Python datasets.get_dataset方法代码示例

# 需要导入模块: from mmdet import datasets [as 别名]
# 或者: from mmdet.datasets import get_dataset [as 别名]
def __init__(self,
                 config_file,
                 checkpoint_file):
        # init RoITransformer
        self.config_file = config_file
        self.checkpoint_file = checkpoint_file
        self.cfg = Config.fromfile(self.config_file)
        self.data_test = self.cfg.data['test']
        self.dataset = get_dataset(self.data_test)
        self.classnames = self.dataset.CLASSES
        self.model = init_detector(config_file, checkpoint_file, device='cuda:0') 

# 需要导入模块: from mmdet import datasets [as 别名]
# 或者: from mmdet.datasets import get_dataset [as 别名]
def main():
    args = parse_args()
    os.makedirs(args.output, exist_ok=True)
    cfg = Config.fromfile(args.config)
    dataset = get_dataset(cfg.data.train)
    for i in tqdm(np.random.randint(0, len(dataset), 500)):
        data = dataset[i]
        img = data['img'].data.numpy().transpose(1, 2, 0)
        masks = data['gt_masks'].data.transpose(1, 2, 0).astype(bool)
        bboxes = data['gt_bboxes'].data.numpy()
        img = mmcv.imdenormalize(img, mean=cfg.img_norm_cfg.mean, std=cfg.img_norm_cfg.std, to_bgr=False)
        img = draw_masks(img, masks).astype(np.uint8)
        draw_bounding_boxes_on_image_array(img, bboxes, use_normalized_coordinates=False, thickness=5)
        cv2.imwrite(osp.join(args.output, f'{i}_{np.random.randint(0, 10000)}.jpg'), img[..., ::-1]) 

# 需要导入模块: from mmdet import datasets [as 别名]
# 或者: from mmdet.datasets import get_dataset [as 别名]
def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            CLASSES=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

# 需要导入模块: from mmdet import datasets [as 别名]
# 或者: from mmdet.datasets import get_dataset [as 别名]
def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__,
            config=cfg.text,
            classes=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

# 需要导入模块: from mmdet import datasets [as 别名]
# 或者: from mmdet.datasets import get_dataset [as 别名]
def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from

    cfg.gpus = args.gpus
    if cfg.checkpoint_config is not None:
        # save mmdet version in checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

# 需要导入模块: from mmdet import datasets [as 别名]
# 或者: from mmdet.datasets import get_dataset [as 别名]
def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    if cfg.checkpoint_config is not None:
        # save mmdet version, config file content and class names in
        # checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text,
            classes=train_dataset.CLASSES)
    # add an attribute for visualization convenience
    model.CLASSES = train_dataset.CLASSES
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger) 

# 需要导入模块: from mmdet import datasets [as 别名]
# 或者: from mmdet.datasets import get_dataset [as 别名]
def main():
    args = parse_args()

    cfg = Config.fromfile(args.config)
    # set cudnn_benchmark
    if cfg.get('cudnn_benchmark', False):
        torch.backends.cudnn.benchmark = True
    # update configs according to CLI args
    if args.work_dir is not None:
        cfg.work_dir = args.work_dir
    if args.resume_from is not None:
        cfg.resume_from = args.resume_from
    cfg.gpus = args.gpus
    if cfg.checkpoint_config is not None:
        # save mmdet version in checkpoints as meta data
        cfg.checkpoint_config.meta = dict(
            mmdet_version=__version__, config=cfg.text)

    # init distributed env first, since logger depends on the dist info.
    if args.launcher == 'none':
        distributed = False
    else:
        distributed = True
        init_dist(args.launcher, **cfg.dist_params)

    # init logger before other steps
    logger = get_root_logger(cfg.log_level)
    logger.info('Distributed training: {}'.format(distributed))

    # set random seeds
    if args.seed is not None:
        logger.info('Set random seed to {}'.format(args.seed))
        set_random_seed(args.seed)

    model = build_detector(
        cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)

    train_dataset = get_dataset(cfg.data.train)
    train_detector(
        model,
        train_dataset,
        cfg,
        distributed=distributed,
        validate=args.validate,
        logger=logger)