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Referência para ultralytics/hub/session.py

Nota

Este ficheiro está disponível em https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/hub/session .py. Se detectares um problema, por favor ajuda a corrigi-lo contribuindo com um Pull Request 🛠️. Obrigado 🙏!



ultralytics.hub.session.HUBTrainingSession

Sessão de treinamento do HUB para os modelos Ultralytics HUB YOLO . Lida com a inicialização do modelo, batimentos cardíacos e checkpointing.

Atributos:

Nome Tipo Descrição
model_id str

Identificador do modelo YOLO que está a ser treinado.

model_url str

URL para o modelo em Ultralytics HUB.

rate_limits dict

Limites de taxa para diferentes chamadas de API (em segundos).

timers dict

Temporizadores para limitação do débito.

metrics_queue dict

Fila de espera para as métricas do modelo.

model dict

Obtém os dados do modelo em Ultralytics HUB.

Código fonte em ultralytics/hub/session.py
class HUBTrainingSession:
    """
    HUB training session for Ultralytics HUB YOLO models. Handles model initialization, heartbeats, and checkpointing.

    Attributes:
        model_id (str): Identifier for the YOLO model being trained.
        model_url (str): URL for the model in Ultralytics HUB.
        rate_limits (dict): Rate limits for different API calls (in seconds).
        timers (dict): Timers for rate limiting.
        metrics_queue (dict): Queue for the model's metrics.
        model (dict): Model data fetched from Ultralytics HUB.
    """

    def __init__(self, identifier):
        """
        Initialize the HUBTrainingSession with the provided model identifier.

        Args:
            identifier (str): Model identifier used to initialize the HUB training session.
                It can be a URL string or a model key with specific format.

        Raises:
            ValueError: If the provided model identifier is invalid.
            ConnectionError: If connecting with global API key is not supported.
            ModuleNotFoundError: If hub-sdk package is not installed.
        """
        from hub_sdk import HUBClient

        self.rate_limits = {"metrics": 3, "ckpt": 900, "heartbeat": 300}  # rate limits (seconds)
        self.metrics_queue = {}  # holds metrics for each epoch until upload
        self.metrics_upload_failed_queue = {}  # holds metrics for each epoch if upload failed
        self.timers = {}  # holds timers in ultralytics/utils/callbacks/hub.py
        self.model = None
        self.model_url = None

        # Parse input
        api_key, model_id, self.filename = self._parse_identifier(identifier)

        # Get credentials
        active_key = api_key or SETTINGS.get("api_key")
        credentials = {"api_key": active_key} if active_key else None  # set credentials

        # Initialize client
        self.client = HUBClient(credentials)

        # Load models if authenticated
        if self.client.authenticated:
            if model_id:
                self.load_model(model_id)  # load existing model
            else:
                self.model = self.client.model()  # load empty model

    @classmethod
    def create_session(cls, identifier, args=None):
        """Class method to create an authenticated HUBTrainingSession or return None."""
        try:
            session = cls(identifier)
            if not session.client.authenticated:
                if identifier.startswith(f"{HUB_WEB_ROOT}/models/"):
                    LOGGER.warning(f"{PREFIX}WARNING ⚠️ Login to Ultralytics HUB with 'yolo hub login API_KEY'.")
                    exit()
                return None
            if args and not identifier.startswith(f"{HUB_WEB_ROOT}/models/"):  # not a HUB model URL
                session.create_model(args)
                assert session.model.id, "HUB model not loaded correctly"
            return session
        # PermissionError and ModuleNotFoundError indicate hub-sdk not installed
        except (PermissionError, ModuleNotFoundError, AssertionError):
            return None

    def load_model(self, model_id):
        """Loads an existing model from Ultralytics HUB using the provided model identifier."""
        self.model = self.client.model(model_id)
        if not self.model.data:  # then model does not exist
            raise ValueError(emojis("❌ The specified HUB model does not exist"))  # TODO: improve error handling

        self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

        self._set_train_args()

        # Start heartbeats for HUB to monitor agent
        self.model.start_heartbeat(self.rate_limits["heartbeat"])
        LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

    def create_model(self, model_args):
        """Initializes a HUB training session with the specified model identifier."""
        payload = {
            "config": {
                "batchSize": model_args.get("batch", -1),
                "epochs": model_args.get("epochs", 300),
                "imageSize": model_args.get("imgsz", 640),
                "patience": model_args.get("patience", 100),
                "device": str(model_args.get("device", "")),  # convert None to string
                "cache": str(model_args.get("cache", "ram")),  # convert True, False, None to string
            },
            "dataset": {"name": model_args.get("data")},
            "lineage": {
                "architecture": {"name": self.filename.replace(".pt", "").replace(".yaml", "")},
                "parent": {},
            },
            "meta": {"name": self.filename},
        }

        if self.filename.endswith(".pt"):
            payload["lineage"]["parent"]["name"] = self.filename

        self.model.create_model(payload)

        # Model could not be created
        # TODO: improve error handling
        if not self.model.id:
            return None

        self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

        # Start heartbeats for HUB to monitor agent
        self.model.start_heartbeat(self.rate_limits["heartbeat"])

        LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

    @staticmethod
    def _parse_identifier(identifier):
        """
        Parses the given identifier to determine the type of identifier and extract relevant components.

        The method supports different identifier formats:
            - A HUB URL, which starts with HUB_WEB_ROOT followed by '/models/'
            - An identifier containing an API key and a model ID separated by an underscore
            - An identifier that is solely a model ID of a fixed length
            - A local filename that ends with '.pt' or '.yaml'

        Args:
            identifier (str): The identifier string to be parsed.

        Returns:
            (tuple): A tuple containing the API key, model ID, and filename as applicable.

        Raises:
            HUBModelError: If the identifier format is not recognized.
        """

        # Initialize variables
        api_key, model_id, filename = None, None, None

        # Check if identifier is a HUB URL
        if identifier.startswith(f"{HUB_WEB_ROOT}/models/"):
            # Extract the model_id after the HUB_WEB_ROOT URL
            model_id = identifier.split(f"{HUB_WEB_ROOT}/models/")[-1]
        else:
            # Split the identifier based on underscores only if it's not a HUB URL
            parts = identifier.split("_")

            # Check if identifier is in the format of API key and model ID
            if len(parts) == 2 and len(parts[0]) == 42 and len(parts[1]) == 20:
                api_key, model_id = parts
            # Check if identifier is a single model ID
            elif len(parts) == 1 and len(parts[0]) == 20:
                model_id = parts[0]
            # Check if identifier is a local filename
            elif identifier.endswith(".pt") or identifier.endswith(".yaml"):
                filename = identifier
            else:
                raise HUBModelError(
                    f"model='{identifier}' could not be parsed. Check format is correct. "
                    f"Supported formats are Ultralytics HUB URL, apiKey_modelId, modelId, local pt or yaml file."
                )

        return api_key, model_id, filename

    def _set_train_args(self):
        """
        Initializes training arguments and creates a model entry on the Ultralytics HUB.

        This method sets up training arguments based on the model's state and updates them with any additional
        arguments provided. It handles different states of the model, such as whether it's resumable, pretrained,
        or requires specific file setup.

        Raises:
            ValueError: If the model is already trained, if required dataset information is missing, or if there are
                issues with the provided training arguments.
        """
        if self.model.is_trained():
            raise ValueError(emojis(f"Model is already trained and uploaded to {self.model_url} 🚀"))

        if self.model.is_resumable():
            # Model has saved weights
            self.train_args = {"data": self.model.get_dataset_url(), "resume": True}
            self.model_file = self.model.get_weights_url("last")
        else:
            # Model has no saved weights
            self.train_args = self.model.data.get("train_args")  # new response

            # Set the model file as either a *.pt or *.yaml file
            self.model_file = (
                self.model.get_weights_url("parent") if self.model.is_pretrained() else self.model.get_architecture()
            )

        if "data" not in self.train_args:
            # RF bug - datasets are sometimes not exported
            raise ValueError("Dataset may still be processing. Please wait a minute and try again.")

        self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False)  # YOLOv5->YOLOv5u
        self.model_id = self.model.id

    def request_queue(
        self,
        request_func,
        retry=3,
        timeout=30,
        thread=True,
        verbose=True,
        progress_total=None,
        stream_response=None,
        *args,
        **kwargs,
    ):
        """Attempts to execute `request_func` with retries, timeout handling, optional threading, and progress."""

        def retry_request():
            """Attempts to call `request_func` with retries, timeout, and optional threading."""
            t0 = time.time()  # Record the start time for the timeout
            response = None
            for i in range(retry + 1):
                if (time.time() - t0) > timeout:
                    LOGGER.warning(f"{PREFIX}Timeout for request reached. {HELP_MSG}")
                    break  # Timeout reached, exit loop

                response = request_func(*args, **kwargs)
                if response is None:
                    LOGGER.warning(f"{PREFIX}Received no response from the request. {HELP_MSG}")
                    time.sleep(2**i)  # Exponential backoff before retrying
                    continue  # Skip further processing and retry

                if progress_total:
                    self._show_upload_progress(progress_total, response)
                elif stream_response:
                    self._iterate_content(response)

                if HTTPStatus.OK <= response.status_code < HTTPStatus.MULTIPLE_CHOICES:
                    # if request related to metrics upload
                    if kwargs.get("metrics"):
                        self.metrics_upload_failed_queue = {}
                    return response  # Success, no need to retry

                if i == 0:
                    # Initial attempt, check status code and provide messages
                    message = self._get_failure_message(response, retry, timeout)

                    if verbose:
                        LOGGER.warning(f"{PREFIX}{message} {HELP_MSG} ({response.status_code})")

                if not self._should_retry(response.status_code):
                    LOGGER.warning(f"{PREFIX}Request failed. {HELP_MSG} ({response.status_code}")
                    break  # Not an error that should be retried, exit loop

                time.sleep(2**i)  # Exponential backoff for retries

            # if request related to metrics upload and exceed retries
            if response is None and kwargs.get("metrics"):
                self.metrics_upload_failed_queue.update(kwargs.get("metrics", None))

            return response

        if thread:
            # Start a new thread to run the retry_request function
            threading.Thread(target=retry_request, daemon=True).start()
        else:
            # If running in the main thread, call retry_request directly
            return retry_request()

    @staticmethod
    def _should_retry(status_code):
        """Determines if a request should be retried based on the HTTP status code."""
        retry_codes = {
            HTTPStatus.REQUEST_TIMEOUT,
            HTTPStatus.BAD_GATEWAY,
            HTTPStatus.GATEWAY_TIMEOUT,
        }
        return status_code in retry_codes

    def _get_failure_message(self, response: requests.Response, retry: int, timeout: int):
        """
        Generate a retry message based on the response status code.

        Args:
            response: The HTTP response object.
            retry: The number of retry attempts allowed.
            timeout: The maximum timeout duration.

        Returns:
            (str): The retry message.
        """
        if self._should_retry(response.status_code):
            return f"Retrying {retry}x for {timeout}s." if retry else ""
        elif response.status_code == HTTPStatus.TOO_MANY_REQUESTS:  # rate limit
            headers = response.headers
            return (
                f"Rate limit reached ({headers['X-RateLimit-Remaining']}/{headers['X-RateLimit-Limit']}). "
                f"Please retry after {headers['Retry-After']}s."
            )
        else:
            try:
                return response.json().get("message", "No JSON message.")
            except AttributeError:
                return "Unable to read JSON."

    def upload_metrics(self):
        """Upload model metrics to Ultralytics HUB."""
        return self.request_queue(self.model.upload_metrics, metrics=self.metrics_queue.copy(), thread=True)

    def upload_model(
        self,
        epoch: int,
        weights: str,
        is_best: bool = False,
        map: float = 0.0,
        final: bool = False,
    ) -> None:
        """
        Upload a model checkpoint to Ultralytics HUB.

        Args:
            epoch (int): The current training epoch.
            weights (str): Path to the model weights file.
            is_best (bool): Indicates if the current model is the best one so far.
            map (float): Mean average precision of the model.
            final (bool): Indicates if the model is the final model after training.
        """
        if Path(weights).is_file():
            progress_total = Path(weights).stat().st_size if final else None  # Only show progress if final
            self.request_queue(
                self.model.upload_model,
                epoch=epoch,
                weights=weights,
                is_best=is_best,
                map=map,
                final=final,
                retry=10,
                timeout=3600,
                thread=not final,
                progress_total=progress_total,
                stream_response=True,
            )
        else:
            LOGGER.warning(f"{PREFIX}WARNING ⚠️ Model upload issue. Missing model {weights}.")

    @staticmethod
    def _show_upload_progress(content_length: int, response: requests.Response) -> None:
        """
        Display a progress bar to track the upload progress of a file download.

        Args:
            content_length (int): The total size of the content to be downloaded in bytes.
            response (requests.Response): The response object from the file download request.

        Returns:
            None
        """
        with TQDM(total=content_length, unit="B", unit_scale=True, unit_divisor=1024) as pbar:
            for data in response.iter_content(chunk_size=1024):
                pbar.update(len(data))

    @staticmethod
    def _iterate_content(response: requests.Response) -> None:
        """
        Process the streamed HTTP response data.

        Args:
            response (requests.Response): The response object from the file download request.

        Returns:
            None
        """
        for _ in response.iter_content(chunk_size=1024):
            pass  # Do nothing with data chunks

__init__(identifier)

Inicializa o HUBTrainingSession com o identificador de modelo fornecido.

Parâmetros:

Nome Tipo Descrição Predefinição
identifier str

Identificador do modelo utilizado para inicializar a sessão de formação do HUB. Pode ser uma cadeia de URL ou uma chave de modelo com um formato específico.

necessário

Aumenta:

Tipo Descrição
ValueError

Se o identificador de modelo fornecido for inválido.

ConnectionError

Se a ligação com a chave global da API não for suportada.

ModuleNotFoundError

Se o pacote hub-sdk não estiver instalado.

Código fonte em ultralytics/hub/session.py
def __init__(self, identifier):
    """
    Initialize the HUBTrainingSession with the provided model identifier.

    Args:
        identifier (str): Model identifier used to initialize the HUB training session.
            It can be a URL string or a model key with specific format.

    Raises:
        ValueError: If the provided model identifier is invalid.
        ConnectionError: If connecting with global API key is not supported.
        ModuleNotFoundError: If hub-sdk package is not installed.
    """
    from hub_sdk import HUBClient

    self.rate_limits = {"metrics": 3, "ckpt": 900, "heartbeat": 300}  # rate limits (seconds)
    self.metrics_queue = {}  # holds metrics for each epoch until upload
    self.metrics_upload_failed_queue = {}  # holds metrics for each epoch if upload failed
    self.timers = {}  # holds timers in ultralytics/utils/callbacks/hub.py
    self.model = None
    self.model_url = None

    # Parse input
    api_key, model_id, self.filename = self._parse_identifier(identifier)

    # Get credentials
    active_key = api_key or SETTINGS.get("api_key")
    credentials = {"api_key": active_key} if active_key else None  # set credentials

    # Initialize client
    self.client = HUBClient(credentials)

    # Load models if authenticated
    if self.client.authenticated:
        if model_id:
            self.load_model(model_id)  # load existing model
        else:
            self.model = self.client.model()  # load empty model

create_model(model_args)

Inicializa uma sessão de treinamento do HUB com o identificador de modelo especificado.

Código fonte em ultralytics/hub/session.py
def create_model(self, model_args):
    """Initializes a HUB training session with the specified model identifier."""
    payload = {
        "config": {
            "batchSize": model_args.get("batch", -1),
            "epochs": model_args.get("epochs", 300),
            "imageSize": model_args.get("imgsz", 640),
            "patience": model_args.get("patience", 100),
            "device": str(model_args.get("device", "")),  # convert None to string
            "cache": str(model_args.get("cache", "ram")),  # convert True, False, None to string
        },
        "dataset": {"name": model_args.get("data")},
        "lineage": {
            "architecture": {"name": self.filename.replace(".pt", "").replace(".yaml", "")},
            "parent": {},
        },
        "meta": {"name": self.filename},
    }

    if self.filename.endswith(".pt"):
        payload["lineage"]["parent"]["name"] = self.filename

    self.model.create_model(payload)

    # Model could not be created
    # TODO: improve error handling
    if not self.model.id:
        return None

    self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

    # Start heartbeats for HUB to monitor agent
    self.model.start_heartbeat(self.rate_limits["heartbeat"])

    LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

create_session(identifier, args=None) classmethod

Método de classe para criar um HUBTrainingSession autenticado ou retornar None.

Código fonte em ultralytics/hub/session.py
@classmethod
def create_session(cls, identifier, args=None):
    """Class method to create an authenticated HUBTrainingSession or return None."""
    try:
        session = cls(identifier)
        if not session.client.authenticated:
            if identifier.startswith(f"{HUB_WEB_ROOT}/models/"):
                LOGGER.warning(f"{PREFIX}WARNING ⚠️ Login to Ultralytics HUB with 'yolo hub login API_KEY'.")
                exit()
            return None
        if args and not identifier.startswith(f"{HUB_WEB_ROOT}/models/"):  # not a HUB model URL
            session.create_model(args)
            assert session.model.id, "HUB model not loaded correctly"
        return session
    # PermissionError and ModuleNotFoundError indicate hub-sdk not installed
    except (PermissionError, ModuleNotFoundError, AssertionError):
        return None

load_model(model_id)

Carrega um modelo existente do Ultralytics HUB usando o identificador de modelo fornecido.

Código fonte em ultralytics/hub/session.py
def load_model(self, model_id):
    """Loads an existing model from Ultralytics HUB using the provided model identifier."""
    self.model = self.client.model(model_id)
    if not self.model.data:  # then model does not exist
        raise ValueError(emojis("❌ The specified HUB model does not exist"))  # TODO: improve error handling

    self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

    self._set_train_args()

    # Start heartbeats for HUB to monitor agent
    self.model.start_heartbeat(self.rate_limits["heartbeat"])
    LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

request_queue(request_func, retry=3, timeout=30, thread=True, verbose=True, progress_total=None, stream_response=None, *args, **kwargs)

Tentativas de execução request_func com novas tentativas, tratamento de tempo limite, threading opcional e progresso.

Código fonte em ultralytics/hub/session.py
def request_queue(
    self,
    request_func,
    retry=3,
    timeout=30,
    thread=True,
    verbose=True,
    progress_total=None,
    stream_response=None,
    *args,
    **kwargs,
):
    """Attempts to execute `request_func` with retries, timeout handling, optional threading, and progress."""

    def retry_request():
        """Attempts to call `request_func` with retries, timeout, and optional threading."""
        t0 = time.time()  # Record the start time for the timeout
        response = None
        for i in range(retry + 1):
            if (time.time() - t0) > timeout:
                LOGGER.warning(f"{PREFIX}Timeout for request reached. {HELP_MSG}")
                break  # Timeout reached, exit loop

            response = request_func(*args, **kwargs)
            if response is None:
                LOGGER.warning(f"{PREFIX}Received no response from the request. {HELP_MSG}")
                time.sleep(2**i)  # Exponential backoff before retrying
                continue  # Skip further processing and retry

            if progress_total:
                self._show_upload_progress(progress_total, response)
            elif stream_response:
                self._iterate_content(response)

            if HTTPStatus.OK <= response.status_code < HTTPStatus.MULTIPLE_CHOICES:
                # if request related to metrics upload
                if kwargs.get("metrics"):
                    self.metrics_upload_failed_queue = {}
                return response  # Success, no need to retry

            if i == 0:
                # Initial attempt, check status code and provide messages
                message = self._get_failure_message(response, retry, timeout)

                if verbose:
                    LOGGER.warning(f"{PREFIX}{message} {HELP_MSG} ({response.status_code})")

            if not self._should_retry(response.status_code):
                LOGGER.warning(f"{PREFIX}Request failed. {HELP_MSG} ({response.status_code}")
                break  # Not an error that should be retried, exit loop

            time.sleep(2**i)  # Exponential backoff for retries

        # if request related to metrics upload and exceed retries
        if response is None and kwargs.get("metrics"):
            self.metrics_upload_failed_queue.update(kwargs.get("metrics", None))

        return response

    if thread:
        # Start a new thread to run the retry_request function
        threading.Thread(target=retry_request, daemon=True).start()
    else:
        # If running in the main thread, call retry_request directly
        return retry_request()

upload_metrics()

Carrega as métricas do modelo para Ultralytics HUB.

Código fonte em ultralytics/hub/session.py
def upload_metrics(self):
    """Upload model metrics to Ultralytics HUB."""
    return self.request_queue(self.model.upload_metrics, metrics=self.metrics_queue.copy(), thread=True)

upload_model(epoch, weights, is_best=False, map=0.0, final=False)

Carrega um ponto de controlo de modelo para Ultralytics HUB.

Parâmetros:

Nome Tipo Descrição Predefinição
epoch int

A época de treino atual.

necessário
weights str

Caminho para o ficheiro de pesos do modelo.

necessário
is_best bool

Indica se o modelo atual é o melhor até agora.

False
map float

Precisão média do modelo.

0.0
final bool

Indica se o modelo é o modelo final após o treino.

False
Código fonte em ultralytics/hub/session.py
def upload_model(
    self,
    epoch: int,
    weights: str,
    is_best: bool = False,
    map: float = 0.0,
    final: bool = False,
) -> None:
    """
    Upload a model checkpoint to Ultralytics HUB.

    Args:
        epoch (int): The current training epoch.
        weights (str): Path to the model weights file.
        is_best (bool): Indicates if the current model is the best one so far.
        map (float): Mean average precision of the model.
        final (bool): Indicates if the model is the final model after training.
    """
    if Path(weights).is_file():
        progress_total = Path(weights).stat().st_size if final else None  # Only show progress if final
        self.request_queue(
            self.model.upload_model,
            epoch=epoch,
            weights=weights,
            is_best=is_best,
            map=map,
            final=final,
            retry=10,
            timeout=3600,
            thread=not final,
            progress_total=progress_total,
            stream_response=True,
        )
    else:
        LOGGER.warning(f"{PREFIX}WARNING ⚠️ Model upload issue. Missing model {weights}.")





Criado em 12/11/2023, Atualizado em 02/06/2024
Autores: Glenn-Jocher (5), Burhan-Q (1)