Update train.py `import val as validate` (#9037)

* Update train.py `import val as validate` Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ciSigned-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Update train.py `import val as validate` (#9037)
fdcb92a9 · Glenn Jocher · GitHub · 4a8ab3bc · fdcb92a9
--- a/train.py
+++ b/train.py
@@ -36,7 +36,7 @@ if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

-import val  # for end-of-epoch mAP
+import val as validate  # for end-of-epoch mAP
 from models.experimental import attempt_load
 from models.yolo import Model
 from utils.autoanchor import check_anchors
@@ -347,17 +347,17 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
            ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
            final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
            if not noval or final_epoch:  # Calculate mAP
-                results, maps, _ = val.run(data_dict,
-                                           batch_size=batch_size // WORLD_SIZE * 2,
-                                           imgsz=imgsz,
-                                           half=amp,
-                                           model=ema.ema,
-                                           single_cls=single_cls,
-                                           dataloader=val_loader,
-                                           save_dir=save_dir,
-                                           plots=False,
-                                           callbacks=callbacks,
-                                           compute_loss=compute_loss)
+                results, maps, _ = validate.run(data_dict,
+                                                batch_size=batch_size // WORLD_SIZE * 2,
+                                                imgsz=imgsz,
+                                                half=amp,
+                                                model=ema.ema,
+                                                single_cls=single_cls,
+                                                dataloader=val_loader,
+                                                save_dir=save_dir,
+                                                plots=False,
+                                                callbacks=callbacks,
+                                                compute_loss=compute_loss)

            # Update best mAP
            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
@@ -407,12 +407,12 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                strip_optimizer(f)  # strip optimizers
                if f is best:
                    LOGGER.info(f'\nValidating {f}...')
-                    results, _, _ = val.run(
+                    results, _, _ = validate.run(
                        data_dict,
                        batch_size=batch_size // WORLD_SIZE * 2,
                        imgsz=imgsz,
                        model=attempt_load(f, device).half(),
-                        iou_thres=0.65 if is_coco else 0.60,  # best pycocotools results at 0.65
+                        iou_thres=0.65 if is_coco else 0.60,  # best pycocotools at iou 0.65
                        single_cls=single_cls,
                        dataloader=val_loader,
                        save_dir=save_dir,