[assistant][ops]New operator implementation, include Food101Dataset

docs/api/api_python/dataset/mindspore.dataset.Food101Dataset.rst

@@ -0,0 +1,102 @@
+mindspore.dataset.Food101Dataset
+================================
+
+.. py:class:: mindspore.dataset.Food101Dataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None)
+
+    读取和解析Food101数据集的源文件构建数据集。
+
+    生成的数据集有两列: `[image, label]` 。 `image` 列的数据类型为uint8。 `label` 列的数据类型为string。
+
+    参数：
+        - **dataset_dir** (str) - 包含数据集文件的根目录路径。
+        - **usage** (str, 可选) - 指定数据集的子集，可取值为 'train'、'test' 或 'all'。
+          取值为'train'时将会读取75,750个训练样本，取值为'test'时将会读取25,250个测试样本，取值为'all'时将会读取全部101,000个样本。默认值：None，读取全部样本图片。
+        - **num_samples** (int, 可选) - 指定从数据集中读取的样本数，可以小于数据集总数。默认值：None，读取全部样本图片。
+        - **num_parallel_workers** (int, 可选) - 指定读取数据的工作线程数。默认值：None，使用mindspore.dataset.config中配置的线程数。
+        - **shuffle** (bool, 可选) - 是否混洗数据集。默认值：None，下表中会展示不同参数配置的预期行为。
+        - **decode** (bool, 可选) - 是否对读取的图片进行解码操作。默认值：False，不解码。
+        - **sampler** (Sampler, 可选) - 指定从数据集中选取样本的采样器。默认值：None，下表中会展示不同配置的预期行为。
+        - **num_shards** (int, 可选) - 指定分布式训练时将数据集进行划分的分片数。默认值：None。指定此参数后， `num_samples` 表示每个分片的最大样本数。
+        - **shard_id** (int, 可选) - 指定分布式训练时使用的分片ID号。默认值：None。只有当指定了 `num_shards` 时才能指定此参数。
+        - **cache** (DatasetCache, 可选) - 单节点数据缓存服务，用于加快数据集处理，详情请阅读 `单节点数据缓存 <https://www.mindspore.cn/tutorials/experts/zh-CN/master/dataset/cache.html>`_ 。默认值：None，不使用缓存。
+
+    异常：
+        - **RuntimeError** - `dataset_dir` 路径下不包含数据文件。
+        - **RuntimeError** - 同时指定了 `sampler` 和 `shuffle` 参数。
+        - **RuntimeError** - 同时指定了 `sampler` 和 `num_shards` 参数或同时指定了 `sampler` 和 `shard_id` 参数。
+        - **RuntimeError** - 指定了 `num_shards` 参数，但是未指定 `shard_id` 参数。
+        - **RuntimeError** - 指定了 `shard_id` 参数，但是未指定 `num_shards` 参数。
+        - **ValueError** - `shard_id` 参数错误，小于0或者大于等于 `num_shards` 。
+        - **ValueError** - `num_parallel_workers` 参数超过系统最大线程数。
+        - **ValueError** - `usage` 参数取值不为'train'、'test'或'all'。
+        - **ValueError** - `dataset_dir` 指定的文件夹不存在。
+
+    .. note:: 此数据集可以指定参数 `sampler` ，但参数 `sampler` 和参数 `shuffle` 的行为是互斥的。下表展示了几种合法的输入参数组合及预期的行为。
+
+    .. list-table:: 配置 `sampler` 和 `shuffle` 的不同组合得到的预期排序结果
+       :widths: 25 25 50
+       :header-rows: 1
+
+       * - 参数 `sampler`
+         - 参数 `shuffle`
+         - 预期数据顺序
+       * - None
+         - None
+         - 随机排列
+       * - None
+         - True
+         - 随机排列
+       * - None
+         - False
+         - 顺序排列
+       * - `sampler` 实例
+         - None
+         - 由 `sampler` 行为定义的顺序
+       * - `sampler` 实例
+         - True
+         - 不允许
+       * - `sampler` 实例
+         - False
+         - 不允许
+
+    **关于Food101数据集：**
+    
+    Food101是一个具有挑战性的数据集，包含101种食品类别，共101000张图片。每一个类别有250张测试图片和750张训练图片。所有图像都被重新缩放，最大边长为512像素。
+
+    以下为原始Food101数据集的结构，您可以将数据集文件解压得到如下的文件结构，并通过MindSpore的API进行读取。
+
+    .. code-block::
+
+        .
+        └── food101_dir
+             ├── images
+             │    ├── apple_pie
+             │    │    ├── 1005649.jpg
+             │    │    ├── 1014775.jpg
+             │    │    ├──...
+             │    ├── baby_back_rips
+             │    │    ├── 1005293.jpg
+             │    │    ├── 1007102.jpg
+             │    │    ├──...
+             │    └──...
+             └── meta
+                  ├── train.txt
+                  ├── test.txt
+                  ├── classes.txt
+                  ├── train.json
+                  ├── test.json
+                  └── train.txt
+
+    **引用：**
+
+    .. code-block::
+
+        @inproceedings{bossard14,
+        title     = {Food-101 -- Mining Discriminative Components with Random Forests},
+        author    = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
+        booktitle = {European Conference on Computer Vision},
+        year      = {2014}
+        }
+
+
+.. include:: mindspore.dataset.api_list_vision.rst

docs/api/api_python/mindspore.dataset.rst

@@ -112,6 +112,7 @@ mindspore.dataset
     mindspore.dataset.FashionMnistDataset
     mindspore.dataset.FlickrDataset
     mindspore.dataset.Flowers102Dataset
+    mindspore.dataset.Food101Dataset
     mindspore.dataset.ImageFolderDataset
     mindspore.dataset.KMnistDataset
     mindspore.dataset.ManifestDataset

docs/api/api_python_en/mindspore.dataset.rst

@@ -24,6 +24,7 @@ Vision
     mindspore.dataset.FashionMnistDataset
     mindspore.dataset.FlickrDataset
     mindspore.dataset.Flowers102Dataset
+    mindspore.dataset.Food101Dataset
     mindspore.dataset.ImageFolderDataset
     mindspore.dataset.KMnistDataset
     mindspore.dataset.ManifestDataset

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

@@ -92,6 +92,7 @@
 #include "minddata/dataset/engine/ir/datasetops/source/fake_image_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/fashion_mnist_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/flickr_node.h"
+#include "minddata/dataset/engine/ir/datasetops/source/food101_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/gtzan_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/image_folder_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/imdb_node.h"
@@ -1303,6 +1304,28 @@ FlickrDataset::FlickrDataset(const std::vector<char> &dataset_dir, const std::ve
   ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
 }
 
+Food101Dataset::Food101Dataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage, bool decode,
+                               const std::shared_ptr<Sampler> &sampler, const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler ? sampler->Parse() : nullptr;
+  auto ds = std::make_shared<Food101Node>(CharToString(dataset_dir), CharToString(usage), decode, sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
+Food101Dataset::Food101Dataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage, bool decode,
+                               const Sampler *sampler, const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler ? sampler->Parse() : nullptr;
+  auto ds = std::make_shared<Food101Node>(CharToString(dataset_dir), CharToString(usage), decode, sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
+Food101Dataset::Food101Dataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage, bool decode,
+                               const std::reference_wrapper<Sampler> &sampler,
+                               const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler.get().Parse();
+  auto ds = std::make_shared<Food101Node>(CharToString(dataset_dir), CharToString(usage), decode, sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
 GTZANDataset::GTZANDataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage,
                            const std::shared_ptr<Sampler> &sampler, const std::shared_ptr<DatasetCache> &cache) {
   auto sampler_obj = sampler ? sampler->Parse() : nullptr;

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/ir/datasetops/source/bindings.cc

@@ -43,6 +43,7 @@
 #include "minddata/dataset/engine/ir/datasetops/source/fake_image_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/fashion_mnist_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/flickr_node.h"
+#include "minddata/dataset/engine/ir/datasetops/source/food101_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/generator_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/gtzan_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/image_folder_node.h"
@@ -326,6 +327,18 @@ PYBIND_REGISTER(FlickrNode, 2, ([](const py::module *m) {
                     }));
                 }));
 
+PYBIND_REGISTER(Food101Node, 2, ([](const py::module *m) {
+                  (void)py::class_<Food101Node, DatasetNode, std::shared_ptr<Food101Node>>(*m, "Food101Node",
+                                                                                           "to create a Food101Node")
+                    .def(py::init([](const std::string &dataset_dir, const std::string &usage, bool decode,
+                                     const py::handle &sampler) {
+                      auto food101 =
+                        std::make_shared<Food101Node>(dataset_dir, usage, decode, toSamplerObj(sampler), nullptr);
+                      THROW_IF_ERROR(food101->ValidateParams());
+                      return food101;
+                    }));
+                }));
+
 PYBIND_REGISTER(GeneratorNode, 2, ([](const py::module *m) {
                   (void)py::class_<GeneratorNode, DatasetNode, std::shared_ptr<GeneratorNode>>(
                     *m, "GeneratorNode", "to create a GeneratorNode")

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

@@ -22,6 +22,7 @@ set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
     fake_image_op.cc
     fashion_mnist_op.cc
     flickr_op.cc
+    food101_op.cc
     gtzan_op.cc
     image_folder_op.cc
     imdb_op.cc

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/food101_op.cc

@@ -0,0 +1,164 @@
+/**
+ * Copyright 2022 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/datasetops/source/food101_op.h"
+
+#include <algorithm>
+#include <iomanip>
+#include <regex>
+
+#include "minddata/dataset/core/config_manager.h"
+#include "minddata/dataset/engine/data_schema.h"
+#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
+#include "minddata/dataset/engine/execution_tree.h"
+#include "minddata/dataset/kernels/image/image_utils.h"
+#include "minddata/dataset/util/path.h"
+#include "utils/file_utils.h"
+
+namespace mindspore {
+namespace dataset {
+Food101Op::Food101Op(const std::string &folder_path, const std::string &usage, int32_t num_workers, int32_t queue_size,
+                     bool decode, std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler)
+    : MappableLeafOp(num_workers, queue_size, std::move(sampler)),
+      folder_path_(folder_path),
+      decode_(decode),
+      usage_(usage),
+      data_schema_(std::move(data_schema)) {}
+
+Status Food101Op::PrepareData() {
+  auto realpath = FileUtils::GetRealPath(folder_path_.c_str());
+  if (!realpath.has_value()) {
+    MS_LOG(ERROR) << "Invalid file path, Food101 dataset dir: " << folder_path_ << " does not exist.";
+    RETURN_STATUS_UNEXPECTED("Invalid file path, Food101 dataset dir: " + folder_path_ + " does not exist.");
+  }
+
+  std::string image_root_path = (Path(realpath.value()) / Path("images")).ToString();
+  std::string train_list_txt_ = (Path(realpath.value()) / Path("meta") / Path("train.txt")).ToString();
+  std::string test_list_txt_ = (Path(realpath.value()) / Path("meta") / Path("test.txt")).ToString();
+
+  Path img_folder(image_root_path);
+  CHECK_FAIL_RETURN_UNEXPECTED(img_folder.Exists(),
+                               "Invalid path, Food101 image path: " + image_root_path + " does not exist.");
+  CHECK_FAIL_RETURN_UNEXPECTED(img_folder.IsDirectory(),
+                               "Invalid path, Food101 image path: " + image_root_path + " is not a folder.");
+  std::shared_ptr<Path::DirIterator> img_folder_itr = Path::DirIterator::OpenDirectory(&img_folder);
+
+  RETURN_UNEXPECTED_IF_NULL(img_folder_itr);
+  int32_t dirname_offset_ = img_folder.ToString().length() + 1;
+
+  while (img_folder_itr->HasNext()) {
+    Path sub_dir = img_folder_itr->Next();
+    if (sub_dir.IsDirectory()) {
+      classes_.insert(sub_dir.ToString().substr(dirname_offset_));
+    }
+  }
+  CHECK_FAIL_RETURN_UNEXPECTED(!classes_.empty(),
+                               "Invalid path, no subfolder found under path: " + img_folder.ToString());
+
+  if (usage_ == "test") {
+    RETURN_IF_NOT_OK(GetAllImageList(test_list_txt_));
+  } else if (usage_ == "train") {
+    RETURN_IF_NOT_OK(GetAllImageList(train_list_txt_));
+  } else {
+    RETURN_IF_NOT_OK(GetAllImageList(train_list_txt_));
+    RETURN_IF_NOT_OK(GetAllImageList(test_list_txt_));
+  }
+  CHECK_FAIL_RETURN_UNEXPECTED(!all_img_lists_.empty(),
+                               "No valid train.txt or test.txt file under path: " + image_root_path);
+  all_img_lists_.shrink_to_fit();
+  num_rows_ = all_img_lists_.size();
+
+  return Status::OK();
+}
+
+void Food101Op::Print(std::ostream &out, bool show_all) const {
+  if (!show_all) {
+    ParallelOp::Print(out, show_all);
+    out << "\n";
+  } else {
+    ParallelOp::Print(out, show_all);
+    out << "\nNumber of rows: " << num_rows_ << "\nFood101 dataset dir: " << folder_path_
+        << "\nDecode: " << (decode_ ? "yes" : "no") << "\n\n";
+  }
+}
+
+Status Food101Op::LoadTensorRow(row_id_type row_id, TensorRow *trow) {
+  RETURN_UNEXPECTED_IF_NULL(trow);
+  std::string img_name = (Path(folder_path_) / Path("images") / Path(all_img_lists_[row_id])).ToString() + ".jpg";
+  std::shared_ptr<Tensor> image, label;
+  std::string label_str;
+  for (auto it : classes_) {
+    if (all_img_lists_[row_id].find(it) != all_img_lists_[row_id].npos) {
+      label_str = it;
+      break;
+    }
+  }
+  RETURN_IF_NOT_OK(Tensor::CreateScalar(label_str, &label));
+  RETURN_IF_NOT_OK(ReadImageToTensor(img_name, &image));
+  (*trow) = TensorRow(row_id, {std::move(image), std::move(label)});
+  trow->setPath({img_name, std::string("")});
+  return Status::OK();
+}
+
+Status Food101Op::ReadImageToTensor(const std::string &image_path, std::shared_ptr<Tensor> *tensor) {
+  RETURN_UNEXPECTED_IF_NULL(tensor);
+  RETURN_IF_NOT_OK(Tensor::CreateFromFile(image_path, tensor));
+  if (decode_) {
+    Status rc = Decode(*tensor, tensor);
+    CHECK_FAIL_RETURN_UNEXPECTED(
+      rc.IsOk(), "Invalid file, failed to decode image, the image may be broken or permission denied: " + image_path);
+  }
+  return Status::OK();
+}
+
+// Get dataset size.
+Status Food101Op::CountTotalRows(int64_t *count) {
+  RETURN_UNEXPECTED_IF_NULL(count);
+  if (all_img_lists_.size() == 0) {
+    RETURN_IF_NOT_OK(PrepareData());
+  }
+  *count = static_cast<int64_t>(all_img_lists_.size());
+  return Status::OK();
+}
+
+Status Food101Op::GetAllImageList(const std::string &file_path) {
+  std::ifstream handle(file_path);
+  if (!handle.is_open()) {
+    RETURN_STATUS_UNEXPECTED("Invalid file, failed to open text:" + file_path +
+                             ", the file is damaged or permission denied.");
+  }
+
+  std::string line;
+  while (getline(handle, line)) {
+    if (!line.empty()) {
+      all_img_lists_.push_back(line);
+    }
+  }
+  return Status::OK();
+}
+
+Status Food101Op::ComputeColMap() {
+  // Set the column name map (base class field).
+  if (column_name_id_map_.empty()) {
+    for (int32_t index = 0; index < data_schema_->NumColumns(); index++) {
+      column_name_id_map_[data_schema_->Column(index).Name()] = index;
+    }
+  } else {
+    MS_LOG(WARNING) << "Column name map is already set!";
+  }
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/datasetops/source/food101_op.h

@@ -0,0 +1,103 @@
+/**
+ * Copyright 2022 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_DATASETOPS_SOURCE_FOOD101_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_FOOD101_OP_H_
+
+#include <fstream>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/engine/data_schema.h"
+#include "minddata/dataset/engine/datasetops/parallel_op.h"
+#include "minddata/dataset/engine/datasetops/source/io_block.h"
+#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
+#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
+#include "minddata/dataset/util/queue.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+class Food101Op : public MappableLeafOp {
+ public:
+  /// \brief Constructor.
+  /// \param[in] folder_path Directory of Food101 dataset.
+  /// \param[in] usage Usage.
+  /// \param[in] num_workers Number of workers reading images in parallel.
+  /// \param[in] queue_size Connector queue size.
+  /// \param[in] decode Whether to decode images.
+  /// \param[in] schema Data schema of Food101 dataset.
+  /// \param[in] sampler Sampler tells Food101 what to read.
+  Food101Op(const std::string &folder_path, const std::string &usage, int32_t num_workers, int32_t queue_size,
+            bool decode, std::unique_ptr<DataSchema> schema, std::shared_ptr<SamplerRT> sampler);
+
+  /// \brief Deconstructor.
+  ~Food101Op() override = default;
+
+  /// A print method typically used for debugging.
+  /// \param[out] out Out stream.
+  /// \param[in] show_all Whether to show all information.
+  void Print(std::ostream &out, bool show_all) const override;
+
+  /// Op name getter.
+  /// \return Name of the current Op.
+  std::string Name() const override { return "Food101Op"; }
+
+  /// Function to count the number of samples in the Food101 dataset.
+  /// \param[in] count Output arg that will hold the actual dataset size.
+  /// \return The status code returned.
+  Status CountTotalRows(int64_t *count);
+
+ private:
+  /// Load a tensor row.
+  /// \param[in] row_id Row id.
+  /// \param[in] row Read all features into this tensor row.
+  /// \return The status code returned.
+  Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
+
+  /// \param[in] image_path Path of image data.
+  /// \param[in] tensor Get image tensor.
+  /// \return The status code returned.
+  Status ReadImageToTensor(const std::string &image_path, std::shared_ptr<Tensor> *tensor);
+
+  /// Called first when function is called. Get file_name, img_path info from ".txt" files.
+  /// \return Status - The status code returned.
+  Status PrepareData();
+
+  /// Private function for computing the assignment of the column name map.
+  /// \return Status-the status code returned.
+  Status ComputeColMap() override;
+
+  /// Private function for getting all the image files;
+  /// \param[in] file_path The path of the dataset.
+  /// \return Status-the status code returned.
+  Status GetAllImageList(const std::string &file_path);
+
+  std::string folder_path_;  // directory of Food101 folder.
+  std::string usage_;
+  bool decode_;
+  std::unique_ptr<DataSchema> data_schema_;
+  std::set<std::string> classes_;
+  std::vector<std::string> all_img_lists_;
+  std::map<std::string, int32_t> class_index_;
+  std::map<std::string, std::vector<int32_t>> annotation_map_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_FOOD101_OP_H_

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/dataset_node.h

@@ -95,6 +95,7 @@ constexpr char kEnWik9Node[] = "EnWik9Dataset";
 constexpr char kFakeImageNode[] = "FakeImageDataset";
 constexpr char kFashionMnistNode[] = "FashionMnistDataset";
 constexpr char kFlickrNode[] = "FlickrDataset";
+constexpr char kFood101Node[] = "Food101Dataset";
 constexpr char kGeneratorNode[] = "GeneratorDataset";
 constexpr char kGTZANNode[] = "GTZANDataset";
 constexpr char kImageFolderNode[] = "ImageFolderDataset";

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/CMakeLists.txt

@@ -23,6 +23,7 @@ set(DATASET_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES
         fake_image_node.cc
         fashion_mnist_node.cc
         flickr_node.cc
+        food101_node.cc
         gtzan_node.cc
         image_folder_node.cc
         imdb_node.cc

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/food101_node.cc

@@ -0,0 +1,150 @@
+/**
+ * Copyright 2022 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/ir/datasetops/source/food101_node.h"
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/engine/datasetops/source/food101_op.h"
+#ifndef ENABLE_ANDROID
+#include "minddata/dataset/engine/serdes.h"
+#endif
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+Food101Node::Food101Node(const std::string &dataset_dir, const std::string &usage, bool decode,
+                         const std::shared_ptr<SamplerObj> &sampler, std::shared_ptr<DatasetCache> cache)
+    : MappableSourceNode(std::move(cache)),
+      dataset_dir_(dataset_dir),
+      usage_(usage),
+      decode_(decode),
+      sampler_(sampler) {}
+
+std::shared_ptr<DatasetNode> Food101Node::Copy() {
+  std::shared_ptr<SamplerObj> sampler = (sampler_ == nullptr) ? nullptr : sampler_->SamplerCopy();
+  auto node = std::make_shared<Food101Node>(dataset_dir_, usage_, decode_, sampler, cache_);
+  (void)node->SetNumWorkers(num_workers_);
+  (void)node->SetConnectorQueueSize(connector_que_size_);
+  return node;
+}
+
+void Food101Node::Print(std::ostream &out) const { out << Name(); }
+
+Status Food101Node::ValidateParams() {
+  RETURN_IF_NOT_OK(DatasetNode::ValidateParams());
+  RETURN_IF_NOT_OK(ValidateDatasetDirParam("Food101Dataset", dataset_dir_));
+  RETURN_IF_NOT_OK(ValidateDatasetSampler("Food101Dataset", sampler_));
+  RETURN_IF_NOT_OK(ValidateStringValue("Food101Dataset", usage_, {"train", "test", "all"}));
+
+  return Status::OK();
+}
+
+Status Food101Node::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops) {
+  // Do internal Schema generation.
+  RETURN_UNEXPECTED_IF_NULL(node_ops);
+  auto schema = std::make_unique<DataSchema>();
+  RETURN_IF_NOT_OK(schema->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kCv, 1)));
+  TensorShape scalar = TensorShape::CreateScalar();
+  RETURN_IF_NOT_OK(
+    schema->AddColumn(ColDescriptor("label", DataType(DataType::DE_STRING), TensorImpl::kFlexible, 0, &scalar)));
+  std::shared_ptr<SamplerRT> sampler_rt = nullptr;
+  RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt));
+  auto op = std::make_shared<Food101Op>(dataset_dir_, usage_, num_workers_, connector_que_size_, decode_,
+                                        std::move(schema), std::move(sampler_rt));
+  op->SetTotalRepeats(GetTotalRepeats());
+  op->SetNumRepeatsPerEpoch(GetNumRepeatsPerEpoch());
+  node_ops->push_back(op);
+
+  return Status::OK();
+}
+
+// Get the shard id of node
+Status Food101Node::GetShardId(int32_t *const shard_id) {
+  *shard_id = sampler_->ShardId();
+
+  return Status::OK();
+}
+
+// Get Dataset size
+Status Food101Node::GetDatasetSize(const std::shared_ptr<DatasetSizeGetter> &size_getter, bool estimate,
+                                   int64_t *dataset_size) {
+  RETURN_UNEXPECTED_IF_NULL(dataset_size);
+  if (dataset_size_ > 0) {
+    *dataset_size = dataset_size_;
+    return Status::OK();
+  }
+  int64_t num_rows = 0, sample_size;
+  std::vector<std::shared_ptr<DatasetOp>> ops;
+  RETURN_IF_NOT_OK(Build(&ops));
+  CHECK_FAIL_RETURN_UNEXPECTED(!ops.empty(), "Unable to build Food101Op.");
+  auto op = std::dynamic_pointer_cast<Food101Op>(ops.front());
+  RETURN_IF_NOT_OK(op->CountTotalRows(&num_rows));
+  std::shared_ptr<SamplerRT> sampler_rt = nullptr;
+  RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt));
+  sample_size = sampler_rt->CalculateNumSamples(num_rows);
+  if (sample_size == -1) {
+    RETURN_IF_NOT_OK(size_getter->DryRun(shared_from_this(), &sample_size));
+  }
+  *dataset_size = sample_size;
+  dataset_size_ = *dataset_size;
+  return Status::OK();
+}
+
+Status Food101Node::to_json(nlohmann::json *out_json) {
+  nlohmann::json args, sampler_args;
+  RETURN_IF_NOT_OK(sampler_->to_json(&sampler_args));
+  args["sampler"] = sampler_args;
+  args["num_parallel_workers"] = num_workers_;
+  args["connector_queue_size"] = connector_que_size_;
+  args["dataset_dir"] = dataset_dir_;
+  args["decode"] = decode_;
+  args["usage"] = usage_;
+  if (cache_ != nullptr) {
+    nlohmann::json cache_args;
+    RETURN_IF_NOT_OK(cache_->to_json(&cache_args));
+    args["cache"] = cache_args;
+  }
+  *out_json = args;
+  return Status::OK();
+}
+
+#ifndef ENABLE_ANDROID
+Status Food101Node::from_json(nlohmann::json json_obj, std::shared_ptr<DatasetNode> *ds) {
+  RETURN_IF_NOT_OK(ValidateParamInJson(json_obj, "num_parallel_workers", kFood101Node));
+  RETURN_IF_NOT_OK(ValidateParamInJson(json_obj, "connector_queue_size", kFood101Node));
+  RETURN_IF_NOT_OK(ValidateParamInJson(json_obj, "dataset_dir", kFood101Node));
+  RETURN_IF_NOT_OK(ValidateParamInJson(json_obj, "decode", kFood101Node));
+  RETURN_IF_NOT_OK(ValidateParamInJson(json_obj, "usage", kFood101Node));
+  RETURN_IF_NOT_OK(ValidateParamInJson(json_obj, "sampler", kFood101Node));
+  std::string dataset_dir = json_obj["dataset_dir"];
+  bool decode = json_obj["decode"];
+  std::string usage = json_obj["usage"];
+  std::shared_ptr<SamplerObj> sampler;
+  RETURN_IF_NOT_OK(Serdes::ConstructSampler(json_obj["sampler"], &sampler));
+  std::shared_ptr<DatasetCache> cache = nullptr;
+  RETURN_IF_NOT_OK(DatasetCache::from_json(json_obj, &cache));
+  *ds = std::make_shared<Food101Node>(dataset_dir, usage, decode, sampler, cache);
+  (*ds)->SetNumWorkers(json_obj["num_parallel_workers"]);
+  (*ds)->SetConnectorQueueSize(json_obj["connector_queue_size"]);
+  return Status::OK();
+}
+#endif
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/food101_node.h

@@ -0,0 +1,104 @@
+/**
+ * Copyright 2022 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_IR_DATASETOPS_SOURCE_FOOD101_NODE_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_FOOD101_NODE_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "minddata/dataset/engine/ir/datasetops/dataset_node.h"
+
+namespace mindspore {
+namespace dataset {
+class Food101Node : public MappableSourceNode {
+ public:
+  /// \brief Constructor.
+  Food101Node(const std::string &dataset_dir, const std::string &usage, bool decode,
+              const std::shared_ptr<SamplerObj> &sampler, std::shared_ptr<DatasetCache> cache);
+
+  /// \brief Destructor.
+  ~Food101Node() override = default;
+
+  /// \brief Node name getter.
+  /// \return Name of the current node.
+  std::string Name() const override { return kFood101Node; }
+
+  /// \brief Print the description.
+  /// \param[out] out The output stream to write output to.
+  void Print(std::ostream &out) const override;
+
+  /// \brief Copy the node to a new object.
+  /// \return A shared pointer to the new copy.
+  std::shared_ptr<DatasetNode> Copy() override;
+
+  /// \brief a base class override function to create the required runtime dataset op objects for this class.
+  /// \param[in] node_ops A vector containing shared pointer to the Dataset Ops that this object will create.
+  /// \return Status Status::OK() if build successfully.
+  Status Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops) override;
+
+  /// \brief Parameters validation.
+  /// \return Status Status::OK() if all the parameters are valid.
+  Status ValidateParams() override;
+
+  /// \brief Get the shard id of node.
+  /// \param[in] shard_id The shard id of node.
+  /// \return Status Status::OK() if get shard id successfully.
+  Status GetShardId(int32_t *const shard_id) override;
+
+  /// \brief Base-class override for GetDatasetSize.
+  /// \param[in] size_getter Shared pointer to DatasetSizeGetter.
+  /// \param[in] estimate This is only supported by some of the ops and it's used to speed up the process of getting
+  ///     dataset size at the expense of accuracy.
+  /// \param[out] dataset_size The size of the dataset.
+  /// \return Status of the function.
+  Status GetDatasetSize(const std::shared_ptr<DatasetSizeGetter> &size_getter, bool estimate,
+                        int64_t *dataset_size) override;
+
+  /// \brief Getter functions.
+  const std::string &DatasetDir() const { return dataset_dir_; }
+  const std::string &Usage() const { return usage_; }
+
+  /// \brief Get the arguments of node.
+  /// \param[out] out_json JSON string of all attributes.
+  /// \return Status of the function.
+  Status to_json(nlohmann::json *out_json) override;
+
+#ifndef ENABLE_ANDROID
+  /// \brief Function to read dataset in json.
+  /// \param[in] json_obj The JSON object to be deserialized.
+  /// \param[out] ds Deserialized dataset.
+  /// \return Status The status code returned.
+  static Status from_json(nlohmann::json json_obj, std::shared_ptr<DatasetNode> *ds);
+#endif
+
+  /// \brief Sampler getter.
+  /// \return SamplerObj of the current node.
+  std::shared_ptr<SamplerObj> Sampler() override { return sampler_; }
+
+  /// \brief Sampler setter.
+  void SetSampler(std::shared_ptr<SamplerObj> sampler) override { sampler_ = sampler; }
+
+ private:
+  std::string dataset_dir_;
+  std::string usage_;
+  bool decode_;
+  std::shared_ptr<SamplerObj> sampler_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_FOOD101_NODE_H_

mindspore/ccsrc/minddata/dataset/include/dataset/datasets.h

@@ -2763,6 +2763,99 @@ inline std::shared_ptr<FlickrDataset> DATASET_API Flickr(const std::string &data
                                          cache);
 }
 
+/// \class Food101Dataset
+/// \brief A source dataset for reading and parsing Food101 dataset.
+class DATASET_API Food101Dataset : public Dataset {
+ public:
+  /// \brief Constructor of Food101Dataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] usage The type of dataset. Acceptable usages include "train", "test" or "all".
+  /// \param[in] decode Decode the images after reading.
+  /// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset.
+  /// \param[in] cache Tensor cache to use.
+  Food101Dataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage, bool decode,
+                 const std::shared_ptr<Sampler> &sampler, const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Constructor of Food101Dataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] usage The type of dataset. Acceptable usages include "train", "test" or "all".
+  /// \param[in] decode Decode the images after reading.
+  /// \param[in] sampler Raw pointer to a sampler object used to choose samples from the dataset.
+  /// \param[in] cache Tensor cache to use.
+  Food101Dataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage, bool decode,
+                 const Sampler *sampler, const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Constructor of Food101Dataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] usage The type of dataset. Acceptable usages include "train", "test" or "all".
+  /// \param[in] decode Decode the images after reading.
+  /// \param[in] sampler Sampler object used to choose samples from the dataset.
+  /// \param[in] cache Tensor cache to use.
+  Food101Dataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage, bool decode,
+                 const std::reference_wrapper<Sampler> &sampler, const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Destructor of Food101Dataset.
+  ~Food101Dataset() override = default;
+};
+
+/// \brief Function to create a Food101Dataset.
+/// \note The generated dataset has two columns ["image", "label"].
+/// \param[in] dataset_dir Path to the root directory that contains the dataset.
+/// \param[in] usage The type of dataset. Acceptable usages include "train", "test" or "all". Default: "all".
+/// \param[in] decode Decode the images after reading. Default: false.
+/// \param[in] sampler Shared pointer to a sampler object used to choose samples from the dataset. If sampler is not
+///     given, a `RandomSampler` will be used to randomly iterate the entire dataset. Default: RandomSampler().
+/// \param[in] cache Tensor cache to use. Default: nullptr, which means no cache is used.
+/// \return Shared pointer to the Food101Dataset.
+/// \par Example
+/// \code
+///      /* Define dataset path and MindData object */
+///      std::string dataset_path = "/path/to/Food101_dataset_directory";
+///      std::shared_ptr<Dataset> ds = Food101(dataset_path);
+///
+///      /* Create iterator to read dataset */
+///      std::shared_ptr<Iterator> iter = ds->CreateIterator();
+///      std::unordered_map<std::string, mindspore::MSTensor> row;
+///      iter->GetNextRow(&row);
+///
+///      /* Note: In Food101 dataset, each data dictionary has keys "image" and "label" */
+///      auto image = row["image"];
+/// \endcode
+inline std::shared_ptr<Food101Dataset> DATASET_API
+Food101(const std::string &dataset_dir, const std::string &usage = "all", bool decode = false,
+        const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
+        const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<Food101Dataset>(StringToChar(dataset_dir), StringToChar(usage), decode, sampler, cache);
+}
+
+/// \brief Function to create a Food101Dataset
+/// \note The generated dataset has two columns ["image", "label"].
+/// \param[in] dataset_dir Path to the root directory that contains the dataset.
+/// \param[in] usage The type of dataset. Acceptable usages include "train", "test" or "all" .
+/// \param[in] decode Decode the images after reading.
+/// \param[in] sampler Raw pointer to a sampler object used to choose samples from the dataset.
+/// \param[in] cache Tensor cache to use. Default: nullptr, which means no cache is used.
+/// \return Shared pointer to the Food101Dataset.
+inline std::shared_ptr<Food101Dataset> DATASET_API Food101(const std::string &dataset_dir, const std::string &usage,
+                                                           bool decode, const Sampler *sampler,
+                                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<Food101Dataset>(StringToChar(dataset_dir), StringToChar(usage), decode, sampler, cache);
+}
+
+/// \brief Function to create a Food101Dataset.
+/// \note The generated dataset has two columns ["image", "label"].
+/// \param[in] dataset_dir Path to the root directory that contains the dataset.
+/// \param[in] usage The type of dataset. Acceptable usages include "train", "test" or "all".
+/// \param[in] decode Decode the images after reading.
+/// \param[in] sampler Sampler object used to choose samples from the dataset.
+/// \param[in] cache Tensor cache to use. Default: nullptr, which means no cache is used.
+/// \return Shared pointer to the Food101Dataset.
+inline std::shared_ptr<Food101Dataset> DATASET_API Food101(const std::string &dataset_dir, const std::string &usage,
+                                                           bool decode, const std::reference_wrapper<Sampler> &sampler,
+                                                           const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<Food101Dataset>(StringToChar(dataset_dir), StringToChar(usage), decode, sampler, cache);
+}
+
 /// \class GTZANDataset
 /// \brief A source dataset for reading and parsing GTZAN dataset.
 class DATASET_API GTZANDataset : public Dataset {

mindspore/ccsrc/minddata/dataset/include/dataset/samplers.h

@@ -47,6 +47,7 @@ class DATASET_API Sampler : std::enable_shared_from_this<Sampler> {
   friend class FakeImageDataset;
   friend class FashionMnistDataset;
   friend class FlickrDataset;
+  friend class Food101Dataset;
   friend class GTZANDataset;
   friend class ImageFolderDataset;
   friend class IMDBDataset;

mindspore/python/mindspore/dataset/engine/__init__.py

@@ -50,6 +50,7 @@ __all__ = ["Caltech101Dataset",        # Vision
            "FashionMnistDataset",      # Vision
            "FlickrDataset",            # Vision
            "Flowers102Dataset",        # Vision
+           "Food101Dataset",           # Vision
            "ImageFolderDataset",       # Vision
            "KITTIDataset",             # Vision
            "KMnistDataset",            # Vision

mindspore/python/mindspore/dataset/engine/datasets_vision.py

@@ -32,14 +32,13 @@ import mindspore._c_dataengine as cde
 
 from .datasets import VisionBaseDataset, SourceDataset, MappableDataset, Shuffle, Schema
 from .datasets_user_defined import GeneratorDataset
-from .validators import check_imagefolderdataset, check_kittidataset,\
-    check_mnist_cifar_dataset, check_manifestdataset, check_vocdataset, check_cocodataset, \
-    check_celebadataset, check_flickr_dataset, check_sb_dataset, check_flowers102dataset, check_cityscapes_dataset, \
-    check_usps_dataset, check_div2k_dataset, check_random_dataset, \
-    check_sbu_dataset, check_qmnist_dataset, check_emnist_dataset, check_fake_image_dataset, check_places365_dataset, \
-    check_photo_tour_dataset, check_svhn_dataset, check_stl10_dataset, check_semeion_dataset, \
-    check_caltech101_dataset, check_caltech256_dataset, check_wider_face_dataset, check_lfw_dataset, \
-    check_lsun_dataset, check_omniglotdataset
+from .validators import check_caltech101_dataset, check_caltech256_dataset, check_celebadataset, \
+    check_cityscapes_dataset, check_cocodataset, check_div2k_dataset, check_emnist_dataset, check_fake_image_dataset, \
+    check_flickr_dataset, check_flowers102dataset, check_food101_dataset, check_imagefolderdataset, \
+    check_kittidataset, check_lfw_dataset, check_lsun_dataset, check_manifestdataset, check_mnist_cifar_dataset, \
+    check_omniglotdataset, check_photo_tour_dataset, check_places365_dataset, check_qmnist_dataset, \
+    check_random_dataset, check_sb_dataset, check_sbu_dataset, check_semeion_dataset, check_stl10_dataset, \
+    check_svhn_dataset, check_usps_dataset, check_vocdataset, check_wider_face_dataset
 
 from ..core.validator_helpers import replace_none
 
@@ -2190,6 +2189,140 @@ class Flowers102Dataset(GeneratorDataset):
         return class_dict
 
 
+class Food101Dataset(MappableDataset, VisionBaseDataset):
+    """
+    A source dataset that reads and parses Food101 dataset.
+
+    The generated dataset has two columns :py:obj:`[image, label]` .
+    The tensor of column :py:obj:`image` is of the uint8 type.
+    The tensor of column :py:obj:`label` is of the string type.
+
+    Args:
+        dataset_dir (str): Path to the root directory that contains the dataset.
+        usage (str, optional): Usage of this dataset, can be 'train', 'test', or 'all'. 'train' will read
+            from 75,750 samples, 'test' will read from 25,250 samples, and 'all' will read all 'train'
+            and 'test' samples. Default: None, will be set to 'all'.
+        num_samples (int, optional): The number of images to be included in the dataset.
+            Default: None, will read all images.
+        num_parallel_workers (int, optional): Number of workers to read the data.
+            Default: None, number set in the mindspore.dataset.config.
+        shuffle (bool, optional): Whether or not to perform shuffle on the dataset.
+            Default: None, expected order behavior shown in the table below.
+        decode (bool, optional): Decode the images after reading. Default: False.
+        sampler (Sampler, optional): Object used to choose samples from the dataset.
+            Default: None, expected order behavior shown in the table below.
+        num_shards (int, optional): Number of shards that the dataset will be divided into. When this argument
+            is specified, `num_samples` reflects the maximum sample number of per shard. Default: None.
+        shard_id (int, optional): The shard ID within `num_shards` . This argument can only be specified
+            when `num_shards` is also specified. Default: None.
+        cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing. More details:
+            `Single-Node Data Cache <https://www.mindspore.cn/tutorials/experts/en/master/dataset/cache.html>`_ .
+            Default: None, which means no cache is used.
+
+    Raises:
+        RuntimeError: If `dataset_dir` does not contain data files.
+        RuntimeError: If `sampler` and `shuffle` are specified at the same time.
+        RuntimeError: If `sampler` and `num_shards`/`shard_id` are specified at the same time.
+        RuntimeError: If `num_shards` is specified but `shard_id` is None.
+        RuntimeError: If `shard_id` is specified but `num_shards` is None.
+        ValueError: If `shard_id` is invalid (< 0 or >= `num_shards`).
+        ValueError: If `num_parallel_workers` exceeds the max thread numbers.
+        ValueError: If the value of `usage` is not 'train', 'test', or 'all'.
+        ValueError: If `dataset_dir` is not exist.
+
+    Note:
+        - This dataset can take in a `sampler` . `sampler` and `shuffle` are mutually exclusive.
+          The table below shows what input arguments are allowed and their expected behavior.
+
+    .. list-table:: Expected Order Behavior of Using `sampler` and `shuffle`
+       :widths: 25 25 50
+       :header-rows: 1
+
+       * - Parameter `sampler`
+         - Parameter `shuffle`
+         - Expected Order Behavior
+       * - None
+         - None
+         - random order
+       * - None
+         - True
+         - random order
+       * - None
+         - False
+         - sequential order
+       * - Sampler object
+         - None
+         - order defined by sampler
+       * - Sampler object
+         - True
+         - not allowed
+       * - Sampler object
+         - False
+         - not allowed
+
+    Examples:
+        >>> food101_dataset_dir = "/path/to/food101_dataset_directory"
+        >>>
+        >>> # Read 3 samples from Food101 dataset
+        >>> dataset = ds.Food101Dataset(dataset_dir=food101_dataset_dir, num_samples=3)
+
+    About Food101 dataset:
+
+    The Food101 is a challenging dataset of 101 food categories, with 101,000 images.
+    There are 250 test imgaes and 750 training images in each class. All images were rescaled
+    to have a maximum side length of 512 pixels.
+
+    The following is the original Food101 dataset structure.
+    You can unzip the dataset files into this directory structure and read by MindSpore's API.
+
+    .. code-block::
+
+        .
+        └── food101_dir
+             ├── images
+             │    ├── apple_pie
+             │    │    ├── 1005649.jpg
+             │    │    ├── 1014775.jpg
+             │    │    ├──...
+             │    ├── baby_back_rips
+             │    │    ├── 1005293.jpg
+             │    │    ├── 1007102.jpg
+             │    │    ├──...
+             │    └──...
+             └── meta
+                  ├── train.txt
+                  ├── test.txt
+                  ├── classes.txt
+                  ├── train.json
+                  ├── test.json
+                  └── train.txt
+
+    Citation:
+
+    .. code-block::
+
+        @inproceedings{bossard14,
+        title     = {Food-101 -- Mining Discriminative Components with Random Forests},
+        author    = {Bossard, Lukas and Guillaumin, Matthieu and Van Gool, Luc},
+        booktitle = {European Conference on Computer Vision},
+        year      = {2014}
+        }
+    """
+
+    @check_food101_dataset
+    def __init__(self, dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=None,
+                 decode=False, sampler=None, num_shards=None, shard_id=None, cache=None):
+        super().__init__(num_parallel_workers=num_parallel_workers, sampler=sampler, num_samples=num_samples,
+                         shuffle=shuffle, num_shards=num_shards, shard_id=shard_id, cache=cache)
+
+        self.dataset_dir = dataset_dir
+        self.usage = replace_none(usage, "all")
+        self.decode = replace_none(decode, False)
+
+    def parse(self, children=None):
+        return cde.Food101Node(self.dataset_dir, self.usage, self.decode, self.sampler)
+
+
 class ImageFolderDataset(MappableDataset, VisionBaseDataset):
     """
     A source dataset that reads images from a tree of directories.

mindspore/python/mindspore/dataset/engine/validators.py

@@ -2328,6 +2328,36 @@ def check_flickr_dataset(method):
     return new_method
 
 
+def check_food101_dataset(method):
+    """A wrapper that wraps a parameter checker around the Food101Dataset."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        _, param_dict = parse_user_args(method, *args, **kwargs)
+
+        nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
+        nreq_param_bool = ['decode', 'shuffle']
+
+        dataset_dir = param_dict.get('dataset_dir')
+        check_dir(dataset_dir)
+
+        usage = param_dict.get('usage')
+        if usage is not None:
+            check_valid_str(usage, ["train", "test", "all"], "usage")
+
+        validate_dataset_param_value(nreq_param_int, param_dict, int)
+        validate_dataset_param_value(nreq_param_bool, param_dict, bool)
+
+        check_sampler_shuffle_shard_options(param_dict)
+
+        cache = param_dict.get('cache')
+        check_cache_option(cache)
+
+        return method(self, *args, **kwargs)
+
+    return new_method
+
+
 def check_sb_dataset(method):
     """A wrapper that wraps a parameter checker around the original Semantic Boundaries Dataset."""
 

tests/ut/cpp/dataset/c_api_dataset_food101_test.cc

@@ -0,0 +1,245 @@
+/**
+ * Copyright 2022 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 "common/common.h"
+#include "minddata/dataset/include/dataset/datasets.h"
+
+using namespace mindspore::dataset;
+using mindspore::dataset::DataType;
+using mindspore::dataset::Tensor;
+using mindspore::dataset::TensorShape;
+
+class MindDataTestPipeline : public UT::DatasetOpTesting {
+ protected:
+};
+
+/// Feature: Food101Dataset
+/// Description: Test basic usage of Food101Dataset
+/// Expectation: The data is processed successfully
+TEST_F(MindDataTestPipeline, TestFood101TestDataset) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFood101TestDataset.";
+
+  // Create a Food101 Test Dataset
+  std::string folder_path = datasets_root_path_ + "/testFood101Data/";
+  std::shared_ptr<Dataset> ds = Food101(folder_path, "test", true, std::make_shared<RandomSampler>(false, 4));
+
+  EXPECT_NE(ds, nullptr);
+
+  // 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();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+
+  EXPECT_NE(row.find("image"), row.end());
+  EXPECT_NE(row.find("label"), row.end());
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    ASSERT_OK(iter->GetNextRow(&row));
+  }
+
+  EXPECT_EQ(i, 4);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+/// Feature: Food101Dataset
+/// Description: Test Food101Dataset in pipeline mode
+/// Expectation: The data is processed successfully
+TEST_F(MindDataTestPipeline, TestFood101TestDatasetWithPipeline) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFood101TestDatasetWithPipeline.";
+
+  std::string folder_path = datasets_root_path_ + "/testFood101Data/";
+
+  // Create two Food101 Test Dataset
+  std::shared_ptr<Dataset> ds1 = Food101(folder_path, "test", true, std::make_shared<RandomSampler>(false, 4));
+  std::shared_ptr<Dataset> ds2 = Food101(folder_path, "test", true,std::make_shared<RandomSampler>(false, 4));
+  EXPECT_NE(ds1, nullptr);
+  EXPECT_NE(ds2, nullptr);
+
+  // Create two Repeat operation on ds
+  int32_t repeat_num = 1;
+  ds1 = ds1->Repeat(repeat_num);
+  EXPECT_NE(ds1, nullptr);
+  repeat_num = 1;
+  ds2 = ds2->Repeat(repeat_num);
+  EXPECT_NE(ds2, nullptr);
+
+  // Create two Project operation on ds
+  std::vector<std::string> column_project = {"image", "label"};
+  ds1 = ds1->Project(column_project);
+  EXPECT_NE(ds1, nullptr);
+  ds2 = ds2->Project(column_project);
+  EXPECT_NE(ds2, nullptr);
+
+  // Create a Concat operation on the ds
+  ds1 = ds1->Concat({ds2});
+  EXPECT_NE(ds1, nullptr);
+
+  // 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 = ds1->CreateIterator();
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::unordered_map<std::string, mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+
+  EXPECT_NE(row.find("image"), row.end());
+  EXPECT_NE(row.find("label"), row.end());
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    i++;
+    auto image = row["image"];
+    MS_LOG(INFO) << "Tensor image shape: " << image.Shape();
+    ASSERT_OK(iter->GetNextRow(&row));
+  }
+
+  EXPECT_EQ(i, 8);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+/// Feature: Food101Dataset
+/// Description: Test Food101Dataset GetDatasetSize
+/// Expectation: Correct size of dataset
+TEST_F(MindDataTestPipeline, TestGetFood101TestDatasetSize) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestGetFood101TestDatasetSize.";
+
+  std::string folder_path = datasets_root_path_ + "/testFood101Data/";
+
+  // Create a Food101 Test Dataset
+  std::shared_ptr<Dataset> ds = Food101(folder_path, "all");
+  EXPECT_NE(ds, nullptr);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 8);
+}
+
+/// Feature: Food101Dataset
+/// Description: Test Food101Dataset Getters method
+/// Expectation: Output is equal to the expected output
+TEST_F(MindDataTestPipeline, TestFood101TestDatasetGetters) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFood101TestDatasetGetters.";
+
+  // Create a Food101 Test Dataset
+  std::string folder_path = datasets_root_path_ + "/testFood101Data/";
+  std::shared_ptr<Dataset> ds = Food101(folder_path, "test");
+  EXPECT_NE(ds, nullptr);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 4);
+  std::vector<DataType> types = ToDETypes(ds->GetOutputTypes());
+  std::vector<TensorShape> shapes = ToTensorShapeVec(ds->GetOutputShapes());
+  std::vector<std::string> column_names = {"image", "label"};
+  int64_t num_classes = ds->GetNumClasses();
+  EXPECT_EQ(types.size(), 2);
+  EXPECT_EQ(types[0].ToString(), "uint8");
+  EXPECT_EQ(types[1].ToString(), "string");
+  EXPECT_EQ(shapes.size(), 2);
+  EXPECT_EQ(shapes[1].ToString(), "<>");
+  EXPECT_EQ(num_classes, -1);
+  EXPECT_EQ(ds->GetBatchSize(), 1);
+  EXPECT_EQ(ds->GetRepeatCount(), 1);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 4);
+  EXPECT_EQ(ToDETypes(ds->GetOutputTypes()), types);
+  EXPECT_EQ(ToTensorShapeVec(ds->GetOutputShapes()), shapes);
+  EXPECT_EQ(ds->GetNumClasses(), -1);
+
+  EXPECT_EQ(ds->GetColumnNames(), column_names);
+  EXPECT_EQ(ds->GetDatasetSize(), 4);
+  EXPECT_EQ(ToDETypes(ds->GetOutputTypes()), types);
+  EXPECT_EQ(ToTensorShapeVec(ds->GetOutputShapes()), shapes);
+  EXPECT_EQ(ds->GetBatchSize(), 1);
+  EXPECT_EQ(ds->GetRepeatCount(), 1);
+  EXPECT_EQ(ds->GetNumClasses(), -1);
+  EXPECT_EQ(ds->GetDatasetSize(), 4);
+}
+
+/// Feature: Food101Dataset
+/// Description: Test iterator of Food101Dataset with wrong column
+/// Expectation: Error message is logged, and CreateIterator() for invalid pipeline returns nullptr
+TEST_F(MindDataTestPipeline, TestFood101IteratorWrongColumn) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFood101IteratorOneColumn.";
+  // Create a Food101 Dataset
+  std::string folder_path = datasets_root_path_ + "/testFood101Data/";
+  std::shared_ptr<Dataset> ds = Food101(folder_path, "all", false, std::make_shared<RandomSampler>(false, 4));
+  EXPECT_NE(ds, nullptr);
+
+  // Pass wrong column name
+  std::vector<std::string> columns = {"digital"};
+  std::shared_ptr<ProjectDataset> project_ds = ds->Project(columns);
+  std::shared_ptr<Iterator> iter = project_ds->CreateIterator();
+  EXPECT_EQ(iter, nullptr);
+}
+
+/// Feature: Food101Dataset
+/// Description: Test Food101Dataset with empty string as the folder path
+/// Expectation: Error message is logged, and CreateIterator() for invalid pipeline returns nullptr
+TEST_F(MindDataTestPipeline, TestFood101DatasetFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFood101DatasetFail.";
+
+  // Create a Food101 Dataset
+  std::shared_ptr<Dataset> ds = Food101("", "train", false, std::make_shared<RandomSampler>(false, 2));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  // Expect failure: invalid Food101 input
+  EXPECT_EQ(iter, nullptr);
+}
+
+/// Feature: Food101Dataset
+/// Description: Test Food101Dataset with invalid usage
+/// Expectation: Error message is logged, and CreateIterator() for invalid pipeline returns nullptr
+TEST_F(MindDataTestPipeline, TestFood101DatasetWithInvalidUsageFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFood101DatasetWithInvalidUsageFail.";
+
+  // Create a Food101 Dataset
+  std::string folder_path = datasets_root_path_ + "/testFood101Data/";
+  std::shared_ptr<Dataset> ds = Food101(folder_path, "validation");
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  // Expect failure: invalid Food101 input, validation is not a valid usage
+  EXPECT_EQ(iter, nullptr);
+}
+
+/// Feature: Food101Dataset
+/// Description: Test Food101Dataset with null sampler
+/// Expectation: Error message is logged, and CreateIterator() for invalid pipeline returns nullptr
+TEST_F(MindDataTestPipeline, TestFood101DatasetWithNullSamplerFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFood101UDatasetWithNullSamplerFail.";
+
+  // Create a Food101 Dataset
+  std::string folder_path = datasets_root_path_ + "/testFood101Data/";
+  std::shared_ptr<Dataset> ds = Food101(folder_path, "all", false, nullptr);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  // Expect failure: invalid Food101 input, sampler cannot be nullptr
+  EXPECT_EQ(iter, nullptr);
+}

tests/ut/data/dataset/testFood101Data/meta/test.txt

@@ -0,0 +1,4 @@
+class1/0
+class2/1
+class3/0
+class4/0

tests/ut/data/dataset/testFood101Data/meta/train.txt

@@ -0,0 +1,4 @@
+class1/1
+class2/0
+class3/1
+class4/1

tests/ut/python/dataset/test_food101.py

@@ -0,0 +1,204 @@
+# Copyright 2022 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.
+# ==============================================================================
+"""
+Test Food101 dataset operators
+"""
+import pytest
+import numpy as np
+
+import mindspore.dataset as ds
+import mindspore.dataset.vision as vision
+import mindspore.log as logger
+
+DATA_DIR = "../data/dataset/testFood101Data/"
+
+
+def test_food101_basic():
+    """
+    Feature: Food101 dataset
+    Description: Read all files
+    Expectation: Throw number of data in all files
+    """
+    logger.info("Test Food101Dataset Op")
+
+    # case 1: test loading default usage dataset
+    data1 = ds.Food101Dataset(DATA_DIR)
+    num_iter1 = 0
+    for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter1 += 1
+    assert num_iter1 == 8
+
+    # case 2: test num_samples
+    data2 = ds.Food101Dataset(DATA_DIR, num_samples=1)
+    num_iter2 = 0
+    for _ in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter2 += 1
+    assert num_iter2 == 1
+
+    # case 3: test repeat
+    data3 = ds.Food101Dataset(DATA_DIR, num_samples=2)
+    data3 = data3.repeat(5)
+    num_iter3 = 0
+    for _ in data3.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter3 += 1
+    assert num_iter3 == 10
+
+
+def test_food101_noshuffle():
+    """
+    Feature: Food101 dataset
+    Description: Test noshuffle
+    Expectation: Throw number of data in all files
+    """
+    logger.info("Test Case noShuffle")
+    # define parameters
+    repeat_count = 1
+
+    # apply dataset operations
+    # Note: "all" reads both "train" dataset (2 samples) and "valid" dataset (2 samples)
+    data1 = ds.Food101Dataset(DATA_DIR, shuffle=False)
+    data1 = data1.repeat(repeat_count)
+
+    num_iter = 0
+    # each data is a dictionary
+    for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter += 1
+
+    assert num_iter == 8
+
+
+def test_food101_usage():
+    """
+    Feature: Food101 dataset
+    Description: Test Usage
+    Expectation: Throw number of data in all files
+    """
+    logger.info("Test Food101Dataset usage flag")
+
+    def test_config(usage, food101_path=DATA_DIR):
+        try:
+            data = ds.Food101Dataset(food101_path, usage=usage)
+            num_rows = 0
+            for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
+                num_rows += 1
+        except (ValueError, TypeError, RuntimeError) as e:
+            return str(e)
+        return num_rows
+
+    # test the usage of Food101
+    assert test_config("test") == 4
+    assert test_config("train") == 4
+    assert test_config("all") == 8
+    assert "usage is not within the valid set of ['train', 'test', 'all']" in test_config(
+        "invalid")
+
+    # change to the folder that contains all Food101 files
+    all_food101 = None
+    if all_food101 is not None:
+        assert test_config("test", all_food101) == 25250
+        assert test_config("train", all_food101) == 75750
+        assert test_config("all", all_food101) == 101000
+        assert ds.Food101Dataset(all_food101, usage="test").get_dataset_size() == 25250
+        assert ds.Food101Dataset(all_food101, usage="train").get_dataset_size() == 75750
+        assert ds.Food101Dataset(all_food101, usage="all").get_dataset_size() == 101000
+
+
+def test_food101_sequential_sampler():
+    """
+    Feature: Food101 dataset
+    Description: Test SequentialSampler
+    Expectation: Get correct number of data
+    """
+    num_samples = 1
+    sampler = ds.SequentialSampler(num_samples=num_samples)
+    data1 = ds.Food101Dataset(DATA_DIR, 'test', sampler=sampler)
+    data2 = ds.Food101Dataset(DATA_DIR, 'test', shuffle=False, num_samples=num_samples)
+    matches_list1, matches_list2 = [], []
+    num_iter = 0
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
+        matches_list1.append(item1["image"].asnumpy())
+        matches_list2.append(item2["image"].asnumpy())
+        num_iter += 1
+    np.testing.assert_array_equal(matches_list1, matches_list2)
+    assert num_iter == num_samples
+
+
+def test_food101_pipeline():
+    """
+    Feature: Pipeline test
+    Description: Read a sample
+    Expectation: The amount of each function are equal
+    """
+    dataset = ds.Food101Dataset(DATA_DIR, "test", num_samples=1, decode=True)
+    resize_op = vision.Resize((100, 100))
+    dataset = dataset.map(input_columns=["image"], operations=resize_op)
+    num_iter = 0
+    for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
+        num_iter += 1
+    assert num_iter == 1
+
+
+def test_food101_exception():
+    """
+    Feature: Food101 dataset
+    Description: Throw error messages when certain errors occur
+    Expectation: Error message
+    """
+    logger.info("Test error cases for Food101Dataset")
+    error_msg_1 = "sampler and shuffle cannot be specified at the same time"
+    with pytest.raises(RuntimeError, match=error_msg_1):
+        ds.Food101Dataset(DATA_DIR, shuffle=False, sampler=ds.PKSampler(3))
+
+    error_msg_2 = "sampler and sharding cannot be specified at the same time"
+    with pytest.raises(RuntimeError, match=error_msg_2):
+        ds.Food101Dataset(DATA_DIR, sampler=ds.PKSampler(
+            3), num_shards=2, shard_id=0)
+
+    error_msg_3 = "num_shards is specified and currently requires shard_id as well"
+    with pytest.raises(RuntimeError, match=error_msg_3):
+        ds.Food101Dataset(DATA_DIR, num_shards=10)
+
+    error_msg_4 = "shard_id is specified but num_shards is not"
+    with pytest.raises(RuntimeError, match=error_msg_4):
+        ds.Food101Dataset(DATA_DIR, shard_id=0)
+
+    error_msg_5 = "Input shard_id is not within the required interval"
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.Food101Dataset(DATA_DIR, num_shards=5, shard_id=-1)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.Food101Dataset(DATA_DIR, num_shards=5, shard_id=5)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.Food101Dataset(DATA_DIR, num_shards=2, shard_id=5)
+
+    error_msg_6 = "num_parallel_workers exceeds"
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.Food101Dataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.Food101Dataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.Food101Dataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
+
+    error_msg_7 = "Argument shard_id"
+    with pytest.raises(TypeError, match=error_msg_7):
+        ds.Food101Dataset(DATA_DIR, num_shards=2, shard_id="0")
+
+
+if __name__ == '__main__':
+    test_food101_basic()
+    test_food101_sequential_sampler()
+    test_food101_noshuffle()
+    test_food101_usage()
+    test_food101_pipeline()
+    test_food101_exception()