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>

val.py

@@ -25,9 +25,9 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
-from utils.general import coco80_to_coco91_class, check_dataset, check_img_size, check_requirements, \
-    check_suffix, check_yaml, box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, \
-    increment_path, colorstr, print_args
+from utils.general import box_iou, coco80_to_coco91_class, colorstr, check_dataset, check_img_size, \
+    check_requirements, check_suffix, check_yaml, increment_path, non_max_suppression, print_args, scale_coords, \
+    xyxy2xywh, xywh2xyxy, LOGGER
 from utils.metrics import ap_per_class, ConfusionMatrix
 from utils.plots import output_to_target, plot_images, plot_val_study
 from utils.torch_utils import select_device, time_sync
@@ -242,18 +242,18 @@ def run(data,
 
     # Print results
     pf = '%20s' + '%11i' * 2 + '%11.3g' * 4  # print format
-    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
+    LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
 
     # Print results per class
     if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
         for i, c in enumerate(ap_class):
-            print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
+            LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
 
     # Print speeds
     t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
     if not training:
         shape = (batch_size, 3, imgsz, imgsz)
-        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
+        LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
 
     # Plots
     if plots:
@@ -265,7 +265,7 @@ def run(data,
         w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
         anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json')  # annotations json
         pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
-        print(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
+        LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
         with open(pred_json, 'w') as f:
             json.dump(jdict, f)
 
@@ -284,13 +284,13 @@ def run(data,
             eval.summarize()
             map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
         except Exception as e:
-            print(f'pycocotools unable to run: {e}')
+            LOGGER.info(f'pycocotools unable to run: {e}')
 
     # Return results
     model.float()  # for training
     if not training:
         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}")
     maps = np.zeros(nc) + map
     for i, c in enumerate(ap_class):
         maps[c] = ap[i]
@@ -327,8 +327,7 @@ def parse_opt():
 
 
 def main(opt):
-    set_logging()
-    check_requirements(exclude=('tensorboard', 'thop'))
+    check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
 
     if opt.task in ('train', 'val', 'test'):  # run normally
         run(**vars(opt))
@@ -346,7 +345,7 @@ def main(opt):
             f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
             y = []  # y axis
             for i in x:  # img-size
-                print(f'\nRunning {f} point {i}...')
+                LOGGER.info(f'\nRunning {f} point {i}...')
                 r, _, t = run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
                               iou_thres=opt.iou_thres, device=opt.device, save_json=opt.save_json, plots=False)
                 y.append(r + t)  # results and times