TransformersVisualQandA

Visual Q&A task

Subclass of Task.

Module: implementation.tasks

Default predictor

This task uses TransformersModel by default with this configuration:

model = AutoModelForImageClassification.from_pretrained(
    "dandelin/vilt-b32-finetuned-vqa"
)
predictor=TransformersModel(
    TransformersModelConfig(
        model=model
    ),
    input_class=TransformersImageModelInput,
    output_class=TransformersLogitsOutput,
)

See:

• TransformersModel

• TransformersModelConfig

• TransformersImageModelInput

• TransformersLogitsOutput

Methods and properties

Main methods and properties

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__init__

Arguments:

• predictor (Predictor[Any, Any], optional): Predictor that will be used in task. If equals to None, default predictor will be used. Defaults to None.

• preprocess (Optional[Component], optional): Component executed before predictor. If equals to None, default component will be used. Defaults to None. Default component: VisualQandAPreprocessor If default chain is used, VisualQandAPreprocessor will use ViltProcessor from model used in predictor.

• postprocess (Optional[Component], optional): Component executed after predictor. If equals to None, default component will be used. Defaults to None. Default component: VisualQandASingleAnswerPostprocessor If default chain is used, VisualQandASingleAnswerPostprocessor will use labels from model used in predictor.

• input_class (Type[Input], optional): Class for input validation. Defaults to TransformersVisualQandAInput.

• output_class (Type[Output], optional): Class for output validation. Defaults to TransformersVisualQandAOutput.

• name (Optional[str], optional): Name for identification. If equals to None, class name will be used. Defaults to None.

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TransformersVisualQandAOutput

Subclass of IOModel.

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__init__

Arguments:

• answer (Optional[Tuple[str, float]])

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TransformersVisualQandAMultianswerOutput

Subclass of IOModel.

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__init__

Arguments:

• answers (Dict[str, float])

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VisualQandAPreprocessor

Prepare model input. Subclass of Action. Type of Action[Dict[str, Any], Dict[str, Any]].

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__init__

Arguments:

• processor (Processor): Feature extractor.

• name (Optional[str], optional): Name for identification. If equals to None, class name will be used. Defaults to None.

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execute

Arguments:

• input_data (Dict[str, Any]): Expected keys:

  • "image" (Image.Image): Image to analyze;

  • "question" (str): Question to answer;

Returns:

• Dict[str, Any]: Expected keys:

  • "input_ids" (Any);

  • "token_type_ids" (Any);

  • "attention_mask" (Any);

  • "pixel_values" (Any);

  • "pixel_mask" (Any);

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VisualQandASingleAnswerPostprocessor

Process model output. Subclass of VisualQandAMultianswerPostprocessor.

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execute

Arguments:

• input_data (Dict[str, Any]): Expected keys:

  • "logits" (Any): Model output;

Returns:

• Dict[str, Any]: Expected keys:

  • "answer" (Optional[Tuple[str, float]]): Answer with highest score, if score higher or equal to threshold, else - None.

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VisualQandAMultianswerPostprocessor

Process model output. Subclass of Action. Type of Action[Dict[str, Any], Dict[str, Any]].

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__init__

Arguments:

• labels (List[str]): Labels for classification.

• threshold (float): Labels threshold score. Defaults to 0.

• name (Optional[str], optional): Name for identification. If equals to None, class name will be used. Defaults to None.

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execute

Arguments:

• input_data (Dict[str, Any]): Expected keys:

  • "logits" (Any): Model output;

Returns:

• Dict[str, Any]: Expected keys:

  • "answers" (Dict[str, float]): Classified labels and scores.

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