def __init__(self):
        super(Model, self).__init__()
        self.name = 'Mask RCNN'

        # Configuration and weights options
        # By default, we use ResNet50 backbone architecture, you can switch to
        # ResNet101 to increase quality if your GPU memory is higher than 8GB.
        # To do so, you will need to download both .yaml and .pkl ResNet101 files
        # then replace the below 'cfg_file' with the following:
        # self.cfg_file = 'models/mrcnn/e2e_mask_rcnn_X-101-64x4d-FPN_2x.yaml'
        self.cfg_file = 'models/mrcnn/e2e_mask_rcnn_R-50-FPN_2x.yaml'
        self.weights = 'models/mrcnn/model_final.pkl'
        self.default_cfg = copy.deepcopy(AttrDict(cfg)) # cfg from detectron.core.config
        self.mrcnn_cfg = AttrDict()
        self.dummy_coco_dataset = dummy_datasets.get_coco_dataset()

        # Inference options
        self.show_box = True
        self.show_class = True
        self.thresh = 0.7
        self.alpha = 0.4
        self.show_border = True
        self.border_thick = 1
        self.bbox_thick = 1
        self.font_scale = 0.35
        self.binary_masks = False

        # Define exposed options
        self.options = (
            'show_box', 'show_class', 'thresh', 'alpha', 'show_border',
            'border_thick', 'bbox_thick', 'font_scale', 'binary_masks',
            )
        # Define inputs/outputs
        self.inputs = {'input': 3}
        self.outputs = {'output': 3} 

def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=args.thresh,
            kp_thresh=args.kp_thresh,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(envu.yaml_dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    ) 

def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=0.7,
            kp_thresh=2,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(yaml.dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    ) 

def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=0.7,
            kp_thresh=2,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(yaml.dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    ) 

def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=args.thresh,
            kp_thresh=args.kp_thresh,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(envu.yaml_dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    ) 

def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=args.thresh,
            kp_thresh=args.kp_thresh,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(envu.yaml_dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    ) 

def main(args):
    logger = logging.getLogger(__name__)

    merge_cfg_from_file(args.cfg)
    cfg.NUM_GPUS = 1
    args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
    assert_and_infer_cfg(cache_urls=False)

    assert not cfg.MODEL.RPN_ONLY, \
        'RPN models are not supported'
    assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
        'Models that require precomputed proposals are not supported'

    model = infer_engine.initialize_model_from_cfg(args.weights)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()

    if os.path.isdir(args.im_or_folder):
        im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
    else:
        im_list = [args.im_or_folder]

    for i, im_name in enumerate(im_list):
        out_name = os.path.join(
            args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
        )
        logger.info('Processing {} -> {}'.format(im_name, out_name))
        im = cv2.imread(im_name)
        timers = defaultdict(Timer)
        t = time.time()
        with c2_utils.NamedCudaScope(0):
            cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
                model, im, None, timers=timers
            )
        logger.info('Inference time: {:.3f}s'.format(time.time() - t))
        for k, v in timers.items():
            logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
        if i == 0:
            logger.info(
                ' \ Note: inference on the first image will be slower than the '
                'rest (caches and auto-tuning need to warm up)'
            )

        vis_utils.vis_one_image(
            im[:, :, ::-1],  # BGR -> RGB for visualization
            im_name,
            args.output_dir,
            cls_boxes,
            cls_segms,
            cls_keyps,
            dataset=dummy_coco_dataset,
            box_alpha=0.3,
            show_class=True,
            thresh=0.7,
            kp_thresh=2,
            ext=args.output_ext,
            out_when_no_box=args.out_when_no_box
        ) 

def main(args):
    logger = logging.getLogger(__name__)
    dummy_coco_dataset = dummy_datasets.get_coco_dataset()
    cfg_orig = load_cfg(yaml.dump(cfg))
    im = cv2.imread(args.im_file)

    if args.rpn_pkl is not None:
        proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
        workspace.ResetWorkspace()
    else:
        proposal_boxes = None

    cls_boxes, cls_segms, cls_keyps = None, None, None
    for i in range(0, len(args.models_to_run), 2):
        pkl = args.models_to_run[i]
        yml = args.models_to_run[i + 1]
        cfg.immutable(False)
        merge_cfg_from_cfg(cfg_orig)
        merge_cfg_from_file(yml)
        if len(pkl) > 0:
            weights_file = pkl
        else:
            weights_file = cfg.TEST.WEIGHTS
        cfg.NUM_GPUS = 1
        assert_and_infer_cfg(cache_urls=False)
        model = model_engine.initialize_model_from_cfg(weights_file)
        with c2_utils.NamedCudaScope(0):
            cls_boxes_, cls_segms_, cls_keyps_ = \
                model_engine.im_detect_all(model, im, proposal_boxes)
        cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
        cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
        cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
        workspace.ResetWorkspace()

    out_name = os.path.join(
        args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
    )
    logger.info('Processing {} -> {}'.format(args.im_file, out_name))

    vis_utils.vis_one_image(
        im[:, :, ::-1],
        args.im_file,
        args.output_dir,
        cls_boxes,
        cls_segms,
        cls_keyps,
        dataset=dummy_coco_dataset,
        box_alpha=0.3,
        show_class=True,
        thresh=0.7,
        kp_thresh=2
    )