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mmedit.apis.inferencers.unconditional_inferencer

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Classes

UnconditionalInferencer

inferencer that predicts with unconditional models.

class mmedit.apis.inferencers.unconditional_inferencer.UnconditionalInferencer(config: Union[mmedit.utils.ConfigType, str], ckpt: Optional[str], device: Optional[str] = None, extra_parameters: Optional[Dict] = None, seed: int = 2022, **kwargs)[source]

Bases: mmedit.apis.inferencers.base_mmedit_inferencer.BaseMMEditInferencer

inferencer that predicts with unconditional models.

func_kwargs[source]
extra_parameters[source]
preprocess() Dict[source]

Process the inputs into a model-feedable format.

Returns

Results of preprocess.

Return type

results(Dict)

forward(inputs: mmedit.apis.inferencers.base_mmedit_inferencer.InputsType) mmedit.apis.inferencers.base_mmedit_inferencer.PredType[source]

Forward the inputs to the model.

visualize(preds: mmedit.apis.inferencers.base_mmedit_inferencer.PredType, result_out_dir: str = '') List[numpy.ndarray][source]

Visualize predictions.

Parameters
  • preds (List[Union[str, np.ndarray]]) – Forward results by the inferencer.

  • data (List[Dict]) – Not needed by this kind of inferencer.

  • result_out_dir (str) – Output directory of image. Defaults to ‘’.

Returns

Result of visualize

Return type

List[np.ndarray]

_pred2dict(data_sample: mmedit.structures.EditDataSample) Dict[source]

Extract elements necessary to represent a prediction into a dictionary. It’s better to contain only basic data elements such as strings and numbers in order to guarantee it’s json-serializable.

Parameters

data_sample (EditDataSample) – The data sample to be converted.

Returns

The output dictionary.

Return type

dict

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