Inference

BaseRequest

Bases: BaseModel

Base request for inference.

Attributes:

Name	Type	Description
id	str_	A unique request identifier.
api_key	Optional[str]	Roboflow API Key that will be passed to the model during initialization for artifact retrieval.
start	Optional[float]	start time of request

Source code in inference/core/entities/requests/inference.py

class BaseRequest(BaseModel):
    """Base request for inference.

    Attributes:
        id (str_): A unique request identifier.
        api_key (Optional[str]): Roboflow API Key that will be passed to the model during initialization for artifact retrieval.
        start (Optional[float]): start time of request
    """

    def __init__(self, **kwargs):
        kwargs["id"] = kwargs.get("id", str(uuid4()))
        super().__init__(**kwargs)

    model_config = ConfigDict(protected_namespaces=())
    id: str
    api_key: Optional[str] = ApiKey
    usage_billable: bool = True
    start: Optional[float] = None
    source: Optional[str] = None
    source_info: Optional[str] = None

CVInferenceRequest

Bases: InferenceRequest

Computer Vision inference request.

Attributes:

Name	Type	Description
image	Union[List[InferenceRequestImage], InferenceRequestImage]	Image(s) for inference.
disable_preproc_auto_orient	Optional[bool]	If true, the auto orient preprocessing step is disabled for this call. Default is False.
disable_preproc_contrast	Optional[bool]	If true, the auto contrast preprocessing step is disabled for this call. Default is False.
disable_preproc_grayscale	Optional[bool]	If true, the grayscale preprocessing step is disabled for this call. Default is False.
disable_preproc_static_crop	Optional[bool]	If true, the static crop preprocessing step is disabled for this call. Default is False.

Source code in inference/core/entities/requests/inference.py

class CVInferenceRequest(InferenceRequest):
    """Computer Vision inference request.

    Attributes:
        image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) for inference.
        disable_preproc_auto_orient (Optional[bool]): If true, the auto orient preprocessing step is disabled for this call. Default is False.
        disable_preproc_contrast (Optional[bool]): If true, the auto contrast preprocessing step is disabled for this call. Default is False.
        disable_preproc_grayscale (Optional[bool]): If true, the grayscale preprocessing step is disabled for this call. Default is False.
        disable_preproc_static_crop (Optional[bool]): If true, the static crop preprocessing step is disabled for this call. Default is False.
    """

    image: Union[List[InferenceRequestImage], InferenceRequestImage]
    disable_preproc_auto_orient: Optional[bool] = Field(
        default=False,
        description="If true, the auto orient preprocessing step is disabled for this call.",
    )
    disable_preproc_contrast: Optional[bool] = Field(
        default=False,
        description="If true, the auto contrast preprocessing step is disabled for this call.",
    )
    disable_preproc_grayscale: Optional[bool] = Field(
        default=False,
        description="If true, the grayscale preprocessing step is disabled for this call.",
    )
    disable_preproc_static_crop: Optional[bool] = Field(
        default=False,
        description="If true, the static crop preprocessing step is disabled for this call.",
    )

ClassificationInferenceRequest

Bases: CVInferenceRequest

Classification inference request.

Attributes:

Name	Type	Description
confidence	Optional[float]	The confidence threshold used to filter out predictions.
visualization_stroke_width	Optional[int]	The stroke width used when visualizing predictions.
visualize_predictions	Optional[bool]	If true, the predictions will be drawn on the original image and returned as a base64 string.

Source code in inference/core/entities/requests/inference.py

class ClassificationInferenceRequest(CVInferenceRequest):
    """Classification inference request.

    Attributes:
        confidence (Optional[float]): The confidence threshold used to filter out predictions.
        visualization_stroke_width (Optional[int]): The stroke width used when visualizing predictions.
        visualize_predictions (Optional[bool]): If true, the predictions will be drawn on the original image and returned as a base64 string.
    """

    def __init__(self, **kwargs):
        kwargs["model_type"] = "classification"
        super().__init__(**kwargs)

    confidence: Optional[float] = Field(
        default=0.4,
        examples=[0.5],
        description="The confidence threshold used to filter out predictions",
    )
    visualization_stroke_width: Optional[int] = Field(
        default=1,
        examples=[1],
        description="The stroke width used when visualizing predictions",
    )
    visualize_predictions: Optional[bool] = Field(
        default=False,
        examples=[False],
        description="If true, the predictions will be drawn on the original image and returned as a base64 string",
    )
    disable_active_learning: Optional[bool] = Field(
        default=False,
        examples=[False],
        description="If true, the predictions will be prevented from registration by Active Learning (if the functionality is enabled)",
    )
    active_learning_target_dataset: Optional[str] = Field(
        default=None,
        examples=["my_dataset"],
        description="Parameter to be used when Active Learning data registration should happen against different dataset than the one pointed by model_id",
    )

DepthEstimationRequest

Bases: InferenceRequest

Request for depth estimation.

Attributes:

Name	Type	Description
image	Union[List[InferenceRequestImage], InferenceRequestImage]	Image(s) to be estimated.
model_id	str	The model ID to use for depth estimation.
depth_version_id	Optional[str]	The version ID of the depth estimation model.

Source code in inference/core/entities/requests/inference.py

class DepthEstimationRequest(InferenceRequest):
    """Request for depth estimation.

    Attributes:
        image (Union[List[InferenceRequestImage], InferenceRequestImage]): Image(s) to be estimated.
        model_id (str): The model ID to use for depth estimation.
        depth_version_id (Optional[str]): The version ID of the depth estimation model.
    """

    image: Union[List[InferenceRequestImage], InferenceRequestImage]
    model_id: Optional[str] = Field(None)
    depth_version_id: Optional[str] = Field(
        default="small",
        examples=["small"],
        description="The version ID of the depth estimation model",
    )

    @validator("model_id", always=True)
    def validate_model_id(cls, value, values):
        if value is not None:
            return value
        if values.get("depth_version_id") is None:
            return None
        return f"depth-anything-v2/{values['depth_version_id']}"

InferenceRequest

Bases: BaseRequest

Base request for inference.

Attributes:

Name	Type	Description
model_id	str	A unique model identifier.
model_type	Optional[str]	The type of the model, usually referring to what task the model performs.

Source code in inference/core/entities/requests/inference.py

class InferenceRequest(BaseRequest):
    """Base request for inference.

    Attributes:
        model_id (str): A unique model identifier.
        model_type (Optional[str]): The type of the model, usually referring to what task the model performs.
    """

    model_id: Optional[str] = ModelID
    model_type: Optional[str] = ModelType

InferenceRequestImage

Bases: BaseModel

Image data for inference request.

Attributes:

Name	Type	Description
type	str	The type of image data provided, one of 'url', 'base64', or 'numpy'.
value	Optional[Any]	Image data corresponding to the image type.

Source code in inference/core/entities/requests/inference.py

class InferenceRequestImage(BaseModel):
    """Image data for inference request.

    Attributes:
        type (str): The type of image data provided, one of 'url', 'base64', or 'numpy'.
        value (Optional[Any]): Image data corresponding to the image type.
    """

    type: str = Field(
        examples=["url"],
        description="The type of image data provided, one of 'url', 'base64', or 'numpy'",
    )
    value: Optional[Any] = Field(
        None,
        examples=["http://www.example-image-url.com"],
        description="Image data corresponding to the image type, if type = 'url' then value is a string containing the url of an image, else if type = 'base64' then value is a string containing base64 encoded image data, else if type = 'numpy' then value is binary numpy data serialized using pickle.dumps(); array should 3 dimensions, channels last, with values in the range [0,255].",
    )

InstanceSegmentationInferenceRequest

Bases: ObjectDetectionInferenceRequest

Instance Segmentation inference request.

Attributes:

Name	Type	Description
mask_decode_mode	Optional[str]	The mode used to decode instance segmentation masks, one of 'accurate', 'fast', 'tradeoff'.
tradeoff_factor	Optional[float]	The amount to tradeoff between 0='fast' and 1='accurate'.

Source code in inference/core/entities/requests/inference.py

class InstanceSegmentationInferenceRequest(ObjectDetectionInferenceRequest):
    """Instance Segmentation inference request.

    Attributes:
        mask_decode_mode (Optional[str]): The mode used to decode instance segmentation masks, one of 'accurate', 'fast', 'tradeoff'.
        tradeoff_factor (Optional[float]): The amount to tradeoff between 0='fast' and 1='accurate'.
    """

    mask_decode_mode: Optional[str] = Field(
        default="accurate",
        examples=["accurate"],
        description="The mode used to decode instance segmentation masks, one of 'accurate', 'fast', 'tradeoff'",
    )
    tradeoff_factor: Optional[float] = Field(
        default=0.0,
        examples=[0.5],
        description="The amount to tradeoff between 0='fast' and 1='accurate'",
    )

ObjectDetectionInferenceRequest

Bases: CVInferenceRequest

Object Detection inference request.

Attributes:

Name	Type	Description
class_agnostic_nms	Optional[bool]	If true, NMS is applied to all detections at once, if false, NMS is applied per class.
class_filter	Optional[List[str]]	If provided, only predictions for the listed classes will be returned.
confidence	Optional[float]	The confidence threshold used to filter out predictions.
fix_batch_size	Optional[bool]	If true, the batch size will be fixed to the maximum batch size configured for this server.
iou_threshold	Optional[float]	The IoU threshold that must be met for a box pair to be considered duplicate during NMS.
max_detections	Optional[int]	The maximum number of detections that will be returned.
max_candidates	Optional[int]	The maximum number of candidate detections passed to NMS.
visualization_labels	Optional[bool]	If true, labels will be rendered on prediction visualizations.
visualization_stroke_width	Optional[int]	The stroke width used when visualizing predictions.
visualize_predictions	Optional[bool]	If true, the predictions will be drawn on the original image and returned as a base64 string.

Source code in inference/core/entities/requests/inference.py

class ObjectDetectionInferenceRequest(CVInferenceRequest):
    """Object Detection inference request.

    Attributes:
        class_agnostic_nms (Optional[bool]): If true, NMS is applied to all detections at once, if false, NMS is applied per class.
        class_filter (Optional[List[str]]): If provided, only predictions for the listed classes will be returned.
        confidence (Optional[float]): The confidence threshold used to filter out predictions.
        fix_batch_size (Optional[bool]): If true, the batch size will be fixed to the maximum batch size configured for this server.
        iou_threshold (Optional[float]): The IoU threshold that must be met for a box pair to be considered duplicate during NMS.
        max_detections (Optional[int]): The maximum number of detections that will be returned.
        max_candidates (Optional[int]): The maximum number of candidate detections passed to NMS.
        visualization_labels (Optional[bool]): If true, labels will be rendered on prediction visualizations.
        visualization_stroke_width (Optional[int]): The stroke width used when visualizing predictions.
        visualize_predictions (Optional[bool]): If true, the predictions will be drawn on the original image and returned as a base64 string.
    """

    class_agnostic_nms: Optional[bool] = Field(
        default=False,
        examples=[False],
        description="If true, NMS is applied to all detections at once, if false, NMS is applied per class",
    )
    class_filter: Optional[List[str]] = Field(
        default=None,
        examples=[["class-1", "class-2", "class-n"]],
        description="If provided, only predictions for the listed classes will be returned",
    )
    confidence: Optional[float] = Field(
        default=0.4,
        examples=[0.5],
        description="The confidence threshold used to filter out predictions",
    )
    fix_batch_size: Optional[bool] = Field(
        default=False,
        examples=[False],
        description="If true, the batch size will be fixed to the maximum batch size configured for this server",
    )
    iou_threshold: Optional[float] = Field(
        default=0.3,
        examples=[0.5],
        description="The IoU threhsold that must be met for a box pair to be considered duplicate during NMS",
    )
    max_detections: Optional[int] = Field(
        default=300,
        examples=[300],
        description="The maximum number of detections that will be returned",
    )
    max_candidates: Optional[int] = Field(
        default=3000,
        description="The maximum number of candidate detections passed to NMS",
    )
    visualization_labels: Optional[bool] = Field(
        default=False,
        examples=[False],
        description="If true, labels will be rendered on prediction visualizations",
    )
    visualization_stroke_width: Optional[int] = Field(
        default=1,
        examples=[1],
        description="The stroke width used when visualizing predictions",
    )
    visualize_predictions: Optional[bool] = Field(
        default=False,
        examples=[False],
        description="If true, the predictions will be drawn on the original image and returned as a base64 string",
    )
    disable_active_learning: Optional[bool] = Field(
        default=False,
        examples=[False],
        description="If true, the predictions will be prevented from registration by Active Learning (if the functionality is enabled)",
    )
    active_learning_target_dataset: Optional[str] = Field(
        default=None,
        examples=["my_dataset"],
        description="Parameter to be used when Active Learning data registration should happen against different dataset than the one pointed by model_id",
    )

request_from_type(model_type, request_dict)

Uses original request id

Source code in inference/core/entities/requests/inference.py

def request_from_type(model_type, request_dict):
    """Uses original request id"""
    if model_type == "classification":
        request = ClassificationInferenceRequest(**request_dict)
    elif model_type == "instance-segmentation":
        request = InstanceSegmentationInferenceRequest(**request_dict)
    elif model_type == "object-detection":
        request = ObjectDetectionInferenceRequest(**request_dict)
    else:
        raise ValueError(f"Unknown task type {model_type}")
    request.id = request_dict.get("id", request.id)
    return request