natas_hw
/
openvino
0
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Changed style of some headers (#3898)

This commit is contained in:
Ilya Churaev 2021-01-19 12:55:26 +03:00 committed by GitHub
parent 07f95e37b1
commit 7ea2d668ad
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 140 additions and 140 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

200
ngraph/core/include/ngraph/chrome_trace.hpp
View File

@ -37,107 +37,103 @@ namespace ngraph
{
namespace event
{
class Duration;
class Object;
class Manager;
//
// This class records timestamps for a given user defined event and
// produces output in the chrome tracing format that can be used to view
// the events of a running program
//
// Following is the format of a trace event
//
// {
// "name": "myName",
// "cat": "category,list",
// "ph": "B",
// "ts": 12345,
// "pid": 123,
// "tid": 456,
// "args": {
// "someArg": 1,
// "anotherArg": {
// "value": "my value"
// }
// }
// }
//
// The trace file format is defined here:
// https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview
//
// The trace file can be viewed by Chrome browser using the
// URL: chrome://tracing/
//
// More information about this is at:
// http://dev.chromium.org/developers/how-tos/trace-event-profiling-tool
class Manager
{
friend class Duration;
friend class Object;
public:
static void open(const std::string& path = "runtime_event_trace.json");
static void close();
static bool is_tracing_enabled() { return s_tracing_enabled; }
static void enable_event_tracing();
static void disable_event_tracing();
static bool is_event_tracing_enabled();
private:
static std::ofstream& get_output_stream();
static const std::string& get_process_id();
static size_t get_current_microseconds()
{
return std::chrono::high_resolution_clock::now().time_since_epoch().count() / 1000;
}
static std::string get_thread_id();
static std::mutex& get_mutex() { return s_file_mutex; }
static std::ostream s_ostream;
static std::mutex s_file_mutex;
static bool s_tracing_enabled;
};
class NGRAPH_API Duration
{
public:
explicit Duration(const std::string& name,
const std::string& category,
const std::string& args = "");
~Duration() { write(); }
/// \brief stop the timer without writing the data to the log file. To write the data
/// call the `write` method
/// Calls to stop() are optional
void stop();
/// \brief write the log data to the log file for this event
/// This funtion has an implicit stop() if stop() has not been previously called
void write();
Duration(const Duration&) = delete;
Duration& operator=(Duration const&) = delete;
private:
std::string to_json() const;
size_t m_start{0};
size_t m_stop{0};
std::string m_name;
std::string m_category;
std::string m_args;
};
class Object
{
public:
Object(const std::string& name, const std::string& args);
void snapshot(const std::string& args);
void destroy();
private:
void write_snapshot(std::ostream& out, const std::string& args);
const std::string m_name;
size_t m_id{0};
};
}
}
//
// This class records timestamps for a given user defined event and
// produces output in the chrome tracing format that can be used to view
// the events of a running program
//
// Following is the format of a trace event
//
// {
// "name": "myName",
// "cat": "category,list",
// "ph": "B",
// "ts": 12345,
// "pid": 123,
// "tid": 456,
// "args": {
// "someArg": 1,
// "anotherArg": {
// "value": "my value"
// }
// }
// }
//
// The trace file format is defined here:
// https://docs.google.com/document/d/1CvAClvFfyA5R-PhYUmn5OOQtYMH4h6I0nSsKchNAySU/preview
//
// The trace file can be viewed by Chrome browser using the
// URL: chrome://tracing/
//
// More information about this is at:
// http://dev.chromium.org/developers/how-tos/trace-event-profiling-tool
class ngraph::event::Manager
{
friend class Duration;
friend class Object;
public:
static void open(const std::string& path = "runtime_event_trace.json");
static void close();
static bool is_tracing_enabled() { return s_tracing_enabled; }
static void enable_event_tracing();
static void disable_event_tracing();
static bool is_event_tracing_enabled();
private:
static std::ofstream& get_output_stream();
static const std::string& get_process_id();
static size_t get_current_microseconds()
{
return std::chrono::high_resolution_clock::now().time_since_epoch().count() / 1000;
}
static std::string get_thread_id();
static std::mutex& get_mutex() { return s_file_mutex; }
static std::ostream s_ostream;
static std::mutex s_file_mutex;
static bool s_tracing_enabled;
};
class NGRAPH_API ngraph::event::Duration
{
public:
explicit Duration(const std::string& name,
const std::string& category,
const std::string& args = "");
~Duration() { write(); }
/// \brief stop the timer without writing the data to the log file. To write the data
/// call the `write` method
/// Calls to stop() are optional
void stop();
/// \brief write the log data to the log file for this event
/// This funtion has an implicit stop() if stop() has not been previously called
void write();
Duration(const Duration&) = delete;
Duration& operator=(Duration const&) = delete;
private:
std::string to_json() const;
size_t m_start{0};
size_t m_stop{0};
std::string m_name;
std::string m_category;
std::string m_args;
};
class ngraph::event::Object
{
public:
Object(const std::string& name, const std::string& args);
void snapshot(const std::string& args);
void destroy();
private:
void write_snapshot(std::ostream& out, const std::string& args);
const std::string m_name;
size_t m_id{0};
};

80
ngraph/core/include/ngraph/pass/low_latency.hpp
View File

@ -13,43 +13,47 @@ namespace ngraph
{
namespace pass
{
class NGRAPH_API LowLatency;
/**
* @brief The transformation finds all TensorIterator layers in the network, processes all
* back
* edges that describe a connection between Result and Parameter of the TensorIterator body,
* and inserts ReadValue layer between Parameter and the next layers after this Parameter,
* and Assign layer after the layers before the Result layer.
* Supported platforms: CPU, GNA.
*
* The example below describes the changes to the inner part (body, back edges) of the
* Tensor
* Iterator layer.
* [] - TensorIterator body
* () - new layer
*
* before applying the transformation:
* back_edge_1 -> [Parameter -> some layers ... -> Result ] -> back_edge_1
*
* after applying the transformation:
* back_edge_1 -> [Parameter -> (ReadValue layer) -> some layers ... -> (Assign layer) ]
* \
* -> Result ] -> back_edge_1
*
* It is recommended to use this transformation in conjunction with the Reshape feature to
* set
* sequence dimension to 1 and with the UnrollTensorIterator transformation.
* For convenience, we have already enabled the unconditional execution of the
* UnrollTensorIterator
* transformation when using the LowLatency transformation for CPU, GNA plugins, no action
* is
* required here.
* After applying both of these transformations, the resulting network can be inferred step
* by
* step, the states will store between inferences.
*
*/
class NGRAPH_API LowLatency : public ngraph::pass::MatcherPass
{
public:
NGRAPH_RTTI_DECLARATION;
LowLatency();
};
} // namespace pass
} // namespace ngraph
/**
* @brief The transformation finds all TensorIterator layers in the network, processes all back
* edges that describe a connection between Result and Parameter of the TensorIterator body,
* and inserts ReadValue layer between Parameter and the next layers after this Parameter,
* and Assign layer after the layers before the Result layer.
* Supported platforms: CPU, GNA.
*
* The example below describes the changes to the inner part (body, back edges) of the Tensor
* Iterator layer.
* [] - TensorIterator body
* () - new layer
*
* before applying the transformation:
* back_edge_1 -> [Parameter -> some layers ... -> Result ] -> back_edge_1
*
* after applying the transformation:
* back_edge_1 -> [Parameter -> (ReadValue layer) -> some layers ... -> (Assign layer) ]
* \
* -> Result ] -> back_edge_1
*
* It is recommended to use this transformation in conjunction with the Reshape feature to set
* sequence dimension to 1 and with the UnrollTensorIterator transformation.
* For convenience, we have already enabled the unconditional execution of the UnrollTensorIterator
* transformation when using the LowLatency transformation for CPU, GNA plugins, no action is
* required here.
* After applying both of these transformations, the resulting network can be inferred step by
* step, the states will store between inferences.
*
*/
class ngraph::pass::LowLatency : public ngraph::pass::MatcherPass
{
public:
NGRAPH_RTTI_DECLARATION;
LowLatency();
};