Update get_loggers() (#4854)

* Update `set_logging()`

* Update export.py

* pre-commit fixes

* Update LoadImages

* Update LoadStreams

* Update print_args

* Single LOGGER definition

* yolo.py fix

Co-authored-by: pre-commit <pre-commit@example.com>

detect.py

@@ -25,8 +25,7 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.experimental import attempt_load
 from utils.datasets import LoadImages, LoadStreams
 from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
-    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
-    strip_optimizer, xyxy2xywh
+    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, strip_optimizer, xyxy2xywh, LOGGER
 from utils.plots import Annotator, colors
 from utils.torch_utils import load_classifier, select_device, time_sync
 
@@ -68,7 +67,6 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
 
     # Initialize
-    set_logging()
     device = select_device(device)
     half &= device.type != 'cpu'  # half precision only supported on CUDA
 
@@ -132,7 +130,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
     if pt and device.type != 'cpu':
         model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters())))  # run once
     dt, seen = [0.0, 0.0, 0.0], 0
-    for path, img, im0s, vid_cap in dataset:
+    for path, img, im0s, vid_cap, s in dataset:
         t1 = time_sync()
         if onnx:
             img = img.astype('float32')
@@ -191,9 +189,10 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
         for i, det in enumerate(pred):  # per image
             seen += 1
             if webcam:  # batch_size >= 1
-                p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
+                p, im0, frame = path[i], im0s[i].copy(), dataset.count
+                s += f'{i}: '
             else:
-                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
+                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
 
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # img.jpg
@@ -227,7 +226,7 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
                             save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 
             # Print time (inference-only)
-            print(f'{s}Done. ({t3 - t2:.3f}s)')
+            LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
 
             # Stream results
             im0 = annotator.result()
@@ -256,10 +255,10 @@ def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
 
     # Print results
     t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
-    print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
+    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
     if save_txt or save_img:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        print(f"Results saved to {colorstr('bold', save_dir)}{s}")
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
     if update:
         strip_optimizer(weights)  # update model (to fix SourceChangeWarning)