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from typing import Any, List, Optional, Union
from uuid import uuid4
from pydantic import BaseModel, ConfigDict, Field
from inference.core.entities.common import ApiKey, ModelID, ModelType
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"] = str(uuid4())
super().__init__(**kwargs)
model_config = ConfigDict(protected_namespaces=())
id: str
api_key: Optional[str] = ApiKey
start: Optional[float] = None
source: Optional[str] = None
source_info: Optional[str] = None
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
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].",
)
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.",
)
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)",
)
class KeypointsDetectionInferenceRequest(ObjectDetectionInferenceRequest):
keypoint_confidence: Optional[float] = Field(
default=0.0,
examples=[0.5],
description="The confidence threshold used to filter out non visible keypoints",
)
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'",
)
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.
"""
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)",
)
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"Uknown task type {model_type}")
request.id = request_dict.get("id")
return request
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