提交 ae99a3a3 编写于 作者: Y YangLuo

C++ API support for TakeDatasetOp and VOCDatasetOp

上级 1f4222ed
......@@ -24,21 +24,25 @@
#include "minddata/dataset/engine/datasetops/source/cifar_op.h"
#include "minddata/dataset/engine/datasetops/source/image_folder_op.h"
#include "minddata/dataset/engine/datasetops/source/mnist_op.h"
#include "minddata/dataset/engine/datasetops/source/voc_op.h"
// Dataset operator headers (in alphabetical order)
#include "minddata/dataset/engine/datasetops/batch_op.h"
#include "minddata/dataset/engine/datasetops/map_op.h"
#include "minddata/dataset/engine/datasetops/project_op.h"
#include "minddata/dataset/engine/datasetops/rename_op.h"
#include "minddata/dataset/engine/datasetops/repeat_op.h"
#include "minddata/dataset/engine/datasetops/shuffle_op.h"
#include "minddata/dataset/engine/datasetops/skip_op.h"
#include "minddata/dataset/engine/datasetops/project_op.h"
#include "minddata/dataset/engine/datasetops/take_op.h"
#include "minddata/dataset/engine/datasetops/zip_op.h"
#include "minddata/dataset/engine/datasetops/rename_op.h"
// Sampler headers (in alphabetical order)
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#include "minddata/dataset/engine/datasetops/source/sampler/random_sampler.h"
#include "minddata/dataset/core/config_manager.h"
#include "minddata/dataset/util/random.h"
#include "minddata/dataset/util/path.h"
namespace mindspore {
namespace dataset {
......@@ -123,6 +127,16 @@ std::shared_ptr<MnistDataset> Mnist(std::string dataset_dir, std::shared_ptr<Sam
return ds->ValidateParams() ? ds : nullptr;
}
// Function to create a VOCDataset.
std::shared_ptr<VOCDataset> VOC(const std::string &dataset_dir, const std::string &task, const std::string &mode,
const std::map<std::string, int32_t> &class_index, bool decode,
std::shared_ptr<SamplerObj> sampler) {
auto ds = std::make_shared<VOCDataset>(dataset_dir, task, mode, class_index, decode, sampler);
// Call derived class validation method.
return ds->ValidateParams() ? ds : nullptr;
}
// FUNCTIONS TO CREATE DATASETS FOR DATASET OPS
// (In alphabetical order)
......@@ -232,6 +246,26 @@ std::shared_ptr<SkipDataset> Dataset::Skip(int32_t count) {
return ds;
}
// Function to create a TakeDataset.
std::shared_ptr<Dataset> Dataset::Take(int32_t count) {
// If count is greater than the number of element in dataset or equal to -1,
// all the element in dataset will be taken
if (count == -1) {
return shared_from_this();
}
auto ds = std::make_shared<TakeDataset>(count);
// Call derived class validation method.
if (!ds->ValidateParams()) {
return nullptr;
}
ds->children.push_back(shared_from_this());
return ds;
}
// Function to create a Zip dataset
std::shared_ptr<ZipDataset> Dataset::Zip(const std::vector<std::shared_ptr<Dataset>> &datasets) {
// Default values
......@@ -392,6 +426,71 @@ std::vector<std::shared_ptr<DatasetOp>> MnistDataset::Build() {
return node_ops;
}
// Constructor for VOCDataset
VOCDataset::VOCDataset(const std::string &dataset_dir, const std::string &task, const std::string &mode,
const std::map<std::string, int32_t> &class_index, bool decode,
std::shared_ptr<SamplerObj> sampler)
: dataset_dir_(dataset_dir),
task_(task),
mode_(mode),
class_index_(class_index),
decode_(decode),
sampler_(sampler) {}
bool VOCDataset::ValidateParams() {
Path dir(dataset_dir_);
if (!dir.IsDirectory()) {
MS_LOG(ERROR) << "Invalid dataset path or no dataset path is specified.";
return false;
}
if (task_ == "Segmentation") {
if (!class_index_.empty()) {
MS_LOG(ERROR) << "class_indexing is invalid in Segmentation task.";
return false;
}
Path imagesets_file = dir / "ImageSets" / "Segmentation" / mode_ + ".txt";
if (!imagesets_file.Exists()) {
MS_LOG(ERROR) << "[Segmentation] imagesets_file is invalid or not exist";
return false;
}
} else if (task_ == "Detection") {
Path imagesets_file = dir / "ImageSets" / "Main" / mode_ + ".txt";
if (!imagesets_file.Exists()) {
MS_LOG(ERROR) << "[Detection] imagesets_file is invalid or not exist.";
return false;
}
} else {
MS_LOG(ERROR) << "Invalid task: " << task_;
return false;
}
return true;
}
// Function to build VOCDataset
std::vector<std::shared_ptr<DatasetOp>> VOCDataset::Build() {
// A vector containing shared pointer to the Dataset Ops that this object will create
std::vector<std::shared_ptr<DatasetOp>> node_ops;
// If user does not specify Sampler, create a default sampler based on the shuffle variable.
if (sampler_ == nullptr) {
sampler_ = CreateDefaultSampler();
}
std::shared_ptr<VOCOp::Builder> builder = std::make_shared<VOCOp::Builder>();
(void)builder->SetDir(dataset_dir_);
(void)builder->SetTask(task_);
(void)builder->SetMode(mode_);
(void)builder->SetNumWorkers(num_workers_);
(void)builder->SetSampler(std::move(sampler_->Build()));
(void)builder->SetDecode(decode_);
(void)builder->SetClassIndex(class_index_);
std::shared_ptr<VOCOp> op;
RETURN_EMPTY_IF_ERROR(builder->Build(&op));
node_ops.push_back(op);
return node_ops;
}
// DERIVED DATASET CLASSES LEAF-NODE DATASETS
// (In alphabetical order)
......@@ -580,6 +679,28 @@ bool SkipDataset::ValidateParams() {
return true;
}
// Constructor for TakeDataset
TakeDataset::TakeDataset(int32_t count) : take_count_(count) {}
// Function to build the TakeOp
std::vector<std::shared_ptr<DatasetOp>> TakeDataset::Build() {
// A vector containing shared pointer to the Dataset Ops that this object will create
std::vector<std::shared_ptr<DatasetOp>> node_ops;
node_ops.push_back(std::make_shared<TakeOp>(take_count_, connector_que_size_));
return node_ops;
}
// Function to validate the parameters for TakeDataset
bool TakeDataset::ValidateParams() {
if (take_count_ < -1) {
MS_LOG(ERROR) << "Take: Invalid input, take_count: " << take_count_;
return false;
}
return true;
}
// Function to build ZipOp
ZipDataset::ZipDataset() {}
......
......@@ -45,6 +45,7 @@ class Cifar10Dataset;
class Cifar100Dataset;
class ImageFolderDataset;
class MnistDataset;
class VOCDataset;
// Dataset Op classes (in alphabetical order)
class BatchDataset;
class MapDataset;
......@@ -53,6 +54,7 @@ class RenameDataset;
class RepeatDataset;
class ShuffleDataset;
class SkipDataset;
class TakeDataset;
class ZipDataset;
/// \brief Function to create a Cifar10 Dataset
......@@ -96,6 +98,24 @@ std::shared_ptr<ImageFolderDataset> ImageFolder(std::string dataset_dir, bool de
/// \return Shared pointer to the current MnistDataset
std::shared_ptr<MnistDataset> Mnist(std::string dataset_dir, std::shared_ptr<SamplerObj> sampler = nullptr);
/// \brief Function to create a VOCDataset
/// \notes The generated dataset has multi-columns :
/// - task='Detection', column: [['image', dtype=uint8], ['bbox', dtype=float32], ['label', dtype=uint32],
/// ['difficult', dtype=uint32], ['truncate', dtype=uint32]].
/// - task='Segmentation', column: [['image', dtype=uint8], ['target',dtype=uint8]].
/// \param[in] dataset_dir Path to the root directory that contains the dataset
/// \param[in] task Set the task type of reading voc data, now only support "Segmentation" or "Detection"
/// \param[in] mode Set the data list txt file to be readed
/// \param[in] class_indexing A str-to-int mapping from label name to index
/// \param[in] decode Decode the images after reading
/// \param[in] sampler Object used to choose samples from the dataset. If sampler is `nullptr`, A `RandomSampler`
/// will be used to randomly iterate the entire dataset
/// \return Shared pointer to the current Dataset
std::shared_ptr<VOCDataset> VOC(const std::string &dataset_dir, const std::string &task = "Segmentation",
const std::string &mode = "train",
const std::map<std::string, int32_t> &class_index = {}, bool decode = false,
std::shared_ptr<SamplerObj> sampler = nullptr);
/// \class Dataset datasets.h
/// \brief A base class to represent a dataset in the data pipeline.
class Dataset : public std::enable_shared_from_this<Dataset> {
......@@ -192,6 +212,12 @@ class Dataset : public std::enable_shared_from_this<Dataset> {
/// \return Shared pointer to the current SkipDataset
std::shared_ptr<SkipDataset> Skip(int32_t count);
/// \brief Function to create a TakeDataset
/// \notes Takes count elements in this dataset.
/// \param[in] count Number of elements the dataset to be taken.
/// \return Shared pointer to the current Dataset
std::shared_ptr<Dataset> Take(int32_t count = -1);
/// \brief Function to create a Zip Dataset
/// \notes Applies zip to the dataset
/// \param[in] datasets A list of shared pointer to the datasets that we want to zip
......@@ -300,6 +326,32 @@ class MnistDataset : public Dataset {
std::shared_ptr<SamplerObj> sampler_;
};
class VOCDataset : public Dataset {
public:
/// \brief Constructor
VOCDataset(const std::string &dataset_dir, const std::string &task, const std::string &mode,
const std::map<std::string, int32_t> &class_index, bool decode, std::shared_ptr<SamplerObj> sampler);
/// \brief Destructor
~VOCDataset() = default;
/// \brief a base class override function to create the required runtime dataset op objects for this class
/// \return shared pointer to the list of newly created DatasetOps
std::vector<std::shared_ptr<DatasetOp>> Build() override;
/// \brief Parameters validation
/// \return bool true if all the params are valid
bool ValidateParams() override;
private:
std::string dataset_dir_;
std::string task_;
std::string mode_;
std::map<std::string, int32_t> class_index_;
bool decode_;
std::shared_ptr<SamplerObj> sampler_;
};
class BatchDataset : public Dataset {
public:
/// \brief Constructor
......@@ -446,6 +498,26 @@ class SkipDataset : public Dataset {
int32_t skip_count_;
};
class TakeDataset : public Dataset {
public:
/// \brief Constructor
explicit TakeDataset(int32_t count);
/// \brief Destructor
~TakeDataset() = default;
/// \brief a base class override function to create the required runtime dataset op objects for this class
/// \return shared pointer to the list of newly created DatasetOps
std::vector<std::shared_ptr<DatasetOp>> Build() override;
/// \brief Parameters validation
/// \return bool true if all the params are valid
bool ValidateParams() override;
private:
int32_t take_count_;
};
class ZipDataset : public Dataset {
public:
/// \brief Constructor
......
......@@ -29,13 +29,20 @@
#include "minddata/dataset/include/transforms.h"
#include "minddata/dataset/include/iterator.h"
#include "minddata/dataset/core/constants.h"
#include "minddata/dataset/core/tensor_shape.h"
#include "minddata/dataset/core/tensor.h"
#include "minddata/dataset/include/samplers.h"
#include "minddata/dataset/engine/datasetops/source/voc_op.h"
using namespace mindspore::dataset::api;
using mindspore::MsLogLevel::ERROR;
using mindspore::ExceptionType::NoExceptionType;
using mindspore::LogStream;
using mindspore::dataset::Tensor;
using mindspore::dataset::TensorShape;
using mindspore::dataset::TensorImpl;
using mindspore::dataset::DataType;
using mindspore::dataset::Status;
using mindspore::dataset::BorderType;
......@@ -638,8 +645,96 @@ TEST_F(MindDataTestPipeline, TestSkipDatasetError1) {
EXPECT_EQ(ds, nullptr);
}
TEST_F(MindDataTestPipeline, TestCifar10Dataset) {
TEST_F(MindDataTestPipeline, TestTakeDatasetDefault) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTakeDatasetDefault.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 7));
EXPECT_NE(ds, nullptr);
// Create a Take operation on ds, dafault count = -1
ds = ds->Take();
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, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
MS_LOG(INFO) << "Number of rows: " << i;
// Expect 7 rows
EXPECT_EQ(i, 7);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestTakeDatasetNormal) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTakeDatasetNormal.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 8));
EXPECT_NE(ds, nullptr);
// Create a Take operation on ds
ds = ds->Take(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, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
uint64_t i = 0;
while (row.size() != 0) {
i++;
auto image = row["image"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
iter->GetNextRow(&row);
}
MS_LOG(INFO) << "Number of rows: " << i;
// Expect 5 rows
EXPECT_EQ(i, 5);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestTakeDatasetError1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestTakeDatasetError1.";
// Create an ImageFolder Dataset
std::string folder_path = datasets_root_path_ + "/testPK/data/";
std::shared_ptr<Dataset> ds = ImageFolder(folder_path, true, RandomSampler(false, 10));
EXPECT_NE(ds, nullptr);
// Create a Take operation on ds with invalid count input
int32_t count = -5;
ds = ds->Take(count);
// Expect nullptr for invalid input take_count
EXPECT_EQ(ds, nullptr);
}
TEST_F(MindDataTestPipeline, TestCifar10Dataset) {
// Create a Cifar10 Dataset
std::string folder_path = datasets_root_path_ + "/testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, RandomSampler(false, 10));
......@@ -1029,3 +1124,168 @@ TEST_F(MindDataTestPipeline, TestRenameFail) {
ds = ds->Rename({"image", "label"}, {"col2"});
EXPECT_EQ(ds, nullptr);
}
TEST_F(MindDataTestPipeline, TestVOCSegmentation) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCSegmentation.";
// Create a VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Segmentation", "train", {}, false, SequentialSampler(0, 3));
EXPECT_NE(ds, nullptr);
// Create a Repeat operation on ds
int32_t repeat_num = 2;
ds = ds->Repeat(repeat_num);
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, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
// Check if VOCOp read correct images/targets
using Tensor = mindspore::dataset::Tensor;
std::string expect_file[] = {"32", "33", "39", "32", "33", "39"};
uint64_t i = 0;
while (row.size() != 0) {
auto image = row["image"];
auto target = row["target"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
MS_LOG(INFO) << "Tensor target shape: " << target->shape();
std::shared_ptr<Tensor> expect_image;
Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);
EXPECT_EQ(*image, *expect_image);
std::shared_ptr<Tensor> expect_target;
Tensor::CreateFromFile(folder_path + "/SegmentationClass/" + expect_file[i] + ".png", &expect_target);
EXPECT_EQ(*target, *expect_target);
iter->GetNextRow(&row);
i++;
}
EXPECT_EQ(i, 6);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestVOCSegmentationError1) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCSegmentationError1.";
// Create a VOC Dataset
std::map<std::string, int32_t> class_index;
class_index["car"] = 0;
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Segmentation", "train", class_index, false, RandomSampler(false, 6));
// Expect nullptr for segmentation task with class_index
EXPECT_EQ(ds, nullptr);
}
TEST_F(MindDataTestPipeline, TestVOCInvalidTaskOrMode) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCInvalidTaskOrMode.";
// Create a VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds_1 = VOC(folder_path, "Classification", "train", {}, false, SequentialSampler(0, 3));
// Expect nullptr for invalid task
EXPECT_EQ(ds_1, nullptr);
std::shared_ptr<Dataset> ds_2 = VOC(folder_path, "Segmentation", "validation", {}, false, RandomSampler(false, 4));
// Expect nullptr for invalid mode
EXPECT_EQ(ds_2, nullptr);
}
TEST_F(MindDataTestPipeline, TestVOCDetection) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCDetection.";
// Create a VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", {}, false, SequentialSampler(0, 4));
EXPECT_NE(ds, nullptr);
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.
std::shared_ptr<Iterator> iter = ds->CreateIterator();
EXPECT_NE(iter, nullptr);
// Iterate the dataset and get each row
std::unordered_map<std::string, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
// Check if VOCOp read correct images/labels
std::string expect_file[] = {"15", "32", "33", "39"};
uint32_t expect_num[] = {5, 5, 4, 3};
uint64_t i = 0;
while (row.size() != 0) {
auto image = row["image"];
auto label = row["label"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
MS_LOG(INFO) << "Tensor label shape: " << label->shape();
std::shared_ptr<Tensor> expect_image;
Tensor::CreateFromFile(folder_path + "/JPEGImages/" + expect_file[i] + ".jpg", &expect_image);
EXPECT_EQ(*image, *expect_image);
std::shared_ptr<Tensor> expect_label;
Tensor::CreateFromMemory(TensorShape({1, 1}), DataType(DataType::DE_UINT32), nullptr, &expect_label);
expect_label->SetItemAt({0, 0}, expect_num[i]);
EXPECT_EQ(*label, *expect_label);
iter->GetNextRow(&row);
i++;
}
EXPECT_EQ(i, 4);
// Manually terminate the pipeline
iter->Stop();
}
TEST_F(MindDataTestPipeline, TestVOCClassIndex) {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestVOCClassIndex.";
// Create a VOC Dataset
std::string folder_path = datasets_root_path_ + "/testVOC2012_2";
std::map<std::string, int32_t> class_index;
class_index["car"] = 0;
class_index["cat"] = 1;
class_index["train"] = 9;
std::shared_ptr<Dataset> ds = VOC(folder_path, "Detection", "train", class_index, false, SequentialSampler(0, 6));
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, std::shared_ptr<Tensor>> row;
iter->GetNextRow(&row);
// Check if VOCOp read correct labels
// When we provide class_index, label of ["car","cat","train"] become [0,1,9]
std::shared_ptr<Tensor> expect_label;
Tensor::CreateFromMemory(TensorShape({1, 1}), DataType(DataType::DE_UINT32), nullptr, &expect_label);
uint32_t expect[] = {9, 9, 9, 1, 1, 0};
uint64_t i = 0;
while (row.size() != 0) {
auto image = row["image"];
auto label = row["label"];
MS_LOG(INFO) << "Tensor image shape: " << image->shape();
MS_LOG(INFO) << "Tensor label shape: " << label->shape();
expect_label->SetItemAt({0, 0}, expect[i]);
EXPECT_EQ(*label, *expect_label);
iter->GetNextRow(&row);
i++;
}
EXPECT_EQ(i, 6);
// Manually terminate the pipeline
iter->Stop();
}
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