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 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)
