Versions

Version

Class representing a Roboflow dataset version.

Source code in roboflow/core/version.py

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

    def __init__(
        self,
        version_dict,
        type,
        api_key,
        name,
        version,
        model_format,
        local,
        workspace,
        project,
        public,
        colors=None,
    ):
        """
        Initialize a Version object.
        """
        if api_key in DEMO_KEYS:
            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"
        else:
            self.__api_key = api_key
            self.name = name

            # FIXME: the version argument is inconsistently passed into this object.
            # Sometimes it is passed as: test-workspace/test-project/2
            # Other times, it is passed as: 2
            self.version = 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))

            if 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)
            else:
                self.model = None

    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"] == 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 = True):
        """
        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 prevent dataset overwrite when dataset is 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
        """

        self.__wait_if_generating()

        if model_format == "yolov8":
            # if ultralytics is installed, we will assume users will want to use yolov8 and we check for the supported version
            try:
                import_module("ultralytics")
                print_warn_for_wrong_dependencies_versions(
                    [("ultralytics", "==", "8.0.134")]
                )
            except ImportError as e:
                print(
                    "[WARNING] we noticed you are downloading a `yolov8` datasets but you don't have `ultralytics` installed. Roboflow `.deploy` supports only models trained with `ultralytics==8.0.134`, to intall it `pip install ultralytics==8.0.134`."
                )
                # silently fail
                pass

        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 requests.exceptions.JSONDecodeError:
                    response.raise_for_status()

        self.__download_zip(link, location, model_format)
        self.__extract_zip(location, model_format)
        self.__reformat_yaml(location, model_format)

        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
        """

        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 requests.exceptions.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 requests.exceptions.JSONDecodeError:
                response.raise_for_status()

    def train(self, speed=None, checkpoint=None, plot_in_notebook=False) -> bool:
        """
        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
        """

        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 requests.exceptions.JSONDecodeError:
                response.raise_for_status()

        status = "training"

        if plot_in_notebook:
            import collections

            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 = []
        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

            if "roboflow-train" in models.keys():
                # 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(
                    [
                        (
                            float(epoch["box_loss"])
                            + float(epoch["class_loss"])
                            + float(epoch["obj_loss"])
                        )
                        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)

        # return the model object
        return self.model

    # @warn_for_wrong_dependencies_versions([("ultralytics", "==", "8.0.134")])
    def deploy(self, model_type: str, model_path: str) -> None:
        """Uploads provided weights file to Roboflow

        Args:
            model_path (str): File path to model weights to be uploaded
        """

        supported_models = ["yolov5", "yolov7-seg", "yolov8"]

        if not any(
            supported_model in model_type for supported_model in supported_models
        ):
            raise (
                ValueError(
                    f"Model type {model_type} not supported. Supported models are {supported_models}"
                )
            )

        if "yolov8" in model_type:
            try:
                import torch
                import ultralytics

            except ImportError as e:
                raise (
                    "The ultralytics python package is required to deploy yolov8 models. Please install it with `pip install ultralytics`"
                )

            print_warn_for_wrong_dependencies_versions(
                [("ultralytics", "==", "8.0.134")]
            )

        elif "yolov5" in model_type or "yolov7" in model_type:
            try:
                import torch
            except ImportError as e:
                raise (
                    "The torch python package is required to deploy yolov5 models. Please install it with `pip install torch`"
                )

        model = torch.load(os.path.join(model_path, "weights/best.pt"))

        if isinstance(model["model"].names, list):
            class_names = model["model"].names
        else:
            class_names = []
            for i, val in enumerate(model["model"].names):
                class_names.append((val, model["model"].names[val]))
            class_names.sort(key=lambda x: x[0])
            class_names = [x[1] for x in class_names]

        if "yolov8" in model_type:
            # try except for backwards compatibility with older versions of ultralytics
            if "-cls" in model_type:
                nc = model["model"].yaml["nc"]
                args = model["train_args"]
            else:
                nc = model["model"].nc
                args = model["model"].args
            try:
                model_artifacts = {
                    "names": class_names,
                    "yaml": model["model"].yaml,
                    "nc": nc,
                    "args": {
                        k: val
                        for k, val in args.items()
                        if ((k == "model") or (k == "imgsz") or (k == "batch"))
                    },
                    "ultralytics_version": ultralytics.__version__,
                    "model_type": model_type,
                }
            except:
                model_artifacts = {
                    "names": class_names,
                    "yaml": model["model"].yaml,
                    "nc": nc,
                    "args": {
                        k: val
                        for k, val in args.__dict__.items()
                        if ((k == "model") or (k == "imgsz") or (k == "batch"))
                    },
                    "ultralytics_version": ultralytics.__version__,
                    "model_type": model_type,
                }
        elif "yolov5" in model_type or "yolov7" in model_type:
            # parse from yaml for yolov5

            with open(os.path.join(model_path, "opt.yaml"), "r") as stream:
                opts = yaml.safe_load(stream)

            model_artifacts = {
                "names": class_names,
                "nc": model["model"].nc,
                "args": {
                    "imgsz": opts["imgsz"] if "imgsz" in opts else opts["img_size"],
                    "batch": opts["batch_size"],
                },
                "model_type": model_type,
            }
            if hasattr(model["model"], "yaml"):
                model_artifacts["yaml"] = model["model"].yaml

        with open(os.path.join(model_path, "model_artifacts.json"), "w") as fp:
            json.dump(model_artifacts, fp)

        torch.save(
            model["model"].state_dict(), os.path.join(model_path, "state_dict.pt")
        )

        lista_files = [
            "results.csv",
            "results.png",
            "model_artifacts.json",
            "state_dict.pt",
        ]

        with zipfile.ZipFile(
            os.path.join(model_path, "roboflow_deploy.zip"), "w"
        ) as zipMe:
            for file in lista_files:
                if os.path.exists(os.path.join(model_path, file)):
                    zipMe.write(
                        os.path.join(model_path, file),
                        arcname=file,
                        compress_type=zipfile.ZIP_DEFLATED,
                    )
                else:
                    if file in ["model_artifacts.json", "state_dict.pt"]:
                        raise (
                            ValueError(
                                f"File {file} not found. Please make sure to provide a valid model path."
                            )
                        )

        res = requests.get(
            f"{API_URL}/{self.workspace}/{self.project}/{self.version}/uploadModel?api_key={self.__api_key}&modelType={model_type}&nocache=true"
        )
        try:
            if res.status_code == 429:
                raise RuntimeError(
                    f"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, "roboflow_deploy.zip"), "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(
                    f"Share your model with the world at: {UNIVERSE_URL}/{self.workspace}/{self.project}/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
        """
        if not os.path.exists(location):
            os.makedirs(location)

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

        try:
            wget.download(link, out=location + "/roboflow.zip", bar=bar_progress)
        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
        """
        with zipfile.ZipFile(location + "/roboflow.zip", "r") as zip_ref:
            for member in tqdm(
                zip_ref.infolist(),
                desc=f"Extracting Dataset Version Zip to {location} in {format}:",
            ):
                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
        """
        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
        """
        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", "yolov8"]:
                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
                if format == "yolov8" and not get_wrong_dependencies_versions(
                    dependencies_versions=[("ultralytics", "==", "8.0.134")]
                ):
                    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"]:
            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
    """
    if not os.path.exists(location):
        os.makedirs(location)

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

    try:
        wget.download(link, out=location + "/roboflow.zip", bar=bar_progress)
    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
    """
    with zipfile.ZipFile(location + "/roboflow.zip", "r") as zip_ref:
        for member in tqdm(
            zip_ref.infolist(),
            desc=f"Extracting Dataset Version Zip to {location} in {format}:",
        ):
            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
    """
    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,
    workspace,
    project,
    public,
    colors=None,
):
    """
    Initialize a Version object.
    """
    if api_key in DEMO_KEYS:
        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"
    else:
        self.__api_key = api_key
        self.name = name

        # FIXME: the version argument is inconsistently passed into this object.
        # Sometimes it is passed as: test-workspace/test-project/2
        # Other times, it is passed as: 2
        self.version = 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))

        if 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)
        else:
            self.model = None

__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
    """
    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", "yolov8"]:
            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
            if format == "yolov8" and not get_wrong_dependencies_versions(
                dependencies_versions=[("ultralytics", "==", "8.0.134")]
            ):
                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"]:
        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)

Uploads provided weights file to Roboflow

Parameters:

Name	Type	Description	Default
model_path	str	File path to model weights to be uploaded	required

Source code in roboflow/core/version.py

def deploy(self, model_type: str, model_path: str) -> None:
    """Uploads provided weights file to Roboflow

    Args:
        model_path (str): File path to model weights to be uploaded
    """

    supported_models = ["yolov5", "yolov7-seg", "yolov8"]

    if not any(
        supported_model in model_type for supported_model in supported_models
    ):
        raise (
            ValueError(
                f"Model type {model_type} not supported. Supported models are {supported_models}"
            )
        )

    if "yolov8" in model_type:
        try:
            import torch
            import ultralytics

        except ImportError as e:
            raise (
                "The ultralytics python package is required to deploy yolov8 models. Please install it with `pip install ultralytics`"
            )

        print_warn_for_wrong_dependencies_versions(
            [("ultralytics", "==", "8.0.134")]
        )

    elif "yolov5" in model_type or "yolov7" in model_type:
        try:
            import torch
        except ImportError as e:
            raise (
                "The torch python package is required to deploy yolov5 models. Please install it with `pip install torch`"
            )

    model = torch.load(os.path.join(model_path, "weights/best.pt"))

    if isinstance(model["model"].names, list):
        class_names = model["model"].names
    else:
        class_names = []
        for i, val in enumerate(model["model"].names):
            class_names.append((val, model["model"].names[val]))
        class_names.sort(key=lambda x: x[0])
        class_names = [x[1] for x in class_names]

    if "yolov8" in model_type:
        # try except for backwards compatibility with older versions of ultralytics
        if "-cls" in model_type:
            nc = model["model"].yaml["nc"]
            args = model["train_args"]
        else:
            nc = model["model"].nc
            args = model["model"].args
        try:
            model_artifacts = {
                "names": class_names,
                "yaml": model["model"].yaml,
                "nc": nc,
                "args": {
                    k: val
                    for k, val in args.items()
                    if ((k == "model") or (k == "imgsz") or (k == "batch"))
                },
                "ultralytics_version": ultralytics.__version__,
                "model_type": model_type,
            }
        except:
            model_artifacts = {
                "names": class_names,
                "yaml": model["model"].yaml,
                "nc": nc,
                "args": {
                    k: val
                    for k, val in args.__dict__.items()
                    if ((k == "model") or (k == "imgsz") or (k == "batch"))
                },
                "ultralytics_version": ultralytics.__version__,
                "model_type": model_type,
            }
    elif "yolov5" in model_type or "yolov7" in model_type:
        # parse from yaml for yolov5

        with open(os.path.join(model_path, "opt.yaml"), "r") as stream:
            opts = yaml.safe_load(stream)

        model_artifacts = {
            "names": class_names,
            "nc": model["model"].nc,
            "args": {
                "imgsz": opts["imgsz"] if "imgsz" in opts else opts["img_size"],
                "batch": opts["batch_size"],
            },
            "model_type": model_type,
        }
        if hasattr(model["model"], "yaml"):
            model_artifacts["yaml"] = model["model"].yaml

    with open(os.path.join(model_path, "model_artifacts.json"), "w") as fp:
        json.dump(model_artifacts, fp)

    torch.save(
        model["model"].state_dict(), os.path.join(model_path, "state_dict.pt")
    )

    lista_files = [
        "results.csv",
        "results.png",
        "model_artifacts.json",
        "state_dict.pt",
    ]

    with zipfile.ZipFile(
        os.path.join(model_path, "roboflow_deploy.zip"), "w"
    ) as zipMe:
        for file in lista_files:
            if os.path.exists(os.path.join(model_path, file)):
                zipMe.write(
                    os.path.join(model_path, file),
                    arcname=file,
                    compress_type=zipfile.ZIP_DEFLATED,
                )
            else:
                if file in ["model_artifacts.json", "state_dict.pt"]:
                    raise (
                        ValueError(
                            f"File {file} not found. Please make sure to provide a valid model path."
                        )
                    )

    res = requests.get(
        f"{API_URL}/{self.workspace}/{self.project}/{self.version}/uploadModel?api_key={self.__api_key}&modelType={model_type}&nocache=true"
    )
    try:
        if res.status_code == 429:
            raise RuntimeError(
                f"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, "roboflow_deploy.zip"), "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(
                f"Share your model with the world at: {UNIVERSE_URL}/{self.workspace}/{self.project}/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}")

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

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 prevent dataset overwrite when dataset is already downloaded	True

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 = True):
    """
    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 prevent dataset overwrite when dataset is 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
    """

    self.__wait_if_generating()

    if model_format == "yolov8":
        # if ultralytics is installed, we will assume users will want to use yolov8 and we check for the supported version
        try:
            import_module("ultralytics")
            print_warn_for_wrong_dependencies_versions(
                [("ultralytics", "==", "8.0.134")]
            )
        except ImportError as e:
            print(
                "[WARNING] we noticed you are downloading a `yolov8` datasets but you don't have `ultralytics` installed. Roboflow `.deploy` supports only models trained with `ultralytics==8.0.134`, to intall it `pip install ultralytics==8.0.134`."
            )
            # silently fail
            pass

    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 requests.exceptions.JSONDecodeError:
                response.raise_for_status()

    self.__download_zip(link, location, model_format)
    self.__extract_zip(location, model_format)
    self.__reformat_yaml(location, model_format)

    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
    """

    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 requests.exceptions.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 requests.exceptions.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
bool	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) -> bool:
    """
    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
    """

    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 requests.exceptions.JSONDecodeError:
            response.raise_for_status()

    status = "training"

    if plot_in_notebook:
        import collections

        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 = []
    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

        if "roboflow-train" in models.keys():
            # 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(
                [
                    (
                        float(epoch["box_loss"])
                        + float(epoch["class_loss"])
                        + float(epoch["obj_loss"])
                    )
                    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)

    # return the model object
    return self.model