File size: 7,150 Bytes
06f26d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
import datetime
import logging
import time

from .dist_util import get_dist_info, master_only

initialized_logger = {}


class AvgTimer():

    def __init__(self, window=200):
        self.window = window  # average window
        self.current_time = 0
        self.total_time = 0
        self.count = 0
        self.avg_time = 0
        self.start()

    def start(self):
        self.start_time = self.tic = time.time()

    def record(self):
        self.count += 1
        self.toc = time.time()
        self.current_time = self.toc - self.tic
        self.total_time += self.current_time
        # calculate average time
        self.avg_time = self.total_time / self.count

        # reset
        if self.count > self.window:
            self.count = 0
            self.total_time = 0

        self.tic = time.time()

    def get_current_time(self):
        return self.current_time

    def get_avg_time(self):
        return self.avg_time


class MessageLogger():
    """Message logger for printing.

    Args:
        opt (dict): Config. It contains the following keys:
            name (str): Exp name.
            logger (dict): Contains 'print_freq' (str) for logger interval.
            train (dict): Contains 'total_iter' (int) for total iters.
            use_tb_logger (bool): Use tensorboard logger.
        start_iter (int): Start iter. Default: 1.
        tb_logger (obj:`tb_logger`): Tensorboard logger. Default: None.
    """

    def __init__(self, opt, start_iter=1, tb_logger=None):
        self.exp_name = opt['name']
        self.interval = opt['logger']['print_freq']
        self.start_iter = start_iter
        self.max_iters = opt['train']['total_iter']
        self.use_tb_logger = opt['logger']['use_tb_logger']
        self.tb_logger = tb_logger
        self.start_time = time.time()
        self.logger = get_root_logger()

    def reset_start_time(self):
        self.start_time = time.time()

    @master_only
    def __call__(self, log_vars):
        """Format logging message.

        Args:
            log_vars (dict): It contains the following keys:
                epoch (int): Epoch number.
                iter (int): Current iter.
                lrs (list): List for learning rates.

                time (float): Iter time.
                data_time (float): Data time for each iter.
        """
        # epoch, iter, learning rates
        epoch = log_vars.pop('epoch')
        current_iter = log_vars.pop('iter')
        lrs = log_vars.pop('lrs')

        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, iter:{current_iter:8,d}, lr:(')
        for v in lrs:
            message += f'{v:.3e},'
        message += ')] '

        # time and estimated time
        if 'time' in log_vars.keys():
            iter_time = log_vars.pop('time')
            data_time = log_vars.pop('data_time')

            total_time = time.time() - self.start_time
            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
            message += f'[eta: {eta_str}, '
            message += f'time (data): {iter_time:.3f} ({data_time:.3f})] '

        # other items, especially losses
        for k, v in log_vars.items():
            message += f'{k}: {v:.4e} '
            # tensorboard logger
            if self.use_tb_logger and 'debug' not in self.exp_name:
                if k.startswith('l_'):
                    self.tb_logger.add_scalar(f'losses/{k}', v, current_iter)
                else:
                    self.tb_logger.add_scalar(k, v, current_iter)
        self.logger.info(message)


@master_only
def init_tb_logger(log_dir):
    from torch.utils.tensorboard import SummaryWriter
    tb_logger = SummaryWriter(log_dir=log_dir)
    return tb_logger


@master_only
def init_wandb_logger(opt):
    """We now only use wandb to sync tensorboard log."""
    import wandb
    logger = get_root_logger()

    project = opt['logger']['wandb']['project']
    resume_id = opt['logger']['wandb'].get('resume_id')
    if resume_id:
        wandb_id = resume_id
        resume = 'allow'
        logger.warning(f'Resume wandb logger with id={wandb_id}.')
    else:
        wandb_id = wandb.util.generate_id()
        resume = 'never'

    wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True)

    logger.info(f'Use wandb logger with id={wandb_id}; project={project}.')


def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
    """Get the root logger.

    The logger will be initialized if it has not been initialized. By default a
    StreamHandler will be added. If `log_file` is specified, a FileHandler will
    also be added.

    Args:
        logger_name (str): root logger name. Default: 'basicsr'.
        log_file (str | None): The log filename. If specified, a FileHandler
            will be added to the root logger.
        log_level (int): The root logger level. Note that only the process of
            rank 0 is affected, while other processes will set the level to
            "Error" and be silent most of the time.

    Returns:
        logging.Logger: The root logger.
    """
    logger = logging.getLogger(logger_name)
    # if the logger has been initialized, just return it
    if logger_name in initialized_logger:
        return logger

    format_str = '%(asctime)s %(levelname)s: %(message)s'
    stream_handler = logging.StreamHandler()
    stream_handler.setFormatter(logging.Formatter(format_str))
    logger.addHandler(stream_handler)
    logger.propagate = False
    rank, _ = get_dist_info()
    if rank != 0:
        logger.setLevel('ERROR')
    elif log_file is not None:
        logger.setLevel(log_level)
        # add file handler
        file_handler = logging.FileHandler(log_file, 'w')
        file_handler.setFormatter(logging.Formatter(format_str))
        file_handler.setLevel(log_level)
        logger.addHandler(file_handler)
    initialized_logger[logger_name] = True
    return logger


def get_env_info():
    """Get environment information.

    Currently, only log the software version.
    """
    import torch
    import torchvision

    from basicsr.version import __version__
    msg = r"""
                ____                _       _____  ____
               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
            /_____/ \__,_//____//_/ \___//____//_/ |_|
     ______                   __   __                 __      __
    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
    """
    msg += ('\nVersion Information: '
            f'\n\tBasicSR: {__version__}'
            f'\n\tPyTorch: {torch.__version__}'
            f'\n\tTorchVision: {torchvision.__version__}')
    return msg