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import numpy as np
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import os
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import random
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import time
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import torch
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from os import path as osp
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from .dist_util import master_only
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def set_random_seed(seed):
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"""Set random seeds."""
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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def get_time_str():
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return time.strftime('%Y%m%d_%H%M%S', time.localtime())
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def mkdir_and_rename(path):
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"""mkdirs. If path exists, rename it with timestamp and create a new one.
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Args:
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path (str): Folder path.
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"""
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if osp.exists(path):
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new_name = path + '_archived_' + get_time_str()
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print(f'Path already exists. Rename it to {new_name}', flush=True)
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os.rename(path, new_name)
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os.makedirs(path, exist_ok=True)
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@master_only
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def make_exp_dirs(opt):
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"""Make dirs for experiments."""
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path_opt = opt['path'].copy()
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if opt['is_train']:
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mkdir_and_rename(path_opt.pop('experiments_root'))
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else:
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mkdir_and_rename(path_opt.pop('results_root'))
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for key, path in path_opt.items():
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if ('strict_load' in key) or ('pretrain_network' in key) or ('resume' in key) or ('param_key' in key):
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continue
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else:
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os.makedirs(path, exist_ok=True)
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def scandir(dir_path, suffix=None, recursive=False, full_path=False):
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"""Scan a directory to find the interested files.
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Args:
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dir_path (str): Path of the directory.
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suffix (str | tuple(str), optional): File suffix that we are
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interested in. Default: None.
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recursive (bool, optional): If set to True, recursively scan the
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directory. Default: False.
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full_path (bool, optional): If set to True, include the dir_path.
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Default: False.
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Returns:
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A generator for all the interested files with relative paths.
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"""
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if (suffix is not None) and not isinstance(suffix, (str, tuple)):
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raise TypeError('"suffix" must be a string or tuple of strings')
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root = dir_path
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def _scandir(dir_path, suffix, recursive):
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for entry in os.scandir(dir_path):
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if not entry.name.startswith('.') and entry.is_file():
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if full_path:
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return_path = entry.path
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else:
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return_path = osp.relpath(entry.path, root)
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if suffix is None:
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yield return_path
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elif return_path.endswith(suffix):
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yield return_path
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else:
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if recursive:
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yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
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else:
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continue
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return _scandir(dir_path, suffix=suffix, recursive=recursive)
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def check_resume(opt, resume_iter):
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"""Check resume states and pretrain_network paths.
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Args:
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opt (dict): Options.
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resume_iter (int): Resume iteration.
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"""
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if opt['path']['resume_state']:
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networks = [key for key in opt.keys() if key.startswith('network_')]
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flag_pretrain = False
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for network in networks:
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if opt['path'].get(f'pretrain_{network}') is not None:
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flag_pretrain = True
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if flag_pretrain:
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print('pretrain_network path will be ignored during resuming.')
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for network in networks:
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name = f'pretrain_{network}'
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basename = network.replace('network_', '')
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if opt['path'].get('ignore_resume_networks') is None or (network
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not in opt['path']['ignore_resume_networks']):
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opt['path'][name] = osp.join(opt['path']['models'], f'net_{basename}_{resume_iter}.pth')
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print(f"Set {name} to {opt['path'][name]}")
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param_keys = [key for key in opt['path'].keys() if key.startswith('param_key')]
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for param_key in param_keys:
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if opt['path'][param_key] == 'params_ema':
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opt['path'][param_key] = 'params'
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print(f'Set {param_key} to params')
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def sizeof_fmt(size, suffix='B'):
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"""Get human readable file size.
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Args:
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size (int): File size.
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suffix (str): Suffix. Default: 'B'.
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Return:
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str: Formatted file siz.
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"""
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for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
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if abs(size) < 1024.0:
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return f'{size:3.1f} {unit}{suffix}'
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size /= 1024.0
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return f'{size:3.1f} Y{suffix}'
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