def run(args):
statistics = None
try:
if args.number_streams is None:
logger.warning(" -nstreams default value is determined automatically for a 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. ")
command_line_arguments = get_command_line_arguments(sys.argv)
if args.report_type:
statistics = StatisticsReport(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_nstreams_value_per_device(devices, args.number_streams)
config = {}
if args.load_config:
load_config(args.load_config, config)
is_network_compiled = False
_, ext = os.path.splitext(args.path_to_model)
if ext == BLOB_EXTENSION:
is_network_compiled = True
print("Model is compiled")
# ------------------------------ 2. Loading OpenVINO ---------------------------------------------------
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)
## CPU (MKLDNN) extensions
if CPU_DEVICE_NAME in device_name and args.path_to_extension:
benchmark.add_extension(path_to_extension=args.path_to_extension)
## 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)
if not args.perf_hint:
for device in devices:
supported_config_keys = benchmark.core.get_property(device, 'SUPPORTED_CONFIG_KEYS')
if 'PERFORMANCE_HINT' in supported_config_keys:
logger.warning(f"-hint default value is determined as 'THROUGHPUT' automatically for {device} device" +
"For more detailed information look at README.")
args.perf_hint = "throughput"
version = benchmark.get_version_info()
logger.info(version)
# --------------------- 3. Setting device configuration --------------------------------------------------------
next_step()
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
for device in devices:
if device not in config.keys():
config[device] = {}
## Set performance counter
if is_flag_set_in_command_line('pc'):
## set to user defined value
config[device]['PERF_COUNT'] = 'YES' if args.perf_counts else 'NO'
elif 'PERF_COUNT' in config[device].keys() and config[device]['PERF_COUNT'] == 'YES':
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]['PERF_COUNT'] = 'YES'
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]['PERF_COUNT'] = 'YES'
else:
## set to default value
config[device]['PERF_COUNT'] = 'YES' if args.perf_counts else 'NO'
perf_counts = True if config[device]['PERF_COUNT'] == 'YES' else perf_counts
## high-level performance hints
if is_flag_set_in_command_line('hint') or args.perf_hint:
config[device]['PERFORMANCE_HINT'] = args.perf_hint.upper()
if is_flag_set_in_command_line('nireq'):
config[device]['PERFORMANCE_HINT_NUM_REQUESTS'] = str(args.number_infer_requests)
## 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
supported_config_keys = benchmark.core.get_property(device, 'SUPPORTED_CONFIG_KEYS')
if key not in supported_config_keys:
raise Exception(f"Device {device} doesn't support config key '{key}'! " +
"Please specify -nstreams for correct devices in format <dev1>:<nstreams1>,<dev2>:<nstreams2>")
config[device][key] = device_number_streams[device]
elif key not in config[device].keys() and args.api_type == "async" and not is_flag_set_in_command_line('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 device != MYRIAD_DEVICE_NAME: ## MYRIAD sets the default number of streams implicitly
config[device][key] = get_device_type_from_name(device) + "_THROUGHPUT_AUTO"
if key in config[device].keys():
device_number_streams[device] = config[device][key]
if CPU_DEVICE_NAME in device: # CPU supports few special performance-oriented keys
# limit threading for CPU portion of inference
if args.number_threads and is_flag_set_in_command_line("nthreads"):
config[device]['CPU_THREADS_NUM'] = str(args.number_threads)
if is_flag_set_in_command_line("enforcebf16") or is_flag_set_in_command_line("enforce_bfloat16"):
config[device]['ENFORCE_BF16'] = 'YES' if args.enforce_bfloat16 else 'NO'
if is_flag_set_in_command_line('pin'):
## set to user defined value
config[device]['CPU_BIND_THREAD'] = args.infer_threads_pinning
elif 'CPU_BIND_THREAD' not in config[device].keys():
if MULTI_DEVICE_NAME in device_name and GPU_DEVICE_NAME in device_name:
logger.warning(f"Turn off threads pinning for {device} " +
"device since multi-scenario with GPU device is used.")
config[device]['CPU_BIND_THREAD'] = 'NO'
## for CPU execution, more throughput-oriented execution via streams
set_throughput_streams()
elif GPU_DEVICE_NAME in device:
## for GPU execution, more throughput-oriented execution via streams
set_throughput_streams()
if MULTI_DEVICE_NAME in device_name and CPU_DEVICE_NAME in device_name:
logger.warning("Turn on GPU throttling. Multi-device execution with the CPU + GPU performs best with GPU throttling hint, " +
"which releases another CPU thread (that is otherwise used by the GPU driver for active polling)")
config[device]['GPU_PLUGIN_THROTTLE'] = '1'
elif MYRIAD_DEVICE_NAME in device:
set_throughput_streams()
config[device]['LOG_LEVEL'] = 'LOG_INFO'
elif GNA_DEVICE_NAME in device:
if is_flag_set_in_command_line('qb'):
if args.qb == 8:
config[device]['GNA_PRECISION'] = 'I8'
else:
config[device]['GNA_PRECISION'] = 'I16'
else:
supported_config_keys = benchmark.core.get_property(device, 'SUPPORTED_CONFIG_KEYS')
if 'CPU_THREADS_NUM' in supported_config_keys and args.number_threads and is_flag_set_in_command_line("nthreads"):
config[device]['CPU_THREADS_NUM'] = str(args.number_threads)
if 'CPU_THROUGHPUT_STREAMS' in supported_config_keys and args.number_streams and is_flag_set_in_command_line("streams"):
config[device]['CPU_THROUGHPUT_STREAMS'] = args.number_streams
if 'CPU_BIND_THREAD' in supported_config_keys and args.infer_threads_pinning and is_flag_set_in_command_line("pin"):
config[device]['CPU_BIND_THREAD'] = args.infer_threads_pinning
perf_counts = perf_counts
benchmark.set_config(config)
if args.cache_dir:
benchmark.set_cache_dir(args.cache_dir)
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:
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_time = datetime.utcnow()
compiled_model = benchmark.core.compile_model(args.path_to_model, benchmark.device)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Compile model took {duration_ms} ms")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('load network time (ms)', duration_ms)
])
app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, 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()
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")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('read network time (ms)', duration_ms)
])
# --------------------- 5. Resizing network to match image sizes and given batch ---------------------------
next_step()
app_inputs_info, reshape = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, model.inputs)
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 network time (ms)', duration_ms)
])
# use batch size according to provided layout and shapes
batch_size = get_network_batch_size(app_inputs_info)
logger.info(f'Network batch size: {batch_size}')
# --------------------- 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_time = datetime.utcnow()
compiled_model = benchmark.core.compile_model(model, benchmark.device)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Compile model took {duration_ms} ms")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('load network time (ms)', duration_ms)
])
else:
next_step()
print("Skipping the step for compiled network")
next_step()
print("Skipping the step for compiled network")
next_step()
print("Skipping the step for compiled network")
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_time = datetime.utcnow()
compiled_model = benchmark.core.import_model(args.path_to_model)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Import model took {duration_ms} ms")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('import network time (ms)', duration_ms)
])
app_inputs_info, _ = get_inputs_info(args.shape, args.data_shape, args.layout, args.batch_size, args.input_scale, args.input_mean, compiled_model.inputs)
batch_size = get_network_batch_size(app_inputs_info)
# --------------------- 8. Querying optimal runtime parameters --------------------------------------------------
next_step()
## actual device-deduced settings
for device in devices:
keys = benchmark.core.get_property(device, 'SUPPORTED_CONFIG_KEYS')
logger.info(f'DEVICE: {device}')
for k in keys:
try:
logger.info(f' {k} , {benchmark.core.get_property(device, k)}')
except:
pass
# Update number of streams
for device in device_number_streams.keys():
key = get_device_type_from_name(device) + '_THROUGHPUT_STREAMS'
device_number_streams[device] = benchmark.core.get_property(device, key)
# ------------------------------------ 9. Creating infer requests and preparing input data ----------------------
next_step()
# Create infer requests
start_time = datetime.utcnow()
requests = benchmark.create_infer_requests(compiled_model)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Create {benchmark.nireq} infer requests took {duration_ms} ms")
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('create infer requests time (ms)', duration_ms)
])
# 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 -nstreams 1 -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!")
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).")
# update batch size in case dynamic network with one data_shape
if benchmark.inference_only 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:
print(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
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)
progress_bar_total_count = 10000
if benchmark.niter and not benchmark.duration_seconds:
progress_bar_total_count = benchmark.niter
progress_bar = ProgressBar(progress_bar_total_count, args.stream_output, args.progress) if args.progress else None
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)
])
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, progress_bar, 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:
print_perf_counters(perfs_count_list)
if statistics:
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 and static_mode:
#latency_prefix = 'median latency (ms)'
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()
print(f'Count: {iteration} iterations')
print(f'Duration: {get_duration_in_milliseconds(total_duration_sec):.2f} ms')
if MULTI_DEVICE_NAME not in device_name:
print('Latency:')
if args.latency_percentile == 50 and static_mode:
print(f' Median: {median_latency_ms:.2f} ms')
elif args.latency_percentile != 50:
print(f'({args.latency_percentile} percentile): {median_latency_ms:.2f} ms')
print(f' AVG: {avg_latency_ms:.2f} ms')
print(f' MIN: {min_latency_ms:.2f} ms')
print(f' MAX: {max_latency_ms:.2f} ms')
if pcseq:
print("Latency for each data shape group: ")
for group in benchmark.latency_groups:
print(f" {str(group)}")
print(f' AVG: {group.avg:.2f} ms')
print(f' MIN: {group.min:.2f} ms')
print(f' MAX: {group.max:.2f} ms')
print(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)
def main(args):
statistics = None
try:
if args.number_streams is None:
logger.warn(" -nstreams default value is determined automatically for a 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 args.report_type:
statistics = StatisticsReport(StatisticsReport.Config(args.report_type, args.report_folder))
statistics.add_parameters(StatisticsReport.Category.COMMAND_LINE_PARAMETERS, get_command_line_arguments(sys.argv))
# ------------------------------ 2. Loading Inference Engine ---------------------------------------------------
next_step()
device_name = args.target_device.upper()
benchmark = Benchmark(args.target_device, args.number_infer_requests,
args.number_iterations, args.time, args.api_type)
benchmark.add_extension(args.path_to_extension, args.path_to_cldnn_config)
version = benchmark.get_version_info()
logger.info(version)
# --------------------- 3. Read the Intermediate Representation of the network ---------------------------------
next_step()
start_time = datetime.now()
ie_network = read_network(args.path_to_model)
duration_ms = "{:.2f}".format((datetime.now() - start_time).total_seconds() * 1000)
if statistics:
logger.info("Read network took {} ms".format(duration_ms))
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('read network time (ms)', duration_ms)
])
# --------------------- 4. Resizing network to match image sizes and given batch -------------------------------
next_step()
if args.batch_size and args.batch_size != ie_network.batch_size:
benchmark.reshape(ie_network, args.batch_size)
batch_size = ie_network.batch_size
logger.info('Network batch size: {}, precision: {}'.format(ie_network.batch_size, ie_network.precision))
# --------------------- 5. Configuring input of the model ------------------------------------------------------
next_step()
config_network_inputs(ie_network)
# --------------------- 6. Setting device configuration --------------------------------------------------------
next_step()
benchmark.set_config(args.number_streams, args.api_type, args.number_threads,
args.infer_threads_pinning)
# --------------------- 7. Loading the model to the device -----------------------------------------------------
next_step()
start_time = datetime.now()
perf_counts = True if args.perf_counts or \
args.report_type in [ averageCntReport, detailedCntReport ] or \
args.exec_graph_path else False
exe_network = benchmark.load_network(ie_network, perf_counts, args.number_infer_requests)
duration_ms = "{:.2f}".format((datetime.now() - start_time).total_seconds() * 1000)
if statistics:
logger.info("Load network took {} ms".format(duration_ms))
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('load network time (ms)', duration_ms)
])
# --------------------- 8. Setting optimal runtime parameters --------------------------------------------------
next_step()
# Number of requests
infer_requests = exe_network.requests
benchmark.nireq = len(infer_requests)
# Iteration limit
benchmark.niter = get_number_iterations(benchmark.niter, len(exe_network.requests), args.api_type)
# ------------------------------------ 9. Creating infer requests and filling input blobs ----------------------
next_step()
request_queue = InferRequestsQueue(infer_requests)
path_to_input = os.path.abspath(args.path_to_input) if args.path_to_input else None
requests_input_data = get_inputs(path_to_input, batch_size, ie_network.inputs, infer_requests)
if statistics:
statistics.add_parameters(StatisticsReport.Category.RUNTIME_CONFIG,
[
('topology', ie_network.name),
('target device', device_name),
('API', args.api_type),
('precision', str(ie_network.precision)),
('batch size', str(ie_network.batch_size)),
('number of iterations', str(benchmark.niter) if benchmark.niter else "0"),
('number of parallel infer requests', str(benchmark.nireq)),
('duration (ms)', str(get_duration_in_milliseconds(benchmark.duration_seconds))),
])
for nstreams in benchmark.device_number_streams.items():
statistics.add_parameters(StatisticsReport.Category.RUNTIME_CONFIG,
[
("number of {} streams".format(nstreams[0]), str(nstreams[1])),
])
# ------------------------------------ 10. Measuring performance -----------------------------------------------
output_string = process_help_inference_string(benchmark)
next_step(output_string)
progress_bar_total_count = 10000
if benchmark.niter and not benchmark.duration_seconds:
progress_bar_total_count = benchmark.niter
progress_bar = ProgressBar(progress_bar_total_count, args.stream_output, args.progress)
fps, latency_ms, total_duration_sec, iteration = benchmark.infer(request_queue, requests_input_data,
batch_size, progress_bar)
# ------------------------------------ 11. Dumping statistics report -------------------------------------------
next_step()
if args.exec_graph_path:
dump_exec_graph(exe_network, args.exec_graph_path)
if perf_counts:
perfs_count_list = []
for ni in range(int(benchmark.nireq)):
perfs_count_list.append(exe_network.requests[ni].get_perf_counts())
if args.perf_counts:
print_perf_counters(perfs_count_list)
if statistics:
statistics.dump_performance_counters(perfs_count_list)
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('total execution time (ms)', '{:.2f}'.format(get_duration_in_milliseconds(total_duration_sec))),
('total number of iterations', str(iteration)),
])
if MULTI_DEVICE_NAME not in device_name:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('latency (ms)', '{:.2f}'.format(latency_ms)),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('throughput', '{:.2f}'.format(fps)),
])
if statistics:
statistics.dump()
print('Count: {} iterations'.format(iteration))
print('Duration: {:.2f} ms'.format(get_duration_in_milliseconds(total_duration_sec)))
if MULTI_DEVICE_NAME not in device_name:
print('Latency: {:.2f} ms'.format(latency_ms))
print('Throughput: {:.2f} FPS'.format(fps))
del exe_network
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()
def run(args):
statistics = None
try:
if args.number_streams is None:
logger.warning(
" -nstreams default value is determined automatically for a 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. "
)
command_line_arguments = get_command_line_arguments(sys.argv)
if args.report_type:
statistics = StatisticsReport(
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_nstreams_value_per_device(
devices, args.number_streams)
config = {}
if args.load_config:
load_config(args.load_config, config)
is_network_compiled = False
_, ext = os.path.splitext(args.path_to_model)
if ext == BLOB_EXTENSION:
is_network_compiled = True
print("Network is compiled")
# ------------------------------ 2. Loading Inference Engine ---------------------------------------------------
next_step(step_id=2)
benchmark = Benchmark(args.target_device, args.number_infer_requests,
args.number_iterations, args.time, args.api_type)
## CPU (MKLDNN) extensions
if CPU_DEVICE_NAME in device_name and args.path_to_extension:
benchmark.add_extension(path_to_extension=args.path_to_extension)
## 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)
version = benchmark.get_version_info()
logger.info(version)
# --------------------- 3. Setting device configuration --------------------------------------------------------
next_step()
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
for device in devices:
if device not in config.keys():
config[device] = {}
## Set performance counter
if is_flag_set_in_command_line('pc'):
## set to user defined value
config[device][
'PERF_COUNT'] = 'YES' if args.perf_counts else 'NO'
elif 'PERF_COUNT' in config[device].keys(
) and config[device]['PERF_COUNT'] == 'YES':
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]['PERF_COUNT'] = 'YES'
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]['PERF_COUNT'] = 'YES'
else:
## set to default value
config[device][
'PERF_COUNT'] = 'YES' if args.perf_counts else 'NO'
perf_counts = True if config[device][
'PERF_COUNT'] == 'YES' else perf_counts
## high-level performance hints
if is_flag_set_in_command_line('hint'):
config[device]['PERFORMANCE_HINT'] = args.perf_hint.upper()
if is_flag_set_in_command_line('nireq'):
config[device]['PERFORMANCE_HINT_NUM_REQUESTS'] = str(
args.number_infer_requests)
## 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
supported_config_keys = benchmark.ie.get_metric(
device, 'SUPPORTED_CONFIG_KEYS')
if key not in supported_config_keys:
raise Exception(
f"Device {device} doesn't support config key '{key}'! "
+
"Please specify -nstreams for correct devices in format <dev1>:<nstreams1>,<dev2>:<nstreams2>"
)
config[device][key] = device_number_streams[device]
elif key not in config[device].keys(
) and args.api_type == "async" and not is_flag_set_in_command_line(
'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 device != MYRIAD_DEVICE_NAME: ## MYRIAD sets the default number of streams implicitly
config[device][key] = get_device_type_from_name(
device) + "_THROUGHPUT_AUTO"
if key in config[device].keys():
device_number_streams[device] = config[device][key]
if CPU_DEVICE_NAME in device: # CPU supports few special performance-oriented keys
# limit threading for CPU portion of inference
if args.number_threads and is_flag_set_in_command_line(
"nthreads"):
config[device]['CPU_THREADS_NUM'] = str(
args.number_threads)
if is_flag_set_in_command_line(
"enforcebf16") or is_flag_set_in_command_line(
"enforce_bfloat16"):
config[device][
'ENFORCE_BF16'] = 'YES' if args.enforce_bfloat16 else 'NO'
if is_flag_set_in_command_line('pin'):
## set to user defined value
config[device][
'CPU_BIND_THREAD'] = args.infer_threads_pinning
elif 'CPU_BIND_THREAD' not in config[device].keys():
if MULTI_DEVICE_NAME in device_name and GPU_DEVICE_NAME in device_name:
logger.warning(
f"Turn off threads pinning for {device} " +
"device since multi-scenario with GPU device is used."
)
config[device]['CPU_BIND_THREAD'] = 'NO'
## for CPU execution, more throughput-oriented execution via streams
set_throughput_streams()
elif GPU_DEVICE_NAME in device:
## for GPU execution, more throughput-oriented execution via streams
set_throughput_streams()
if MULTI_DEVICE_NAME in device_name and CPU_DEVICE_NAME in device_name:
logger.warning(
"Turn on GPU throttling. Multi-device execution with the CPU + GPU performs best with GPU throttling hint, "
+
"which releases another CPU thread (that is otherwise used by the GPU driver for active polling)"
)
config[device]['GPU_PLUGIN_THROTTLE'] = '1'
elif MYRIAD_DEVICE_NAME in device:
set_throughput_streams()
config[device]['LOG_LEVEL'] = 'LOG_INFO'
elif GNA_DEVICE_NAME in device:
if is_flag_set_in_command_line('qb'):
if args.qb == 8:
config[device]['GNA_PRECISION'] = 'I8'
else:
config[device]['GNA_PRECISION'] = 'I16'
if args.number_threads and is_flag_set_in_command_line(
"nthreads"):
config[device]['GNA_LIB_N_THREADS'] = str(
args.number_threads)
else:
supported_config_keys = benchmark.ie.get_metric(
device, 'SUPPORTED_CONFIG_KEYS')
if 'CPU_THREADS_NUM' in supported_config_keys and args.number_threads and is_flag_set_in_command_line(
"nthreads"):
config[device]['CPU_THREADS_NUM'] = str(
args.number_threads)
if 'CPU_THROUGHPUT_STREAMS' in supported_config_keys and args.number_streams and is_flag_set_in_command_line(
"streams"):
config[device][
'CPU_THROUGHPUT_STREAMS'] = args.number_streams
if 'CPU_BIND_THREAD' in supported_config_keys and args.infer_threads_pinning and is_flag_set_in_command_line(
"pin"):
config[device][
'CPU_BIND_THREAD'] = args.infer_threads_pinning
perf_counts = perf_counts
benchmark.set_config(config)
batch_size = args.batch_size
if args.cache_dir:
benchmark.set_cache_dir(args.cache_dir)
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:
next_step()
print("Skipping the step for loading network from file")
next_step()
print("Skipping the step for loading network from file")
next_step()
print("Skipping the step for loading network from file")
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_time = datetime.utcnow()
exe_network = benchmark.load_network(args.path_to_model)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Load network took {duration_ms} ms")
if statistics:
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS,
[('load network time (ms)', duration_ms)])
app_inputs_info, _ = get_inputs_info(args.shape, args.layout,
args.batch_size,
args.input_scale,
args.input_mean,
exe_network.input_info)
if batch_size == 0:
batch_size = 1
elif not is_network_compiled:
# --------------------- 4. Read the Intermediate Representation of the network -----------------------------
next_step()
start_time = datetime.utcnow()
ie_network = benchmark.read_network(args.path_to_model)
topology_name = ie_network.name
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Read network took {duration_ms} ms")
if statistics:
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS,
[('read network time (ms)', duration_ms)])
# --------------------- 5. Resizing network to match image sizes and given batch ---------------------------
next_step()
app_inputs_info, reshape = get_inputs_info(args.shape, args.layout,
args.batch_size,
args.input_scale,
args.input_mean,
ie_network.input_info)
if reshape:
start_time = datetime.utcnow()
shapes = {k: v.shape for k, v in app_inputs_info.items()}
logger.info('Reshaping network: {}'.format(', '.join(
"'{}': {}".format(k, v) for k, v in shapes.items())))
ie_network.reshape(shapes)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Reshape network took {duration_ms} ms")
if statistics:
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS,
[('reshape network time (ms)', duration_ms)])
# use batch size according to provided layout and shapes
batch_size = get_batch_size(
app_inputs_info) if args.layout else ie_network.batch_size
logger.info(f'Network batch size: {batch_size}')
# --------------------- 6. Configuring inputs and outputs of the model --------------------------------------------------
next_step()
process_precision(ie_network, app_inputs_info,
args.input_precision, args.output_precision,
args.input_output_precision)
print_inputs_and_outputs_info(ie_network)
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_time = datetime.utcnow()
exe_network = benchmark.load_network(ie_network)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Load network took {duration_ms} ms")
if statistics:
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS,
[('load network time (ms)', duration_ms)])
else:
next_step()
print("Skipping the step for compiled network")
next_step()
print("Skipping the step for compiled network")
next_step()
print("Skipping the step for compiled network")
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_time = datetime.utcnow()
exe_network = benchmark.import_network(args.path_to_model)
duration_ms = f"{(datetime.utcnow() - start_time).total_seconds() * 1000:.2f}"
logger.info(f"Import network took {duration_ms} ms")
if statistics:
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS,
[('import network time (ms)', duration_ms)])
app_inputs_info, _ = get_inputs_info(args.shape, args.layout,
args.batch_size,
args.input_scale,
args.input_mean,
exe_network.input_info)
if batch_size == 0:
batch_size = 1
# --------------------- 8. Querying optimal runtime parameters --------------------------------------------------
next_step()
if is_flag_set_in_command_line('hint'):
## actual device-deduced settings for the hint
for device in devices:
keys = benchmark.ie.get_metric(device, 'SUPPORTED_CONFIG_KEYS')
logger.info(f'DEVICE: {device}')
for k in keys:
logger.info(f' {k} , {exe_network.get_config(k)}')
# Update number of streams
for device in device_number_streams.keys():
key = get_device_type_from_name(device) + '_THROUGHPUT_STREAMS'
device_number_streams[device] = benchmark.ie.get_config(
device, key)
# Number of requests
infer_requests = exe_network.requests
# Iteration limit
benchmark.niter = get_number_iterations(benchmark.niter,
benchmark.nireq, args.api_type)
# ------------------------------------ 9. Creating infer requests and filling input blobs ----------------------
next_step()
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))
set_inputs(paths_to_input, batch_size, app_inputs_info, infer_requests)
if statistics:
statistics.add_parameters(
StatisticsReport.Category.RUNTIME_CONFIG, [
('topology', topology_name),
('target device', device_name),
('API', args.api_type),
('precision', "UNSPECIFIED"),
('batch size', str(batch_size)),
('number of iterations',
str(benchmark.niter) if benchmark.niter else "0"),
('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)
progress_bar_total_count = 10000
if benchmark.niter and not benchmark.duration_seconds:
progress_bar_total_count = benchmark.niter
progress_bar = ProgressBar(progress_bar_total_count,
args.stream_output,
args.progress) if args.progress else None
duration_ms = f"{benchmark.first_infer(exe_network):.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)])
fps, latency_ms, total_duration_sec, iteration = benchmark.infer(
exe_network, batch_size, args.latency_percentile, progress_bar)
# ------------------------------------ 11. Dumping statistics report -------------------------------------------
next_step()
if args.dump_config:
dump_config(args.dump_config, config)
logger.info(
f"Inference Engine configuration settings were dumped to {args.dump_config}"
)
if args.exec_graph_path:
dump_exec_graph(exe_network, args.exec_graph_path)
if perf_counts:
perfs_count_list = []
for ni in range(int(benchmark.nireq)):
perfs_count_list.append(
exe_network.requests[ni].get_perf_counts())
if args.perf_counts:
print_perf_counters(perfs_count_list)
if statistics:
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:
if args.latency_percentile == 50:
latency_prefix = 'latency (ms)'
else:
latency_prefix = 'latency (' + args.latency_percentile + ' percentile) (ms)'
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS, [
(latency_prefix, f'{latency_ms:.2f}'),
])
statistics.add_parameters(
StatisticsReport.Category.EXECUTION_RESULTS, [
('throughput', f'{fps:.2f}'),
])
if statistics:
statistics.dump()
print(f'Count: {iteration} iterations')
print(
f'Duration: {get_duration_in_milliseconds(total_duration_sec):.2f} ms'
)
if MULTI_DEVICE_NAME not in device_name:
if args.latency_percentile == 50:
print(f'Latency: {latency_ms:.2f} ms')
else:
print(
f'Latency ({args.latency_percentile} percentile): {latency_ms:.2f} ms'
)
print(f'Throughput: {fps:.2f} FPS')
del exe_network
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)
def run(args):
statistics = None
try:
if args.number_streams is None:
logger.warn(" -nstreams default value is determined automatically for a 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. ")
command_line_arguments = get_command_line_arguments(sys.argv)
if args.report_type:
statistics = StatisticsReport(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_nstreams_value_per_device(devices, args.number_streams)
config = {}
if args.load_config:
load_config(args.load_config, config)
is_network_compiled = False
_, ext = os.path.splitext(args.path_to_model)
if ext == BLOB_EXTENSION:
is_network_compiled = True
print("Network is compiled")
# ------------------------------ 2. Loading Inference Engine ---------------------------------------------------
next_step(step_id=2)
benchmark = Benchmark(args.target_device, args.number_infer_requests,
args.number_iterations, args.time, args.api_type)
## CPU (MKLDNN) extensions
if CPU_DEVICE_NAME in device_name and args.path_to_extension:
benchmark.add_extension(path_to_extension=args.path_to_extension)
## 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)
version = benchmark.get_version_info()
logger.info(version)
# --------------------- 3. Setting device configuration --------------------------------------------------------
next_step()
perf_counts = False
for device in devices:
if device not in config.keys():
config[device] = {}
## Set performance counter
if is_flag_set_in_command_line('pc'):
## set to user defined value
config[device]['PERF_COUNT'] = 'YES' if args.perf_counts else 'NO'
elif 'PERF_COUNT' in config[device].keys() and config[device]['PERF_COUNT'] == 'YES':
logger.warn("Performance counters for {} device is turned on. ".format(device) +
"To print results use -pc option.")
elif args.report_type in [ averageCntReport, detailedCntReport ]:
logger.warn("Turn on performance counters for {} device ".format(device) +
"since report type is {}.".format(args.report_type))
config[device]['PERF_COUNT'] = 'YES'
elif args.exec_graph_path is not None:
logger.warn("Turn on performance counters for {} device ".format(device) +
"due to execution graph dumping.")
config[device]['PERF_COUNT'] = 'YES'
else:
## set to default value
config[device]['PERF_COUNT'] = 'YES' if args.perf_counts else 'NO'
perf_counts = True if config[device]['PERF_COUNT'] == 'YES' else perf_counts
def set_throughput_streams():
key = device + "_THROUGHPUT_STREAMS"
if device in device_number_streams.keys():
## set to user defined value
supported_config_keys = benchmark.ie.get_metric(device, 'SUPPORTED_CONFIG_KEYS')
if key not in supported_config_keys:
raise Exception("Device {} doesn't support config key '{}'! ".format(device, key) +
"Please specify -nstreams for correct devices in format <dev1>:<nstreams1>,<dev2>:<nstreams2>")
config[device][key] = device_number_streams[device]
elif key not in config[device].keys() and args.api_type == "async":
logger.warn("-nstreams default value is determined automatically for {} device. ".format(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.")
config[device][key] = device + "_THROUGHPUT_AUTO"
if key in config[device].keys():
device_number_streams[device] = config[device][key]
if device == CPU_DEVICE_NAME: # CPU supports few special performance-oriented keys
# limit threading for CPU portion of inference
if args.number_threads and is_flag_set_in_command_line("nthreads"):
config[device]['CPU_THREADS_NUM'] = str(args.number_threads)
if is_flag_set_in_command_line("enforcebf16") or is_flag_set_in_command_line("enforce_bfloat16"):
config[device]['ENFORCE_BF16'] = 'YES' if args.enforce_bfloat16 else 'NO'
if is_flag_set_in_command_line('pin'):
## set to user defined value
config[device]['CPU_BIND_THREAD'] = args.infer_threads_pinning
elif 'CPU_BIND_THREAD' not in config[device].keys():
if MULTI_DEVICE_NAME in device_name and GPU_DEVICE_NAME in device_name:
logger.warn("Turn off threads pinning for {}".format(device) +
"device since multi-scenario with GPU device is used.")
config[device]['CPU_BIND_THREAD'] = 'NO'
else:
## set to default value
config[device]['CPU_BIND_THREAD'] = args.infer_threads_pinning
## for CPU execution, more throughput-oriented execution via streams
set_throughput_streams()
elif device == GPU_DEVICE_NAME:
## for GPU execution, more throughput-oriented execution via streams
set_throughput_streams()
if MULTI_DEVICE_NAME in device_name and CPU_DEVICE_NAME in device_name:
logger.warn("Turn on GPU trottling. Multi-device execution with the CPU + GPU performs best with GPU trottling hint, " +
"which releases another CPU thread (that is otherwise used by the GPU driver for active polling)")
config[device]['CLDNN_PLUGIN_THROTTLE'] = '1'
elif device == MYRIAD_DEVICE_NAME:
config[device]['LOG_LEVEL'] = 'LOG_INFO'
elif device == GNA_DEVICE_NAME:
if is_flag_set_in_command_line('qb'):
if args.qb == 8:
config[device]['GNA_PRECISION'] = 'I8'
else:
config[device]['GNA_PRECISION'] = 'I16'
if args.number_threads and is_flag_set_in_command_line("nthreads"):
config[device]['GNA_LIB_N_THREADS'] = str(args.number_threads)
perf_counts = perf_counts
benchmark.set_config(config)
batch_size = args.batch_size
if not is_network_compiled:
# --------------------- 4. Read the Intermediate Representation of the network -----------------------------
next_step()
start_time = datetime.utcnow()
ie_network = benchmark.read_network(args.path_to_model)
duration_ms = "{:.2f}".format((datetime.utcnow() - start_time).total_seconds() * 1000)
logger.info("Read network took {} ms".format(duration_ms))
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('read network time (ms)', duration_ms)
])
# --------------------- 5. Resizing network to match image sizes and given batch ---------------------------
next_step()
shapes = {k: v.input_data.shape.copy() for k, v in ie_network.input_info.items()}
reshape = False
if args.shape:
reshape |= update_shapes(shapes, args.shape, ie_network.input_info)
if args.batch_size and args.batch_size != ie_network.batch_size:
reshape |= adjust_shapes_batch(shapes, args.batch_size, ie_network.input_info)
if reshape:
start_time = datetime.utcnow()
logger.info(
'Reshaping network: {}'.format(', '.join("'{}': {}".format(k, v) for k, v in shapes.items())))
ie_network.reshape(shapes)
duration_ms = "{:.2f}".format((datetime.utcnow() - start_time).total_seconds() * 1000)
logger.info("Reshape network took {} ms".format(duration_ms))
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('reshape network time (ms)', duration_ms)
])
batch_size = ie_network.batch_size
logger.info('Network batch size: {}'.format(ie_network.batch_size))
# --------------------- 6. Configuring input of the model --------------------------------------------------
next_step()
config_network_inputs(ie_network)
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_time = datetime.utcnow()
exe_network = benchmark.load_network(ie_network)
duration_ms = "{:.2f}".format((datetime.utcnow() - start_time).total_seconds() * 1000)
logger.info("Load network took {} ms".format(duration_ms))
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('load network time (ms)', duration_ms)
])
else:
next_step()
print("Skipping the step for compiled network")
next_step()
print("Skipping the step for compiled network")
next_step()
print("Skipping the step for compiled network")
# --------------------- 7. Loading the model to the device -------------------------------------------------
next_step()
start_time = datetime.utcnow()
exe_network = benchmark.import_network(args.path_to_model)
duration_ms = "{:.2f}".format((datetime.utcnow() - start_time).total_seconds() * 1000)
logger.info("Import network took {} ms".format(duration_ms))
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('import network time (ms)', duration_ms)
])
if batch_size == 0:
batch_size = 1
# --------------------- 8. Setting optimal runtime parameters --------------------------------------------------
next_step()
# Update number of streams
for device in device_number_streams.keys():
key = device + '_THROUGHPUT_STREAMS'
device_number_streams[device] = benchmark.ie.get_config(device, key)
# Number of requests
infer_requests = exe_network.requests
# Iteration limit
benchmark.niter = get_number_iterations(benchmark.niter, benchmark.nireq, args.api_type)
# ------------------------------------ 9. Creating infer requests and filling input blobs ----------------------
next_step()
paths_to_input = list()
if args.paths_to_input:
for path in args.paths_to_input:
paths_to_input.append(os.path.abspath(*path) if args.paths_to_input else None)
set_inputs(paths_to_input, batch_size, exe_network.input_info, infer_requests)
if statistics:
statistics.add_parameters(StatisticsReport.Category.RUNTIME_CONFIG,
[
('topology', ie_network.name),
('target device', device_name),
('API', args.api_type),
('precision', "UNSPECIFIED"),
('batch size', str(batch_size)),
('number of iterations', str(benchmark.niter) if benchmark.niter else "0"),
('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,
[
("number of {} streams".format(nstreams[0]), str(nstreams[1])),
])
# ------------------------------------ 10. Measuring performance -----------------------------------------------
output_string = process_help_inference_string(benchmark)
next_step(additional_info=output_string)
progress_bar_total_count = 10000
if benchmark.niter and not benchmark.duration_seconds:
progress_bar_total_count = benchmark.niter
progress_bar = ProgressBar(progress_bar_total_count, args.stream_output, args.progress) if args.progress else None
fps, latency_ms, total_duration_sec, iteration = benchmark.infer(exe_network, batch_size, progress_bar)
# ------------------------------------ 11. Dumping statistics report -------------------------------------------
next_step()
if args.dump_config:
dump_config(args.dump_config, config)
logger.info("Inference Engine configuration settings were dumped to {}".format(args.dump_config))
if args.exec_graph_path:
dump_exec_graph(exe_network, args.exec_graph_path)
if perf_counts:
perfs_count_list = []
for ni in range(int(benchmark.nireq)):
perfs_count_list.append(exe_network.requests[ni].get_perf_counts())
if args.perf_counts:
print_perf_counters(perfs_count_list)
if statistics:
statistics.dump_performance_counters(perfs_count_list)
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('total execution time (ms)', '{:.2f}'.format(get_duration_in_milliseconds(total_duration_sec))),
('total number of iterations', str(iteration)),
])
if MULTI_DEVICE_NAME not in device_name:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('latency (ms)', '{:.2f}'.format(latency_ms)),
])
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('throughput', '{:.2f}'.format(fps)),
])
if statistics:
statistics.dump()
print('Count: {} iterations'.format(iteration))
print('Duration: {:.2f} ms'.format(get_duration_in_milliseconds(total_duration_sec)))
if MULTI_DEVICE_NAME not in device_name:
print('Latency: {:.2f} ms'.format(latency_ms))
print('Throughput: {:.2f} FPS'.format(fps))
del exe_network
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)
def benchmark_app(args):
try:
# ------------------------------ 2. Loading Inference Engine ---------------------------------------------------
next_step()
benchmark = Benchmark(args.target_device, args.number_infer_requests,
args.number_iterations, args.time, args.api_type)
benchmark.add_extension(args.path_to_extension, args.path_to_cldnn_config)
version = benchmark.get_version_info()
logger.info(version)
# --------------------- 3. Read the Intermediate Representation of the network ---------------------------------
next_step()
ie_network = read_network(args.path_to_model)
# --------------------- 4. Resizing network to match image sizes and given batch -------------------------------
next_step()
if args.batch_size and args.batch_size != ie_network.batch_size:
benchmark.reshape(ie_network, args.batch_size)
batch_size = ie_network.batch_size
logger.info('Network batch size: %s, precision: %s', ie_network.batch_size, ie_network.precision)
# --------------------- 5. Configuring input of the model ------------------------------------------------------
next_step()
config_network_inputs(ie_network)
# --------------------- 6. Setting device configuration --------------------------------------------------------
next_step()
benchmark.set_config(args.number_streams, args.api_type, args.number_threads, args.infer_threads_pinning)
# --------------------- 7. Loading the model to the device -----------------------------------------------------
next_step()
perf_counts = False
if args.perf_counts or args.exec_graph_path:
perf_counts = True
exe_network = benchmark.load_network(ie_network, perf_counts, args.number_infer_requests)
# --------------------- 8. Setting optimal runtime parameters --------------------------------------------------
next_step()
# Number of requests
infer_requests = exe_network.requests
benchmark.nireq = len(infer_requests)
# Iteration limit
benchmark.niter = get_number_iterations(benchmark.niter, len(exe_network.requests), args.api_type)
# ------------------------------------ 9. Creating infer requests and filling input blobs ----------------------
next_step()
request_queue = InferRequestsQueue(infer_requests)
path_to_input = os.path.abspath(args.path_to_input) if args.path_to_input else None
requests_input_data = get_inputs(path_to_input, batch_size, ie_network.inputs, infer_requests)
# ------------------------------------ 10. Measuring performance -----------------------------------------------
output_string = process_help_inference_string(benchmark)
next_step(output_string)
progress_bar_total_count = 10000
if benchmark.niter and not benchmark.duration_seconds:
progress_bar_total_count = benchmark.niter
progress_bar = ProgressBar(progress_bar_total_count, args.stream_output, args.progress)
benchmark.infer(request_queue, requests_input_data, batch_size, progress_bar)
# ------------------------------------ 11. Dumping statistics report -------------------------------------------
next_step()
if args.exec_graph_path:
dump_exec_graph(exe_network, args.exec_graph_path)
del exe_network
next_step.step_id = 0
except Exception as exc:
logger.exception(exc)