forked from mindspore-Ecosystem/mindspore
[feat][assistant][I3J6V2] add new data operator EMnist
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2c1d3baace
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@ -96,6 +96,7 @@
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#include "minddata/dataset/engine/ir/datasetops/source/coco_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/csv_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/div2k_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/emnist_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/flickr_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/image_folder_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/random_node.h"
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@ -1042,6 +1043,33 @@ DIV2KDataset::DIV2KDataset(const std::vector<char> &dataset_dir, const std::vect
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ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
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}
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EMnistDataset::EMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &name,
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const std::vector<char> &usage, const std::shared_ptr<Sampler> &sampler,
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const std::shared_ptr<DatasetCache> &cache) {
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auto sampler_obj = sampler ? sampler->Parse() : nullptr;
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auto ds = std::make_shared<EMnistNode>(CharToString(dataset_dir), CharToString(name), CharToString(usage),
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sampler_obj, cache);
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ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
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}
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EMnistDataset::EMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &name,
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const std::vector<char> &usage, const Sampler *sampler,
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const std::shared_ptr<DatasetCache> &cache) {
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auto sampler_obj = sampler ? sampler->Parse() : nullptr;
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auto ds = std::make_shared<EMnistNode>(CharToString(dataset_dir), CharToString(name), CharToString(usage),
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sampler_obj, cache);
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ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
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}
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EMnistDataset::EMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &name,
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const std::vector<char> &usage, const std::reference_wrapper<Sampler> sampler,
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const std::shared_ptr<DatasetCache> &cache) {
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auto sampler_obj = sampler.get().Parse();
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auto ds = std::make_shared<EMnistNode>(CharToString(dataset_dir), CharToString(name), CharToString(usage),
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sampler_obj, cache);
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ir_node_ = std::static_pointer_cast<DatasetNode>(ds);
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}
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FlickrDataset::FlickrDataset(const std::vector<char> &dataset_dir, const std::vector<char> &annotation_file,
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bool decode, const std::shared_ptr<Sampler> &sampler,
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const std::shared_ptr<DatasetCache> &cache) {
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@ -33,6 +33,7 @@
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#include "minddata/dataset/engine/ir/datasetops/source/coco_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/csv_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/div2k_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/emnist_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/flickr_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/generator_node.h"
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#include "minddata/dataset/engine/ir/datasetops/source/image_folder_node.h"
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@ -152,6 +153,17 @@ PYBIND_REGISTER(DIV2KNode, 2, ([](const py::module *m) {
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}));
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}));
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PYBIND_REGISTER(EMnistNode, 2, ([](const py::module *m) {
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(void)py::class_<EMnistNode, DatasetNode, std::shared_ptr<EMnistNode>>(*m, "EMnistNode",
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"to create an EMnistNode")
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.def(py::init([](std::string dataset_dir, std::string name, std::string usage, py::handle sampler) {
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auto emnist =
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std::make_shared<EMnistNode>(dataset_dir, name, usage, toSamplerObj(sampler), nullptr);
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THROW_IF_ERROR(emnist->ValidateParams());
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return emnist;
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}));
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}));
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PYBIND_REGISTER(
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FlickrNode, 2, ([](const py::module *m) {
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(void)py::class_<FlickrNode, DatasetNode, std::shared_ptr<FlickrNode>>(*m, "FlickrNode", "to create a FlickrNode")
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@ -22,6 +22,7 @@ set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
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div2k_op.cc
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flickr_op.cc
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qmnist_op.cc
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emnist_op.cc
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)
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set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
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@ -0,0 +1,146 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "minddata/dataset/engine/datasetops/source/emnist_op.h"
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#include <algorithm>
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#include <fstream>
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#include <iomanip>
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#include <set>
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#include <utility>
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#include "debug/common.h"
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#include "minddata/dataset/core/config_manager.h"
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#include "minddata/dataset/core/tensor_shape.h"
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#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
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#include "minddata/dataset/engine/execution_tree.h"
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#include "utils/file_utils.h"
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#include "utils/ms_utils.h"
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namespace mindspore {
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namespace dataset {
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EMnistOp::EMnistOp(const std::string &name, const std::string &usage, int32_t num_workers,
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const std::string &folder_path, int32_t queue_size, std::unique_ptr<DataSchema> data_schema,
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std::shared_ptr<SamplerRT> sampler)
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: MnistOp(usage, num_workers, folder_path, queue_size, std::move(data_schema), std::move(sampler)), name_(name) {}
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void EMnistOp::Print(std::ostream &out, bool show_all) const {
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if (!show_all) {
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// Call the super class for displaying any common 1-liner info.
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ParallelOp::Print(out, show_all);
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// Then show any custom derived-internal 1-liner info for this op.
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out << "\n";
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} else {
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// Call the super class for displaying any common detailed info.
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ParallelOp::Print(out, show_all);
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// Then show any custom derived-internal stuff.
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out << "\nNumber of rows:" << num_rows_ << "\n"
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<< DatasetName(true) << " directory: " << folder_path_ << "\nName: " << name_ << "\nUsage: " << usage_
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<< "\n\n";
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}
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}
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Status EMnistOp::WalkAllFiles() {
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const std::string img_ext = "-images-idx3-ubyte";
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const std::string lbl_ext = "-labels-idx1-ubyte";
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const std::string train_prefix = "-train";
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const std::string test_prefix = "-test";
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auto realpath = FileUtils::GetRealPath(folder_path_.data());
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CHECK_FAIL_RETURN_UNEXPECTED(realpath.has_value(), "Get real path failed: " + folder_path_);
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Path dir(realpath.value());
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auto dir_it = Path::DirIterator::OpenDirectory(&dir);
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if (dir_it == nullptr) {
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RETURN_STATUS_UNEXPECTED("Invalid path, failed to open directory: " + dir.ToString());
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}
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std::string prefix;
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prefix = "emnist-" + name_; // used to match usage == "all".
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if (usage_ == "train" || usage_ == "test") {
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prefix += (usage_ == "test" ? test_prefix : train_prefix);
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}
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if (dir_it != nullptr) {
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while (dir_it->HasNext()) {
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Path file = dir_it->Next();
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std::string fname = file.Basename(); // name of the emnist file.
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if ((fname.find(prefix) != std::string::npos) && (fname.find(img_ext) != std::string::npos)) {
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image_names_.push_back(file.ToString());
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MS_LOG(INFO) << DatasetName(true) << " operator found image file at " << fname << ".";
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} else if ((fname.find(prefix) != std::string::npos) && (fname.find(lbl_ext) != std::string::npos)) {
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label_names_.push_back(file.ToString());
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MS_LOG(INFO) << DatasetName(true) << " operator found label file at " << fname << ".";
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}
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}
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} else {
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MS_LOG(WARNING) << DatasetName(true) << " operator unable to open directory " << dir.ToString() << ".";
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}
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std::sort(image_names_.begin(), image_names_.end());
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std::sort(label_names_.begin(), label_names_.end());
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CHECK_FAIL_RETURN_UNEXPECTED(image_names_.size() == label_names_.size(),
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"Invalid data, num of images does not equal to num of labels.");
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return Status::OK();
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}
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Status EMnistOp::CountTotalRows(const std::string &dir, const std::string &name, const std::string &usage,
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int64_t *count) {
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// the logic of counting the number of samples is copied from ParseEMnistData() and uses CheckReader().
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RETURN_UNEXPECTED_IF_NULL(count);
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*count = 0;
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const int64_t num_samples = 0;
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const int64_t start_index = 0;
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auto sampler = std::make_shared<SequentialSamplerRT>(start_index, num_samples);
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auto schema = std::make_unique<DataSchema>();
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RETURN_IF_NOT_OK(schema->AddColumn(ColDescriptor("image", DataType(DataType::DE_UINT8), TensorImpl::kCv, 1)));
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TensorShape scalar = TensorShape::CreateScalar();
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RETURN_IF_NOT_OK(
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schema->AddColumn(ColDescriptor("label", DataType(DataType::DE_UINT32), TensorImpl::kFlexible, 0, &scalar)));
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std::shared_ptr<ConfigManager> cfg = GlobalContext::config_manager();
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int32_t num_workers = cfg->num_parallel_workers();
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int32_t op_connect_size = cfg->op_connector_size();
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auto op =
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std::make_shared<EMnistOp>(name, usage, num_workers, dir, op_connect_size, std::move(schema), std::move(sampler));
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RETURN_IF_NOT_OK(op->WalkAllFiles());
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for (size_t i = 0; i < op->image_names_.size(); ++i) {
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std::ifstream image_reader;
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image_reader.open(op->image_names_[i], std::ios::binary);
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CHECK_FAIL_RETURN_UNEXPECTED(image_reader.is_open(),
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"Invalid file, failed to open image file: " + op->image_names_[i]);
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std::ifstream label_reader;
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label_reader.open(op->label_names_[i], std::ios::binary);
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CHECK_FAIL_RETURN_UNEXPECTED(label_reader.is_open(),
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"Invalid file, failed to open label file: " + op->label_names_[i]);
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uint32_t num_images;
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Status s = op->CheckImage(op->image_names_[i], &image_reader, &num_images);
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image_reader.close();
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RETURN_IF_NOT_OK(s);
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uint32_t num_labels;
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s = op->CheckLabel(op->label_names_[i], &label_reader, &num_labels);
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label_reader.close();
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RETURN_IF_NOT_OK(s);
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CHECK_FAIL_RETURN_UNEXPECTED((num_images == num_labels),
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"Invalid data, num of images is not equal to num of labels.");
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*count = *count + num_images;
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}
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return Status::OK();
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}
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} // namespace dataset
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} // namespace mindspore
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@ -0,0 +1,84 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_EMNIST_OP_H_
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_EMNIST_OP_H_
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#include <algorithm>
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#include <map>
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "minddata/dataset/engine/datasetops/source/mnist_op.h"
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namespace mindspore {
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namespace dataset {
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// Forward declares
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template <typename T>
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class Queue;
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class EMnistOp : public MnistOp {
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public:
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// Constructor.
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// @param const std::string &name - Class of this dataset, can be
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// "byclass","bymerge","balanced","letters","digits","mnist".
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// @param const std::string &usage - Usage of this dataset, can be 'train', 'test' or 'all'.
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// @param int32_t num_workers - Number of workers reading images in parallel.
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// @param const std::string &folder_path - Dir directory of emnist.
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// @param int32_t queue_size - Connector queue size.
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// @param std::unique_ptr<DataSchema> data_schema - The schema of the Emnist dataset.
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// @param std::shared_ptr<SamplerRT> sampler - Sampler tells EMnistOp what to read.
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EMnistOp(const std::string &name, const std::string &usage, int32_t num_workers, const std::string &folder_path,
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int32_t queue_size, std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler);
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// Destructor.
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~EMnistOp() = default;
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// A print method typically used for debugging.
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// @param std::ostream &out - Out stream.
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// @param bool show_all - Whether to show all information.
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void Print(std::ostream &out, bool show_all) const override;
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// Function to count the number of samples in the EMNIST dataset.
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// @param const std::string &dir - Path to the EMNIST directory.
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// @param const std::string &name - Class of this dataset, can be
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// "byclass","bymerge","balanced","letters","digits","mnist".
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// @param const std::string &usage - Usage of this dataset, can be 'train', 'test' or 'all'.
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// @param int64_t *count - Output arg that will hold the minimum of the actual dataset size and numSamples.
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// @return Status The status code returned.
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static Status CountTotalRows(const std::string &dir, const std::string &name, const std::string &usage,
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int64_t *count);
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// Op name getter.
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// @return Name of the current Op.
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std::string Name() const override { return "EMnistOp"; }
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// DatasetName name getter.
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// \return DatasetName of the current Op.
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std::string DatasetName(bool upper = false) const override { return upper ? "EMnist" : "emnist"; }
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private:
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// Read all files in the directory.
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// @return Status The status code returned.
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Status WalkAllFiles() override;
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const std::string name_; // can be "byclass", "bymerge", "balanced", "letters", "digits", "mnist".
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};
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} // namespace dataset
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_EMNIST_OP_H_
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@ -83,6 +83,7 @@ constexpr char kCLUENode[] = "CLUEDataset";
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constexpr char kCocoNode[] = "CocoDataset";
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constexpr char kCSVNode[] = "CSVDataset";
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constexpr char kDIV2KNode[] = "DIV2KDataset";
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constexpr char kEMnistNode[] = "EMnistDataset";
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constexpr char kFlickrNode[] = "FlickrDataset";
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constexpr char kGeneratorNode[] = "GeneratorDataset";
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constexpr char kImageFolderNode[] = "ImageFolderDataset";
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@ -12,6 +12,7 @@ set(DATASET_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES
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coco_node.cc
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csv_node.cc
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div2k_node.cc
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emnist_node.cc
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flickr_node.cc
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image_folder_node.cc
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manifest_node.cc
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@ -33,4 +34,4 @@ if(ENABLE_PYTHON)
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)
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endif()
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add_library(engine-ir-datasetops-source OBJECT ${DATASET_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES})
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add_library(engine-ir-datasetops-source OBJECT ${DATASET_ENGINE_IR_DATASETOPS_SOURCE_SRC_FILES})
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@ -0,0 +1,121 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "minddata/dataset/engine/ir/datasetops/source/emnist_node.h"
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "minddata/dataset/engine/datasetops/source/emnist_op.h"
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#include "minddata/dataset/util/status.h"
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namespace mindspore {
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namespace dataset {
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EMnistNode::EMnistNode(const std::string &dataset_dir, const std::string &name, const std::string &usage,
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std::shared_ptr<SamplerObj> sampler, std::shared_ptr<DatasetCache> cache)
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: MappableSourceNode(std::move(cache)), dataset_dir_(dataset_dir), name_(name), usage_(usage), sampler_(sampler) {}
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std::shared_ptr<DatasetNode> EMnistNode::Copy() {
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std::shared_ptr<SamplerObj> sampler = (sampler_ == nullptr) ? nullptr : sampler_->SamplerCopy();
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auto node = std::make_shared<EMnistNode>(dataset_dir_, name_, usage_, sampler, cache_);
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return node;
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}
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void EMnistNode::Print(std::ostream &out) const {
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out << (Name() + "(cache: " + ((cache_ != nullptr) ? "true" : "false") + ")");
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}
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Status EMnistNode::ValidateParams() {
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RETURN_IF_NOT_OK(DatasetNode::ValidateParams());
|
||||
RETURN_IF_NOT_OK(ValidateDatasetDirParam("EMnistNode", dataset_dir_));
|
||||
|
||||
RETURN_IF_NOT_OK(ValidateDatasetSampler("EMnistNode", sampler_));
|
||||
|
||||
RETURN_IF_NOT_OK(ValidateStringValue("EMnistNode", usage_, {"train", "test", "all"}));
|
||||
|
||||
RETURN_IF_NOT_OK(
|
||||
ValidateStringValue("EMnistNode", name_, {"byclass", "bymerge", "balanced", "letters", "digits", "mnist"}));
|
||||
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
Status EMnistNode::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<EMnistOp>(name_, 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 EMnistNode::GetShardId(int32_t *shard_id) {
|
||||
*shard_id = sampler_->ShardId();
|
||||
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
// Get Dataset size.
|
||||
Status EMnistNode::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(EMnistOp::CountTotalRows(dataset_dir_, name_, 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 EMnistNode::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["name"] = name_;
|
||||
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
|
|
@ -0,0 +1,111 @@
|
|||
/**
|
||||
* 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_EMNIST_NODE_H_
|
||||
#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_EMNIST_NODE_H_
|
||||
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include "minddata/dataset/engine/ir/datasetops/dataset_node.h"
|
||||
|
||||
namespace mindspore {
|
||||
namespace dataset {
|
||||
class EMnistNode : public MappableSourceNode {
|
||||
public:
|
||||
/// \brief Constructor.
|
||||
/// \param[in] dataset_dir Dataset directory of emnist.
|
||||
/// \param[in] name Class of this dataset, can be "byclass", "bymerge", "balanced", "letters", "digits", "mnist".
|
||||
/// \param[in] usage Usage of this dataset, can be 'train', 'test' or 'all'.
|
||||
/// \param[in] sampler Tells EMnistOp what to read.
|
||||
/// \param[in] cache Tensor cache to use.
|
||||
EMnistNode(const std::string &dataset_dir, const std::string &name, const std::string &usage,
|
||||
std::shared_ptr<SamplerObj> sampler, std::shared_ptr<DatasetCache> cache);
|
||||
|
||||
/// \brief Destructor.
|
||||
~EMnistNode() = default;
|
||||
|
||||
/// \brief Node name getter.
|
||||
/// \return Name of the current node.
|
||||
std::string Name() const override { return "EMnistNode"; }
|
||||
|
||||
/// \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[in] node_ops A vector containing shared pointer to the Dataset Ops that this object will create.
|
||||
/// \return Status Status::OK() if build successfully.
|
||||
Status Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops) override;
|
||||
|
||||
/// \brief Parameters validation.
|
||||
/// \return Status Status::OK() if all the parameters are valid.
|
||||
Status ValidateParams() override;
|
||||
|
||||
/// \brief Get the shard id of node.
|
||||
/// \param[in] shard_id The shard id.
|
||||
/// \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.
|
||||
/// \return Dataset direction.
|
||||
const std::string &DatasetDir() const { return dataset_dir_; }
|
||||
|
||||
/// \brief Getter functions.
|
||||
/// \return Usage.
|
||||
const std::string &Usage() const { return usage_; }
|
||||
|
||||
/// \brief Getter functions.
|
||||
/// \return Name.
|
||||
const std::string &GetName() const { return name_; }
|
||||
|
||||
/// \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.
|
||||
/// \param[in] sampler Tells EMnistOp what to read.
|
||||
void SetSampler(std::shared_ptr<SamplerObj> sampler) override { sampler_ = sampler; }
|
||||
|
||||
private:
|
||||
std::string dataset_dir_;
|
||||
std::string name_;
|
||||
std::string usage_;
|
||||
std::shared_ptr<SamplerObj> sampler_;
|
||||
};
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_IR_DATASETOPS_SOURCE_EMNIST_NODE_H_
|
|
@ -1628,6 +1628,93 @@ inline std::shared_ptr<DIV2KDataset> DIV2K(const std::string &dataset_dir, const
|
|||
decode, sampler, cache);
|
||||
}
|
||||
|
||||
/// \class EMnistDataset
|
||||
/// \brief A source dataset for reading and parsing EMnist dataset.
|
||||
class EMnistDataset : public Dataset {
|
||||
public:
|
||||
/// \brief Constructor of EMnistDataset.
|
||||
/// \param[in] dataset_dir Path to the root directory that contains the dataset.
|
||||
/// \param[in] name Name of splits for EMNIST, can be "byclass", "bymerge", "balanced", "letters", "digits"
|
||||
/// or "mnist".
|
||||
/// \param[in] usage Part of dataset of EMNIST, 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 EMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &name,
|
||||
const std::vector<char> &usage, const std::shared_ptr<Sampler> &sampler,
|
||||
const std::shared_ptr<DatasetCache> &cache);
|
||||
|
||||
/// \brief Constructor of EMnistDataset.
|
||||
/// \param[in] dataset_dir Path to the root directory that contains the dataset.
|
||||
/// \param[in] name Name of splits for EMNIST, can be "byclass", "bymerge", "balanced", "letters", "digits"
|
||||
/// or "mnist".
|
||||
/// \param[in] usage Part of dataset of EMNIST, 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 EMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &name,
|
||||
const std::vector<char> &usage, const Sampler *sampler,
|
||||
const std::shared_ptr<DatasetCache> &cache);
|
||||
|
||||
/// \brief Constructor of EMnistDataset.
|
||||
/// \param[in] dataset_dir Path to the root directory that contains the dataset.
|
||||
/// \param[in] name Name of splits for EMNIST, can be "byclass", "bymerge", "balanced", "letters", "digits"
|
||||
/// or "mnist".
|
||||
/// \param[in] usage Part of dataset of EMNIST, 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 EMnistDataset(const std::vector<char> &dataset_dir, const std::vector<char> &name,
|
||||
const std::vector<char> &usage, const std::reference_wrapper<Sampler> sampler,
|
||||
const std::shared_ptr<DatasetCache> &cache);
|
||||
~EMnistDataset() = default;
|
||||
};
|
||||
|
||||
/// \brief Function to create a EMnistDataset.
|
||||
/// \notes The generated dataset has two columns ["image", "label"].
|
||||
/// \param[in] dataset_dir Path to the root directory that contains the dataset.
|
||||
/// \param[in] name Name of splits for EMNIST, can be "byclass", "bymerge", "balanced", "letters", "digits" or "mnist".
|
||||
/// \param[in] usage Usage of EMNIST, 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 EMnistDataset.
|
||||
inline std::shared_ptr<EMnistDataset> EMnist(
|
||||
const std::string &dataset_dir, const std::string &name, 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<EMnistDataset>(StringToChar(dataset_dir), StringToChar(name), StringToChar(usage), sampler,
|
||||
cache);
|
||||
}
|
||||
|
||||
/// \brief Function to create a EMnistDataset.
|
||||
/// \notes The generated dataset has two columns ["image", "label"].
|
||||
/// \param[in] dataset_dir Path to the root directory that contains the dataset
|
||||
/// \param[in] name Name of splits for EMNIST, can be "byclass", "bymerge", "balanced", "letters", "digits" or "mnist".
|
||||
/// \param[in] usage Usage of EMNIST, 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 EMnistDataset.
|
||||
inline std::shared_ptr<EMnistDataset> EMnist(const std::string &dataset_dir, const std::string &usage,
|
||||
const std::string &name, const Sampler *sampler,
|
||||
const std::shared_ptr<DatasetCache> &cache = nullptr) {
|
||||
return std::make_shared<EMnistDataset>(StringToChar(dataset_dir), StringToChar(name), StringToChar(usage), sampler,
|
||||
cache);
|
||||
}
|
||||
|
||||
/// \brief Function to create a EMnistDataset.
|
||||
/// \notes The generated dataset has two columns ["image", "label"].
|
||||
/// \param[in] dataset_dir Path to the root directory that contains the dataset.
|
||||
/// \param[in] name Name of splits for EMNIST, can be "byclass", "bymerge", "balanced", "letters", "digits" or "mnist".
|
||||
/// \param[in] usage Usage of EMNIST, 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 EMnistDataset.
|
||||
inline std::shared_ptr<EMnistDataset> EMnist(const std::string &dataset_dir, const std::string &name,
|
||||
const std::string &usage, const std::reference_wrapper<Sampler> sampler,
|
||||
const std::shared_ptr<DatasetCache> &cache = nullptr) {
|
||||
return std::make_shared<EMnistDataset>(StringToChar(dataset_dir), StringToChar(name), StringToChar(usage), sampler,
|
||||
cache);
|
||||
}
|
||||
|
||||
/// \class FlickrDataset
|
||||
/// \brief A source dataset for reading and parsing Flickr dataset.
|
||||
class FlickrDataset : public Dataset {
|
||||
|
|
|
@ -39,6 +39,7 @@ class Sampler : std::enable_shared_from_this<Sampler> {
|
|||
friend class CocoDataset;
|
||||
friend class CSVDataset;
|
||||
friend class DIV2KDataset;
|
||||
friend class EMnistDataset;
|
||||
friend class FlickrDataset;
|
||||
friend class ImageFolderDataset;
|
||||
friend class ManifestDataset;
|
||||
|
|
|
@ -66,7 +66,7 @@ from .validators import check_batch, check_shuffle, check_map, check_filter, che
|
|||
check_bucket_batch_by_length, check_cluedataset, check_save, check_csvdataset, check_paddeddataset, \
|
||||
check_tuple_iterator, check_dict_iterator, check_schema, check_to_device_send, check_flickr_dataset, \
|
||||
check_sb_dataset, check_flowers102dataset, check_cityscapes_dataset, check_usps_dataset, check_div2k_dataset, \
|
||||
check_sbu_dataset, check_qmnist_dataset
|
||||
check_sbu_dataset, check_qmnist_dataset, check_emnist_dataset
|
||||
from ..core.config import get_callback_timeout, _init_device_info, get_enable_shared_mem, get_num_parallel_workers, \
|
||||
get_prefetch_size
|
||||
from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
|
||||
|
@ -6350,6 +6350,138 @@ class PaddedDataset(GeneratorDataset):
|
|||
self.padded_samples = padded_samples
|
||||
|
||||
|
||||
class EMnistDataset(MappableDataset):
|
||||
"""
|
||||
A source dataset for reading and parsing the EMNIST 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.
|
||||
name (str): Name of splits for this dataset, can be "byclass", "bymerge", "balanced", "letters", "digits"
|
||||
or "mnist".
|
||||
usage (str, optional): Usage of this dataset, can be "train", "test" or "all".
|
||||
(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 max 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 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:
|
||||
>>> emnist_dataset_dir = "/path/to/emnist_dataset_directory"
|
||||
>>>
|
||||
>>> # Read 3 samples from EMNIST dataset
|
||||
>>> dataset = ds.EMnistDataset(dataset_dir=emnist_dataset_dir, name="mnist", num_samples=3)
|
||||
>>>
|
||||
>>> # Note: In emnist_dataset dataset, each dictionary has keys "image" and "label"
|
||||
|
||||
About EMNIST dataset:
|
||||
|
||||
The EMNIST dataset is a set of handwritten character digits derived from the NIST Special
|
||||
Database 19 and converted to a 28x28 pixel image format and dataset structure that directly
|
||||
matches the MNIST dataset. Further information on the dataset contents and conversion process
|
||||
can be found in the paper available at https://arxiv.org/abs/1702.05373v1.
|
||||
|
||||
The numbers of characters and classes of each split of EMNIST are as follows:
|
||||
|
||||
By Class: 814,255 characters and 62 unbalanced classes.
|
||||
By Merge: 814,255 characters and 47 unbalanced classes.
|
||||
Balanced: 131,600 characters and 47 balanced classes.
|
||||
Letters: 145,600 characters and 26 balanced classes.
|
||||
Digits: 280,000 characters and 10 balanced classes.
|
||||
MNIST: 70,000 characters and 10 balanced classes.
|
||||
|
||||
Here is the original EMNIST dataset structure.
|
||||
You can unzip the dataset files into this directory structure and read by MindSpore's API.
|
||||
|
||||
.. code-block::
|
||||
|
||||
.
|
||||
└── mnist_dataset_dir
|
||||
├── emnist-mnist-train-images-idx3-ubyte
|
||||
├── emnist-mnist-train-labels-idx1-ubyte
|
||||
├── emnist-mnist-test-images-idx3-ubyte
|
||||
├── emnist-mnist-test-labels-idx1-ubyte
|
||||
├── ...
|
||||
|
||||
Citation:
|
||||
|
||||
.. code-block::
|
||||
|
||||
@article{cohen_afshar_tapson_schaik_2017,
|
||||
title = {EMNIST: Extending MNIST to handwritten letters},
|
||||
DOI = {10.1109/ijcnn.2017.7966217},
|
||||
journal = {2017 International Joint Conference on Neural Networks (IJCNN)},
|
||||
author = {Cohen, Gregory and Afshar, Saeed and Tapson, Jonathan and Schaik, Andre Van},
|
||||
year = {2017},
|
||||
howpublished = {https://www.westernsydney.edu.au/icns/reproducible_research/
|
||||
publication_support_materials/emnist}
|
||||
}
|
||||
"""
|
||||
|
||||
@check_emnist_dataset
|
||||
def __init__(self, dataset_dir, name, 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.name = name
|
||||
self.usage = replace_none(usage, "all")
|
||||
|
||||
def parse(self, children=None):
|
||||
return cde.EMnistNode(self.dataset_dir, self.name, self.usage, self.sampler)
|
||||
|
||||
|
||||
class FlickrDataset(MappableDataset):
|
||||
"""
|
||||
A source dataset for reading and parsing Flickr8k and Flickr30k dataset.
|
||||
|
|
|
@ -1,4 +1,4 @@
|
|||
# Copyright 2019 Huawei Technologies Co., Ltd
|
||||
# Copyright 2019-2021 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.
|
||||
|
@ -1463,6 +1463,39 @@ def check_to_device_send(method):
|
|||
return new_method
|
||||
|
||||
|
||||
def check_emnist_dataset(method):
|
||||
"""A wrapper that wraps a parameter checker emnist dataset"""
|
||||
|
||||
@wraps(method)
|
||||
def new_method(self, *args, **kwargs):
|
||||
_, param_dict = parse_user_args(method, *args, **kwargs)
|
||||
|
||||
nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
|
||||
nreq_param_bool = ['shuffle']
|
||||
|
||||
validate_dataset_param_value(nreq_param_int, param_dict, int)
|
||||
validate_dataset_param_value(nreq_param_bool, param_dict, bool)
|
||||
|
||||
dataset_dir = param_dict.get('dataset_dir')
|
||||
check_dir(dataset_dir)
|
||||
|
||||
name = param_dict.get('name')
|
||||
check_valid_str(name, ["byclass", "bymerge", "balanced", "letters", "digits", "mnist"], "name")
|
||||
|
||||
usage = param_dict.get('usage')
|
||||
if usage is not None:
|
||||
check_valid_str(usage, ["train", "test", "all"], "usage")
|
||||
|
||||
check_sampler_shuffle_shard_options(param_dict)
|
||||
|
||||
cache = param_dict.get('cache')
|
||||
check_cache_option(cache)
|
||||
|
||||
return method(self, *args, **kwargs)
|
||||
|
||||
return new_method
|
||||
|
||||
|
||||
def check_flickr_dataset(method):
|
||||
"""A wrapper that wraps a parameter checker around the original Dataset(Flickr8k, Flickr30k)."""
|
||||
|
||||
|
|
|
@ -23,6 +23,7 @@ SET(DE_UT_SRCS
|
|||
c_api_dataset_config_test.cc
|
||||
c_api_dataset_csv_test.cc
|
||||
c_api_dataset_div2k_test.cc
|
||||
c_api_dataset_emnist_test.cc
|
||||
c_api_dataset_flickr_test.cc
|
||||
c_api_dataset_iterator_test.cc
|
||||
c_api_dataset_manifest_test.cc
|
||||
|
|
|
@ -0,0 +1,368 @@
|
|||
/**
|
||||
* 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:
|
||||
};
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistTrainDataset) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistTrainDataset.";
|
||||
|
||||
// Create a EMnist Train Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "train", std::make_shared<RandomSampler>(false, 5));
|
||||
|
||||
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, 5);
|
||||
// Manually terminate the pipeline
|
||||
iter->Stop();
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistTestDataset) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistTestDataset.";
|
||||
|
||||
// Create a EMNIST Test Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "train", std::make_shared<RandomSampler>(false, 5));
|
||||
|
||||
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, 5);
|
||||
|
||||
// Manually terminate the pipeline
|
||||
iter->Stop();
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistTrainDatasetWithPipeline) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistTrainDatasetWithPipeline.";
|
||||
|
||||
// Create two Emnist Train Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
|
||||
std::shared_ptr<Dataset> ds1 = EMnist(folder_path, "mnist", "train", std::make_shared<RandomSampler>(false, 5));
|
||||
std::shared_ptr<Dataset> ds2 = EMnist(folder_path, "byclass", "train", std::make_shared<RandomSampler>(false, 5));
|
||||
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, 10);
|
||||
|
||||
// Manually terminate the pipeline
|
||||
iter->Stop();
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistTestDatasetWithPipeline) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistTestDatasetWithPipeline.";
|
||||
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
|
||||
// Create two EMnist Test Dataset
|
||||
std::shared_ptr<Dataset> ds1 = EMnist(folder_path, "mnist", "test", std::make_shared<RandomSampler>(false, 5));
|
||||
std::shared_ptr<Dataset> ds2 = EMnist(folder_path, "mnist", "test", std::make_shared<RandomSampler>(false, 5));
|
||||
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, 10);
|
||||
|
||||
// Manually terminate the pipeline
|
||||
iter->Stop();
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestGetEMnistTrainDatasetSize) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestGetEMnistTrainDatasetSize.";
|
||||
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
// Create a EMnist Train Dataset
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "train");
|
||||
EXPECT_NE(ds, nullptr);
|
||||
|
||||
EXPECT_EQ(ds->GetDatasetSize(), 10);
|
||||
|
||||
std::shared_ptr<Dataset> ds2 = EMnist(folder_path, "byclass", "train");
|
||||
EXPECT_NE(ds2, nullptr);
|
||||
|
||||
EXPECT_EQ(ds2->GetDatasetSize(), 10);
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestGetEMnistTestDatasetSize) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestGetEMnistTestDatasetSize.";
|
||||
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
|
||||
// Create a EMnist Test Dataset
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "test");
|
||||
EXPECT_NE(ds, nullptr);
|
||||
|
||||
EXPECT_EQ(ds->GetDatasetSize(), 10);
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistTrainDatasetGetters) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistTrainDatasetGetters.";
|
||||
|
||||
// Create a EMnist Train Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "train");
|
||||
EXPECT_NE(ds, nullptr);
|
||||
|
||||
EXPECT_EQ(ds->GetDatasetSize(), 10);
|
||||
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(), 10);
|
||||
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(), 10);
|
||||
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(), 10);
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistTestDatasetGetters) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistTestDatasetGetters.";
|
||||
|
||||
// Create a EMnist Test Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "test");
|
||||
EXPECT_NE(ds, nullptr);
|
||||
|
||||
EXPECT_EQ(ds->GetDatasetSize(), 10);
|
||||
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(), 10);
|
||||
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(), 10);
|
||||
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(), 10);
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistDatasetWithInvalidDir) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistDatasetWithInvalidDir.";
|
||||
|
||||
// Create a EMnist Dataset
|
||||
std::shared_ptr<Dataset> ds = EMnist("", "mnist", "train", std::make_shared<RandomSampler>(false, 5));
|
||||
EXPECT_NE(ds, nullptr);
|
||||
|
||||
// Create an iterator over the result of the above dataset
|
||||
std::shared_ptr<Iterator> iter = ds->CreateIterator();
|
||||
// Expect failure: invalid EMnist input
|
||||
EXPECT_EQ(iter, nullptr);
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistDatasetWithInvalidUsage) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistDatasetWithInvalidUsage.";
|
||||
|
||||
// Create a EMnist Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "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 EMnist input, validation is not a valid usage
|
||||
EXPECT_EQ(iter, nullptr);
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistDatasetWithInvalidName) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistDatasetWithInvalidName.";
|
||||
|
||||
// Create a EMnist Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "validation", "train");
|
||||
EXPECT_NE(ds, nullptr);
|
||||
|
||||
// Create an iterator over the result of the above dataset
|
||||
std::shared_ptr<Iterator> iter = ds->CreateIterator();
|
||||
// Expect failure: invalid EMnist input, validation is not a valid name
|
||||
EXPECT_EQ(iter, nullptr);
|
||||
}
|
||||
|
||||
TEST_F(MindDataTestPipeline, TestEMnistDatasetWithNullSampler) {
|
||||
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestEMnistDatasetWithNullSampler.";
|
||||
|
||||
// Create a EMnist Dataset
|
||||
std::string folder_path = datasets_root_path_ + "/testEMnistDataset";
|
||||
std::shared_ptr<Dataset> ds = EMnist(folder_path, "mnist", "train", 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 EMnist input, sampler cannot be nullptr
|
||||
EXPECT_EQ(iter, nullptr);
|
||||
}
|
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|
@ -0,0 +1,481 @@
|
|||
# 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 EMnist 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/testEMnistDataset"
|
||||
|
||||
|
||||
def load_emnist(path, usage, name):
|
||||
"""
|
||||
load EMnist data
|
||||
"""
|
||||
image_path = []
|
||||
label_path = []
|
||||
image_ext = "images-idx3-ubyte"
|
||||
label_ext = "labels-idx1-ubyte"
|
||||
train_prefix = "emnist-" + name + "-train-"
|
||||
test_prefix = "emnist-" + name + "-test-"
|
||||
assert usage in ["train", "test", "all"]
|
||||
if usage == "train":
|
||||
image_path.append(os.path.realpath(os.path.join(path, train_prefix + image_ext)))
|
||||
label_path.append(os.path.realpath(os.path.join(path, train_prefix + label_ext)))
|
||||
elif usage == "test":
|
||||
image_path.append(os.path.realpath(os.path.join(path, test_prefix + image_ext)))
|
||||
label_path.append(os.path.realpath(os.path.join(path, test_prefix + label_ext)))
|
||||
elif usage == "all":
|
||||
image_path.append(os.path.realpath(os.path.join(path, test_prefix + image_ext)))
|
||||
label_path.append(os.path.realpath(os.path.join(path, test_prefix + label_ext)))
|
||||
image_path.append(os.path.realpath(os.path.join(path, train_prefix + image_ext)))
|
||||
label_path.append(os.path.realpath(os.path.join(path, train_prefix + label_ext)))
|
||||
assert len(image_path) == len(label_path)
|
||||
images = []
|
||||
labels = []
|
||||
for i, _ in enumerate(image_path):
|
||||
with open(image_path[i], 'rb') as image_file:
|
||||
image_file.read(16)
|
||||
image = np.fromfile(image_file, dtype=np.uint8)
|
||||
image = image.reshape(-1, 28, 28, 1)
|
||||
image[image > 0] = 255 # Perform binarization to maintain consistency with our API
|
||||
images.append(image)
|
||||
with open(label_path[i], 'rb') as label_file:
|
||||
label_file.read(8)
|
||||
label = np.fromfile(label_file, dtype=np.uint8)
|
||||
labels.append(label)
|
||||
|
||||
images = np.concatenate(images, 0)
|
||||
labels = np.concatenate(labels, 0)
|
||||
|
||||
return images, labels
|
||||
|
||||
|
||||
def visualize_dataset(images, labels):
|
||||
"""
|
||||
Helper function to visualize the dataset samples
|
||||
"""
|
||||
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_emnist_content_check():
|
||||
"""
|
||||
Validate EMnistDataset image readings
|
||||
"""
|
||||
logger.info("Test EMnistDataset Op with content check")
|
||||
# train mnist
|
||||
train_data = ds.EMnistDataset(DATA_DIR, name="mnist", usage="train", num_samples=10, shuffle=False)
|
||||
images, labels = load_emnist(DATA_DIR, "train", "mnist")
|
||||
num_iter = 0
|
||||
# in this example, each dictionary has keys "image" and "label"
|
||||
image_list, label_list = [], []
|
||||
for i, data in enumerate(train_data.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 == 10
|
||||
|
||||
# train byclass
|
||||
train_data = ds.EMnistDataset(DATA_DIR, name="byclass", usage="train", num_samples=10, shuffle=False)
|
||||
images, labels = load_emnist(DATA_DIR, "train", "byclass")
|
||||
num_iter = 0
|
||||
# in this example, each dictionary has keys "image" and "label"
|
||||
image_list, label_list = [], []
|
||||
for i, data in enumerate(train_data.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 == 10
|
||||
|
||||
# test
|
||||
test_data = ds.EMnistDataset(DATA_DIR, name="mnist", usage="test", num_samples=10, shuffle=False)
|
||||
images, labels = load_emnist(DATA_DIR, "test", "mnist")
|
||||
num_iter = 0
|
||||
# in this example, each dictionary has keys "image" and "label"
|
||||
image_list, label_list = [], []
|
||||
for i, data in enumerate(test_data.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 == 10
|
||||
|
||||
|
||||
def test_emnist_basic():
|
||||
"""
|
||||
Validate EMnistDataset
|
||||
"""
|
||||
logger.info("Test EMnistDataset Op")
|
||||
|
||||
# case 1: test loading whole dataset
|
||||
train_data = ds.EMnistDataset(DATA_DIR, "mnist", "train")
|
||||
num_iter1 = 0
|
||||
for _ in train_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter1 += 1
|
||||
assert num_iter1 == 10
|
||||
|
||||
test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test")
|
||||
num_iter = 0
|
||||
for _ in test_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter += 1
|
||||
assert num_iter == 10
|
||||
|
||||
# case 2: test num_samples
|
||||
train_data = ds.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=5)
|
||||
num_iter2 = 0
|
||||
for _ in train_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter2 += 1
|
||||
assert num_iter2 == 5
|
||||
|
||||
test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=5)
|
||||
num_iter2 = 0
|
||||
for _ in test_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter2 += 1
|
||||
assert num_iter2 == 5
|
||||
|
||||
# case 3: test repeat
|
||||
train_data = ds.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=2)
|
||||
train_data = train_data.repeat(5)
|
||||
num_iter3 = 0
|
||||
for _ in train_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter3 += 1
|
||||
assert num_iter3 == 10
|
||||
|
||||
test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=2)
|
||||
test_data = test_data.repeat(5)
|
||||
num_iter3 = 0
|
||||
for _ in test_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter3 += 1
|
||||
assert num_iter3 == 10
|
||||
|
||||
# case 4: test batch with drop_remainder=False
|
||||
train_data = ds.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=10)
|
||||
assert train_data.get_dataset_size() == 10
|
||||
assert train_data.get_batch_size() == 1
|
||||
|
||||
train_data = train_data.batch(batch_size=7) # drop_remainder is default to be False
|
||||
assert train_data.get_dataset_size() == 2
|
||||
assert train_data.get_batch_size() == 7
|
||||
num_iter4 = 0
|
||||
for _ in train_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter4 += 1
|
||||
assert num_iter4 == 2
|
||||
|
||||
test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=10)
|
||||
assert test_data.get_dataset_size() == 10
|
||||
assert test_data.get_batch_size() == 1
|
||||
|
||||
test_data = test_data.batch(
|
||||
batch_size=7) # drop_remainder is default to be False
|
||||
assert test_data.get_dataset_size() == 2
|
||||
assert test_data.get_batch_size() == 7
|
||||
num_iter4 = 0
|
||||
for _ in test_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter4 += 1
|
||||
assert num_iter4 == 2
|
||||
|
||||
# case 5: test batch with drop_remainder=True
|
||||
train_data = ds.EMnistDataset(DATA_DIR, "byclass", "train", num_samples=10)
|
||||
assert train_data.get_dataset_size() == 10
|
||||
assert train_data.get_batch_size() == 1
|
||||
train_data = train_data.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
|
||||
assert train_data.get_dataset_size() == 1
|
||||
assert train_data.get_batch_size() == 7
|
||||
num_iter5 = 0
|
||||
for _ in train_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter5 += 1
|
||||
assert num_iter5 == 1
|
||||
|
||||
test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=10)
|
||||
assert test_data.get_dataset_size() == 10
|
||||
assert test_data.get_batch_size() == 1
|
||||
test_data = test_data.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
|
||||
assert test_data.get_dataset_size() == 1
|
||||
assert test_data.get_batch_size() == 7
|
||||
num_iter5 = 0
|
||||
for _ in test_data.create_dict_iterator(num_epochs=1):
|
||||
num_iter5 += 1
|
||||
assert num_iter5 == 1
|
||||
|
||||
# case 6: test get_col_names
|
||||
dataset = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=10)
|
||||
assert dataset.get_col_names() == ["image", "label"]
|
||||
|
||||
|
||||
def test_emnist_pk_sampler():
|
||||
"""
|
||||
Test EMnistDataset with PKSampler
|
||||
"""
|
||||
logger.info("Test EMnistDataset Op with PKSampler")
|
||||
golden = [0, 0, 0, 1, 1, 1]
|
||||
|
||||
sampler = ds.PKSampler(3)
|
||||
train_data = ds.EMnistDataset(DATA_DIR, "mnist", "train", sampler=sampler)
|
||||
num_iter = 0
|
||||
label_list = []
|
||||
for item in train_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 == 6
|
||||
|
||||
sampler = ds.PKSampler(3)
|
||||
test_data = ds.EMnistDataset(DATA_DIR, "mnist", "train", sampler=sampler)
|
||||
num_iter = 0
|
||||
label_list = []
|
||||
for item in test_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 == 6
|
||||
|
||||
|
||||
def test_emnist_sequential_sampler():
|
||||
"""
|
||||
Test EMnistDataset with SequentialSampler
|
||||
"""
|
||||
logger.info("Test EMnistDataset Op with SequentialSampler")
|
||||
num_samples = 10
|
||||
sampler = ds.SequentialSampler(num_samples=num_samples)
|
||||
train_data1 = ds.EMnistDataset(DATA_DIR, "mnist", "train", sampler=sampler)
|
||||
train_data2 = ds.EMnistDataset(DATA_DIR, "mnist", "train", shuffle=False, num_samples=num_samples)
|
||||
label_list1, label_list2 = [], []
|
||||
num_iter = 0
|
||||
for item1, item2 in zip(train_data1.create_dict_iterator(num_epochs=1),
|
||||
train_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
|
||||
|
||||
num_samples = 10
|
||||
sampler = ds.SequentialSampler(num_samples=num_samples)
|
||||
test_data1 = ds.EMnistDataset(DATA_DIR, "mnist", "test", sampler=sampler)
|
||||
test_data2 = ds.EMnistDataset(DATA_DIR, "mnist", "test", shuffle=False, num_samples=num_samples)
|
||||
label_list1, label_list2 = [], []
|
||||
num_iter = 0
|
||||
for item1, item2 in zip(test_data1.create_dict_iterator(num_epochs=1),
|
||||
test_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_emnist_exception():
|
||||
"""
|
||||
Test error cases for EMnistDataset
|
||||
"""
|
||||
logger.info("Test error cases for EMnistDataset")
|
||||
error_msg_1 = "sampler and shuffle cannot be specified at the same time"
|
||||
with pytest.raises(RuntimeError, match=error_msg_1):
|
||||
ds.EMnistDataset(DATA_DIR, "byclass", "train", shuffle=False, sampler=ds.PKSampler(3))
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "mnist", "train", sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", 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.EMnistDataset(DATA_DIR, "byclass", "train", num_shards=10)
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", num_shards=10)
|
||||
|
||||
error_msg_4 = "shard_id is specified but num_shards is not"
|
||||
with pytest.raises(RuntimeError, match=error_msg_4):
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "train", shard_id=0)
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", shard_id=0)
|
||||
|
||||
error_msg_5 = "Input shard_id is not within the required interval"
|
||||
with pytest.raises(ValueError, match=error_msg_5):
|
||||
ds.EMnistDataset(DATA_DIR, "byclass", "train", num_shards=5, shard_id=-1)
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", num_shards=5, shard_id=-1)
|
||||
with pytest.raises(ValueError, match=error_msg_5):
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "train", num_shards=5, shard_id=5)
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", num_shards=5, shard_id=5)
|
||||
with pytest.raises(ValueError, match=error_msg_5):
|
||||
ds.EMnistDataset(DATA_DIR, "byclass", "train", num_shards=2, shard_id=5)
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", num_shards=2, shard_id=5)
|
||||
|
||||
error_msg_6 = "num_parallel_workers exceeds"
|
||||
with pytest.raises(ValueError, match=error_msg_6):
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "train", shuffle=False, num_parallel_workers=0)
|
||||
ds.EMnistDataset(DATA_DIR, "mnist", "test", shuffle=False, num_parallel_workers=0)
|
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with pytest.raises(ValueError, match=error_msg_6):
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ds.EMnistDataset(DATA_DIR, "byclass", "train", shuffle=False, num_parallel_workers=256)
|
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ds.EMnistDataset(DATA_DIR, "mnist", "test", shuffle=False, num_parallel_workers=256)
|
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with pytest.raises(ValueError, match=error_msg_6):
|
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ds.EMnistDataset(DATA_DIR, "mnist", "train", shuffle=False, num_parallel_workers=-2)
|
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ds.EMnistDataset(DATA_DIR, "mnist", "test", shuffle=False, num_parallel_workers=-2)
|
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|
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error_msg_7 = "Argument shard_id"
|
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with pytest.raises(TypeError, match=error_msg_7):
|
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ds.EMnistDataset(DATA_DIR, "mnist", "train", num_shards=2, shard_id="0")
|
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ds.EMnistDataset(DATA_DIR, "mnist", "test", num_shards=2, shard_id="0")
|
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|
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def exception_func(item):
|
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raise Exception("Error occur!")
|
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|
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error_msg_8 = "The corresponding data files"
|
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with pytest.raises(RuntimeError, match=error_msg_8):
|
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data = ds.EMnistDataset(DATA_DIR, "mnist", "train")
|
||||
data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
|
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for _ in data.__iter__():
|
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pass
|
||||
with pytest.raises(RuntimeError, match=error_msg_8):
|
||||
data = ds.EMnistDataset(DATA_DIR, "mnist", "train")
|
||||
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.EMnistDataset(DATA_DIR, "mnist", "train")
|
||||
data = data.map(operations=exception_func, input_columns=["label"], num_parallel_workers=1)
|
||||
for _ in data.__iter__():
|
||||
pass
|
||||
|
||||
|
||||
def test_emnist_visualize(plot=False):
|
||||
"""
|
||||
Visualize EMnistDataset results
|
||||
"""
|
||||
logger.info("Test EMnistDataset visualization")
|
||||
|
||||
train_data = ds.EMnistDataset(DATA_DIR, "mnist", "train", num_samples=10, shuffle=False)
|
||||
num_iter = 0
|
||||
image_list, label_list = [], []
|
||||
for item in train_data.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)
|
||||
|
||||
test_data = ds.EMnistDataset(DATA_DIR, "mnist", "test", num_samples=10, shuffle=False)
|
||||
num_iter = 0
|
||||
image_list, label_list = [], []
|
||||
for item in test_data.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_emnist_usage():
|
||||
"""
|
||||
Validate EMnistDataset image readings
|
||||
"""
|
||||
logger.info("Test EMnistDataset usage flag")
|
||||
|
||||
def test_config(usage, emnist_path=None):
|
||||
emnist_path = DATA_DIR if emnist_path is None else emnist_path
|
||||
try:
|
||||
data = ds.EMnistDataset(emnist_path, "mnist", 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("train") == 10
|
||||
assert test_config("test") == 10
|
||||
assert test_config("all") == 20
|
||||
|
||||
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 emnist 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) == 10000
|
||||
assert test_config("test", all_files_path) == 60000
|
||||
assert test_config("all", all_files_path) == 70000
|
||||
assert ds.EMnistDataset(all_files_path, "mnist", usage="test").get_dataset_size() == 10000
|
||||
assert ds.EMnistDataset(all_files_path, "mnist", usage="test").get_dataset_size() == 60000
|
||||
assert ds.EMnistDataset(all_files_path, "mnist", usage="all").get_dataset_size() == 70000
|
||||
|
||||
|
||||
def test_emnist_name():
|
||||
"""
|
||||
Validate EMnistDataset image readings
|
||||
"""
|
||||
def test_config(name, usage, emnist_path=None):
|
||||
emnist_path = DATA_DIR if emnist_path is None else emnist_path
|
||||
try:
|
||||
data = ds.EMnistDataset(emnist_path, name, 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("mnist", "train") == 10
|
||||
assert test_config("mnist", "test") == 10
|
||||
assert test_config("byclass", "train") == 10
|
||||
assert "name is not within the valid set of " + \
|
||||
"['byclass', 'bymerge', 'balanced', 'letters', 'digits', 'mnist']" in test_config("invalid", "train")
|
||||
assert "Argument name with value ['list'] is not of type [<class 'str'>]" in test_config(["list"], "train")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_emnist_content_check()
|
||||
test_emnist_basic()
|
||||
test_emnist_pk_sampler()
|
||||
test_emnist_sequential_sampler()
|
||||
test_emnist_exception()
|
||||
test_emnist_visualize(plot=True)
|
||||
test_emnist_usage()
|
||||
test_emnist_name()
|
Loading…
Reference in New Issue