evp / depth /utils_depth /logging.py
nick_93
init
bcec54e
# ------------------------------------------------------------------------------
# The code is from GLPDepth (https://github.com/vinvino02/GLPDepth).
# For non-commercial purpose only (research, evaluation etc).
# ------------------------------------------------------------------------------
import os
import cv2
import sys
import time
import numpy as np
import torch
TOTAL_BAR_LENGTH = 30.
last_time = time.time()
begin_time = last_time
def progress_bar(current, total, epochs, cur_epoch, msg=None):
_, term_width = os.popen('stty size', 'r').read().split()
term_width = int(term_width)
global last_time, begin_time
if current == 0:
begin_time = time.time() # Reset for new bar.
cur_len = int(TOTAL_BAR_LENGTH * current / total)
rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1
sys.stdout.write(' [')
for i in range(cur_len):
sys.stdout.write('=')
sys.stdout.write('>')
for i in range(rest_len):
sys.stdout.write('.')
sys.stdout.write(']')
cur_time = time.time()
step_time = cur_time - last_time
last_time = cur_time
tot_time = cur_time - begin_time
remain_time = step_time * (total - current) + \
(epochs - cur_epoch) * step_time * total
L = []
L.append(' Step: %s' % format_time(step_time))
L.append(' | Tot: %s' % format_time(tot_time))
L.append(' | Rem: %s' % format_time(remain_time))
if msg:
L.append(' | ' + msg)
msg = ''.join(L)
sys.stdout.write(msg)
for i in range(157 - int(TOTAL_BAR_LENGTH) - len(msg) - 3):
sys.stdout.write(' ')
# Go back to the center of the bar.
for i in range(157 - int(TOTAL_BAR_LENGTH / 2) + 2):
sys.stdout.write('\b')
sys.stdout.write(' %d/%d ' % (current + 1, total))
if current < total - 1:
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
sys.stdout.flush()
class AverageMeter():
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def format_time(seconds):
days = int(seconds / 3600 / 24)
seconds = seconds - days * 3600 * 24
hours = int(seconds / 3600)
seconds = seconds - hours * 3600
minutes = int(seconds / 60)
seconds = seconds - minutes * 60
secondsf = int(seconds)
seconds = seconds - secondsf
millis = int(seconds * 1000)
f = ''
i = 1
if days > 0:
f += str(days) + 'D'
i += 1
if hours > 0 and i <= 2:
f += str(hours) + 'h'
i += 1
if minutes > 0 and i <= 2:
f += str(minutes).zfill(2) + 'm'
i += 1
if secondsf > 0 and i <= 2:
f += str(secondsf).zfill(2) + 's'
i += 1
if millis > 0 and i <= 2:
f += str(millis).zfill(3) + 'ms'
i += 1
if f == '':
f = '0ms'
return f
def display_result(result_dict):
line = "\n"
line += "=" * 100 + '\n'
for metric, value in result_dict.items():
line += "{:>10} ".format(metric)
line += "\n"
for metric, value in result_dict.items():
line += "{:10.4f} ".format(value)
line += "\n"
line += "=" * 100 + '\n'
return line
def save_images(pred, save_path):
if len(pred.shape) > 3:
pred = pred.squeeze()
if isinstance(pred, torch.Tensor):
pred = pred.cpu().numpy().astype(np.uint8)
if pred.shape[0] < 4:
pred = np.transpose(pred, (1, 2, 0))
cv2.imwrite(save_path, pred, [cv2.IMWRITE_PNG_COMPRESSION, 0])
def check_and_make_dirs(paths):
if not isinstance(paths, list):
paths = [paths]
for path in paths:
if not os.path.exists(path):
os.makedirs(path)
def log_args_to_txt(log_txt, args):
if not os.path.exists(log_txt):
with open(log_txt, 'w') as txtfile:
args_ = vars(args)
args_str = ''
for k, v in args_.items():
args_str = args_str + str(k) + ':' + str(v) + ',\t\n'
txtfile.write(args_str + '\n')