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import logging
import os
import random
import sys
import time
from shutil import get_terminal_size
import numpy as np
import torch
logger = logging.getLogger('base')
def make_exp_dirs(opt):
"""Make dirs for experiments."""
path_opt = opt['path'].copy()
if opt['is_train']:
overwrite = True if 'debug' in opt['name'] else False
os.makedirs(path_opt.pop('experiments_root'), exist_ok=overwrite)
os.makedirs(path_opt.pop('models'), exist_ok=overwrite)
else:
os.makedirs(path_opt.pop('results_root'))
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)
class ProgressBar(object):
"""A progress bar which can print the progress.
Modified from:
https://github.com/hellock/cvbase/blob/master/cvbase/progress.py
"""
def __init__(self, task_num=0, bar_width=50, start=True):
self.task_num = task_num
max_bar_width = self._get_max_bar_width()
self.bar_width = (
bar_width if bar_width <= max_bar_width else max_bar_width)
self.completed = 0
if start:
self.start()
def _get_max_bar_width(self):
terminal_width, _ = get_terminal_size()
max_bar_width = min(int(terminal_width * 0.6), terminal_width - 50)
if max_bar_width < 10:
print(f'terminal width is too small ({terminal_width}), '
'please consider widen the terminal for better '
'progressbar visualization')
max_bar_width = 10
return max_bar_width
def start(self):
if self.task_num > 0:
sys.stdout.write(f"[{' ' * self.bar_width}] 0/{self.task_num}, "
f'elapsed: 0s, ETA:\nStart...\n')
else:
sys.stdout.write('completed: 0, elapsed: 0s')
sys.stdout.flush()
self.start_time = time.time()
def update(self, msg='In progress...'):
self.completed += 1
elapsed = time.time() - self.start_time
fps = self.completed / elapsed
if self.task_num > 0:
percentage = self.completed / float(self.task_num)
eta = int(elapsed * (1 - percentage) / percentage + 0.5)
mark_width = int(self.bar_width * percentage)
bar_chars = '>' * mark_width + '-' * (self.bar_width - mark_width)
sys.stdout.write('\033[2F') # cursor up 2 lines
sys.stdout.write(
'\033[J'
) # clean the output (remove extra chars since last display)
sys.stdout.write(
f'[{bar_chars}] {self.completed}/{self.task_num}, '
f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, '
f'ETA: {eta:5}s\n{msg}\n')
else:
sys.stdout.write(
f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s, '
f'{fps:.1f} tasks/s')
sys.stdout.flush()
class AverageMeter(object):
"""
Computes and stores the average and current value
Imported from
https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0 # running average = running sum / running count
self.sum = 0 # running sum
self.count = 0 # running count
def update(self, val, n=1):
# n = batch_size
# val = batch accuracy for an attribute
# self.val = val
# sum = 100 * accumulative correct predictions for this attribute
self.sum += val * n
# count = total samples so far
self.count += n
# avg = 100 * avg accuracy for this attribute
# for all the batches so far
self.avg = self.sum / self.count
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