diff --git a/mindspore/ccsrc/minddata/dataset/api/datasets.cc b/mindspore/ccsrc/minddata/dataset/api/datasets.cc index 5630a9106c4..9f45458108f 100644 --- a/mindspore/ccsrc/minddata/dataset/api/datasets.cc +++ b/mindspore/ccsrc/minddata/dataset/api/datasets.cc @@ -103,6 +103,7 @@ #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/kmnist_node.h" #include "minddata/dataset/engine/ir/datasetops/source/lj_speech_node.h" #include "minddata/dataset/engine/ir/datasetops/source/manifest_node.h" #include "minddata/dataset/engine/ir/datasetops/source/minddata_node.h" @@ -1212,6 +1213,28 @@ ImageFolderDataset::ImageFolderDataset(const std::vector &dataset_dir, boo ir_node_ = std::static_pointer_cast(ds); } +KMnistDataset::KMnistDataset(const std::vector &dataset_dir, const std::vector &usage, + const std::shared_ptr &sampler, const std::shared_ptr &cache) { + auto sampler_obj = sampler ? sampler->Parse() : nullptr; + auto ds = std::make_shared(CharToString(dataset_dir), CharToString(usage), sampler_obj, cache); + ir_node_ = std::static_pointer_cast(ds); +} + +KMnistDataset::KMnistDataset(const std::vector &dataset_dir, const std::vector &usage, + const Sampler *sampler, const std::shared_ptr &cache) { + auto sampler_obj = sampler ? sampler->Parse() : nullptr; + auto ds = std::make_shared(CharToString(dataset_dir), CharToString(usage), sampler_obj, cache); + ir_node_ = std::static_pointer_cast(ds); +} + +KMnistDataset::KMnistDataset(const std::vector &dataset_dir, const std::vector &usage, + const std::reference_wrapper sampler, + const std::shared_ptr &cache) { + auto sampler_obj = sampler.get().Parse(); + auto ds = std::make_shared(CharToString(dataset_dir), CharToString(usage), sampler_obj, cache); + ir_node_ = std::static_pointer_cast(ds); +} + LJSpeechDataset::LJSpeechDataset(const std::vector &dataset_dir, const std::shared_ptr &sampler, const std::shared_ptr &cache) { auto sampler_obj = sampler ? sampler->Parse() : nullptr; diff --git a/mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/ir/datasetops/source/bindings.cc b/mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/ir/datasetops/source/bindings.cc index 0fedd3082f6..d815c99693e 100644 --- a/mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/ir/datasetops/source/bindings.cc +++ b/mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/ir/datasetops/source/bindings.cc @@ -41,6 +41,7 @@ #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" +#include "minddata/dataset/engine/ir/datasetops/source/kmnist_node.h" #include "minddata/dataset/engine/ir/datasetops/source/mnist_node.h" #include "minddata/dataset/engine/ir/datasetops/source/random_node.h" #include "minddata/dataset/engine/ir/datasetops/source/speech_commands_node.h" @@ -267,6 +268,16 @@ PYBIND_REGISTER(ImageFolderNode, 2, ([](const py::module *m) { })); })); +PYBIND_REGISTER(KMnistNode, 2, ([](const py::module *m) { + (void)py::class_>(*m, "KMnistNode", + "to create a KMnistNode") + .def(py::init([](std::string dataset_dir, std::string usage, py::handle sampler) { + auto kmnist = std::make_shared(dataset_dir, usage, toSamplerObj(sampler), nullptr); + THROW_IF_ERROR(kmnist->ValidateParams()); + return kmnist; + })); + })); + PYBIND_REGISTER(LJSpeechNode, 2, ([](const py::module *m) { (void)py::class_>(*m, "LJSpeechNode", "to create a LJSpeechNode") diff --git a/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/CMakeLists.txt b/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/CMakeLists.txt index 86edae5fc7f..184fa893c88 100644 --- a/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/CMakeLists.txt +++ b/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/CMakeLists.txt @@ -19,6 +19,7 @@ set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES flickr_op.cc image_folder_op.cc io_block.cc + kmnist_op.cc lj_speech_op.cc mappable_leaf_op.cc mnist_op.cc diff --git a/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/kmnist_op.cc b/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/kmnist_op.cc new file mode 100644 index 00000000000..cbba8cd511b --- /dev/null +++ b/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/kmnist_op.cc @@ -0,0 +1,80 @@ +/** + * 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/kmnist_op.h" + +#include +#include + +#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 { +KMnistOp::KMnistOp(const std::string &usage, int32_t num_workers, const std::string &folder_path, int32_t queue_size, + std::unique_ptr data_schema, std::shared_ptr sampler) + : MnistOp(usage, num_workers, folder_path, queue_size, std::move(data_schema), std::move(sampler)) {} + +Status KMnistOp::CountTotalRows(const std::string &dir, const std::string &usage, int64_t *count) { + // the logic of counting the number of samples is copied from ParseKMnistData() 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(start_index, num_samples); + auto schema = std::make_unique(); + 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 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(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 diff --git a/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/kmnist_op.h b/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/kmnist_op.h new file mode 100644 index 00000000000..276920387b3 --- /dev/null +++ b/mindspore/ccsrc/minddata/dataset/engine/datasetops/source/kmnist_op.h @@ -0,0 +1,65 @@ +/** + * 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_KMNIST_OP_H_ +#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_KMNIST_OP_H_ + +#include +#include +#include +#include + +#include "minddata/dataset/engine/datasetops/source/mnist_op.h" + +namespace mindspore { +namespace dataset { +/// \brief Forward declares. +template +class Queue; + +class KMnistOp : 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 kmnist. + /// \param[in] queue_size Connector queue size. + /// \param[in] data_schema The schema of the kmnist dataset. + /// \param[in] Sampler Tells KMnistOp what to read. + KMnistOp(const std::string &usage, int32_t num_workers, const std::string &folder_path, int32_t queue_size, + std::unique_ptr data_schema, std::shared_ptr sampler); + + /// \brief Destructor. + ~KMnistOp() = default; + + /// \brief Function to count the number of samples in the KMNIST dataset. + /// \param[in] dir Path to the KMNIST 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 "KMnistOp"; } + + /// \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 ? "KMnist" : "kmnist"; } +}; +} // namespace dataset +} // namespace mindspore +#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_KMNIST_OP_H_ diff --git a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/dataset_node.h b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/dataset_node.h index 6079947c109..ec30b54408d 100644 --- a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/dataset_node.h +++ b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/dataset_node.h @@ -93,6 +93,7 @@ constexpr char kFashionMnistNode[] = "FashionMnistDataset"; constexpr char kFlickrNode[] = "FlickrDataset"; constexpr char kGeneratorNode[] = "GeneratorDataset"; constexpr char kImageFolderNode[] = "ImageFolderDataset"; +constexpr char kKMnistNode[] = "KMnistDataset"; constexpr char kLJSpeechNode[] = "LJSpeechDataset"; constexpr char kManifestNode[] = "ManifestDataset"; constexpr char kMindDataNode[] = "MindDataDataset"; diff --git a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/CMakeLists.txt b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/CMakeLists.txt index b78d8bcf543..32fea350bb9 100644 --- a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/CMakeLists.txt +++ b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/CMakeLists.txt @@ -19,6 +19,7 @@ set(DATASET_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES fashion_mnist_node.cc flickr_node.cc image_folder_node.cc + kmnist_node.cc lj_speech_node.cc manifest_node.cc minddata_node.cc diff --git a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/kmnist_node.cc b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/kmnist_node.cc new file mode 100644 index 00000000000..b315dab8b37 --- /dev/null +++ b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/kmnist_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/kmnist_node.h" + +#include +#include +#include +#include + +#include "minddata/dataset/engine/datasetops/source/kmnist_op.h" +#include "minddata/dataset/util/status.h" + +namespace mindspore { +namespace dataset { +KMnistNode::KMnistNode(const std::string &dataset_dir, const std::string &usage, std::shared_ptr sampler, + std::shared_ptr cache) + : MappableSourceNode(std::move(cache)), dataset_dir_(dataset_dir), usage_(usage), sampler_(sampler) {} + +std::shared_ptr KMnistNode::Copy() { + std::shared_ptr sampler = (sampler_ == nullptr) ? nullptr : sampler_->SamplerCopy(); + auto node = std::make_shared(dataset_dir_, usage_, sampler, cache_); + return node; +} + +void KMnistNode::Print(std::ostream &out) const { out << Name(); } + +Status KMnistNode::ValidateParams() { + RETURN_IF_NOT_OK(DatasetNode::ValidateParams()); + RETURN_IF_NOT_OK(ValidateDatasetDirParam("KMnistNode", dataset_dir_)); + + RETURN_IF_NOT_OK(ValidateDatasetSampler("KMnistNode", sampler_)); + + RETURN_IF_NOT_OK(ValidateStringValue("KMnistNode", usage_, {"train", "test", "all"})); + + return Status::OK(); +} + +Status KMnistNode::Build(std::vector> *const node_ops) { + // Do internal Schema generation. + auto schema = std::make_unique(); + 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 sampler_rt = nullptr; + RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt)); + + auto op = std::make_shared(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 KMnistNode::GetShardId(int32_t *shard_id) { + *shard_id = sampler_->ShardId(); + + return Status::OK(); +} + +// Get Dataset size. +Status KMnistNode::GetDatasetSize(const std::shared_ptr &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(KMnistOp::CountTotalRows(dataset_dir_, usage_, &num_rows)); + std::shared_ptr 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 KMnistNode::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 diff --git a/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/kmnist_node.h b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/kmnist_node.h new file mode 100644 index 00000000000..d25478fea29 --- /dev/null +++ b/mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/kmnist_node.h @@ -0,0 +1,101 @@ +/** + * 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_KMNIST_NODE_H_ +#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_KMNIST_NODE_H_ + +#include +#include +#include + +#include "minddata/dataset/engine/ir/datasetops/dataset_node.h" + +namespace mindspore { +namespace dataset { +class KMnistNode : public MappableSourceNode { + public: + /// \brief Constructor. + /// \param[in] dataset_dir Dataset directory of kmnist. + /// \param[in] usage Usage of this dataset, can be 'train', 'test' or 'all'. + /// \param[in] sampler Tells KMnistOp what to read. + /// \param[in] cache Tensor cache to use. + KMnistNode(const std::string &dataset_dir, const std::string &usage, std::shared_ptr sampler, + std::shared_ptr cache); + + /// \brief Destructor. + ~KMnistNode() = default; + + /// \brief Node name getter. + /// \return Name of the current node. + std::string Name() const override { return kKMnistNode; } + + /// \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 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> *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[out] shard_id The shard id. + /// \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 &size_getter, bool estimate, + int64_t *dataset_size) override; + + /// \brief Getter functions. + const std::string &DatasetDir() const { return dataset_dir_; } + + /// \brief Getter functions. + 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 Sampler() override { return sampler_; } + + /// \brief Sampler setter. + void SetSampler(std::shared_ptr sampler) override { sampler_ = sampler; } + + private: + std::string dataset_dir_; + std::string usage_; + std::shared_ptr sampler_; +}; +} // namespace dataset +} // namespace mindspore +#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_KMNIST_NODE_H_ diff --git a/mindspore/ccsrc/minddata/dataset/include/dataset/datasets.h b/mindspore/ccsrc/minddata/dataset/include/dataset/datasets.h index e792aa3f07c..11e2dc67e3b 100644 --- a/mindspore/ccsrc/minddata/dataset/include/dataset/datasets.h +++ b/mindspore/ccsrc/minddata/dataset/include/dataset/datasets.h @@ -2510,6 +2510,83 @@ inline std::shared_ptr MS_API ImageFolder(const std::string MapStringToChar(class_indexing), cache); } +/// \class KMnistDataset. +/// \brief A source dataset for reading and parsing KMnist dataset. +class MS_API KMnistDataset : public Dataset { + public: + /// \brief Function to create a KMnistDataset. + /// \param[in] dataset_dir Path to the root directory that contains the dataset. + /// \param[in] usage of KMNIST, 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. + /// \return Shared pointer to the current KMnistDataset. + explicit KMnistDataset(const std::vector &dataset_dir, const std::vector &usage, + const std::shared_ptr &sampler, const std::shared_ptr &cache); + + /// \brief Function to create a KMnistDataset. + /// \param[in] dataset_dir Path to the root directory that contains the dataset. + /// \param[in] usage of Kmnist, 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. + /// \return Shared pointer to the current KMnistDataset. + explicit KMnistDataset(const std::vector &dataset_dir, const std::vector &usage, const Sampler *sampler, + const std::shared_ptr &cache); + + /// \brief Function to create a KMnistDataset. + /// \param[in] dataset_dir Path to the root directory that contains the dataset. + /// \param[in] usage of Kmnist, 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. + /// \return Shared pointer to the current KMnistDataset. + explicit KMnistDataset(const std::vector &dataset_dir, const std::vector &usage, + const std::reference_wrapper sampler, const std::shared_ptr &cache); + + /// \brief Destructor of KMnistDataset. + ~KMnistDataset() = default; +}; + +/// \brief Function to create a KMnistDataset. +/// \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 of KMNIST, 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 current KMnistDataset. +inline std::shared_ptr MS_API +KMnist(const std::string &dataset_dir, const std::string &usage = "all", + const std::shared_ptr &sampler = std::make_shared(), + const std::shared_ptr &cache = nullptr) { + return std::make_shared(StringToChar(dataset_dir), StringToChar(usage), sampler, cache); +} + +/// \brief Function to create a KMnistDataset. +/// \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 of Kmnist, 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 current KMnistDataset. +inline std::shared_ptr MS_API KMnist(const std::string &dataset_dir, const std::string &usage, + const Sampler *sampler, + const std::shared_ptr &cache = nullptr) { + return std::make_shared(StringToChar(dataset_dir), StringToChar(usage), sampler, cache); +} + +/// \brief Function to create a KMnistDataset. +/// \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 of Kmnist, 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 current KMnistDataset. +inline std::shared_ptr MS_API KMnist(const std::string &dataset_dir, const std::string &usage, + const std::reference_wrapper sampler, + const std::shared_ptr &cache = nullptr) { + return std::make_shared(StringToChar(dataset_dir), StringToChar(usage), sampler, cache); +} + /// \class LJSpeechDataset /// \brief A source dataset for reading and parsing LJSpeech dataset. class MS_API LJSpeechDataset : public Dataset { diff --git a/mindspore/ccsrc/minddata/dataset/include/dataset/samplers.h b/mindspore/ccsrc/minddata/dataset/include/dataset/samplers.h index 907c24906df..a9dfe5d269d 100644 --- a/mindspore/ccsrc/minddata/dataset/include/dataset/samplers.h +++ b/mindspore/ccsrc/minddata/dataset/include/dataset/samplers.h @@ -46,6 +46,7 @@ class MS_API Sampler : std::enable_shared_from_this { friend class FashionMnistDataset; friend class FlickrDataset; friend class ImageFolderDataset; + friend class KMnistDataset; friend class LJSpeechDataset; friend class ManifestDataset; friend class MindDataDataset; diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py index c10346bc262..6bc9143fdd4 100644 --- a/mindspore/dataset/engine/datasets.py +++ b/mindspore/dataset/engine/datasets.py @@ -3706,6 +3706,127 @@ class ImageFolderDataset(MappableDataset): return cde.ImageFolderNode(self.dataset_dir, self.decode, self.sampler, self.extensions, self.class_indexing) +class KMnistDataset(MappableDataset): + """ + A source dataset for reading and parsing the KMNIST 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 (out of range [0, `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: + >>> kmnist_dataset_dir = "/path/to/kmnist_dataset_directory" + >>> + >>> # Read 3 samples from KMNIST dataset + >>> dataset = ds.KMnistDataset(dataset_dir=kmnist_dataset_dir, num_samples=3) + >>> + >>> # Note: In kmnist_dataset dataset, each dictionary has keys "image" and "label" + + About KMNIST dataset: + + KMNIST is a dataset, adapted from Kuzushiji Dataset, as a drop-in replacement for MNIST dataset, + which is the most famous dataset in the machine learning community. + + Here is the original KMNIST dataset structure. + You can unzip the dataset files into this directory structure and read by MindSpore's API. + + .. code-block:: + + . + └── kmnist_dataset_dir + ├── t10k-images-idx3-ubyte + ├── t10k-labels-idx1-ubyte + ├── train-images-idx3-ubyte + └── train-labels-idx1-ubyte + + Citation: + + .. code-block:: + + @online{clanuwat2018deep, + author = {Tarin Clanuwat and Mikel Bober-Irizar and Asanobu Kitamoto and + Alex Lamb and Kazuaki Yamamoto and David Ha}, + title = {Deep Learning for Classical Japanese Literature}, + date = {2018-12-03}, + year = {2018}, + eprintclass = {cs.CV}, + eprinttype = {arXiv}, + eprint = {cs.CV/1812.01718}, + } + """ + + @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.KMnistNode(self.dataset_dir, self.usage, self.sampler) + + class MnistDataset(MappableDataset): """ A source dataset for reading and parsing the MNIST dataset. diff --git a/tests/ut/cpp/dataset/CMakeLists.txt b/tests/ut/cpp/dataset/CMakeLists.txt index 9ba0127933d..966a4b61ec4 100644 --- a/tests/ut/cpp/dataset/CMakeLists.txt +++ b/tests/ut/cpp/dataset/CMakeLists.txt @@ -29,6 +29,7 @@ SET(DE_UT_SRCS c_api_dataset_fashion_mnist_test.cc c_api_dataset_flickr_test.cc c_api_dataset_iterator_test.cc + c_api_dataset_kmnist_test.cc c_api_dataset_lj_speech_test.cc c_api_dataset_manifest_test.cc c_api_dataset_minddata_test.cc diff --git a/tests/ut/cpp/dataset/c_api_dataset_kmnist_test.cc b/tests/ut/cpp/dataset/c_api_dataset_kmnist_test.cc new file mode 100644 index 00000000000..d2e70e0daeb --- /dev/null +++ b/tests/ut/cpp/dataset/c_api_dataset_kmnist_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: KMnistTestDataset. +/// Description: test basic usage of KMnistTestDataset. +/// Expectation: get correct data. +TEST_F(MindDataTestPipeline, TestKMnistTestDataset) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistTestDataset."; + + // Create a KMnist Test Dataset + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + std::shared_ptr ds = KMnist(folder_path, "test", std::make_shared(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 iter = ds->CreateIterator(); + EXPECT_NE(iter, nullptr); + + // Iterate the dataset and get each row + std::unordered_map 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: KMnistTestDatasetWithPipeline. +/// Description: test KMnistTestDataset with pipeline. +/// Expectation: get correct data. +TEST_F(MindDataTestPipeline, TestKMnistTestDatasetWithPipeline) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistTestDatasetWithPipeline."; + + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + + // Create two KMnist Test Dataset + std::shared_ptr ds1 = KMnist(folder_path, "test", std::make_shared(false, 10)); + std::shared_ptr ds2 = KMnist(folder_path, "test", std::make_shared(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 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 iter = ds1->CreateIterator(); + EXPECT_NE(iter, nullptr); + + // Iterate the dataset and get each row + std::unordered_map 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: KMnistIteratorOneColumn. +/// Description: test iterator of KMnistDataset with only the "image" column. +/// Expectation: get correct data. +TEST_F(MindDataTestPipeline, TestKMnistIteratorOneColumn) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistIteratorOneColumn."; + // Create a KMnist Dataset + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + std::shared_ptr ds = KMnist(folder_path, "all", std::make_shared(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 columns = {"image"}; + std::shared_ptr iter = ds->CreateIterator(columns, -1); + EXPECT_NE(iter, nullptr); + + // Iterate the dataset and get each row + std::vector row; + ASSERT_OK(iter->GetNextRow(&row)); + std::vector 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: KMnistTestDatasetSize. +/// Description: test usage of get the size of KMnistTestDataset. +/// Expectation: get correct data. +TEST_F(MindDataTestPipeline, TestGetKMnistTestDatasetSize) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestGetKMnistTestDatasetSize."; + + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + + // Create a KMnist Test Dataset + std::shared_ptr ds = KMnist(folder_path, "test"); + EXPECT_NE(ds, nullptr); + + EXPECT_EQ(ds->GetDatasetSize(), 10000); +} + +/// Feature: KMnistTestDatasetGetters. +/// Description: test DatasetGetters of KMnistTestDataset. +/// Expectation: get correct the value. +TEST_F(MindDataTestPipeline, TestKMnistTestDatasetGetters) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistTestDatasetGetters."; + + // Create a KMnist Test Dataset + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + std::shared_ptr ds = KMnist(folder_path, "test"); + EXPECT_NE(ds, nullptr); + + EXPECT_EQ(ds->GetDatasetSize(), 10000); + std::vector types = ToDETypes(ds->GetOutputTypes()); + std::vector shapes = ToTensorShapeVec(ds->GetOutputShapes()); + std::vector 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: KMnistIteratorWrongColumn. +/// Description: test iterator of KMnistDataset with wrong column. +/// Expectation: get none piece of data. +TEST_F(MindDataTestPipeline, TestKMnistIteratorWrongColumn) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistIteratorOneColumn."; + // Create a KMnist Dataset + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + std::shared_ptr ds = KMnist(folder_path, "all", std::make_shared(false, 4)); + EXPECT_NE(ds, nullptr); + + // Pass wrong column name + std::vector columns = {"digital"}; + std::shared_ptr iter = ds->CreateIterator(columns); + EXPECT_EQ(iter, nullptr); +} + +/// Feature: KMnistDatasetFail. +/// Description: test failure of KMnistDataset. +/// Expectation: get none piece of data. +TEST_F(MindDataTestPipeline, TestKMnistDatasetFail) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistDatasetFail."; + + // Create a KMnist Dataset + std::shared_ptr ds = KMnist("", "train", std::make_shared(false, 10)); + EXPECT_NE(ds, nullptr); + + // Create an iterator over the result of the above dataset + std::shared_ptr iter = ds->CreateIterator(); + // Expect failure: invalid KMnist input + EXPECT_EQ(iter, nullptr); +} + +/// Feature: KMnistDatasetWithInvalidUsageFail. +/// Description: test KMnistDataset with invalid usage. +/// Expectation: get none piece of data. +TEST_F(MindDataTestPipeline, TestKMnistDatasetWithInvalidUsageFail) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistDatasetWithInvalidUsageFail."; + + // Create a KMnist Dataset + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + std::shared_ptr ds = KMnist(folder_path, "validation"); + EXPECT_NE(ds, nullptr); + + // Create an iterator over the result of the above dataset + std::shared_ptr iter = ds->CreateIterator(); + // Expect failure: invalid KMnist input, validation is not a valid usage + EXPECT_EQ(iter, nullptr); +} + +/// Feature: KMnistDatasetWithNullSamplerFail. +/// Description: test KMnistDataset with null sampler. +/// Expectation: get none piece of data. +TEST_F(MindDataTestPipeline, TestKMnistDatasetWithNullSamplerFail) { + MS_LOG(INFO) << "Doing MindDataTestPipeline-TestKMnistUDatasetWithNullSamplerFail."; + + // Create a KMnist Dataset + std::string folder_path = datasets_root_path_ + "/testMnistData/"; + std::shared_ptr ds = KMnist(folder_path, "all", nullptr); + EXPECT_NE(ds, nullptr); + + // Create an iterator over the result of the above dataset + std::shared_ptr iter = ds->CreateIterator(); + // Expect failure: invalid KMnist input, sampler cannot be nullptr + EXPECT_EQ(iter, nullptr); +} diff --git a/tests/ut/python/dataset/test_datasets_kmnist.py b/tests/ut/python/dataset/test_datasets_kmnist.py new file mode 100644 index 00000000000..918cd46acaa --- /dev/null +++ b/tests/ut/python/dataset/test_datasets_kmnist.py @@ -0,0 +1,329 @@ +# 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 KMnist 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_kmnist(path): + """ + Feature: load_kmnist. + Description: load KMnistDataset. + Expectation: get data of KMnistDataset. + """ + 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(os.path.realpath(labels_path), 'rb') as lbpath: + lbpath.read(8) + labels = np.fromfile(lbpath, dtype=np.uint8) + with open(os.path.realpath(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 KMnistDataset. + 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_kmnist_content_check(): + """ + Feature: test_kmnist_content_check. + Description: validate KMnistDataset image readings. + Expectation: get correct value. + """ + logger.info("Test KMnistDataset Op with content check") + data1 = ds.KMnistDataset(DATA_DIR, num_samples=100, shuffle=False) + images, labels = load_kmnist(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_kmnist_basic(): + """ + Feature: test_kmnist_basic. + Description: test basic usage of KMnistDataset. + Expectation: get correct data. + """ + logger.info("Test KMnistDataset Op") + + # case 1: test loading whole dataset + data1 = ds.KMnistDataset(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.KMnistDataset(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.KMnistDataset(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.KMnistDataset(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.KMnistDataset(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.KMnistDataset(DATA_DIR, "train", num_samples=10) + assert data6.get_col_names() == ["image", "label"] + + #case 7: test batch + data7 = ds.KMnistDataset(DATA_DIR, num_samples=200) + data7 = data7.batch(100, drop_remainder=True) + num_iter7 = 0 + for _ in data7.create_dict_iterator(num_epochs=1): + num_iter7 += 1 + assert num_iter7 == 2 + + +def test_kmnist_pk_sampler(): + """ + Feature: test_kmnist_pk_sampler. + Description: test usage of KMnistDataset with PKSampler. + Expectation: get correct data. + """ + logger.info("Test KMnistDataset 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.KMnistDataset(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_kmnist_sequential_sampler(): + """ + Feature: test_kmnist_sequential_sampler. + Description: test usage of KMnistDataset with SequentialSampler. + Expectation: get correct data. + """ + logger.info("Test KMnistDataset Op with SequentialSampler") + num_samples = 50 + sampler = ds.SequentialSampler(num_samples=num_samples) + data1 = ds.KMnistDataset(DATA_DIR, sampler=sampler) + data2 = ds.KMnistDataset(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_kmnist_exception(): + """ + Feature: test_kmnist_exception. + Description: test error cases for KMnistDataset. + Expectation: raise exception. + """ + logger.info("Test error cases for KMnistDataset") + error_msg_1 = "sampler and shuffle cannot be specified at the same time" + with pytest.raises(RuntimeError, match=error_msg_1): + ds.KMnistDataset(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.KMnistDataset(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.KMnistDataset(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.KMnistDataset(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.KMnistDataset(DATA_DIR, num_shards=5, shard_id=-1) + with pytest.raises(ValueError, match=error_msg_5): + ds.KMnistDataset(DATA_DIR, num_shards=5, shard_id=5) + with pytest.raises(ValueError, match=error_msg_5): + ds.KMnistDataset(DATA_DIR, num_shards=2, shard_id=5) + + error_msg_6 = "num_parallel_workers exceeds" + with pytest.raises(ValueError, match=error_msg_6): + ds.KMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=0) + with pytest.raises(ValueError, match=error_msg_6): + ds.KMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=256) + with pytest.raises(ValueError, match=error_msg_6): + ds.KMnistDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2) + + error_msg_7 = "Argument shard_id" + with pytest.raises(TypeError, match=error_msg_7): + ds.KMnistDataset(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.KMnistDataset(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.KMnistDataset(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.KMnistDataset(DATA_DIR) + data = data.map(operations=exception_func, input_columns=["label"], num_parallel_workers=1) + for _ in data.__iter__(): + pass + + +def test_kmnist_visualize(plot=False): + """ + Feature: test_kmnist_visualize. + Description: visualize KMnistDataset results. + Expectation: get correct data and plot them. + """ + logger.info("Test KMnistDataset visualization") + + data1 = ds.KMnistDataset(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_kmnist_usage(): + """ + Feature: test_kmnist_usage. + Description: validate KMnistDataset image readings. + Expectation: get correct data. + """ + logger.info("Test KMnistDataset usage flag") + + def test_config(usage, kmnist_path=None): + kmnist_path = DATA_DIR if kmnist_path is None else kmnist_path + try: + data = ds.KMnistDataset(kmnist_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 "KMnistDataset 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 []" in test_config(["list"]) + + # change this directory to the folder that contains all kmnist 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.KMnistDataset(all_files_path, usage="train").get_dataset_size() == 60000 + assert ds.KMnistDataset(all_files_path, usage="test").get_dataset_size() == 10000 + assert ds.KMnistDataset(all_files_path, usage="all").get_dataset_size() == 70000 + + +if __name__ == '__main__': + test_kmnist_content_check() + test_kmnist_basic() + test_kmnist_pk_sampler() + test_kmnist_sequential_sampler() + test_kmnist_exception() + test_kmnist_visualize(plot=True) + test_kmnist_usage()