!18018 [assistant][ops]New operator implementation, include FashionMnistDataset

Merge pull request !18018 from 张璇/fashionmnist_dataset

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

@@ -98,6 +98,7 @@
 #include "minddata/dataset/engine/ir/datasetops/source/div2k_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/emnist_node.h"
 #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/image_folder_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/random_node.h"
@@ -1097,6 +1098,29 @@ FakeImageDataset::FakeImageDataset(int32_t num_images, const std::vector<int32_t
   ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
 }
 
+FashionMnistDataset::FashionMnistDataset(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;
+  auto ds = std::make_shared<FashionMnistNode>(CharToString(dataset_dir), CharToString(usage), sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
+FashionMnistDataset::FashionMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage,
+                                         const Sampler *sampler, const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler ? sampler->Parse() : nullptr;
+  auto ds = std::make_shared<FashionMnistNode>(CharToString(dataset_dir), CharToString(usage), sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
+FashionMnistDataset::FashionMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage,
+                                         const std::reference_wrapper<Sampler> sampler,
+                                         const std::shared_ptr<DatasetCache> &cache) {
+  auto sampler_obj = sampler.get().Parse();
+  auto ds = std::make_shared<FashionMnistNode>(CharToString(dataset_dir), CharToString(usage), sampler_obj, cache);
+  ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
+}
+
 FlickrDataset::FlickrDataset(const std::vector<char> &dataset_dir, const std::vector<char> &annotation_file,
                              bool decode, const std::shared_ptr<Sampler> &sampler,
                              const std::shared_ptr<DatasetCache> &cache) {

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

@@ -35,6 +35,7 @@
 #include "minddata/dataset/engine/ir/datasetops/source/div2k_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/emnist_node.h"
 #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/generator_node.h"
 #include "minddata/dataset/engine/ir/datasetops/source/image_folder_node.h"
@@ -179,6 +180,17 @@ PYBIND_REGISTER(FakeImageNode, 2, ([](const py::module *m) {
                     }));
                 }));
 
+PYBIND_REGISTER(FashionMnistNode, 2, ([](const py::module *m) {
+                  (void)py::class_<FashionMnistNode, DatasetNode, std::shared_ptr<FashionMnistNode>>(
+                    *m, "FashionMnistNode", "to create a FashionMnistNode")
+                    .def(py::init([](std::string dataset_dir, std::string usage, py::handle sampler) {
+                      auto fashion_mnist =
+                        std::make_shared<FashionMnistNode>(dataset_dir, usage, toSamplerObj(sampler), nullptr);
+                      THROW_IF_ERROR(fashion_mnist->ValidateParams());
+                      return fashion_mnist;
+                    }));
+                }));
+
 PYBIND_REGISTER(
   FlickrNode, 2, ([](const py::module *m) {
     (void)py::class_<FlickrNode, DatasetNode, std::shared_ptr<FlickrNode>>(*m, "FlickrNode", "to create a FlickrNode")

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

@@ -26,6 +26,7 @@ set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
     fake_image_op.cc
     places365_op.cc
     photo_tour_op.cc
+    fashion_mnist_op.cc
     )
 
 set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES

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

@@ -0,0 +1,84 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "minddata/dataset/engine/datasetops/source/fashion_mnist_op.h"
+
+#include <fstream>
+#include <iomanip>
+#include <set>
+
+#include "minddata/dataset/core/config_manager.h"
+#include "minddata/dataset/core/tensor_shape.h"
+#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
+#include "minddata/dataset/engine/execution_tree.h"
+#include "utils/ms_utils.h"
+
+namespace mindspore {
+namespace dataset {
+FashionMnistOp::FashionMnistOp(const std::string &usage, int32_t num_workers, const std::string &folder_path,
+                               int32_t queue_size, std::unique_ptr<DataSchema> data_schema,
+                               std::shared_ptr<SamplerRT> sampler)
+    : MnistOp(usage, num_workers, folder_path, queue_size, std::move(data_schema), std::move(sampler)) {}
+
+Status FashionMnistOp::CountTotalRows(const std::string &dir, const std::string &usage, int64_t *count) {
+  // the logic of counting the number of samples is copied from ParseMnistData() and uses CheckReader().
+  RETURN_UNEXPECTED_IF_NULL(count);
+  *count = 0;
+
+  const int64_t num_samples = 0;
+  const int64_t start_index = 0;
+  auto sampler = std::make_shared<SequentialSamplerRT>(start_index, num_samples);
+  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_UINT32), TensorImpl::kFlexible, 0, &scalar)));
+  std::shared_ptr<ConfigManager> cfg = GlobalContext::config_manager();
+  int32_t num_workers = cfg->num_parallel_workers();
+  int32_t op_connect_size = cfg->op_connector_size();
+  auto op =
+    std::make_shared<FashionMnistOp>(usage, num_workers, dir, op_connect_size, std::move(schema), std::move(sampler));
+
+  RETURN_IF_NOT_OK(op->WalkAllFiles());
+
+  for (size_t i = 0; i < op->image_names_.size(); ++i) {
+    std::ifstream image_reader;
+    image_reader.open(op->image_names_[i], std::ios::binary);
+    CHECK_FAIL_RETURN_UNEXPECTED(image_reader.is_open(),
+                                 "Invalid file, failed to open image file: " + op->image_names_[i]);
+    std::ifstream label_reader;
+    label_reader.open(op->label_names_[i], std::ios::binary);
+    CHECK_FAIL_RETURN_UNEXPECTED(label_reader.is_open(),
+                                 "Invalid file, failed to open label file: " + op->label_names_[i]);
+    uint32_t num_images;
+    Status s = op->CheckImage(op->image_names_[i], &image_reader, &num_images);
+    image_reader.close();
+    RETURN_IF_NOT_OK(s);
+
+    uint32_t num_labels;
+    s = op->CheckLabel(op->label_names_[i], &label_reader, &num_labels);
+    label_reader.close();
+    RETURN_IF_NOT_OK(s);
+
+    CHECK_FAIL_RETURN_UNEXPECTED((num_images == num_labels),
+                                 "Invalid data, num of images is not equal to num of labels.");
+    *count = *count + num_images;
+  }
+
+  return Status::OK();
+}
+
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,67 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_FASHION_MNIST_OP_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_FASHION_MNIST_OP_H_
+
+#include <algorithm>
+#include <map>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/engine/datasetops/source/mnist_op.h"
+
+namespace mindspore {
+namespace dataset {
+/// \brief Forward declares.
+template <typename T>
+class Queue;
+
+class FashionMnistOp : public MnistOp {
+ public:
+  /// \brief Constructor.
+  /// \param[in] usage Usage of this dataset, can be 'train', 'test' or 'all'.
+  /// \param[in] num_workers Number of workers reading images in parallel.
+  /// \param[in] folder_path Dir directory of fashionmnist.
+  /// \param[in] queue_size Connector queue size.
+  /// \param[in] data_schema The schema of the fashionmnist dataset.
+  /// \param[in] Sampler Tells FashionMnistOp what to read.
+  FashionMnistOp(const std::string &usage, int32_t num_workers, const std::string &folder_path, int32_t queue_size,
+                 std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler);
+
+  /// \brief Destructor.
+  ~FashionMnistOp() = default;
+
+  /// \brief Function to count the number of samples in the Fashion-MNIST dataset.
+  /// \param[in] dir Path to the Fashion-MNIST directory.
+  /// \param[in] usage Usage of this dataset, can be 'train', 'test' or 'all'.
+  /// \param[in] count Output arg that will hold the minimum of the actual dataset size and numSamples.
+  /// \return Status The status code returned.
+  static Status CountTotalRows(const std::string &dir, const std::string &usage, int64_t *count);
+
+  /// \brief Op name getter.
+  /// \return Name of the current Op.
+  std::string Name() const override { return "FashionMnistOp"; }
+
+  /// \brief Dataset name getter.
+  /// \param[in] upper Whether to get upper name.
+  /// \return Dataset name of the current Op.
+  std::string DatasetName(bool upper = false) const override { return upper ? "FashionMnist" : "fashion mnist"; }
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_FASHION_MNIST_OP_H_

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

@@ -85,6 +85,7 @@ constexpr char kCSVNode[] = "CSVDataset";
 constexpr char kDIV2KNode[] = "DIV2KDataset";
 constexpr char kEMnistNode[] = "EMnistDataset";
 constexpr char kFakeImageNode[] = "FakeImageDataset";
+constexpr char kFashionMnistNode[] = "FashionMnistDataset";
 constexpr char kFlickrNode[] = "FlickrDataset";
 constexpr char kGeneratorNode[] = "GeneratorDataset";
 constexpr char kImageFolderNode[] = "ImageFolderDataset";

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

@@ -14,6 +14,7 @@ set(DATASET_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES
         div2k_node.cc
         emnist_node.cc
         fake_image_node.cc
+        fashion_mnist_node.cc
         flickr_node.cc
         image_folder_node.cc
         manifest_node.cc

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

@@ -0,0 +1,114 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "minddata/dataset/engine/ir/datasetops/source/fashion_mnist_node.h"
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "minddata/dataset/engine/datasetops/source/fashion_mnist_op.h"
+#include "minddata/dataset/util/status.h"
+
+namespace mindspore {
+namespace dataset {
+FashionMnistNode::FashionMnistNode(const std::string &dataset_dir, const std::string &usage,
+                                   std::shared_ptr<SamplerObj> sampler, std::shared_ptr<DatasetCache> cache)
+    : MappableSourceNode(std::move(cache)), dataset_dir_(dataset_dir), usage_(usage), sampler_(sampler) {}
+
+std::shared_ptr<DatasetNode> FashionMnistNode::Copy() {
+  std::shared_ptr<SamplerObj> sampler = (sampler_ == nullptr) ? nullptr : sampler_->SamplerCopy();
+  auto node = std::make_shared<FashionMnistNode>(dataset_dir_, usage_, sampler, cache_);
+  return node;
+}
+
+void FashionMnistNode::Print(std::ostream &out) const { out << Name(); }
+
+Status FashionMnistNode::ValidateParams() {
+  RETURN_IF_NOT_OK(DatasetNode::ValidateParams());
+  RETURN_IF_NOT_OK(ValidateDatasetDirParam("FashionMnistNode", dataset_dir_));
+
+  RETURN_IF_NOT_OK(ValidateDatasetSampler("FashionMnistNode", sampler_));
+
+  RETURN_IF_NOT_OK(ValidateStringValue("FashionMnistNode", usage_, {"train", "test", "all"}));
+
+  return Status::OK();
+}
+
+Status FashionMnistNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops) {
+  // Do internal Schema generation.
+  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_UINT32), TensorImpl::kFlexible, 0, &scalar)));
+  std::shared_ptr<SamplerRT> sampler_rt = nullptr;
+  RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt));
+
+  auto op = std::make_shared<FashionMnistOp>(usage_, num_workers_, dataset_dir_, connector_que_size_, 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 FashionMnistNode::GetShardId(int32_t *shard_id) {
+  *shard_id = sampler_->ShardId();
+
+  return Status::OK();
+}
+
+// Get Dataset size.
+Status FashionMnistNode::GetDatasetSize(const std::shared_ptr<DatasetSizeGetter> &size_getter, bool estimate,
+                                        int64_t *dataset_size) {
+  if (dataset_size_ > 0) {
+    *dataset_size = dataset_size_;
+    return Status::OK();
+  }
+  int64_t num_rows, sample_size;
+  RETURN_IF_NOT_OK(FashionMnistOp::CountTotalRows(dataset_dir_, usage_, &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 FashionMnistNode::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["dataset_dir"] = dataset_dir_;
+  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();
+}
+}  // namespace dataset
+}  // namespace mindspore

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

@@ -0,0 +1,94 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_FASHION_MNIST_NODE_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_FASHION_MNIST_NODE_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "minddata/dataset/engine/ir/datasetops/dataset_node.h"
+
+namespace mindspore {
+namespace dataset {
+class FashionMnistNode : public MappableSourceNode {
+ public:
+  /// \brief Constructor.
+  FashionMnistNode(const std::string &dataset_dir, const std::string &usage, std::shared_ptr<SamplerObj> sampler,
+                   std::shared_ptr<DatasetCache> cache);
+
+  /// \brief Destructor.
+  ~FashionMnistNode() = default;
+
+  /// \brief Node name getter.
+  /// \return Name of the current node.
+  std::string Name() const override { return kFashionMnistNode; }
+
+  /// \brief Print the description.
+  /// \param[in] 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[out] 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.
+  /// \return Status Status::OK() if get shard id successfully.
+  Status GetShardId(int32_t *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;
+
+  /// \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_;
+  std::shared_ptr<SamplerObj> sampler_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_FASHION_MNIST_NODE_H_

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

@@ -2109,6 +2109,81 @@ inline std::shared_ptr<FakeImageDataset> FakeImage(int32_t num_images, const std
   return std::make_shared<FakeImageDataset>(num_images, image_size, num_classes, base_seed, sampler, cache);
 }
 
+/// \class FashionMnistDataset
+/// \brief A source dataset that reads and parses FASHION-MNIST dataset.
+class FashionMnistDataset : public Dataset {
+ public:
+  /// \brief Constructor of FashionMnistDataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] usage Usage of FASHION-MNIST, can be "train", "test" or "all".
+  /// \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.
+  /// \param[in] cache Tensor cache to use.
+  explicit FashionMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage,
+                               const std::shared_ptr<Sampler> &sampler, const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Constructor of FashionMnistDataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] usage Usage of FASHION-MNIST, can be "train", "test" or "all".
+  /// \param[in] sampler Raw pointer to a sampler object used to choose samples from the dataset.
+  /// \param[in] cache Tensor cache to use.
+  explicit FashionMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage,
+                               const Sampler *sampler, const std::shared_ptr<DatasetCache> &cache);
+
+  /// \brief Constructor of FashionMnistDataset.
+  /// \param[in] dataset_dir Path to the root directory that contains the dataset.
+  /// \param[in] usage Usage of FASHION-MNIST, can be "train", "test" or "all".
+  /// \param[in] sampler Sampler object used to choose samples from the dataset.
+  /// \param[in] cache Tensor cache to use.
+  explicit FashionMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &usage,
+                               const std::reference_wrapper<Sampler> sampler,
+                               const std::shared_ptr<DatasetCache> &cache);
+
+  /// Destructor of FashionMnistDataset.
+  ~FashionMnistDataset() = default;
+};
+
+/// \brief Function to create a FashionMnistDataset.
+/// \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 Usage of FASHION-MNIST, can be "train", "test" or "all" (default = "all").
+/// \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 FashionMnistDataset.
+inline std::shared_ptr<FashionMnistDataset> FashionMnist(
+  const std::string &dataset_dir, const std::string &usage = "all",
+  const std::shared_ptr<Sampler> &sampler = std::make_shared<RandomSampler>(),
+  const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<FashionMnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
+}
+
+/// \brief Function to create a FashionMnistDataset.
+/// \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 Usage of FASHION-MNIST, can be "train", "test" or "all".
+/// \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 FashionMnistDataset.
+inline std::shared_ptr<FashionMnistDataset> FashionMnist(const std::string &dataset_dir, const std::string &usage,
+                                                         const Sampler *sampler,
+                                                         const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<FashionMnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
+}
+
+/// \brief Function to create a FashionMnistDataset.
+/// \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 Usage of FASHION-MNIST, can be "train", "test" or "all".
+/// \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 FashionMnistDataset.
+inline std::shared_ptr<FashionMnistDataset> FashionMnist(const std::string &dataset_dir, const std::string &usage,
+                                                         const std::reference_wrapper<Sampler> sampler,
+                                                         const std::shared_ptr<DatasetCache> &cache = nullptr) {
+  return std::make_shared<FashionMnistDataset>(StringToChar(dataset_dir), StringToChar(usage), sampler, cache);
+}
+
 /// \class FlickrDataset
 /// \brief A source dataset for reading and parsing Flickr dataset.
 class FlickrDataset : public Dataset {

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

@@ -41,6 +41,7 @@ class Sampler : std::enable_shared_from_this<Sampler> {
   friend class DIV2KDataset;
   friend class EMnistDataset;
   friend class FakeImageDataset;
+  friend class FashionMnistDataset;
   friend class FlickrDataset;
   friend class ImageFolderDataset;
   friend class ManifestDataset;

mindspore/dataset/engine/datasets.py

@@ -3279,6 +3279,128 @@ class RangeDataset(MappableDataset):
         return self.dataset_size
 
 
+class FashionMnistDataset(MappableDataset):
+    """
+    A source dataset for reading and parsing the FASHION-MNIST 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 a scalar of the uint32 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 60,000
+            train samples, `test` will read from 10,000 test samples, `all` will read from all 70,000 samples.
+            (default=None, will read all samples)
+        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, will use value set in the config).
+        shuffle (bool, optional): Whether or not to perform shuffle on the dataset
+            (default=None, expected order behavior shown in the table).
+        sampler (Sampler, optional): Object used to choose samples from the
+            dataset (default=None, expected order behavior shown in the table).
+        num_shards (int, optional): Number of shards that the dataset will be divided into (default=None).
+            When this argument is specified, `num_samples` reflects the maximum sample number of per shard.
+        shard_id (int, optional): The shard ID within `num_shards` (default=None). This
+            argument can only be specified when `num_shards` is also specified.
+        cache (DatasetCache, optional): Use tensor caching service to speed up dataset processing.
+            (default=None, which means no cache is used).
+
+    Raises:
+        RuntimeError: If dataset_dir does not contain data files.
+        RuntimeError: If num_parallel_workers exceeds the max thread numbers.
+        RuntimeError: If sampler and shuffle are specified at the same time.
+        RuntimeError: If sampler and sharding 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).
+
+    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:
+        >>> fashion_mnist_dataset_dir = "/path/to/fashion_mnist_dataset_directory"
+        >>>
+        >>> # Read 3 samples from FASHIONMNIST dataset
+        >>> dataset = ds.FashionMnistDataset(dataset_dir=fashion_mnist_dataset_dir, num_samples=3)
+        >>>
+        >>> # Note: In FASHIONMNIST dataset, each dictionary has keys "image" and "label"
+
+    About Fashion-MNIST dataset:
+
+    Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and
+    a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
+    We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking
+    machine learning algorithms. It shares the same image size and structure of training and testing splits.
+
+    Here is the original Fashion-MNIST dataset structure.
+    You can unzip the dataset files into this directory structure and read by MindSpore's API.
+
+    .. code-block::
+
+        .
+        └── fashionmnist_dataset_dir
+             ├── t10k-images-idx3-ubyte
+             ├── t10k-labels-idx1-ubyte
+             ├── train-images-idx3-ubyte
+             └── train-labels-idx1-ubyte
+
+    Citation:
+
+    .. code-block::
+
+        @online{xiao2017/online,
+          author       = {Han Xiao and Kashif Rasul and Roland Vollgraf},
+          title        = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms},
+          date         = {2017-08-28},
+          year         = {2017},
+          eprintclass  = {cs.LG},
+          eprinttype   = {arXiv},
+          eprint       = {cs.LG/1708.07747},
+        }
+    """
+
+    @check_mnist_cifar_dataset
+    def __init__(self, dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=None,
+                 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")
+
+    def parse(self, children=None):
+        return cde.FashionMnistNode(self.dataset_dir, self.usage, self.sampler)
+
+
 class ImageFolderDataset(MappableDataset):
     """
     A source dataset that reads images from a tree of directories.

tests/ut/cpp/dataset/CMakeLists.txt

@@ -25,6 +25,7 @@ SET(DE_UT_SRCS
         c_api_dataset_div2k_test.cc
         c_api_dataset_emnist_test.cc
         c_api_dataset_fake_image_test.cc
+        c_api_dataset_fashion_mnist_test.cc
         c_api_dataset_flickr_test.cc
         c_api_dataset_iterator_test.cc
         c_api_dataset_manifest_test.cc

tests/ut/cpp/dataset/c_api_dataset_fashion_mnist_test.cc

@@ -0,0 +1,287 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#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: FashionMnistTestDataset.
+/// Description: test basic usage of FashionMnistTestDataset.
+/// Expectation: get correct data.
+TEST_F(MindDataTestPipeline, TestFashionMnistTestDataset) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistTestDataset.";
+
+  // Create a FashionMnist Test Dataset
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+  std::shared_ptr<Dataset> ds = FashionMnist(folder_path, "test", std::make_shared<RandomSampler>(false, 10));
+
+  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, 10);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+/// Feature: FashionMnistTestDatasetWithPipeline.
+/// Description: test FashionMnistTestDataset with pipeline.
+/// Expectation: get correct data.
+TEST_F(MindDataTestPipeline, TestFashionMnistTestDatasetWithPipeline) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistTestDatasetWithPipeline.";
+
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+
+  // Create two FashionMnist Test Dataset
+  std::shared_ptr<Dataset> ds1 = FashionMnist(folder_path, "test", std::make_shared<RandomSampler>(false, 10));
+  std::shared_ptr<Dataset> ds2 = FashionMnist(folder_path, "test", std::make_shared<RandomSampler>(false, 10));
+  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, 20);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+/// Feature: FashionMnistIteratorOneColumn.
+/// Description: test iterator of FashionMnistDataset with only the "image" column.
+/// Expectation: get correct data.
+TEST_F(MindDataTestPipeline, TestFashionMnistIteratorOneColumn) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistIteratorOneColumn.";
+  // Create a FashionMnist Dataset
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+  std::shared_ptr<Dataset> ds = FashionMnist(folder_path, "all", std::make_shared<RandomSampler>(false, 4));
+  EXPECT_NE(ds, nullptr);
+
+  // Create a Batch operation on ds
+  int32_t batch_size = 2;
+  ds = ds->Batch(batch_size);
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  // Only select "image" column and drop others
+  std::vector<std::string> columns = {"image"};
+  std::shared_ptr<Iterator> iter = ds->CreateIterator(columns, -1);
+  EXPECT_NE(iter, nullptr);
+
+  // Iterate the dataset and get each row
+  std::vector<mindspore::MSTensor> row;
+  ASSERT_OK(iter->GetNextRow(&row));
+  std::vector<int64_t> expect_image = {2, 28, 28, 1};
+
+  uint64_t i = 0;
+  while (row.size() != 0) {
+    for (auto &v : row) {
+      MS_LOG(INFO) << "image shape:" << v.Shape();
+      EXPECT_EQ(expect_image, v.Shape());
+    }
+    ASSERT_OK(iter->GetNextRow(&row));
+    i++;
+  }
+
+  EXPECT_EQ(i, 2);
+
+  // Manually terminate the pipeline
+  iter->Stop();
+}
+
+/// Feature: FashionMnistTestDatasetSize.
+/// Description: test usage of get the size of FashionMnistTestDataset.
+/// Expectation: get correct data.
+TEST_F(MindDataTestPipeline, TestGetFashionMnistTestDatasetSize) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestGetFashionMnistTestDatasetSize.";
+
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+
+  // Create a FashionMnist Test Dataset
+  std::shared_ptr<Dataset> ds = FashionMnist(folder_path, "test");
+  EXPECT_NE(ds, nullptr);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 10000);
+}
+
+/// Feature: FashionMnistTestDatasetGetters.
+/// Description: test DatasetGetters of FashionMnistTestDataset.
+/// Expectation: get correct the value.
+TEST_F(MindDataTestPipeline, TestFashionMnistTestDatasetGetters) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistTestDatasetGetters.";
+
+  // Create a FashionMnist Test Dataset
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+  std::shared_ptr<Dataset> ds = FashionMnist(folder_path, "test");
+  EXPECT_NE(ds, nullptr);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 10000);
+  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(), "uint32");
+  EXPECT_EQ(shapes.size(), 2);
+  EXPECT_EQ(shapes[0].ToString(), "<28,28,1>");
+  EXPECT_EQ(shapes[1].ToString(), "<>");
+  EXPECT_EQ(num_classes, -1);
+  EXPECT_EQ(ds->GetBatchSize(), 1);
+  EXPECT_EQ(ds->GetRepeatCount(), 1);
+
+  EXPECT_EQ(ds->GetDatasetSize(), 10000);
+  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(), 10000);
+  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(), 10000);
+}
+
+/// Feature: FashionMnistIteratorWrongColumn.
+/// Description: test iterator of FashionMnistDataset with wrong column.
+/// Expectation: get none piece of data.
+TEST_F(MindDataTestPipeline, TestFashionMnistIteratorWrongColumn) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistIteratorOneColumn.";
+  // Create a FashionMnist Dataset
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+  std::shared_ptr<Dataset> ds = FashionMnist(folder_path, "all", std::make_shared<RandomSampler>(false, 4));
+  EXPECT_NE(ds, nullptr);
+
+  // Pass wrong column name
+  std::vector<std::string> columns = {"digital"};
+  std::shared_ptr<Iterator> iter = ds->CreateIterator(columns);
+  EXPECT_EQ(iter, nullptr);
+}
+
+/// Feature: FashionMnistDatasetFail.
+/// Description: test failure of FashionMnistDataset.
+/// Expectation: get none piece of data.
+TEST_F(MindDataTestPipeline, TestFashionMnistDatasetFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistDatasetFail.";
+
+  // Create a FashionMnist Dataset
+  std::shared_ptr<Dataset> ds = FashionMnist("", "train", std::make_shared<RandomSampler>(false, 10));
+  EXPECT_NE(ds, nullptr);
+
+  // Create an iterator over the result of the above dataset
+  std::shared_ptr<Iterator> iter = ds->CreateIterator();
+  // Expect failure: invalid FashionMnist input
+  EXPECT_EQ(iter, nullptr);
+}
+
+/// Feature: FashionMnistDatasetWithInvalidUsageFail.
+/// Description: test FashionMnistDataset with invalid usage.
+/// Expectation: get none piece of data.
+TEST_F(MindDataTestPipeline, TestFashionMnistDatasetWithInvalidUsageFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistDatasetWithInvalidUsageFail.";
+
+  // Create a FashionMnist Dataset
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+  std::shared_ptr<Dataset> ds = FashionMnist(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 FashionMnist input, validation is not a valid usage
+  EXPECT_EQ(iter, nullptr);
+}
+
+/// Feature: FashionMnistDatasetWithNullSamplerFail.
+/// Description: test FashionMnistDataset with null sampler.
+/// Expectation: get none piece of data.
+TEST_F(MindDataTestPipeline, TestFashionMnistDatasetWithNullSamplerFail) {
+  MS_LOG(INFO) << "Doing MindDataTestPipeline-TestFashionMnistUDatasetWithNullSamplerFail.";
+
+  // Create a FashionMnist Dataset
+  std::string folder_path = datasets_root_path_ + "/testMnistData/";
+  std::shared_ptr<Dataset> ds = FashionMnist(folder_path, "all", 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 FashionMnist input, sampler cannot be nullptr
+  EXPECT_EQ(iter, nullptr);
+}

tests/ut/python/dataset/test_datasets_fashion_mnist.py

@@ -0,0 +1,322 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""
+Test FashionMnist dataset operators
+"""
+
+import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+
+import mindspore.dataset as ds
+import mindspore.dataset.vision.c_transforms as vision
+from mindspore import log as logger
+
+DATA_DIR = "../data/dataset/testMnistData"
+
+
+def load_fashion_mnist(path):
+    """
+    Feature: load_fashion_mnist.
+    Description: load FashionMnistDataset.
+    Expectation: get data of FashionMnistDataset.
+    """
+    labels_path = os.path.realpath(os.path.join(path, 't10k-labels-idx1-ubyte'))
+    images_path = os.path.realpath(os.path.join(path, 't10k-images-idx3-ubyte'))
+    with open(labels_path, 'rb') as lbpath:
+        lbpath.read(8)
+        labels = np.fromfile(lbpath, dtype=np.uint8)
+    with open(images_path, 'rb') as imgpath:
+        imgpath.read(16)
+        images = np.fromfile(imgpath, dtype=np.uint8)
+        images = images.reshape(-1, 28, 28, 1)
+        images[images > 0] = 255  # Perform binarization to maintain consistency with our API
+    return images, labels
+
+
+def visualize_dataset(images, labels):
+    """
+    Feature: visualize_dataset.
+    Description: visualize FashionMnistDataset.
+    Expectation: plot images.
+    """
+    num_samples = len(images)
+    for i in range(num_samples):
+        plt.subplot(1, num_samples, i + 1)
+        plt.imshow(images[i].squeeze(), cmap=plt.cm.gray)
+        plt.title(labels[i])
+    plt.show()
+
+
+def test_fashion_mnist_content_check():
+    """
+    Feature: test_fashion_mnist_content_check.
+    Description: validate FashionMnistDataset image readings.
+    Expectation: get correct value.
+    """
+    logger.info("Test FashionMnistDataset Op with content check")
+    data1 = ds.FashionMnistDataset(DATA_DIR, num_samples=100, shuffle=False)
+    images, labels = load_fashion_mnist(DATA_DIR)
+    num_iter = 0
+    # in this example, each dictionary has keys "image" and "label"
+    image_list, label_list = [], []
+    for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
+        image_list.append(data["image"])
+        label_list.append("label {}".format(data["label"]))
+        np.testing.assert_array_equal(data["image"], images[i])
+        np.testing.assert_array_equal(data["label"], labels[i])
+        num_iter += 1
+    assert num_iter == 100
+
+
+def test_fashion_mnist_basic():
+    """
+    Feature: test_fashion_mnist_basic.
+    Description: test basic usage of FashionMnistDataset.
+    Expectation: get correct data.
+    """
+    logger.info("Test FashionMnistDataset Op")
+
+    # case 1: test loading whole dataset
+    data1 = ds.FashionMnistDataset(DATA_DIR)
+    num_iter1 = 0
+    for _ in data1.create_dict_iterator(num_epochs=1):
+        num_iter1 += 1
+    assert num_iter1 == 10000
+
+    # case 2: test num_samples
+    data2 = ds.FashionMnistDataset(DATA_DIR, num_samples=500)
+    num_iter2 = 0
+    for _ in data2.create_dict_iterator(num_epochs=1):
+        num_iter2 += 1
+    assert num_iter2 == 500
+
+    # case 3: test repeat
+    data3 = ds.FashionMnistDataset(DATA_DIR, num_samples=200)
+    data3 = data3.repeat(5)
+    num_iter3 = 0
+    for _ in data3.create_dict_iterator(num_epochs=1):
+        num_iter3 += 1
+    assert num_iter3 == 1000
+
+    # case 4: test batch with drop_remainder=False
+    data4 = ds.FashionMnistDataset(DATA_DIR, num_samples=100)
+    assert data4.get_dataset_size() == 100
+    assert data4.get_batch_size() == 1
+    data4 = data4.batch(batch_size=7)  # drop_remainder is default to be False
+    assert data4.get_dataset_size() == 15
+    assert data4.get_batch_size() == 7
+    num_iter4 = 0
+    for _ in data4.create_dict_iterator(num_epochs=1):
+        num_iter4 += 1
+    assert num_iter4 == 15
+
+    # case 5: test batch with drop_remainder=True
+    data5 = ds.FashionMnistDataset(DATA_DIR, num_samples=100)
+    assert data5.get_dataset_size() == 100
+    assert data5.get_batch_size() == 1
+    data5 = data5.batch(batch_size=7, drop_remainder=True)  # the rest of incomplete batch will be dropped
+    assert data5.get_dataset_size() == 14
+    assert data5.get_batch_size() == 7
+    num_iter5 = 0
+    for _ in data5.create_dict_iterator(num_epochs=1):
+        num_iter5 += 1
+    assert num_iter5 == 14
+
+    # case 6: test get_col_names
+    data6 = ds.FashionMnistDataset(DATA_DIR, "train", num_samples=10)
+    assert data6.get_col_names() == ["image", "label"]
+
+
+def test_fashion_mnist_pk_sampler():
+    """
+    Feature: test_fashion_mnist_pk_sampler.
+    Description: test usage of FashionMnistDataset with PKSampler.
+    Expectation: get correct data.
+    """
+    logger.info("Test FashionMnistDataset Op with PKSampler")
+    golden = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4,
+              5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9]
+    sampler = ds.PKSampler(3)
+    data = ds.FashionMnistDataset(DATA_DIR, sampler=sampler)
+    num_iter = 0
+    label_list = []
+    for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
+        label_list.append(item["label"])
+        num_iter += 1
+    np.testing.assert_array_equal(golden, label_list)
+    assert num_iter == 30
+
+
+def test_fashion_mnist_sequential_sampler():
+    """
+    Feature: test_fashion_mnist_sequential_sampler.
+    Description: test usage of FashionMnistDataset with SequentialSampler.
+    Expectation: get correct data.
+    """
+    logger.info("Test FashionMnistDataset Op with SequentialSampler")
+    num_samples = 50
+    sampler = ds.SequentialSampler(num_samples=num_samples)
+    data1 = ds.FashionMnistDataset(DATA_DIR, sampler=sampler)
+    data2 = ds.FashionMnistDataset(DATA_DIR, shuffle=False, num_samples=num_samples)
+    label_list1, label_list2 = [], []
+    num_iter = 0
+    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
+        label_list1.append(item1["label"].asnumpy())
+        label_list2.append(item2["label"].asnumpy())
+        num_iter += 1
+    np.testing.assert_array_equal(label_list1, label_list2)
+    assert num_iter == num_samples
+
+
+def test_fashion_mnist_exception():
+    """
+    Feature: test_fashion_mnist_exception.
+    Description: test error cases for FashionMnistDataset.
+    Expectation: raise exception.
+    """
+    logger.info("Test error cases for FashionMnistDataset")
+    error_msg_1 = "sampler and shuffle cannot be specified at the same time"
+    with pytest.raises(RuntimeError, match=error_msg_1):
+        ds.FashionMnistDataset(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.FashionMnistDataset(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.FashionMnistDataset(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.FashionMnistDataset(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.FashionMnistDataset(DATA_DIR, num_shards=5, shard_id=-1)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.FashionMnistDataset(DATA_DIR, num_shards=5, shard_id=5)
+    with pytest.raises(ValueError, match=error_msg_5):
+        ds.FashionMnistDataset(DATA_DIR, num_shards=2, shard_id=5)
+
+    error_msg_6 = "num_parallel_workers exceeds"
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.FashionMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.FashionMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
+    with pytest.raises(ValueError, match=error_msg_6):
+        ds.FashionMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
+
+    error_msg_7 = "Argument shard_id"
+    with pytest.raises(TypeError, match=error_msg_7):
+        ds.FashionMnistDataset(DATA_DIR, num_shards=2, shard_id="0")
+
+    def exception_func(item):
+        raise Exception("Error occur!")
+
+    error_msg_8 = "The corresponding data files"
+    with pytest.raises(RuntimeError, match=error_msg_8):
+        data = ds.FashionMnistDataset(DATA_DIR)
+        data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
+        for _ in data.__iter__():
+            pass
+    with pytest.raises(RuntimeError, match=error_msg_8):
+        data = ds.FashionMnistDataset(DATA_DIR)
+        data = data.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
+        data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
+        for _ in data.__iter__():
+            pass
+    with pytest.raises(RuntimeError, match=error_msg_8):
+        data = ds.FashionMnistDataset(DATA_DIR)
+        data = data.map(operations=exception_func, input_columns=["label"], num_parallel_workers=1)
+        for _ in data.__iter__():
+            pass
+
+
+def test_fashion_mnist_visualize(plot=False):
+    """
+    Feature: test_fashion_mnist_visualize.
+    Description: visualize FashionMnistDataset results.
+    Expectation: get correct data and plot them.
+    """
+    logger.info("Test FashionMnistDataset visualization")
+
+    data1 = ds.FashionMnistDataset(DATA_DIR, num_samples=10, shuffle=False)
+    num_iter = 0
+    image_list, label_list = [], []
+    for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
+        image = item["image"]
+        label = item["label"]
+        image_list.append(image)
+        label_list.append("label {}".format(label))
+        assert isinstance(image, np.ndarray)
+        assert image.shape == (28, 28, 1)
+        assert image.dtype == np.uint8
+        assert label.dtype == np.uint32
+        num_iter += 1
+    assert num_iter == 10
+    if plot:
+        visualize_dataset(image_list, label_list)
+
+
+def test_fashion_mnist_usage():
+    """
+    Feature: test_fashion_mnist_usage.
+    Description: validate FashionMnistDataset image readings.
+    Expectation: get correct data.
+    """
+    logger.info("Test FashionMnistDataset usage flag")
+
+    def test_config(usage, fashion_mnist_path=None):
+        fashion_mnist_path = DATA_DIR if fashion_mnist_path is None else fashion_mnist_path
+        try:
+            data = ds.FashionMnistDataset(fashion_mnist_path, usage=usage, shuffle=False)
+            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
+
+    assert test_config("test") == 10000
+    assert test_config("all") == 10000
+    assert "FashionMnistDataset API can't read the data file (interface mismatch or no data found)" \
+           in test_config("train")
+    assert "usage is not within the valid set of ['train', 'test', 'all']" in test_config("invalid")
+    assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
+
+    # change this directory to the folder that contains all fashionmnist files
+    all_files_path = None
+    # the following tests on the entire datasets
+    if all_files_path is not None:
+        assert test_config("train", all_files_path) == 60000
+        assert test_config("test", all_files_path) == 10000
+        assert test_config("all", all_files_path) == 70000
+        assert ds.FashionMnistDataset(all_files_path, usage="train").get_dataset_size() == 60000
+        assert ds.FashionMnistDataset(all_files_path, usage="test").get_dataset_size() == 10000
+        assert ds.FashionMnistDataset(all_files_path, usage="all").get_dataset_size() == 70000
+
+
+if __name__ == '__main__':
+    test_fashion_mnist_content_check()
+    test_fashion_mnist_basic()
+    test_fashion_mnist_pk_sampler()
+    test_fashion_mnist_sequential_sampler()
+    test_fashion_mnist_exception()
+    test_fashion_mnist_visualize(plot=True)
+    test_fashion_mnist_usage()