Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #21477 from TolyaTalamanov:at/pipeline-builder-tool

Browse Source 
[G-API] Implement G-API pipeline modeling tool

* Implement G-API pipeline builder tool

* Remove whitespaces from config

* Remove unused unittest stuff

* Fix comments to review

* Fix MAC warning

* Rename to pipeline_modeling_tool

* Move to opencv apps

* Move config to gapi/samples/data

* gapi: samples sources are installed automatically
pull/21452/head
Anatoliy Talamanov 3 years ago committed by GitHub
parent f4a7754cc0
commit a92cba8484
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 2389 additions and 1 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 6
      modules/gapi/CMakeLists.txt
  2. 192
      modules/gapi/samples/data/config_template.yml
  3. 406
      modules/gapi/samples/pipeline_modeling_tool.cpp
  4. 66
      modules/gapi/samples/pipeline_modeling_tool/dummy_source.hpp
  5. 204
      modules/gapi/samples/pipeline_modeling_tool/pipeline.hpp
  6. 502
      modules/gapi/samples/pipeline_modeling_tool/pipeline_builder.hpp
  7. 931
      modules/gapi/samples/pipeline_modeling_tool/test_pipeline_modeling_tool.py
  8. 81
      modules/gapi/samples/pipeline_modeling_tool/utils.hpp

6
modules/gapi/CMakeLists.txt
Unescape View File

@ -356,6 +356,12 @@ if (TARGET example_gapi_onevpl_infer_single_roi)
endif() endif()
endif() endif()
if(TARGET example_gapi_pipeline_modeling_tool)
if(WIN32)
ocv_target_link_libraries(example_gapi_pipeline_modeling_tool winmm.lib)
endif()
endif()
# perf test dependencies postprocessing # perf test dependencies postprocessing
if(HAVE_GAPI_ONEVPL) if(HAVE_GAPI_ONEVPL)
# NB: TARGET opencv_perf_gapi doesn't exist before `ocv_add_perf_tests` # NB: TARGET opencv_perf_gapi doesn't exist before `ocv_add_perf_tests`

192
modules/gapi/samples/data/config_template.yml
Unescape View File

@ -0,0 +1,192 @@
%YAML:1.0
# Application running time in milliseconds: integer.
work_time: 2000
Pipelines:
PL1:
source:
name: 'Src'
latency: 33.0
output:
dims: [1, 3, 1280, 720]
precision: 'U8'
nodes:
- name: 'PP'
type: 'Dummy'
time: 0.2
output:
dims: [1, 3, 300, 300]
precision: 'U8'
- name: 'Infer'
type: 'Infer'
xml: 'face-detection-retail-0004.xml'
bin: 'face-detection-retail-0004.bin'
device: 'CPU'
input_layers:
- 'data'
output_layers:
- 'detection_out'
edges:
- from: 'Src'
to: 'PP'
- from: 'PP'
to: 'Infer'
# Path to the dump file (*.dot)'
dump: 'pl1.dot'
PL2:
source:
name: 'Src'
latency: 50.0
output:
dims: [1, 3, 1280, 720]
precision: 'U8'
nodes:
- name: 'M1_PP'
type: 'Dummy'
time: 0.2
output:
dims: [1, 3, 300, 300]
precision: 'U8'
- name: 'M1_Infer'
type: 'Infer'
xml: 'face-detection-retail-0004.xml'
bin: 'face-detection-retail-0004.bin'
device: 'CPU'
input_layers:
- 'data'
output_layers:
- 'detection_out'
- name: 'M2_PP'
type: 'Dummy'
time: 0.2
output:
dims: [1, 3, 300, 300]
precision: 'U8'
- name: 'M2_Infer'
type: 'Infer'
xml: 'face-detection-retail-0004.xml'
bin: 'face-detection-retail-0004.bin'
device: 'CPU'
input_layers:
- 'data'
output_layers:
- 'detection_out'
- name: 'M3_PP'
type: 'Dummy'
time: 0.2
output:
dims: [1, 3, 300, 300]
precision: 'U8'
- name: 'M3_Infer'
type: 'Infer'
xml: 'face-detection-retail-0004.xml'
bin: 'face-detection-retail-0004.bin'
device: 'CPU'
input_layers:
- 'data'
output_layers:
- 'detection_out'
- name: 'M4_PP'
type: 'Dummy'
time: 0.2
output:
dims: [1, 3, 300, 300]
precision: 'U8'
- name: 'M4_Infer'
type: 'Infer'
xml: 'face-detection-retail-0004.xml'
bin: 'face-detection-retail-0004.bin'
device: 'CPU'
input_layers:
- 'data'
output_layers:
- 'detection_out'
- name: 'M5_PP'
type: 'Dummy'
time: 0.2
output:
dims: [1, 3, 300, 300]
precision: 'U8'
- name: 'M5_Infer'
type: 'Infer'
xml: 'face-detection-retail-0004.xml'
bin: 'face-detection-retail-0004.bin'
device: 'CPU'
input_layers:
- 'data'
output_layers:
- 'detection_out'
edges:
- from: 'Src'
to: 'M1_PP'
- from: 'M1_PP'
to: 'M1_Infer'
- from: 'M1_Infer'
to: 'M2_PP'
- from: 'M2_PP'
to: 'M2_Infer'
- from: 'M2_Infer'
to: 'M3_PP'
- from: 'M3_PP'
to: 'M3_Infer'
- from: 'M3_Infer'
to: 'M4_PP'
- from: 'M4_PP'
to: 'M4_Infer'
- from: 'M4_Infer'
to: 'M5_PP'
- from: 'M5_PP'
to: 'M5_Infer'
dump: 'pl2.dot'
PL3:
source:
name: 'Src'
latency: 33.0
output:
dims: [1, 3, 1280, 720]
precision: 'U8'
nodes:
- name: 'PP'
type: 'Dummy'
time: 0.2
output:
dims: [1, 3, 300, 300]
precision: 'U8'
- name: 'Infer'
type: 'Infer'
xml: 'face-detection-retail-0004.xml'
bin: 'face-detection-retail-0004.bin'
device: 'CPU'
input_layers:
- 'data'
output_layers:
- 'detection_out'
edges:
- from: 'Src'
to: 'PP'
- from: 'PP'
to: 'Infer'
dump: 'pl3.dot'

406
modules/gapi/samples/pipeline_modeling_tool.cpp
Unescape View File

@ -0,0 +1,406 @@
#include <iostream>
#include <fstream>
#include <thread>
#include <exception>
#include <unordered_map>
#include <vector>
#include <opencv2/gapi.hpp>
#include <opencv2/highgui.hpp> // cv::CommandLineParser
#include <opencv2/core/utils/filesystem.hpp>
#if defined(_WIN32)
#include <windows.h>
#endif
#include "pipeline_modeling_tool/dummy_source.hpp"
#include "pipeline_modeling_tool/utils.hpp"
#include "pipeline_modeling_tool/pipeline_builder.hpp"
enum class AppMode {
REALTIME,
BENCHMARK
};
static AppMode strToAppMode(const std::string& mode_str) {
if (mode_str == "realtime") {
return AppMode::REALTIME;
} else if (mode_str == "benchmark") {
return AppMode::BENCHMARK;
} else {
throw std::logic_error("Unsupported AppMode: " + mode_str +
"\nPlease chose between: realtime and benchmark");
}
}
template <typename T>
T read(const cv::FileNode& node) {
return static_cast<T>(node);
}
static cv::FileNode check_and_get_fn(const cv::FileNode& fn,
const std::string& field,
const std::string& uplvl) {
const bool is_map = fn.isMap();
if (!is_map || fn[field].empty()) {
throw std::logic_error(uplvl + " must contain field: " + field);
}
return fn[field];
}
static cv::FileNode check_and_get_fn(const cv::FileStorage& fs,
const std::string& field,
const std::string& uplvl) {
auto fn = fs[field];
if (fn.empty()) {
throw std::logic_error(uplvl + " must contain field: " + field);
}
return fn;
}
template <typename T, typename FileT>
T check_and_read(const FileT& f,
const std::string& field,
const std::string& uplvl) {
auto fn = check_and_get_fn(f, field, uplvl);
return read<T>(fn);
}
template <typename T>
cv::optional<T> readOpt(const cv::FileNode& fn) {
return fn.empty() ? cv::optional<T>() : cv::optional<T>(read<T>(fn));
}
template <typename T>
std::vector<T> readList(const cv::FileNode& fn,
const std::string& field,
const std::string& uplvl) {
auto fn_field = check_and_get_fn(fn, field, uplvl);
if (!fn_field.isSeq()) {
throw std::logic_error(field + " in " + uplvl + " must be a sequence");
}
std::vector<T> vec;
for (auto iter : fn_field) {
vec.push_back(read<T>(iter));
}
return vec;
}
template <typename T>
std::vector<T> readVec(const cv::FileNode& fn,
const std::string& field,
const std::string& uplvl) {
auto fn_field = check_and_get_fn(fn, field, uplvl);
std::vector<T> vec;
fn_field >> vec;
return vec;
}
static int strToPrecision(const std::string& precision) {
static std::unordered_map<std::string, int> str_to_precision = {
{"U8", CV_8U}, {"FP32", CV_32F}, {"FP16", CV_16F}
};
auto it = str_to_precision.find(precision);
if (it == str_to_precision.end()) {
throw std::logic_error("Unsupported precision: " + precision);
}
return it->second;
}
template <>
OutputDescr read<OutputDescr>(const cv::FileNode& fn) {
auto dims = readVec<int>(fn, "dims", "output");
auto str_prec = check_and_read<std::string>(fn, "precision", "output");
return OutputDescr{dims, strToPrecision(str_prec)};
}
template <>
Edge read<Edge>(const cv::FileNode& fn) {
auto from = check_and_read<std::string>(fn, "from", "edge");
auto to = check_and_read<std::string>(fn, "to", "edge");
auto splitNameAndPort = [](const std::string& str) {
auto pos = str.find(':');
auto name =
pos == std::string::npos ? str : std::string(str.c_str(), pos);
size_t port =
pos == std::string::npos ? 0 : std::atoi(str.c_str() + pos + 1);
return std::make_pair(name, port);
};
auto p1 = splitNameAndPort(from);
auto p2 = splitNameAndPort(to);
return Edge{Edge::P{p1.first, p1.second}, Edge::P{p2.first, p2.second}};
}
static std::string getModelsPath() {
static char* models_path_c = std::getenv("PIPELINE_MODELS_PATH");
static std::string models_path = models_path_c ? models_path_c : ".";
return models_path;
}
template <>
ModelPath read<ModelPath>(const cv::FileNode& fn) {
using cv::utils::fs::join;
if (!fn["xml"].empty() && !fn["bin"].empty()) {
return ModelPath{LoadPath{join(getModelsPath(), fn["xml"].string()),
join(getModelsPath(), fn["bin"].string())}};
} else if (!fn["blob"].empty()){
return ModelPath{ImportPath{join(getModelsPath(), fn["blob"].string())}};
} else {
const std::string emsg = R""""(
Path to OpenVINO model must be specified in either of two formats:
1.
xml: path to *.xml
bin: path to *.bin
2.
blob: path to *.blob
)"""";
throw std::logic_error(emsg);
}
}
static PLMode strToPLMode(const std::string& mode_str) {
if (mode_str == "streaming") {
return PLMode::STREAMING;
} else if (mode_str == "regular") {
return PLMode::REGULAR;
} else {
throw std::logic_error("Unsupported PLMode: " + mode_str +
"\nPlease chose between: streaming and regular");
}
}
static std::vector<std::string> parseExecList(const std::string& exec_list) {
std::vector<std::string> pl_types;
std::stringstream ss(exec_list);
std::string pl_type;
while (getline(ss, pl_type, ',')) {
pl_types.push_back(pl_type);
}
return pl_types;
}
static void loadConfig(const std::string& filename,
std::map<std::string, std::string>& config) {
cv::FileStorage fs(filename, cv::FileStorage::READ);
if (!fs.isOpened()) {
throw std::runtime_error("Failed to load config: " + filename);
}
cv::FileNode root = fs.root();
for (auto it = root.begin(); it != root.end(); ++it) {
auto device = *it;
if (!device.isMap()) {
throw std::runtime_error("Failed to parse config: " + filename);
}
for (auto item : device) {
config.emplace(item.name(), item.string());
}
}
}
int main(int argc, char* argv[]) {
#if defined(_WIN32)
timeBeginPeriod(1);
#endif
try {
const std::string keys =
"{ h help | | Print this help message. }"
"{ cfg | | Path to the config which is either"
" YAML file or string. }"
"{ load_config | | Optional. Path to XML/YAML/JSON file"
" to load custom IE parameters. }"
"{ cache_dir | | Optional. Enables caching of loaded models"
" to specified directory. }"
"{ log_file | | Optional. If file is specified, app will"
" dump expanded execution information. }"
"{ pl_mode | streaming | Optional. Pipeline mode: streaming/regular"
" if it's specified will be applied for"
" every pipeline. }"
"{ qc | 1 | Optional. Calculated automatically by G-API"
" if set to 0. If it's specified will be"
" applied for every pipeline. }"
"{ app_mode | realtime | Application mode (realtime/benchmark). }"
"{ exec_list | | A comma-separated list of pipelines that"
" will be executed. Spaces around commas"
" are prohibited. }";
cv::CommandLineParser cmd(argc, argv, keys);
if (cmd.has("help")) {
cmd.printMessage();
return 0;
}
const auto cfg = cmd.get<std::string>("cfg");
const auto load_config = cmd.get<std::string>("load_config");
const auto cached_dir = cmd.get<std::string>("cache_dir");
const auto log_file = cmd.get<std::string>("log_file");
const auto pl_mode = strToPLMode(cmd.get<std::string>("pl_mode"));
const auto qc = cmd.get<int>("qc");
const auto app_mode = strToAppMode(cmd.get<std::string>("app_mode"));
const auto exec_str = cmd.get<std::string>("exec_list");
cv::FileStorage fs;
if (cfg.empty()) {
throw std::logic_error("Config must be specified via --cfg option");
}
// NB: *.yml
if (cfg.size() < 5) {
throw std::logic_error("--cfg string must contain at least 5 symbols"
" to determine if it's a file (*.yml) a or string");
}
if (cfg.substr(cfg.size() - 4, cfg.size()) == ".yml") {
if (!fs.open(cfg, cv::FileStorage::READ)) {
throw std::logic_error("Failed to open config file: " + cfg);
}
} else {
fs = cv::FileStorage(cfg, cv::FileStorage::FORMAT_YAML |
cv::FileStorage::MEMORY);
}
std::map<std::string, std::string> config;
if (!load_config.empty()) {
loadConfig(load_config, config);
}
// NB: Takes priority over config from file
if (!cached_dir.empty()) {
config =
std::map<std::string, std::string>{{"CACHE_DIR", cached_dir}};
}
const double work_time_ms =
check_and_read<double>(fs, "work_time", "Config");
if (work_time_ms < 0) {
throw std::logic_error("work_time must be positive");
}
auto pipelines_fn = check_and_get_fn(fs, "Pipelines", "Config");
if (!pipelines_fn.isMap()) {
throw std::logic_error("Pipelines field must be a map");
}
auto exec_list = !exec_str.empty() ? parseExecList(exec_str)
: pipelines_fn.keys();
std::vector<Pipeline::Ptr> pipelines;
pipelines.reserve(exec_list.size());
// NB: Build pipelines based on config information
PipelineBuilder builder;
for (const auto& name : exec_list) {
const auto& pl_fn = check_and_get_fn(pipelines_fn, name, "Pipelines");
builder.setName(name);
// NB: Set source
{
const auto& src_fn = check_and_get_fn(pl_fn, "source", name);
auto src_name =
check_and_read<std::string>(src_fn, "name", "source");
auto latency =
check_and_read<double>(src_fn, "latency", "source");
auto output =
check_and_read<OutputDescr>(src_fn, "output", "source");
// NB: In case BENCHMARK mode sources work with zero latency.
if (app_mode == AppMode::BENCHMARK) {
latency = 0.0;
}
builder.setSource(src_name, latency, output);
}
const auto& nodes_fn = check_and_get_fn(pl_fn, "nodes", name);
if (!nodes_fn.isSeq()) {
throw std::logic_error("nodes in " + name + " must be a sequence");
}
for (auto node_fn : nodes_fn) {
auto node_name =
check_and_read<std::string>(node_fn, "name", "node");
auto node_type =
check_and_read<std::string>(node_fn, "type", "node");
if (node_type == "Dummy") {
auto time =
check_and_read<double>(node_fn, "time", node_name);
if (time < 0) {
throw std::logic_error(node_name + " time must be positive");
}
auto output =
check_and_read<OutputDescr>(node_fn, "output", node_name);
builder.addDummy(node_name, time, output);
} else if (node_type == "Infer") {
InferParams params;
params.path = read<ModelPath>(node_fn);
params.device =
check_and_read<std::string>(node_fn, "device", node_name);
params.input_layers =
readList<std::string>(node_fn, "input_layers", node_name);
params.output_layers =
readList<std::string>(node_fn, "output_layers", node_name);
params.config = config;
builder.addInfer(node_name, params);
} else {
throw std::logic_error("Unsupported node type: " + node_type);
}
}
const auto edges_fn = check_and_get_fn(pl_fn, "edges", name);
if (!edges_fn.isSeq()) {
throw std::logic_error("edges in " + name + " must be a sequence");
}
for (auto edge_fn : edges_fn) {
auto edge = read<Edge>(edge_fn);
builder.addEdge(edge);
}
// NB: Pipeline mode from config takes priority over cmd.
auto mode = readOpt<std::string>(pl_fn["mode"]);
builder.setMode(mode.has_value() ? strToPLMode(mode.value()) : pl_mode);
// NB: Queue capacity from config takes priority over cmd.
auto config_qc = readOpt<int>(pl_fn["queue_capacity"]);
auto queue_capacity = config_qc.has_value() ? config_qc.value() : qc;
// NB: 0 is special constant that means
// queue capacity should be calculated automatically.
if (queue_capacity != 0) {
builder.setQueueCapacity(queue_capacity);
}
auto dump = readOpt<std::string>(pl_fn["dump"]);
if (dump) {
builder.setDumpFilePath(dump.value());
}
pipelines.emplace_back(builder.build());
}
// NB: Compille pipelines
for (size_t i = 0; i < pipelines.size(); ++i) {
pipelines[i]->compile();
}
// NB: Execute pipelines
std::vector<std::thread> threads(pipelines.size());
for (size_t i = 0; i < pipelines.size(); ++i) {
threads[i] = std::thread([&, i]() {
pipelines[i]->run(work_time_ms);
});
}
std::ofstream file;
if (!log_file.empty()) {
file.open(log_file);
}
for (size_t i = 0; i < threads.size(); ++i) {
threads[i].join();
if (file.is_open()) {
file << pipelines[i]->report().toStr(true) << std::endl;
}
std::cout << pipelines[i]->report().toStr() << std::endl;
}
} catch (std::exception& e) {
std::cout << e.what() << std::endl;
throw;
}
return 0;
}

66
modules/gapi/samples/pipeline_modeling_tool/dummy_source.hpp
Unescape View File

@ -0,0 +1,66 @@
#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP
#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP
#include <thread>
#include <memory>
#include <chrono>
#include <opencv2/gapi.hpp>
#include <opencv2/gapi/streaming/cap.hpp> // cv::gapi::wip::IStreamSource
#include "utils.hpp"
class DummySource final: public cv::gapi::wip::IStreamSource {
public:
using Ptr = std::shared_ptr<DummySource>;
DummySource(const double latency,
const OutputDescr& output);
bool pull(cv::gapi::wip::Data& data) override;
cv::GMetaArg descr_of() const override;
private:
double m_latency;
cv::Mat m_mat;
using TimePoint =
std::chrono::time_point<std::chrono::high_resolution_clock>;
cv::optional<TimePoint> m_prev_pull_tp;
};
DummySource::DummySource(const double latency,
const OutputDescr& output)
: m_latency(latency), m_mat(output.dims, output.precision) {
if (output.dims.size() == 1) {
//FIXME: Well-known 1D mat WA
m_mat.dims = 1;
}
utils::generateRandom(m_mat);
}
bool DummySource::pull(cv::gapi::wip::Data& data) {
using namespace std::chrono;
using namespace cv::gapi::streaming;
// NB: In case it's the first pull.
if (!m_prev_pull_tp) {
m_prev_pull_tp = cv::util::make_optional(high_resolution_clock::now());
}
// NB: Just increase reference counter not to release mat memory
// after assigning it to the data.
cv::Mat mat = m_mat;
auto end = high_resolution_clock::now();
auto elapsed =
duration_cast<duration<double, std::milli>>(end - *m_prev_pull_tp).count();
auto delta = m_latency - elapsed;
if (delta > 0) {
utils::sleep(delta);
}
data.meta[meta_tag::timestamp] = int64_t{utils::timestamp<milliseconds>()};
data = mat;
m_prev_pull_tp = cv::util::make_optional(high_resolution_clock::now());
return true;
}
cv::GMetaArg DummySource::descr_of() const {
return cv::GMetaArg{cv::descr_of(m_mat)};
}
#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_DUMMY_SOURCE_HPP

204
modules/gapi/samples/pipeline_modeling_tool/pipeline.hpp
Unescape View File

@ -0,0 +1,204 @@
#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_HPP
#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_HPP
struct PerfReport {
std::string name;
double avg_latency = 0.0;
double throughput = 0.0;
int64_t first_run_latency = 0;
int64_t elapsed = 0;
int64_t compilation_time = 0;
std::vector<int64_t> latencies;
std::string toStr(bool expanded = false) const;
};
std::string PerfReport::toStr(bool expand) const {
std::stringstream ss;
ss << name << ": Compilation time: " << compilation_time << " ms; "
<< "Average latency: " << avg_latency << " ms; Throughput: "
<< throughput << " FPS; First latency: "
<< first_run_latency << " ms";
if (expand) {
ss << "\nTotal processed frames: " << latencies.size()
<< "\nTotal elapsed time: " << elapsed << " ms" << std::endl;
for (size_t i = 0; i < latencies.size(); ++i) {
ss << std::endl;
ss << "Frame:" << i << "\nLatency: "
<< latencies[i] << " ms";
}
}
return ss.str();
}
class Pipeline {
public:
using Ptr = std::shared_ptr<Pipeline>;
Pipeline(std::string&& name,
cv::GComputation&& comp,
cv::gapi::wip::IStreamSource::Ptr&& src,
cv::GCompileArgs&& args,
const size_t num_outputs);
void compile();
void run(double work_time_ms);
const PerfReport& report() const;
virtual ~Pipeline() = default;
protected:
struct RunPerf {
int64_t elapsed = 0;
std::vector<int64_t> latencies;
};
virtual void _compile() = 0;
virtual RunPerf _run(double work_time_ms) = 0;
std::string m_name;
cv::GComputation m_comp;
cv::gapi::wip::IStreamSource::Ptr m_src;
cv::GCompileArgs m_args;
size_t m_num_outputs;
PerfReport m_perf;
};
Pipeline::Pipeline(std::string&& name,
cv::GComputation&& comp,
cv::gapi::wip::IStreamSource::Ptr&& src,
cv::GCompileArgs&& args,
const size_t num_outputs)
: m_name(std::move(name)),
m_comp(std::move(comp)),
m_src(std::move(src)),
m_args(std::move(args)),
m_num_outputs(num_outputs) {
m_perf.name = m_name;
}
void Pipeline::compile() {
m_perf.compilation_time =
utils::measure<std::chrono::milliseconds>([this]() {
_compile();
});
}
void Pipeline::run(double work_time_ms) {
auto run_perf = _run(work_time_ms);
m_perf.elapsed = run_perf.elapsed;
m_perf.latencies = std::move(run_perf.latencies);
m_perf.avg_latency =
std::accumulate(m_perf.latencies.begin(),
m_perf.latencies.end(),
0.0) / static_cast<double>(m_perf.latencies.size());
m_perf.throughput =
(m_perf.latencies.size() / static_cast<double>(m_perf.elapsed)) * 1000;
m_perf.first_run_latency = m_perf.latencies[0];
}
const PerfReport& Pipeline::report() const {
return m_perf;
}
class StreamingPipeline : public Pipeline {
public:
using Pipeline::Pipeline;
private:
void _compile() override {
m_compiled =
m_comp.compileStreaming({m_src->descr_of()},
cv::GCompileArgs(m_args));
}
Pipeline::RunPerf _run(double work_time_ms) override {
// NB: Setup.
using namespace std::chrono;
// NB: N-1 buffers + timestamp.
std::vector<cv::Mat> out_mats(m_num_outputs - 1);
int64_t start_ts = -1;
cv::GRunArgsP pipeline_outputs;
for (auto& m : out_mats) {
pipeline_outputs += cv::gout(m);
}
pipeline_outputs += cv::gout(start_ts);
m_compiled.setSource(m_src);
// NB: Start execution & measure performance statistics.
Pipeline::RunPerf perf;
auto start = high_resolution_clock::now();
m_compiled.start();
while (m_compiled.pull(cv::GRunArgsP{pipeline_outputs})) {
int64_t latency = utils::timestamp<milliseconds>() - start_ts;
perf.latencies.push_back(latency);
perf.elapsed = duration_cast<milliseconds>(
high_resolution_clock::now() - start).count();
if (perf.elapsed >= work_time_ms) {
m_compiled.stop();
break;
}
};
return perf;
}
cv::GStreamingCompiled m_compiled;
};
class RegularPipeline : public Pipeline {
public:
using Pipeline::Pipeline;
private:
void _compile() override {
m_compiled =
m_comp.compile({m_src->descr_of()},
cv::GCompileArgs(m_args));
}
Pipeline::RunPerf _run(double work_time_ms) override {
// NB: Setup
using namespace std::chrono;
cv::gapi::wip::Data d;
std::vector<cv::Mat> out_mats(m_num_outputs);
cv::GRunArgsP pipeline_outputs;
for (auto& m : out_mats) {
pipeline_outputs += cv::gout(m);
}
// NB: Start execution & measure performance statistics.
Pipeline::RunPerf perf;
auto start = high_resolution_clock::now();
while (m_src->pull(d)) {
auto in_mat = cv::util::get<cv::Mat>(d);
int64_t latency = utils::measure<milliseconds>([&]{
m_compiled(cv::gin(in_mat), cv::GRunArgsP{pipeline_outputs});
});
perf.latencies.push_back(latency);
perf.elapsed = duration_cast<milliseconds>(
high_resolution_clock::now() - start).count();
if (perf.elapsed >= work_time_ms) {
break;
}
};
return perf;
}
cv::GCompiled m_compiled;
};
enum class PLMode {
REGULAR,
STREAMING
};
#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_HPP

502
modules/gapi/samples/pipeline_modeling_tool/pipeline_builder.hpp
Unescape View File

@ -0,0 +1,502 @@
#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_BUILDER_HPP
#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_BUILDER_HPP
#include <map>
#include <opencv2/gapi/infer.hpp> // cv::gapi::GNetPackage
#include <opencv2/gapi/streaming/cap.hpp> // cv::gapi::wip::IStreamSource
#include <opencv2/gapi/infer/ie.hpp> // cv::gapi::ie::Params
#include <opencv2/gapi/gcommon.hpp> // cv::gapi::GCompileArgs
#include <opencv2/gapi/cpu/gcpukernel.hpp> // GAPI_OCV_KERNEL
#include <opencv2/gapi/gkernel.hpp> // G_API_OP
#include "pipeline.hpp"
#include "utils.hpp"
struct Edge {
struct P {
std::string name;
size_t port;
};
P src;
P dst;
};
struct CallNode {
using F = std::function<void(const cv::GProtoArgs&, cv::GProtoArgs&)>;
std::string name;
F run;
};
struct DataNode {
cv::optional<cv::GProtoArg> arg;
};
struct Node {
using Ptr = std::shared_ptr<Node>;
using WPtr = std::weak_ptr<Node>;
using Kind = cv::util::variant<CallNode, DataNode>;
std::vector<Node::WPtr> in_nodes;
std::vector<Node::Ptr> out_nodes;
Kind kind;
};
struct DummyCall {
G_API_OP(GDummy,
<cv::GMat(cv::GMat, double, OutputDescr)>,
"custom.dummy") {
static cv::GMatDesc outMeta(const cv::GMatDesc& /* in */,
double /* time */,
const OutputDescr& output) {
if (output.dims.size() == 2) {
return cv::GMatDesc(output.precision,
1,
cv::Size(output.dims[0], output.dims[1]));
}
return cv::GMatDesc(output.precision, output.dims);
}
};
struct DummyState {
cv::Mat mat;
};
// NB: Generate random mat once and then
// copy to dst buffer on every iteration.
GAPI_OCV_KERNEL_ST(GCPUDummy, GDummy, DummyState) {
static void setup(const cv::GMatDesc& /*in*/,
double /*time*/,
const OutputDescr& output,
std::shared_ptr<DummyState>& state,
const cv::GCompileArgs& /*args*/) {
state.reset(new DummyState{});
state->mat.create(output.dims, output.precision);
utils::generateRandom(state->mat);
}
static void run(const cv::Mat& /*in_mat*/,
double time,
const OutputDescr& /*output*/,
cv::Mat& out_mat,
DummyState& state) {
using namespace std::chrono;
double total = 0;
auto start = high_resolution_clock::now();
state.mat.copyTo(out_mat);
while (total < time) {
total = duration_cast<duration<double, std::milli>>(
high_resolution_clock::now() - start).count();
}
}
};
void operator()(const cv::GProtoArgs& inputs, cv::GProtoArgs& outputs);
size_t numInputs() const { return 1; }
size_t numOutputs() const { return 1; }
double time;
OutputDescr output;
};
void DummyCall::operator()(const cv::GProtoArgs& inputs,
cv::GProtoArgs& outputs) {
GAPI_Assert(inputs.size() == 1u);
GAPI_Assert(cv::util::holds_alternative<cv::GMat>(inputs[0]));
GAPI_Assert(outputs.empty());
auto in = cv::util::get<cv::GMat>(inputs[0]);
outputs.emplace_back(GDummy::on(in, time, output));
}
struct InferCall {
void operator()(const cv::GProtoArgs& inputs, cv::GProtoArgs& outputs);
size_t numInputs() const { return input_layers.size(); }
size_t numOutputs() const { return output_layers.size(); }
std::string tag;
std::vector<std::string> input_layers;
std::vector<std::string> output_layers;
};
void InferCall::operator()(const cv::GProtoArgs& inputs,
cv::GProtoArgs& outputs) {
GAPI_Assert(inputs.size() == input_layers.size());
GAPI_Assert(outputs.empty());
cv::GInferInputs g_inputs;
// TODO: Add an opportunity not specify input/output layers in case
// there is only single layer.
for (size_t i = 0; i < inputs.size(); ++i) {
// TODO: Support GFrame as well.
GAPI_Assert(cv::util::holds_alternative<cv::GMat>(inputs[i]));
auto in = cv::util::get<cv::GMat>(inputs[i]);
g_inputs[input_layers[i]] = in;
}
auto g_outputs = cv::gapi::infer<cv::gapi::Generic>(tag, g_inputs);
for (size_t i = 0; i < output_layers.size(); ++i) {
outputs.emplace_back(g_outputs.at(output_layers[i]));
}
}
struct SourceCall {
void operator()(const cv::GProtoArgs& inputs, cv::GProtoArgs& outputs);
size_t numInputs() const { return 0; }
size_t numOutputs() const { return 1; }
};
void SourceCall::operator()(const cv::GProtoArgs& inputs,
cv::GProtoArgs& outputs) {
GAPI_Assert(inputs.empty());
GAPI_Assert(outputs.empty());
// NB: Since NV12 isn't exposed source always produce GMat.
outputs.emplace_back(cv::GMat());
}
struct LoadPath {
std::string xml;
std::string bin;
};
struct ImportPath {
std::string blob;
};
using ModelPath = cv::util::variant<ImportPath, LoadPath>;
struct InferParams {
std::string name;
ModelPath path;
std::string device;
std::vector<std::string> input_layers;
std::vector<std::string> output_layers;
std::map<std::string, std::string> config;
};
class PipelineBuilder {
public:
PipelineBuilder();
void addDummy(const std::string& name,
const double time,
const OutputDescr& output);
void addInfer(const std::string& name, const InferParams& params);
void setSource(const std::string& name,
double latency,
const OutputDescr& output);
void addEdge(const Edge& edge);
void setMode(PLMode mode);
void setDumpFilePath(const std::string& dump);
void setQueueCapacity(const size_t qc);
void setName(const std::string& name);
Pipeline::Ptr build();
private:
template <typename CallT>
void addCall(const std::string& name,
CallT&& call);
Pipeline::Ptr construct();
template <typename K, typename V>
using M = std::unordered_map<K, V>;
struct State {
struct NodeEdges {
std::vector<Edge> input_edges;
std::vector<Edge> output_edges;
};
M<std::string, Node::Ptr> calls_map;
std::vector<Node::Ptr> all_calls;
cv::gapi::GNetPackage networks;
cv::gapi::GKernelPackage kernels;
cv::GCompileArgs compile_args;
cv::gapi::wip::IStreamSource::Ptr src;
PLMode mode = PLMode::STREAMING;
std::string name;
};
std::unique_ptr<State> m_state;
};
PipelineBuilder::PipelineBuilder() : m_state(new State{}) { };
void PipelineBuilder::addDummy(const std::string& name,
const double time,
const OutputDescr& output) {
m_state->kernels.include<DummyCall::GCPUDummy>();
addCall(name, DummyCall{time, output});
}
template <typename CallT>
void PipelineBuilder::addCall(const std::string& name,
CallT&& call) {
size_t num_inputs = call.numInputs();
size_t num_outputs = call.numOutputs();
Node::Ptr call_node(new Node{{},{},Node::Kind{CallNode{name, std::move(call)}}});
// NB: Create placeholders for inputs.
call_node->in_nodes.resize(num_inputs);
// NB: Create outputs with empty data.
for (size_t i = 0; i < num_outputs; ++i) {
call_node->out_nodes.emplace_back(new Node{{call_node},
{},
Node::Kind{DataNode{}}});
}
auto it = m_state->calls_map.find(name);
if (it != m_state->calls_map.end()) {
throw std::logic_error("Node: " + name + " already exists!");
}
m_state->calls_map.emplace(name, call_node);
m_state->all_calls.emplace_back(call_node);
}
void PipelineBuilder::addInfer(const std::string& name,
const InferParams& params) {
// NB: No default ctor for Params.
std::unique_ptr<cv::gapi::ie::Params<cv::gapi::Generic>> pp;
if (cv::util::holds_alternative<LoadPath>(params.path)) {
auto load_path = cv::util::get<LoadPath>(params.path);
pp.reset(new cv::gapi::ie::Params<cv::gapi::Generic>(name,
load_path.xml,
load_path.bin,
params.device));
} else {
GAPI_Assert(cv::util::holds_alternative<ImportPath>(params.path));
auto import_path = cv::util::get<ImportPath>(params.path);
pp.reset(new cv::gapi::ie::Params<cv::gapi::Generic>(name,
import_path.blob,
params.device));
}
pp->pluginConfig(params.config);
m_state->networks += cv::gapi::networks(*pp);
addCall(name, InferCall{name, params.input_layers, params.output_layers});
}
void PipelineBuilder::addEdge(const Edge& edge) {
const auto& src_it = m_state->calls_map.find(edge.src.name);
if (src_it == m_state->calls_map.end()) {
throw std::logic_error("Failed to find node: " + edge.src.name);
}
auto src_node = src_it->second;
if (src_node->out_nodes.size() <= edge.src.port) {
throw std::logic_error("Failed to access node: " + edge.src.name +
" by out port: " + std::to_string(edge.src.port));
}
auto dst_it = m_state->calls_map.find(edge.dst.name);
if (dst_it == m_state->calls_map.end()) {
throw std::logic_error("Failed to find node: " + edge.dst.name);
}
auto dst_node = dst_it->second;
if (dst_node->in_nodes.size() <= edge.dst.port) {
throw std::logic_error("Failed to access node: " + edge.dst.name +
" by in port: " + std::to_string(edge.dst.port));
}
auto out_data = src_node->out_nodes[edge.src.port];
auto& in_data = dst_node->in_nodes[edge.dst.port];
// NB: in_data != nullptr.
if (!in_data.expired()) {
throw std::logic_error("Node: " + edge.dst.name +
" already connected by in port: " +
std::to_string(edge.dst.port));
}
dst_node->in_nodes[edge.dst.port] = out_data;
out_data->out_nodes.push_back(dst_node);
}
void PipelineBuilder::setSource(const std::string& name,
double latency,
const OutputDescr& output) {
GAPI_Assert(!m_state->src);
m_state->src = std::make_shared<DummySource>(latency, output);
addCall(name, SourceCall{});
}
void PipelineBuilder::setMode(PLMode mode) {
m_state->mode = mode;
}
void PipelineBuilder::setDumpFilePath(const std::string& dump) {
m_state->compile_args.emplace_back(cv::graph_dump_path{dump});
}
void PipelineBuilder::setQueueCapacity(const size_t qc) {
m_state->compile_args.emplace_back(cv::gapi::streaming::queue_capacity{qc});
}
void PipelineBuilder::setName(const std::string& name) {
m_state->name = name;
}
static bool visit(Node::Ptr node,
std::vector<Node::Ptr>& sorted,
std::unordered_map<Node::Ptr, int>& visited) {
if (!node) {
throw std::logic_error("Found null node");
}
visited[node] = 1;
for (auto in : node->in_nodes) {
auto in_node = in.lock();
if (visited[in_node] == 0) {
if (visit(in_node, sorted, visited)) {
return true;
}
} else if (visited[in_node] == 1) {
return true;
}
}
visited[node] = 2;
sorted.push_back(node);
return false;
}
static cv::optional<std::vector<Node::Ptr>>
toposort(const std::vector<Node::Ptr> nodes) {
std::vector<Node::Ptr> sorted;
std::unordered_map<Node::Ptr, int> visited;
for (auto n : nodes) {
if (visit(n, sorted, visited)) {
return cv::optional<std::vector<Node::Ptr>>{};
}
}
return cv::util::make_optional(sorted);
}
Pipeline::Ptr PipelineBuilder::construct() {
// NB: Unlike G-API, pipeline_builder_tool graph always starts with CALL node
// (not data) that produce datas, so the call node which doesn't have
// inputs is considered as "producer" node.
//
// Graph always starts with CALL node and ends with DATA node.
// Graph example: [source] -> (source:0) -> [PP] -> (PP:0)
//
// The algorithm is quite simple:
// 0. Verify that every call input node exists (connected).
// 1. Sort all nodes by visiting only call nodes,
// since there is no data nodes that's not connected with any call node,
// it's guarantee that every node will be visited.
// 2. Fillter call nodes.
// 3. Go through every call node.
// FIXME: Add toposort in case user passed nodes
// in arbitrary order which is unlikely happened.
// 4. Extract proto input from every input node
// 5. Run call and get outputs
// 6. If call node doesn't have inputs it means that it's "producer" node,
// so collect all outputs to graph_inputs vector.
// 7. Assign proto outputs to output data nodes,
// so the next calls can use them as inputs.
cv::GProtoArgs graph_inputs;
cv::GProtoArgs graph_outputs;
// 0. Verify that every call input node exists (connected).
for (auto call_node : m_state->all_calls) {
for (size_t i = 0; i < call_node->in_nodes.size(); ++i) {
const auto& in_data_node = call_node->in_nodes[i];
// NB: in_data_node == nullptr.
if (in_data_node.expired()) {
const auto& call = cv::util::get<CallNode>(call_node->kind);
throw std::logic_error(
"Node: " + call.name + " in Pipeline: " + m_state->name +
" has dangling input by in port: " + std::to_string(i));
}
}
}
// (0) Sort all nodes;
auto has_sorted = toposort(m_state->all_calls);
if (!has_sorted) {
throw std::logic_error(
"Pipeline: " + m_state->name + " has cyclic dependencies") ;
}
auto& sorted = has_sorted.value();
// (1). Fillter call nodes.
std::vector<Node::Ptr> sorted_calls;
for (auto n : sorted) {
if (cv::util::holds_alternative<CallNode>(n->kind)) {
sorted_calls.push_back(n);
}
}
// (2). Go through every call node.
for (auto call_node : sorted_calls) {
cv::GProtoArgs outputs;
cv::GProtoArgs inputs;
for (size_t i = 0; i < call_node->in_nodes.size(); ++i) {
auto in_node = call_node->in_nodes.at(i);
auto in_data = cv::util::get<DataNode>(in_node.lock()->kind);
if (!in_data.arg.has_value()) {
throw std::logic_error("data hasn't been provided");
}
// (3). Extract proto input from every input node.
inputs.push_back(in_data.arg.value());
}
// (4). Run call and get outputs.
auto call = cv::util::get<CallNode>(call_node->kind);
call.run(inputs, outputs);
// (5) If call node doesn't have inputs
// it means that it's input producer node (Source).
if (call_node->in_nodes.empty()) {
for (auto out : outputs) {
graph_inputs.push_back(out);
}
}
// (6). Assign proto outputs to output data nodes,
// so the next calls can use them as inputs.
GAPI_Assert(outputs.size() == call_node->out_nodes.size());
for (size_t i = 0; i < outputs.size(); ++i) {
auto out_node = call_node->out_nodes[i];
auto& out_data = cv::util::get<DataNode>(out_node->kind);
out_data.arg = cv::util::make_optional(outputs[i]);
if (out_node->out_nodes.empty()) {
graph_outputs.push_back(out_data.arg.value());
}
}
}
m_state->compile_args.emplace_back(m_state->networks);
m_state->compile_args.emplace_back(m_state->kernels);
if (m_state->mode == PLMode::STREAMING) {
GAPI_Assert(graph_inputs.size() == 1);
GAPI_Assert(cv::util::holds_alternative<cv::GMat>(graph_inputs[0]));
// FIXME: Handle GFrame when NV12 comes.
const auto& graph_input = cv::util::get<cv::GMat>(graph_inputs[0]);
// NB: In case streaming mode need to expose timestamp in order to
// calculate performance metrics.
graph_outputs.emplace_back(
cv::gapi::streaming::timestamp(graph_input).strip());
return std::make_shared<StreamingPipeline>(std::move(m_state->name),
cv::GComputation(
cv::GProtoInputArgs{graph_inputs},
cv::GProtoOutputArgs{graph_outputs}),
std::move(m_state->src),
std::move(m_state->compile_args),
graph_outputs.size());
}
GAPI_Assert(m_state->mode == PLMode::REGULAR);
return std::make_shared<RegularPipeline>(std::move(m_state->name),
cv::GComputation(
cv::GProtoInputArgs{graph_inputs},
cv::GProtoOutputArgs{graph_outputs}),
std::move(m_state->src),
std::move(m_state->compile_args),
graph_outputs.size());
}
Pipeline::Ptr PipelineBuilder::build() {
auto pipeline = construct();
m_state.reset(new State{});
return pipeline;
}
#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_PIPELINE_BUILDER_HPP

931
modules/gapi/samples/pipeline_modeling_tool/test_pipeline_modeling_tool.py
Unescape View File

@ -0,0 +1,931 @@
import os
import subprocess
pipeline_modeling_tool = os.getenv('PIPELINE_MODELING_TOOL')
def get_output(exec_str):
try:
out = subprocess.check_output(exec_str,
stderr=subprocess.STDOUT,
shell=True).strip().decode()
except subprocess.CalledProcessError as exc:
out = exc.output.strip().decode()
return out
def test_error_no_config_specified():
out = get_output(pipeline_modeling_tool)
assert out.startswith('Config must be specified via --cfg option')
def test_error_no_config_exists():
cfg_file = 'not_existing_cfg.yml'
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert 'Failed to open config file: not_existing_cfg.yml' in out
def test_error_no_work_time():
cfg_file = """\"%YAML:1.0\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Config must contain field: work_time')
def test_error_work_time_not_positive():
cfg_file = """\"%YAML:1.0
work_time: -1\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('work_time must be positive')
def test_error_no_pipelines():
cfg_file = """\"%YAML:1.0
work_time: 1000\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Config must contain field: Pipelines')
def test_error_pipelines_node_not_map():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Pipelines field must be a map')
def test_error_config_not_contain_pl():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:\" """
exec_str = '{} --cfg={} --exec_list=PL2'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Pipelines must contain field: PL2')
def test_error_no_source():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('PL1 must contain field: source')
def test_error_source_no_name():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('source must contain field: name')
def test_error_source_no_latency():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('source must contain field: latency')
def test_error_source_no_output():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('source must contain field: output')
def test_error_source_output_no_dims():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('output must contain field: dims')
def test_error_source_output_no_precision():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('output must contain field: precision')
def test_error_no_nodes():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('PL1 must contain field: nodes')
def test_error_nodes_not_sequence():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('nodes in PL1 must be a sequence')
def test_error_node_no_name():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
-\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('node must contain field: name')
def test_error_node_no_type():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('node must contain field: type')
def test_error_node_unknown_type():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Unknown'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Unsupported node type: Unknown')
def test_error_node_dummy_no_time():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node0 must contain field: time')
def test_error_node_dummy_not_positive_time():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: -0.2\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node0 time must be positive')
def test_error_node_dummy_no_output():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node0 must contain field: output')
def test_error_node_infer_no_model_path():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Infer'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
error_msg = """Path to OpenVINO model must be specified in either of two formats:
1.
xml: path to *.xml
bin: path to *.bin
2.
blob: path to *.blob"""
assert out.startswith(error_msg)
def test_error_node_infer_no_input_layers():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Infer'
blob: model.blob
device: 'CPU'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node0 must contain field: input_layers')
def test_error_node_infer_input_layers_are_empty():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Infer'
blob: model.blob
device: 'CPU'
input_layers:
\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('input_layers in Node0 must be a sequence')
def test_error_node_infer_no_output_layers():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Infer'
blob: model.blob
device: 'CPU'
input_layers:
- 'layer_name'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node0 must contain field: output_layers')
def test_error_node_infer_output_layers_are_empty():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Infer'
blob: model.blob
device: 'CPU'
input_layers:
- 'layer_name'
output_layers:\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('output_layers in Node0 must be a sequence')
def test_error_no_edges():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('PL1 must contain field: edges')
def test_error_edges_not_sequence():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('edges in PL1 must be a sequence')
def test_error_edges_no_from():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
-\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('edge must contain field: from')
def test_error_edges_no_to():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Node0'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('edge must contain field: to')
def test_error_edges_from_not_exists():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Node1'
to: 'Node2'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Failed to find node: Node1')
def test_error_edges_from_port_not_exists():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Node0:10'
to: 'Node2'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Failed to access node: Node0 by out port: 10')
def test_error_edges_to_not_exists():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node2'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Failed to find node: Node2')
def test_error_edges_to_port_not_exists():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node0:3'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Failed to access node: Node0 by in port: 3')
def test_error_connect_to_source():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Node0'
to: 'Src'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Failed to access node: Src by in port: 0')
def test_error_double_edge():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node0'
- from: 'Src'
to: 'Node0'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node: Node0 already connected by in port: 0')
def test_error_double_edge():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node0'
- from: 'Src'
to: 'Node0'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node: Node0 already connected by in port: 0')
def test_node_has_dangling_input():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
- name: 'Node1'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Node0'
to: 'Node1'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Node: Node0 in Pipeline: PL1 has dangling input by in port: 0')
def test_error_has_cycle_0():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node'
type: 'Infer'
blob: 'model.blob'
device: 'CPU'
input_layers:
- 'in_layer_name_0'
- 'in_layer_name_1'
output_layers:
- 'out_layer_name'
edges:
- from: 'Src'
to: 'Node:0'
- from: 'Node:0'
to: 'Node:1'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Pipeline: PL1 has cyclic dependencies')
def test_error_has_cycle_0():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Infer'
blob: 'model.blob'
device: 'CPU'
input_layers:
- 'in_layer_name_0'
- 'in_layer_name_1'
output_layers:
- 'out_layer_name'
- name: 'Node1'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node0:0'
- from: 'Node0:0'
to: 'Node1:0'
- from: 'Node1'
to: 'Node0:1'\" """
exec_str = '{} --cfg={}'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Pipeline: PL1 has cyclic dependencies')
def test_error_no_load_config_exists():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node0'\" """
exec_str = '{} --cfg={} --load_config=not_existing.yml'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert 'Failed to load config: not_existing.yml' in out
def test_error_invalid_app_mode():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node0'\" """
exec_str = '{} --cfg={} --pl_mode=unknown'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Unsupported PLMode: unknown\n'
'Please chose between: streaming and regular')
def test_error_invalid_pl_mode():
cfg_file = """\"%YAML:1.0
work_time: 1000
Pipelines:
PL1:
source:
name: 'Src'
latency: 20
output:
dims: [1,2,3,4]
precision: 'U8'
nodes:
- name: 'Node0'
type: 'Dummy'
time: 0.2
output:
dims: [1,2,3,4]
precision: 'U8'
edges:
- from: 'Src'
to: 'Node0'\" """
exec_str = '{} --cfg={} --app_mode=unknown'.format(pipeline_modeling_tool, cfg_file)
out = get_output(exec_str)
assert out.startswith('Unsupported AppMode: unknown\n'
'Please chose between: realtime and benchmark')

81
modules/gapi/samples/pipeline_modeling_tool/utils.hpp
Unescape View File

@ -0,0 +1,81 @@
#ifndef OPENCV_GAPI_PIPELINE_MODELING_TOOL_UTILS_HPP
#define OPENCV_GAPI_PIPELINE_MODELING_TOOL_UTILS_HPP
#include <opencv2/core.hpp>
#if defined(_WIN32)
#include <windows.h>
#endif
// FIXME: It's better to place it somewhere in common.hpp
struct OutputDescr {
std::vector<int> dims;
int precision;
};
namespace utils {
inline void generateRandom(cv::Mat& out) {
switch (out.depth()) {
case CV_8U:
cv::randu(out, 0, 255);
break;
case CV_32F:
cv::randu(out, 0.f, 1.f);
break;
case CV_16F: {
cv::Mat fp32_mat(out.size(), CV_MAKETYPE(CV_32F, out.channels()));
cv::randu(fp32_mat, 0.f, 1.f);
fp32_mat.convertTo(out, out.type());
break;
}
default:
throw std::logic_error("Unsupported preprocessing depth");
}
}
inline void sleep(double ms) {
#if defined(_WIN32)
// NB: It takes portions of 100 nanoseconds.
int64_t ns_units = static_cast<int64_t>(ms * 1e4);
// FIXME: Wrap it to RAII and instance only once.
HANDLE timer = CreateWaitableTimer(NULL, true, NULL);
if (!timer) {
throw std::logic_error("Failed to create timer");
}
LARGE_INTEGER li;
li.QuadPart = -ns_units;
if(!SetWaitableTimer(timer, &li, 0, NULL, NULL, false)){
CloseHandle(timer);
throw std::logic_error("Failed to set timer");
}
if (WaitForSingleObject(timer, INFINITE) != WAIT_OBJECT_0) {
CloseHandle(timer);
throw std::logic_error("Failed to wait timer");
}
CloseHandle(timer);
#else
using namespace std::chrono;
std::this_thread::sleep_for(duration<double, std::milli>(ms));
#endif
}
template <typename duration_t>
typename duration_t::rep measure(std::function<void()> f) {
using namespace std::chrono;
auto start = high_resolution_clock::now();
f();
return duration_cast<duration_t>(
high_resolution_clock::now() - start).count();
}
template <typename duration_t>
typename duration_t::rep timestamp() {
using namespace std::chrono;
auto now = high_resolution_clock::now();
return duration_cast<duration_t>(now.time_since_epoch()).count();
}
} // namespace utils
#endif // OPENCV_GAPI_PIPELINE_MODELING_TOOL_UTILS_HPP
Write Preview
Loading…
Cancel
Save