def main():
args = build_argparser().parse_args()
full_name = path.basename(args.input)
extension = path.splitext(full_name)[1]
if '.txt' in extension:
with open(args.input) as f:
videos = [line.strip() for line in f.read().split('\n')]
else:
videos = [args.input]
if not args.input:
raise ValueError('--input option is expected')
if args.labels:
with open(args.labels) as f:
labels = [l.strip() for l in f.read().strip().split('\n')]
else:
labels = None
ie = IECore()
if 'MYRIAD' in args.device:
myriad_config = {'VPU_HW_STAGES_OPTIMIZATION': 'YES'}
ie.set_config(myriad_config, 'MYRIAD')
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, 'CPU')
decoder_target_device = 'CPU'
if args.device != 'CPU':
encoder_target_device = args.device
else:
encoder_target_device = decoder_target_device
encoder_xml = args.m_encoder
encoder_bin = args.m_encoder.replace('.xml', '.bin')
encoder = IEModel(encoder_xml, encoder_bin, ie, encoder_target_device,
num_requests=(3 if args.device == 'MYRIAD' else 1))
if args.m_decoder is not None:
decoder_xml = args.m_decoder
decoder_bin = args.m_decoder.replace('.xml', '.bin')
decoder = IEModel(decoder_xml, decoder_bin, ie, decoder_target_device, num_requests=2)
decoder_seq_size = decoder.input_size[1]
else:
decoder = DummyDecoder(num_requests=2)
decoder_seq_size = args.decoder_seq_size
presenter = monitors.Presenter(args.utilization_monitors, 70)
result_presenter = ResultRenderer(no_show=args.no_show, presenter=presenter, labels=labels,
label_smoothing_window=args.label_smoothing)
run_pipeline(videos, encoder, decoder, result_presenter.render_frame, decoder_seq_size=decoder_seq_size, fps=args.fps)
print(presenter.reportMeans())
def main():
args = build_argparser().parse_args()
if args.labels:
with open(args.labels) as f:
labels = [line.strip() for line in f]
else:
labels = None
log.info('OpenVINO Inference Engine')
log.info('\tbuild: {}'.format(get_version()))
core = Core()
if 'MYRIAD' in args.device:
myriad_config = {'VPU_HW_STAGES_OPTIMIZATION': 'YES'}
core.set_config(myriad_config, 'MYRIAD')
decoder_target_device = 'CPU'
if args.device != 'CPU':
encoder_target_device = args.device
else:
encoder_target_device = decoder_target_device
models = [IEModel(args.m_encoder, core, encoder_target_device, model_type='Action Recognition Encoder',
num_requests=(3 if args.device == 'MYRIAD' else 1))]
if args.architecture_type == 'en-de':
if args.m_decoder is None:
raise RuntimeError('No decoder for encoder-decoder model type (-m_de) provided')
models.append(IEModel(args.m_decoder, core, decoder_target_device, model_type='Action Recognition Decoder', num_requests=2))
seq_size = models[1].input_shape[1]
elif args.architecture_type == 'en-mean':
models.append(DummyDecoder(num_requests=2))
seq_size = args.decoder_seq_size
elif args.architecture_type == 'i3d-rgb':
seq_size = models[0].input_shape[1]
presenter = monitors.Presenter(args.utilization_monitors, 70)
result_presenter = ResultRenderer(no_show=args.no_show, presenter=presenter, output=args.output, limit=args.output_limit, labels=labels,
label_smoothing_window=args.label_smoothing)
cap = open_images_capture(args.input, args.loop)
run_pipeline(cap, args.architecture_type, models, result_presenter.render_frame, args.raw_output_message,
seq_size=seq_size, fps=cap.fps())
for rep in presenter.reportMeans():
log.info(rep)
def main():
args = build_argparser().parse_args()
if args.labels:
with open(args.labels) as f:
labels = [line.strip() for line in f]
else:
labels = None
ie = IECore()
if 'MYRIAD' in args.device:
myriad_config = {'VPU_HW_STAGES_OPTIMIZATION': 'YES'}
ie.set_config(myriad_config, 'MYRIAD')
if args.cpu_extension and 'CPU' in args.device:
ie.add_extension(args.cpu_extension, 'CPU')
decoder_target_device = 'CPU'
if args.device != 'CPU':
encoder_target_device = args.device
else:
encoder_target_device = decoder_target_device
encoder_xml = args.m_encoder
encoder_bin = args.m_encoder.replace('.xml', '.bin')
models = [
IEModel(encoder_xml,
encoder_bin,
ie,
encoder_target_device,
num_requests=(3 if args.device == 'MYRIAD' else 1))
]
if args.architecture_type == 'en-de':
if args.m_decoder is None:
raise RuntimeError(
'No decoder for encoder-decoder model type (-m_de) provided')
decoder_xml = args.m_decoder
decoder_bin = args.m_decoder.replace('.xml', '.bin')
models.append(
IEModel(decoder_xml,
decoder_bin,
ie,
decoder_target_device,
num_requests=2))
seq_size = models[1].input_size[1]
elif args.architecture_type == 'en-mean':
models.append(DummyDecoder(num_requests=2))
seq_size = args.decoder_seq_size
elif args.architecture_type == 'i3d-rgb':
seq_size = models[0].input_size[2]
presenter = monitors.Presenter(args.utilization_monitors, 70)
result_presenter = ResultRenderer(
no_show=args.no_show,
presenter=presenter,
output=args.output,
limit=args.output_limit,
labels=labels,
label_smoothing_window=args.label_smoothing)
cap = open_images_capture(args.input, args.loop)
run_pipeline(cap,
args.architecture_type,
models,
result_presenter.render_frame,
seq_size=seq_size,
fps=cap.fps())
print(presenter.reportMeans())