precommit: yapf (#5494)
* precommit: yapf * align isort * fix # Conflicts: # utils/plots.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update setup.cfg * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update setup.cfg * Update setup.cfg * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update wandb_utils.py * Update augmentations.py * Update setup.cfg * Update yolo.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update val.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * simplify colorstr * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * val run fix * export.py last comma * Update export.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update hubconf.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * PyTorch Hub tuple fix * PyTorch Hub tuple fix2 * PyTorch Hub tuple fix3 * Update setup Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -62,11 +62,7 @@ RANK = int(os.getenv('RANK', -1))
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WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
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def train(hyp, # path/to/hyp.yaml or hyp dictionary
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opt,
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device,
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callbacks
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):
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def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary
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save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
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Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
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opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
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@@ -220,20 +216,38 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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LOGGER.info('Using SyncBatchNorm()')
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# Trainloader
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train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
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hyp=hyp, augment=True, cache=None if opt.cache == 'val' else opt.cache,
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rect=opt.rect, rank=LOCAL_RANK, workers=workers,
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image_weights=opt.image_weights, quad=opt.quad,
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prefix=colorstr('train: '), shuffle=True)
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train_loader, dataset = create_dataloader(train_path,
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imgsz,
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batch_size // WORLD_SIZE,
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gs,
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single_cls,
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hyp=hyp,
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augment=True,
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cache=None if opt.cache == 'val' else opt.cache,
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rect=opt.rect,
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rank=LOCAL_RANK,
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workers=workers,
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image_weights=opt.image_weights,
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quad=opt.quad,
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prefix=colorstr('train: '),
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shuffle=True)
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mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class
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nb = len(train_loader) # number of batches
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assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
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# Process 0
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if RANK in [-1, 0]:
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val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
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hyp=hyp, cache=None if noval else opt.cache,
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rect=True, rank=-1, workers=workers * 2, pad=0.5,
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val_loader = create_dataloader(val_path,
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imgsz,
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batch_size // WORLD_SIZE * 2,
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gs,
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single_cls,
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hyp=hyp,
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cache=None if noval else opt.cache,
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rect=True,
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rank=-1,
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workers=workers * 2,
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pad=0.5,
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prefix=colorstr('val: '))[0]
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if not resume:
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@@ -350,8 +364,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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if RANK in [-1, 0]:
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
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pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
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f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
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pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
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(f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
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callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
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if callbacks.stop_training:
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return
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@@ -387,14 +401,15 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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# Save model
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if (not nosave) or (final_epoch and not evolve): # if save
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ckpt = {'epoch': epoch,
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'best_fitness': best_fitness,
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'model': deepcopy(de_parallel(model)).half(),
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'ema': deepcopy(ema.ema).half(),
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'updates': ema.updates,
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'optimizer': optimizer.state_dict(),
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'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
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'date': datetime.now().isoformat()}
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ckpt = {
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'epoch': epoch,
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'best_fitness': best_fitness,
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'model': deepcopy(de_parallel(model)).half(),
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'ema': deepcopy(ema.ema).half(),
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'updates': ema.updates,
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'optimizer': optimizer.state_dict(),
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'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
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'date': datetime.now().isoformat()}
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# Save last, best and delete
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torch.save(ckpt, last)
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@@ -428,19 +443,20 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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strip_optimizer(f) # strip optimizers
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if f is best:
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LOGGER.info(f'\nValidating {f}...')
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results, _, _ = val.run(data_dict,
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batch_size=batch_size // WORLD_SIZE * 2,
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imgsz=imgsz,
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model=attempt_load(f, device).half(),
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iou_thres=0.65 if is_coco else 0.60, # best pycocotools results at 0.65
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single_cls=single_cls,
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dataloader=val_loader,
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save_dir=save_dir,
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save_json=is_coco,
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verbose=True,
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plots=True,
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callbacks=callbacks,
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compute_loss=compute_loss) # val best model with plots
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results, _, _ = val.run(
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data_dict,
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batch_size=batch_size // WORLD_SIZE * 2,
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imgsz=imgsz,
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model=attempt_load(f, device).half(),
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iou_thres=0.65 if is_coco else 0.60, # best pycocotools results at 0.65
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single_cls=single_cls,
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dataloader=val_loader,
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save_dir=save_dir,
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save_json=is_coco,
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verbose=True,
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plots=True,
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callbacks=callbacks,
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compute_loss=compute_loss) # val best model with plots
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if is_coco:
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callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
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@@ -546,35 +562,36 @@ def main(opt, callbacks=Callbacks()):
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# Evolve hyperparameters (optional)
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else:
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# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
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meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
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'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
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'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
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'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
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'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
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'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
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'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
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'box': (1, 0.02, 0.2), # box loss gain
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'cls': (1, 0.2, 4.0), # cls loss gain
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'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
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'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
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'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
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'iou_t': (0, 0.1, 0.7), # IoU training threshold
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'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
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'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
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'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
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'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
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'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
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'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
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'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
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'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
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'scale': (1, 0.0, 0.9), # image scale (+/- gain)
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'shear': (1, 0.0, 10.0), # image shear (+/- deg)
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'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
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'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
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'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
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'mosaic': (1, 0.0, 1.0), # image mixup (probability)
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'mixup': (1, 0.0, 1.0), # image mixup (probability)
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'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
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meta = {
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'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
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'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
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'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
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'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
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'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
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'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
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'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
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'box': (1, 0.02, 0.2), # box loss gain
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'cls': (1, 0.2, 4.0), # cls loss gain
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'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
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'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
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'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
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'iou_t': (0, 0.1, 0.7), # IoU training threshold
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'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
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'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
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'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
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'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
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'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
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'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
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'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
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'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
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'scale': (1, 0.0, 0.9), # image scale (+/- gain)
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'shear': (1, 0.0, 10.0), # image shear (+/- deg)
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'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
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'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
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'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
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'mosaic': (1, 0.0, 1.0), # image mixup (probability)
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'mixup': (1, 0.0, 1.0), # image mixup (probability)
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'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
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with open(opt.hyp, errors='ignore') as f:
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hyp = yaml.safe_load(f) # load hyps dict
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