ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -53,7 +53,7 @@ class Model(nn.Module):
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list(ultralytics.engine.results.Results): The prediction results.
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"""
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def __init__(self, model: Union[str, Path] = 'yolov8n.pt', task=None) -> None:
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def __init__(self, model: Union[str, Path] = "yolov8n.pt", task=None) -> None:
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"""
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Initializes the YOLO model.
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@ -89,7 +89,7 @@ class Model(nn.Module):
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# Load or create new YOLO model
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model = checks.check_model_file_from_stem(model) # add suffix, i.e. yolov8n -> yolov8n.pt
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if Path(model).suffix in ('.yaml', '.yml'):
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if Path(model).suffix in (".yaml", ".yml"):
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self._new(model, task)
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else:
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self._load(model, task)
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@ -112,16 +112,20 @@ class Model(nn.Module):
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def is_triton_model(model):
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"""Is model a Triton Server URL string, i.e. <scheme>://<netloc>/<endpoint>/<task_name>"""
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from urllib.parse import urlsplit
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url = urlsplit(model)
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return url.netloc and url.path and url.scheme in {'http', 'grpc'}
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return url.netloc and url.path and url.scheme in {"http", "grpc"}
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@staticmethod
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def is_hub_model(model):
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"""Check if the provided model is a HUB model."""
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return any((
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model.startswith(f'{HUB_WEB_ROOT}/models/'), # i.e. https://hub.ultralytics.com/models/MODEL_ID
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[len(x) for x in model.split('_')] == [42, 20], # APIKEY_MODELID
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len(model) == 20 and not Path(model).exists() and all(x not in model for x in './\\'))) # MODELID
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return any(
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(
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model.startswith(f"{HUB_WEB_ROOT}/models/"), # i.e. https://hub.ultralytics.com/models/MODEL_ID
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[len(x) for x in model.split("_")] == [42, 20], # APIKEY_MODELID
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len(model) == 20 and not Path(model).exists() and all(x not in model for x in "./\\"),
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)
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) # MODELID
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def _new(self, cfg: str, task=None, model=None, verbose=True):
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"""
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@ -136,9 +140,9 @@ class Model(nn.Module):
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cfg_dict = yaml_model_load(cfg)
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self.cfg = cfg
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self.task = task or guess_model_task(cfg_dict)
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self.model = (model or self._smart_load('model'))(cfg_dict, verbose=verbose and RANK == -1) # build model
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self.overrides['model'] = self.cfg
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self.overrides['task'] = self.task
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self.model = (model or self._smart_load("model"))(cfg_dict, verbose=verbose and RANK == -1) # build model
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self.overrides["model"] = self.cfg
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self.overrides["task"] = self.task
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# Below added to allow export from YAMLs
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self.model.args = {**DEFAULT_CFG_DICT, **self.overrides} # combine default and model args (prefer model args)
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@ -153,9 +157,9 @@ class Model(nn.Module):
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task (str | None): model task
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"""
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suffix = Path(weights).suffix
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if suffix == '.pt':
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if suffix == ".pt":
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self.model, self.ckpt = attempt_load_one_weight(weights)
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self.task = self.model.args['task']
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self.task = self.model.args["task"]
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self.overrides = self.model.args = self._reset_ckpt_args(self.model.args)
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self.ckpt_path = self.model.pt_path
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else:
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@ -163,12 +167,12 @@ class Model(nn.Module):
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self.model, self.ckpt = weights, None
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self.task = task or guess_model_task(weights)
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self.ckpt_path = weights
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self.overrides['model'] = weights
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self.overrides['task'] = self.task
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self.overrides["model"] = weights
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self.overrides["task"] = self.task
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def _check_is_pytorch_model(self):
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"""Raises TypeError is model is not a PyTorch model."""
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pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == '.pt'
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pt_str = isinstance(self.model, (str, Path)) and Path(self.model).suffix == ".pt"
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pt_module = isinstance(self.model, nn.Module)
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if not (pt_module or pt_str):
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raise TypeError(
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@ -176,19 +180,20 @@ class Model(nn.Module):
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f"PyTorch models can train, val, predict and export, i.e. 'model.train(data=...)', but exported "
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f"formats like ONNX, TensorRT etc. only support 'predict' and 'val' modes, "
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f"i.e. 'yolo predict model=yolov8n.onnx'.\nTo run CUDA or MPS inference please pass the device "
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f"argument directly in your inference command, i.e. 'model.predict(source=..., device=0)'")
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f"argument directly in your inference command, i.e. 'model.predict(source=..., device=0)'"
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)
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def reset_weights(self):
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"""Resets the model modules parameters to randomly initialized values, losing all training information."""
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self._check_is_pytorch_model()
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for m in self.model.modules():
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if hasattr(m, 'reset_parameters'):
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if hasattr(m, "reset_parameters"):
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m.reset_parameters()
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for p in self.model.parameters():
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p.requires_grad = True
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return self
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def load(self, weights='yolov8n.pt'):
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def load(self, weights="yolov8n.pt"):
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"""Transfers parameters with matching names and shapes from 'weights' to model."""
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self._check_is_pytorch_model()
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if isinstance(weights, (str, Path)):
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@ -226,8 +231,8 @@ class Model(nn.Module):
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Returns:
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(List[torch.Tensor]): A list of image embeddings.
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"""
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if not kwargs.get('embed'):
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kwargs['embed'] = [len(self.model.model) - 2] # embed second-to-last layer if no indices passed
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if not kwargs.get("embed"):
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kwargs["embed"] = [len(self.model.model) - 2] # embed second-to-last layer if no indices passed
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return self.predict(source, stream, **kwargs)
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def predict(self, source=None, stream=False, predictor=None, **kwargs):
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@ -249,21 +254,22 @@ class Model(nn.Module):
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source = ASSETS
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LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
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is_cli = (sys.argv[0].endswith('yolo') or sys.argv[0].endswith('ultralytics')) and any(
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x in sys.argv for x in ('predict', 'track', 'mode=predict', 'mode=track'))
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is_cli = (sys.argv[0].endswith("yolo") or sys.argv[0].endswith("ultralytics")) and any(
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x in sys.argv for x in ("predict", "track", "mode=predict", "mode=track")
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)
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custom = {'conf': 0.25, 'save': is_cli} # method defaults
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args = {**self.overrides, **custom, **kwargs, 'mode': 'predict'} # highest priority args on the right
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prompts = args.pop('prompts', None) # for SAM-type models
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custom = {"conf": 0.25, "save": is_cli} # method defaults
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args = {**self.overrides, **custom, **kwargs, "mode": "predict"} # highest priority args on the right
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prompts = args.pop("prompts", None) # for SAM-type models
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if not self.predictor:
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self.predictor = predictor or self._smart_load('predictor')(overrides=args, _callbacks=self.callbacks)
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self.predictor = predictor or self._smart_load("predictor")(overrides=args, _callbacks=self.callbacks)
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self.predictor.setup_model(model=self.model, verbose=is_cli)
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else: # only update args if predictor is already setup
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self.predictor.args = get_cfg(self.predictor.args, args)
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if 'project' in args or 'name' in args:
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if "project" in args or "name" in args:
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self.predictor.save_dir = get_save_dir(self.predictor.args)
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if prompts and hasattr(self.predictor, 'set_prompts'): # for SAM-type models
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if prompts and hasattr(self.predictor, "set_prompts"): # for SAM-type models
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self.predictor.set_prompts(prompts)
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return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
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@ -280,11 +286,12 @@ class Model(nn.Module):
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Returns:
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(List[ultralytics.engine.results.Results]): The tracking results.
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"""
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if not hasattr(self.predictor, 'trackers'):
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if not hasattr(self.predictor, "trackers"):
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from ultralytics.trackers import register_tracker
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register_tracker(self, persist)
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kwargs['conf'] = kwargs.get('conf') or 0.1 # ByteTrack-based method needs low confidence predictions as input
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kwargs['mode'] = 'track'
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kwargs["conf"] = kwargs.get("conf") or 0.1 # ByteTrack-based method needs low confidence predictions as input
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kwargs["mode"] = "track"
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return self.predict(source=source, stream=stream, **kwargs)
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def val(self, validator=None, **kwargs):
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@ -295,10 +302,10 @@ class Model(nn.Module):
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validator (BaseValidator): Customized validator.
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**kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
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"""
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custom = {'rect': True} # method defaults
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args = {**self.overrides, **custom, **kwargs, 'mode': 'val'} # highest priority args on the right
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custom = {"rect": True} # method defaults
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args = {**self.overrides, **custom, **kwargs, "mode": "val"} # highest priority args on the right
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validator = (validator or self._smart_load('validator'))(args=args, _callbacks=self.callbacks)
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validator = (validator or self._smart_load("validator"))(args=args, _callbacks=self.callbacks)
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validator(model=self.model)
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self.metrics = validator.metrics
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return validator.metrics
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@ -313,16 +320,17 @@ class Model(nn.Module):
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self._check_is_pytorch_model()
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from ultralytics.utils.benchmarks import benchmark
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custom = {'verbose': False} # method defaults
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args = {**DEFAULT_CFG_DICT, **self.model.args, **custom, **kwargs, 'mode': 'benchmark'}
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custom = {"verbose": False} # method defaults
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args = {**DEFAULT_CFG_DICT, **self.model.args, **custom, **kwargs, "mode": "benchmark"}
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return benchmark(
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model=self,
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data=kwargs.get('data'), # if no 'data' argument passed set data=None for default datasets
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imgsz=args['imgsz'],
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half=args['half'],
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int8=args['int8'],
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device=args['device'],
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verbose=kwargs.get('verbose'))
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data=kwargs.get("data"), # if no 'data' argument passed set data=None for default datasets
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imgsz=args["imgsz"],
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half=args["half"],
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int8=args["int8"],
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device=args["device"],
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verbose=kwargs.get("verbose"),
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)
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def export(self, **kwargs):
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"""
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@ -334,8 +342,8 @@ class Model(nn.Module):
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self._check_is_pytorch_model()
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from .exporter import Exporter
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custom = {'imgsz': self.model.args['imgsz'], 'batch': 1, 'data': None, 'verbose': False} # method defaults
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args = {**self.overrides, **custom, **kwargs, 'mode': 'export'} # highest priority args on the right
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custom = {"imgsz": self.model.args["imgsz"], "batch": 1, "data": None, "verbose": False} # method defaults
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args = {**self.overrides, **custom, **kwargs, "mode": "export"} # highest priority args on the right
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return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
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def train(self, trainer=None, **kwargs):
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@ -347,32 +355,32 @@ class Model(nn.Module):
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**kwargs (Any): Any number of arguments representing the training configuration.
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"""
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self._check_is_pytorch_model()
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if hasattr(self.session, 'model') and self.session.model.id: # Ultralytics HUB session with loaded model
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if hasattr(self.session, "model") and self.session.model.id: # Ultralytics HUB session with loaded model
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if any(kwargs):
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LOGGER.warning('WARNING ⚠️ using HUB training arguments, ignoring local training arguments.')
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LOGGER.warning("WARNING ⚠️ using HUB training arguments, ignoring local training arguments.")
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kwargs = self.session.train_args # overwrite kwargs
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checks.check_pip_update_available()
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overrides = yaml_load(checks.check_yaml(kwargs['cfg'])) if kwargs.get('cfg') else self.overrides
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custom = {'data': DEFAULT_CFG_DICT['data'] or TASK2DATA[self.task]} # method defaults
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args = {**overrides, **custom, **kwargs, 'mode': 'train'} # highest priority args on the right
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if args.get('resume'):
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args['resume'] = self.ckpt_path
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overrides = yaml_load(checks.check_yaml(kwargs["cfg"])) if kwargs.get("cfg") else self.overrides
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custom = {"data": DEFAULT_CFG_DICT["data"] or TASK2DATA[self.task]} # method defaults
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args = {**overrides, **custom, **kwargs, "mode": "train"} # highest priority args on the right
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if args.get("resume"):
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args["resume"] = self.ckpt_path
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self.trainer = (trainer or self._smart_load('trainer'))(overrides=args, _callbacks=self.callbacks)
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if not args.get('resume'): # manually set model only if not resuming
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self.trainer = (trainer or self._smart_load("trainer"))(overrides=args, _callbacks=self.callbacks)
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if not args.get("resume"): # manually set model only if not resuming
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self.trainer.model = self.trainer.get_model(weights=self.model if self.ckpt else None, cfg=self.model.yaml)
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self.model = self.trainer.model
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if SETTINGS['hub'] is True and not self.session:
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if SETTINGS["hub"] is True and not self.session:
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# Create a model in HUB
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try:
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self.session = self._get_hub_session(self.model_name)
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if self.session:
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self.session.create_model(args)
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# Check model was created
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if not getattr(self.session.model, 'id', None):
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if not getattr(self.session.model, "id", None):
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self.session = None
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except PermissionError:
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# Ignore permission error
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@ -385,7 +393,7 @@ class Model(nn.Module):
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ckpt = self.trainer.best if self.trainer.best.exists() else self.trainer.last
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self.model, _ = attempt_load_one_weight(ckpt)
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self.overrides = self.model.args
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self.metrics = getattr(self.trainer.validator, 'metrics', None) # TODO: no metrics returned by DDP
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self.metrics = getattr(self.trainer.validator, "metrics", None) # TODO: no metrics returned by DDP
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return self.metrics
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def tune(self, use_ray=False, iterations=10, *args, **kwargs):
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@ -398,12 +406,13 @@ class Model(nn.Module):
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self._check_is_pytorch_model()
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if use_ray:
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from ultralytics.utils.tuner import run_ray_tune
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return run_ray_tune(self, max_samples=iterations, *args, **kwargs)
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else:
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from .tuner import Tuner
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custom = {} # method defaults
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args = {**self.overrides, **custom, **kwargs, 'mode': 'train'} # highest priority args on the right
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args = {**self.overrides, **custom, **kwargs, "mode": "train"} # highest priority args on the right
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return Tuner(args=args, _callbacks=self.callbacks)(model=self, iterations=iterations)
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def _apply(self, fn):
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@ -411,13 +420,13 @@ class Model(nn.Module):
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self._check_is_pytorch_model()
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self = super()._apply(fn) # noqa
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self.predictor = None # reset predictor as device may have changed
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self.overrides['device'] = self.device # was str(self.device) i.e. device(type='cuda', index=0) -> 'cuda:0'
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self.overrides["device"] = self.device # was str(self.device) i.e. device(type='cuda', index=0) -> 'cuda:0'
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return self
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@property
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def names(self):
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"""Returns class names of the loaded model."""
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return self.model.names if hasattr(self.model, 'names') else None
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return self.model.names if hasattr(self.model, "names") else None
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@property
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def device(self):
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@ -427,7 +436,7 @@ class Model(nn.Module):
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@property
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def transforms(self):
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"""Returns transform of the loaded model."""
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return self.model.transforms if hasattr(self.model, 'transforms') else None
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return self.model.transforms if hasattr(self.model, "transforms") else None
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def add_callback(self, event: str, func):
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"""Add a callback."""
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@ -445,7 +454,7 @@ class Model(nn.Module):
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@staticmethod
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def _reset_ckpt_args(args):
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"""Reset arguments when loading a PyTorch model."""
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include = {'imgsz', 'data', 'task', 'single_cls'} # only remember these arguments when loading a PyTorch model
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include = {"imgsz", "data", "task", "single_cls"} # only remember these arguments when loading a PyTorch model
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return {k: v for k, v in args.items() if k in include}
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# def __getattr__(self, attr):
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@ -461,7 +470,8 @@ class Model(nn.Module):
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name = self.__class__.__name__
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mode = inspect.stack()[1][3] # get the function name.
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raise NotImplementedError(
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emojis(f"WARNING ⚠️ '{name}' model does not support '{mode}' mode for '{self.task}' task yet.")) from e
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emojis(f"WARNING ⚠️ '{name}' model does not support '{mode}' mode for '{self.task}' task yet.")
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) from e
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@property
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def task_map(self):
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@ -471,4 +481,4 @@ class Model(nn.Module):
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Returns:
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task_map (dict): The map of model task to mode classes.
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"""
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raise NotImplementedError('Please provide task map for your model!')
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raise NotImplementedError("Please provide task map for your model!")
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