Interface API

Reference information for the Interface API.

eva.Interface

A high-level interface for training and validating a machine learning model.

This class provides a convenient interface to connect a model, data, and trainer to train and validate a model.

fit

Perform model training and evaluation out-of-place.

This method uses the specified trainer to fit the model using the provided data.

Example use cases:

• Using a model consisting of a frozen backbone and a head, the backbone will generate the embeddings on the fly which are then used as input features to train the head on the downstream task specified by the given dataset.
• Fitting only the head network using a dataset that loads pre-computed embeddings.

Parameters:

Name	Type	Description	Default
trainer	Trainer	The base trainer to use but not modify.	required
model	ModelModule	The model module to use but not modify.	required
data	DataModule	The data module.	required

Source code in src/eva/core/interface/interface.py

def fit(
    self,
    trainer: eva_trainer.Trainer,
    model: modules.ModelModule,
    data: datamodules.DataModule,
) -> None:
    """Perform model training and evaluation out-of-place.

    This method uses the specified trainer to fit the model using the provided data.

    Example use cases:

    - Using a model consisting of a frozen backbone and a head, the backbone will generate
      the embeddings on the fly which are then used as input features to train the head on
      the downstream task specified by the given dataset.
    - Fitting only the head network using a dataset that loads pre-computed embeddings.

    Args:
        trainer: The base trainer to use but not modify.
        model: The model module to use but not modify.
        data: The data module.
    """
    trainer.run_evaluation_session(model=model, datamodule=data)

predict

Perform model prediction out-of-place.

This method performs inference with a pre-trained foundation model to compute embeddings.

Parameters:

Name	Type	Description	Default
trainer	Trainer	The base trainer to use but not modify.	required
model	ModelModule	The model module to use but not modify.	required
data	DataModule	The data module.	required

Source code in src/eva/core/interface/interface.py

def predict(
    self,
    trainer: eva_trainer.Trainer,
    model: modules.ModelModule,
    data: datamodules.DataModule,
) -> None:
    """Perform model prediction out-of-place.

    This method performs inference with a pre-trained foundation model to compute embeddings.

    Args:
        trainer: The base trainer to use but not modify.
        model: The model module to use but not modify.
        data: The data module.
    """
    eva_trainer.infer_model(
        base_trainer=trainer,
        base_model=model,
        datamodule=data,
        return_predictions=False,
    )

predict_fit

Combines the predict and fit commands in one method.

This method performs the following two steps: 1. predict: perform inference with a pre-trained foundation model to compute embeddings. 2. fit: training the head network using the embeddings generated in step 1.

Parameters:

Name	Type	Description	Default
trainer	Trainer	The base trainer to use but not modify.	required
model	ModelModule	The model module to use but not modify.	required
data	DataModule	The data module.	required

Source code in src/eva/core/interface/interface.py

def predict_fit(
    self,
    trainer: eva_trainer.Trainer,
    model: modules.ModelModule,
    data: datamodules.DataModule,
) -> None:
    """Combines the predict and fit commands in one method.

    This method performs the following two steps:
    1. predict: perform inference with a pre-trained foundation model to compute embeddings.
    2. fit: training the head network using the embeddings generated in step 1.

    Args:
        trainer: The base trainer to use but not modify.
        model: The model module to use but not modify.
        data: The data module.
    """
    self.predict(trainer=trainer, model=model, data=data)
    self.fit(trainer=trainer, model=model, data=data)