DBNet / DB /concern /log.py
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import os
import logging
import functools
import json
import time
from datetime import datetime
from tensorboardX import SummaryWriter
import yaml
import cv2
import numpy as np
from concern.config import Configurable, State
class Logger(Configurable):
SUMMARY_DIR_NAME = 'summaries'
VISUALIZE_NAME = 'visualize'
LOG_FILE_NAME = 'output.log'
ARGS_FILE_NAME = 'args.log'
METRICS_FILE_NAME = 'metrics.log'
database_dir = State(default='./outputs/')
log_dir = State(default='workspace')
verbose = State(default=False)
level = State(default='info')
log_interval = State(default=100)
def __init__(self, **kwargs):
self.load_all(**kwargs)
self._make_storage()
cmd = kwargs['cmd']
self.name = cmd['name']
self.log_dir = os.path.join(self.log_dir, self.name)
try:
self.verbose = cmd['verbose']
except:
print('verbose:', self.verbose)
if self.verbose:
print('Initializing log dir for', self.log_dir)
if not os.path.exists(self.log_dir):
os.makedirs(self.log_dir)
self.message_logger = self._init_message_logger()
summary_path = os.path.join(self.log_dir, self.SUMMARY_DIR_NAME)
self.tf_board_logger = SummaryWriter(summary_path)
self.metrics_writer = open(os.path.join(
self.log_dir, self.METRICS_FILE_NAME), 'at')
self.timestamp = time.time()
self.logged = -1
self.speed = None
self.eta_time = None
def _make_storage(self):
application = os.path.basename(os.getcwd())
storage_dir = os.path.join(
self.database_dir, self.log_dir, application)
if not os.path.exists(storage_dir):
os.makedirs(storage_dir)
if not os.path.exists(self.log_dir):
os.symlink(storage_dir, self.log_dir)
def save_dir(self, dir_name):
return os.path.join(self.log_dir, dir_name)
def _init_message_logger(self):
message_logger = logging.getLogger('messages')
message_logger.setLevel(
logging.DEBUG if self.verbose else logging.INFO)
formatter = logging.Formatter(
'[%(levelname)s] [%(asctime)s] %(message)s')
std_handler = logging.StreamHandler()
std_handler.setLevel(message_logger.level)
std_handler.setFormatter(formatter)
file_handler = logging.FileHandler(
os.path.join(self.log_dir, self.LOG_FILE_NAME))
file_handler.setLevel(message_logger.level)
file_handler.setFormatter(formatter)
message_logger.addHandler(std_handler)
message_logger.addHandler(file_handler)
return message_logger
def report_time(self, name: str):
if self.verbose:
self.info(name + " time :" + str(time.time() - self.timestamp))
self.timestamp = time.time()
def report_eta(self, steps, total, epoch):
self.logged = self.logged % total + 1
steps = steps % total
if self.eta_time is None:
self.eta_time = time.time()
speed = -1
else:
eta_time = time.time()
speed = eta_time - self.eta_time
if self.speed is not None:
speed = ((self.logged - 1) * self.speed + speed) / self.logged
self.speed = speed
self.eta_time = eta_time
seconds = (total - steps) * speed
hours = seconds // 3600
minutes = (seconds - (hours * 3600)) // 60
seconds = seconds % 60
print('%d/%d batches processed in epoch %d, ETA: %2d:%2d:%2d' %
(steps, total, epoch,
hours, minutes, seconds), end='\r')
def args(self, parameters=None):
if parameters is None:
with open(os.path.join(self.log_dir, self.ARGS_FILE_NAME), 'rt') as reader:
return yaml.load(reader.read())
with open(os.path.join(self.log_dir, self.ARGS_FILE_NAME), 'wt') as writer:
yaml.dump(parameters.dump(), writer)
def metrics(self, epoch, steps, metrics_dict):
results = {}
for name, a in metrics_dict.items():
results[name] = {'count': a.count, 'value': float(a.avg)}
self.add_scalar('metrics/' + name, a.avg, steps)
result_dict = {
str(datetime.now()): {
'epoch': epoch,
'steps': steps,
**results
}
}
string_result = yaml.dump(result_dict)
self.info(string_result)
self.metrics_writer.write(string_result)
self.metrics_writer.flush()
def named_number(self, name, num=None, default=0):
if num is None:
return int(self.has_signal(name)) or default
else:
with open(os.path.join(self.log_dir, name), 'w') as writer:
writer.write(str(num))
return num
epoch = functools.partialmethod(named_number, 'epoch')
iter = functools.partialmethod(named_number, 'iter')
def message(self, level, content):
self.message_logger.__getattribute__(level)(content)
def images(self, prefix, image_dict, step):
for name, image in image_dict.items():
self.add_image(prefix + '/' + name, image, step, dataformats='HWC')
def merge_save_images(self, name, images):
for i, image in enumerate(images):
if i == 0:
result = image
else:
result = np.concatenate([result, image], 0)
cv2.imwrite(os.path.join(self.vis_dir(), name+'.jpg'), result)
def vis_dir(self):
vis_dir = os.path.join(self.log_dir, self.VISUALIZE_NAME)
if not os.path.exists(vis_dir):
os.mkdir(vis_dir)
return vis_dir
def save_image_dict(self, images, max_size=1024):
for file_name, image in images.items():
height, width = image.shape[:2]
if height > width:
actual_height = min(height, max_size)
actual_width = int(round(actual_height * width / height))
else:
actual_width = min(width, max_size)
actual_height = int(round(actual_width * height / width))
image = cv2.resize(image, (actual_width, actual_height))
cv2.imwrite(os.path.join(self.vis_dir(), file_name+'.jpg'), image)
def __getattr__(self, name):
message_levels = set(['debug', 'info', 'warning', 'error', 'critical'])
if name == '__setstate__':
raise AttributeError('haha')
if name in message_levels:
return functools.partial(self.message, name)
elif hasattr(self.__dict__.get('tf_board_logger'), name):
return self.tf_board_logger.__getattribute__(name)
else:
super()