Standardize warnings with `WARNING ⚠️ ...` (#9467)

benchmarks.py

@@ -91,7 +91,7 @@ def run(
         except Exception as e:
             if hard_fail:
                 assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}'
-            LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}')
+            LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}')
             y.append([name, None, None, None])  # mAP, t_inference
         if pt_only and i == 0:
             break  # break after PyTorch

classify/train.py

@@ -114,7 +114,7 @@ def train(opt, device):
             m = hub.list('ultralytics/yolov5')  # + hub.list('pytorch/vision')  # models
             raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m))
         if isinstance(model, DetectionModel):
-            LOGGER.warning("WARNING: pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
+            LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'")
             model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10)  # convert to classification model
         reshape_classifier_output(model, nc)  # update class count
     for m in model.modules():

export.py

@@ -282,7 +282,7 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
 
     if dynamic:
         if im.shape[0] <= 1:
-            LOGGER.warning(f"{prefix}WARNING: --dynamic model requires maximum --batch-size argument")
+            LOGGER.warning(f"{prefix}WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
         profile = builder.create_optimization_profile()
         for inp in inputs:
             profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)

hubconf.py

@@ -47,7 +47,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
                 model = DetectMultiBackend(path, device=device, fuse=autoshape)  # detection model
                 if autoshape:
                     if model.pt and isinstance(model.model, ClassificationModel):
-                        LOGGER.warning('WARNING: ⚠️ YOLOv5 v6.2 ClassificationModel is not yet AutoShape compatible. '
+                        LOGGER.warning('WARNING ⚠️ YOLOv5 v6.2 ClassificationModel is not yet AutoShape compatible. '
                                        'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
                     else:
                         model = AutoShape(model)  # for file/URI/PIL/cv2/np inputs and NMS

segment/train.py

@@ -176,7 +176,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
     # DP mode
     if cuda and RANK == -1 and torch.cuda.device_count() > 1:
-        LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
+        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)
 

segment/val.py

@@ -345,7 +345,7 @@ def run(
     pf = '%22s' + '%11i' * 2 + '%11.3g' * 8  # print format
     LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
     if nt.sum() == 0:
-        LOGGER.warning(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):
@@ -438,9 +438,9 @@ 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(f'WARNING: confidence threshold {opt.conf_thres} > 0.001 produces invalid results ⚠️')
+            LOGGER.warning(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results')
         if opt.save_hybrid:
-            LOGGER.info('WARNING: --save-hybrid will return high mAP from hybrid labels, not from predictions alone ⚠️')
+            LOGGER.warning('WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone')
         run(**vars(opt))
 
     else:

train.py

@@ -173,7 +173,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
     # DP mode
     if cuda and RANK == -1 and torch.cuda.device_count() > 1:
-        LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
+        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)
 

utils/__init__.py

@@ -4,9 +4,15 @@ utils/initialization
 """
 
 import contextlib
+import platform
 import threading
 
 
+def emojis(str=''):
+    # Return platform-dependent emoji-safe version of string
+    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
+
+
 class TryExcept(contextlib.ContextDecorator):
     # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
     def __init__(self, msg=''):
@@ -17,7 +23,7 @@ class TryExcept(contextlib.ContextDecorator):
 
     def __exit__(self, exc_type, value, traceback):
         if value:
-            print(f'{self.msg}{value}')
+            print(emojis(f'{self.msg}{value}'))
         return True
 
 
@@ -38,7 +44,7 @@ def notebook_init(verbose=True):
     import os
     import shutil
 
-    from utils.general import check_font, check_requirements, emojis, is_colab
+    from utils.general import check_font, check_requirements, is_colab
     from utils.torch_utils import select_device  # imports
 
     check_requirements(('psutil', 'IPython'))

utils/autoanchor.py

@@ -122,7 +122,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
     # Filter
     i = (wh0 < 3.0).any(1).sum()
     if i:
-        LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found: {i} of {len(wh0)} labels are < 3 pixels in size')
+        LOGGER.info(f'{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size')
     wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32)  # filter > 2 pixels
     # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
 
@@ -134,7 +134,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
         k = kmeans(wh / s, n, iter=30)[0] * s  # points
         assert n == len(k)  # kmeans may return fewer points than requested if wh is insufficient or too similar
     except Exception:
-        LOGGER.warning(f'{PREFIX}WARNING: switching strategies from kmeans to random init')
+        LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init')
         k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size  # random init
     wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
     k = print_results(k, verbose=False)

utils/autobatch.py

@@ -65,7 +65,7 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
             b = batch_sizes[max(i - 1, 0)]  # select prior safe point
     if b < 1 or b > 1024:  # b outside of safe range
         b = batch_size
-        LOGGER.warning(f'{prefix}WARNING: ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
+        LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
 
     fraction = np.polyval(p, b) / t  # actual fraction predicted
     LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')

utils/dataloaders.py

@@ -116,7 +116,7 @@ def create_dataloader(path,
                       prefix='',
                       shuffle=False):
     if rect and shuffle:
-        LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False')
+        LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
         shuffle = False
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = LoadImagesAndLabels(
@@ -328,7 +328,7 @@ class LoadStreams:
         self.auto = auto and self.rect
         self.transforms = transforms  # optional
         if not self.rect:
-            LOGGER.warning('WARNING: Stream shapes differ. For optimal performance supply similarly-shaped streams.')
+            LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
 
     def update(self, i, cap, stream):
         # Read stream `i` frames in daemon thread
@@ -341,7 +341,7 @@ class LoadStreams:
                 if success:
                     self.imgs[i] = im
                 else:
-                    LOGGER.warning('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] = np.zeros_like(self.imgs[i])
                     cap.open(stream)  # re-open stream if signal was lost
             time.sleep(0.0)  # wait time
@@ -543,7 +543,7 @@ class LoadImagesAndLabels(Dataset):
         if msgs:
             LOGGER.info('\n'.join(msgs))
         if nf == 0:
-            LOGGER.warning(f'{prefix}WARNING: No labels found in {path}. {HELP_URL}')
+            LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}')
         x['hash'] = get_hash(self.label_files + self.im_files)
         x['results'] = nf, nm, ne, nc, len(self.im_files)
         x['msgs'] = msgs  # warnings
@@ -553,7 +553,7 @@ class LoadImagesAndLabels(Dataset):
             path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
             LOGGER.info(f'{prefix}New cache created: {path}')
         except Exception as e:
-            LOGGER.warning(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}')  # not writeable
+            LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}')  # not writeable
         return x
 
     def __len__(self):
@@ -917,7 +917,7 @@ def verify_image_label(args):
                 f.seek(-2, 2)
                 if f.read() != b'\xff\xd9':  # corrupt JPEG
                     ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
-                    msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'
+                    msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
 
         # verify labels
         if os.path.isfile(lb_file):
@@ -939,7 +939,7 @@ def verify_image_label(args):
                     lb = lb[i]  # remove duplicates
                     if segments:
                         segments = [segments[x] for x in i]
-                    msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'
+                    msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
             else:
                 ne = 1  # label empty
                 lb = np.zeros((0, 5), dtype=np.float32)
@@ -949,7 +949,7 @@ def verify_image_label(args):
         return im_file, lb, shape, segments, nm, nf, ne, nc, msg
     except Exception as e:
         nc = 1
-        msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'
+        msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
         return [None, None, None, None, nm, nf, ne, nc, msg]
 
 
@@ -1012,7 +1012,7 @@ class HUBDatasetStats():
                 im = im.resize((int(im.width * r), int(im.height * r)))
             im.save(f_new, 'JPEG', quality=50, optimize=True)  # save
         except Exception as e:  # use OpenCV
-            print(f'WARNING: HUB ops PIL failure {f}: {e}')
+            LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
             im = cv2.imread(f)
             im_height, im_width = im.shape[:2]
             r = max_dim / max(im_height, im_width)  # ratio

utils/general.py

@@ -34,7 +34,7 @@ import torch
 import torchvision
 import yaml
 
-from utils import TryExcept
+from utils import TryExcept, emojis
 from utils.downloads import gsutil_getsize
 from utils.metrics import box_iou, fitness
 
@@ -248,11 +248,6 @@ def get_latest_run(search_dir='.'):
     return max(last_list, key=os.path.getctime) if last_list else ''
 
 
-def emojis(str=''):
-    # Return platform-dependent emoji-safe version of string
-    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
 def file_age(path=__file__):
     # Return days since last file update
     dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime))  # delta
@@ -333,7 +328,7 @@ def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=Fals
     # Check version vs. required version
     current, minimum = (pkg.parse_version(x) for x in (current, minimum))
     result = (current == minimum) if pinned else (current >= minimum)  # bool
-    s = f'WARNING: ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed'  # string
+    s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed'  # string
     if hard:
         assert result, emojis(s)  # assert min requirements met
     if verbose and not result:
@@ -373,7 +368,7 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
                 f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
             LOGGER.info(s)
         except Exception as e:
-            LOGGER.warning(f'{prefix} {e}')
+            LOGGER.warning(f'{prefix} ❌ {e}')
 
 
 def check_img_size(imgsz, s=32, floor=0):
@@ -384,7 +379,7 @@ def check_img_size(imgsz, s=32, floor=0):
         imgsz = list(imgsz)  # convert to list if tuple
         new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
     if new_size != imgsz:
-        LOGGER.warning(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
+        LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}')
     return new_size
 
 
@@ -399,7 +394,7 @@ def check_imshow():
         cv2.waitKey(1)
         return True
     except Exception as e:
-        LOGGER.warning(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
+        LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
         return False
 
 
@@ -589,9 +584,9 @@ def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry
                 if success:
                     break
                 elif i < retry:
-                    LOGGER.warning(f'Download failure, retrying {i + 1}/{retry} {url}...')
+                    LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')
                 else:
-                    LOGGER.warning(f'Failed to download {url}...')
+                    LOGGER.warning(f'❌ Failed to download {url}...')
 
         if unzip and success and f.suffix in ('.zip', '.tar', '.gz'):
             LOGGER.info(f'Unzipping {f}...')
@@ -908,7 +903,7 @@ def non_max_suppression(
 
         output[xi] = x[i]
         if (time.time() - t) > time_limit:
-            LOGGER.warning(f'WARNING: NMS time limit {time_limit:.3f}s exceeded')
+            LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded')
             break  # time limit exceeded
 
     return output

utils/loggers/__init__.py

@@ -11,7 +11,7 @@ import pkg_resources as pkg
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
-from utils.general import colorstr, cv2
+from utils.general import LOGGER, 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_labels, plot_results
@@ -393,7 +393,7 @@ def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
             warnings.simplefilter('ignore')  # suppress jit trace warning
             tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
     except Exception as e:
-        print(f'WARNING: TensorBoard graph visualization failure {e}')
+        LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}')
 
 
 def web_project_name(project):

utils/metrics.py

@@ -186,7 +186,7 @@ class ConfusionMatrix:
         # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
         return tp[:-1], fp[:-1]  # remove background class
 
-    @TryExcept('WARNING: ConfusionMatrix plot failure: ')
+    @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure: ')
     def plot(self, normalize=True, save_dir='', names=()):
         import seaborn as sn
 

utils/segment/dataloaders.py

@@ -37,7 +37,7 @@ def create_dataloader(path,
                       mask_downsample_ratio=1,
                       overlap_mask=False):
     if rect and shuffle:
-        LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False')
+        LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
         shuffle = False
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = LoadImagesAndLabelsAndMasks(

utils/torch_utils.py

@@ -47,7 +47,7 @@ def smartCrossEntropyLoss(label_smoothing=0.0):
     if check_version(torch.__version__, '1.10.0'):
         return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
     if label_smoothing > 0:
-        LOGGER.warning(f'WARNING: label smoothing {label_smoothing} requires torch>=1.10.0')
+        LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0')
     return nn.CrossEntropyLoss()
 
 

val.py

@@ -282,7 +282,7 @@ def run(
     pf = '%22s' + '%11i' * 2 + '%11.3g' * 4  # print format
     LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
     if nt.sum() == 0:
-        LOGGER.warning(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):
@@ -374,9 +374,9 @@ 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(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')
         if opt.save_hybrid:
-            LOGGER.info('WARNING: --save-hybrid will return high mAP from hybrid labels, not from predictions alone ⚠️')
+            LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone')
         run(**vars(opt))
 
     else: