[tools] new api and support dynamic models in python benchmark app (o…

…penvinotoolkit#8582) * Preprocessing API - base classes Includes API definition for trivial mean/scale operations (which don't require layout) Mean/scale with 'layout' support will be done under separate task together with Layout Current test code coverage: 100% * Python bindings for base preprocessing API * remove pre_post_process directory from ngraph/core * remove files from ngraph/python dir * move pyngraph pre_post_process files from ngraph/python to runtime * remove pre_post_process test from CMakeList * move include to the header * update include path for pre_post_process * style fix * bind InputTensorInfo::set_layout * cleaned test_preprocess * fix test expected output * remove duplicate test * update description of set_element_type * fix style * move preprocess from pyngraph to pyopenvino/graph * update test_preprocess imports and remove unnecessary test * remove duplicate import * update custom method * update test * update test * create decorator that changes Node into Output<Node> * create function that cast Node to Output<Node> * update test_preprocess to use decorator for custom function * change _cast_to_output -> _from_node * style fix * add tests fro scale and mean with vector input * style fix * add docstring for custom_preprocess_function * bind InputInfo network method * style fix * bind OutputInfo * fix description of preprocess submodule * fix style * update copyright year * bind OutputTensorInfo * bind OutputNetworkInfo and InputNetworkInfo * Bind exec core ov (openvinotoolkit#50) * Output const node python tests (openvinotoolkit#52) * add python bindings tests for Output<const ov::None> * add proper tests * add new line * rename ie_version to version * Pszmel/bind infer request (openvinotoolkit#51) * remove set_batch, get_blob and set_blob * update InferRequest class * change InferenceEngine::InferRequest to ov::runtime::InferRequest * update set_callback body * update bindings to reflect ov::runtime::InferRequest * bind set_input_tensor and get_input_tensor * style fix * clen ie_infer_queue.cpp * Bind exec core ov (openvinotoolkit#50) * bind core, exec_net classes * rm unused function * add new line * rename ie_infer_request -> infer_request * update imports * update __init__.py * update ie_api.py * Replace old containers with the new one * create impl for create_infer_request * comment out infer_queue to avoid errors with old infer_request * update infer_request bind to reflect new infer_request api * comment out inpuit_info from ie_network to avoid errors with old containers * Register new containers and comment out InferQueue * update infer request tests * style fix * remove unused imports * remove unused imports and 2 methods * add tests to cover all new methods from infer_request * style fix * add test * remove registration of InferResults * update name of exception_ptr parameter * update the loops that iterate through inputs and outputs * clean setCustomCallbacks * style fix * add Tensor import * style fix * update infer and normalize_inputs * style fix * rename startTime and endTime * Create test for mixed keys as infer arguments * update infer function * update return type of infer Co-authored-by: Bartek Szmelczynski <[email protected]> * fix get_version * fix opaque issue * some cosmetic changes * fix codestyle in tests * make tests green * Extend python InferRequest * Extend python Function * Change return value of infer call * Fix missing precisions conversions in CPU plugin * Rework of runtime for new tests * Fixed onnx reading in python tests * Edit compatibility tests * Edit tests * Add FLOAT_LIKE xfails * bind ColorFormat and ResizeAlgorithm * clean imports * fix typo * [Python API] bind ProfilingInfo (openvinotoolkit#55) * bind ProfilingInfo * Add tests * Fix code style * Add property * fix codestyle * Infer new request method (openvinotoolkit#56) * fix conflicts, add infer_new_request function * remove redundant functions, fix style * revert the unwanted changes * revert removal of the Blob * revert removal of isTblob * add add_extension from path * codestyle * add PostProcessSteps to init * bind PreProcessSteps * create additional tests * fix win build * add inputs-outputs to function * update infer queue * fix code style * Hot-fix CPU plugin with precision * fix start_async * add performance hint to time infer (openvinotoolkit#8480) * Updated common migration pipeline (openvinotoolkit#8176) * Updated common migration pipeline * Fixed merge issue * Added new model and extended example * Fixed typo * Added v10-v11 comparison * Avoid redundant graph nodes scans (openvinotoolkit#8415) * Refactor work with env variables (openvinotoolkit#8208) * del MO_ROOT * del MO_ROOT from common_utils.py * add MO_PATH to common_utils.py * change mo_path * [IE Sample Scripts] Use cmake to build samples (openvinotoolkit#8442) * Use cmake to build samples * Add the option to set custom build output folder * Remove opset8 from compatibility ngraph python API (openvinotoolkit#8452) * [GPU] OneDNN gpu submodule update to version 2.5 (openvinotoolkit#8449) * [GPU] OneDNN gpu submodule update to version 2.5 * [GPU] Updated onednn submodule and added layout optimizer fix * Install rules for static libraries case (openvinotoolkit#8384) * Proper cmake install for static libraries case * Added an ability to skip template plugin * Added install rules for VPU / GPU * Install more libraries * Fixed absolute TBB include paths * Disable GNA * Fixed issue with linker * Some fixes * Fixed linkage issues in tests * Disabled some tests * Updated CI pipelines * Fixed Windows linkage * Fixed custom_opset test for static casr * Fixed CVS-70313 * Continue on error * Fixed clanf-format * Try to fix Windows linker * Fixed compilation * Disable samples * Fixed samples build with THREADING=SEQ * Fixed link error on Windows * Fixed ieFuncTests * Added static Azure CI * Revert "Fixed link error on Windows" This reverts commit 78cca36. * Merge static and dynamic linux pipelines * Fixed Azure * fix codestyle * rename all methods in this class to snake_case * some updates * code style * fix code style in tests * update statistics reporting * update filling inputs * change ngraph.Type to ov.Type * fix typo * save work * save work * save work * compute latency in callback * save work * Fix get_idle_request * save work * fix latency * Fix code style * update AppInputInfo * add iteration to PatrialShape * fix rebasing * bind result::get_layout() * correct mistakes * fix setup * use parameters/results instead inputs/outputs * move _from_node to node_output.hpp * add read_model from buffer * update imports * revert package struct * add new line * remove bad quotes * update imports * style fix * add new line * Fix preprocessing * rename functin args * set NCHW layout to image as default * Fix input fillings * remove Type import * update tests * style fix * test clean * remove blank line * Add tensor_shape * fix comments * update PrePostProcessor init and build methods * create test with model update tests with new PrePostProcessor init and build * Change filling inputs * fix preprocessing * basic support dynamic shapes * fix legacy mode * rename ie to core * fix cpp code style * fix input files parsing * fix binary filling * support dynamic batch size * process images with original shapes if no tensor shapes were given * fix fps and number of iterations * Add new metrics * support pass path to folder into input mapping * add pcseq flag * fix resolving conflicts * dump statistic per group * check for compatibility with partial shape * revert statistic report names * code refactoring * update parameters * enable legacy_mode if data size less than nireq * add serialize to offline_transformations * Fix preprocessing import * change log output due to ci parsing * fix layout * allow to pass batch size with undefined layout * add serializer * fix comments from jiwaszki * Fix latency parsing for ci * code style * rename tensor_shape to data_shape * add message if image is processed with original shape * fix syntax warning * remove default legacy_mode if requests cover all data * rewrite all file parsing * fix preprocessing * Fix preprocessing #2 * Use layout instead str * Fix file extensions * Fix image sizes filling * sort input files * [Python API] quick fix of packaging * update tests * fix setup.py * small fix * small fixes according to comments * skip mo frontend tests * full mode is default for dynamic models only * backward compatibility * Fix package * set layout in runtime * static mode for dynamic models with all equal data shapes * use get_tensor instead set_tensor in legacy mode * benchmarking dynamic model available in full mode only * fix layout detection * use batch_size * iteration instead processed_frames in legacy mode * fix tensor naming * represent --inference_only * refactoring main loop * Fix number of iterations for full mode Co-authored-by: Michael Nosov <[email protected]> Co-authored-by: pszmel <[email protected]> Co-authored-by: Bartek Szmelczynski <[email protected]> Co-authored-by: Anastasia Kuporosova <[email protected]> Co-authored-by: jiwaszki <[email protected]> Co-authored-by: Victor Kuznetsov <[email protected]> Co-authored-by: Ilya Churaev <[email protected]> Co-authored-by: Tomasz Jankowski <[email protected]> Co-authored-by: Dmitry Pigasin <[email protected]> Co-authored-by: Artur Kulikowski <[email protected]> Co-authored-by: Ilya Znamenskiy <[email protected]> Co-authored-by: Ilya Lavrenov <[email protected]>
elilobanova · Dec 2, 2021 · f9bd740 · f9bd740
1 parent 87ea55f
commit f9bd740
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diff --git a/src/bindings/python/src/pyopenvino/graph/partial_shape.cpp b/src/bindings/python/src/pyopenvino/graph/partial_shape.cpp
@@ -192,6 +192,25 @@ void regclass_graph_PartialShape(py::module m) {
         },
         py::is_operator());
 
+    shape.def("__len__", [](const ov::PartialShape& self) {
+        return self.size();
+    });
+
+    shape.def("__setitem__", [](ov::PartialShape& self, size_t key, ov::Dimension& d) {
+        self[key] = d;
+    });
+
+    shape.def("__getitem__", [](const ov::PartialShape& self, size_t key) {
+        return self[key];
+    });
+
+    shape.def(
+        "__iter__",
+        [](ov::PartialShape& self) {
+            return py::make_iterator(self.begin(), self.end());
+        },
+        py::keep_alive<0, 1>()); /* Keep vector alive while iterator is used */
+
     shape.def("__str__", [](const ov::PartialShape& self) -> std::string {
         std::stringstream ss;
         ss << self;

diff --git a/src/bindings/python/src/pyopenvino/graph/shape.cpp b/src/bindings/python/src/pyopenvino/graph/shape.cpp
@@ -11,6 +11,7 @@
 #include <sstream>
 #include <string>
 
+#include "openvino/core/dimension.hpp"  // ov::Dimension
 #include "pyopenvino/graph/shape.hpp"
 
 namespace py = pybind11;
@@ -24,7 +25,13 @@ void regclass_graph_Shape(py::module m) {
     shape.def("__len__", [](const ov::Shape& v) {
         return v.size();
     });
-    shape.def("__getitem__", [](const ov::Shape& v, int key) {
+    shape.def("__setitem__", [](ov::Shape& self, size_t key, size_t d) {
+        self[key] = d;
+    });
+    shape.def("__setitem__", [](ov::Shape& self, size_t key, ov::Dimension d) {
+        self[key] = d.get_length();
+    });
+    shape.def("__getitem__", [](const ov::Shape& v, size_t key) {
         return v[key];
     });
 

diff --git a/tools/benchmark_tool/openvino/tools/benchmark/benchmark.py b/tools/benchmark_tool/openvino/tools/benchmark/benchmark.py
@@ -4,7 +4,8 @@
 import os
 from datetime import datetime
 from math import ceil
-from openvino.inference_engine import IENetwork, IECore, get_version, StatusCode
+from typing import Union
+from openvino.runtime import Core, get_version, AsyncInferQueue
 
 from .utils.constants import MULTI_DEVICE_NAME, HETERO_DEVICE_NAME, CPU_DEVICE_NAME, GPU_DEVICE_NAME, XML_EXTENSION, BIN_EXTENSION
 from .utils.logging import logger
@@ -15,151 +16,195 @@ def percentile(values, percent):
     return values[ceil(len(values) * percent / 100) - 1]
 
 class Benchmark:
-    def __init__(self, device: str, number_infer_requests: int = None, number_iterations: int = None,
-                 duration_seconds: int = None, api_type: str = 'async'):
+    def __init__(self, device: str, number_infer_requests: int = 0, number_iterations: int = None,
+                 duration_seconds: int = None, api_type: str = 'async', inference_only = None):
         self.device = device
-        self.ie = IECore()
-        self.nireq = number_infer_requests
+        self.core = Core()
+        self.nireq = number_infer_requests if api_type == 'async' else 1
         self.niter = number_iterations
         self.duration_seconds = get_duration_seconds(duration_seconds, self.niter, self.device)
         self.api_type = api_type
+        self.inference_only = inference_only
+        self.latency_groups = []
 
     def __del__(self):
-        del self.ie
+        del self.core
 
     def add_extension(self, path_to_extension: str=None, path_to_cldnn_config: str=None):
         if path_to_cldnn_config:
-            self.ie.set_config({'CONFIG_FILE': path_to_cldnn_config}, GPU_DEVICE_NAME)
+            self.core.set_config({'CONFIG_FILE': path_to_cldnn_config}, GPU_DEVICE_NAME)
             logger.info(f'GPU extensions is loaded {path_to_cldnn_config}')
 
         if path_to_extension:
-            self.ie.add_extension(extension_path=path_to_extension, device_name=CPU_DEVICE_NAME)
+            self.core.add_extension(extension_path=path_to_extension)
             logger.info(f'CPU extensions is loaded {path_to_extension}')
 
     def get_version_info(self) -> str:
         logger.info(f"InferenceEngine:\n{'': <9}{'API version':.<24} {get_version()}")
         version_string = 'Device info\n'
-        for device, version in self.ie.get_versions(self.device).items():
+        for device, version in self.core.get_versions(self.device).items():
             version_string += f"{'': <9}{device}\n"
             version_string += f"{'': <9}{version.description:.<24}{' version'} {version.major}.{version.minor}\n"
             version_string += f"{'': <9}{'Build':.<24} {version.build_number}\n"
         return version_string
 
     def set_config(self, config = {}):
         for device in config.keys():
-            self.ie.set_config(config[device], device)
+            self.core.set_config(config[device], device)
 
     def set_cache_dir(self, cache_dir: str):
-        self.ie.set_config({'CACHE_DIR': cache_dir}, '')
+        self.core.set_config({'CACHE_DIR': cache_dir}, '')
 
-    def read_network(self, path_to_model: str):
+    def read_model(self, path_to_model: str):
         model_filename = os.path.abspath(path_to_model)
         head, ext = os.path.splitext(model_filename)
         weights_filename = os.path.abspath(head + BIN_EXTENSION) if ext == XML_EXTENSION else ""
-        ie_network = self.ie.read_network(model_filename, weights_filename)
-        return ie_network
-
-    def load_network(self, ie_network: IENetwork, config = {}):
-        exe_network = self.ie.load_network(ie_network,
-                                           self.device,
-                                           config=config,
-                                           num_requests=1 if self.api_type == 'sync' else self.nireq or 0)
-        # Number of requests
-        self.nireq = len(exe_network.requests)
-
-        return exe_network
-
-    def load_network_from_file(self, path_to_model: str, config = {}):
-        exe_network = self.ie.load_network(path_to_model,
-                                           self.device,
-                                           config=config,
-                                           num_requests=1 if self.api_type == 'sync' else self.nireq or 0)
-        # Number of requests
-        self.nireq = len(exe_network.requests)
-
-        return exe_network
-
-    def import_network(self, path_to_file : str, config = {}):
-        exe_network = self.ie.import_network(model_file=path_to_file,
-                                             device_name=self.device,
-                                             config=config,
-                                             num_requests=1 if self.api_type == 'sync' else self.nireq or 0)
-        # Number of requests
-        self.nireq = len(exe_network.requests)
-        return exe_network
-
-    def first_infer(self, exe_network):
-        infer_request = exe_network.requests[0]
-
-        # warming up - out of scope
+        return self.core.read_model(model_filename, weights_filename)
+
+    def create_infer_requests(self, exe_network):
         if self.api_type == 'sync':
-            infer_request.infer()
+            requests = [exe_network.create_infer_request()]
         else:
-            infer_request.async_infer()
-            status = infer_request.wait()
-            if status != StatusCode.OK:
-                raise Exception(f"Wait for all requests is failed with status code {status}!")
-        return infer_request.latency
+            requests = AsyncInferQueue(exe_network, self.nireq)
+            self.nireq = len(requests)
+        return requests
 
-    def infer(self, exe_network, batch_size, latency_percentile, progress_bar=None):
+    def first_infer(self, requests):
+        if self.api_type == 'sync':
+            requests[0].infer()
+            return requests[0].latency
+        else:
+            id = requests.get_idle_request_id()
+            requests.start_async()
+            requests.wait_all()
+            return requests[id].latency
+
+    def update_progress_bar(self, progress_bar, exec_time, progress_count):
+        if self.duration_seconds:
+            # calculate how many progress intervals are covered by current iteration.
+            # depends on the current iteration time and time of each progress interval.
+            # Previously covered progress intervals must be skipped.
+            progress_interval_time = self.duration_seconds / progress_bar.total_num
+            new_progress = int(exec_time / progress_interval_time - progress_count)
+            progress_bar.add_progress(new_progress)
+            progress_count += new_progress
+        elif self.niter:
+            progress_bar.add_progress(1)
+        return progress_count
+
+    def sync_inference(self, request, data_queue, progress_bar):
         progress_count = 0
-        infer_requests = exe_network.requests
-
+        exec_time = 0
+        iteration = 0
+        times = []
         start_time = datetime.utcnow()
+        while (self.niter and iteration < self.niter) or \
+              (self.duration_seconds and exec_time < self.duration_seconds):
+            if self.inference_only == False:
+                request.set_input_tensors(data_queue.get_next_input())
+            request.infer()
+            times.append(request.latency)
+            iteration += 1
+
+            exec_time = (datetime.utcnow() - start_time).total_seconds()
+
+            if progress_bar:
+                progress_count = self.update_progress_bar(progress_bar, exec_time, progress_count)
+
+        total_duration_sec = (datetime.utcnow() - start_time).total_seconds()
+        return sorted(times), total_duration_sec, iteration
+
+    def async_inference_only(self, infer_queue, progress_bar):
+        progress_count = 0
         exec_time = 0
         iteration = 0
+        times = []
+        in_fly = set()
+        start_time = datetime.utcnow()
+        while (self.niter and iteration < self.niter) or \
+              (self.duration_seconds and exec_time < self.duration_seconds) or \
+              (iteration % self.nireq):
+            idle_id = infer_queue.get_idle_request_id()
+            if idle_id in in_fly:
+                times.append(infer_queue[idle_id].latency)
+            else:
+                in_fly.add(idle_id)
+            infer_queue.start_async()
+            iteration += 1
+
+            exec_time = (datetime.utcnow() - start_time).total_seconds()
+
+            if progress_bar:
+                progress_count = self.update_progress_bar(progress_bar, exec_time, progress_count)
 
+        infer_queue.wait_all()
+        total_duration_sec = (datetime.utcnow() - start_time).total_seconds()
+        for infer_request_id in in_fly:
+            times.append(infer_queue[infer_request_id].latency)
+        return sorted(times), total_duration_sec, iteration
+
+    def async_inference_full_mode(self, infer_queue, data_queue, progress_bar, pcseq):
+        progress_count = 0
+        processed_frames = 0
+        exec_time = 0
+        iteration = 0
         times = []
+        num_groups = len(self.latency_groups)
         in_fly = set()
-        # Start inference & calculate performance
-        # to align number if iterations to guarantee that last infer requests are executed in the same conditions **/
+        start_time = datetime.utcnow()
         while (self.niter and iteration < self.niter) or \
               (self.duration_seconds and exec_time < self.duration_seconds) or \
-              (self.api_type == 'async' and iteration % self.nireq):
-            if self.api_type == 'sync':
-                infer_requests[0].infer()
-                times.append(infer_requests[0].latency)
+              (iteration % num_groups):
+            processed_frames += data_queue.get_next_batch_size()
+            idle_id = infer_queue.get_idle_request_id()
+            if idle_id in in_fly:
+                times.append(infer_queue[idle_id].latency)
+                if pcseq:
+                    self.latency_groups[infer_queue.userdata[idle_id]].times.append(infer_queue[idle_id].latency)
             else:
-                infer_request_id = exe_network.get_idle_request_id()
-                if infer_request_id < 0:
-                    status = exe_network.wait(num_requests=1)
-                    if status != StatusCode.OK:
-                        raise Exception("Wait for idle request failed!")
-                    infer_request_id = exe_network.get_idle_request_id()
-                    if infer_request_id < 0:
-                        raise Exception("Invalid request id!")
-                if infer_request_id in in_fly:
-                    times.append(infer_requests[infer_request_id].latency)
-                else:
-                    in_fly.add(infer_request_id)
-                infer_requests[infer_request_id].async_infer()
+                in_fly.add(idle_id)
+            group_id = data_queue.current_group_id
+            infer_queue[idle_id].set_input_tensors(data_queue.get_next_input())
+            infer_queue.start_async(userdata=group_id)
             iteration += 1
 
             exec_time = (datetime.utcnow() - start_time).total_seconds()
 
             if progress_bar:
-              if self.duration_seconds:
-                  # calculate how many progress intervals are covered by current iteration.
-                  # depends on the current iteration time and time of each progress interval.
-                  # Previously covered progress intervals must be skipped.
-                  progress_interval_time = self.duration_seconds / progress_bar.total_num
-                  new_progress = int(exec_time / progress_interval_time - progress_count)
-                  progress_bar.add_progress(new_progress)
-                  progress_count += new_progress
-              elif self.niter:
-                  progress_bar.add_progress(1)
-
-        # wait the latest inference executions
-        status = exe_network.wait()
-        if status != StatusCode.OK:
-            raise Exception(f"Wait for all requests is failed with status code {status}!")
+                progress_count = self.update_progress_bar(progress_bar, exec_time, progress_count)
 
+        infer_queue.wait_all()
         total_duration_sec = (datetime.utcnow() - start_time).total_seconds()
         for infer_request_id in in_fly:
-            times.append(infer_requests[infer_request_id].latency)
-        times.sort()
-        latency_ms = percentile(times, latency_percentile)
-        fps = batch_size * 1000 / latency_ms if self.api_type == 'sync' else batch_size * iteration / total_duration_sec
+            times.append(infer_queue[infer_request_id].latency)
+        return sorted(times), total_duration_sec, processed_frames, iteration
+
+    def main_loop(self, requests, data_queue, batch_size, latency_percentile, progress_bar, pcseq):
+        if self.api_type == 'sync':
+            times, total_duration_sec, iteration = self.sync_inference(requests[0], data_queue, progress_bar)
+        elif self.inference_only:
+            times, total_duration_sec, iteration = self.async_inference_only(requests, progress_bar)
+            fps = len(batch_size) * iteration / total_duration_sec
+        else:
+            times, total_duration_sec, processed_frames, iteration = self.async_inference_full_mode(requests, data_queue, progress_bar, pcseq)
+            fps = processed_frames / total_duration_sec
+
+        median_latency_ms = percentile(times, latency_percentile)
+        avg_latency_ms = sum(times) / len(times)
+        min_latency_ms = times[0]
+        max_latency_ms = times[-1]
+
+        if self.api_type == 'sync':
+            fps = len(batch_size) * 1000 / median_latency_ms
+
+        if pcseq:
+            for group in self.latency_groups:
+                if group.times:
+                    group.times.sort()
+                    group.avg = sum(group.times) / len(group.times)
+                    group.min = group.times[0]
+                    group.max = group.times[-1]
+
         if progress_bar:
             progress_bar.finish()
-        return fps, latency_ms, total_duration_sec, iteration
+        return fps, median_latency_ms, avg_latency_ms, min_latency_ms, max_latency_ms, total_duration_sec, iteration