Versions

Version

Class representing a Roboflow dataset version.

Source code in roboflow/core/version.py

class Version:
    """
    Class representing a Roboflow dataset version.
    """

    model: Optional[InferenceModel]

    def __init__(
        self,
        version_dict,
        type,
        api_key,
        name,
        version,
        model_format,
        local: Optional[str],
        workspace,
        project,
        public,
        colors=None,
    ):
        """
        Initialize a Version object.
        """
        if api_key:
            self.__api_key = api_key
            self.name = name
            self.version = unwrap_version_id(version_id=version)
            self.type = type
            self.augmentation = version_dict["augmentation"]
            self.created = version_dict["created"]
            self.id = version_dict["id"]
            self.images = version_dict["images"]
            self.preprocessing = version_dict["preprocessing"]
            self.splits = version_dict["splits"]
            self.model_format = model_format
            self.workspace = workspace
            self.project = project
            self.public = public
            self.colors = {} if colors is None else colors

            self.colors = colors
            if "exports" in version_dict.keys():
                self.exports = version_dict["exports"]
            else:
                self.exports = []

            version_without_workspace = os.path.basename(str(version))

            response = requests.get(f"{API_URL}/{workspace}/{project}/{self.version}?api_key={self.__api_key}")
            if response.ok:
                version_info = response.json()["version"]
                has_model = bool(version_info.get("train", {}).get("model"))
            else:
                has_model = False

            if not has_model:
                self.model = None
            elif self.type == TYPE_OBJECT_DETECTION:
                self.model = ObjectDetectionModel(
                    self.__api_key,
                    self.id,
                    self.name,
                    version_without_workspace,
                    local=local,
                    colors=self.colors,
                    preprocessing=self.preprocessing,
                )
            elif self.type == TYPE_CLASSICATION:
                self.model = ClassificationModel(
                    self.__api_key,
                    self.id,
                    self.name,
                    version_without_workspace,
                    local=local,
                    colors=self.colors,
                    preprocessing=self.preprocessing,
                )
            elif self.type == TYPE_INSTANCE_SEGMENTATION:
                self.model = InstanceSegmentationModel(
                    self.__api_key,
                    self.id,
                    colors=self.colors,
                    preprocessing=self.preprocessing,
                    local=local,
                )
            elif self.type == TYPE_SEMANTIC_SEGMENTATION:
                self.model = SemanticSegmentationModel(self.__api_key, self.id)
            elif self.type == TYPE_KEYPOINT_DETECTION:
                self.model = KeypointDetectionModel(self.__api_key, self.id, version=version_without_workspace)
            else:
                self.model = None

        elif DEMO_KEYS:
            api_key = DEMO_KEYS[0]
            if api_key == "coco-128-sample":
                self.__api_key = api_key
                self.model_format = model_format
                self.name = "coco-128"
                self.version = "1"
            else:
                self.__api_key = api_key
                self.model_format = model_format
                self.name = "chess-pieces-new"
                self.version = "23"
                self.id = "joseph-nelson/chess-pieces-new"

    def __check_if_generating(self):
        # check Roboflow API to see if this version is still generating

        url = f"{API_URL}/{self.workspace}/{self.project}/{self.version}?nocache=true"
        response = requests.get(url, params={"api_key": self.__api_key})
        response.raise_for_status()
        if response.json()["version"]["progress"] is None:
            progress = 0.0
        else:
            progress = float(response.json()["version"]["progress"])

        return response.json()["version"]["generating"], progress

    def __wait_if_generating(self, recurse=False):
        # checks if a given version is still in the progress of generating

        still_generating, progress = self.__check_if_generating()

        if still_generating:
            progress_message = "Generating version still in progress. Progress: " + str(round(progress * 100, 2)) + "%"
            sys.stdout.write("\r" + progress_message)
            sys.stdout.flush()
            time.sleep(5)
            return self.__wait_if_generating(recurse=True)

        else:
            if recurse:
                sys.stdout.write("\n")
                sys.stdout.flush()
            return

    def download(self, model_format=None, location=None, overwrite: bool = False):
        """
        Download and extract a ZIP of a version's dataset in a given format

        :param model_format: A format to use for downloading
        :param location: An optional path for saving the file
        :param overwrite: An optional flag to prevent dataset overwrite when dataset is already downloaded

        Args:
            model_format (str): A format to use for downloading
            location (str): An optional path for saving the file
            overwrite (bool): An optional flag to overwrite an existing dataset if the dataset has already downloaded

        Returns:
            Dataset Object

        Raises:
            RuntimeError: If the Roboflow API returns an error with a helpful JSON body
            HTTPError: If the Network/Roboflow API fails and does not return JSON
        """  # noqa: E501 // docs

        self.__wait_if_generating()

        model_format = self.__get_format_identifier(model_format)

        if model_format not in self.exports:
            self.export(model_format)

        # if model_format is not in

        if location is None:
            location = self.__get_download_location()
        if os.path.exists(location) and not overwrite:
            return Dataset(self.name, self.version, model_format, os.path.abspath(location))

        if self.__api_key == "coco-128-sample":
            link = "https://app.roboflow.com/ds/n9QwXwUK42?key=NnVCe2yMxP"
        else:
            url = self.__get_download_url(model_format)
            response = requests.get(url, params={"api_key": self.__api_key})
            if response.status_code == 200:
                link = response.json()["export"]["link"]
            else:
                try:
                    raise RuntimeError(response.json())
                except json.JSONDecodeError:
                    response.raise_for_status()

        self.__download_zip(link, location, model_format)
        self.__extract_zip(location, model_format)
        self.__reformat_yaml(location, model_format)  # TODO: is roboflow-python a place to be munging yaml files?

        return Dataset(self.name, self.version, model_format, os.path.abspath(location))

    def export(self, model_format=None):
        """
        Ask the Roboflow API to generate a version's dataset in a given format so that it can be downloaded via the `download()` method.

        The export will be asynchronously generated and available for download after some amount of seconds - depending on dataset size.

        Args:
            model_format (str): A format to use for downloading

        Returns:
            True

        Raises:
            RuntimeError: If the Roboflow API returns an error with a helpful JSON body
            HTTPError: If the Network/Roboflow API fails and does not return JSON
        """  # noqa: E501 // docs

        model_format = self.__get_format_identifier(model_format)

        self.__wait_if_generating()

        url = self.__get_download_url(model_format)
        response = requests.get(url, params={"api_key": self.__api_key})
        if not response.ok:
            try:
                raise RuntimeError(response.json())
            except json.JSONDecodeError:
                response.raise_for_status()

        # the rest api returns 202 if the export is still in progress
        if response.status_code == 202:
            status_code_check = 202
            while status_code_check == 202:
                time.sleep(1)
                response = requests.get(url, params={"api_key": self.__api_key})
                status_code_check = response.status_code
                if status_code_check == 202:
                    progress = response.json()["progress"]
                    progress_message = (
                        "Exporting format " + model_format + " in progress : " + str(round(progress * 100, 2)) + "%"
                    )
                    sys.stdout.write("\r" + progress_message)
                    sys.stdout.flush()

        if response.status_code == 200:
            sys.stdout.write("\n")
            print("\r" + "Version export complete for " + model_format + " format")
            sys.stdout.flush()
            return True
        else:
            try:
                raise RuntimeError(response.json())
            except json.JSONDecodeError:
                response.raise_for_status()

    def train(self, speed=None, checkpoint=None, plot_in_notebook=False) -> InferenceModel:
        """
        Ask the Roboflow API to train a previously exported version's dataset.

        Args:
            speed: Whether to train quickly or accurately. Note: accurate training is a paid feature. Default speed is `fast`.
            checkpoint: A string representing the checkpoint to use while training
            plot: Whether to plot the training results. Default is `False`.

        Returns:
            An instance of the trained model class

        Raises:
            RuntimeError: If the Roboflow API returns an error with a helpful JSON body
            HTTPError: If the Network/Roboflow API fails and does not return JSON
        """  # noqa: E501 // docs

        self.__wait_if_generating()

        train_model_format = "yolov5pytorch"

        if self.type == TYPE_CLASSICATION:
            train_model_format = "folder"

        if self.type == TYPE_INSTANCE_SEGMENTATION:
            train_model_format = "yolov5pytorch"

        if self.type == TYPE_SEMANTIC_SEGMENTATION:
            train_model_format = "png-mask-semantic"

        # if classification
        if train_model_format not in self.exports:
            self.export(train_model_format)

        workspace, project, *_ = self.id.rsplit("/")
        url = f"{API_URL}/{workspace}/{project}/{self.version}/train"

        data = {}
        if speed:
            data["speed"] = speed

        if checkpoint:
            data["checkpoint"] = checkpoint

        write_line("Reaching out to Roboflow to start training...")

        response = requests.post(url, json=data, params={"api_key": self.__api_key})
        if not response.ok:
            try:
                raise RuntimeError(response.json())
            except json.JSONDecodeError:
                response.raise_for_status()

        status = "training"

        if plot_in_notebook:
            from IPython.display import clear_output
            from matplotlib import pyplot as plt

            def live_plot(epochs, mAP, loss, title=""):
                clear_output(wait=True)

                plt.subplot(2, 1, 1)
                plt.plot(epochs, mAP, "#00FFCE")
                plt.title(title)
                plt.ylabel("mAP")

                plt.subplot(2, 1, 2)
                plt.plot(epochs, loss, "#A351FB")
                plt.xlabel("epochs")
                plt.ylabel("loss")
                plt.show()

        first_graph_write = False
        previous_epochs: Union[np.ndarray, list] = []
        num_machine_spin_dots = []

        while status == "training" or status == "running":
            url = f"{API_URL}/{self.workspace}/{self.project}/{self.version}?nocache=true"
            response = requests.get(url, params={"api_key": self.__api_key})
            response.raise_for_status()
            version = response.json()["version"]
            if "models" in version.keys():
                models = version["models"]
            else:
                models = {}

            if "train" in version.keys():
                if "results" in version["train"].keys():
                    status = "finished"
                    break
                if "status" in version["train"].keys():
                    if version["train"]["status"] == "failed":
                        write_line(line="Training failed")
                        break

            epochs: Union[np.ndarray, list]
            mAP: Union[np.ndarray, list]
            loss: Union[np.ndarray, list]

            if "roboflow-train" in models.keys():
                import numpy as np

                # training has started
                epochs = np.array([int(epoch["epoch"]) for epoch in models["roboflow-train"]["epochs"]])
                mAP = np.array([float(epoch["mAP"]) for epoch in models["roboflow-train"]["epochs"]])
                loss = np.array(
                    [
                        sum(float(epoch[key]) for key in ["box_loss", "class_loss", "obj_loss"] if key in epoch)
                        for epoch in models["roboflow-train"]["epochs"]
                    ]
                )

                title = "Training in Progress"
                # plottling logic
            else:
                num_machine_spin_dots.append(".")
                if len(num_machine_spin_dots) > 5:
                    num_machine_spin_dots = ["."]
                title = "Training Machine Spinning Up" + "".join(num_machine_spin_dots)

                epochs = []
                mAP = []
                loss = []

            if (len(epochs) > len(previous_epochs)) or (len(epochs) == 0):
                if plot_in_notebook:
                    live_plot(epochs, mAP, loss, title)
                else:
                    if len(epochs) > 0:
                        title = (
                            title + ": Epoch: " + str(epochs[-1]) + " mAP: " + str(mAP[-1]) + " loss: " + str(loss[-1])
                        )
                    if not first_graph_write:
                        write_line(title)
                        first_graph_write = True

            previous_epochs = copy.deepcopy(epochs)

            time.sleep(5)

        if not self.model:
            if self.type == TYPE_OBJECT_DETECTION:
                self.model = ObjectDetectionModel(
                    self.__api_key,
                    self.id,
                    self.name,
                    self.version,
                    colors=self.colors,
                    preprocessing=self.preprocessing,
                )
            elif self.type == TYPE_CLASSICATION:
                self.model = ClassificationModel(
                    self.__api_key,
                    self.id,
                    self.name,
                    self.version,
                    colors=self.colors,
                    preprocessing=self.preprocessing,
                )
            elif self.type == TYPE_INSTANCE_SEGMENTATION:
                self.model = InstanceSegmentationModel(
                    self.__api_key,
                    self.id,
                    colors=self.colors,
                    preprocessing=self.preprocessing,
                )
            elif self.type == TYPE_SEMANTIC_SEGMENTATION:
                self.model = SemanticSegmentationModel(self.__api_key, self.id)
            elif self.type == TYPE_KEYPOINT_DETECTION:
                self.model = KeypointDetectionModel(self.__api_key, self.id, version=self.version)
            else:
                raise ValueError(f"Unsupported model type: {self.type}")

        # return the model object
        assert self.model
        return self.model

    # @warn_for_wrong_dependencies_versions([("ultralytics", "==", "8.0.196")])
    def deploy(self, model_type: str, model_path: str, filename: str = "weights/best.pt") -> None:
        """Uploads provided weights file to Roboflow.

        Args:
            model_type (str): The type of the model to be deployed.
            model_path (str): File path to the model weights to be uploaded.
            filename (str, optional): The name of the weights file. Defaults to "weights/best.pt".
        """
        model_type = normalize_yolo_model_type(model_type)
        zip_file_name = process(model_type, model_path, filename)

        if zip_file_name is None:
            raise RuntimeError("Failed to process model")

        self._upload_zip(model_type, model_path, zip_file_name)

    def _upload_zip(self, model_type: str, model_path: str, model_file_name: str):
        res = requests.get(
            f"{API_URL}/{self.workspace}/{self.project}/{self.version}"
            f"/uploadModel?api_key={self.__api_key}&modelType={model_type}&nocache=true"
        )
        try:
            if res.status_code == 429:
                raise RuntimeError(
                    "This version already has a trained model. Please generate and"
                    " train a new version in order to upload model to Roboflow."
                )
            else:
                res.raise_for_status()
        except Exception as e:
            print(f"An error occured when getting the model upload URL: {e}")
            return

        res = requests.put(
            res.json()["url"],
            data=open(os.path.join(model_path, model_file_name), "rb"),
        )
        try:
            res.raise_for_status()

            if self.public:
                print(
                    f"View the status of your deployment at: {APP_URL}/{self.workspace}/{self.project}/{self.version}"
                )
                print(
                    "Share your model with the world at:"
                    f" {UNIVERSE_URL}/{self.workspace}/{self.project}/"
                    f"model/{self.version}"
                )
            else:
                print(
                    f"View the status of your deployment at: {APP_URL}/{self.workspace}/{self.project}/{self.version}"
                )

        except Exception as e:
            print(f"An error occured when uploading the model: {e}")

    def __download_zip(self, link, location, format):
        """
        Download a dataset's zip file from the given URL and save it in the desired location

        Args:
            link (str): link the URL of the remote zip file
            location (str): filepath of the data directory to save the zip file to
            format (str): the format identifier string
        """  # noqa: E501 // docs
        if not os.path.exists(location):
            os.makedirs(location)

        def bar_progress(current, total, width=80):
            progress_message = (
                f"Downloading Dataset Version Zip in {location} to {format}: "
                f"{current / total * 100:.0f}% [{current} / {total}] bytes"
            )
            sys.stdout.write("\r" + progress_message)
            sys.stdout.flush()

        try:
            response = requests.get(link, stream=True)

            # write the zip file to the desired location
            with open(location + "/roboflow.zip", "wb") as f:
                total_length = int(response.headers.get("content-length"))  # type: ignore[arg-type]
                desc = None if TQDM_DISABLE else f"Downloading Dataset Version Zip in {location} to {format}:"
                for chunk in tqdm(
                    response.iter_content(chunk_size=1024),
                    desc=desc,
                    total=int(total_length / 1024) + 1,
                ):
                    if chunk:
                        f.write(chunk)
                        f.flush()

        except Exception as e:
            print(f"Error when trying to download dataset @ {link}")
            raise e
        sys.stdout.write("\n")
        sys.stdout.flush()

    def __extract_zip(self, location, format):
        """
        Extracts the contents of a downloaded ZIP file and then deletes the zipped file.

        Args:
            location (str): filepath of the data directory that contains the ZIP file
            format (str): the format identifier string

        Raises:
            RuntimeError: If there is an error unzipping the file
        """  # noqa: E501 // docs
        desc = None if TQDM_DISABLE else f"Extracting Dataset Version Zip to {location} in {format}:"
        with zipfile.ZipFile(location + "/roboflow.zip", "r") as zip_ref:
            for member in tqdm(
                zip_ref.infolist(),
                desc=desc,
            ):
                try:
                    zip_ref.extract(member, location)
                except zipfile.error:
                    raise RuntimeError("Error unzipping download")

        os.remove(location + "/roboflow.zip")

    def __get_download_location(self):
        """
        Get the local path to save a downloaded dataset to

        Returns:
            str: the local path
        """
        version_slug = self.name.replace(" ", "-")
        filename = f"{version_slug}-{self.version}"

        directory = os.environ.get("DATASET_DIRECTORY")
        if directory:
            return f"{directory}/{filename}"

        return filename

    def __get_download_url(self, format):
        """
        Get the Roboflow API URL for downloading (and exporting downloadable zips)

        Args:
            format (str): the format identifier string

        Returns:
            str: the Roboflow API URL
        """
        workspace, project, *_ = self.id.rsplit("/")
        return f"{API_URL}/{workspace}/{project}/{self.version}/{format}"

    def __get_format_identifier(self, format):
        """
        If `format` is none, fall back to the instance's `model_format` value.

        If a human readable format name was passed, return the identifier that should be used for Roboflow API calls

        Otherwise, assume that the passed in format is also the identifier

        Args:
            format (str): a human readable format string

        Returns:
            str: format identifier string
        """  # noqa: E501 // docs
        if not format:
            format = self.model_format

        if not format:
            raise RuntimeError(
                "You must pass a format argument to version.download() or define a model in your Roboflow object"
            )

        friendly_formats = {"yolov5": "yolov5pytorch", "yolov7": "yolov7pytorch"}

        return friendly_formats.get(format, format)

    def __reformat_yaml(self, location: str, format: str):
        """
        Certain formats seem to require reformatting the downloaded YAML.

        Args:
            location (str): filepath of the data directory that contains the yaml file
            format (str): the format identifier string
        """  # noqa: E501 // docs
        data_path = os.path.join(location, "data.yaml")

        def data_yaml_callback(content: dict) -> dict:
            if format == "mt-yolov6":
                content["train"] = location + content["train"].lstrip(".")
                content["val"] = location + content["val"].lstrip(".")
                content["test"] = location + content["test"].lstrip(".")
            if format in ["yolov5pytorch", "yolov7pytorch"]:
                content["train"] = location + content["train"].lstrip("..")
                content["val"] = location + content["val"].lstrip("..")
            try:
                # get_wrong_dependencies_versions raises exception if ultralytics is not installed at all  # noqa: E501 // docs
                if format == "yolov8" and not get_wrong_dependencies_versions(
                    dependencies_versions=[("ultralytics", "==", "8.0.196")]
                ):
                    content["train"] = "train/images"
                    content["val"] = "valid/images"
                    content["test"] = "test/images"
            except ModuleNotFoundError:
                pass
            return content

        if format in ["yolov5pytorch", "mt-yolov6", "yolov7pytorch", "yolov8", "yolov9"]:
            amend_data_yaml(path=data_path, callback=data_yaml_callback)

    def __str__(self):
        """
        String representation of version object.
        """
        json_value = {
            "name": self.name,
            "type": self.type,
            "version": self.version,
            "augmentation": self.augmentation,
            "created": self.created,
            "preprocessing": self.preprocessing,
            "splits": self.splits,
            "workspace": self.workspace,
        }
        return json.dumps(json_value, indent=2)

__download_zip(link, location, format)

Download a dataset's zip file from the given URL and save it in the desired location

Parameters:

Name	Type	Description	Default
link	str	link the URL of the remote zip file	required
location	str	filepath of the data directory to save the zip file to	required
format	str	the format identifier string	required

Source code in roboflow/core/version.py

def __download_zip(self, link, location, format):
    """
    Download a dataset's zip file from the given URL and save it in the desired location

    Args:
        link (str): link the URL of the remote zip file
        location (str): filepath of the data directory to save the zip file to
        format (str): the format identifier string
    """  # noqa: E501 // docs
    if not os.path.exists(location):
        os.makedirs(location)

    def bar_progress(current, total, width=80):
        progress_message = (
            f"Downloading Dataset Version Zip in {location} to {format}: "
            f"{current / total * 100:.0f}% [{current} / {total}] bytes"
        )
        sys.stdout.write("\r" + progress_message)
        sys.stdout.flush()

    try:
        response = requests.get(link, stream=True)

        # write the zip file to the desired location
        with open(location + "/roboflow.zip", "wb") as f:
            total_length = int(response.headers.get("content-length"))  # type: ignore[arg-type]
            desc = None if TQDM_DISABLE else f"Downloading Dataset Version Zip in {location} to {format}:"
            for chunk in tqdm(
                response.iter_content(chunk_size=1024),
                desc=desc,
                total=int(total_length / 1024) + 1,
            ):
                if chunk:
                    f.write(chunk)
                    f.flush()

    except Exception as e:
        print(f"Error when trying to download dataset @ {link}")
        raise e
    sys.stdout.write("\n")
    sys.stdout.flush()

__extract_zip(location, format)

Extracts the contents of a downloaded ZIP file and then deletes the zipped file.

Parameters:

Name	Type	Description	Default
location	str	filepath of the data directory that contains the ZIP file	required
format	str	the format identifier string	required

Raises:

Type	Description
RuntimeError	If there is an error unzipping the file

Source code in roboflow/core/version.py

def __extract_zip(self, location, format):
    """
    Extracts the contents of a downloaded ZIP file and then deletes the zipped file.

    Args:
        location (str): filepath of the data directory that contains the ZIP file
        format (str): the format identifier string

    Raises:
        RuntimeError: If there is an error unzipping the file
    """  # noqa: E501 // docs
    desc = None if TQDM_DISABLE else f"Extracting Dataset Version Zip to {location} in {format}:"
    with zipfile.ZipFile(location + "/roboflow.zip", "r") as zip_ref:
        for member in tqdm(
            zip_ref.infolist(),
            desc=desc,
        ):
            try:
                zip_ref.extract(member, location)
            except zipfile.error:
                raise RuntimeError("Error unzipping download")

    os.remove(location + "/roboflow.zip")

__get_download_location()

Get the local path to save a downloaded dataset to

Returns:

Name	Type	Description
str		the local path

Source code in roboflow/core/version.py

def __get_download_location(self):
    """
    Get the local path to save a downloaded dataset to

    Returns:
        str: the local path
    """
    version_slug = self.name.replace(" ", "-")
    filename = f"{version_slug}-{self.version}"

    directory = os.environ.get("DATASET_DIRECTORY")
    if directory:
        return f"{directory}/{filename}"

    return filename

__get_download_url(format)

Get the Roboflow API URL for downloading (and exporting downloadable zips)

Parameters:

Name	Type	Description	Default
format	str	the format identifier string	required

Returns:

Name	Type	Description
str		the Roboflow API URL

Source code in roboflow/core/version.py

def __get_download_url(self, format):
    """
    Get the Roboflow API URL for downloading (and exporting downloadable zips)

    Args:
        format (str): the format identifier string

    Returns:
        str: the Roboflow API URL
    """
    workspace, project, *_ = self.id.rsplit("/")
    return f"{API_URL}/{workspace}/{project}/{self.version}/{format}"

__get_format_identifier(format)

If format is none, fall back to the instance's model_format value.

If a human readable format name was passed, return the identifier that should be used for Roboflow API calls

Otherwise, assume that the passed in format is also the identifier

Parameters:

Name	Type	Description	Default
format	str	a human readable format string	required

Returns:

Name	Type	Description
str		format identifier string

Source code in roboflow/core/version.py

def __get_format_identifier(self, format):
    """
    If `format` is none, fall back to the instance's `model_format` value.

    If a human readable format name was passed, return the identifier that should be used for Roboflow API calls

    Otherwise, assume that the passed in format is also the identifier

    Args:
        format (str): a human readable format string

    Returns:
        str: format identifier string
    """  # noqa: E501 // docs
    if not format:
        format = self.model_format

    if not format:
        raise RuntimeError(
            "You must pass a format argument to version.download() or define a model in your Roboflow object"
        )

    friendly_formats = {"yolov5": "yolov5pytorch", "yolov7": "yolov7pytorch"}

    return friendly_formats.get(format, format)

__init__(version_dict, type, api_key, name, version, model_format, local, workspace, project, public, colors=None)

Initialize a Version object.

Source code in roboflow/core/version.py

def __init__(
    self,
    version_dict,
    type,
    api_key,
    name,
    version,
    model_format,
    local: Optional[str],
    workspace,
    project,
    public,
    colors=None,
):
    """
    Initialize a Version object.
    """
    if api_key:
        self.__api_key = api_key
        self.name = name
        self.version = unwrap_version_id(version_id=version)
        self.type = type
        self.augmentation = version_dict["augmentation"]
        self.created = version_dict["created"]
        self.id = version_dict["id"]
        self.images = version_dict["images"]
        self.preprocessing = version_dict["preprocessing"]
        self.splits = version_dict["splits"]
        self.model_format = model_format
        self.workspace = workspace
        self.project = project
        self.public = public
        self.colors = {} if colors is None else colors

        self.colors = colors
        if "exports" in version_dict.keys():
            self.exports = version_dict["exports"]
        else:
            self.exports = []

        version_without_workspace = os.path.basename(str(version))

        response = requests.get(f"{API_URL}/{workspace}/{project}/{self.version}?api_key={self.__api_key}")
        if response.ok:
            version_info = response.json()["version"]
            has_model = bool(version_info.get("train", {}).get("model"))
        else:
            has_model = False

        if not has_model:
            self.model = None
        elif self.type == TYPE_OBJECT_DETECTION:
            self.model = ObjectDetectionModel(
                self.__api_key,
                self.id,
                self.name,
                version_without_workspace,
                local=local,
                colors=self.colors,
                preprocessing=self.preprocessing,
            )
        elif self.type == TYPE_CLASSICATION:
            self.model = ClassificationModel(
                self.__api_key,
                self.id,
                self.name,
                version_without_workspace,
                local=local,
                colors=self.colors,
                preprocessing=self.preprocessing,
            )
        elif self.type == TYPE_INSTANCE_SEGMENTATION:
            self.model = InstanceSegmentationModel(
                self.__api_key,
                self.id,
                colors=self.colors,
                preprocessing=self.preprocessing,
                local=local,
            )
        elif self.type == TYPE_SEMANTIC_SEGMENTATION:
            self.model = SemanticSegmentationModel(self.__api_key, self.id)
        elif self.type == TYPE_KEYPOINT_DETECTION:
            self.model = KeypointDetectionModel(self.__api_key, self.id, version=version_without_workspace)
        else:
            self.model = None

    elif DEMO_KEYS:
        api_key = DEMO_KEYS[0]
        if api_key == "coco-128-sample":
            self.__api_key = api_key
            self.model_format = model_format
            self.name = "coco-128"
            self.version = "1"
        else:
            self.__api_key = api_key
            self.model_format = model_format
            self.name = "chess-pieces-new"
            self.version = "23"
            self.id = "joseph-nelson/chess-pieces-new"

__reformat_yaml(location, format)

Certain formats seem to require reformatting the downloaded YAML.

Parameters:

Name	Type	Description	Default
location	str	filepath of the data directory that contains the yaml file	required
format	str	the format identifier string	required

Source code in roboflow/core/version.py

def __reformat_yaml(self, location: str, format: str):
    """
    Certain formats seem to require reformatting the downloaded YAML.

    Args:
        location (str): filepath of the data directory that contains the yaml file
        format (str): the format identifier string
    """  # noqa: E501 // docs
    data_path = os.path.join(location, "data.yaml")

    def data_yaml_callback(content: dict) -> dict:
        if format == "mt-yolov6":
            content["train"] = location + content["train"].lstrip(".")
            content["val"] = location + content["val"].lstrip(".")
            content["test"] = location + content["test"].lstrip(".")
        if format in ["yolov5pytorch", "yolov7pytorch"]:
            content["train"] = location + content["train"].lstrip("..")
            content["val"] = location + content["val"].lstrip("..")
        try:
            # get_wrong_dependencies_versions raises exception if ultralytics is not installed at all  # noqa: E501 // docs
            if format == "yolov8" and not get_wrong_dependencies_versions(
                dependencies_versions=[("ultralytics", "==", "8.0.196")]
            ):
                content["train"] = "train/images"
                content["val"] = "valid/images"
                content["test"] = "test/images"
        except ModuleNotFoundError:
            pass
        return content

    if format in ["yolov5pytorch", "mt-yolov6", "yolov7pytorch", "yolov8", "yolov9"]:
        amend_data_yaml(path=data_path, callback=data_yaml_callback)

__str__()

String representation of version object.

Source code in roboflow/core/version.py

def __str__(self):
    """
    String representation of version object.
    """
    json_value = {
        "name": self.name,
        "type": self.type,
        "version": self.version,
        "augmentation": self.augmentation,
        "created": self.created,
        "preprocessing": self.preprocessing,
        "splits": self.splits,
        "workspace": self.workspace,
    }
    return json.dumps(json_value, indent=2)

deploy(model_type, model_path, filename='weights/best.pt')

Uploads provided weights file to Roboflow.

Parameters:

Name	Type	Description	Default
model_type	str	The type of the model to be deployed.	required
model_path	str	File path to the model weights to be uploaded.	required
filename	str	The name of the weights file. Defaults to "weights/best.pt".	'weights/best.pt'

Source code in roboflow/core/version.py

def deploy(self, model_type: str, model_path: str, filename: str = "weights/best.pt") -> None:
    """Uploads provided weights file to Roboflow.

    Args:
        model_type (str): The type of the model to be deployed.
        model_path (str): File path to the model weights to be uploaded.
        filename (str, optional): The name of the weights file. Defaults to "weights/best.pt".
    """
    model_type = normalize_yolo_model_type(model_type)
    zip_file_name = process(model_type, model_path, filename)

    if zip_file_name is None:
        raise RuntimeError("Failed to process model")

    self._upload_zip(model_type, model_path, zip_file_name)

download(model_format=None, location=None, overwrite=False)

Download and extract a ZIP of a version's dataset in a given format

:param model_format: A format to use for downloading :param location: An optional path for saving the file :param overwrite: An optional flag to prevent dataset overwrite when dataset is already downloaded

Parameters:

Name	Type	Description	Default
model_format	str	A format to use for downloading	None
location	str	An optional path for saving the file	None
overwrite	bool	An optional flag to overwrite an existing dataset if the dataset has already downloaded	False

Returns:

Type	Description
	Dataset Object

Raises:

Type	Description
RuntimeError	If the Roboflow API returns an error with a helpful JSON body
HTTPError	If the Network/Roboflow API fails and does not return JSON

Source code in roboflow/core/version.py

def download(self, model_format=None, location=None, overwrite: bool = False):
    """
    Download and extract a ZIP of a version's dataset in a given format

    :param model_format: A format to use for downloading
    :param location: An optional path for saving the file
    :param overwrite: An optional flag to prevent dataset overwrite when dataset is already downloaded

    Args:
        model_format (str): A format to use for downloading
        location (str): An optional path for saving the file
        overwrite (bool): An optional flag to overwrite an existing dataset if the dataset has already downloaded

    Returns:
        Dataset Object

    Raises:
        RuntimeError: If the Roboflow API returns an error with a helpful JSON body
        HTTPError: If the Network/Roboflow API fails and does not return JSON
    """  # noqa: E501 // docs

    self.__wait_if_generating()

    model_format = self.__get_format_identifier(model_format)

    if model_format not in self.exports:
        self.export(model_format)

    # if model_format is not in

    if location is None:
        location = self.__get_download_location()
    if os.path.exists(location) and not overwrite:
        return Dataset(self.name, self.version, model_format, os.path.abspath(location))

    if self.__api_key == "coco-128-sample":
        link = "https://app.roboflow.com/ds/n9QwXwUK42?key=NnVCe2yMxP"
    else:
        url = self.__get_download_url(model_format)
        response = requests.get(url, params={"api_key": self.__api_key})
        if response.status_code == 200:
            link = response.json()["export"]["link"]
        else:
            try:
                raise RuntimeError(response.json())
            except json.JSONDecodeError:
                response.raise_for_status()

    self.__download_zip(link, location, model_format)
    self.__extract_zip(location, model_format)
    self.__reformat_yaml(location, model_format)  # TODO: is roboflow-python a place to be munging yaml files?

    return Dataset(self.name, self.version, model_format, os.path.abspath(location))

export(model_format=None)

Ask the Roboflow API to generate a version's dataset in a given format so that it can be downloaded via the download() method.

The export will be asynchronously generated and available for download after some amount of seconds - depending on dataset size.

Parameters:

Name	Type	Description	Default
model_format	str	A format to use for downloading	None

Returns:

Type	Description
	True

Raises:

Type	Description
RuntimeError	If the Roboflow API returns an error with a helpful JSON body
HTTPError	If the Network/Roboflow API fails and does not return JSON

Source code in roboflow/core/version.py

def export(self, model_format=None):
    """
    Ask the Roboflow API to generate a version's dataset in a given format so that it can be downloaded via the `download()` method.

    The export will be asynchronously generated and available for download after some amount of seconds - depending on dataset size.

    Args:
        model_format (str): A format to use for downloading

    Returns:
        True

    Raises:
        RuntimeError: If the Roboflow API returns an error with a helpful JSON body
        HTTPError: If the Network/Roboflow API fails and does not return JSON
    """  # noqa: E501 // docs

    model_format = self.__get_format_identifier(model_format)

    self.__wait_if_generating()

    url = self.__get_download_url(model_format)
    response = requests.get(url, params={"api_key": self.__api_key})
    if not response.ok:
        try:
            raise RuntimeError(response.json())
        except json.JSONDecodeError:
            response.raise_for_status()

    # the rest api returns 202 if the export is still in progress
    if response.status_code == 202:
        status_code_check = 202
        while status_code_check == 202:
            time.sleep(1)
            response = requests.get(url, params={"api_key": self.__api_key})
            status_code_check = response.status_code
            if status_code_check == 202:
                progress = response.json()["progress"]
                progress_message = (
                    "Exporting format " + model_format + " in progress : " + str(round(progress * 100, 2)) + "%"
                )
                sys.stdout.write("\r" + progress_message)
                sys.stdout.flush()

    if response.status_code == 200:
        sys.stdout.write("\n")
        print("\r" + "Version export complete for " + model_format + " format")
        sys.stdout.flush()
        return True
    else:
        try:
            raise RuntimeError(response.json())
        except json.JSONDecodeError:
            response.raise_for_status()

train(speed=None, checkpoint=None, plot_in_notebook=False)

Ask the Roboflow API to train a previously exported version's dataset.

Parameters:

Name	Type	Description	Default
speed		Whether to train quickly or accurately. Note: accurate training is a paid feature. Default speed is fast.	None
checkpoint		A string representing the checkpoint to use while training	None
plot		Whether to plot the training results. Default is False.	required

Returns:

Type	Description
InferenceModel	An instance of the trained model class

Raises:

Type	Description
RuntimeError	If the Roboflow API returns an error with a helpful JSON body
HTTPError	If the Network/Roboflow API fails and does not return JSON

Source code in roboflow/core/version.py

def train(self, speed=None, checkpoint=None, plot_in_notebook=False) -> InferenceModel:
    """
    Ask the Roboflow API to train a previously exported version's dataset.

    Args:
        speed: Whether to train quickly or accurately. Note: accurate training is a paid feature. Default speed is `fast`.
        checkpoint: A string representing the checkpoint to use while training
        plot: Whether to plot the training results. Default is `False`.

    Returns:
        An instance of the trained model class

    Raises:
        RuntimeError: If the Roboflow API returns an error with a helpful JSON body
        HTTPError: If the Network/Roboflow API fails and does not return JSON
    """  # noqa: E501 // docs

    self.__wait_if_generating()

    train_model_format = "yolov5pytorch"

    if self.type == TYPE_CLASSICATION:
        train_model_format = "folder"

    if self.type == TYPE_INSTANCE_SEGMENTATION:
        train_model_format = "yolov5pytorch"

    if self.type == TYPE_SEMANTIC_SEGMENTATION:
        train_model_format = "png-mask-semantic"

    # if classification
    if train_model_format not in self.exports:
        self.export(train_model_format)

    workspace, project, *_ = self.id.rsplit("/")
    url = f"{API_URL}/{workspace}/{project}/{self.version}/train"

    data = {}
    if speed:
        data["speed"] = speed

    if checkpoint:
        data["checkpoint"] = checkpoint

    write_line("Reaching out to Roboflow to start training...")

    response = requests.post(url, json=data, params={"api_key": self.__api_key})
    if not response.ok:
        try:
            raise RuntimeError(response.json())
        except json.JSONDecodeError:
            response.raise_for_status()

    status = "training"

    if plot_in_notebook:
        from IPython.display import clear_output
        from matplotlib import pyplot as plt

        def live_plot(epochs, mAP, loss, title=""):
            clear_output(wait=True)

            plt.subplot(2, 1, 1)
            plt.plot(epochs, mAP, "#00FFCE")
            plt.title(title)
            plt.ylabel("mAP")

            plt.subplot(2, 1, 2)
            plt.plot(epochs, loss, "#A351FB")
            plt.xlabel("epochs")
            plt.ylabel("loss")
            plt.show()

    first_graph_write = False
    previous_epochs: Union[np.ndarray, list] = []
    num_machine_spin_dots = []

    while status == "training" or status == "running":
        url = f"{API_URL}/{self.workspace}/{self.project}/{self.version}?nocache=true"
        response = requests.get(url, params={"api_key": self.__api_key})
        response.raise_for_status()
        version = response.json()["version"]
        if "models" in version.keys():
            models = version["models"]
        else:
            models = {}

        if "train" in version.keys():
            if "results" in version["train"].keys():
                status = "finished"
                break
            if "status" in version["train"].keys():
                if version["train"]["status"] == "failed":
                    write_line(line="Training failed")
                    break

        epochs: Union[np.ndarray, list]
        mAP: Union[np.ndarray, list]
        loss: Union[np.ndarray, list]

        if "roboflow-train" in models.keys():
            import numpy as np

            # training has started
            epochs = np.array([int(epoch["epoch"]) for epoch in models["roboflow-train"]["epochs"]])
            mAP = np.array([float(epoch["mAP"]) for epoch in models["roboflow-train"]["epochs"]])
            loss = np.array(
                [
                    sum(float(epoch[key]) for key in ["box_loss", "class_loss", "obj_loss"] if key in epoch)
                    for epoch in models["roboflow-train"]["epochs"]
                ]
            )

            title = "Training in Progress"
            # plottling logic
        else:
            num_machine_spin_dots.append(".")
            if len(num_machine_spin_dots) > 5:
                num_machine_spin_dots = ["."]
            title = "Training Machine Spinning Up" + "".join(num_machine_spin_dots)

            epochs = []
            mAP = []
            loss = []

        if (len(epochs) > len(previous_epochs)) or (len(epochs) == 0):
            if plot_in_notebook:
                live_plot(epochs, mAP, loss, title)
            else:
                if len(epochs) > 0:
                    title = (
                        title + ": Epoch: " + str(epochs[-1]) + " mAP: " + str(mAP[-1]) + " loss: " + str(loss[-1])
                    )
                if not first_graph_write:
                    write_line(title)
                    first_graph_write = True

        previous_epochs = copy.deepcopy(epochs)

        time.sleep(5)

    if not self.model:
        if self.type == TYPE_OBJECT_DETECTION:
            self.model = ObjectDetectionModel(
                self.__api_key,
                self.id,
                self.name,
                self.version,
                colors=self.colors,
                preprocessing=self.preprocessing,
            )
        elif self.type == TYPE_CLASSICATION:
            self.model = ClassificationModel(
                self.__api_key,
                self.id,
                self.name,
                self.version,
                colors=self.colors,
                preprocessing=self.preprocessing,
            )
        elif self.type == TYPE_INSTANCE_SEGMENTATION:
            self.model = InstanceSegmentationModel(
                self.__api_key,
                self.id,
                colors=self.colors,
                preprocessing=self.preprocessing,
            )
        elif self.type == TYPE_SEMANTIC_SEGMENTATION:
            self.model = SemanticSegmentationModel(self.__api_key, self.id)
        elif self.type == TYPE_KEYPOINT_DETECTION:
            self.model = KeypointDetectionModel(self.__api_key, self.id, version=self.version)
        else:
            raise ValueError(f"Unsupported model type: {self.type}")

    # return the model object
    assert self.model
    return self.model