Source code for pyiqa.utils.misc

import numpy as np
import os
import random
import time
import torch
from os import path as osp

from .dist_util import master_only




def set_random_seed(seed=123):
    """Set random seeds."""
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True





def get_time_str():
    return time.strftime('%Y%m%d_%H%M%S', time.localtime())





def mkdir_and_rename(path):
    """mkdirs. If path exists, rename it with timestamp, create a new one, and move it to archive folder.

    Args:
        path (str): Folder path.
    """
    if osp.exists(path):
        new_name = path + '_archived_' + get_time_str()
        new_name = new_name.replace('tb_logger', 'tb_logger_archived')
        print(f'Path already exists. Rename it to {new_name}', flush=True)
        os.rename(path, new_name)
    os.makedirs(path, exist_ok=True)



@master_only


def make_exp_dirs(opt):
    """Make dirs for experiments."""
    path_opt = opt['path'].copy()
    if opt['is_train']:
        mkdir_and_rename(path_opt.pop('experiments_root'))
    else:
        mkdir_and_rename(path_opt.pop('results_root'))
    for key, path in path_opt.items():
        if (
            ('strict_load' in key)
            or ('pretrain_network' in key)
            or ('resume' in key)
            or ('param_key' in key)
        ):
            continue
        else:
            os.makedirs(path, exist_ok=True)





def scandir(dir_path, suffix=None, recursive=False, full_path=False):
    """Scan a directory to find the interested files.

    Args:
        dir_path (str): Path of the directory.
        suffix (str | tuple(str), optional): File suffix that we are
            interested in. Default: None.
        recursive (bool, optional): If set to True, recursively scan the
            directory. Default: False.
        full_path (bool, optional): If set to True, include the dir_path.
            Default: False.

    Returns:
        A generator for all the interested files with relative paths.
    """

    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
        raise TypeError('"suffix" must be a string or tuple of strings')

    root = dir_path

    def _scandir(dir_path, suffix, recursive):
        for entry in os.scandir(dir_path):
            if not entry.name.startswith('.') and entry.is_file():
                if full_path:
                    return_path = entry.path
                else:
                    return_path = osp.relpath(entry.path, root)

                if suffix is None:
                    yield return_path
                elif return_path.endswith(suffix):
                    yield return_path
            else:
                if recursive:
                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
                else:
                    continue

    return _scandir(dir_path, suffix=suffix, recursive=recursive)





def check_resume(opt, resume_iter):
    """Check resume states and pretrain_network paths.

    Args:
        opt (dict): Options.
        resume_iter (int): Resume iteration.
    """
    if opt['path']['resume_state']:
        # get all the networks
        networks = [key for key in opt.keys() if key.startswith('network_')]
        flag_pretrain = False
        for network in networks:
            if opt['path'].get(f'pretrain_{network}') is not None:
                flag_pretrain = True
        if flag_pretrain:
            print('pretrain_network path will be ignored during resuming.')
        # set pretrained model paths
        for network in networks:
            name = f'pretrain_{network}'
            basename = network.replace('network_', '')
            if opt['path'].get('ignore_resume_networks') is None or (
                network not in opt['path']['ignore_resume_networks']
            ):
                opt['path'][name] = osp.join(
                    opt['path']['models'], f'net_{basename}_{resume_iter}.pth'
                )
                print(f'Set {name} to {opt["path"][name]}')

        # change param_key to params in resume
        param_keys = [key for key in opt['path'].keys() if key.startswith('param_key')]
        for param_key in param_keys:
            if opt['path'][param_key] == 'params_ema':
                opt['path'][param_key] = 'params'
                print(f'Set {param_key} to params')





def sizeof_fmt(size, suffix='B'):
    """Get human readable file size.

    Args:
        size (int): File size.
        suffix (str): Suffix. Default: 'B'.

    Return:
        str: Formatted file size.
    """
    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
        if abs(size) < 1024.0:
            return f'{size:3.1f} {unit}{suffix}'
        size /= 1024.0
    return f'{size:3.1f} Y{suffix}'