Update Actions with Lychee and GitHub Token (#12592)
* Add Ultralytics Actions * Auto-format by Ultralytics actions * Update format.yml Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -21,77 +21,79 @@ from utils.torch_utils import select_device
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# Project Configuration
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config = comet_ml.config.get_config()
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COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
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COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
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def get_args(known=False):
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
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parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
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parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
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parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
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parser.add_argument('--epochs', type=int, default=300, help='total training epochs')
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parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
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parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
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parser.add_argument('--rect', action='store_true', help='rectangular training')
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parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--noval', action='store_true', help='only validate final epoch')
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parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
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parser.add_argument('--noplots', action='store_true', help='save no plot files')
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parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
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parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
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parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
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parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
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parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
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parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
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parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
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parser.add_argument('--name', default='exp', help='save to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--quad', action='store_true', help='quad dataloader')
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parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
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parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
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parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
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parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
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parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
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parser.add_argument('--seed', type=int, default=0, help='Global training seed')
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parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
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parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path")
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parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
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parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
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parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path")
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parser.add_argument("--epochs", type=int, default=300, help="total training epochs")
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parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch")
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parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)")
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parser.add_argument("--rect", action="store_true", help="rectangular training")
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parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training")
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parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
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parser.add_argument("--noval", action="store_true", help="only validate final epoch")
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parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor")
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parser.add_argument("--noplots", action="store_true", help="save no plot files")
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parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations")
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parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
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parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"')
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parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training")
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parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
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parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%")
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parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class")
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parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer")
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parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode")
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parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
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parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name")
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parser.add_argument("--name", default="exp", help="save to project/name")
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parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
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parser.add_argument("--quad", action="store_true", help="quad dataloader")
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parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler")
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parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon")
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parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)")
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parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2")
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parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)")
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parser.add_argument("--seed", type=int, default=0, help="Global training seed")
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parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
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# Weights & Biases arguments
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parser.add_argument('--entity', default=None, help='W&B: Entity')
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parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')
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parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
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parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
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parser.add_argument("--entity", default=None, help="W&B: Entity")
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parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='W&B: Upload data, "val" option')
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parser.add_argument("--bbox_interval", type=int, default=-1, help="W&B: Set bounding-box image logging interval")
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parser.add_argument("--artifact_alias", type=str, default="latest", help="W&B: Version of dataset artifact to use")
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# Comet Arguments
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parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.')
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parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.')
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parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.")
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parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.')
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parser.add_argument('--comet_optimizer_workers',
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type=int,
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default=1,
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help='Comet: Number of Parallel Workers to use with the Comet Optimizer.')
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parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.")
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parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.")
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parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.")
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parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.")
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parser.add_argument(
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"--comet_optimizer_workers",
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type=int,
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default=1,
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help="Comet: Number of Parallel Workers to use with the Comet Optimizer.",
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)
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return parser.parse_known_args()[0] if known else parser.parse_args()
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def run(parameters, opt):
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hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']}
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hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]}
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opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
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opt.batch_size = parameters.get('batch_size')
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opt.epochs = parameters.get('epochs')
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opt.batch_size = parameters.get("batch_size")
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opt.epochs = parameters.get("epochs")
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device = select_device(opt.device, batch_size=opt.batch_size)
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train(hyp_dict, opt, device, callbacks=Callbacks())
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if __name__ == '__main__':
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if __name__ == "__main__":
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opt = get_args(known=True)
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opt.weights = str(opt.weights)
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@@ -99,7 +101,7 @@ if __name__ == '__main__':
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opt.data = str(opt.data)
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opt.project = str(opt.project)
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optimizer_id = os.getenv('COMET_OPTIMIZER_ID')
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optimizer_id = os.getenv("COMET_OPTIMIZER_ID")
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if optimizer_id is None:
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with open(opt.comet_optimizer_config) as f:
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optimizer_config = json.load(f)
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@@ -110,9 +112,9 @@ if __name__ == '__main__':
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opt.comet_optimizer_id = optimizer.id
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status = optimizer.status()
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opt.comet_optimizer_objective = status['spec']['objective']
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opt.comet_optimizer_metric = status['spec']['metric']
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opt.comet_optimizer_objective = status["spec"]["objective"]
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opt.comet_optimizer_metric = status["spec"]["metric"]
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logger.info('COMET INFO: Starting Hyperparameter Sweep')
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logger.info("COMET INFO: Starting Hyperparameter Sweep")
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for parameter in optimizer.get_parameters():
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run(parameter['parameters'], opt)
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run(parameter["parameters"], opt)
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