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ANSLibs/OpenVINO/python/openvino/tools/benchmark/main.py

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Initial version that excludes the C:\ANSLibs\Python311
2026-03-29 14:17:11 +11:00
# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import os
import sys
import platform
from datetime import datetime
from openvino import Dimension, properties
from openvino.tools.benchmark.benchmark import Benchmark
from openvino.tools.benchmark.parameters import parse_args
from openvino.tools.benchmark.utils.constants import MULTI_DEVICE_NAME, \
CPU_DEVICE_NAME, GPU_DEVICE_NAME, \
BLOB_EXTENSION, AUTO_DEVICE_NAME
from openvino.tools.benchmark.utils.inputs_filling import get_input_data
from openvino.tools.benchmark.utils.logging import logger
from openvino.tools.benchmark.utils.utils import next_step, get_number_iterations, pre_post_processing, \
process_help_inference_string, print_perf_counters, print_perf_counters_sort, dump_exec_graph, get_duration_in_milliseconds, \
get_command_line_arguments, parse_value_per_device, parse_devices, get_inputs_info, \
print_inputs_and_outputs_info, get_network_batch_size, load_config, dump_config, get_latency_groups, \
check_for_static, can_measure_as_static, parse_value_for_virtual_device, is_virtual_device, is_virtual_device_found
from openvino.tools.benchmark.utils.statistics_report import StatisticsReport, JsonStatisticsReport, CsvStatisticsReport, \
averageCntReport, detailedCntReport
def get_peak_memory_usage():
if platform.system() == "Linux":
with open("/proc/self/status", "r") as f:
for line in f:
if line.startswith("VmPeak:"):
return int(line.split()[1]) # The value in KB
raise RuntimeError("VmPeak attribute not found. Unable to determine peak memory usage.")
# No Windows support due to the lack of the ‘psutil’ module in the CI infrastructure
# No Macos support due to no /proc/self/status file
return None
def log_memory_usage(logger, start_mem_usage, end_mem_usage, action_name):
if start_mem_usage is None or end_mem_usage is None:
return
capitalized_action_name = action_name.capitalize()
action_name = "compilation" if action_name == "compile" else action_name
logger.info(f"Start of {action_name} memory usage: Peak {start_mem_usage} KB")
logger.info(f"End of {action_name} memory usage: Peak {end_mem_usage} KB")
logger.info(f"{capitalized_action_name} model ram used {end_mem_usage - start_mem_usage} KB")
def parse_and_check_command_line():
def arg_not_empty(arg_value,empty_value):
return not arg_value is None and not arg_value == empty_value
parser = parse_args()
args = parser.parse_args()
if args.latency_percentile < 1 or args.latency_percentile > 100:
parser.print_help()
raise RuntimeError("The percentile value is incorrect. The applicable values range is [1, 100].")
if not args.perf_hint == "none" and (arg_not_empty(args.number_streams, "") or arg_not_empty(args.number_threads, 0) or arg_not_empty(args.infer_threads_pinning, "")):
raise Exception("-nstreams, -nthreads and -pin options are fine tune options. To use them you " \
"should explicitely set -hint option to none. This is not OpenVINO limitation " \
"(those options can be used in OpenVINO together), but a benchmark_app UI rule.")
if args.report_type == "average_counters" and MULTI_DEVICE_NAME in args.target_device:
raise Exception("only detailed_counters report type is supported for MULTI device")
_, ext = os.path.splitext(args.path_to_model)
is_network_compiled = True if ext == BLOB_EXTENSION else False
is_precisiton_set = not (args.input_precision == "" and args.output_precision == "" and args.input_output_precision == "")
if is_network_compiled and is_precisiton_set:
raise Exception("Cannot set precision for a compiled model. " \
"Please re-compile your model with required precision.")
if args.api_type == "":
args.api_type = "sync" if args.perf_hint == "latency" else "async"
if args.api_type == "sync":
if args.time == 0 and (args.number_infer_requests > args.number_iterations):
raise Exception("Number of infer requests should be less than or equal to number of iterations in sync mode.")
return args, is_network_compiled
def main():
statistics = None
try:
# ------------------------------ 1. Parsing and validating input arguments ------------------------------
next_step()
logger.info("Parsing input parameters")
args, is_network_compiled = parse_and_check_command_line()
command_line_arguments = get_command_line_arguments(sys.argv)
if args.report_type:
_statistics_class = JsonStatisticsReport if args.json_stats else CsvStatisticsReport
statistics = _statistics_class(StatisticsReport.Config(args.report_type, args.report_folder))
statistics.add_parameters(StatisticsReport.Category.COMMAND_LINE_PARAMETERS, command_line_arguments)
def is_flag_set_in_command_line(flag):
return any(x.strip('-') == flag for x, y in command_line_arguments)
device_name = args.target_device
devices = parse_devices(device_name)
device_number_streams = parse_value_per_device(devices, args.number_streams, "nstreams")
device_infer_precision = parse_value_per_device(devices, args.infer_precision, "infer_precision")
config = {}
if args.load_config:
load_config(args.load_config, config)
if is_network_compiled:
logger.info("Model is compiled")
# ------------------------------ 2. Loading OpenVINO Runtime -------------------------------------------
next_step(step_id=2)
benchmark = Benchmark(args.target_device, args.number_infer_requests,
args.number_iterations, args.time, args.api_type,
args.inference_only, args.maximum_inference_rate)
if args.extensions:
benchmark.add_extension(path_to_extensions=args.extensions)
## GPU (clDNN) Extensions
if GPU_DEVICE_NAME in device_name and args.path_to_cldnn_config:
if GPU_DEVICE_NAME not in config.keys():
config[GPU_DEVICE_NAME] = {}
config[GPU_DEVICE_NAME]['CONFIG_FILE'] = args.path_to_cldnn_config
if GPU_DEVICE_NAME in config.keys() and 'CONFIG_FILE' in config[GPU_DEVICE_NAME].keys():
cldnn_config = config[GPU_DEVICE_NAME]['CONFIG_FILE']
benchmark.add_extension(path_to_cldnn_config=cldnn_config)
benchmark.print_version_info()
# --------------------- 3. Setting device configuration --------------------------------------------------------
next_step()
def set_performance_hint(device):
perf_hint = properties.hint.PerformanceMode.THROUGHPUT
supported_properties = benchmark.core.get_property(device, properties.supported_properties())
if properties.hint.performance_mode() in supported_properties:
if is_flag_set_in_command_line('hint'):
if args.perf_hint == "throughput" or args.perf_hint == "tput":
perf_hint = properties.hint.PerformanceMode.THROUGHPUT
elif args.perf_hint == "latency":
perf_hint = properties.hint.PerformanceMode.LATENCY
elif args.perf_hint == "cumulative_throughput" or args.perf_hint == "ctput":
perf_hint = properties.hint.PerformanceMode.CUMULATIVE_THROUGHPUT
elif args.perf_hint=='none':
# Not set PerformanceMode, and plugin will apply its internal default PerformanceMode
return
else:
raise RuntimeError("Incorrect performance hint. Please set -hint option to"
"`throughput`(tput), `latency', 'cumulative_throughput'(ctput) value or 'none'.")
else:
perf_hint = properties.hint.PerformanceMode.LATENCY if benchmark.api_type == "sync" else properties.hint.PerformanceMode.THROUGHPUT
logger.warning(f"Performance hint was not explicitly specified in command line. " +
f"Device({device}) performance hint will be set to {perf_hint}.")
config[device][properties.hint.performance_mode()] = perf_hint
else:
logger.warning(f"Device {device} does not support performance hint property(-hint).")
def get_device_type_from_name(name) :
new_name = str(name)
new_name = new_name.split(".", 1)[0]
new_name = new_name.split("(", 1)[0]
return new_name
## Set default values from dumped config
default_devices = set()
for device in devices:
device_type = get_device_type_from_name(device)
if device_type in config and device not in config:
config[device] = config[device_type].copy()
default_devices.add(device_type)
for def_device in default_devices:
config.pop(def_device)
perf_counts = False
# check if using the virtual device
hw_devices_list = devices.copy()
# Remove the hardware devices if AUTO/MULTI/HETERO appears in the devices list.
is_virtual = is_virtual_device_found(devices)
if is_virtual:
devices.clear()
# Parse out the currect virtual device as the target device.
virtual_device = device_name.partition(":")[0]
hw_devices_list.remove(virtual_device)
devices.append(virtual_device)
parse_value_for_virtual_device(virtual_device, device_number_streams)
parse_value_for_virtual_device(virtual_device, device_infer_precision)
for device in devices:
supported_properties = benchmark.core.get_property(device, properties.supported_properties())
if device not in config.keys():
config[device] = {}
## high-level performance modes
set_performance_hint(device)
if is_flag_set_in_command_line('nireq'):
config[device][properties.hint.num_requests()] = str(args.number_infer_requests)
## Set performance counter
if is_flag_set_in_command_line('pc'):
## set to user defined value
config[device][properties.enable_profiling()] = True if args.perf_counts else False
elif properties.enable_profiling() in config[device].keys() and config[device][properties.enable_profiling()] == True:
logger.warning(f"Performance counters for {device} device is turned on. " +
"To print results use -pc option.")
elif args.report_type in [ averageCntReport, detailedCntReport ]:
logger.warning(f"Turn on performance counters for {device} device " +
f"since report type is {args.report_type}.")
config[device][properties.enable_profiling()] = True
elif args.exec_graph_path is not None:
logger.warning(f"Turn on performance counters for {device} device " +
"due to execution graph dumping.")
config[device][properties.enable_profiling()] = True
elif is_flag_set_in_command_line('pcsort'):
## set to default value
logger.warning(f"Turn on performance counters for {device} device " +
f"since pcsort value is {args.perf_counts_sort}.")
config[device][properties.enable_profiling()] = True if args.perf_counts_sort else False
else:
## set to default value
config[device][properties.enable_profiling()] = args.perf_counts
perf_counts = True if config[device][properties.enable_profiling()] == True else perf_counts
## insert or append property into hw device properties list
def update_configs(hw_device, property_name, property_value):
(key, value) = properties.device.properties({hw_device:{property_name:property_value}})
# add property into hw device properties list.
if key not in config[device].keys():
config[device][key] = value
else:
current_config = config[device][key].get()
if hw_device not in current_config.keys():
current_config.update(value.get())
else:
current_device_config = current_config[hw_device]
for prop in value.get().items():
current_device_config.update(prop[1])
current_config[hw_device].update(current_device_config)
config[device][key].set(current_config)
def update_device_config_for_virtual_device(value, config, key):
# check if the element contains the hardware device property
if len(value.split(':')) == 1:
config[device][key] = device_infer_precision[device]
else:
# set device nstreams properties in the AUTO/MULTI plugin
value_vec = value[value.find('{') + 1:value.rfind('}')].split(',')
device_properties = {value_vec[i].split(':')[0] : value_vec[i].split(':')[1] for i in range(0, len(value_vec))}
for hw_device in device_properties.keys():
update_configs(hw_device, key, device_properties[hw_device])
## infer precision
def set_infer_precision():
key = properties.hint.inference_precision()
if device in device_infer_precision.keys():
## set to user defined value
if key in supported_properties:
config[device][key] = device_infer_precision[device]
elif is_virtual_device(device):
update_device_config_for_virtual_device(device_infer_precision[device], config, key)
else:
raise Exception(f"Device {device} doesn't support config key INFERENCE_PRECISION_HINT!" \
" Please specify -infer_precision for correct devices in format" \
" <dev1>:<infer_precision1>,<dev2>:<infer_precision2> or via configuration file.")
return
## the rest are individual per-device settings (overriding the values the device will deduce from perf hint)
def set_throughput_streams():
key = get_device_type_from_name(device) + "_THROUGHPUT_STREAMS"
if device in device_number_streams.keys():
## set to user defined value
if key in supported_properties:
config[device][key] = device_number_streams[device]
elif properties.streams.num() in supported_properties:
key = properties.streams.num()
config[device][key] = device_number_streams[device]
elif is_virtual_device(device):
key = properties.streams.num()
update_device_config_for_virtual_device(device_number_streams[device], config, key)
else:
raise Exception(f"Device {device} doesn't support config key '{key}'! " +
"Please specify -nstreams for correct devices in format <dev1>:<nstreams1>,<dev2>:<nstreams2>")
elif key not in config[device].keys() and args.api_type == "async" and key not in config[device].keys() \
and 'PERFORMANCE_HINT' in config[device].keys() and config[device]['PERFORMANCE_HINT'] == '':
## set the _AUTO value for the #streams
logger.warning(f"-nstreams default value is determined automatically for {device} device. " +
"Although the automatic selection usually provides a reasonable performance, "
"but it still may be non-optimal for some cases, for more information look at README.")
if key in supported_properties:
config[device][key] = get_device_type_from_name(device) + "_THROUGHPUT_AUTO"
elif properties.streams.Num() in supported_properties:
key = properties.streams.Num()
config[device][key] = "-1" # Set AUTO mode for streams number
elif is_virtual_device(device):
# Set nstreams to default value auto if no nstreams specified from cmd line.
for hw_device in hw_devices_list:
hw_supported_properties = benchmark.core.get_property(hw_device, properties.supported_properties())
key = get_device_type_from_name(hw_device) + "_THROUGHPUT_STREAMS"
value = get_device_type_from_name(hw_device) + "_THROUGHPUT_AUTO"
if key not in hw_supported_properties:
key = properties.streams.Num()
value = properties.streams.Num.AUTO
if key in hw_supported_properties:
update_configs(hw_device, key, value)
if key in config[device].keys():
device_number_streams[device] = config[device][key]
return
def set_nthreads_pin(property_name, property_value):
if property_name in supported_properties or device_name == AUTO_DEVICE_NAME:
# create nthreads/pin primary property for HW device or AUTO if -d is AUTO directly.
config[device][property_name] = property_value
elif is_virtual:
# Create secondary property of -nthreads/-pin only for CPU if CPU device appears in the devices
# list specified by -d.
if CPU_DEVICE_NAME in hw_devices_list:
update_configs(CPU_DEVICE_NAME, property_name, property_value)
return
if args.number_threads and is_flag_set_in_command_line("nthreads"):
# limit threading for CPU portion of inference
set_nthreads_pin(properties.inference_num_threads(), str(args.number_threads))
if is_flag_set_in_command_line('pin'):
## set for CPU to user defined value
set_nthreads_pin(properties.hint.enable_cpu_pinning(), args.infer_threads_pinning)
set_throughput_streams()
set_infer_precision()
if is_virtual_device(device):
if device in device_number_streams.keys():
del device_number_streams[device]
device_config = {}
# In case of multiple devices found prefer the one given in CLI argument
if benchmark.device.find(device_name) == 0 and device_name in config.keys():
device_config = config[device_name]
else:
for device in config:
if benchmark.device.find(device) == 0:
device_config = config[device]
if args.cache_dir:
benchmark.set_cache_dir(args.cache_dir)
## If set batch size, disable the auto batching
if args.batch_size:
logger.warning("Batch size is set. Auto batching will be disabled")
device_config["ALLOW_AUTO_BATCHING"] = False
topology_name = ""
load_from_file_enabled = is_flag_set_in_command_line('load_from_file') or is_flag_set_in_command_line('lfile')
if load_from_file_enabled and not is_network_compiled:
if args.mean_values or args.scale_values:
raise RuntimeError("--mean_values and --scale_values aren't supported with --load_from_file. "
"The values can be set via model_optimizer while generating xml")
next_step()
print("Skipping the step for loading model from file")
next_step()
print("Skipping the step for loading model from file")
next_step()
print("Skipping the step for loading model from file")
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_mem_usage = get_peak_memory_usage()
start_time = datetime.utcnow()
compiled_model = benchmark.core.compile_model(args.path_to_model, benchmark.device, device_config)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
end_mem_usage = get_peak_memory_usage()
logger.info(f"Compile model took {duration_ms} ms")
log_memory_usage(logger, start_mem_usage, end_mem_usage, "compile")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('compile model time (ms)', duration_ms)
])
app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.scale_values, args.mean_values, compiled_model.inputs)
batch_size = get_network_batch_size(app_inputs_info)
elif not is_network_compiled:
# --------------------- 4. Read the Intermediate Representation of the network -----------------------------
next_step()
logger.info("Loading model files")
start_time = datetime.utcnow()
model = benchmark.read_model(args.path_to_model)
topology_name = model.get_name()
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Read model took {duration_ms} ms")
logger.info("Original model I/O parameters:")
print_inputs_and_outputs_info(model)
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('read model time (ms)', duration_ms)
])
# --------------------- 5. Resizing network to match image sizes and given batch ---------------------------
next_step()
for port in model.inputs:
if not port.get_names():
port.set_names({port.node.get_friendly_name()})
app_inputs_info, reshape = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.scale_values, args.mean_values, model.inputs)
# use batch size according to provided layout and shapes
batch_size = get_network_batch_size(app_inputs_info)
logger.info(f'Model batch size: {batch_size}')
if reshape:
start_time = datetime.utcnow()
shapes = { info.name : info.partial_shape for info in app_inputs_info }
logger.info(
'Reshaping model: {}'.format(', '.join("'{}': {}".format(k, str(v)) for k, v in shapes.items())))
model.reshape(shapes)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Reshape model took {duration_ms} ms")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('reshape model time (ms)', duration_ms)
])
# --------------------- 6. Configuring inputs and outputs of the model --------------------------------------------------
next_step()
pre_post_processing(model, app_inputs_info, args.input_precision, args.output_precision, args.input_output_precision)
print_inputs_and_outputs_info(model)
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_mem_usage = get_peak_memory_usage()
start_time = datetime.utcnow()
compiled_model = benchmark.core.compile_model(model, benchmark.device, device_config)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
end_mem_usage = get_peak_memory_usage()
logger.info(f"Compile model took {duration_ms} ms")
log_memory_usage(logger, start_mem_usage, end_mem_usage, "compile")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('compile model time (ms)', duration_ms)
])
else:
if args.mean_values or args.scale_values:
raise RuntimeError("--mean_values and --scale_values aren't supported for compiled model. "
"The values can be set via model_optimizer while generating xml")
next_step()
print("Skipping the step for compiled model")
next_step()
print("Skipping the step for compiled model")
next_step()
print("Skipping the step for compiled model")
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_mem_usage = get_peak_memory_usage()
start_time = datetime.utcnow()
compiled_model = benchmark.core.import_model(args.path_to_model, benchmark.device, device_config)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
end_mem_usage = get_peak_memory_usage()
logger.info(f"Import model took {duration_ms} ms")
log_memory_usage(logger, start_mem_usage, end_mem_usage, "import")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('import model time (ms)', duration_ms)
])
app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.scale_values, args.mean_values, compiled_model.inputs)
batch_size = get_network_batch_size(app_inputs_info)
# --------------------- 8. Querying optimal runtime parameters --------------------------------------------------
next_step()
## actual device-deduced settings
keys = compiled_model.get_property(properties.supported_properties())
logger.info("Model:")
for k in keys:
skip_keys = (properties.supported_properties())
if k not in skip_keys:
value = compiled_model.get_property(k)
if k == properties.device.properties():
for device_key in value.keys():
logger.info(f' {device_key}:')
for k2, value2 in value.get(device_key).items():
if k2 not in skip_keys:
logger.info(f' {k2}: {value2}')
else:
logger.info(f' {k}: {value}')
# Update number of streams
for device in device_number_streams.keys():
try:
key = get_device_type_from_name(device) + '_THROUGHPUT_STREAMS'
device_number_streams[device] = compiled_model.get_property(key)
except:
key = 'NUM_STREAMS'
device_number_streams[device] = compiled_model.get_property(key)
# ------------------------------------ 9. Creating infer requests and preparing input data ----------------------
next_step()
# Create infer requests
requests = benchmark.create_infer_requests(compiled_model)
# Prepare input data
paths_to_input = list()
if args.paths_to_input:
for path in args.paths_to_input:
if ":" in next(iter(path), ""):
paths_to_input.extend(path)
else:
paths_to_input.append(os.path.abspath(*path))
data_queue = get_input_data(paths_to_input, app_inputs_info)
static_mode = check_for_static(app_inputs_info)
allow_inference_only_or_sync = can_measure_as_static(app_inputs_info)
if not allow_inference_only_or_sync and benchmark.api_type == 'sync':
raise Exception("Benchmarking of the model with dynamic shapes is available for async API only. "
"Please use -api async -hint latency -nireq 1 to emulate sync behavior.")
if benchmark.inference_only == None:
if static_mode:
benchmark.inference_only = True
else:
benchmark.inference_only = False
elif benchmark.inference_only and not allow_inference_only_or_sync:
raise Exception("Benchmarking dynamic model available with input filling in measurement loop only!")
# update batch size in case dynamic network with one data_shape
if allow_inference_only_or_sync and batch_size.is_dynamic:
batch_size = Dimension(data_queue.batch_sizes[data_queue.current_group_id])
benchmark.latency_groups = get_latency_groups(app_inputs_info)
if len(benchmark.latency_groups) > 1:
logger.info(f"Defined {len(benchmark.latency_groups)} tensor groups:")
for group in benchmark.latency_groups:
logger.info(f"\t{str(group)}")
# Iteration limit
benchmark.niter = get_number_iterations(benchmark.niter, benchmark.nireq, max(len(info.shapes) for info in app_inputs_info), benchmark.api_type)
# Set input tensors before first inference
for request in requests:
data_tensors = data_queue.get_next_input()
for port, data_tensor in data_tensors.items():
input_tensor = request.get_input_tensor(port)
if not static_mode:
input_tensor.shape = data_tensor.shape
if not len(input_tensor.shape):
input_tensor.data.flat[:] = data_tensor.data
else:
input_tensor.data[:] = data_tensor.data
if statistics:
statistics.add_parameters(StatisticsReport.Category.RUNTIME_CONFIG,
[
('topology', topology_name),
('target device', device_name),
('API', args.api_type),
('inference_only', benchmark.inference_only),
('precision', "UNSPECIFIED"),
('batch size', str(batch_size)),
('number of iterations', str(benchmark.niter)),
('number of parallel infer requests', str(benchmark.nireq)),
('duration (ms)', str(get_duration_in_milliseconds(benchmark.duration_seconds))),
])
for nstreams in device_number_streams.items():
statistics.add_parameters(StatisticsReport.Category.RUNTIME_CONFIG,
[
(f"number of {nstreams[0]} streams", str(nstreams[1])),
])
# ------------------------------------ 10. Measuring performance -----------------------------------------------
output_string = process_help_inference_string(benchmark, device_number_streams)
next_step(additional_info=output_string)
if benchmark.inference_only:
logger.info("Benchmarking in inference only mode (inputs filling are not included in measurement loop).")
else:
logger.info("Benchmarking in full mode (inputs filling are included in measurement loop).")
if not args.no_warmup:
duration_ms = f"{benchmark.first_infer(requests):.2f}"
logger.info(f"First inference took {duration_ms} ms")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('first inference time (ms)', duration_ms)
])
else:
logger.info("Skipping warmup inference due to -no_warmup flag")
pcseq = args.pcseq
if static_mode or len(benchmark.latency_groups) == 1:
pcseq = False
fps, median_latency_ms, avg_latency_ms, min_latency_ms, max_latency_ms, total_duration_sec, iteration = benchmark.main_loop(requests, data_queue, batch_size, args.latency_percentile, pcseq)
# ------------------------------------ 11. Dumping statistics report -------------------------------------------
next_step()
if args.dump_config:
dump_config(args.dump_config, config)
logger.info(f"OpenVINO configuration settings were dumped to {args.dump_config}")
if args.exec_graph_path:
dump_exec_graph(compiled_model, args.exec_graph_path)
if perf_counts:
perfs_count_list = []
for request in requests:
perfs_count_list.append(request.profiling_info)
if args.perf_counts_sort:
total_sorted_list = print_perf_counters_sort(perfs_count_list,sort_flag=args.perf_counts_sort)
if statistics:
statistics.dump_performance_counters_sorted(total_sorted_list)
elif args.perf_counts:
print_perf_counters(perfs_count_list)
if statistics:
# if not args.perf_counts_sort:
statistics.dump_performance_counters(perfs_count_list)
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('total execution time (ms)', f'{get_duration_in_milliseconds(total_duration_sec):.2f}'),
('total number of iterations', str(iteration)),
])
if MULTI_DEVICE_NAME not in device_name:
latency_prefix = None
if args.latency_percentile == 50:
latency_prefix = 'latency (ms)'
elif args.latency_percentile != 50:
latency_prefix = 'latency (' + str(args.latency_percentile) + ' percentile) (ms)'
if latency_prefix:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
(latency_prefix, f'{median_latency_ms:.2f}'),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
("avg latency", f'{avg_latency_ms:.2f}'),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
("min latency", f'{min_latency_ms:.2f}'),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
("max latency", f'{max_latency_ms:.2f}'),
])
if pcseq:
for group in benchmark.latency_groups:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
("group", str(group)),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
("avg latency", f'{group.avg:.2f}'),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
("min latency", f'{group.min:.2f}'),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
("max latency", f'{group.max:.2f}'),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('throughput', f'{fps:.2f}'),
])
statistics.dump()
try:
exeDevice = compiled_model.get_property("EXECUTION_DEVICES")
logger.info(f'Execution Devices:{exeDevice}')
except:
pass
logger.info(f'Count: {iteration} iterations')
logger.info(f'Duration: {get_duration_in_milliseconds(total_duration_sec):.2f} ms')
if MULTI_DEVICE_NAME not in device_name:
logger.info('Latency:')
if args.latency_percentile == 50:
logger.info(f' Median: {median_latency_ms:.2f} ms')
elif args.latency_percentile != 50:
logger.info(f' {args.latency_percentile} percentile: {median_latency_ms:.2f} ms')
logger.info(f' Average: {avg_latency_ms:.2f} ms')
logger.info(f' Min: {min_latency_ms:.2f} ms')
logger.info(f' Max: {max_latency_ms:.2f} ms')
if pcseq:
logger.info("Latency for each data shape group:")
for idx,group in enumerate(benchmark.latency_groups):
logger.info(f"{idx+1}.{str(group)}")
if args.latency_percentile == 50:
logger.info(f' Median: {group.median:.2f} ms')
elif args.latency_percentile != 50:
logger.info(f' {args.latency_percentile} percentile: {group.median:.2f} ms')
logger.info(f' Average: {group.avg:.2f} ms')
logger.info(f' Min: {group.min:.2f} ms')
logger.info(f' Max: {group.max:.2f} ms')
logger.info(f'Throughput: {fps:.2f} FPS')
del compiled_model
next_step.step_id = 0
except Exception as e:
logger.exception(e)
if statistics:
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS,
[('error', str(e))]
)
statistics.dump()
sys.exit(1)
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