File size: 4,655 Bytes
bfa59ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
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}'