manavisrani07's picture
Upload 319 files
6c343a2
raw
history blame
No virus
4.66 kB
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):
"""Set random seeds."""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
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 and create a new one.
Args:
path (str): Folder path.
"""
if osp.exists(path):
new_name = path + '_archived_' + get_time_str()
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}'