Fix logging naming bug when doing dataset split

.gitignore

@@ -4,3 +4,6 @@ ckpt/
 logs/
 results/
 __pycache__/
+.DS_Store
+
+datasets/

code/utils/logging.py → code/utils/logging_utils.py

@@ -1,156 +1,156 @@
-import os
-import cv2
-import sys
-import time
-import numpy as np
-
-import torch
-
-_, term_width = os.popen('stty size', 'r').read().split()
-term_width = int(term_width)
-
-TOTAL_BAR_LENGTH = 30.
-last_time = time.time()
-begin_time = last_time
-
-
-def progress_bar(current, total, epochs, cur_epoch, msg=None):
-    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(term_width - int(TOTAL_BAR_LENGTH) - len(msg) - 3):
-        sys.stdout.write(' ')
-
-    # Go back to the center of the bar.
-    for i in range(term_width - 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'
+import os
+import cv2
+import sys
+import time
+import numpy as np
+
+import torch
+
+_, term_width = os.popen('stty size', 'r').read().split()
+term_width = int(term_width)
+
+TOTAL_BAR_LENGTH = 30.
+last_time = time.time()
+begin_time = last_time
+
+
+def progress_bar(current, total, epochs, cur_epoch, msg=None):
+    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(term_width - int(TOTAL_BAR_LENGTH) - len(msg) - 3):
+        sys.stdout.write(' ')
+
+    # Go back to the center of the bar.
+    for i in range(term_width - 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')