!8930 Add Jni code for integration

From: @ezphlow
Reviewed-by: @liucunwei,@jonyguo
Signed-off-by: @liucunwei

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

@@ -225,20 +225,21 @@ Status AlbumOp::WorkerEntry(int32_t worker_id) {
 
 // Only support JPEG/PNG/GIF/BMP
 // Optimization: Could take in a tensor
-Status AlbumOp::CheckImageType(const std::string &file_name, bool *valid) {
+// This function does not return status because we want to just skip bad input, not crash
+bool AlbumOp::CheckImageType(const std::string &file_name, bool *valid) {
   std::ifstream file_handle;
   constexpr int read_num = 3;
   *valid = false;
   file_handle.open(file_name, std::ios::binary | std::ios::in);
   if (!file_handle.is_open()) {
-    RETURN_STATUS_UNEXPECTED("Invalid file, can not open image file: " + file_name);
+    return false;
   }
   unsigned char file_type[read_num];
   (void)file_handle.read(reinterpret_cast<char *>(file_type), read_num);
 
   if (file_handle.fail()) {
     file_handle.close();
-    RETURN_STATUS_UNEXPECTED("Invalid data, failed to read image file: " + file_name);
+    return false;
   }
   file_handle.close();
   if (file_type[0] == 0xff && file_type[1] == 0xd8 && file_type[2] == 0xff) {
@@ -246,17 +247,8 @@ Status AlbumOp::CheckImageType(const std::string &file_name, bool *valid) {
     // JPEG with EXIF stats with \xff\xd8\xff\xe1
     // Use \xff\xd8\xff to cover both.
     *valid = true;
-  } else if (file_type[0] == 0x89 && file_type[1] == 0x50 && file_type[2] == 0x4e) {
-    // It's a PNG
-    *valid = true;
-  } else if (file_type[0] == 0x47 && file_type[1] == 0x49 && file_type[2] == 0x46) {
-    // It's a GIF
-    *valid = true;
-  } else if (file_type[0] == 0x42 && file_type[1] == 0x4d) {
-    // It's a BMP
-    *valid = true;
   }
-  return Status::OK();
+  return true;
 }
 
 Status AlbumOp::LoadImageTensor(const std::string &image_file_path, uint32_t col_num, TensorRow *row) {
@@ -264,22 +256,44 @@ Status AlbumOp::LoadImageTensor(const std::string &image_file_path, uint32_t col
   std::ifstream fs;
   fs.open(image_file_path, std::ios::binary | std::ios::in);
   if (fs.fail()) {
-    MS_LOG(INFO) << "Image file not found:" << image_file_path << ".";
+    MS_LOG(WARNING) << "File not found:" << image_file_path << ".";
     // If file doesn't exist, we don't flag this as error in input check, simply push back empty tensor
-    RETURN_STATUS_UNEXPECTED("Invalid file_path, failed to read file: " + image_file_path);
+    RETURN_IF_NOT_OK(LoadEmptyTensor(col_num, row));
+    return Status::OK();
+  }
+  // Hack logic to replace png images with empty tensor
+  Path file(image_file_path);
+  std::set<std::string> png_ext = {".png", ".PNG"};
+  if (png_ext.find(file.Extension()) != png_ext.end()) {
+    // load empty tensor since image is not jpg
+    MS_LOG(INFO) << "PNG!" << image_file_path << ".";
+    RETURN_IF_NOT_OK(LoadEmptyTensor(col_num, row));
+    return Status::OK();
+  }
+  // treat bin files separately
+  std::set<std::string> bin_ext = {".bin", ".BIN"};
+  if (bin_ext.find(file.Extension()) != bin_ext.end()) {
+    // load empty tensor since image is not jpg
+    MS_LOG(INFO) << "Bin file found" << image_file_path << ".";
+    RETURN_IF_NOT_OK(Tensor::CreateFromFile(image_file_path, &image));
+    row->push_back(std::move(image));
+    return Status::OK();
   }
-
-  MS_LOG(INFO) << "Image file found: " << image_file_path << ".";
 
   // check that the file is an image before decoding
   bool valid = false;
-  RETURN_IF_NOT_OK(CheckImageType(image_file_path, &valid));
+  bool check_success = CheckImageType(image_file_path, &valid);
+  if (!check_success || !valid) {
+    RETURN_IF_NOT_OK(LoadEmptyTensor(col_num, row));
+    return Status::OK();
+  }
+  // if it is a jpeg image, load and try to decode
   RETURN_IF_NOT_OK(Tensor::CreateFromFile(image_file_path, &image));
   if (decode_ && valid) {
     Status rc = Decode(image, &image);
     if (rc.IsError()) {
-      std::string err = "Invalid data, failed to decode image: " + image_file_path;
-      RETURN_STATUS_UNEXPECTED(err);
+      RETURN_IF_NOT_OK(LoadEmptyTensor(col_num, row));
+      return Status::OK();
     }
   }
   row->push_back(std::move(image));

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

@@ -189,8 +189,8 @@ class AlbumOp : public ParallelOp, public RandomAccessOp {
 
   /// \brief Check if image ia valid.Only support JPEG/PNG/GIF/BMP
   ///     This function could be optimized to return the tensor to reduce open/closing files
-  /// \return Status - The error code returned
-  Status CheckImageType(const std::string &file_name, bool *valid);
+  /// \return bool - if file is bad then return false
+  bool CheckImageType(const std::string &file_name, bool *valid);
 
   // Base-class override for NodePass visitor acceptor.
   // @param p - Pointer to the NodePass to be accepted.

mindspore/ccsrc/minddata/dataset/kernels/image/lite_image_utils.cc

@@ -429,10 +429,11 @@ Status Pad(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output
     bool ret = Pad(lite_mat_rgb, lite_mat_pad, pad_top, pad_bottom, pad_left, pad_right,
                    PaddBorderType::PADD_BORDER_CONSTANT, fill_r, fill_g, fill_b);
     CHECK_FAIL_RETURN_UNEXPECTED(ret, "Pad failed in lite cv");
-
+    // new shape for output tensor
+    TensorShape new_shape = TensorShape({lite_mat_pad.height_, lite_mat_pad.width_, input->shape()[2]});
     std::shared_ptr<Tensor> output_tensor;
-    RETURN_IF_NOT_OK(Tensor::CreateFromMemory(input->shape(), input->type(),
-                                              static_cast<uchar *>(lite_mat_pad.data_ptr_), &output_tensor));
+    RETURN_IF_NOT_OK(
+      Tensor::CreateFromMemory(new_shape, input->type(), static_cast<uchar *>(lite_mat_pad.data_ptr_), &output_tensor));
     *output = output_tensor;
   } catch (std::runtime_error &e) {
     RETURN_STATUS_UNEXPECTED("Error in image Pad.");

mindspore/lite/minddata/CMakeLists.txt

@@ -237,9 +237,9 @@ if (BUILD_MINDDATA STREQUAL "full")
             )
     include_directories("${CMAKE_BINARY_DIR}/minddata/dataset/engine/cache")
 
-#    if (BUILD_MINDDATA_EXAMPLE AND (PLATFORM_ARM32 OR PLATFORM_ARM64))
-#      set(MINDDATA_EXAMPLE_SRC ${CMAKE_CURRENT_SOURCE_DIR}/example/jni-example.cc)
-#    endif ()
+    if (BUILD_MINDDATA_EXAMPLE AND (PLATFORM_ARM32 OR PLATFORM_ARM64))
+	    set(MINDDATA_TODAPI_SRC ${CMAKE_CURRENT_SOURCE_DIR}/wrapper/MDToDApi.cc)
+    endif ()
 
     add_library(minddata-lite SHARED
             ${MINDDATA_API_SRC_FILES}

mindspore/lite/minddata/wrapper/MDToDApi.cc

@@ -0,0 +1,451 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "MDToDApi.h"
+
+#include <string>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+#include "minddata/dataset/include/datasets.h"
+#include "minddata/dataset/include/execute.h"
+#include "minddata/dataset/util/path.h"
+#include "minddata/dataset/include/vision.h"
+#include "minddata/dataset/util/data_helper.h"
+#if defined(__ANDROID__) || defined(ANDROID)
+#include <android/log.h>
+#include <android/asset_manager.h>
+#endif
+
+using mindspore::dataset::Path;
+using mindspore::dataset::Tensor;
+
+using mindspore::dataset;
+
+using mindspore::LogStream;
+using mindspore::MsLogLevel::DEBUG;
+using mindspore::MsLogLevel::ERROR;
+using mindspore::MsLogLevel::INFO;
+
+using mindspore::dataset::BorderType;
+using mindspore::dataset::InterpolationMode;
+using mindspore::dataset::Status;
+
+class MDToDApi {
+ public:
+  std::shared_ptr<Dataset> _ds;
+  std::shared_ptr<Iterator> _iter;
+  std::vector<std::shared_ptr<TensorOperation>> _augs;
+  std::string _storage_folder;
+  std::string _folder_path;
+  bool _hasBatch;
+  int64_t _file_id;
+
+  MDToDApi() : _ds(nullptr), _iter(nullptr), _augs({}), _storage_folder(""), _file_id(-1), _hasBatch(false) {
+    MS_LOG(WARNING) << "MDToDAPI Call constructor";
+  }
+  ~MDToDApi() {
+    MS_LOG(WARNING) << "MDToDAPI Call destructor";
+    _augs.clear();
+    _ds = nullptr;
+    _iter = nullptr;
+  }
+};
+
+std::vector<std::string> MDToDBuffToVector(MDToDBuff_t StrBuff) {
+  std::vector<std::string> strVector;
+  if (StrBuff.DataSize > 0) {
+    const char *p = reinterpret_cast<char *>(StrBuff.Buff);
+    do {
+      strVector.push_back(std::string(p));
+      p += strVector.back().size() + 1;
+    } while (p < reinterpret_cast<char *>(StrBuff.Buff) + StrBuff.DataSize);
+  }
+  return strVector;
+}
+
+extern "C" int MDToDApi_pathTest(const char *path) {
+  Path f(path);
+  MS_LOG(WARNING) << f.Exists() << f.IsDirectory() << f.ParentPath();
+  // Print out the first few items in the directory
+  auto dir_it = Path::DirIterator::OpenDirectory(&f);
+  MS_LOG(WARNING) << dir_it.get();
+  int i = 0;
+  while (dir_it->hasNext()) {
+    Path v = dir_it->next();
+    MS_LOG(WARNING) << v.toString() << "\n";
+    i++;
+    if (i > 5) break;
+  }
+  return 0;
+}
+
+extern "C" MDToDApi *MDToDApi_createPipeLine(MDToDConf_t MDConf) {
+  MS_LOG(WARNING) << "Start createPipeLine";
+  std::string folder_path(MDConf.pFolderPath);
+  std::string schema_file(MDConf.pSchemFile);
+  std::vector<std::string> column_names = MDToDBuffToVector(MDConf.columnsToReadBuff);
+  if (std::find(column_names.begin(), column_names.end(), "id") == column_names.end()) {
+    MS_LOG(WARNING) << "Column id not foud adding it ";
+    column_names.push_back("id");
+  }
+  std::vector<std::shared_ptr<TensorOperation>> mapOperations;
+  if (std::find(column_names.begin(), column_names.end(), "image") != column_names.end()) {
+    MS_LOG(WARNING) << "Found column image create map with:";
+    MS_LOG(WARNING) << "resize: { " << MDConf.ResizeSizeWH[0] << ", " << MDConf.ResizeSizeWH[1] << " }";
+    MS_LOG(WARNING) << "crop: { " << MDConf.CropSizeWH[0] << ", " << MDConf.CropSizeWH[1] << " }";
+    MS_LOG(WARNING) << "MEAN: { " << MDConf.MEAN[0] << ", " << MDConf.MEAN[1] << ", " << MDConf.MEAN[2] << " }";
+    MS_LOG(WARNING) << "STD: { " << MDConf.STD[0] << ", " << MDConf.STD[1] << ", " << MDConf.STD[2] << " }";
+
+    if ((MDConf.ResizeSizeWH[0] != 0) && (MDConf.ResizeSizeWH[1] != 0)) {
+      std::vector<int> Resize(MDConf.ResizeSizeWH, MDConf.ResizeSizeWH + 2);
+      std::shared_ptr<TensorOperation> resize_op = vision::Resize(Resize);
+      assert(resize_op != nullptr);
+      MS_LOG(WARNING) << "Push back resize";
+      mapOperations.push_back(resize_op);
+    }
+    if ((MDConf.CropSizeWH[0] != 0) && (MDConf.CropSizeWH[1] != 0)) {
+      std::vector<int> Crop(MDConf.CropSizeWH, MDConf.CropSizeWH + 2);
+      std::shared_ptr<TensorOperation> center_crop_op = vision::CenterCrop(Crop);
+      assert(center_crop_op != nullptr);
+      MS_LOG(WARNING) << "Push back crop";
+      mapOperations.push_back(center_crop_op);
+    }
+  }
+  std::shared_ptr<Dataset> ds = nullptr;
+  MS_LOG(INFO) << "Read id =" << MDConf.fileid << " (-1) for all";
+  if (MDConf.fileid > -1) {
+    // read specific image using SequentialSampler
+    ds = Album(folder_path, schema_file, column_names, true, SequentialSampler(MDConf.fileid, 1L));
+  } else {
+    // Distributed sampler takes num_shards then shard_id
+    ds = Album(folder_path, schema_file, column_names, true, SequentialSampler());
+  }
+  ds = ds->SetNumWorkers(1);
+
+  assert(ds != nullptr);
+
+  // Create a Repeat operation on ds
+  int32_t repeat_num = 1;
+  ds = ds->Repeat(repeat_num);
+  assert(ds != nullptr);
+
+  // Create objects for the tensor ops
+  MS_LOG(INFO) << " Create pipline parameters";
+  MS_LOG(INFO) << "floder path: " << folder_path << " , schema json: " << schema_file;
+  MS_LOG(INFO) << "Reading columns:";
+  for (auto str : column_names) {
+    MS_LOG(INFO) << str << " ";
+  }
+  bool hasBatch = false;
+
+  // Create an iterator over the result of the above dataset
+  // This will trigger the creation of the Execution Tree and launch it.
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  if (nullptr == iter) {
+    MS_LOG(ERROR) << "Iterator creation failed";
+    return nullptr;
+  }
+  assert(iter != nullptr);
+  MDToDApi *pMDToDApi = new MDToDApi;
+  pMDToDApi->_ds = ds;
+  pMDToDApi->_iter = iter;
+  pMDToDApi->_augs = mapOperations;
+  pMDToDApi->_storage_folder = std::string(MDConf.pStoragePath);
+  pMDToDApi->_folder_path = folder_path;
+  pMDToDApi->_hasBatch = hasBatch;
+  return pMDToDApi;
+}
+
+template <typename T>
+void MDBuffToVector(MDToDBuff_t MDBuff, std::vector<T> *vec) {
+  vec.clear();
+  if (MDBuff.DataSize > 0) {
+    int nofElements = MDBuff.DataSize / sizeof(T);
+    *vec.assign(reinterpret_cast<T *>(MDBuff.Buff), reinterpret_cast<T *>(MDBuff.Buff) + nofElements);
+  }
+}
+
+template <typename T>
+void GetValue(std::unordered_map<std::string, std::shared_ptr<Tensor>> row, std::string columnName, T *o) {
+  auto column = row[columnName];
+  if (NULL != column) {
+    MS_LOG(INFO) << "Tensor " << columnName << " shape: " << column->shape() << " type: " << column->type()
+                 << " bytes: " << column->SizeInBytes();
+    column->GetItemAt<T>(o, {});
+    MS_LOG(INFO) << columnName << ": " << +*o;
+  } else {
+    MS_LOG(INFO) << "Tensor " << columnName << " Not found"
+                 << ".";
+    *o = 0;
+  }
+}
+
+void GetTensorToBuff(std::unordered_map<std::string, std::shared_ptr<Tensor>> row, std::string columnName,
+                     bool hasBatch, MDToDBuff_t *resBuff) {
+  auto column = row[columnName];
+  resBuff->TensorSize[0] = resBuff->TensorSize[1] = resBuff->TensorSize[2] = resBuff->TensorSize[3] =
+    0;  // Mark all dims do not exist in tensor
+  int firstDim = (hasBatch) ? 1 : 0;
+  if (NULL != column) {
+    MS_LOG(INFO) << "Tensor " << columnName << " shape: " << column->shape() << " type: " << column->type()
+                 << " bytes: " << column->SizeInBytes() << "nof elements: " << column->shape()[firstDim];
+    auto tesoreShape = column->shape().AsVector();
+    for (int ix = 0; ix < tesoreShape.size(); ix++) {
+      MS_LOG(INFO) << "Tensor " << columnName << " shape[" << ix << "] = " << tesoreShape[ix];
+      resBuff->TensorSize[ix] = tesoreShape[ix];
+    }
+    if (!hasBatch) {
+      for (int ix = 3; ix > 0; ix--) {
+        resBuff->TensorSize[ix] = resBuff->TensorSize[ix - 1];
+      }
+      resBuff->TensorSize[0] = 1;
+    }
+    if (column->shape()[firstDim] > 0) {
+      if (DataType::DE_STRING == column->type()) {
+        std::string str;
+        for (int ix = 0; ix < column->shape()[firstDim]; ix++) {
+          std::string_view strView;
+          if (hasBatch) {
+            column->GetItemAt(&strView, {0, ix});
+          } else {
+            column->GetItemAt(&strView, {ix});
+          }
+          MS_LOG(INFO) << "string " << columnName << "[" << ix << "]:" << strView << " (size: " << strView.size()
+                       << ")";
+          str.append(strView);
+          str.push_back('\0');
+        }
+        resBuff->DataSize = str.size();
+        errno_t ret = memcpy_s(resBuff->Buff, resBuff->MaxBuffSize, str.data(), resBuff->DataSize);
+        if (ret != 0) {
+          resBuff->DataSize = 0;  // memcpy fail amount of data copied is 0
+          MS_LOG(ERROR) << "memcpy_s return: " << ret;
+        }
+      } else {
+        DataHelper dh;
+        resBuff->DataSize =
+          dh.DumpData(column->GetBuffer(), column->SizeInBytes(), resBuff->Buff, resBuff->MaxBuffSize);
+      }
+      MS_LOG(INFO) << columnName << " " << resBuff->DataSize
+                   << " bytesCopyed to buff (MaxBuffSize: " << resBuff->MaxBuffSize << ") ";
+      if (0 == resBuff->DataSize) {
+        MS_LOG(ERROR) << "Copy Failed!!!! " << columnName << " Too large"
+                      << ".";  // memcpy failed
+      }
+    } else {
+      MS_LOG(INFO) << "Tensor " << columnName << " is empty (has size 0)";
+    }
+  } else {
+    MS_LOG(INFO) << "Tensor " << columnName << " was not read.";
+  }
+}
+
+extern "C" int MDToDApi_GetNext(MDToDApi *pMDToDApi, MDToDResult_t *results) {
+  MS_LOG(INFO) << "Start GetNext";
+  if (pMDToDApi == nullptr) {
+    MS_LOG(ERROR) << "GetNext called with nullptr. Abort";
+    assert(pMDToDApi != nullptr);
+  }
+
+  // Set defualt
+  results->fileid = -1;
+  results->embeddingBuff.DataSize = 0;
+  results->imageBuff.DataSize = 0;
+  MS_LOG(INFO) << "Start GetNext [1]" << pMDToDApi;
+  // get next row for dataset
+  std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
+  if (pMDToDApi->_iter == nullptr) {
+    MS_LOG(ERROR) << "GetNext called with no iterator. abort";
+    return -1;
+  }
+  // create Execute functions, this replaces Map in Pipeline
+  pMDToDApi->_iter->GetNextRow(&row);
+  if (row.size() != 0) {
+    if ((pMDToDApi->_augs).size() > 0) {
+      // String and Tensors
+      GetTensorToBuff(row, "image_filename", pMDToDApi->_hasBatch, &results->fileNameBuff);
+      // for each operation, run eager mode, single threaded operation, will have to memcpy
+      // regardless
+      for (int i = 0; i < (pMDToDApi->_augs).size(); i++) {
+        // each Execute call will invoke a memcpy, this cannot really be optimized further
+        // for this use case, std move is added for fail save.
+        row["image"] = Execute((pMDToDApi->_augs)[i])(std::move(row["image"]));
+        if (row["image"] == nullptr) {
+          // nullptr means that the eager mode image processing failed, we fail in this case
+          return -1;
+        }
+      }
+    }
+    // FILE ID
+    GetValue<int64_t>(row, "id", &results->fileid);
+    pMDToDApi->_file_id = results->fileid;  // hold current file id to enable embeddings update (no itr->getCurrent)
+    // IS FOR TRAIN
+    GetValue<int32_t>(row, "_isForTrain", &results->isForTrain);
+    GetValue<int32_t>(row, "_noOfFaces", &results->noOfFaces);
+    // String and Tensors
+    GetTensorToBuff(row, "image_filename", pMDToDApi->_hasBatch, &results->fileNameBuff);
+    GetTensorToBuff(row, "image", pMDToDApi->_hasBatch, &results->imageBuff);
+    GetTensorToBuff(row, "_embedding", pMDToDApi->_hasBatch, &results->embeddingBuff);
+    GetTensorToBuff(row, "label", pMDToDApi->_hasBatch, &results->labelBuff);
+    GetTensorToBuff(row, "_boundingBoxes", pMDToDApi->_hasBatch, &results->boundingBoxesBuff);
+    GetTensorToBuff(row, "_confidences", pMDToDApi->_hasBatch, &results->confidencesBuff);
+    GetTensorToBuff(row, "_landmarks", pMDToDApi->_hasBatch, &results->landmarksBuff);
+    GetTensorToBuff(row, "_faceFileNames", pMDToDApi->_hasBatch, &results->faceFileNamesBuff);
+    GetTensorToBuff(row, "_imageQualities", pMDToDApi->_hasBatch, &results->imageQualitiesBuff);
+    GetTensorToBuff(row, "_faceEmbeddings", pMDToDApi->_hasBatch, &results->faceEmbeddingsBuff);
+    return 0;
+  }
+  return -1;
+}
+
+extern "C" int MDToDApi_Stop(MDToDApi *pMDToDApi) {
+  // Manually terminate the pipeline
+  pMDToDApi->_iter->Stop();
+  MS_LOG(WARNING) << "pipline stoped";
+  return 0;
+}
+
+extern "C" int MDToDApi_Destroy(MDToDApi *pMDToDApi) {
+  MS_LOG(WARNING) << "pipeline deleted start";
+  pMDToDApi->_iter->Stop();
+  delete pMDToDApi;
+  MS_LOG(WARNING) << "pipeline deleted end";
+  return 0;
+}
+
+int GetJsonFullFileName(MDToDApi *pMDToDApi, std::string *filePath) {
+  int64_t file_id = pMDToDApi->_file_id;
+  if (file_id < 0) {
+    MS_LOG(ERROR) << "Illigal file ID to update: " << file_id << ".";
+    return -1;
+  }
+  std::string converted = std::to_string(pMDToDApi->_file_id);
+  *filePath = pMDToDApi->_folder_path + "/" + converted + ".json";
+  return 0;
+}
+
+extern "C" int MDToDApi_UpdateEmbeding(MDToDApi *pMDToDApi, const char *column, float *emmbeddings,
+                                       size_t emmbeddingsSize) {
+  auto columnName = std::string(column);
+  MS_LOG(INFO) << "Start update " << columnName;
+
+  std::string converted = std::to_string(pMDToDApi->_file_id);
+  std::string embedding_file_path = pMDToDApi->_storage_folder + "/" + converted + columnName + ".bin";
+  DataHelper dh;
+  MS_LOG(INFO) << "Try to save file " << embedding_file_path;
+  std::vector<float> bin_content(emmbeddings, emmbeddings + emmbeddingsSize);
+  Status rc = dh.template WriteBinFile<float>(embedding_file_path, bin_content);
+  if (rc.IsError()) {
+    MS_LOG(ERROR) << "Fail to write embedding file: " << embedding_file_path << ".";
+    return -1;
+  }
+  MS_LOG(INFO) << "Saved file " << embedding_file_path;
+
+  std::string file_path;
+  if (0 != GetJsonFullFileName(pMDToDApi, &file_path)) {
+    MS_LOG(ERROR) << "Failed to update " << columnName;
+    return -1;
+  }
+
+  MS_LOG(INFO) << "Updating json file: " << file_path;
+  rc = dh.UpdateValue(file_path, std::string(column), embedding_file_path);
+  if (rc.IsError()) {
+    MS_LOG(ERROR) << "Fail to update json: " << file_path << ".";
+    return -1;
+  }
+  return 0;
+}
+
+extern "C" int MDToDApi_UpdateStringArray(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff) {
+  auto columnName = std::string(column);
+  std::string file_path;
+  if (0 != GetJsonFullFileName(pMDToDApi, &file_path)) {
+    MS_LOG(ERROR) << "Failed to update " << columnName;
+    return -1;
+  }
+  MS_LOG(INFO) << "Start Update string array column: " << columnName << " in file " << file_path;
+  DataHelper dh;
+  std::vector<std::string> strVec;
+  if (MDbuff.DataSize > 0) {
+    const char *p = reinterpret_cast<char *>(MDbuff.Buff);
+    do {
+      strVec.push_back(std::string(p));
+      p += strVec.back().size() + 1;
+    } while (p < reinterpret_cast<char *>(MDbuff.Buff) + MDbuff.DataSize);
+  }
+  Status rc = dh.UpdateArray(file_path, columnName, strVec);
+  if (rc.IsError()) {
+    MS_LOG(ERROR) << "Fail to update json: " << file_path << ".";
+    return -1;
+  }
+  return 0;
+}
+
+extern "C" int MDToDApi_UpdateFloatArray(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDBuff) {
+  auto columnName = std::string(column);
+  std::string file_path;
+  if (0 != GetJsonFullFileName(pMDToDApi, &file_path)) {
+    MS_LOG(ERROR) << "Faile to updaet " << columnName;
+    return -1;
+  }
+  MS_LOG(INFO) << "Start Update float Array column: " << columnName << " in file " << file_path;
+  DataHelper dh;
+  std::vector<float> vec;
+  MDBuffToVector<float>(MDBuff, &vec);
+  Status rc = dh.UpdateArray<float>(file_path, columnName, vec);
+  if (rc.IsError()) {
+    MS_LOG(ERROR) << "Fail to update json: " << file_path << ".";
+    return -1;
+  }
+  return 0;
+}
+
+extern "C" int MDToDApi_UpdateIsForTrain(MDToDApi *pMDToDApi, int32_t isForTrain) {
+  int64_t file_id = pMDToDApi->_file_id;
+  MS_LOG(INFO) << "Start Update isForTRain for id: " << file_id << " To " << isForTrain;
+
+  if (file_id < 0) return -1;
+  std::string converted = std::to_string(pMDToDApi->_file_id);
+  std::string file_path = pMDToDApi->_folder_path + "/" + converted + ".json";
+  DataHelper dh;
+  MS_LOG(INFO) << "Updating file: " << file_path;
+  Status rc = dh.UpdateValue<int32_t>(file_path, "_isForTrain", isForTrain, "");
+  if (rc.IsError()) {
+    MS_LOG(ERROR) << "Fail to update json: " << file_path << ".";
+    return -1;
+  }
+  return 0;
+}
+
+extern "C" int MDToDApi_UpdateNoOfFaces(MDToDApi *pMDToDApi, int32_t noOfFaces) {
+  int64_t file_id = pMDToDApi->_file_id;
+  MS_LOG(INFO) << "Start Update noOfFaces for id: " << file_id << " To " << noOfFaces;
+
+  if (file_id < 0) return -1;
+  std::string converted = std::to_string(pMDToDApi->_file_id);
+  std::string file_path = pMDToDApi->_folder_path + "/" + converted + ".json";
+  DataHelper dh;
+  MS_LOG(INFO) << "Updating file: " << file_path;
+  Status rc = dh.UpdateValue<int32_t>(file_path, "_noOfFaces", noOfFaces, "");
+  if (rc.IsError()) {
+    MS_LOG(ERROR) << "Fail to update json: " << file_path << ".";
+    return -1;
+  }
+  return 0;
+}

mindspore/lite/minddata/wrapper/MDToDApi.h

@@ -0,0 +1,70 @@
+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef DATASET_MDTODAPI_H_
+#define DATASET_MDTODAPI_H_
+
+#include <stdint.h>
+class MDToDApi;
+
+typedef struct MDToDBuff {
+  void *Buff;
+  size_t DataSize;
+  size_t TensorSize[4];
+  size_t MaxBuffSize;
+} MDToDBuff_t;
+
+typedef struct MDToDConf {
+  const char *pFolderPath;
+  const char *pSchemFile;
+  const char *pStoragePath;
+  MDToDBuff_t columnsToReadBuff;
+  float MEAN[3];
+  float STD[3];
+  int ResizeSizeWH[2];
+  int CropSizeWH[2];
+  int64_t fileid;  // -1 All files, otherwise get a single specifc file
+} MDToDConf_t;
+
+typedef struct MDToDResult {
+  int64_t fileid;
+  int32_t isForTrain;
+  int32_t noOfFaces;
+  MDToDBuff_t fileNameBuff;
+  MDToDBuff_t labelBuff;
+  MDToDBuff_t imageBuff;
+  MDToDBuff_t embeddingBuff;
+  MDToDBuff_t boundingBoxesBuff;
+  MDToDBuff_t confidencesBuff;
+  MDToDBuff_t landmarksBuff;
+  MDToDBuff_t faceFileNamesBuff;
+  MDToDBuff_t imageQualitiesBuff;
+  MDToDBuff_t faceEmbeddingsBuff;
+} MDToDResult_t;
+
+typedef int (*MDToDApi_pathTest_t)(const char *path);
+typedef int (*MDToDApi_testAlbum_t)();
+typedef MDToDApi *(*MDToDApi_createPipeLine_t)(MDToDConf_t MDConf);
+typedef int (*MDToDApi_GetNext_t)(MDToDApi *pMDToDApi, MDToDResult_t *results);
+typedef int (*MDToDApi_UpdateEmbeding_t)(MDToDApi *pMDToDApi, const char *column, float *emmbeddings,
+                                         size_t emmbeddingsSize);
+typedef int (*MDToDApi_UpdateStringArray_t)(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff);
+typedef int (*MDToDApi_UpdateFloatArray_t)(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff);
+typedef int (*MDToDApi_UpdateIsForTrain_t)(MDToDApi *pMDToDApi, uint8_t isForTrain);
+typedef int (*MDToDApi_UpdateNoOfFaces_t)(MDToDApi *pMDToDApi, int32_t noOfFaces);
+typedef int (*MDToDApi_Stop_t)(MDToDApi *pMDToDApi);
+typedef int (*MDToDApi_Destroy_t)(MDToDApi *pMDToDApi);
+
+#endif

mindspore/lite/test/ut/src/dataset/eager_test.cc

@@ -27,10 +27,10 @@
 
 using MSTensor = mindspore::tensor::MSTensor;
 using DETensor = mindspore::tensor::DETensor;
-using mindspore::dataset::api::vision::Decode;
-using mindspore::dataset::api::vision::Normalize;
-using mindspore::dataset::api::vision::Resize;
-using Execute = mindspore::dataset::api::Execute;
+using mindspore::dataset::vision::Decode;
+using mindspore::dataset::vision::Normalize;
+using mindspore::dataset::vision::Resize;
+using Execute = mindspore::dataset::Execute;
 using Path = mindspore::dataset::Path;
 
 class MindDataTestEager : public mindspore::CommonTest {