emoji-safe default logging

utils/autoanchor.py

@@ -10,7 +10,7 @@
 import yaml
 from tqdm import tqdm
 
-from utils.general import LOGGER, colorstr, emojis
+from utils.general import LOGGER, colorstr
 
 PREFIX = colorstr('AutoAnchor: ')
 
@@ -45,9 +45,9 @@ def metric(k):  # compute metric
     bpr, aat = metric(anchors.cpu().view(-1, 2))
     s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
     if bpr > 0.98:  # threshold to recompute
-        LOGGER.info(emojis(f'{s}Current anchors are a good fit to dataset ✅'))
+        LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅')
     else:
-        LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...'))
+        LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...')
         na = m.anchors.numel() // 2  # number of anchors
         try:
             anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
@@ -62,7 +62,7 @@ def metric(k):  # compute metric
             s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)'
         else:
             s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)'
-        LOGGER.info(emojis(s))
+        LOGGER.info(s)
 
 
 def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):

utils/autobatch.py

@@ -8,7 +8,7 @@
 import numpy as np
 import torch
 
-from utils.general import LOGGER, colorstr, emojis
+from utils.general import LOGGER, colorstr
 from utils.torch_utils import profile
 
 
@@ -62,5 +62,5 @@ def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
             b = batch_sizes[max(i - 1, 0)]  # select prior safe point
 
     fraction = np.polyval(p, b) / t  # actual fraction predicted
-    LOGGER.info(emojis(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅'))
+    LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
     return b

utils/general.py

@@ -97,6 +97,8 @@ def set_logging(name=None, verbose=VERBOSE):
 
 set_logging()  # run before defining LOGGER
 LOGGER = logging.getLogger("yolov5")  # define globally (used in train.py, val.py, detect.py, etc.)
+for fn in LOGGER.info, LOGGER.warning:
+    _fn, fn = fn, lambda x: _fn(emojis(x))  # emoji safe logging
 
 
 def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
@@ -334,7 +336,7 @@ def check_git_status(repo='ultralytics/yolov5'):
         s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `{pull}` or `git clone {url}` to update."
     else:
         s += f'up to date with {url} ✅'
-    LOGGER.info(emojis(s))  # emoji-safe
+    LOGGER.info(s)
 
 
 def check_python(minimum='3.7.0'):
@@ -388,7 +390,7 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
         source = file.resolve() if 'file' in locals() else requirements
         s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
             f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
-        LOGGER.info(emojis(s))
+        LOGGER.info(s)
 
 
 def check_img_size(imgsz, s=32, floor=0):
@@ -489,9 +491,9 @@ def check_dataset(data, autodownload=True):
 
     # Checks
     for k in 'train', 'val', 'nc':
-        assert k in data, emojis(f"data.yaml '{k}:' field missing ❌")
+        assert k in data, f"data.yaml '{k}:' field missing ❌"
     if 'names' not in data:
-        LOGGER.warning(emojis("data.yaml 'names:' field missing ⚠️, assigning default names 'class0', 'class1', etc."))
+        LOGGER.warning("data.yaml 'names:' field missing ⚠️, assigning default names 'class0', 'class1', etc.")
         data['names'] = [f'class{i}' for i in range(data['nc'])]  # default names
 
     # Resolve paths
@@ -507,9 +509,9 @@ def check_dataset(data, autodownload=True):
     if val:
         val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
         if not all(x.exists() for x in val):
-            LOGGER.info(emojis('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]))
+            LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()])
             if not s or not autodownload:
-                raise Exception(emojis('Dataset not found ❌'))
+                raise Exception('Dataset not found ❌')
             t = time.time()
             root = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'
             if s.startswith('http') and s.endswith('.zip'):  # URL
@@ -527,7 +529,7 @@ def check_dataset(data, autodownload=True):
                 r = exec(s, {'yaml': data})  # return None
             dt = f'({round(time.time() - t, 1)}s)'
             s = f"success ✅ {dt}, saved to {colorstr('bold', root)}" if r in (0, None) else f"failure {dt} ❌"
-            LOGGER.info(emojis(f"Dataset download {s}"))
+            LOGGER.info(f"Dataset download {s}")
     check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts
     return data  # dictionary
 
@@ -552,11 +554,11 @@ def amp_allclose(model, im):
     im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3))
     try:
         assert amp_allclose(model, im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im)
-        LOGGER.info(emojis(f'{prefix}checks passed ✅'))
+        LOGGER.info(f'{prefix}checks passed ✅')
         return True
     except Exception:
         help_url = 'https://github.com/ultralytics/yolov5/issues/7908'
-        LOGGER.warning(emojis(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}'))
+        LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}')
         return False
 
 

utils/loggers/__init__.py

@@ -10,7 +10,7 @@
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
-from utils.general import colorstr, cv2, emojis
+from utils.general import colorstr, cv2
 from utils.loggers.clearml.clearml_utils import ClearmlLogger
 from utils.loggers.wandb.wandb_utils import WandbLogger
 from utils.plots import plot_images, plot_results
@@ -73,11 +73,11 @@ def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None,
         if not wandb:
             prefix = colorstr('Weights & Biases: ')
             s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases"
-            self.logger.info(emojis(s))
+            self.logger.info(s)
         if not clearml:
             prefix = colorstr('ClearML: ')
             s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 runs in ClearML"
-            self.logger.info(emojis(s))
+            self.logger.info(s)
 
         # TensorBoard
         s = self.save_dir

utils/torch_utils.py

@@ -97,7 +97,7 @@ def select_device(device='', batch_size=0, newline=True):
 
     if not newline:
         s = s.rstrip()
-    LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
+    LOGGER.info(s)
     return torch.device(arg)
 
 

val.py

@@ -38,7 +38,7 @@
 from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
 from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_yaml,
-                           coco80_to_coco91_class, colorstr, emojis, increment_path, non_max_suppression, print_args,
+                           coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
                            scale_coords, xywh2xyxy, xyxy2xywh)
 from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
 from utils.plots import output_to_target, plot_images, plot_val_study
@@ -274,7 +274,7 @@ def run(
     pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
     LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
     if nt.sum() == 0:
-        LOGGER.warning(emojis(f'WARNING: no labels found in {task} set, can not compute metrics without labels ⚠️'))
+        LOGGER.warning(f'WARNING: no labels found in {task} set, can not compute metrics without labels ⚠️')
 
     # Print results per class
     if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
@@ -365,7 +365,7 @@ def main(opt):
 
     if opt.task in ('train', 'val', 'test'):  # run normally
         if opt.conf_thres > 0.001:  # https://github.com/ultralytics/yolov5/issues/1466
-            LOGGER.info(emojis(f'WARNING: confidence threshold {opt.conf_thres} > 0.001 produces invalid results ⚠️'))
+            LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} > 0.001 produces invalid results ⚠️')
         run(**vars(opt))
 
     else: