Ejemplo n.º 1
0
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())
Ejemplo n.º 2
0
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)
Ejemplo n.º 3
0
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())