Python utils.read_json方法代碼示例

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def setup_logging(save_dir, log_config='logger/logger_config.json', default_level=logging.INFO):
    """
    Setup logging configuration
    """
    log_config = Path(log_config)
    if log_config.is_file():
        config = read_json(log_config)
        # modify logging paths based on run config
        for _, handler in config['handlers'].items():
            if 'filename' in handler:
                handler['filename'] = str(save_dir / handler['filename'])

        logging.config.dictConfig(config)
    else:
        print("Warning: logging configuration file is not found in {}.".format(log_config))
        logging.basicConfig(level=default_level) 

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def __init__(self, acc_model, don_model, features_path=None):
        self.don_model = joblib.load(don_model)
        self.acc_model = joblib.load(acc_model)
        if features_path is None:
            features_path = os.path.join(this_dir, "../features.json")
        self.features_metadata = read_json(features_path)
        # acceptor and donor site indexes are unified across SOI
        # NB! This indexes are pos=1 of the region, and index-1 is already pos=-1, not 0!
        self.don_i = 3
        self.acc_i = -21
        self.labranchor = kipoi.get_model("labranchor", with_dataloader=False)
        # add current dir to python path for multiprocessing
        sys.path.append(this_dir) 

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def __init__(self, acc_model, don_model, features_path=None):
        self.don_model = joblib.load(don_model)
        self.acc_model = joblib.load(acc_model)
        if features_path is None:
            features_path = os.path.join(this_dir, "../features.json")
        self.features_metadata = read_json(features_path)
        # acceptor and donor site indexes are unified across SOI
        # NB! This indexes are pos=1 of the region, and index-1 is already pos=-1, not 0!
        self.don_i = 3
        self.acc_i = -21
        # add current dir to python path for multiprocessing
        sys.path.append(this_dir) 

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def from_args(cls, args, options=''):
        """
        Initialize this class from some cli arguments. Used in train, test.
        """
        for opt in options:
            args.add_argument(*opt.flags, default=None, type=opt.type)
        if not isinstance(args, tuple):
            args = args.parse_args()

        if args.device is not None:
            os.environ["CUDA_VISIBLE_DEVICES"] = args.device
        if args.resume is not None:
            resume = Path(args.resume)
            cfg_fname = resume.parent / 'config.json'
        else:
            msg_no_cfg = "Configuration file need to be specified. Add '-c config.json', for example."
            assert args.config is not None, msg_no_cfg
            resume = None
            cfg_fname = Path(args.config)
        
        config = read_json(cfg_fname)
        if args.config and resume:
            # update new config for fine-tuning
            config.update(read_json(args.config))

        # parse custom cli options into dictionary
        modification = {opt.target : getattr(args, _get_opt_name(opt.flags)) for opt in options}
        return cls(config, resume, modification) 

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def __init__(self, root='data', split_id=0, **kwargs):
        self.dataset_dir = osp.join(root, self.dataset_dir)
        self.dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
        self.cam_a_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
        self.cam_b_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
        self.split_path = osp.join(self.dataset_dir, 'splits.json')

        self._download_data()
        self._check_before_run()
        
        self._prepare_split()
        splits = read_json(self.split_path)
        if split_id >= len(splits):
            raise ValueError("split_id exceeds range, received {}, but expected between 0 and {}".format(split_id, len(splits)-1))
        split = splits[split_id]

        train = split['train']
        query = split['query'] # query and gallery share the same images
        gallery = split['gallery']

        train = [tuple(item) for item in train]
        query = [tuple(item) for item in query]
        gallery = [tuple(item) for item in gallery]
        
        num_train_pids = split['num_train_pids']
        num_query_pids = split['num_query_pids']
        num_gallery_pids = split['num_gallery_pids']
        
        num_train_imgs = len(train)
        num_query_imgs = len(query)
        num_gallery_imgs = len(gallery)

        num_total_pids = num_train_pids + num_query_pids
        num_total_imgs = num_train_imgs + num_query_imgs

        print("=> VIPeR loaded")
        print("Dataset statistics:")
        print("  ------------------------------")
        print("  subset   | # ids | # images")
        print("  ------------------------------")
        print("  train    | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
        print("  query    | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
        print("  gallery  | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
        print("  ------------------------------")
        print("  total    | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
        print("  ------------------------------")

        self.train = train
        self.query = query
        self.gallery = gallery

        self.num_train_pids = num_train_pids
        self.num_query_pids = num_query_pids
        self.num_gallery_pids = num_gallery_pids 

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def _process_dir(self, dir_path, json_path, relabel):
        if osp.exists(json_path):
            print("=> {} generated before, awesome!".format(json_path))
            split = read_json(json_path)
            return split['tracklets'], split['num_tracklets'], split['num_pids'], split['num_imgs_per_tracklet']

        print("=> Automatically generating split (might take a while for the first time, have a coffe)")
        pdirs = glob.glob(osp.join(dir_path, '*')) # avoid .DS_Store
        print("Processing {} with {} person identities".format(dir_path, len(pdirs)))

        pid_container = set()
        for pdir in pdirs:
            pid = int(osp.basename(pdir))
            pid_container.add(pid)
        pid2label = {pid:label for label, pid in enumerate(pid_container)}

        tracklets = []
        num_imgs_per_tracklet = []
        for pdir in pdirs:
            pid = int(osp.basename(pdir))
            if relabel: pid = pid2label[pid]
            tdirs = glob.glob(osp.join(pdir, '*'))
            for tdir in tdirs:
                raw_img_paths = glob.glob(osp.join(tdir, '*.jpg'))
                num_imgs = len(raw_img_paths)

                if num_imgs < self.min_seq_len:
                    continue

                num_imgs_per_tracklet.append(num_imgs)
                img_paths = []
                for img_idx in range(num_imgs):
                    # some tracklet starts from 0002 instead of 0001
                    img_idx_name = 'F' + str(img_idx+1).zfill(4)
                    res = glob.glob(osp.join(tdir, '*' + img_idx_name + '*.jpg'))
                    if len(res) == 0:
                        print("Warn: index name {} in {} is missing, jump to next".format(img_idx_name, tdir))
                        continue
                    img_paths.append(res[0])
                img_name = osp.basename(img_paths[0])
                camid = int(img_name[6]) - 1 # index-0
                img_paths = tuple(img_paths)
                tracklets.append((img_paths, pid, camid))

        num_pids = len(pid_container)
        num_tracklets = len(tracklets)

        print("Saving split to {}".format(json_path))
        split_dict = {
            'tracklets': tracklets,
            'num_tracklets': num_tracklets,
            'num_pids': num_pids,
            'num_imgs_per_tracklet': num_imgs_per_tracklet,
        }
        write_json(split_dict, json_path)

        return tracklets, num_tracklets, num_pids, num_imgs_per_tracklet 

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def __init__(self, args, options='', timestamp=True):
        # parse default and custom cli options
        for opt in options:
            args.add_argument(*opt.flags, default=None, type=opt.type)
        args = args.parse_args()

        if args.device:
            os.environ["CUDA_VISIBLE_DEVICES"] = args.device
        if args.resume:
            self.resume = Path(args.resume)
            self.cfg_fname = self.resume.parent / 'config.json'
        else:
            msg_no_cfg = "Configuration file need to be specified. Add '-c config.json', for example."
            assert args.config is not None, msg_no_cfg
            self.resume = None
            self.cfg_fname = Path(args.config)

        # load config file and apply custom cli options
        config = read_json(self.cfg_fname)
        self.__config = _update_config(config, options, args)

        # set save_dir where trained model and log will be saved.
        save_dir = Path(self.config['trainer']['save_dir'])
        timestamp = datetime.now().strftime(r'%m%d_%H%M%S') if timestamp else ''

        exper_name = self.config['name']
        self.__save_dir = save_dir / 'models' / exper_name / timestamp
        self.__log_dir = save_dir / 'log' / exper_name / timestamp

        self.save_dir.mkdir(parents=True, exist_ok=True)
        self.log_dir.mkdir(parents=True, exist_ok=True)

        # save updated config file to the checkpoint dir
        write_json(self.config, self.save_dir / 'config.json')

        # configure logging module
        setup_logging(self.log_dir)
        self.log_levels = {
            0: logging.WARNING,
            1: logging.INFO,
            2: logging.DEBUG
        } 

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import read_json [as 別名]
def __init__(self, split_id=0):
        self._download_data()
        self._check_before_run()

        self._prepare_split()
        splits = read_json(self.split_path)
        if split_id >= len(splits):
            raise ValueError("split_id exceeds range, received {}, but expected between 0 and {}".format(split_id, len(splits)-1))
        split = splits[split_id]
        train_dirs, test_dirs = split['train'], split['test']
        print("# train identites: {}, # test identites {}".format(len(train_dirs), len(test_dirs)))

        train, num_train_tracklets, num_train_pids, num_imgs_train = \
          self._process_data(train_dirs, cam1=True, cam2=True)
        query, num_query_tracklets, num_query_pids, num_imgs_query = \
          self._process_data(test_dirs, cam1=True, cam2=False)
        gallery, num_gallery_tracklets, num_gallery_pids, num_imgs_gallery = \
          self._process_data(test_dirs, cam1=False, cam2=True)

        num_imgs_per_tracklet = num_imgs_train + num_imgs_query + num_imgs_gallery
        min_num = np.min(num_imgs_per_tracklet)
        max_num = np.max(num_imgs_per_tracklet)
        avg_num = np.mean(num_imgs_per_tracklet)

        num_total_pids = num_train_pids + num_query_pids
        num_total_tracklets = num_train_tracklets + num_query_tracklets + num_gallery_tracklets

        print("=> iLIDS-VID loaded")
        print("Dataset statistics:")
        print("  ------------------------------")
        print("  subset   | # ids | # tracklets")
        print("  ------------------------------")
        print("  train    | {:5d} | {:8d}".format(num_train_pids, num_train_tracklets))
        print("  query    | {:5d} | {:8d}".format(num_query_pids, num_query_tracklets))
        print("  gallery  | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_tracklets))
        print("  ------------------------------")
        print("  total    | {:5d} | {:8d}".format(num_total_pids, num_total_tracklets))
        print("  number of images per tracklet: {} ~ {}, average {:.1f}".format(min_num, max_num, avg_num))
        print("  ------------------------------")

        self.train = train
        self.query = query
        self.gallery = gallery

        self.num_train_pids = num_train_pids
        self.num_query_pids = num_query_pids
        self.num_gallery_pids = num_gallery_pids