PyTorch Hub `_verbose=False` fix2 (#7550)

hubconf.py

@@ -36,6 +36,7 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
 
     if not verbose:
         LOGGER.setLevel(logging.WARNING)
+
     check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
     name = Path(name)
     path = name.with_suffix('.pt') if name.suffix == '' else name  # checkpoint path
@@ -65,63 +66,63 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
         raise Exception(s) from e
 
 
-def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
+def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
     # YOLOv5 custom or local model
-    return _create(path, autoshape=autoshape, verbose=verbose, device=device)
+    return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
 
 
-def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-nano model https://github.com/ultralytics/yolov5
-    return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-small model https://github.com/ultralytics/yolov5
-    return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-medium model https://github.com/ultralytics/yolov5
-    return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-large model https://github.com/ultralytics/yolov5
-    return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
-    return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device)
 
 
-def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
+def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
     # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)
+    return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device)
 
 
 if __name__ == '__main__':
-    model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)  # pretrained
+    model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
     # model = custom(path='path/to/model.pt')  # custom
 
     # Verify inference

train.py

@@ -54,7 +54,7 @@ from utils.loggers import Loggers
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.loss import ComputeLoss
 from utils.metrics import fitness
-from utils.plots import check_font, plot_evolve, plot_labels
+from utils.plots import plot_evolve, plot_labels
 from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
@@ -105,8 +105,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     init_seeds(1 + RANK)
     with torch_distributed_zero_first(LOCAL_RANK):
         data_dict = data_dict or check_dataset(data)  # check if None
-        if not is_ascii(data_dict['names']):  # non-latin labels, i.e. asian, arabic, cyrillic
-            check_font('Arial.Unicode.ttf', progress=True)
     train_path, val_path = data_dict['train'], data_dict['val']
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names

utils/downloads.py

@@ -3,6 +3,7 @@
 Download utils
 """
 
+import logging
 import os
 import platform
 import subprocess
@@ -23,27 +24,30 @@ def gsutil_getsize(url=''):
 
 def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
     # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
+    from utils.general import LOGGER
+
     file = Path(file)
     assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
     try:  # url1
-        print(f'Downloading {url} to {file}...')
-        torch.hub.download_url_to_file(url, str(file))
+        LOGGER.info(f'Downloading {url} to {file}...')
+        torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
         assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
     except Exception as e:  # url2
         file.unlink(missing_ok=True)  # remove partial downloads
-        print(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
+        LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
         os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
     finally:
         if not file.exists() or file.stat().st_size < min_bytes:  # check
             file.unlink(missing_ok=True)  # remove partial downloads
-            print(f"ERROR: {assert_msg}\n{error_msg}")
-        print('')
+            LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
+        LOGGER.info('')
 
 
 def attempt_download(file, repo='ultralytics/yolov5'):  # from utils.downloads import *; attempt_download()
     # Attempt file download if does not exist
-    file = Path(str(file).strip().replace("'", ''))
+    from utils.general import LOGGER
 
+    file = Path(str(file).strip().replace("'", ''))
     if not file.exists():
         # URL specified
         name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
@@ -51,7 +55,7 @@ def attempt_download(file, repo='ultralytics/yolov5'):  # from utils.downloads i
             url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/
             file = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
             if Path(file).is_file():
-                print(f'Found {url} locally at {file}')  # file already exists
+                LOGGER.info(f'Found {url} locally at {file}')  # file already exists
             else:
                 safe_download(file=file, url=url, min_bytes=1E5)
             return file

utils/general.py

@@ -490,6 +490,7 @@ def check_dataset(data, autodownload=True):
             else:
                 raise Exception(emojis('Dataset not found ❌'))
 
+    check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts
     return data  # dictionary
 
 

utils/plots.py

@@ -66,9 +66,6 @@ def check_pil_font(font=FONT, size=10):
 
 
 class Annotator:
-    if RANK in (-1, 0):
-        check_pil_font()  # download TTF if necessary
-
     # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
     def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
         assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'