LOGGER consolidation

models/common.py

@@ -3,7 +3,6 @@
 Common modules
 """
 
-import logging
 import math
 import warnings
 from copy import copy
@@ -18,12 +17,10 @@
 from torch.cuda import amp
 
 from utils.datasets import exif_transpose, letterbox
-from utils.general import colorstr, increment_path, make_divisible, non_max_suppression, scale_coords, xyxy2xywh
+from utils.general import LOGGER, colorstr, increment_path, make_divisible, non_max_suppression, scale_coords, xyxy2xywh
 from utils.plots import Annotator, colors, save_one_box
 from utils.torch_utils import time_sync
 
-LOGGER = logging.getLogger(__name__)
-
 
 def autopad(k, p=None):  # kernel, padding
     # Pad to 'same'

train.py

@@ -7,7 +7,6 @@
 """
 
 import argparse
-import logging
 import math
 import os
 import random
@@ -201,8 +200,8 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
 
     # DP mode
     if cuda and RANK == -1 and torch.cuda.device_count() > 1:
-        logging.warning('DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n'
-                        'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
+        LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
+                       'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
         model = torch.nn.DataParallel(model)
 
     # SyncBatchNorm

utils/augmentations.py

@@ -3,14 +3,13 @@
 Image augmentation functions
 """
 
-import logging
 import math
 import random
 
 import cv2
 import numpy as np
 
-from utils.general import check_version, colorstr, resample_segments, segment2box
+from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
 from utils.metrics import bbox_ioa
 
 
@@ -32,11 +31,11 @@ def __init__(self):
                 A.ImageCompression(quality_lower=75, p=0.0)],
                 bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
 
-            logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
+            LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
         except ImportError:  # package not installed, skip
             pass
         except Exception as e:
-            logging.info(colorstr('albumentations: ') + f'{e}')
+            LOGGER.info(colorstr('albumentations: ') + f'{e}')
 
     def __call__(self, im, labels, p=1.0):
         if self.transform and random.random() < p:

utils/datasets.py

@@ -6,7 +6,6 @@
 import glob
 import hashlib
 import json
-import logging
 import os
 import random
 import shutil
@@ -335,7 +334,7 @@ def update(self, i, cap, stream):
                 if success:
                     self.imgs[i] = im
                 else:
-                    LOGGER.warn('WARNING: Video stream unresponsive, please check your IP camera connection.')
+                    LOGGER.warning('WARNING: Video stream unresponsive, please check your IP camera connection.')
                     self.imgs[i] *= 0
                     cap.open(stream)  # re-open stream if signal was lost
             time.sleep(1 / self.fps[i])  # wait time
@@ -427,7 +426,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r
             d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
             tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
             if cache['msgs']:
-                logging.info('\n'.join(cache['msgs']))  # display warnings
+                LOGGER.info('\n'.join(cache['msgs']))  # display warnings
         assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {HELP_URL}'
 
         # Read cache
@@ -525,19 +524,19 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''):
 
         pbar.close()
         if msgs:
-            logging.info('\n'.join(msgs))
+            LOGGER.info('\n'.join(msgs))
         if nf == 0:
-            logging.info(f'{prefix}WARNING: No labels found in {path}. See {HELP_URL}')
+            LOGGER.warning(f'{prefix}WARNING: No labels found in {path}. See {HELP_URL}')
         x['hash'] = get_hash(self.label_files + self.img_files)
         x['results'] = nf, nm, ne, nc, len(self.img_files)
         x['msgs'] = msgs  # warnings
         x['version'] = self.cache_version  # cache version
         try:
             np.save(path, x)  # save cache for next time
             path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
-            logging.info(f'{prefix}New cache created: {path}')
+            LOGGER.info(f'{prefix}New cache created: {path}')
         except Exception as e:
-            logging.info(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}')  # path not writeable
+            LOGGER.warning(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}')  # not writeable
         return x
 
     def __len__(self):

utils/general.py

@@ -45,7 +45,7 @@
 def set_logging(name=None, verbose=True):
     # Sets level and returns logger
     rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
-    logging.basicConfig(format="%(message)s", level=logging.INFO if (verbose and rank in (-1, 0)) else logging.WARN)
+    logging.basicConfig(format="%(message)s", level=logging.INFO if (verbose and rank in (-1, 0)) else logging.WARNING)
     return logging.getLogger(name)
 
 

utils/torch_utils.py

@@ -4,7 +4,6 @@
 """
 
 import datetime
-import logging
 import math
 import os
 import platform
@@ -100,7 +99,6 @@ def profile(input, ops, n=10, device=None):
     #     profile(input, [m1, m2], n=100)  # profile over 100 iterations
 
     results = []
-    logging.basicConfig(format="%(message)s", level=logging.INFO)
     device = device or select_device()
     print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
           f"{'input':>24s}{'output':>24s}")