Namespace `VERBOSE` env variable to `YOLOv5_VERBOSE` (#6428)

hubconf.py

@@ -12,10 +12,10 @@ import torch
 
 
 def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    """Creates a specified YOLOv5 model
+    """Creates or loads a YOLOv5 model
 
     Arguments:
-        name (str): name of model, i.e. 'yolov5s'
+        name (str): model name 'yolov5s' or path 'path/to/best.pt'
         pretrained (bool): load pretrained weights into the model
         channels (int): number of input channels
         classes (int): number of model classes
@@ -24,19 +24,19 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
         device (str, torch.device, None): device to use for model parameters
 
     Returns:
-        YOLOv5 pytorch model
+        YOLOv5 model
     """
     from pathlib import Path
 
     from models.common import AutoShape, DetectMultiBackend
     from models.yolo import Model
     from utils.downloads import attempt_download
-    from utils.general import check_requirements, intersect_dicts, set_logging
+    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
     from utils.torch_utils import select_device
 
+    if not verbose:
+        LOGGER.setLevel(logging.WARNING)
     check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
-    set_logging(verbose=verbose)
-
     name = Path(name)
     path = name.with_suffix('.pt') if name.suffix == '' else name  # checkpoint path
     try:

utils/general.py

@@ -36,7 +36,7 @@ from utils.metrics import box_iou, fitness
 FILE = Path(__file__).resolve()
 ROOT = FILE.parents[1]  # YOLOv5 root directory
 NUM_THREADS = min(8, max(1, os.cpu_count() - 1))  # number of YOLOv5 multiprocessing threads
-VERBOSE = str(os.getenv('VERBOSE', True)).lower() == 'true'  # global verbose mode
+VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true'  # global verbose mode
 
 torch.set_printoptions(linewidth=320, precision=5, profile='long')
 np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
@@ -241,20 +241,20 @@ def check_online():
 def check_git_status():
     # Recommend 'git pull' if code is out of date
     msg = ', for updates see https://github.com/ultralytics/yolov5'
-    print(colorstr('github: '), end='')
-    assert Path('.git').exists(), 'skipping check (not a git repository)' + msg
-    assert not is_docker(), 'skipping check (Docker image)' + msg
-    assert check_online(), 'skipping check (offline)' + msg
+    s = colorstr('github: ')  # string
+    assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg
+    assert not is_docker(), s + 'skipping check (Docker image)' + msg
+    assert check_online(), s + 'skipping check (offline)' + msg
 
     cmd = 'git fetch && git config --get remote.origin.url'
     url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git')  # git fetch
     branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out
     n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True))  # commits behind
     if n > 0:
-        s = f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
+        s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update."
     else:
-        s = f'up to date with {url} ✅'
-    print(emojis(s))  # emoji-safe
+        s += f'up to date with {url} ✅'
+    LOGGER.info(emojis(s))  # emoji-safe
 
 
 def check_python(minimum='3.6.2'):
@@ -294,21 +294,21 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
         except Exception as e:  # DistributionNotFound or VersionConflict if requirements not met
             s = f"{prefix} {r} not found and is required by YOLOv5"
             if install:
-                print(f"{s}, attempting auto-update...")
+                LOGGER.info(f"{s}, attempting auto-update...")
                 try:
                     assert check_online(), f"'pip install {r}' skipped (offline)"
-                    print(check_output(f"pip install '{r}'", shell=True).decode())
+                    LOGGER.info(check_output(f"pip install '{r}'", shell=True).decode())
                     n += 1
                 except Exception as e:
-                    print(f'{prefix} {e}')
+                    LOGGER.warning(f'{prefix} {e}')
             else:
-                print(f'{s}. Please install and rerun your command.')
+                LOGGER.info(f'{s}. Please install and rerun your command.')
 
     if n:  # if packages updated
         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"
-        print(emojis(s))
+        LOGGER.info(emojis(s))
 
 
 def check_img_size(imgsz, s=32, floor=0):
@@ -318,7 +318,7 @@ def check_img_size(imgsz, s=32, floor=0):
     else:  # list i.e. img_size=[640, 480]
         new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
     if new_size != imgsz:
-        print(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
 
 
@@ -333,7 +333,7 @@ def check_imshow():
         cv2.waitKey(1)
         return True
     except Exception as e:
-        print(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
 
 
@@ -363,9 +363,9 @@ def check_file(file, suffix=''):
         url = str(Path(file)).replace(':/', '://')  # Pathlib turns :// -> :/
         file = Path(urllib.parse.unquote(file).split('?')[0]).name  # '%2F' to '/', split 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:
-            print(f'Downloading {url} to {file}...')
+            LOGGER.info(f'Downloading {url} to {file}...')
             torch.hub.download_url_to_file(url, file)
             assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}'  # check
         return file
@@ -407,23 +407,23 @@ 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):
-            print('\nWARNING: Dataset not found, nonexistent 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 s and autodownload:  # download script
                 root = path.parent if 'path' in data else '..'  # unzip directory i.e. '../'
                 if s.startswith('http') and s.endswith('.zip'):  # URL
                     f = Path(s).name  # filename
-                    print(f'Downloading {s} to {f}...')
+                    LOGGER.info(f'Downloading {s} to {f}...')
                     torch.hub.download_url_to_file(s, f)
                     Path(root).mkdir(parents=True, exist_ok=True)  # create root
                     ZipFile(f).extractall(path=root)  # unzip
                     Path(f).unlink()  # remove zip
                     r = None  # success
                 elif s.startswith('bash '):  # bash script
-                    print(f'Running {s} ...')
+                    LOGGER.info(f'Running {s} ...')
                     r = os.system(s)
                 else:  # python script
                     r = exec(s, {'yaml': data})  # return None
-                print(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n")
+                LOGGER.info(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n")
             else:
                 raise Exception('Dataset not found.')
 
@@ -445,13 +445,13 @@ def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1):
         if Path(url).is_file():  # exists in current path
             Path(url).rename(f)  # move to dir
         elif not f.exists():
-            print(f'Downloading {url} to {f}...')
+            LOGGER.info(f'Downloading {url} to {f}...')
             if curl:
                 os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -")  # curl download, retry and resume on fail
             else:
                 torch.hub.download_url_to_file(url, f, progress=True)  # torch download
         if unzip and f.suffix in ('.zip', '.gz'):
-            print(f'Unzipping {f}...')
+            LOGGER.info(f'Unzipping {f}...')
             if f.suffix == '.zip':
                 ZipFile(f).extractall(path=dir)  # unzip
             elif f.suffix == '.gz':
@@ -744,7 +744,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
 
         output[xi] = x[i]
         if (time.time() - t) > time_limit:
-            print(f'WARNING: NMS time limit {time_limit}s exceeded')
+            LOGGER.warning(f'WARNING: NMS time limit {time_limit}s exceeded')
             break  # time limit exceeded
 
     return output
@@ -763,7 +763,7 @@ def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_op
         p.requires_grad = False
     torch.save(x, s or f)
     mb = os.path.getsize(s or f) / 1E6  # filesize
-    print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
+    LOGGER.info(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
 
 
 def print_mutation(results, hyp, save_dir, bucket):
@@ -786,8 +786,8 @@ def print_mutation(results, hyp, save_dir, bucket):
         f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
 
     # Print to screen
-    print(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
-    print(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals), end='\n\n\n')
+    LOGGER.info(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
+    LOGGER.info(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals) + '\n\n')
 
     # Save yaml
     with open(evolve_yaml, 'w') as f:

utils/plots.py

@@ -57,7 +57,7 @@ def check_font(font='Arial.ttf', size=10):
         return ImageFont.truetype(str(font) if font.exists() else font.name, size)
     except Exception as e:  # download if missing
         url = "https://ultralytics.com/assets/" + font.name
-        print(f'Downloading {url} to {font}...')
+        LOGGER.info(f'Downloading {url} to {font}...')
         torch.hub.download_url_to_file(url, str(font), progress=False)
         try:
             return ImageFont.truetype(str(font), size)
@@ -143,7 +143,7 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
                 ax[i].imshow(blocks[i].squeeze())  # cmap='gray'
                 ax[i].axis('off')
 
-            print(f'Saving {f}... ({n}/{channels})')
+            LOGGER.info(f'Saving {f}... ({n}/{channels})')
             plt.savefig(f, dpi=300, bbox_inches='tight')
             plt.close()
             np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy())  # npy save
@@ -417,7 +417,7 @@ def plot_results(file='path/to/results.csv', dir=''):
                 # if j in [8, 9, 10]:  # share train and val loss y axes
                 #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
         except Exception as e:
-            print(f'Warning: Plotting error for {f}: {e}')
+            LOGGER.info(f'Warning: Plotting error for {f}: {e}')
     ax[1].legend()
     fig.savefig(save_dir / 'results.png', dpi=200)
     plt.close()