From fdcb92a938ef27d1b277a156af7f7922400279e3 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 19 Aug 2022 16:54:58 +0200 Subject: [PATCH] Update train.py `import val as validate` (#9037) * Update train.py `import val as validate` Signed-off-by: Glenn Jocher * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Signed-off-by: Glenn Jocher Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- train.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/train.py b/train.py index 279d52d..665b4f5 100644 --- 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,