Python cfg.immutable方法代碼示例

# 需要導入模塊: from detectron.core.config import cfg [as 別名]
# 或者: from detectron.core.config.cfg import immutable [as 別名]
def configure_bbox_reg_weights(model, saved_cfg):
    """Compatibility for old models trained with bounding box regression
    mean/std normalization (instead of fixed weights).
    """
    if 'MODEL' not in saved_cfg or 'BBOX_REG_WEIGHTS' not in saved_cfg.MODEL:
        logger.warning('Model from weights file was trained before config key '
                       'MODEL.BBOX_REG_WEIGHTS was added. Forcing '
                       'MODEL.BBOX_REG_WEIGHTS = (1., 1., 1., 1.) to ensure '
                       'correct **inference** behavior.')
        # Generally we don't allow modifying the config, but this is a one-off
        # hack to support some very old models
        is_immutable = cfg.is_immutable()
        cfg.immutable(False)
        cfg.MODEL.BBOX_REG_WEIGHTS = (1., 1., 1., 1.)
        cfg.immutable(is_immutable)
        logger.info('New config:')
        logger.info(pprint.pformat(cfg))
        assert not model.train, (
            'This model was trained with an older version of the code that '
            'used bounding box regression mean/std normalization. It can no '
            'longer be used for training. To upgrade it to a trainable model '
            'please use fb/compat/convert_bbox_reg_normalized_model.py.'
        ) 

# 需要導入模塊: from detectron.core.config import cfg [as 別名]
# 或者: from detectron.core.config.cfg import immutable [as 別名]
def get_rpn_box_proposals(im, args):
    cfg.immutable(False)
    merge_cfg_from_file(args.rpn_cfg)
    cfg.NUM_GPUS = 1
    cfg.MODEL.RPN_ONLY = True
    cfg.TEST.RPN_PRE_NMS_TOP_N = 10000
    cfg.TEST.RPN_POST_NMS_TOP_N = 2000
    assert_and_infer_cfg(cache_urls=False)

    model = model_engine.initialize_model_from_cfg(args.rpn_pkl)
    with c2_utils.NamedCudaScope(0):
        boxes, scores = rpn_engine.im_proposals(model, im)
    return boxes, scores 

# 需要導入模塊: from detectron.core.config import cfg [as 別名]
# 或者: from detectron.core.config.cfg import immutable [as 別名]
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
    ) 

# 需要導入模塊: from detectron.core.config import cfg [as 別名]
# 或者: from detectron.core.config.cfg import immutable [as 別名]
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
    )