_INT64;
+inline DataType DataType::FromCType<int64_t>() {
+  return DataType(DataType::DE_INT64);
 }
 
 template <>
-inline bool DataType::IsCompatible<uint64_t>() const {
-  return type_ == DataType::DE_UINT64;
+inline DataType DataType::FromCType<uint64_t>() {
+  return DataType(DataType::DE_UINT64);
 }
 
 template <>
-inline bool DataType::IsCompatible<int32_t>() const {
-  return type_ == DataType::DE_INT32;
+inline DataType DataType::FromCType<int32_t>() {
+  return DataType(DataType::DE_INT32);
 }
 
 template <>
-inline bool DataType::IsCompatible<uint32_t>() const {
-  return type_ == DataType::DE_UINT32;
+inline DataType DataType::FromCType<uint32_t>() {
+  return DataType(DataType::DE_UINT32);
 }
 
 template <>
-inline bool DataType::IsCompatible<int16_t>() const {
-  return type_ == DataType::DE_INT16;
+inline DataType DataType::FromCType<int16_t>() {
+  return DataType(DataType::DE_INT16);
 }
 
 template <>
-inline bool DataType::IsCompatible<uint16_t>() const {
-  return type_ == DataType::DE_UINT16;
+inline DataType DataType::FromCType<uint16_t>() {
+  return DataType(DataType::DE_UINT16);
 }
 
 template <>
-inline bool DataType::IsCompatible<int8_t>() const {
-  return type_ == DataType::DE_INT8;
+inline DataType DataType::FromCType<int8_t>() {
+  return DataType(DataType::DE_INT8);
 }
 
 template <>
-inline bool DataType::IsCompatible<uint8_t>() const {
-  return type_ == DataType::DE_UINT8;
+inline DataType DataType::FromCType<uint8_t>() {
+  return DataType(DataType::DE_UINT8);
 }
 
 template <>
-inline bool DataType::IsCompatible<std::string_view>() const {
-  return type_ == DataType::DE_STRING;
+inline DataType DataType::FromCType<std::string_view>() {
+  return DataType(DataType::DE_STRING);
 }
 
 template <>

mindspore/ccsrc/dataset/core/tensor.cc

@@ -18,6 +18,7 @@
 #include <algorithm>
 #include <iomanip>
 #include <iostream>
+#include <fstream>
 #include <memory>
 #include <vector>
 #include <utility>
@@ -311,6 +312,50 @@ Status Tensor::CreateTensor(std::shared_ptr<Tensor> *ptr, const dataengine::Byte
   return Status::OK();
 }
 
+Status Tensor::CreateTensor(std::shared_ptr<Tensor> *ptr, const std::string &file_path) {
+  std::ifstream fs;
+  fs.open(file_path, std::ios::binary | std::ios::in);
+  CHECK_FAIL_RETURN_UNEXPECTED(!fs.fail(), "Fail to open file: " + file_path);
+  int64_t num_bytes = fs.seekg(0, std::ios::end).tellg();
+  CHECK_FAIL_RETURN_UNEXPECTED(fs.seekg(0, std::ios::beg).good(), "Fail to find size of file");
+  RETURN_IF_NOT_OK(
+    Tensor::CreateTensor(ptr, TensorImpl::kFlexible, TensorShape{num_bytes}, DataType(DataType::DE_UINT8)));
+  int64_t written_bytes = fs.read(reinterpret_cast<char *>((*ptr)->GetMutableBuffer()), num_bytes).gcount();
+  CHECK_FAIL_RETURN_UNEXPECTED(written_bytes == num_bytes && fs.good(), "Error in writing to tensor");
+  fs.close();
+  return Status::OK();
+}
+
+Status Tensor::CreateTensor(std::shared_ptr<Tensor> *ptr, const dataengine::BytesList &bytes_list,
+                            const TensorShape &shape, const DataType &type, dsize_t pad_size) {
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(ptr, TensorImpl::kFlexible, shape, type));
+
+  unsigned char *current_tensor_addr = (*ptr)->GetMutableBuffer();
+  int64_t tensor_bytes_remaining = bytes_list.value_size() * pad_size;
+
+  for (int i = 0; i < bytes_list.value_size(); i++) {
+    // read string data into tensor
+    const std::string &current_element = bytes_list.value(i);
+    int return_code =
+      memcpy_s(current_tensor_addr, tensor_bytes_remaining, common::SafeCStr(current_element), current_element.size());
+
+    CHECK_FAIL_RETURN_UNEXPECTED(return_code == 0, "memcpy_s failed when reading bytesList element into Tensor");
+
+    current_tensor_addr += current_element.size();
+    tensor_bytes_remaining -= current_element.size();
+
+    // pad
+    int64_t chars_to_pad = pad_size - current_element.size();
+    return_code = memset_s(current_tensor_addr, tensor_bytes_remaining, static_cast<int>(' '), chars_to_pad);
+    CHECK_FAIL_RETURN_UNEXPECTED(return_code == 0, "memcpy_s failed when padding Tensor");
+
+    current_tensor_addr += chars_to_pad;
+    tensor_bytes_remaining -= chars_to_pad;
+  }
+
+  return Status::OK();
+}
+
 // Memcpy the given strided array's used part to consecutive memory
 // Consider a 3-d array
 // A[(i * shape[1] + j) * shape[2] + k] = B[i][j][k] = C[i * strides[0] + j * strides[1] + k * strides[2]]

mindspore/ccsrc/dataset/core/tensor.h

@@ -135,9 +135,41 @@ class Tensor {
   static Status CreateTensor(std::shared_ptr<Tensor> *ptr, const std::vector<std::string> &strings,
                              const TensorShape &shape = TensorShape::CreateUnknownRankShape());
 
+  // create tensor from protobuf bytelist with strings
   static Status CreateTensor(std::shared_ptr<Tensor> *ptr, const dataengine::BytesList &bytes_list,
                              const TensorShape &shape);
 
+  // A static factory method to create a Tensor from a given list of numbers.
+  // @param ptr output argument to hold the created Tensor
+  // @param items elements of the tensor
+  // @param shape shape of the tensor
+  // @return Status Code
+  template <typename T>
+  static Status CreateTensor(std::shared_ptr<Tensor> *ptr, const std::vector<T> &items,
+                             const TensorShape &shape_req = TensorShape::CreateUnknownRankShape()) {
+    DataType type = DataType::FromCType<T>();
+    auto items_ptr = reinterpret_cast<const uchar *>(&items[0]);
+    TensorShape shape = shape_req;
+    if (!shape.known()) {
+      shape = TensorShape({static_cast<dsize_t>(items.size())});
+    }
+    return CreateTensor(ptr, TensorImpl::kFlexible, shape, type, items_ptr);
+  }
+
+  // A static factory method to create a Tensor from a given number.
+  // @param ptr output argument to hold the created Tensor
+  // @param item value
+  // @return Status Code
+  template <typename T>
+  static Status CreateTensor(std::shared_ptr<Tensor> *ptr, const T &item) {
+    return CreateTensor<T>(ptr, {item}, TensorShape::CreateScalar());
+  }
+  // Create tensor from protobuf bytelist with uint8 or int8 types
+  static Status CreateTensor(std::shared_ptr<Tensor> *ptr, const dataengine::BytesList &bytes_list,
+                             const TensorShape &shape, const DataType &type, dsize_t pad_size);
+
+  static Status CreateTensor(std::shared_ptr<Tensor> *ptr, const std::string &path);
+
   // Copy raw data of a array based on shape and strides to the destination pointer
   // @param dst Pointer to the destination array where the content is to be copied
   // @param src Pointer to the source of strided array to be copied
@@ -260,11 +292,6 @@ class Tensor {
   // @return const unsigned char*
   const unsigned char *GetBuffer() const;
 
-  // Get the starting memory address for the data of the tensor.  This potentially
-  // drives an allocation if the data area.
-  // @return unsigned char*
-  unsigned char *GetMutableBuffer();
-
   // Getter of the type
   // @return
   DataType type() const { return type_; }
@@ -518,6 +545,7 @@ class Tensor {
   // @return TensorIterator
   template <typename T>
   TensorIterator<T> begin() {
+    AllocateBuffer(SizeInBytes());
     return TensorIterator<T>(data_);
   }
 
@@ -536,6 +564,11 @@ class Tensor {
   Status CopyLastDimAt(const std::shared_ptr<Tensor> &src, const std::vector<dsize_t> &index);
 
  protected:
+  // Get the starting memory address for the data of the tensor.  This potentially
+  // drives an allocation if the data is null.
+  // @return unsigned char*
+  unsigned char *GetMutableBuffer();
+
   // A function that prints Tensor recursively, first called by print
   // @param out
   // @param cur_dim

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

@@ -254,7 +254,7 @@ Status DeviceQueueOp::MallocForGPUData(std::vector<device::DataItemGpu> *items,
       return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "memory malloc failed.");
     }
     (void)memset_s(sub_item.data_ptr_, sub_item.data_len_, 0, sub_item.data_len_);
-    unsigned char *column_data = curr_row[i]->GetMutableBuffer();
+    const unsigned char *column_data = curr_row[i]->GetBuffer();
     if (memcpy_s(sub_item.data_ptr_, sub_item.data_len_, column_data,
                  static_cast<uint32_t>(curr_row[i++]->SizeInBytes())) != 0) {
       MS_LOG(ERROR) << "memcpy_s failed!";

mindspore/ccsrc/dataset/engine/datasetops/source/celeba_op.cc

@@ -363,19 +363,7 @@ Status CelebAOp::LoadTensorRow(const std::pair<std::string, std::vector<int32_t>
 
   Path path(folder_path_);
   Path image_path = path / image_label.first;
-  std::ifstream handle(image_path.toString(), std::ios::binary | std::ios::in);
-  if (handle.fail()) {
-    std::string err_msg = "Fail to open file: " + image_path.toString();
-    return Status(StatusCode::kFileNotExist, __LINE__, __FILE__, err_msg);
-  }
-
-  (void)handle.seekg(0, std::ios::end);
-  int64_t num_elements = handle.tellg();
-  (void)handle.seekg(0, std::ios::beg);
-  RETURN_IF_NOT_OK(Tensor::CreateTensor(&image, data_schema_->column(0).tensorImpl(),
-                                        TensorShape(std::vector<dsize_t>(1, num_elements)),
-                                        data_schema_->column(0).type()));
-  (void)handle.read(reinterpret_cast<char *>(image->GetMutableBuffer()), num_elements);
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&image, image_path.toString()));
   if (decode_ == true) {
     Status rc = Decode(image, &image);
     if (rc.IsError()) {

mindspore/ccsrc/dataset/engine/datasetops/source/cifar_op.cc

@@ -195,7 +195,7 @@ Status CifarOp::LoadTensorRow(uint64_t index, TensorRow *trow) {
   std::shared_ptr<Tensor> fine_label;
   std::shared_ptr<Tensor> ori_image = cifar_image_label_pairs_[index].first;
   std::shared_ptr<Tensor> copy_image =
-    std::make_shared<Tensor>(ori_image->shape(), ori_image->type(), ori_image->GetMutableBuffer());
+    std::make_shared<Tensor>(ori_image->shape(), ori_image->type(), ori_image->GetBuffer());
   RETURN_IF_NOT_OK(Tensor::CreateTensor(&label, data_schema_->column(1).tensorImpl(), data_schema_->column(1).shape(),
                                         data_schema_->column(1).type(),
                                         reinterpret_cast<unsigned char *>(&cifar_image_label_pairs_[index].second[0])));
@@ -366,10 +366,9 @@ Status CifarOp::ParseCifarData() {
       RETURN_IF_NOT_OK(Tensor::CreateTensor(&image_tensor, data_schema_->column(0).tensorImpl(),
                                             TensorShape({kCifarImageHeight, kCifarImageWidth, kCifarImageChannel}),
                                             data_schema_->column(0).type()));
-      for (int ch = 0; ch < kCifarImageChannel; ++ch) {
-        for (int pix = 0; pix < kCifarImageHeight * kCifarImageWidth; ++pix) {
-          (image_tensor->GetMutableBuffer())[pix * kCifarImageChannel + ch] = block[cur_block_index++];
-        }
+      auto itr = image_tensor->begin<uint8_t>();
+      for (; itr != image_tensor->end<uint8_t>(); itr++) {
+        *itr = block[cur_block_index++];
       }
       cifar_image_label_pairs_.emplace_back(std::make_pair(image_tensor, labels));
     }

mindspore/ccsrc/dataset/engine/datasetops/source/coco_op.cc

@@ -591,17 +591,8 @@ Status CocoOp::LaunchThreadsAndInitOp() {
 }
 
 Status CocoOp::ReadImageToTensor(const std::string &path, const ColDescriptor &col, std::shared_ptr<Tensor> *tensor) {
-  std::ifstream fs;
-  fs.open(path, std::ios::binary | std::ios::in);
-  if (fs.fail()) {
-    RETURN_STATUS_UNEXPECTED("Fail to open file: " + path);
-  }
-  int64_t num_elements = fs.seekg(0, std::ios::end).tellg();
-  (void)fs.seekg(0, std::ios::beg);
-  RETURN_IF_NOT_OK(
-    Tensor::CreateTensor(tensor, col.tensorImpl(), TensorShape(std::vector<dsize_t>(1, num_elements)), col.type()));
-  (void)fs.read(reinterpret_cast<char *>((*tensor)->GetMutableBuffer()), num_elements);
-  fs.close();
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(tensor, path));
+
   if (decode_ == true) {
     Status rc = Decode(*tensor, tensor);
     if (rc.IsError()) {

mindspore/ccsrc/dataset/engine/datasetops/source/image_folder_op.cc

@@ -204,18 +204,8 @@ Status ImageFolderOp::LoadTensorRow(ImageLabelPair pairPtr, TensorRow *trow) {
   RETURN_IF_NOT_OK(Tensor::CreateTensor(&label, data_schema_->column(1).tensorImpl(), data_schema_->column(1).shape(),
                                         data_schema_->column(1).type(),
                                         reinterpret_cast<unsigned char *>(&pairPtr->second)));
-  std::ifstream fs;
-  fs.open(folder_path_ + (pairPtr->first), std::ios::binary | std::ios::in);
-  if (fs.fail()) {
-    RETURN_STATUS_UNEXPECTED("Fail to open file: " + pairPtr->first);
-  }
-  int64_t num_elements = fs.seekg(0, std::ios::end).tellg();
-  (void)fs.seekg(0, std::ios::beg);
-  RETURN_IF_NOT_OK(Tensor::CreateTensor(&image, data_schema_->column(0).tensorImpl(),
-                                        TensorShape(std::vector<dsize_t>(1, num_elements)),
-                                        data_schema_->column(0).type(), nullptr));
-  (void)fs.read(reinterpret_cast<char *>(image->GetMutableBuffer()), num_elements);
-  fs.close();
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&image, folder_path_ + (pairPtr->first)));
+
   if (decode_ == true) {
     Status rc = Decode(image, &image);
     if (rc.IsError()) {

mindspore/ccsrc/dataset/engine/datasetops/source/manifest_op.cc

@@ -198,23 +198,7 @@ Status ManifestOp::LoadTensorRow(const std::pair<std::string, std::vector<std::s
       data_schema_->column(1).type(), reinterpret_cast<unsigned char *>(&label_index[0])));
   }
 
-  std::ifstream fs;
-  fs.open(data.first, std::ios::binary | std::ios::in);
-  if (!fs.is_open()) {
-    RETURN_STATUS_UNEXPECTED("Fail to open file: " + data.first);
-  }
-
-  int64_t num_elements = fs.seekg(0, std::ios::end).tellg();
-  (void)fs.seekg(0, std::ios::beg);
-  RETURN_IF_NOT_OK(Tensor::CreateTensor(&image, data_schema_->column(0).tensorImpl(),
-                                        TensorShape(std::vector<dsize_t>(1, num_elements)),
-                                        data_schema_->column(0).type(), nullptr));
-  (void)fs.read(reinterpret_cast<char *>(image->GetMutableBuffer()), num_elements);
-  if (fs.fail()) {
-    fs.close();
-    RETURN_STATUS_UNEXPECTED("Fail to read file: " + data.first);
-  }
-  fs.close();
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&image, data.first));
   if (decode_ == true) {
     Status rc = Decode(image, &image);
     if (rc.IsError()) {

mindspore/ccsrc/dataset/engine/datasetops/source/mnist_op.cc

@@ -167,7 +167,7 @@ Status MnistOp::LoadTensorRow(const MnistLabelPair &mnist_pair, TensorRow *trow)
   int32_t l = mnist_pair.second;
   // make a copy of cached tensor
   RETURN_IF_NOT_OK(Tensor::CreateTensor(&image, data_schema_->column(0).tensorImpl(), mnist_pair.first->shape(),
-                                        mnist_pair.first->type(), mnist_pair.first->GetMutableBuffer()));
+                                        mnist_pair.first->type(), mnist_pair.first->GetBuffer()));
   RETURN_IF_NOT_OK(Tensor::CreateTensor(&label, data_schema_->column(1).tensorImpl(), data_schema_->column(1).shape(),
                                         data_schema_->column(1).type(), reinterpret_cast<unsigned char *>(&l)));
   (*trow) = {std::move(image), std::move(label)};

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

@@ -68,8 +68,8 @@ Status DistributedSampler::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer
     (*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_));
-    int64_t *id_ptr = reinterpret_cast<int64_t *>(sample_ids->GetMutableBuffer());
-    while (cnt_ < samples_per_buffer_) {
+    auto id_ptr = sample_ids->begin<int64_t>();
+    while (cnt_ < samples_per_buffer_ && id_ptr != sample_ids->end<int64_t>()) {
       int64_t sampled_id = (num_devices_ * cnt_ + device_id_) % num_rows_;
       if (shuffle_) {
         sampled_id = shuffle_vec_[static_cast<size_t>(sampled_id)];

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

@@ -73,8 +73,8 @@ Status PKSampler::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
     std::shared_ptr<Tensor> sample_ids;
     int64_t last_id = (samples_per_buffer_ + next_id_ > num_samples_) ? num_samples_ : samples_per_buffer_ + next_id_;
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sample_ids, last_id - next_id_));
-    int64_t *id_ptr = reinterpret_cast<int64_t *>(sample_ids->GetMutableBuffer());
-    while (next_id_ < last_id) {
+    auto id_ptr = sample_ids->begin<int64_t>();
+    while (next_id_ < last_id && id_ptr != sample_ids->end<int64_t>()) {
       int64_t cls_id = next_id_++ / samples_per_class_;
       const std::vector<int64_t> &samples = label_to_ids_[labels_[cls_id]];
       int64_t rnd_ind = std::uniform_int_distribution<int64_t>(0, samples.size() - 1)(rnd_);

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

@@ -45,7 +45,7 @@ Status RandomSampler::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer) {
     std::shared_ptr<Tensor> sampleIds;
     int64_t last_id = std::min(samples_per_buffer_ + next_id_, num_samples_);
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, last_id - next_id_));
-    int64_t *id_ptr = reinterpret_cast<int64_t *>(sampleIds->GetMutableBuffer());
+    auto id_ptr = sampleIds->begin<int64_t>();
 
     for (int64_t i = 0; i < (last_id - next_id_); i++) {
       int64_t sampled_id = 0;

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

@@ -42,7 +42,7 @@ Status SequentialSampler::GetNextSample(std::unique_ptr<DataBuffer> *out_buffer)
     int64_t num_elements = std::min(remaining_ids, samples_per_buffer_);
 
     RETURN_IF_NOT_OK(CreateSamplerTensor(&sampleIds, num_elements));
-    int64_t *idPtr = reinterpret_cast<int64_t *>(sampleIds->GetMutableBuffer());
+    auto idPtr = sampleIds->begin<int64_t>();
     for (int64_t i = 0; i < num_elements; i++) {
       int64_t sampled_id = current_id_;
       if (HasChildSampler()) {

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

@@ -94,7 +94,7 @@ Status SubsetRandomSampler::GetNextSample(std::unique_ptr<DataBuffer> *out_buffe
     RETURN_IF_NOT_OK(CreateSamplerTensor(&outputIds, last_id - sample_id_));
 
     // Initialize tensor
-    int64_t *id_ptr = reinterpret_cast<int64_t *>(outputIds->GetMutableBuffer());
+    auto id_ptr = outputIds->begin<int64_t>();
     while (sample_id_ < last_id) {
       if (indices_[sample_id_] >= num_rows_) {
         std::string err_msg =

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

@@ -125,7 +125,7 @@ Status WeightedRandomSampler::GetNextSample(std::unique_ptr<DataBuffer> *out_buf
     RETURN_IF_NOT_OK(CreateSamplerTensor(&outputIds, last_id - sample_id_));
 
     // Initialize tensor.
-    int64_t *id_ptr = reinterpret_cast<int64_t *>(outputIds->GetMutableBuffer());
+    auto id_ptr = outputIds->begin<int64_t>();
     // Assign the data to tensor element.
     while (sample_id_ < last_id) {
       int64_t genId;

mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.cc

@@ -771,53 +771,7 @@ Status TFReaderOp::LoadBytesList(const ColDescriptor &current_col, const dataeng
   // know how many elements there are and the total bytes, create tensor here:
   TensorShape current_shape = TensorShape::CreateScalar();
   RETURN_IF_NOT_OK(current_col.MaterializeTensorShape((*num_elements) * pad_size, &current_shape));
-  RETURN_IF_NOT_OK(Tensor::CreateTensor(tensor, current_col.tensorImpl(), current_shape, current_col.type()));
-
-  // Tensors are lazily allocated, this eagerly allocates memory for the tensor.
-  unsigned char *current_tensor_addr = (*tensor)->GetMutableBuffer();
-  int64_t tensor_bytes_remaining = (*num_elements) * pad_size;
-
-  if (current_tensor_addr == nullptr) {
-    std::string err_msg = "tensor memory allocation failed";
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
-
-  RETURN_IF_NOT_OK(LoadAndPadBytes(current_tensor_addr, bytes_list, tensor_bytes_remaining, pad_size));
-
-  return Status::OK();
-}
-
-Status TFReaderOp::LoadAndPadBytes(unsigned char *current_tensor_addr, const dataengine::BytesList &bytes_list,
-                                   int64_t tensor_bytes_remaining, int64_t pad_size) {
-  if (current_tensor_addr == nullptr) {
-    std::string err_msg = "current_tensor_addr is null";
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
-
-  for (int i = 0; i < bytes_list.value_size(); i++) {
-    // read string data into tensor
-    const std::string &current_element = bytes_list.value(i);
-    int return_code =
-      memcpy_s(current_tensor_addr, tensor_bytes_remaining, common::SafeCStr(current_element), current_element.size());
-    if (return_code != 0) {
-      std::string err_msg = "memcpy_s failed when reading bytesList element into Tensor";
-      RETURN_STATUS_UNEXPECTED(err_msg);
-    }
-
-    current_tensor_addr += current_element.size();
-    tensor_bytes_remaining -= current_element.size();
-
-    // pad
-    int64_t chars_to_pad = pad_size - current_element.size();
-    return_code = memset_s(current_tensor_addr, tensor_bytes_remaining, static_cast<int>(' '), chars_to_pad);
-    if (return_code != 0) {
-      std::string err_msg = "memset_s failed when padding bytesList in Tensor";
-      RETURN_STATUS_UNEXPECTED(err_msg);
-    }
-
-    current_tensor_addr += chars_to_pad;
-    tensor_bytes_remaining -= chars_to_pad;
-  }
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(tensor, bytes_list, current_shape, current_col.type(), pad_size));
 
   return Status::OK();
 }

mindspore/ccsrc/dataset/engine/datasetops/source/tf_reader_op.h

@@ -296,17 +296,8 @@ class TFReaderOp : public ParallelOp {
   // @param column_values_list - the cell that contains the bytes list to read from.
   // @param elementStr - the string we read the value into.
   // @return Status - the error code returned.
-  Status LoadBytesList(const ColDescriptor &current_col, const dataengine::Feature &column_values_list,
-                       int32_t *num_elements, std::shared_ptr<Tensor> *tensor);
-
-  // Loads all the strings in bytes_list into the memory at current_tensor_addr.
-  // @param current_tensor_addr - the memory address to load the strings to.
-  // @param bytes_list - the list of strings to load.
-  // @param tensor_bytes_remaining - the number of bytes available for this function to use.
-  // @param pad_size - number of bytes to pad to.
-  // @return Status - the error code returned.
-  Status LoadAndPadBytes(unsigned char *current_tensor_addr, const dataengine::BytesList &bytes_list,
-                         int64_t tensor_bytes_remaining, int64_t pad_size);
+  static Status LoadBytesList(const ColDescriptor &current_col, const dataengine::Feature &column_values_list,
+                              int32_t *num_elements, std::shared_ptr<Tensor> *tensor);
 
   // Reads values from a float list
   // @param current_col - the column descriptor containing the expected shape and type of the data.

mindspore/ccsrc/dataset/engine/datasetops/source/voc_op.cc

@@ -368,17 +368,7 @@ Status VOCOp::LaunchThreadsAndInitOp() {
 }
 
 Status VOCOp::ReadImageToTensor(const std::string &path, const ColDescriptor &col, std::shared_ptr<Tensor> *tensor) {
-  std::ifstream fs;
-  fs.open(path, std::ios::binary | std::ios::in);
-  if (fs.fail()) {
-    RETURN_STATUS_UNEXPECTED("Fail to open file: " + path);
-  }
-  int64_t num_elements = fs.seekg(0, std::ios::end).tellg();
-  (void)fs.seekg(0, std::ios::beg);
-  RETURN_IF_NOT_OK(
-    Tensor::CreateTensor(tensor, col.tensorImpl(), TensorShape(std::vector<dsize_t>(1, num_elements)), col.type()));
-  (void)fs.read(reinterpret_cast<char *>((*tensor)->GetMutableBuffer()), num_elements);
-  fs.close();
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(tensor, path));
   if (decode_ == true) {
     Status rc = Decode(*tensor, tensor);
     if (rc.IsError()) {

mindspore/ccsrc/dataset/engine/gnn/graph.cc

@@ -59,7 +59,7 @@ Status Graph::CreateTensorByVector(const std::vector<std::vector<T>> &data, Data
   size_t n = data[0].size();
   RETURN_IF_NOT_OK(Tensor::CreateTensor(
     &tensor, TensorImpl::kFlexible, TensorShape({static_cast<dsize_t>(m), static_cast<dsize_t>(n)}), type, nullptr));
-  T *ptr = reinterpret_cast<T *>(tensor->GetMutableBuffer());
+  auto ptr = tensor->begin<T>();
   for (const auto &id_m : data) {
     CHECK_FAIL_RETURN_UNEXPECTED(id_m.size() == n, "Each member of the vector has a different size");
     for (const auto &id_n : id_m) {

mindspore/ccsrc/dataset/engine/tdt/tdt_plugin.cc

@@ -119,7 +119,7 @@ TdtStatus TdtPlugin::translate(const TensorRow &ts_row, std::vector<DataItem> &i
     data_item.tensorShape_ = dataShapes;
     data_item.tensorType_ = datatype;
     data_item.dataLen_ = ts->SizeInBytes();
-    data_item.dataPtr_ = std::shared_ptr<void>(reinterpret_cast<void *>(ts->GetMutableBuffer()), [](void *elem) {});
+    data_item.dataPtr_ = std::shared_ptr<void>(reinterpret_cast<uchar *>(&(*ts->begin<uint8_t>())), [](void *elem) {});
     items.emplace_back(data_item);
     MS_LOG(INFO) << "TDT data type is " << datatype << ", data shape is " << dataShapes << ", data length is "
                  << ts->Size() << ".";

mindspore/ccsrc/dataset/kernels/data/data_utils.h

@@ -101,11 +101,11 @@ Status PadEndString(const std::shared_ptr<Tensor> &src, std::shared_ptr<Tensor>
                     const std::vector<dsize_t> &pad_shape, const std::string &pad_val);
 
 // recursive helper function for padding string tensors. This function could be very expensive if called on a
-// multi-dimensional tensor it is only meant to be called by PadEndNumeric.
+// multi-dimensional tensor it is only meant to be called by PadEndString.
 // @tparam T - type of tensor and fill value
 // @param std::shared_ptr<Tensor> src - Tensor to pad from
 // @param std::shared_ptr<Tensor>* dst - Tensor to pad to, return value
-// @param std::vector<dsize_t> cur_ind - recursion helper
+// @param std::vector<dsize_t> cur_ind - recursion helperas text
 // @param std::string pad_val - value to pad tensor with
 // @param size_t cur_dim - recursion helper
 // @return Status - The error code return

mindspore/ccsrc/dataset/kernels/image/image_utils.cc

@@ -119,17 +119,14 @@ Status Resize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
   }
 }
 
-bool HasJpegMagic(const unsigned char *data, size_t data_size) {
+bool HasJpegMagic(const std::shared_ptr<Tensor> &input) {
   const unsigned char *kJpegMagic = (unsigned char *)"\xFF\xD8\xFF";
   constexpr size_t kJpegMagicLen = 3;
-  return data_size >= kJpegMagicLen && memcmp(data, kJpegMagic, kJpegMagicLen) == 0;
+  return input->SizeInBytes() >= kJpegMagicLen && memcmp(input->GetBuffer(), kJpegMagic, kJpegMagicLen) == 0;
 }
 
 Status Decode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
-  if (input->GetMutableBuffer() == nullptr) {
-    RETURN_STATUS_UNEXPECTED("Tensor is nullptr");
-  }
-  if (HasJpegMagic(input->GetMutableBuffer(), input->SizeInBytes())) {
+  if (HasJpegMagic(input)) {
     return JpegCropAndDecode(input, output);
   } else {
     return DecodeCv(input, output);
@@ -283,7 +280,7 @@ Status JpegCropAndDecode(const std::shared_ptr<Tensor> &input, std::shared_ptr<T
   jerr.pub.error_exit = JpegErrorExitCustom;
   try {
     jpeg_create_decompress(&cinfo);
-    JpegSetSource(&cinfo, input->GetMutableBuffer(), input->SizeInBytes());
+    JpegSetSource(&cinfo, input->GetBuffer(), input->SizeInBytes());
     (void)jpeg_read_header(&cinfo, TRUE);
     RETURN_IF_NOT_OK(JpegSetColorSpace(&cinfo));
     jpeg_calc_output_dimensions(&cinfo);
@@ -312,7 +309,7 @@ Status JpegCropAndDecode(const std::shared_ptr<Tensor> &input, std::shared_ptr<T
   TensorShape ts = TensorShape({crop_h, crop_w, kOutNumComponents});
   auto output_tensor = std::make_shared<Tensor>(ts, DataType(DataType::DE_UINT8));
   const int buffer_size = output_tensor->SizeInBytes();
-  JSAMPLE *buffer = static_cast<JSAMPLE *>(output_tensor->GetMutableBuffer());
+  JSAMPLE *buffer = static_cast<JSAMPLE *>(reinterpret_cast<uchar *>(&(*output_tensor->begin<uint8_t>())));
   const int max_scanlines_to_read = skipped_scanlines + crop_h;
   // stride refers to output tensor, which has 3 components at most
   const int stride = crop_w * kOutNumComponents;

mindspore/ccsrc/dataset/kernels/image/image_utils.h

@@ -96,7 +96,7 @@ Status Decode(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
 
 Status DecodeCv(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output);
 
-bool HasJpegMagic(const unsigned char *data, size_t data_size);
+bool HasJpegMagic(const std::shared_ptr<Tensor> &input);
 
 void JpegSetSource(j_decompress_ptr c_info, const void *data, int64_t data_size);
 

mindspore/ccsrc/dataset/kernels/image/random_crop_decode_resize_op.cc

@@ -31,7 +31,7 @@ Status RandomCropDecodeResizeOp::Compute(const std::shared_ptr<Tensor> &input, s
   if (input == nullptr) {
     RETURN_STATUS_UNEXPECTED("input tensor is null");
   }
-  if (!HasJpegMagic(input->GetMutableBuffer(), input->SizeInBytes())) {
+  if (!HasJpegMagic(input)) {
     DecodeOp op(true);
     std::shared_ptr<Tensor> decoded;
     RETURN_IF_NOT_OK(op.Compute(input, &decoded));
@@ -43,7 +43,7 @@ Status RandomCropDecodeResizeOp::Compute(const std::shared_ptr<Tensor> &input, s
     jerr.pub.error_exit = JpegErrorExitCustom;
     try {
       jpeg_create_decompress(&cinfo);
-      JpegSetSource(&cinfo, input->GetMutableBuffer(), input->SizeInBytes());
+      JpegSetSource(&cinfo, input->GetBuffer(), input->SizeInBytes());
       (void)jpeg_read_header(&cinfo, TRUE);
       jpeg_calc_output_dimensions(&cinfo);
     } catch (std::runtime_error &e) {

tests/ut/cpp/dataset/common/cvop_common.cc

@@ -28,9 +28,9 @@
 namespace common = mindspore::common;
 
 using namespace mindspore::dataset;
-using mindspore::MsLogLevel::INFO;
-using mindspore::ExceptionType::NoExceptionType;
 using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::INFO;
 using UT::CVOP::CVOpCommon;
 
 CVOpCommon::CVOpCommon() {}
@@ -52,15 +52,7 @@ std::string CVOpCommon::GetFilename() {
 
 void CVOpCommon::GetInputImage(std::string filename) {
   try {
-    std::ifstream tmp(filename, std::ios::binary | std::ios::ate);
-    dsize_t file_size = tmp.tellg();
-    tmp.close();
-
-    std::ifstream file(filename, std::ios::binary);
-    TensorShape in_shape({file_size});
-    raw_input_tensor_ = std::make_shared<Tensor>(in_shape, DataType(DataType::DE_UINT8));
-
-    file.read(reinterpret_cast<char *>(raw_input_tensor_->GetMutableBuffer()), raw_input_tensor_->SizeInBytes());
+    Tensor::CreateTensor(&raw_input_tensor_, filename);
     raw_cv_image_ = cv::imread(filename, cv::ImreadModes::IMREAD_COLOR);
     input_tensor_ = std::dynamic_pointer_cast<Tensor>(std::make_shared<CVTensor>(raw_cv_image_));
     SwapRedAndBlue(input_tensor_, &input_tensor_);

tests/ut/cpp/dataset/image_folder_op_test.cc

@@ -69,11 +69,9 @@ std::shared_ptr<ImageFolderOp> ImageFolder(int64_t num_works, int64_t rows, int6
 Status Create1DTensor(std::shared_ptr<Tensor> *sample_ids, int64_t num_elements, unsigned char *data = nullptr,
                       DataType::Type data_type = DataType::DE_UINT32) {
   TensorShape shape(std::vector<int64_t>(1, num_elements));
-  RETURN_IF_NOT_OK(
-    Tensor::CreateTensor(sample_ids, TensorImpl::kFlexible, shape, DataType(data_type), data));
-  if (data == nullptr) {
-    (*sample_ids)->GetMutableBuffer();  // allocate memory in case user forgets!
-  }
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(sample_ids, TensorImpl::kFlexible, shape, DataType(data_type), data));
+  (*sample_ids)->AllocateBuffer((*sample_ids)->SizeInBytes());  // allocate memory in case user forgets!
+
   return Status::OK();
 }
 

tests/ut/cpp/dataset/map_op_test.cc

@@ -189,7 +189,7 @@ TEST_F(MindDataTestMapOp, TestByPosition) {
     EXPECT_EQ(tensor_list[i]->type(), golden_types[i]);
     EXPECT_EQ(tensor_list[i]->Rank(), golden_ranks[i]);
     EXPECT_EQ(tensor_list[i]->shape(), golden_shapes[i]);
-    EXPECT_NE(tensor_list[i]->GetMutableBuffer(), nullptr);
+    EXPECT_NE(tensor_list[i]->GetBuffer(), nullptr);
   }
 }
 
@@ -365,7 +365,7 @@ TEST_F(MindDataTestMapOp, Test1to3) {
       EXPECT_EQ(tensor_list[i]->type(), golden_types[i]);
       EXPECT_EQ(tensor_list[i]->Rank(), golden_ranks[i]);
       EXPECT_EQ(tensor_list[i]->shape(), golden_shapes[i]);
-      EXPECT_NE(tensor_list[i]->GetMutableBuffer(), nullptr);
+      EXPECT_NE(tensor_list[i]->GetBuffer(), nullptr);
     }
     rc = di.FetchNextTensorRow(&tensor_list);
     EXPECT_TRUE(rc.IsOk());
@@ -699,7 +699,7 @@ TEST_F(MindDataTestMapOp, ImageFolder_Decode_Repeat_Resize) {
     MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n";
     EXPECT_TRUE(img_class[(i % 44) / 11] == label);
     // Dump all the image into string, to be used as a comparison later.
-    result.append((char *)tensor_map["image"]->GetMutableBuffer(), (int64_t) tensor_map["image"]->Size());
+    result.append((char *)tensor_map["image"]->GetBuffer(), (int64_t)tensor_map["image"]->Size());
     di.GetNextAsMap(&tensor_map);
     i++;
   }
@@ -744,7 +744,7 @@ TEST_F(MindDataTestMapOp, ImageFolder_Decode_Repeat_Resize) {
     tensor_map["label"]->GetItemAt<int32_t>(&label, {});
     MS_LOG(DEBUG) << "row:" << i << "\tlabel:" << label << "\n";
     EXPECT_TRUE(img_class[(i % 44) / 11] == label);
-    result2.append((char *)tensor_map["image"]->GetMutableBuffer(), (int64_t) tensor_map["image"]->Size());
+    result2.append((char *)tensor_map["image"]->GetBuffer(), (int64_t)tensor_map["image"]->Size());
     di2.GetNextAsMap(&tensor_map);
     i++;
   }

tests/ut/cpp/dataset/random_crop_decode_resize_op_test.cc

@@ -54,16 +54,17 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp2) {
   auto decode_and_crop = static_cast<RandomCropAndResizeOp>(crop_and_decode_copy);
   EXPECT_TRUE(crop_and_decode.OneToOne());
   GlobalContext::config_manager()->set_seed(42);
-  for (int i = 0; i < 100; i++) {
+  for (int k = 0; k < 100; k++) {
     (void)crop_and_decode.Compute(raw_input_tensor_, &crop_and_decode_output);
     (void)decode_and_crop.Compute(input_tensor_, &decode_and_crop_output);
-    cv::Mat output1(target_height, target_width, CV_8UC3, crop_and_decode_output->GetMutableBuffer());
-    cv::Mat output2(target_height, target_width, CV_8UC3, decode_and_crop_output->GetMutableBuffer());
+    cv::Mat output1 = CVTensor::AsCVTensor(crop_and_decode_output)->mat().clone();
+    cv::Mat output2 = CVTensor::AsCVTensor(decode_and_crop_output)->mat().clone();
+
     long int mse_sum = 0;
     long int count = 0;
     int a, b;
-    for (int j = 0; j < target_height; j++) {
-      for (int k = 0; k < target_width; k++) {
+    for (int i = 0; i < target_height; i++) {
+      for (int j = 0; j < target_width; j++) {
         a = static_cast<int>(output1.at<cv::Vec3b>(i, j)[1]);
         b = static_cast<int>(output2.at<cv::Vec3b>(i, j)[1]);
         mse_sum += sqrt((a - b) * (a - b));
@@ -133,8 +134,8 @@ TEST_F(MindDataTestRandomCropDecodeResizeOp, TestOp1) {
     crop_and_decode_status = Crop(decoded, &decoded_and_cropped, x, y, crop_width, crop_height);
     decode_and_crop_status = JpegCropAndDecode(raw_input_tensor_, &cropped_and_decoded, x, y, crop_width, crop_height);
     {
-      cv::Mat M1(crop_height, crop_width, CV_8UC3, decoded_and_cropped->GetMutableBuffer());
-      cv::Mat M2(crop_height, crop_width, CV_8UC3, cropped_and_decoded->GetMutableBuffer());
+      cv::Mat M1 = CVTensor::AsCVTensor(decoded_and_cropped)->mat().clone();
+      cv::Mat M2 = CVTensor::AsCVTensor(cropped_and_decoded)->mat().clone();
       for (int i = 0; i < crop_height; ++i) {
         for (int j = 0; j < crop_width; ++j) {
           m1 = M1.at<cv::Vec3b>(i, j)[1];

tests/ut/cpp/dataset/stand_alone_samplers_test.cc

@@ -31,11 +31,9 @@ using namespace mindspore::dataset;
 
 Status CreateINT64Tensor(std::shared_ptr<Tensor> *sample_ids, int64_t num_elements, unsigned char *data = nullptr) {
   TensorShape shape(std::vector<int64_t>(1, num_elements));
-  RETURN_IF_NOT_OK(Tensor::CreateTensor(sample_ids, TensorImpl::kFlexible, shape,
-                                        DataType(DataType::DE_INT64), data));
-  if (data == nullptr) {
-    (*sample_ids)->GetMutableBuffer();  // allocate memory in case user forgets!
-  }
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(sample_ids, TensorImpl::kFlexible, shape, DataType(DataType::DE_INT64), data));
+  (*sample_ids)->AllocateBuffer((*sample_ids)->SizeInBytes());  // allocate memory in case user forgets!
+
   return Status::OK();
 }
 

tests/ut/cpp/dataset/tensor_string_test.cc

@@ -69,10 +69,10 @@ TEST_F(MindDataTestStringTensorDE, Basics2) {
   std::vector<uint32_t> offsets = {0, 4, 9, 12, 18, 22, 26};
   uint32_t ctr = 0;
   for (auto i : offsets) {
-    ASSERT_TRUE(*(reinterpret_cast<uint32_t *>(t->GetMutableBuffer() + ctr)) == i + 28);
+    ASSERT_TRUE(*(reinterpret_cast<const uint32_t *>(t->GetBuffer() + ctr)) == i + 28);
     ctr += 4;
   }
-  const char *buf = reinterpret_cast<char *>(t->GetMutableBuffer()) + 6 * 4 + 4;
+  const char *buf = reinterpret_cast<const char *>(t->GetBuffer()) + 6 * 4 + 4;
   std::vector<uint32_t> starts = {0, 4, 9, 12, 18, 22};
 
   uint32_t index = 0;
@@ -94,10 +94,10 @@ TEST_F(MindDataTestStringTensorDE, Empty) {
   std::vector<uint32_t> offsets = {0, 4, 9, 10, 11, 15, 16};
   uint32_t ctr = 0;
   for (auto i : offsets) {
-    ASSERT_TRUE(*(reinterpret_cast<uint32_t *>(t->GetMutableBuffer() + ctr)) == i + 28);
+    ASSERT_TRUE(*(reinterpret_cast<const uint32_t *>(t->GetBuffer() + ctr)) == i + 28);
     ctr += 4;
   }
-  const char *buf = reinterpret_cast<char *>(t->GetMutableBuffer()) + 6 * 4 + 4;
+  const char *buf = reinterpret_cast<const char *>(t->GetBuffer()) + 6 * 4 + 4;
   std::vector<uint32_t> starts = {0, 4, 9, 10, 11, 15};
 
   uint32_t index = 0;

tests/ut/cpp/dataset/tensor_test.cc

@@ -71,8 +71,9 @@ TEST_F(MindDataTestTensorDE, Basics) {
   ASSERT_TRUE(rc.IsError());
   ASSERT_EQ(t->ToString(), "Tensor (shape: <2,3>, Type: uint64)\n[[1,2,3],[4,5,6]]");
   std::vector<uint64_t> x = {1, 2, 3, 4, 5, 6};
-  std::shared_ptr<Tensor> t2 = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_UINT64),
-                                                        reinterpret_cast<unsigned char *>(&x[0]));
+  std::shared_ptr<Tensor> t2;
+  Tensor::CreateTensor(&t2, x, TensorShape({2, 3}));
+
   ASSERT_EQ(*t == *t2, true);
   ASSERT_EQ(*t != *t2, false);
 }
@@ -81,8 +82,8 @@ TEST_F(MindDataTestTensorDE, Fill) {
   std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 2}), DataType(DataType::DE_FLOAT32));
   t->Fill<float>(2.5);
   std::vector<float> x = {2.5, 2.5, 2.5, 2.5};
-  std::shared_ptr<Tensor> t2 = std::make_shared<Tensor>(TensorShape({2, 2}), DataType(DataType::DE_FLOAT32),
-                                                        reinterpret_cast<unsigned char *>(&x[0]));
+  std::shared_ptr<Tensor> t2;
+  Tensor::CreateTensor(&t2, x, TensorShape({2, 2}));
   ASSERT_EQ(*t == *t2, true);
 }
 
@@ -91,8 +92,9 @@ TEST_F(MindDataTestTensorDE, Reshape) {
   t->Fill<uint8_t>(254);
   t->Reshape(TensorShape({4}));
   std::vector<uint8_t> x = {254, 254, 254, 254};
-  std::shared_ptr<Tensor> t2 = std::make_shared<Tensor>(TensorShape({4}), DataType(DataType::DE_UINT8),
-                                                        reinterpret_cast<unsigned char *>(&x[0]));
+  std::shared_ptr<Tensor> t2;
+  Tensor::CreateTensor(&t2, x);
+
   ASSERT_EQ(*t == *t2, true);
   Status rc = t->Reshape(TensorShape({5}));
   ASSERT_TRUE(rc.IsError());
@@ -112,17 +114,17 @@ TEST_F(MindDataTestTensorDE, CopyTensor) {
   int16_t o;
   t->GetItemAt<int16_t>(&o, {});
   ASSERT_EQ(o, -66);
-  unsigned char *addr = t->GetMutableBuffer();
+  const unsigned char *addr = t->GetBuffer();
   auto t2 = std::make_shared<Tensor>(std::move(*t));
   ASSERT_EQ(t2->shape(), TensorShape({}));
   ASSERT_EQ(t2->type(), DataType::DE_INT16);
   t2->GetItemAt<int16_t>(&o, {});
   ASSERT_EQ(o, -66);
-  unsigned char *new_addr = t2->GetMutableBuffer();
+  const unsigned char *new_addr = t2->GetBuffer();
   ASSERT_EQ(addr, new_addr);
   ASSERT_EQ(t->shape(), TensorShape::CreateUnknownRankShape());
   ASSERT_EQ(t->type(), DataType::DE_UNKNOWN);
-  ASSERT_EQ(t->GetMutableBuffer(), nullptr);
+  ASSERT_EQ(t->GetBuffer(), nullptr);
   Status rc = t->GetItemAt<int16_t>(&o, {});
   ASSERT_TRUE(rc.IsError());
 }
@@ -130,26 +132,30 @@ TEST_F(MindDataTestTensorDE, CopyTensor) {
 TEST_F(MindDataTestTensorDE, InsertTensor) {
   std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT64));
   std::vector<double> x = {1.1, 2.1, 3.1};
-  std::shared_ptr<Tensor> t2 = std::make_shared<Tensor>(TensorShape({3}), DataType(DataType::DE_FLOAT64),
-                                                        reinterpret_cast<unsigned char *>(&x[0]));
+  std::shared_ptr<Tensor> t2;
+  Tensor::CreateTensor(&t2, x);
+
   std::vector<double> y = {1.2, 2.2, 3.2};
-  std::shared_ptr<Tensor> t3 = std::make_shared<Tensor>(TensorShape({3}), DataType(DataType::DE_FLOAT64),
-                                                        reinterpret_cast<unsigned char *>(&y[0]));
+  std::shared_ptr<Tensor> t3;
+  Tensor::CreateTensor(&t3, y);
+
   ASSERT_TRUE(t->InsertTensor({0}, t2).OK());
   ASSERT_TRUE(t->InsertTensor({1}, t3).OK());
   std::vector<double> z = {1.1, 2.1, 3.1, 1.2, 2.2, 3.2};
 
-  std::shared_ptr<Tensor> t4 = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT64),
-                                                        reinterpret_cast<unsigned char *>(&z[0]));
+  std::shared_ptr<Tensor> t4;
+  Tensor::CreateTensor(&t4, z, TensorShape({2, 3}));
+
   ASSERT_EQ(*t == *t4, true);
-  std::vector<double> x2 = {0};
 
-  std::shared_ptr<Tensor> t5 = std::make_shared<Tensor>(TensorShape({}), DataType(DataType::DE_FLOAT64),
-                                                        reinterpret_cast<unsigned char *>(&x2[0]));
+  std::shared_ptr<Tensor> t5;
+  Tensor::CreateTensor<double>(&t5, 0);
+
   ASSERT_TRUE(t->InsertTensor({1, 2}, t5).OK());
   z[5] = 0;
-  std::shared_ptr<Tensor> t6 = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_FLOAT64),
-                                                        reinterpret_cast<unsigned char *>(&z[0]));
+  std::shared_ptr<Tensor> t6;
+  Tensor::CreateTensor(&t6, z, TensorShape({2, 3}));
+
   ASSERT_EQ(*t == *t6, true);
   ASSERT_EQ(t->InsertTensor({2}, t5).get_code(), StatusCode::kUnexpectedError);
   ASSERT_EQ(t->InsertTensor({1}, t5).get_code(), StatusCode::kUnexpectedError);
@@ -238,12 +244,11 @@ TEST_F(MindDataTestTensorDE, Strides) {
 void checkCvMat(TensorShape shape, DataType type) {
   std::shared_ptr<CVTensor> t = std::make_shared<CVTensor>(shape, type);
   cv::Mat m = t->mat();
-  ASSERT_EQ(m.data, t->GetMutableBuffer());
+  ASSERT_EQ(m.data, t->GetBuffer());
   ASSERT_EQ(static_cast<uchar>(m.type()) & static_cast<uchar>(CV_MAT_DEPTH_MASK), type.AsCVType());
   if (shape.Rank() < 4) {
     if (shape.Rank() > 1) {
-      for (dsize_t i = 0; i < 2; i++)
-        ASSERT_EQ(m.size[static_cast<int>(i)], shape[i]);
+      for (dsize_t i = 0; i < 2; i++) ASSERT_EQ(m.size[static_cast<int>(i)], shape[i]);
     } else if (shape.Rank() == 0) {
       ASSERT_EQ(m.size[0], 1);
       ASSERT_EQ(m.size[1], 1);
@@ -312,15 +317,15 @@ TEST_F(MindDataTestTensorDE, CVTensorFromMat) {
 TEST_F(MindDataTestTensorDE, CVTensorAs) {
   std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({3, 2}), DataType(DataType::DE_FLOAT64));
   t->Fill<double>(2.2);
-  unsigned char *addr = t->GetMutableBuffer();
+  const unsigned char *addr = t->GetBuffer();
   std::shared_ptr<Tensor> t2 = std::make_shared<Tensor>(TensorShape({3, 2}), DataType(DataType::DE_FLOAT64));
   t2->Fill<double>(4.4);
   std::shared_ptr<CVTensor> ctv = CVTensor::AsCVTensor(t);
-  ASSERT_EQ(t->GetMutableBuffer(), nullptr);
-  ASSERT_EQ(ctv->GetMutableBuffer(), addr);
+  ASSERT_EQ(t->GetBuffer(), nullptr);
+  ASSERT_EQ(ctv->GetBuffer(), addr);
   cv::Mat m = ctv->mat();
   m = 2 * m;
-  ASSERT_EQ(ctv->GetMutableBuffer(), addr);
+  ASSERT_EQ(ctv->GetBuffer(), addr);
   ASSERT_TRUE(*t2 == *ctv);
   MS_LOG(DEBUG) << *t2 << std::endl << *ctv;
 }
@@ -357,8 +362,9 @@ TEST_F(MindDataTestTensorDE, TensorIterator) {
   std::vector<uint32_t> values = {1, 2, 3, 4, 5, 6};
   std::vector<uint32_t> values2 = {2, 3, 4, 5, 6, 7};
 
-  std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({6}), DataType(DataType::DE_UINT32),
-                                                       reinterpret_cast<unsigned char *>(&values[0]));
+  std::shared_ptr<Tensor> t;
+  Tensor::CreateTensor(&t, values);
+
   auto i = t->begin<uint32_t>();
   auto j = values.begin();
   uint32_t ctr = 0;
@@ -367,7 +373,7 @@ TEST_F(MindDataTestTensorDE, TensorIterator) {
     ctr++;
   }
   ASSERT_TRUE(ctr == 6);
-  t->Reshape(TensorShape {2, 3});
+  t->Reshape(TensorShape{2, 3});
   i = t->begin<uint32_t>();
   j = values.begin();
   ctr = 0;