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Code Issues Projects Releases Wiki Activity

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

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* Standardize warnings

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2022-09-18 16:15:25 +02:00 committed by GitHub
parent 295c5e9d3c
commit ca9c993d6c
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17 changed files with 45 additions and 44 deletions
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2
benchmarks.py
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@ -91,7 +91,7 @@ def run(
except Exception as e: except Exception as e:
if hard_fail: if hard_fail:
assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}' 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 y.append([name, None, None, None]) # mAP, t_inference
if pt_only and i == 0: if pt_only and i == 0:
break # break after PyTorch break # break after PyTorch

2
classify/train.py
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@ -114,7 +114,7 @@ def train(opt, device):
m = hub.list('ultralytics/yolov5') # + hub.list('pytorch/vision') # models 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)) raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m))
if isinstance(model, DetectionModel): 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 model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model
reshape_classifier_output(model, nc) # update class count reshape_classifier_output(model, nc) # update class count
for m in model.modules(): for m in model.modules():

2
export.py
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@ -282,7 +282,7 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
if dynamic: if dynamic:
if im.shape[0] <= 1: 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() profile = builder.create_optimization_profile()
for inp in inputs: for inp in inputs:
profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape) profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)

2
hubconf.py
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@ -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 model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
if autoshape: if autoshape:
if model.pt and isinstance(model.model, ClassificationModel): 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).') 'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
else: else:
model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS

2
segment/train.py
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@ -176,7 +176,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
# DP mode # DP mode
if cuda and RANK == -1 and torch.cuda.device_count() > 1: 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.') 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)

6
segment/val.py
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@ -345,7 +345,7 @@ def run(
pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format
LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results())) LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
if nt.sum() == 0: 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 # Print results per class
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): 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.task in ('train', 'val', 'test'): # run normally
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 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: 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)) run(**vars(opt))
else: else:

2
train.py
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@ -173,7 +173,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
# DP mode # DP mode
if cuda and RANK == -1 and torch.cuda.device_count() > 1: 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.') 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)

10
utils/__init__.py
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@ -4,9 +4,15 @@ utils/initialization
""" """
import contextlib import contextlib
import platform
import threading 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): class TryExcept(contextlib.ContextDecorator):
# YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
def __init__(self, msg=''): def __init__(self, msg=''):
@ -17,7 +23,7 @@ class TryExcept(contextlib.ContextDecorator):
def __exit__(self, exc_type, value, traceback): def __exit__(self, exc_type, value, traceback):
if value: if value:
print(f'{self.msg}{value}') print(emojis(f'{self.msg}{value}'))
return True return True
@ -38,7 +44,7 @@ def notebook_init(verbose=True):
import os import os
import shutil 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 from utils.torch_utils import select_device # imports
check_requirements(('psutil', 'IPython')) check_requirements(('psutil', 'IPython'))

4
utils/autoanchor.py
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@ -122,7 +122,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
# Filter # Filter
i = (wh0 < 3.0).any(1).sum() i = (wh0 < 3.0).any(1).sum()
if i: 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 = 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 # 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 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 assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar
except Exception: 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 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)) wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
k = print_results(k, verbose=False) k = print_results(k, verbose=False)

2
utils/autobatch.py
View File

@ -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 b = batch_sizes[max(i - 1, 0)] # select prior safe point
if b < 1 or b > 1024: # b outside of safe range if b < 1 or b > 1024: # b outside of safe range
b = batch_size 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 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}%) ✅') LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')

18
utils/dataloaders.py
View File

@ -116,7 +116,7 @@ def create_dataloader(path,
prefix='', prefix='',
shuffle=False): shuffle=False):
if rect and shuffle: 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 shuffle = False
with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
dataset = LoadImagesAndLabels( dataset = LoadImagesAndLabels(
@ -328,7 +328,7 @@ class LoadStreams:
self.auto = auto and self.rect self.auto = auto and self.rect
self.transforms = transforms # optional self.transforms = transforms # optional
if not self.rect: 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): def update(self, i, cap, stream):
# Read stream `i` frames in daemon thread # Read stream `i` frames in daemon thread
@ -341,7 +341,7 @@ class LoadStreams:
if success: if success:
self.imgs[i] = im self.imgs[i] = im
else: 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]) self.imgs[i] = np.zeros_like(self.imgs[i])
cap.open(stream) # re-open stream if signal was lost cap.open(stream) # re-open stream if signal was lost
time.sleep(0.0) # wait time time.sleep(0.0) # wait time
@ -543,7 +543,7 @@ class LoadImagesAndLabels(Dataset):
if msgs: if msgs:
LOGGER.info('\n'.join(msgs)) LOGGER.info('\n'.join(msgs))
if nf == 0: 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['hash'] = get_hash(self.label_files + self.im_files)
x['results'] = nf, nm, ne, nc, len(self.im_files) x['results'] = nf, nm, ne, nc, len(self.im_files)
x['msgs'] = msgs # warnings x['msgs'] = msgs # warnings
@ -553,7 +553,7 @@ class LoadImagesAndLabels(Dataset):
path.with_suffix('.cache.npy').rename(path) # remove .npy suffix path.with_suffix('.cache.npy').rename(path) # remove .npy suffix
LOGGER.info(f'{prefix}New cache created: {path}') LOGGER.info(f'{prefix}New cache created: {path}')
except Exception as e: 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 return x
def __len__(self): def __len__(self):
@ -917,7 +917,7 @@ def verify_image_label(args):
f.seek(-2, 2) f.seek(-2, 2)
if f.read() != b'\xff\xd9': # corrupt JPEG if f.read() != b'\xff\xd9': # corrupt JPEG
ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100) 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 # verify labels
if os.path.isfile(lb_file): if os.path.isfile(lb_file):
@ -939,7 +939,7 @@ def verify_image_label(args):
lb = lb[i] # remove duplicates lb = lb[i] # remove duplicates
if segments: if segments:
segments = [segments[x] for x in i] 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: else:
ne = 1 # label empty ne = 1 # label empty
lb = np.zeros((0, 5), dtype=np.float32) 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 return im_file, lb, shape, segments, nm, nf, ne, nc, msg
except Exception as e: except Exception as e:
nc = 1 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] 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 = im.resize((int(im.width * r), int(im.height * r)))
im.save(f_new, 'JPEG', quality=50, optimize=True) # save im.save(f_new, 'JPEG', quality=50, optimize=True) # save
except Exception as e: # use OpenCV 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 = cv2.imread(f)
im_height, im_width = im.shape[:2] im_height, im_width = im.shape[:2]
r = max_dim / max(im_height, im_width) # ratio r = max_dim / max(im_height, im_width) # ratio

21
utils/general.py
View File

@ -34,7 +34,7 @@ import torch
import torchvision import torchvision
import yaml import yaml
from utils import TryExcept from utils import TryExcept, emojis
from utils.downloads import gsutil_getsize from utils.downloads import gsutil_getsize
from utils.metrics import box_iou, fitness 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 '' 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__): def file_age(path=__file__):
# Return days since last file update # Return days since last file update
dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta 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 # Check version vs. required version
current, minimum = (pkg.parse_version(x) for x in (current, minimum)) current, minimum = (pkg.parse_version(x) for x in (current, minimum))
result = (current == minimum) if pinned else (current >= minimum) # bool 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: if hard:
assert result, emojis(s) # assert min requirements met assert result, emojis(s) # assert min requirements met
if verbose and not result: 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" f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
LOGGER.info(s) LOGGER.info(s)
except Exception as e: except Exception as e:
LOGGER.warning(f'{prefix} {e}') LOGGER.warning(f'{prefix} ❌ {e}')
def check_img_size(imgsz, s=32, floor=0): 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 imgsz = list(imgsz) # convert to list if tuple
new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
if new_size != 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 return new_size
@ -399,7 +394,7 @@ def check_imshow():
cv2.waitKey(1) cv2.waitKey(1)
return True return True
except Exception as e: 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 return False
@ -589,9 +584,9 @@ def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry
if success: if success:
break break
elif i < retry: elif i < retry:
LOGGER.warning(f'Download failure, retrying {i + 1}/{retry} {url}...') LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')
else: 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'): if unzip and success and f.suffix in ('.zip', '.tar', '.gz'):
LOGGER.info(f'Unzipping {f}...') LOGGER.info(f'Unzipping {f}...')
@ -908,7 +903,7 @@ def non_max_suppression(
output[xi] = x[i] output[xi] = x[i]
if (time.time() - t) > time_limit: 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 break # time limit exceeded
return output return output

4
utils/loggers/__init__.py
View File

@ -11,7 +11,7 @@ import pkg_resources as pkg
import torch import torch
from torch.utils.tensorboard import SummaryWriter 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.clearml.clearml_utils import ClearmlLogger
from utils.loggers.wandb.wandb_utils import WandbLogger from utils.loggers.wandb.wandb_utils import WandbLogger
from utils.plots import plot_images, plot_labels, plot_results 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 warnings.simplefilter('ignore') # suppress jit trace warning
tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
except Exception as e: 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): def web_project_name(project):

2
utils/metrics.py
View File

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

2
utils/segment/dataloaders.py
View File

@ -37,7 +37,7 @@ def create_dataloader(path,
mask_downsample_ratio=1, mask_downsample_ratio=1,
overlap_mask=False): overlap_mask=False):
if rect and shuffle: 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 shuffle = False
with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
dataset = LoadImagesAndLabelsAndMasks( dataset = LoadImagesAndLabelsAndMasks(

2
utils/torch_utils.py
View File

@ -47,7 +47,7 @@ def smartCrossEntropyLoss(label_smoothing=0.0):
if check_version(torch.__version__, '1.10.0'): if check_version(torch.__version__, '1.10.0'):
return nn.CrossEntropyLoss(label_smoothing=label_smoothing) return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
if label_smoothing > 0: 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() return nn.CrossEntropyLoss()

6
val.py
View File

@ -282,7 +282,7 @@ def run(
pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
if nt.sum() == 0: 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 # Print results per class
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): 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.task in ('train', 'val', 'test'): # run normally
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 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: 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)) run(**vars(opt))
else: else: