Nigel
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!13290 Add a base class for Mappable source ops 

From: @hfarahat
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2021-03-31 03:09:21 +08:00 committed by Gitee
parent 5312cb372e a220a8d74b
commit 1edbbe56ba
24 changed files with 352 additions and 1335 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/CMakeLists.txt
View File

@ -14,6 +14,7 @@ set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES
clue_op.cc clue_op.cc
csv_op.cc csv_op.cc
album_op.cc album_op.cc
mappable_leaf_op.cc
) )
set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES set(DATASET_ENGINE_DATASETOPS_SOURCE_SRC_FILES

139
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/album_op.cc
View File

@ -72,17 +72,15 @@ Status AlbumOp::Builder::SanityCheck() {
AlbumOp::AlbumOp(int32_t num_wkrs, int32_t rows_per_buffer, std::string file_dir, int32_t queue_size, bool do_decode, AlbumOp::AlbumOp(int32_t num_wkrs, int32_t rows_per_buffer, std::string file_dir, int32_t queue_size, bool do_decode,
const std::set<std::string> &exts, std::unique_ptr<DataSchema> data_schema, const std::set<std::string> &exts, std::unique_ptr<DataSchema> data_schema,
std::shared_ptr<SamplerRT> sampler) std::shared_ptr<SamplerRT> sampler)
: ParallelOp(num_wkrs, queue_size, std::move(sampler)), : MappableLeafOp(num_wkrs, queue_size, std::move(sampler), rows_per_buffer),
rows_per_buffer_(rows_per_buffer),
folder_path_(file_dir), folder_path_(file_dir),
decode_(do_decode), decode_(do_decode),
extensions_(exts), extensions_(exts),
data_schema_(std::move(data_schema)), data_schema_(std::move(data_schema)),
row_cnt_(0),
buf_cnt_(0),
sampler_ind_(0), sampler_ind_(0),
dirname_offset_(0), dirname_offset_(0),
sample_ids_(nullptr) { sample_ids_(nullptr),
curr_row_(0) {
// Set the column name map (base class field) // Set the column name map (base class field)
for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) { for (int32_t i = 0; i < data_schema_->NumColumns(); ++i) {
column_name_id_map_[data_schema_->column(i).name()] = i; column_name_id_map_[data_schema_->column(i).name()] = i;
@ -131,97 +129,6 @@ Status AlbumOp::PrescanEntry() {
return Status::OK(); return Status::OK();
} }
// Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work
Status AlbumOp::operator()() {
RETURN_IF_NOT_OK(this->PrescanEntry());
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
while (true) { // each iterator is 1 epoch
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (sampler_buffer->eoe() == false) {
TensorRow sample_row;
RETURN_IF_NOT_OK(sampler_buffer->PopRow(&sample_row));
TensorPtr sample_ids = sample_row[0];
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) >= num_rows_) continue; // index out of bound, skipping
keys.push_back(*itr);
row_cnt_++;
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(
io_block_queues_[buf_cnt_++ % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
keys.clear();
}
}
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
}
if (IsLastIteration()) {
std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
std::unique_ptr<IOBlock> eof_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block)));
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block)));
for (int32_t i = 0; i < num_workers_; ++i) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else { // not the last repeat.
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
// contains the main logic of pulling a IOBlock from IOBlockQueue, load a buffer and push the buffer to out_connector_
// IMPORTANT: 1 IOBlock produces 1 DataBuffer
Status AlbumOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty() == true) return Status::OK(); // empty key is a quit signal for workers
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker.");
}
// Only support JPEG/PNG/GIF/BMP // Only support JPEG/PNG/GIF/BMP
// Optimization: Could take in a tensor // Optimization: Could take in a tensor
// This function does not return status because we want to just skip bad input, not crash // This function does not return status because we want to just skip bad input, not crash
@ -443,7 +350,8 @@ Status AlbumOp::LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num,
// to take a reference to a column descriptor? // to take a reference to a column descriptor?
// the design of this class is to make the code more readable, forgoing minor performance gain like // the design of this class is to make the code more readable, forgoing minor performance gain like
// getting rid of duplicated checks // getting rid of duplicated checks
Status AlbumOp::LoadTensorRow(row_id_type row_id, const std::string &file, TensorRow *row) { Status AlbumOp::LoadTensorRow(row_id_type row_id, TensorRow *row) {
std::string file = image_rows_[row_id];
// testing here is to just print out file path // testing here is to just print out file path
(*row) = TensorRow(row_id, {}); (*row) = TensorRow(row_id, {});
MS_LOG(INFO) << "Image row file: " << file << "."; MS_LOG(INFO) << "Image row file: " << file << ".";
@ -531,19 +439,6 @@ Status AlbumOp::loadColumnData(const std::string &file, int32_t index, nlohmann:
} }
} }
// Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
Status AlbumOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
TensorRow trow;
for (const int64_t &key : keys) {
RETURN_IF_NOT_OK(this->LoadTensorRow(key, image_rows_[key], &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
void AlbumOp::Print(std::ostream &out, bool show_all) const { void AlbumOp::Print(std::ostream &out, bool show_all) const {
// Always show the id and name as first line regardless if this summary or detailed print // Always show the id and name as first line regardless if this summary or detailed print
out << "(" << std::setw(2) << operator_id_ << ") <AlbumOp>:"; out << "(" << std::setw(2) << operator_id_ << ") <AlbumOp>:";
@ -561,24 +456,12 @@ void AlbumOp::Print(std::ostream &out, bool show_all) const {
} }
} }
// Reset Sampler and wakeup Master thread (functor)
Status AlbumOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(sampler_->ResetSampler());
row_cnt_ = 0;
return Status::OK();
}
// hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
Status AlbumOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
Status AlbumOp::LaunchThreadsAndInitOp() { Status AlbumOp::LaunchThreadsAndInitOp() {
if (tree_ == nullptr) { if (tree_ == nullptr) {
return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set."); return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Pipeline init failed, Execution tree not set.");
} }
RETURN_IF_NOT_OK(this->PrescanEntry());
// registers QueueList and individual Queues for interrupt services // registers QueueList and individual Queues for interrupt services
RETURN_IF_NOT_OK(io_block_queues_.Register(tree_->AllTasks())); RETURN_IF_NOT_OK(io_block_queues_.Register(tree_->AllTasks()));
RETURN_IF_NOT_OK(wait_for_workers_post_.Register(tree_->AllTasks())); RETURN_IF_NOT_OK(wait_for_workers_post_.Register(tree_->AllTasks()));
@ -612,13 +495,13 @@ Status AlbumOp::GetNextRow(TensorRow *row) {
RETURN_IF_NOT_OK(sample_buffer->PopRow(&sample_row)); RETURN_IF_NOT_OK(sample_buffer->PopRow(&sample_row));
sample_ids_ = sample_row[0]; sample_ids_ = sample_row[0];
} }
if (row_cnt_ + 1 > sample_ids_->Size()) { if (curr_row_ + 1 > sample_ids_->Size()) {
return Status::OK(); return Status::OK();
} }
int64_t key; int64_t key;
sample_ids_->GetItemAt(&key, {row_cnt_}); RETURN_IF_NOT_OK(sample_ids_->GetItemAt(&key, {curr_row_}));
RETURN_IF_NOT_OK(LoadTensorRow(key, image_rows_[key], row)); RETURN_IF_NOT_OK(LoadTensorRow(key, row));
row_cnt_++; curr_row_++;
return Status::OK(); return Status::OK();
} }
} // namespace dataset } // namespace dataset

40
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/album_op.h
View File

@ -30,6 +30,7 @@
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#include "minddata/dataset/util/path.h" #include "minddata/dataset/util/path.h"
#include "minddata/dataset/util/queue.h" #include "minddata/dataset/util/queue.h"
@ -47,7 +48,7 @@ class Queue;
using FolderImages = std::shared_ptr<std::pair<std::string, std::queue<std::string>>>; using FolderImages = std::shared_ptr<std::pair<std::string, std::queue<std::string>>>;
/// \class AlbumOp album_op.h /// \class AlbumOp album_op.h
class AlbumOp : public ParallelOp, public RandomAccessOp { class AlbumOp : public MappableLeafOp {
public: public:
class Builder { class Builder {
public: public:
@ -171,17 +172,6 @@ class AlbumOp : public ParallelOp, public RandomAccessOp {
/// \return Status The status code returned /// \return Status The status code returned
Status PrescanEntry(); Status PrescanEntry();
/// \brief Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
/// \param[in] int32_t workerId - id of each worker
/// \return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
/// \brief Main Loop of AlbumOp
/// Master thread: Fill IOBlockQueue, then goes to sleep
/// Worker thread: pulls IOBlock from IOBlockQueue, work on it then put buffer to mOutConnector
/// \return Status The status code returned
Status operator()() override;
/// \brief A print method typically used for debugging /// \brief A print method typically used for debugging
/// \param[in] out /// \param[in] out
/// \param[in] show_all /// \param[in] show_all
@ -197,10 +187,6 @@ class AlbumOp : public ParallelOp, public RandomAccessOp {
std::string Name() const override { return "AlbumOp"; } std::string Name() const override { return "AlbumOp"; }
private: private:
/// \brief Initialize Sampler, calls sampler->Init() within
/// \return Status The status code returned
Status InitSampler();
/// \brief Load image to tensor row /// \brief Load image to tensor row
/// \param[in] image_file Image name of file /// \param[in] image_file Image name of file
/// \param[in] col_num Column num in schema /// \param[in] col_num Column num in schema
@ -265,10 +251,9 @@ class AlbumOp : public ParallelOp, public RandomAccessOp {
/// \brief Load a tensor row according to a json file /// \brief Load a tensor row according to a json file
/// \param[in] row_id_type row_id - id for this tensor row /// \param[in] row_id_type row_id - id for this tensor row
/// \param[in] ImageColumns file Json file location
/// \param[in, out] TensorRow row Json content stored into a tensor row /// \param[in, out] TensorRow row Json content stored into a tensor row
/// \return Status The status code returned /// \return Status The status code returned
Status LoadTensorRow(row_id_type row_id, const std::string &file, TensorRow *row); Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
/// \brief Load a tensor column according to a json file /// \brief Load a tensor column according to a json file
/// \param[in] ImageColumns file Json file location /// \param[in] ImageColumns file Json file location
@ -278,23 +263,14 @@ class AlbumOp : public ParallelOp, public RandomAccessOp {
/// \return Status The status code returned /// \return Status The status code returned
Status loadColumnData(const std::string &file, int32_t index, nlohmann::json js, TensorRow *row); Status loadColumnData(const std::string &file, int32_t index, nlohmann::json js, TensorRow *row);
/// \param[in] const std::vector<int64_t> &keys Keys in ioblock
/// \param[in, out] std::unique_ptr<DataBuffer> db Databuffer to push to
/// \return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
/// \brief Called first when function is called /// \brief Called first when function is called
/// \return Status The status code returned /// \return Status The status code returned
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
/// \brief reset Op
/// \return Status The status code returned
Status Reset() override;
Status GetNextRow(TensorRow *row) override; Status GetNextRow(TensorRow *row) override;
// Private function for computing the assignment of the column name map. /// Private function for computing the assignment of the column name map.
// @return Status The status code returned /// \return Status The status code returned
Status ComputeColMap() override; Status ComputeColMap() override;
int32_t rows_per_buffer_; int32_t rows_per_buffer_;
@ -303,12 +279,12 @@ class AlbumOp : public ParallelOp, public RandomAccessOp {
std::set<std::string> extensions_; // extensions allowed std::set<std::string> extensions_; // extensions allowed
std::unordered_map<std::string, int32_t> col_name_map_; std::unordered_map<std::string, int32_t> col_name_map_;
std::unique_ptr<DataSchema> data_schema_; std::unique_ptr<DataSchema> data_schema_;
int64_t row_cnt_;
int64_t buf_cnt_;
int64_t sampler_ind_; int64_t sampler_ind_;
int64_t dirname_offset_; int64_t dirname_offset_;
std::vector<std::string> image_rows_; std::vector<std::string> image_rows_;
TensorPtr sample_ids_; TensorPtr sample_ids_;
int32_t curr_row_;
}; };
} // namespace dataset } // namespace dataset
} // namespace mindspore } // namespace mindspore

127
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/celeba_op.cc
View File

@ -79,8 +79,7 @@ Status CelebAOp::Builder::SanityCheck() {
CelebAOp::CelebAOp(int32_t num_workers, int32_t rows_per_buffer, const std::string &dir, int32_t queue_size, CelebAOp::CelebAOp(int32_t num_workers, int32_t rows_per_buffer, const std::string &dir, int32_t queue_size,
bool decode, const std::string &usage, const std::set<std::string> &exts, bool decode, const std::string &usage, const std::set<std::string> &exts,
std::unique_ptr<DataSchema> schema, std::shared_ptr<SamplerRT> sampler) std::unique_ptr<DataSchema> schema, std::shared_ptr<SamplerRT> sampler)
: ParallelOp(num_workers, queue_size, std::move(sampler)), : MappableLeafOp(num_workers, queue_size, std::move(sampler), rows_per_buffer),
rows_per_buffer_(rows_per_buffer),
folder_path_(dir), folder_path_(dir),
decode_(decode), decode_(decode),
extensions_(exts), extensions_(exts),
@ -269,121 +268,8 @@ std::vector<std::string> CelebAOp::Split(const std::string &line) {
return split; return split;
} }
// Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work Status CelebAOp::LoadTensorRow(row_id_type row_id, TensorRow *row) {
Status CelebAOp::operator()() { std::pair<std::string, std::vector<int32_t>> &image_label = image_labels_vec_[row_id];
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> data_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&data_buffer));
RETURN_IF_NOT_OK(AddIOBlock(&data_buffer));
return Status::OK();
}
Status CelebAOp::AddIOBlock(std::unique_ptr<DataBuffer> *data_buffer) {
int64_t buff_count = 0;
while (true) {
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
int64_t row_count = 0;
while (!(*data_buffer)->eoe()) {
TensorRow sample_row;
RETURN_IF_NOT_OK((*data_buffer)->PopRow(&sample_row));
std::shared_ptr<Tensor> sample_ids = sample_row[0];
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) >= num_rows_) {
MS_LOG(WARNING) << "Sample Id (" << *itr << ") is out of bounds, skipping. Max id is " << num_rows_ << ".";
continue;
}
keys.push_back(*itr);
row_count++;
if (row_count % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(io_block_queues_[buff_count++ % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
keys.clear();
}
}
RETURN_IF_NOT_OK(sampler_->GetNextSample(data_buffer));
}
if (!keys.empty()) {
RETURN_IF_NOT_OK(io_block_queues_[(buff_count++) % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
}
if (IsLastIteration()) {
RETURN_IF_NOT_OK(
io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
RETURN_IF_NOT_OK(
io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
for (int32_t i = 0; i < num_workers_; i++) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else { // not the last repeat.
RETURN_IF_NOT_OK(
io_block_queues_[(buff_count++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(data_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
Status CelebAOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty()) {
return Status::OK(); // empty key is a quit signal for workers
}
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
return Status(StatusCode::kMDUnexpectedError, __LINE__, __FILE__, "Unexpected nullptr received in worker.");
}
Status CelebAOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
for (const auto &key : keys) {
TensorRow row;
RETURN_IF_NOT_OK(LoadTensorRow(key, image_labels_vec_[key], &row));
deq->push_back(std::move(row));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
Status CelebAOp::LoadTensorRow(row_id_type row_id, const std::pair<std::string, std::vector<int32_t>> &image_label,
TensorRow *row) {
std::shared_ptr<Tensor> image; std::shared_ptr<Tensor> image;
std::shared_ptr<Tensor> label; std::shared_ptr<Tensor> label;
@ -432,13 +318,6 @@ void CelebAOp::Print(std::ostream &out, bool show_all) const {
} }
} }
// Reset Sampler and wakeup Master thread (functor)
Status CelebAOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(sampler_->ResetSampler());
return Status::OK();
}
Status CelebAOp::ComputeColMap() { Status CelebAOp::ComputeColMap() {
// Set the column name map (base class field) // Set the column name map (base class field)
if (column_name_id_map_.empty()) { if (column_name_id_map_.empty()) {

34
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/celeba_op.h
View File

@ -27,6 +27,7 @@
#include "minddata/dataset/util/status.h" #include "minddata/dataset/util/status.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#include "minddata/dataset/util/queue.h" #include "minddata/dataset/util/queue.h"
#include "minddata/dataset/engine/datasetops/source/io_block.h" #include "minddata/dataset/engine/datasetops/source/io_block.h"
@ -41,7 +42,7 @@
namespace mindspore { namespace mindspore {
namespace dataset { namespace dataset {
class CelebAOp : public ParallelOp, RandomAccessOp { class CelebAOp : public MappableLeafOp {
public: public:
class Builder { class Builder {
public: public:
@ -148,27 +149,11 @@ class CelebAOp : public ParallelOp, RandomAccessOp {
~CelebAOp() override = default; ~CelebAOp() override = default;
// Main Loop of CelebAOp
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it then put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param int32_t worker_id - id of each worker
// @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
// A print method typically used for debugging // A print method typically used for debugging
// @param out // @param out
// @param show_all // @param show_all
void Print(std::ostream &out, bool show_all) const override; void Print(std::ostream &out, bool show_all) const override;
// Method in operator(), to fill IOBlockQueue
// @param std::unique_ptr<DataBuffer> sampler_buffer - to fill IOBlockQueue
// @return Status The status code returned
Status AddIOBlock(std::unique_ptr<DataBuffer> *data_buffer);
// Op name getter // Op name getter
// @return Name of the current Op // @return Name of the current Op
std::string Name() const override { return "CelebAOp"; } std::string Name() const override { return "CelebAOp"; }
@ -176,7 +161,7 @@ class CelebAOp : public ParallelOp, RandomAccessOp {
private: private:
// Called first when function is called // Called first when function is called
// @return // @return
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
// Parse attribute file // Parse attribute file
// @return // @return
@ -191,32 +176,21 @@ class CelebAOp : public ParallelOp, RandomAccessOp {
// @return std::vector<std::string> - string after split // @return std::vector<std::string> - string after split
std::vector<std::string> Split(const std::string &line); std::vector<std::string> Split(const std::string &line);
// @param const std::vector<int64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
// Load a tensor row according to a pair // Load a tensor row according to a pair
// @param row_id_type row_id - id for this tensor row // @param row_id_type row_id - id for this tensor row
// @param std::pair - <image_file,<label>> // @param std::pair - <image_file,<label>>
// @param TensorRow row - image & label read into this tensor row // @param TensorRow row - image & label read into this tensor row
// @return Status The status code returned // @return Status The status code returned
Status LoadTensorRow(row_id_type row_id, const std::pair<std::string, std::vector<int32_t>> &image_label, Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
TensorRow *row);
// Check if need read according to dataset type // Check if need read according to dataset type
// @return bool - if need read // @return bool - if need read
bool CheckDatasetTypeValid(); bool CheckDatasetTypeValid();
// reset Op
// @return Status The status code returned
Status Reset() override;
// Private function for computing the assignment of the column name map. // Private function for computing the assignment of the column name map.
// @return - Status // @return - Status
Status ComputeColMap() override; Status ComputeColMap() override;
int32_t rows_per_buffer_;
std::string folder_path_; // directory of celeba folder std::string folder_path_; // directory of celeba folder
bool decode_; bool decode_;
std::set<std::string> extensions_; // extensions allowed std::set<std::string> extensions_; // extensions allowed

127
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/cifar_op.cc
View File

@ -88,76 +88,16 @@ Status CifarOp::Builder::SanityCheck() {
CifarOp::CifarOp(CifarType type, const std::string &usage, int32_t num_works, int32_t rows_per_buf, CifarOp::CifarOp(CifarType type, const std::string &usage, int32_t num_works, int32_t rows_per_buf,
const std::string &file_dir, int32_t queue_size, std::unique_ptr<DataSchema> data_schema, const std::string &file_dir, int32_t queue_size, std::unique_ptr<DataSchema> data_schema,
std::shared_ptr<SamplerRT> sampler) std::shared_ptr<SamplerRT> sampler)
: ParallelOp(num_works, queue_size, std::move(sampler)), : MappableLeafOp(num_works, queue_size, std::move(sampler), rows_per_buf),
cifar_type_(type), cifar_type_(type),
usage_(usage), usage_(usage),
rows_per_buffer_(rows_per_buf),
folder_path_(file_dir), folder_path_(file_dir),
data_schema_(std::move(data_schema)), data_schema_(std::move(data_schema)) {
row_cnt_(0),
buf_cnt_(0) {
constexpr uint64_t kUtilQueueSize = 512; constexpr uint64_t kUtilQueueSize = 512;
cifar_raw_data_block_ = std::make_unique<Queue<std::vector<unsigned char>>>(kUtilQueueSize); cifar_raw_data_block_ = std::make_unique<Queue<std::vector<unsigned char>>>(kUtilQueueSize);
io_block_queues_.Init(num_workers_, queue_size); io_block_queues_.Init(num_workers_, queue_size);
} }
// Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work
Status CifarOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
while (true) { // each iterator is 1 epoch
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (sampler_buffer->eoe() == false) {
TensorRow sample_row;
RETURN_IF_NOT_OK(sampler_buffer->PopRow(&sample_row));
std::shared_ptr<Tensor> sample_ids = sample_row[0];
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); itr++) {
keys.push_back(*itr);
row_cnt_++;
if ((*itr) >= num_rows_) continue; // index out of bound, skipping
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
keys.clear();
}
}
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
}
if (IsLastIteration()) {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
for (int32_t i = 0; i < num_workers_; i++) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else { // not the last repeat.
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
Status CifarOp::LaunchThreadsAndInitOp() { Status CifarOp::LaunchThreadsAndInitOp() {
if (tree_ == nullptr) { if (tree_ == nullptr) {
RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set."); RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set.");
@ -175,43 +115,8 @@ Status CifarOp::LaunchThreadsAndInitOp() {
return Status::OK(); return Status::OK();
} }
// contains the main logic of pulling a IOBlock from IOBlockQueue, load a buffer and push the buffer to out_connector_
// IMPORTANT: 1 IOBlock produces 1 DataBuffer
Status CifarOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty() == true) {
return Status::OK(); // empty key is a quit signal for workers
}
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker.");
}
// Load 1 TensorRow (image,label). 1 function call produces 1 TensorTow in a DataBuffer // Load 1 TensorRow (image,label). 1 function call produces 1 TensorTow in a DataBuffer
Status CifarOp::LoadTensorRow(uint64_t index, TensorRow *trow) { Status CifarOp::LoadTensorRow(row_id_type index, TensorRow *trow) {
std::shared_ptr<Tensor> label; std::shared_ptr<Tensor> label;
std::shared_ptr<Tensor> fine_label; std::shared_ptr<Tensor> fine_label;
std::shared_ptr<Tensor> ori_image = cifar_image_label_pairs_[index].first; std::shared_ptr<Tensor> ori_image = cifar_image_label_pairs_[index].first;
@ -234,18 +139,6 @@ Status CifarOp::LoadTensorRow(uint64_t index, TensorRow *trow) {
return Status::OK(); return Status::OK();
} }
// Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
Status CifarOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
for (const int64_t &key : keys) {
TensorRow trow;
RETURN_IF_NOT_OK(LoadTensorRow(key, &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
void CifarOp::Print(std::ostream &out, bool show_all) const { void CifarOp::Print(std::ostream &out, bool show_all) const {
if (!show_all) { if (!show_all) {
// Call the super class for displaying any common 1-liner info // Call the super class for displaying any common 1-liner info
@ -260,20 +153,6 @@ void CifarOp::Print(std::ostream &out, bool show_all) const {
} }
} }
// Reset Sampler and wakeup Master thread (functor)
Status CifarOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(sampler_->ResetSampler());
row_cnt_ = 0;
return Status::OK();
}
// hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
Status CifarOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
Status CifarOp::ReadCifarBlockDataAsync() { Status CifarOp::ReadCifarBlockDataAsync() {
TaskManager::FindMe()->Post(); TaskManager::FindMe()->Post();
RETURN_IF_NOT_OK(GetCifarFiles()); RETURN_IF_NOT_OK(GetCifarFiles());

35
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/cifar_op.h
View File

@ -26,6 +26,7 @@
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#include "minddata/dataset/util/path.h" #include "minddata/dataset/util/path.h"
#include "minddata/dataset/util/queue.h" #include "minddata/dataset/util/queue.h"
@ -35,7 +36,7 @@
namespace mindspore { namespace mindspore {
namespace dataset { namespace dataset {
class CifarOp : public ParallelOp, public RandomAccessOp { class CifarOp : public MappableLeafOp {
public: public:
enum CifarType { kCifar10, kCifar100 }; enum CifarType { kCifar10, kCifar100 };
@ -142,17 +143,6 @@ class CifarOp : public ParallelOp, public RandomAccessOp {
// Destructor. // Destructor.
~CifarOp() = default; ~CifarOp() = default;
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param uint32_t workerId - id of each worker
// @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
// Main Loop of CifarOp
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it then put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// A print method typically used for debugging // A print method typically used for debugging
// @param out // @param out
// @param show_all // @param show_all
@ -170,32 +160,20 @@ class CifarOp : public ParallelOp, public RandomAccessOp {
std::string Name() const override { return "CifarOp"; } std::string Name() const override { return "CifarOp"; }
private: private:
// Initialize Sampler, calls sampler->Init() within
// @return Status The status code returned
Status InitSampler();
// Load a tensor row according to a pair // Load a tensor row according to a pair
// @param uint64_t index - index need to load // @param uint64_t index - index need to load
// @param TensorRow row - image & label read into this tensor row // @param TensorRow row - image & label read into this tensor row
// @return Status The status code returned // @return Status The status code returned
Status LoadTensorRow(uint64_t index, TensorRow *row); Status LoadTensorRow(row_id_type index, TensorRow *trow) override;
// @param const std::vector<uint64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
private:
// Read block data from cifar file // Read block data from cifar file
// @return // @return
Status ReadCifarBlockDataAsync(); Status ReadCifarBlockDataAsync();
// Called first when function is called // Called first when function is called
// @return // @return
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
// reset Op
// @return Status The status code returned
Status Reset() override;
// Get cifar files in dir // Get cifar files in dir
// @return // @return
@ -223,12 +201,9 @@ class CifarOp : public ParallelOp, public RandomAccessOp {
Status ComputeColMap() override; Status ComputeColMap() override;
CifarType cifar_type_; CifarType cifar_type_;
int32_t rows_per_buffer_;
std::string folder_path_; std::string folder_path_;
std::unique_ptr<DataSchema> data_schema_; std::unique_ptr<DataSchema> data_schema_;
int64_t row_cnt_;
int64_t buf_cnt_;
const std::string usage_; // can only be either "train" or "test" const std::string usage_; // can only be either "train" or "test"
std::unique_ptr<Queue<std::vector<unsigned char>>> cifar_raw_data_block_; std::unique_ptr<Queue<std::vector<unsigned char>>> cifar_raw_data_block_;
std::vector<std::string> cifar_files_; std::vector<std::string> cifar_files_;

126
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/coco_op.cc
View File

@ -124,82 +124,15 @@ Status CocoOp::Builder::SanityCheck() {
CocoOp::CocoOp(const TaskType &task_type, const std::string &image_folder_path, const std::string &annotation_path, CocoOp::CocoOp(const TaskType &task_type, const std::string &image_folder_path, const std::string &annotation_path,
int32_t num_workers, int32_t rows_per_buffer, int32_t queue_size, bool decode, int32_t num_workers, int32_t rows_per_buffer, int32_t queue_size, bool decode,
std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler) std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler)
: ParallelOp(num_workers, queue_size, std::move(sampler)), : MappableLeafOp(num_workers, queue_size, std::move(sampler), rows_per_buffer),
decode_(decode), decode_(decode),
row_cnt_(0),
buf_cnt_(0),
task_type_(task_type), task_type_(task_type),
image_folder_path_(image_folder_path), image_folder_path_(image_folder_path),
annotation_path_(annotation_path), annotation_path_(annotation_path),
rows_per_buffer_(rows_per_buffer),
data_schema_(std::move(data_schema)) { data_schema_(std::move(data_schema)) {
io_block_queues_.Init(num_workers_, queue_size); io_block_queues_.Init(num_workers_, queue_size);
} }
Status CocoOp::TraverseSampleIds(const std::shared_ptr<Tensor> &sample_ids, std::vector<int64_t> *keys) {
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) > num_rows_) continue;
keys->push_back(*itr);
row_cnt_++;
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(*keys, IOBlock::kDeIoBlockNone))));
keys->clear();
}
}
return Status::OK();
}
Status CocoOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
while (true) {
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (sampler_buffer->eoe() == false) {
std::shared_ptr<Tensor> sample_ids;
RETURN_IF_NOT_OK(sampler_buffer->GetTensor(&sample_ids, 0, 0));
if (sample_ids->type() != DataType(DataType::DE_INT64)) {
RETURN_STATUS_UNEXPECTED("Invalid parameter, data type of Sampler Tensor isn't int64, got " +
sample_ids->type().ToString());
}
RETURN_IF_NOT_OK(TraverseSampleIds(sample_ids, &keys));
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
}
if (IsLastIteration()) {
std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
std::unique_ptr<IOBlock> eof_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block)));
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block)));
for (int32_t i = 0; i < num_workers_; i++) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
void CocoOp::Print(std::ostream &out, bool show_all) const { void CocoOp::Print(std::ostream &out, bool show_all) const {
if (!show_all) { if (!show_all) {
// Call the super class for displaying any common 1-liner info // Call the super class for displaying any common 1-liner info
@ -215,14 +148,8 @@ void CocoOp::Print(std::ostream &out, bool show_all) const {
} }
} }
Status CocoOp::Reset() { Status CocoOp::LoadTensorRow(row_id_type row_id, TensorRow *trow) {
MS_LOG(DEBUG) << Name() << " performing a self-reset."; std::string image_id = image_ids_[row_id];
RETURN_IF_NOT_OK(sampler_->ResetSampler());
row_cnt_ = 0;
return Status::OK();
}
Status CocoOp::LoadTensorRow(row_id_type row_id, const std::string &image_id, TensorRow *trow) {
std::shared_ptr<Tensor> image, coordinate; std::shared_ptr<Tensor> image, coordinate;
auto itr = coordinate_map_.find(image_id); auto itr = coordinate_map_.find(image_id);
if (itr == coordinate_map_.end()) { if (itr == coordinate_map_.end()) {
@ -374,48 +301,6 @@ Status CocoOp::LoadMixTensorRow(row_id_type row_id, const std::string &image_id,
return Status::OK(); return Status::OK();
} }
Status CocoOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
TensorRow trow;
for (const int64_t &key : keys) {
RETURN_IF_NOT_OK(this->LoadTensorRow(key, image_ids_[key], &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
Status CocoOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, (std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty() == true) return Status::OK();
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker");
}
template <typename T> template <typename T>
Status CocoOp::SearchNodeInJson(const nlohmann::json &input_tree, std::string node_name, T *output_node) { Status CocoOp::SearchNodeInJson(const nlohmann::json &input_tree, std::string node_name, T *output_node) {
auto node = input_tree.find(node_name); auto node = input_tree.find(node_name);
@ -627,11 +512,6 @@ Status CocoOp::CategoriesColumnLoad(const nlohmann::json &categories_tree) {
return Status::OK(); return Status::OK();
} }
Status CocoOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
Status CocoOp::LaunchThreadsAndInitOp() { Status CocoOp::LaunchThreadsAndInitOp() {
if (tree_ == nullptr) { if (tree_ == nullptr) {
RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set."); RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set.");

39
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/coco_op.h
View File

@ -27,6 +27,7 @@
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#ifndef ENABLE_ANDROID #ifndef ENABLE_ANDROID
#include "minddata/dataset/kernels/image/image_utils.h" #include "minddata/dataset/kernels/image/image_utils.h"
@ -46,7 +47,7 @@ class Queue;
using CoordinateRow = std::vector<std::vector<float>>; using CoordinateRow = std::vector<std::vector<float>>;
class CocoOp : public ParallelOp, public RandomAccessOp { class CocoOp : public MappableLeafOp {
public: public:
enum class TaskType { Detection = 0, Stuff = 1, Panoptic = 2, Keypoint = 3 }; enum class TaskType { Detection = 0, Stuff = 1, Panoptic = 2, Keypoint = 3 };
@ -171,17 +172,6 @@ class CocoOp : public ParallelOp, public RandomAccessOp {
// Destructor // Destructor
~CocoOp() = default; ~CocoOp() = default;
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param int32_t workerId - id of each worker
// @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
// Main Loop of CocoOp
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it the put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// A print method typically used for debugging // A print method typically used for debugging
// @param out // @param out
// @param show_all // @param show_all
@ -212,16 +202,12 @@ class CocoOp : public ParallelOp, public RandomAccessOp {
Status GetClassIndexing(std::vector<std::pair<std::string, std::vector<int32_t>>> *output_class_indexing) override; Status GetClassIndexing(std::vector<std::pair<std::string, std::vector<int32_t>>> *output_class_indexing) override;
private: private:
// Initialize Sampler, calls sampler->Init() within
// @return Status The status code returned
Status InitSampler();
// Load a tensor row according to image id // Load a tensor row according to image id
// @param row_id_type row_id - id for this tensor row // @param row_id_type row_id - id for this tensor row
// @param std::string image_id - image id // @param std::string image_id - image id
// @param TensorRow row - image & target read into this tensor row // @param TensorRow row - image & target read into this tensor row
// @return Status The status code returned // @return Status The status code returned
Status LoadTensorRow(row_id_type row_id, const std::string &image_id, TensorRow *row); Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
// Load a tensor row with vector which a vector to a tensor // Load a tensor row with vector which a vector to a tensor
// @param row_id_type row_id - id for this tensor row // @param row_id_type row_id - id for this tensor row
@ -259,27 +245,13 @@ class CocoOp : public ParallelOp, public RandomAccessOp {
// @return Status The status code returned // @return Status The status code returned
Status ReadImageToTensor(const std::string &path, const ColDescriptor &col, std::shared_ptr<Tensor> *tensor); Status ReadImageToTensor(const std::string &path, const ColDescriptor &col, std::shared_ptr<Tensor> *tensor);
// @param const std::vector<uint64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
// Read annotation from Annotation folder // Read annotation from Annotation folder
// @return Status The status code returned // @return Status The status code returned
Status ParseAnnotationIds(); Status ParseAnnotationIds();
// @param const std::shared_ptr<Tensor> &sample_ids - sample ids of tensor
// @param std::vector<int64_t> *keys - image id
// @return Status The status code returned
Status TraverseSampleIds(const std::shared_ptr<Tensor> &sample_ids, std::vector<int64_t> *keys);
// Called first when function is called // Called first when function is called
// @return Status The status code returned // @return Status The status code returned
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
// Reset dataset state
// @return Status The status code returned
Status Reset() override;
// @param nlohmann::json image_tree - image tree of json // @param nlohmann::json image_tree - image tree of json
// @param std::vector<std::string> *image_vec - image id list of json // @param std::vector<std::string> *image_vec - image id list of json
@ -323,12 +295,9 @@ class CocoOp : public ParallelOp, public RandomAccessOp {
Status ComputeColMap() override; Status ComputeColMap() override;
bool decode_; bool decode_;
int64_t row_cnt_;
int64_t buf_cnt_;
std::string image_folder_path_; std::string image_folder_path_;
std::string annotation_path_; std::string annotation_path_;
TaskType task_type_; TaskType task_type_;
int32_t rows_per_buffer_;
std::unique_ptr<DataSchema> data_schema_; std::unique_ptr<DataSchema> data_schema_;
std::vector<std::string> image_ids_; std::vector<std::string> image_ids_;

124
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/image_folder_op.cc
View File

@ -68,16 +68,13 @@ ImageFolderOp::ImageFolderOp(int32_t num_wkrs, int32_t rows_per_buffer, std::str
bool recursive, bool do_decode, const std::set<std::string> &exts, bool recursive, bool do_decode, const std::set<std::string> &exts,
const std::map<std::string, int32_t> &map, std::unique_ptr<DataSchema> data_schema, const std::map<std::string, int32_t> &map, std::unique_ptr<DataSchema> data_schema,
std::shared_ptr<SamplerRT> sampler) std::shared_ptr<SamplerRT> sampler)
: ParallelOp(num_wkrs, queue_size, std::move(sampler)), : MappableLeafOp(num_wkrs, queue_size, std::move(sampler), rows_per_buffer),
rows_per_buffer_(rows_per_buffer),
folder_path_(file_dir), folder_path_(file_dir),
recursive_(recursive), recursive_(recursive),
decode_(do_decode), decode_(do_decode),
extensions_(exts), extensions_(exts),
class_index_(map), class_index_(map),
data_schema_(std::move(data_schema)), data_schema_(std::move(data_schema)),
row_cnt_(0),
buf_cnt_(0),
sampler_ind_(0), sampler_ind_(0),
dirname_offset_(0) { dirname_offset_(0) {
folder_name_queue_ = std::make_unique<Queue<std::string>>(num_wkrs * queue_size); folder_name_queue_ = std::make_unique<Queue<std::string>>(num_wkrs * queue_size);
@ -125,98 +122,9 @@ Status ImageFolderOp::PrescanMasterEntry(const std::string &filedir) {
return Status::OK(); return Status::OK();
} }
// Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work
Status ImageFolderOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
while (true) { // each iterator is 1 epoch
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (sampler_buffer->eoe() == false) {
TensorRow sample_row;
RETURN_IF_NOT_OK(sampler_buffer->PopRow(&sample_row));
std::shared_ptr<Tensor> sample_ids = sample_row[0];
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) >= num_rows_) continue; // index out of bound, skipping
keys.push_back(*itr);
row_cnt_++;
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(
io_block_queues_[buf_cnt_++ % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
keys.clear();
}
}
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
}
if (IsLastIteration()) {
std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
std::unique_ptr<IOBlock> eof_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block)));
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block)));
for (int32_t i = 0; i < num_workers_; ++i) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else { // not the last repeat.
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
// contains the main logic of pulling a IOBlock from IOBlockQueue, load a buffer and push the buffer to out_connector_
// IMPORTANT: 1 IOBlock produces 1 DataBuffer
Status ImageFolderOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty() == true) return Status::OK(); // empty key is a quit signal for workers
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker");
}
// Load 1 TensorRow (image,label) using 1 ImageLabelPair. 1 function call produces 1 TensorTow in a DataBuffer // Load 1 TensorRow (image,label) using 1 ImageLabelPair. 1 function call produces 1 TensorTow in a DataBuffer
Status ImageFolderOp::LoadTensorRow(row_id_type row_id, ImageLabelPair pairPtr, TensorRow *trow) { Status ImageFolderOp::LoadTensorRow(row_id_type row_id, TensorRow *trow) {
ImageLabelPair pairPtr = image_label_pairs_[row_id];
std::shared_ptr<Tensor> image, label; std::shared_ptr<Tensor> image, label;
RETURN_IF_NOT_OK(Tensor::CreateScalar(pairPtr->second, &label)); RETURN_IF_NOT_OK(Tensor::CreateScalar(pairPtr->second, &label));
RETURN_IF_NOT_OK(Tensor::CreateFromFile(folder_path_ + (pairPtr->first), &image)); RETURN_IF_NOT_OK(Tensor::CreateFromFile(folder_path_ + (pairPtr->first), &image));
@ -233,18 +141,6 @@ Status ImageFolderOp::LoadTensorRow(row_id_type row_id, ImageLabelPair pairPtr,
return Status::OK(); return Status::OK();
} }
// Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
Status ImageFolderOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
TensorRow trow;
for (const int64_t &key : keys) {
RETURN_IF_NOT_OK(this->LoadTensorRow(key, image_label_pairs_[key], &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
void ImageFolderOp::Print(std::ostream &out, bool show_all) const { void ImageFolderOp::Print(std::ostream &out, bool show_all) const {
if (!show_all) { if (!show_all) {
// Call the super class for displaying any common 1-liner info // Call the super class for displaying any common 1-liner info
@ -260,20 +156,6 @@ void ImageFolderOp::Print(std::ostream &out, bool show_all) const {
} }
} }
// Reset Sampler and wakeup Master thread (functor)
Status ImageFolderOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(sampler_->ResetSampler());
row_cnt_ = 0;
return Status::OK();
}
// hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
Status ImageFolderOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
// Derived from RandomAccessOp // Derived from RandomAccessOp
Status ImageFolderOp::GetClassIds(std::map<int32_t, std::vector<int64_t>> *cls_ids) const { Status ImageFolderOp::GetClassIds(std::map<int32_t, std::vector<int64_t>> *cls_ids) const {
if (cls_ids == nullptr || !cls_ids->empty() || image_label_pairs_.empty()) { if (cls_ids == nullptr || !cls_ids->empty() || image_label_pairs_.empty()) {

34
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/image_folder_op.h
View File

@ -29,6 +29,7 @@
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#ifndef ENABLE_ANDROID #ifndef ENABLE_ANDROID
#include "minddata/dataset/kernels/image/image_utils.h" #include "minddata/dataset/kernels/image/image_utils.h"
@ -50,7 +51,7 @@ class Queue;
using ImageLabelPair = std::shared_ptr<std::pair<std::string, int32_t>>; using ImageLabelPair = std::shared_ptr<std::pair<std::string, int32_t>>;
using FolderImagesPair = std::shared_ptr<std::pair<std::string, std::queue<ImageLabelPair>>>; using FolderImagesPair = std::shared_ptr<std::pair<std::string, std::queue<ImageLabelPair>>>;
class ImageFolderOp : public ParallelOp, public RandomAccessOp { class ImageFolderOp : public MappableLeafOp {
public: public:
class Builder { class Builder {
public: public:
@ -175,22 +176,11 @@ class ImageFolderOp : public ParallelOp, public RandomAccessOp {
// @return Status The status code returned // @return Status The status code returned
Status PrescanMasterEntry(const std::string &dir); Status PrescanMasterEntry(const std::string &dir);
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param int32_t workerId - id of each worker
// @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector // Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param int32_t workerId - id of each worker // @param int32_t workerId - id of each worker
// @return Status The status code returned // @return Status The status code returned
Status PrescanWorkerEntry(int32_t worker_id); Status PrescanWorkerEntry(int32_t worker_id);
// Main Loop of ImageFolderOp
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it then put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// Method derived from RandomAccess Op, enable Sampler to get all ids for each class // Method derived from RandomAccess Op, enable Sampler to get all ids for each class
// @param (std::map<int64_t, std::vector<int64_t >> * map - key label, val all ids for this class // @param (std::map<int64_t, std::vector<int64_t >> * map - key label, val all ids for this class
// @return Status The status code returned // @return Status The status code returned
@ -217,21 +207,12 @@ class ImageFolderOp : public ParallelOp, public RandomAccessOp {
Status GetNumClasses(int64_t *num_classes) override; Status GetNumClasses(int64_t *num_classes) override;
private: private:
// Initialize Sampler, calls sampler->Init() within
// @return Status The status code returned
Status InitSampler();
// Load a tensor row according to a pair // Load a tensor row according to a pair
// @param row_id_type row_id - id for this tensor row // @param row_id_type row_id - id for this tensor row
// @param ImageLabelPair pair - <imagefile,label> // @param ImageLabelPair pair - <imagefile,label>
// @param TensorRow row - image & label read into this tensor row // @param TensorRow row - image & label read into this tensor row
// @return Status The status code returned // @return Status The status code returned
Status LoadTensorRow(row_id_type row_id, ImageLabelPair pair, TensorRow *row); Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
// @param const std::vector<int64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
// @param std::string & dir - dir to walk all images // @param std::string & dir - dir to walk all images
// @param int64_t * cnt - number of non folder files under the current dir // @param int64_t * cnt - number of non folder files under the current dir
@ -244,25 +225,18 @@ class ImageFolderOp : public ParallelOp, public RandomAccessOp {
// Called first when function is called // Called first when function is called
// @return // @return
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
// reset Op
// @return Status The status code returned
Status Reset() override;
// Private function for computing the assignment of the column name map. // Private function for computing the assignment of the column name map.
// @return - Status // @return - Status
Status ComputeColMap() override; Status ComputeColMap() override;
int32_t rows_per_buffer_;
std::string folder_path_; // directory of image folder std::string folder_path_; // directory of image folder
bool recursive_; bool recursive_;
bool decode_; bool decode_;
std::set<std::string> extensions_; // extensions allowed std::set<std::string> extensions_; // extensions allowed
std::map<std::string, int32_t> class_index_; std::map<std::string, int32_t> class_index_;
std::unique_ptr<DataSchema> data_schema_; std::unique_ptr<DataSchema> data_schema_;
int64_t row_cnt_;
int64_t buf_cnt_;
int64_t sampler_ind_; int64_t sampler_ind_;
int64_t dirname_offset_; int64_t dirname_offset_;
std::vector<ImageLabelPair> image_label_pairs_; std::vector<ImageLabelPair> image_label_pairs_;

133
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/manifest_op.cc
View File

@ -67,82 +67,18 @@ Status ManifestOp::Builder::SanityCheck() {
ManifestOp::ManifestOp(int32_t num_works, int32_t rows_per_buffer, std::string file, int32_t queue_size, bool decode, ManifestOp::ManifestOp(int32_t num_works, int32_t rows_per_buffer, std::string file, int32_t queue_size, bool decode,
const std::map<std::string, int32_t> &class_index, std::unique_ptr<DataSchema> data_schema, const std::map<std::string, int32_t> &class_index, std::unique_ptr<DataSchema> data_schema,
std::shared_ptr<SamplerRT> sampler, std::string usage) std::shared_ptr<SamplerRT> sampler, std::string usage)
: ParallelOp(num_works, queue_size, std::move(sampler)), : MappableLeafOp(num_works, queue_size, std::move(sampler), rows_per_buffer),
rows_per_buffer_(rows_per_buffer),
io_block_pushed_(0), io_block_pushed_(0),
row_cnt_(0),
sampler_ind_(0), sampler_ind_(0),
data_schema_(std::move(data_schema)), data_schema_(std::move(data_schema)),
file_(file), file_(file),
class_index_(class_index), class_index_(class_index),
decode_(decode), decode_(decode),
usage_(usage), usage_(usage) {
buf_cnt_(0) {
io_block_queues_.Init(num_workers_, queue_size); io_block_queues_.Init(num_workers_, queue_size);
(void)std::transform(usage_.begin(), usage_.end(), usage_.begin(), ::tolower); (void)std::transform(usage_.begin(), usage_.end(), usage_.begin(), ::tolower);
} }
// Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work
Status ManifestOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
return AddIoBlock(&sampler_buffer);
}
Status ManifestOp::AddIoBlock(std::unique_ptr<DataBuffer> *sampler_buffer) {
while (true) { // each iterator is 1 epoch
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (!(*sampler_buffer)->eoe()) {
TensorRow sample_row;
RETURN_IF_NOT_OK((*sampler_buffer)->PopRow(&sample_row));
std::shared_ptr<Tensor> sample_ids = sample_row[0];
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) >= num_rows_) continue; // index out of bound, skipping
keys.push_back(*itr);
row_cnt_++;
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
keys.clear();
}
}
RETURN_IF_NOT_OK(sampler_->GetNextSample(sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
}
if (IsLastIteration()) {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
for (int32_t i = 0; i < num_workers_; i++) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
Status ManifestOp::LaunchThreadsAndInitOp() { Status ManifestOp::LaunchThreadsAndInitOp() {
if (tree_ == nullptr) { if (tree_ == nullptr) {
RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set."); RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set.");
@ -159,44 +95,9 @@ Status ManifestOp::LaunchThreadsAndInitOp() {
return Status::OK(); return Status::OK();
} }
// contains the main logic of pulling a IOBlock from IOBlockQueue, load a buffer and push the buffer to out_connector_
// IMPORTANT: 1 IOBlock produces 1 DataBuffer
Status ManifestOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty()) {
return Status::OK(); // empty key is a quit signal for workers
}
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker.");
}
// Load 1 TensorRow (image,label) using 1 ImageLabelPair. 1 function call produces 1 TensorTow in a DataBuffer // Load 1 TensorRow (image,label) using 1 ImageLabelPair. 1 function call produces 1 TensorTow in a DataBuffer
Status ManifestOp::LoadTensorRow(row_id_type row_id, const std::pair<std::string, std::vector<std::string>> &data, Status ManifestOp::LoadTensorRow(row_id_type row_id, TensorRow *trow) {
TensorRow *trow) { std::pair<std::string, std::vector<std::string>> data = image_labelname_[static_cast<size_t>(row_id)];
std::shared_ptr<Tensor> image; std::shared_ptr<Tensor> image;
std::shared_ptr<Tensor> label; std::shared_ptr<Tensor> label;
std::vector<int32_t> label_index(data.second.size()); std::vector<int32_t> label_index(data.second.size());
@ -222,18 +123,6 @@ Status ManifestOp::LoadTensorRow(row_id_type row_id, const std::pair<std::string
return Status::OK(); return Status::OK();
} }
// Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
Status ManifestOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
for (const auto &key : keys) {
TensorRow trow;
RETURN_IF_NOT_OK(LoadTensorRow(key, image_labelname_[static_cast<size_t>(key)], &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
void ManifestOp::Print(std::ostream &out, bool show_all) const { void ManifestOp::Print(std::ostream &out, bool show_all) const {
if (!show_all) { if (!show_all) {
// Call the super class for displaying any common 1-liner info // Call the super class for displaying any common 1-liner info
@ -249,20 +138,6 @@ void ManifestOp::Print(std::ostream &out, bool show_all) const {
} }
} }
// Reset Sampler and wakeup Master thread (functor)
Status ManifestOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(sampler_->ResetSampler());
row_cnt_ = 0;
return Status::OK();
}
// hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
Status ManifestOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
// Derived from RandomAccessOp // Derived from RandomAccessOp
Status ManifestOp::GetClassIds(std::map<int32_t, std::vector<int64_t>> *cls_ids) const { Status ManifestOp::GetClassIds(std::map<int32_t, std::vector<int64_t>> *cls_ids) const {
if (cls_ids == nullptr || !cls_ids->empty() || image_labelname_.empty()) { if (cls_ids == nullptr || !cls_ids->empty() || image_labelname_.empty()) {

40
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/manifest_op.h
View File

@ -26,6 +26,7 @@
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#include "minddata/dataset/kernels/image/image_utils.h" #include "minddata/dataset/kernels/image/image_utils.h"
#include "minddata/dataset/util/queue.h" #include "minddata/dataset/util/queue.h"
@ -35,7 +36,7 @@
namespace mindspore { namespace mindspore {
namespace dataset { namespace dataset {
class ManifestOp : public ParallelOp, public RandomAccessOp { class ManifestOp : public MappableLeafOp {
public: public:
class Builder { class Builder {
public: public:
@ -143,17 +144,6 @@ class ManifestOp : public ParallelOp, public RandomAccessOp {
// Destructor. // Destructor.
~ManifestOp() = default; ~ManifestOp() = default;
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param int32_t worker_id - id of each worker
// @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
// Main Loop of ManifestOp
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it then put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// Method derived from RandomAccess Op, enable Sampler to get all ids for each class // Method derived from RandomAccess Op, enable Sampler to get all ids for each class
// @param (std::map<int64_t, std::vector<int64_t >> * map - key label, val all ids for this class // @param (std::map<int64_t, std::vector<int64_t >> * map - key label, val all ids for this class
// @return Status The status code returned // @return Status The status code returned
@ -194,27 +184,12 @@ class ManifestOp : public ParallelOp, public RandomAccessOp {
Status GetClassIndexing(std::vector<std::pair<std::string, std::vector<int32_t>>> *output_class_indexing) override; Status GetClassIndexing(std::vector<std::pair<std::string, std::vector<int32_t>>> *output_class_indexing) override;
private: private:
// Initialize Sampler, calls sampler->Init() within
// @return Status The status code returned
Status InitSampler();
// Method in operator(), to fill IOBlockQueue
// @param std::unique_ptr<DataBuffer> sampler_buffer - to fill IOBlockQueue
// @return Status The status code returned
Status AddIoBlock(std::unique_ptr<DataBuffer> *sampler_buffer);
// Load a tensor row according to a pair // Load a tensor row according to a pair
// @param row_id_type row_id - id for this tensor row // @param row_id_type row_id - id for this tensor row
// @param std::pair<std::string, std::vector<std::string>> - <imagefile, <label1, label2...>> // @param std::pair<std::string, std::vector<std::string>> - <imagefile, <label1, label2...>>
// @param TensorRow row - image & label read into this tensor row // @param TensorRow row - image & label read into this tensor row
// @return Status The status code returned // @return Status The status code returned
Status LoadTensorRow(row_id_type row_id, const std::pair<std::string, std::vector<std::string>> &data, Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
TensorRow *row);
// @param const std::vector<int64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
// Parse manifest file to get image path and label and so on. // Parse manifest file to get image path and label and so on.
// @return Status The status code returned // @return Status The status code returned
@ -222,11 +197,7 @@ class ManifestOp : public ParallelOp, public RandomAccessOp {
// Called first when function is called // Called first when function is called
// @return Status The status code returned // @return Status The status code returned
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
// reset Op
// @return Status The status code returned
Status Reset() override;
// Check if image ia valid.Only support JPEG/PNG/GIF/BMP // Check if image ia valid.Only support JPEG/PNG/GIF/BMP
// @return // @return
@ -240,16 +211,13 @@ class ManifestOp : public ParallelOp, public RandomAccessOp {
// @return - Status // @return - Status
Status ComputeColMap() override; Status ComputeColMap() override;
int32_t rows_per_buffer_;
int64_t io_block_pushed_; int64_t io_block_pushed_;
int64_t row_cnt_;
int64_t sampler_ind_; int64_t sampler_ind_;
std::unique_ptr<DataSchema> data_schema_; std::unique_ptr<DataSchema> data_schema_;
std::string file_; // file that store the information of images std::string file_; // file that store the information of images
std::map<std::string, int32_t> class_index_; std::map<std::string, int32_t> class_index_;
bool decode_; bool decode_;
std::string usage_; std::string usage_;
int64_t buf_cnt_;
std::map<std::string, int32_t> label_index_; std::map<std::string, int32_t> label_index_;
std::vector<std::pair<std::string, std::vector<std::string>>> image_labelname_; std::vector<std::pair<std::string, std::vector<std::string>>> image_labelname_;

152
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mappable_leaf_op.cc Normal file
View File

@ -0,0 +1,152 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include <fstream>
#include <unordered_set>
#include "utils/ms_utils.h"
#include "minddata/dataset/core/config_manager.h"
#include "minddata/dataset/core/tensor_shape.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
#include "minddata/dataset/engine/db_connector.h"
#include "minddata/dataset/engine/execution_tree.h"
namespace mindspore {
namespace dataset {
MappableLeafOp::MappableLeafOp(int32_t num_wkrs, int32_t queue_size, std::shared_ptr<SamplerRT> sampler,
int32_t rows_per_buffer)
: ParallelOp(num_wkrs, queue_size, std::move(sampler)),
row_cnt_(0),
buf_cnt_(0),
rows_per_buffer_(rows_per_buffer) {}
// Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work
Status MappableLeafOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
while (true) { // each iterator is 1 epoch
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (sampler_buffer->eoe() == false) {
TensorRow sample_row;
RETURN_IF_NOT_OK(sampler_buffer->PopRow(&sample_row));
std::shared_ptr<Tensor> sample_ids = sample_row[0];
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) >= num_rows_) continue; // index out of bound, skipping
keys.push_back(*itr);
row_cnt_++;
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(
io_block_queues_[buf_cnt_++ % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
keys.clear();
}
}
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(keys, IOBlock::kDeIoBlockNone)));
}
if (IsLastIteration()) {
std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
std::unique_ptr<IOBlock> eof_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block)));
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block)));
for (int32_t i = 0; i < num_workers_; ++i) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else { // not the last repeat.
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
// Reset Sampler and wakeup Master thread (functor)
Status MappableLeafOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(sampler_->ResetSampler());
return Status::OK();
}
// hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
Status MappableLeafOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
// contains the main logic of pulling a IOBlock from IOBlockQueue, load a buffer and push the buffer to out_connector_
// IMPORTANT: 1 IOBlock produces 1 DataBuffer
Status MappableLeafOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty() == true) return Status::OK(); // empty key is a quit signal for workers
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker");
}
// Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
Status MappableLeafOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
TensorRow trow;
for (const int64_t &key : keys) {
RETURN_IF_NOT_OK(this->LoadTensorRow(key, &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
} // namespace dataset
} // namespace mindspore

110
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mappable_leaf_op.h Normal file
View File

@ -0,0 +1,110 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_MAPPABLE_LEAF_OP_H_
#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_MAPPABLE_LEAF_OP_H_
#include <deque>
#include <memory>
#include <queue>
#include <string>
#include <algorithm>
#include <map>
#include <set>
#include <utility>
#include <vector>
#include "minddata/dataset/core/tensor.h"
#include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#ifndef ENABLE_ANDROID
#include "minddata/dataset/kernels/image/image_utils.h"
#else
#include "minddata/dataset/kernels/image/lite_image_utils.h"
#endif
#include "minddata/dataset/util/path.h"
#include "minddata/dataset/util/queue.h"
#include "minddata/dataset/util/services.h"
#include "minddata/dataset/util/status.h"
#include "minddata/dataset/util/wait_post.h"
namespace mindspore {
namespace dataset {
// Forward declares
template <typename T>
class Queue;
using ImageLabelPair = std::shared_ptr<std::pair<std::string, int32_t>>;
using FolderImagesPair = std::shared_ptr<std::pair<std::string, std::queue<ImageLabelPair>>>;
class MappableLeafOp : public ParallelOp, public RandomAccessOp {
public:
// Constructor
// @param int32_t num_wkrs - Num of workers reading images in parallel
// @param int32_t - rows_per_buffer Number of images (rows) in each buffer
// @param std::string - dir directory of ImageNetFolder
// @param int32_t queue_size - connector queue size
// @param std::set<std::string> exts - set of file extensions to read, if empty, read everything under the dir
// @param td::unique_ptr<Sampler> sampler - sampler tells the source what to read
MappableLeafOp(int32_t num_wkrs, int32_t queue_size, std::shared_ptr<SamplerRT> sampler, int32_t rows_per_buffer);
// Destructor.
~MappableLeafOp() = default;
// Main Loop of MappableLeaf
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it then put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// Op name getter
// @return Name of the current Op
std::string Name() const override { return "MappableLeafPp"; }
protected:
// Initialize Sampler, calls sampler->Init() within
// @return Status The status code returned
Status InitSampler();
// // Called first when function is called
// // @return
virtual Status LaunchThreadsAndInitOp() = 0;
Status WorkerEntry(int32_t workerId) override;
// @param const std::vector<int64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
// Load a tensor row according to a pair
// @param row_id_type row_id - id for this tensor row
// @param ImageLabelPair pair - <imagefile,label>
// @param TensorRow row - loaded row
// @return Status The status code returned
virtual Status LoadTensorRow(row_id_type row_id, TensorRow *row) = 0;
// reset Op
// @return Status The status code returned
Status Reset() override;
int32_t rows_per_buffer_;
int64_t row_cnt_;
int64_t buf_cnt_;
};
} // namespace dataset
} // namespace mindspore
#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_DATASETOPS_SOURCE_MAPPABLE_LEAF_OP_H_

55
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mindrecord_op.cc
View File

@ -27,6 +27,7 @@
#include "minddata/dataset/core/global_context.h" #include "minddata/dataset/core/global_context.h"
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/datasetops/dataset_op.h" #include "minddata/dataset/engine/datasetops/dataset_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.h"
#include "minddata/dataset/engine/db_connector.h" #include "minddata/dataset/engine/db_connector.h"
#include "minddata/dataset/engine/execution_tree.h" #include "minddata/dataset/engine/execution_tree.h"
#include "minddata/dataset/util/log_adapter.h" #include "minddata/dataset/util/log_adapter.h"
@ -115,16 +116,14 @@ MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buf
const std::vector<std::string> &columns_to_load, const std::vector<std::string> &columns_to_load,
const std::vector<std::shared_ptr<ShardOperator>> &operators, int64_t num_padded, const std::vector<std::shared_ptr<ShardOperator>> &operators, int64_t num_padded,
const mindrecord::json &sample_json, const std::map<std::string, std::string> &sample_bytes) const mindrecord::json &sample_json, const std::map<std::string, std::string> &sample_bytes)
: ParallelOp(num_mind_record_workers, op_connector_queue_size), : MappableLeafOp(num_mind_record_workers, op_connector_queue_size, std::make_shared<SequentialSamplerRT>(0, 0),
rows_per_buffer_(rows_per_buffer), rows_per_buffer),
dataset_file_(dataset_file), dataset_file_(dataset_file),
load_dataset_(load_dataset), load_dataset_(load_dataset),
columns_to_load_(columns_to_load), columns_to_load_(columns_to_load),
operators_(operators), operators_(operators),
num_mind_record_workers_(num_mind_record_workers), num_mind_record_workers_(num_mind_record_workers),
num_rows_(0),
buffers_needed_(0), buffers_needed_(0),
buf_cnt_(0),
ended_worker_(0), ended_worker_(0),
num_padded_(num_padded), num_padded_(num_padded),
sample_json_(sample_json), sample_json_(sample_json),
@ -379,61 +378,19 @@ Status MindRecordOp::LoadTensorRow(TensorRow *tensor_row, const std::vector<uint
return Status::OK(); return Status::OK();
} }
// Class functor operator () override.
// All dataset ops operate by launching a thread (see ExecutionTree). This class functor will
// provide the master loop that drives the logic for performing the work
// Main logic, Register Queue with TaskGroup, launch all threads and do the functor's work
Status MindRecordOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadAndInitOp());
num_rows_ = shard_reader_->GetNumRows();
// Compute how many buffers we would need to accomplish rowsPerBuffer
buffers_needed_ = (num_rows_ + rows_per_buffer_ - 1) / rows_per_buffer_;
while (true) { // each iterator is 1 epoch
for (int32_t i = 0; i < buffers_needed_; ++i) {
std::vector<int64_t> keys(1, i);
RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
}
if (IsLastIteration()) {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
for (int32_t i = 0; i < num_workers_; i++) {
RETURN_IF_NOT_OK(io_block_queues_[i]->Add(
std::move(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone))));
}
return Status::OK();
} else {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) RETURN_IF_NOT_OK(Reset());
UpdateRepeatAndEpochCounter();
}
}
// Overrides base class reset method. When an operator does a reset, it cleans up any state // Overrides base class reset method. When an operator does a reset, it cleans up any state
// info from it's previous execution and then initializes itself so that it can be executed // info from it's previous execution and then initializes itself so that it can be executed
// again. // again.
Status MindRecordOp::Reset() { Status MindRecordOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset."; MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(ParallelOp::Reset()); // Call our super class reset first. RETURN_IF_NOT_OK(MappableLeafOp::Reset()); // Call our super class reset first.
shard_reader_->ShuffleTask(); shard_reader_->ShuffleTask();
return Status::OK(); return Status::OK();
} }
Status MindRecordOp::LaunchThreadAndInitOp() { Status MindRecordOp::LaunchThreadsAndInitOp() {
if (tree_ == nullptr) { if (tree_ == nullptr) {
RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set."); RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set.");
} }
@ -446,6 +403,8 @@ Status MindRecordOp::LaunchThreadAndInitOp() {
// Launch main workers that load DataBuffers by reading all images // Launch main workers that load DataBuffers by reading all images
RETURN_IF_NOT_OK( RETURN_IF_NOT_OK(
tree_->LaunchWorkers(num_workers_, std::bind(&MindRecordOp::WorkerEntry, this, std::placeholders::_1), "", id())); tree_->LaunchWorkers(num_workers_, std::bind(&MindRecordOp::WorkerEntry, this, std::placeholders::_1), "", id()));
num_rows_ = shard_reader_->GetNumRows();
RETURN_IF_NOT_OK(this->InitSampler()); // pass numRows to Sampler
TaskManager::FindMe()->Post(); TaskManager::FindMe()->Post();
return Status::OK(); return Status::OK();
} }

24
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mindrecord_op.h
View File

@ -28,7 +28,7 @@
#include <vector> #include <vector>
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/util/queue.h" #include "minddata/dataset/util/queue.h"
#include "minddata/dataset/util/status.h" #include "minddata/dataset/util/status.h"
#include "minddata/mindrecord/include/shard_column.h" #include "minddata/mindrecord/include/shard_column.h"
@ -50,7 +50,7 @@ using ShardTuple = std::vector<std::tuple<std::vector<uint8_t>, mindrecord::json
const int32_t LOG_INTERVAL = 19; const int32_t LOG_INTERVAL = 19;
class MindRecordOp : public ParallelOp { class MindRecordOp : public MappableLeafOp {
public: public:
// The nested builder class inside of the MindRecordOp is used to help manage all of the arguments // The nested builder class inside of the MindRecordOp is used to help manage all of the arguments
// for constructing it. Use the builder by setting each argument with the provided set methods, // for constructing it. Use the builder by setting each argument with the provided set methods,
@ -167,15 +167,9 @@ class MindRecordOp : public ParallelOp {
// @return Status The status code returned // @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override; Status WorkerEntry(int32_t worker_id) override;
// Class functor operator () override.
// All DatasetOps operate by launching a thread (see ExecutionTree). This class functor will
// provide the master loop that drives the logic for performing the work.
// @return Status The status code returned
Status operator()() override;
// Called first when function is called // Called first when function is called
// @return // @return
Status LaunchThreadAndInitOp(); Status LaunchThreadsAndInitOp() override;
// Overrides base class reset method. When an operator does a reset, it cleans up any state // Overrides base class reset method. When an operator does a reset, it cleans up any state
// info from it's previous execution and then initializes itself so that it can be executed // info from it's previous execution and then initializes itself so that it can be executed
@ -183,15 +177,9 @@ class MindRecordOp : public ParallelOp {
// @return Status The status code returned // @return Status The status code returned
Status Reset() override; Status Reset() override;
// Getter method
int32_t num_rows() const { return num_rows_; }
static Status CountTotalRows(const std::vector<std::string> dataset_path, bool load_dataset, static Status CountTotalRows(const std::vector<std::string> dataset_path, bool load_dataset,
const std::shared_ptr<ShardOperator> &op, int64_t *count, int64_t num_padded); const std::shared_ptr<ShardOperator> &op, int64_t *count, int64_t num_padded);
// Getter method
int32_t rows_per_buffer() const { return rows_per_buffer_; }
// Getter method // Getter method
std::vector<std::string> dataset_file() const { return dataset_file_; } std::vector<std::string> dataset_file() const { return dataset_file_; }
@ -216,19 +204,19 @@ class MindRecordOp : public ParallelOp {
Status LoadTensorRow(TensorRow *tensor_row, const std::vector<uint8_t> &columns_blob, Status LoadTensorRow(TensorRow *tensor_row, const std::vector<uint8_t> &columns_blob,
const mindrecord::json &columns_json, const mindrecord::TaskType task_type); const mindrecord::json &columns_json, const mindrecord::TaskType task_type);
Status LoadTensorRow(row_id_type row_id, TensorRow *row) override {
return Status(StatusCode::kMDSyntaxError, "Cannot call this method.");
}
// Private function for computing the assignment of the column name map. // Private function for computing the assignment of the column name map.
// @return - Status // @return - Status
Status ComputeColMap() override; Status ComputeColMap() override;
int32_t rows_per_buffer_; // The number of requested rows per buffer.
std::vector<std::string> dataset_file_; // dataset files std::vector<std::string> dataset_file_; // dataset files
bool load_dataset_; // load dataset from single file or not bool load_dataset_; // load dataset from single file or not
std::vector<std::string> columns_to_load_; // Columns to load from dataset std::vector<std::string> columns_to_load_; // Columns to load from dataset
std::vector<std::shared_ptr<ShardOperator>> operators_; // ShardOperators to use std::vector<std::shared_ptr<ShardOperator>> operators_; // ShardOperators to use
int32_t num_mind_record_workers_; // number of workers to be spawned by ShardReader int32_t num_mind_record_workers_; // number of workers to be spawned by ShardReader
int32_t buffers_needed_; // Counter for the buffers that were fetched int32_t buffers_needed_; // Counter for the buffers that were fetched
int64_t buf_cnt_; // Buffer counter
int32_t num_rows_; // One more than the last row id in the range for this cache
std::atomic<int32_t> ended_worker_; std::atomic<int32_t> ended_worker_;
int64_t num_padded_; int64_t num_padded_;

131
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mnist_op.cc
View File

@ -75,117 +75,18 @@ Status MnistOp::Builder::SanityCheck() {
MnistOp::MnistOp(const std::string &usage, int32_t num_workers, int32_t rows_per_buffer, std::string folder_path, MnistOp::MnistOp(const std::string &usage, int32_t num_workers, int32_t rows_per_buffer, std::string folder_path,
int32_t queue_size, std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler) int32_t queue_size, std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler)
: ParallelOp(num_workers, queue_size, std::move(sampler)), : MappableLeafOp(num_workers, queue_size, std::move(sampler), rows_per_buffer),
usage_(usage), usage_(usage),
buf_cnt_(0),
row_cnt_(0),
folder_path_(folder_path), folder_path_(folder_path),
rows_per_buffer_(rows_per_buffer),
image_path_({}), image_path_({}),
label_path_({}), label_path_({}),
data_schema_(std::move(data_schema)) { data_schema_(std::move(data_schema)) {
io_block_queues_.Init(num_workers, queue_size); io_block_queues_.Init(num_workers, queue_size);
} }
Status MnistOp::TraversalSampleIds(const std::shared_ptr<Tensor> &sample_ids, std::vector<int64_t> *keys) {
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) >= num_rows_) continue; // index out of bound, skipping
keys->push_back(*itr);
row_cnt_++;
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(*keys, IOBlock::kDeIoBlockNone))));
keys->clear();
}
}
return Status::OK();
}
// functor that contains the main logic of MNIST op
Status MnistOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
while (true) { // each iterator is 1 epoch
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (sampler_buffer->eoe() == false) {
std::shared_ptr<Tensor> sample_ids;
RETURN_IF_NOT_OK(sampler_buffer->GetTensor(&sample_ids, 0, 0));
if (sample_ids->type() != DataType(DataType::DE_INT64)) {
RETURN_STATUS_UNEXPECTED("Invalid parameter, data type of Sampler Tensor isn't int64, got " +
sample_ids->type().ToString());
}
RETURN_IF_NOT_OK(TraversalSampleIds(sample_ids, &keys));
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
}
if (IsLastIteration()) {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof)));
for (int32_t i = 0; i < num_workers_; ++i) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
// contains the logic of pulling a IOBlock from IOBlockQueue, load a buffer and push the buffer to out_connector_
Status MnistOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> iOBlock;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&iOBlock));
while (iOBlock != nullptr) {
if (iOBlock->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (iOBlock->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (iOBlock->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF)));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(iOBlock->GetKeys(&keys));
if (keys.empty() == true) return Status::OK(); // empty key is a quit signal for workers
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&iOBlock));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker.");
}
// Load 1 TensorRow (image,label) using 1 MnistLabelPair. // Load 1 TensorRow (image,label) using 1 MnistLabelPair.
Status MnistOp::LoadTensorRow(row_id_type row_id, const MnistLabelPair &mnist_pair, TensorRow *trow) { Status MnistOp::LoadTensorRow(row_id_type row_id, TensorRow *trow) {
MnistLabelPair mnist_pair = image_label_pairs_[row_id];
std::shared_ptr<Tensor> image, label; std::shared_ptr<Tensor> image, label;
// make a copy of cached tensor // make a copy of cached tensor
RETURN_IF_NOT_OK(Tensor::CreateFromTensor(mnist_pair.first, &image)); RETURN_IF_NOT_OK(Tensor::CreateFromTensor(mnist_pair.first, &image));
@ -196,18 +97,6 @@ Status MnistOp::LoadTensorRow(row_id_type row_id, const MnistLabelPair &mnist_pa
return Status::OK(); return Status::OK();
} }
// Looping over LoadTensorRow to make 1 DataBuffer. 1 function call produces 1 buffer
Status MnistOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
TensorRow trow;
for (const int64_t &key : keys) {
RETURN_IF_NOT_OK(this->LoadTensorRow(key, image_label_pairs_[key], &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
void MnistOp::Print(std::ostream &out, bool show_all) const { void MnistOp::Print(std::ostream &out, bool show_all) const {
if (!show_all) { if (!show_all) {
// Call the super class for displaying any common 1-liner info // Call the super class for displaying any common 1-liner info
@ -222,20 +111,6 @@ void MnistOp::Print(std::ostream &out, bool show_all) const {
} }
} }
// Reset Sampler and wakeup Master thread (functor)
Status MnistOp::Reset() {
MS_LOG(DEBUG) << Name() << " performing a self-reset.";
RETURN_IF_NOT_OK(sampler_->ResetSampler());
row_cnt_ = 0;
return Status::OK();
}
// hand shake with Sampler, allow Sampler to call RandomAccessOp's functions to get NumRows
Status MnistOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
// Derived from RandomAccessOp // Derived from RandomAccessOp
Status MnistOp::GetClassIds(std::map<int32_t, std::vector<int64_t>> *cls_ids) const { Status MnistOp::GetClassIds(std::map<int32_t, std::vector<int64_t>> *cls_ids) const {
if (cls_ids == nullptr || !cls_ids->empty() || image_label_pairs_.empty()) { if (cls_ids == nullptr || !cls_ids->empty() || image_label_pairs_.empty()) {

40
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/mnist_op.h
View File

@ -27,6 +27,7 @@
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#include "minddata/dataset/util/path.h" #include "minddata/dataset/util/path.h"
#include "minddata/dataset/util/queue.h" #include "minddata/dataset/util/queue.h"
@ -41,7 +42,7 @@ class Queue;
using MnistLabelPair = std::pair<std::shared_ptr<Tensor>, uint32_t>; using MnistLabelPair = std::pair<std::shared_ptr<Tensor>, uint32_t>;
class MnistOp : public ParallelOp, public RandomAccessOp { class MnistOp : public MappableLeafOp {
public: public:
class Builder { class Builder {
public: public:
@ -131,17 +132,6 @@ class MnistOp : public ParallelOp, public RandomAccessOp {
// Destructor. // Destructor.
~MnistOp() = default; ~MnistOp() = default;
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param int32_t worker_id - id of each worker
// @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
// Main Loop of MnistOp
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it then put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// Method derived from RandomAccess Op, enable Sampler to get all ids for each class // Method derived from RandomAccess Op, enable Sampler to get all ids for each class
// @param (std::map<uint64_t, std::vector<uint64_t >> * map - key label, val all ids for this class // @param (std::map<uint64_t, std::vector<uint64_t >> * map - key label, val all ids for this class
// @return Status The status code returned // @return Status The status code returned
@ -163,27 +153,12 @@ class MnistOp : public ParallelOp, public RandomAccessOp {
std::string Name() const override { return "MnistOp"; } std::string Name() const override { return "MnistOp"; }
private: private:
// Initialize Sampler, calls sampler->Init() within
// @return Status The status code returned
Status InitSampler();
// Load a tensor row according to a pair // Load a tensor row according to a pair
// @param row_id_type row_id - id for this tensor row // @param row_id_type row_id - id for this tensor row
// @param ImageLabelPair pair - <imagefile,label> // @param ImageLabelPair pair - <imagefile,label>
// @param TensorRow row - image & label read into this tensor row // @param TensorRow row - image & label read into this tensor row
// @return Status The status code returned // @return Status The status code returned
Status LoadTensorRow(row_id_type row_id, const MnistLabelPair &mnist_pair, TensorRow *row); Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
// @param const std::vector<int64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
// Iterate through all members in sampleIds and fill them into IOBlock.
// @param std::shared_ptr<Tensor> sample_ids -
// @param std::vector<int64_t> *keys - keys in ioblock
// @return Status The status code returned
Status TraversalSampleIds(const std::shared_ptr<Tensor> &sample_ids, std::vector<int64_t> *keys);
// Check image file stream. // Check image file stream.
// @param const std::string *file_name - image file name // @param const std::string *file_name - image file name
@ -226,20 +201,13 @@ class MnistOp : public ParallelOp, public RandomAccessOp {
// Called first when function is called // Called first when function is called
// @return Status The status code returned // @return Status The status code returned
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
// reset Op
// @return Status The status code returned
Status Reset() override;
// Private function for computing the assignment of the column name map. // Private function for computing the assignment of the column name map.
// @return - Status // @return - Status
Status ComputeColMap() override; Status ComputeColMap() override;
int64_t buf_cnt_;
int64_t row_cnt_;
std::string folder_path_; // directory of image folder std::string folder_path_; // directory of image folder
int32_t rows_per_buffer_;
const std::string usage_; // can only be either "train" or "test" const std::string usage_; // can only be either "train" or "test"
std::unique_ptr<DataSchema> data_schema_; std::unique_ptr<DataSchema> data_schema_;
std::vector<MnistLabelPair> image_label_pairs_; std::vector<MnistLabelPair> image_label_pairs_;

7
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/sampler.cc
View File

@ -1,5 +1,5 @@
/** /**
* Copyright 2019 Huawei Technologies Co., Ltd * Copyright 2019-2021 Huawei Technologies Co., Ltd
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License. * you may not use this file except in compliance with the License.
@ -26,7 +26,7 @@ Status RandomAccessOp::GetNumRowsInDataset(int64_t *num) const {
// after it has interacted with it's storage layers. // after it has interacted with it's storage layers.
// Here, it is just a getter method to return the value. However, it is invalid if there is // Here, it is just a getter method to return the value. However, it is invalid if there is
// not a value set for this count, so generate a failure if that is the case. // not a value set for this count, so generate a failure if that is the case.
if (num == nullptr || num_rows_ == 0) { if (num == nullptr || num_rows_ == -1) {
RETURN_STATUS_UNEXPECTED("RandomAccessOp has not computed its num rows yet."); RETURN_STATUS_UNEXPECTED("RandomAccessOp has not computed its num rows yet.");
} }
(*num) = num_rows_; (*num) = num_rows_;
@ -70,9 +70,6 @@ Status SamplerRT::HandshakeRandomAccessOp(const RandomAccessOp *op) {
} }
Status SamplerRT::CreateSamplerTensor(std::shared_ptr<Tensor> *sample_ids, int64_t num_elements) { Status SamplerRT::CreateSamplerTensor(std::shared_ptr<Tensor> *sample_ids, int64_t num_elements) {
if (num_elements == 0) {
RETURN_STATUS_UNEXPECTED("Invalid data, num of elements cannot be 0.");
}
if (col_desc_ == nullptr) { if (col_desc_ == nullptr) {
// a ColDescriptor for Tensor that holds SampleIds // a ColDescriptor for Tensor that holds SampleIds
col_desc_ = std::make_unique<ColDescriptor>("sampleIds", DataType(DataType::DE_INT64), TensorImpl::kFlexible, 1); col_desc_ = std::make_unique<ColDescriptor>("sampleIds", DataType(DataType::DE_INT64), TensorImpl::kFlexible, 1);

6
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/sampler/sequential_sampler.cc
View File

@ -1,5 +1,5 @@
/** /**
* Copyright 2019 Huawei Technologies Co., Ltd * Copyright 2019-2021 Huawei Technologies Co., Ltd
* *
* Licensed under the Apache License, Version 2.0 (the "License"); * Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License. * you may not use this file except in compliance with the License.
@ -70,7 +70,7 @@ Status SequentialSamplerRT::InitSampler() {
CHECK_FAIL_RETURN_UNEXPECTED(start_index_ >= 0, CHECK_FAIL_RETURN_UNEXPECTED(start_index_ >= 0,
"Invalid parameter, start_index must be greater than or equal to 0, but got " + "Invalid parameter, start_index must be greater than or equal to 0, but got " +
std::to_string(start_index_) + ".\n"); std::to_string(start_index_) + ".\n");
CHECK_FAIL_RETURN_UNEXPECTED(start_index_ < num_rows_, CHECK_FAIL_RETURN_UNEXPECTED(start_index_ < num_rows_ || (num_rows_ == 0 && start_index_ == 0),
"Invalid parameter, start_index must be less than num_rows, but got start_index: " + "Invalid parameter, start_index must be less than num_rows, but got start_index: " +
std::to_string(start_index_) + ", num_rows: " + std::to_string(num_rows_) + ".\n"); std::to_string(start_index_) + ", num_rows: " + std::to_string(num_rows_) + ".\n");
CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ >= 0, CHECK_FAIL_RETURN_UNEXPECTED(num_samples_ >= 0,
@ -83,7 +83,7 @@ Status SequentialSamplerRT::InitSampler() {
num_samples_ = available_row_count; num_samples_ = available_row_count;
} }
CHECK_FAIL_RETURN_UNEXPECTED( CHECK_FAIL_RETURN_UNEXPECTED(
num_samples_ > 0 && samples_per_buffer_ > 0, (num_samples_ > 0 && samples_per_buffer_ > 0) || num_samples_ == 0,
"Invalid parameter, samples_per_buffer must be greater than 0, but got " + std::to_string(samples_per_buffer_)); "Invalid parameter, samples_per_buffer must be greater than 0, but got " + std::to_string(samples_per_buffer_));
samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_; samples_per_buffer_ = samples_per_buffer_ > num_samples_ ? num_samples_ : samples_per_buffer_;

126
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/voc_op.cc
View File

@ -99,83 +99,16 @@ VOCOp::VOCOp(const TaskType &task_type, const std::string &task_mode, const std:
const std::map<std::string, int32_t> &class_index, int32_t num_workers, int32_t rows_per_buffer, const std::map<std::string, int32_t> &class_index, int32_t num_workers, int32_t rows_per_buffer,
int32_t queue_size, bool decode, std::unique_ptr<DataSchema> data_schema, int32_t queue_size, bool decode, std::unique_ptr<DataSchema> data_schema,
std::shared_ptr<SamplerRT> sampler) std::shared_ptr<SamplerRT> sampler)
: ParallelOp(num_workers, queue_size, std::move(sampler)), : MappableLeafOp(num_workers, queue_size, std::move(sampler), rows_per_buffer),
decode_(decode), decode_(decode),
row_cnt_(0),
buf_cnt_(0),
task_type_(task_type), task_type_(task_type),
usage_(task_mode), usage_(task_mode),
folder_path_(folder_path), folder_path_(folder_path),
class_index_(class_index), class_index_(class_index),
rows_per_buffer_(rows_per_buffer),
data_schema_(std::move(data_schema)) { data_schema_(std::move(data_schema)) {
io_block_queues_.Init(num_workers_, queue_size); io_block_queues_.Init(num_workers_, queue_size);
} }
Status VOCOp::TraverseSampleIds(const std::shared_ptr<Tensor> &sample_ids, std::vector<int64_t> *keys) {
for (auto itr = sample_ids->begin<int64_t>(); itr != sample_ids->end<int64_t>(); ++itr) {
if ((*itr) > num_rows_) continue;
keys->push_back(*itr);
row_cnt_++;
if (row_cnt_ % rows_per_buffer_ == 0) {
RETURN_IF_NOT_OK(io_block_queues_[buf_cnt_++ % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(*keys, IOBlock::kDeIoBlockNone))));
keys->clear();
}
}
return Status::OK();
}
Status VOCOp::operator()() {
RETURN_IF_NOT_OK(LaunchThreadsAndInitOp());
std::unique_ptr<DataBuffer> sampler_buffer;
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
while (true) {
std::vector<int64_t> keys;
keys.reserve(rows_per_buffer_);
while (sampler_buffer->eoe() == false) {
std::shared_ptr<Tensor> sample_ids;
RETURN_IF_NOT_OK(sampler_buffer->GetTensor(&sample_ids, 0, 0));
if (sample_ids->type() != DataType(DataType::DE_INT64)) {
RETURN_STATUS_UNEXPECTED("Invalid parameter, data type of Sampler Tensor isn't int64, got " +
sample_ids->type().ToString());
}
RETURN_IF_NOT_OK(TraverseSampleIds(sample_ids, &keys));
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
if (keys.empty() == false) {
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(
std::make_unique<IOBlock>(IOBlock(keys, IOBlock::kDeIoBlockNone))));
}
if (IsLastIteration()) {
std::unique_ptr<IOBlock> eoe_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe);
std::unique_ptr<IOBlock> eof_block = std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEof);
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eoe_block)));
RETURN_IF_NOT_OK(io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::move(eof_block)));
for (int32_t i = 0; i < num_workers_; i++) {
RETURN_IF_NOT_OK(
io_block_queues_[i]->Add(std::make_unique<IOBlock>(std::vector<int64_t>(), IOBlock::kDeIoBlockNone)));
}
return Status::OK();
} else {
RETURN_IF_NOT_OK(
io_block_queues_[(buf_cnt_++) % num_workers_]->Add(std::make_unique<IOBlock>(IOBlock::kDeIoBlockFlagEoe)));
}
if (epoch_sync_flag_) {
// If epoch_sync_flag_ is set, then master thread sleeps until all the worker threads have finished their job for
// the current epoch.
RETURN_IF_NOT_OK(WaitForWorkers());
}
// If not the last repeat, self-reset and go to loop again.
if (!IsLastIteration()) {
RETURN_IF_NOT_OK(Reset());
RETURN_IF_NOT_OK(sampler_->GetNextSample(&sampler_buffer));
}
UpdateRepeatAndEpochCounter();
}
}
void VOCOp::Print(std::ostream &out, bool show_all) const { void VOCOp::Print(std::ostream &out, bool show_all) const {
if (!show_all) { if (!show_all) {
// Call the super class for displaying any common 1-liner info // Call the super class for displaying any common 1-liner info
@ -191,14 +124,8 @@ void VOCOp::Print(std::ostream &out, bool show_all) const {
} }
} }
Status VOCOp::Reset() { Status VOCOp::LoadTensorRow(row_id_type row_id, TensorRow *trow) {
MS_LOG(DEBUG) << Name() << " performing a self-reset."; std::string image_id = image_ids_[row_id];
RETURN_IF_NOT_OK(sampler_->ResetSampler());
row_cnt_ = 0;
return Status::OK();
}
Status VOCOp::LoadTensorRow(row_id_type row_id, const std::string &image_id, TensorRow *trow) {
if (task_type_ == TaskType::Segmentation) { if (task_type_ == TaskType::Segmentation) {
std::shared_ptr<Tensor> image, target; std::shared_ptr<Tensor> image, target;
const std::string kImageFile = const std::string kImageFile =
@ -226,48 +153,6 @@ Status VOCOp::LoadTensorRow(row_id_type row_id, const std::string &image_id, Ten
return Status::OK(); return Status::OK();
} }
Status VOCOp::LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db) {
std::unique_ptr<TensorQTable> deq = std::make_unique<TensorQTable>();
TensorRow trow;
for (const uint64_t &key : keys) {
RETURN_IF_NOT_OK(this->LoadTensorRow(key, image_ids_[key], &trow));
deq->push_back(std::move(trow));
}
(*db)->set_tensor_table(std::move(deq));
return Status::OK();
}
Status VOCOp::WorkerEntry(int32_t worker_id) {
TaskManager::FindMe()->Post();
int64_t buffer_id = worker_id;
std::unique_ptr<IOBlock> io_block;
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
while (io_block != nullptr) {
if (io_block->wait() == true) {
// Sync io_block is a signal that master thread wants us to pause and sync with other workers.
// The last guy who comes to this sync point should reset the counter and wake up the master thread.
if (++num_workers_paused_ == num_workers_) {
wait_for_workers_post_.Set();
}
} else if (io_block->eoe() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE)));
buffer_id = worker_id;
} else if (io_block->eof() == true) {
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, (std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
} else {
std::vector<int64_t> keys;
RETURN_IF_NOT_OK(io_block->GetKeys(&keys));
if (keys.empty() == true) return Status::OK();
std::unique_ptr<DataBuffer> db = std::make_unique<DataBuffer>(buffer_id, DataBuffer::kDeBFlagNone);
RETURN_IF_NOT_OK(LoadBuffer(keys, &db));
RETURN_IF_NOT_OK(out_connector_->Add(worker_id, std::move(db)));
buffer_id += num_workers_;
}
RETURN_IF_NOT_OK(io_block_queues_[worker_id]->PopFront(&io_block));
}
RETURN_STATUS_UNEXPECTED("Unexpected nullptr received in worker");
}
Status VOCOp::ParseImageIds() { Status VOCOp::ParseImageIds() {
std::string image_sets_file; std::string image_sets_file;
if (task_type_ == TaskType::Segmentation) { if (task_type_ == TaskType::Segmentation) {
@ -378,11 +263,6 @@ Status VOCOp::ParseAnnotationBbox(const std::string &path) {
return Status::OK(); return Status::OK();
} }
Status VOCOp::InitSampler() {
RETURN_IF_NOT_OK(sampler_->HandshakeRandomAccessOp(this));
return Status::OK();
}
Status VOCOp::LaunchThreadsAndInitOp() { Status VOCOp::LaunchThreadsAndInitOp() {
if (tree_ == nullptr) { if (tree_ == nullptr) {
RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set."); RETURN_STATUS_UNEXPECTED("Pipeline init failed, Execution tree not set.");

36
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/voc_op.h
View File

@ -27,6 +27,7 @@
#include "minddata/dataset/engine/data_buffer.h" #include "minddata/dataset/engine/data_buffer.h"
#include "minddata/dataset/engine/data_schema.h" #include "minddata/dataset/engine/data_schema.h"
#include "minddata/dataset/engine/datasetops/parallel_op.h" #include "minddata/dataset/engine/datasetops/parallel_op.h"
#include "minddata/dataset/engine/datasetops/source/mappable_leaf_op.h"
#include "minddata/dataset/engine/datasetops/source/sampler/sampler.h" #include "minddata/dataset/engine/datasetops/source/sampler/sampler.h"
#include "minddata/dataset/kernels/image/image_utils.h" #include "minddata/dataset/kernels/image/image_utils.h"
#include "minddata/dataset/util/path.h" #include "minddata/dataset/util/path.h"
@ -45,7 +46,7 @@ class Queue;
using Annotation = std::vector<std::pair<std::string, std::vector<float>>>; using Annotation = std::vector<std::pair<std::string, std::vector<float>>>;
class VOCOp : public ParallelOp, public RandomAccessOp { class VOCOp : public MappableLeafOp {
public: public:
enum class TaskType { Segmentation = 0, Detection = 1 }; enum class TaskType { Segmentation = 0, Detection = 1 };
@ -175,17 +176,6 @@ class VOCOp : public ParallelOp, public RandomAccessOp {
// Destructor // Destructor
~VOCOp() = default; ~VOCOp() = default;
// Worker thread pulls a number of IOBlock from IOBlock Queue, make a buffer and push it to Connector
// @param int32_t workerId - id of each worker
// @return Status The status code returned
Status WorkerEntry(int32_t worker_id) override;
// Main Loop of VOCOp
// Master thread: Fill IOBlockQueue, then goes to sleep
// Worker thread: pulls IOBlock from IOBlockQueue, work on it the put buffer to mOutConnector
// @return Status The status code returned
Status operator()() override;
// A print method typically used for debugging // A print method typically used for debugging
// @param out // @param out
// @param show_all // @param show_all
@ -219,16 +209,12 @@ class VOCOp : public ParallelOp, public RandomAccessOp {
Status GetClassIndexing(std::vector<std::pair<std::string, std::vector<int32_t>>> *output_class_indexing) override; Status GetClassIndexing(std::vector<std::pair<std::string, std::vector<int32_t>>> *output_class_indexing) override;
private: private:
// Initialize Sampler, calls sampler->Init() within
// @return Status The status code returned
Status InitSampler();
// Load a tensor row according to image id // Load a tensor row according to image id
// @param row_id_type row_id - id for this tensor row // @param row_id_type row_id - id for this tensor row
// @param std::string image_id - image id // @param std::string image_id - image id
// @param TensorRow row - image & target read into this tensor row // @param TensorRow row - image & target read into this tensor row
// @return Status The status code returned // @return Status The status code returned
Status LoadTensorRow(row_id_type row_id, const std::string &image_id, TensorRow *row); Status LoadTensorRow(row_id_type row_id, TensorRow *row) override;
// @param const std::string &path - path to the image file // @param const std::string &path - path to the image file
// @param const ColDescriptor &col - contains tensor implementation and datatype // @param const ColDescriptor &col - contains tensor implementation and datatype
@ -241,11 +227,6 @@ class VOCOp : public ParallelOp, public RandomAccessOp {
// @return Status The status code returned // @return Status The status code returned
Status ReadAnnotationToTensor(const std::string &path, TensorRow *row); Status ReadAnnotationToTensor(const std::string &path, TensorRow *row);
// @param const std::vector<uint64_t> &keys - keys in ioblock
// @param std::unique_ptr<DataBuffer> db
// @return Status The status code returned
Status LoadBuffer(const std::vector<int64_t> &keys, std::unique_ptr<DataBuffer> *db);
// Read image list from ImageSets // Read image list from ImageSets
// @return Status The status code returned // @return Status The status code returned
Status ParseImageIds(); Status ParseImageIds();
@ -264,18 +245,9 @@ class VOCOp : public ParallelOp, public RandomAccessOp {
// @return Status The status code returned // @return Status The status code returned
void ParseNodeValue(XMLElement *bbox_node, const char *name, float *value); void ParseNodeValue(XMLElement *bbox_node, const char *name, float *value);
// @param const std::shared_ptr<Tensor> &sample_ids - sample ids of tensor
// @param std::vector<int64_t> *keys - image id
// @return Status The status code returned
Status TraverseSampleIds(const std::shared_ptr<Tensor> &sample_ids, std::vector<int64_t> *keys);
// Called first when function is called // Called first when function is called
// @return Status The status code returned // @return Status The status code returned
Status LaunchThreadsAndInitOp(); Status LaunchThreadsAndInitOp() override;
// Reset dataset state
// @return Status The status code returned
Status Reset() override;
// Private function for computing the assignment of the column name map. // Private function for computing the assignment of the column name map.
// @return - Status // @return - Status

1
mindspore/lite/minddata/CMakeLists.txt
View File

@ -154,6 +154,7 @@ if(BUILD_MINDDATA STREQUAL "full")
${MINDDATA_DIR}/engine/datasetops/map_op/cpu_map_job.cc ${MINDDATA_DIR}/engine/datasetops/map_op/cpu_map_job.cc
${MINDDATA_DIR}/engine/datasetops/source/album_op.cc ${MINDDATA_DIR}/engine/datasetops/source/album_op.cc
${MINDDATA_DIR}/engine/datasetops/source/mnist_op.cc ${MINDDATA_DIR}/engine/datasetops/source/mnist_op.cc
${MINDDATA_DIR}/engine/datasetops/source/mappable_leaf_op.cc
${MINDDATA_DIR}/engine/datasetops/source/io_block.cc ${MINDDATA_DIR}/engine/datasetops/source/io_block.cc
${MINDDATA_DIR}/engine/opt/pre/getter_pass.cc ${MINDDATA_DIR}/engine/opt/pre/getter_pass.cc