WandbEvalCallback

WandbEvalCallback(
    data_table_columns: List[str],
    pred_table_columns: List[str],
    *args,
    **kwargs
) -> None

class WandbClfEvalCallback(WandbEvalCallback):
    def __init__(self, validation_data, data_table_columns, pred_table_columns):
        super().__init__(data_table_columns, pred_table_columns)

        self.x = validation_data[0]
        self.y = validation_data[1]

    def add_ground_truth(self):
        for idx, (image, label) in enumerate(zip(self.x, self.y)):
            self.data_table.add_data(idx, wandb.Image(image), label)

    def add_model_predictions(self, epoch):
        preds = self.model.predict(self.x, verbose=0)
        preds = tf.argmax(preds, axis=-1)

        data_table_ref = self.data_table_ref
        table_idxs = data_table_ref.get_index()

        for idx in table_idxs:
            pred = preds[idx]
            self.pred_table.add_data(
                epoch,
                data_table_ref.data[idx][0],
                data_table_ref.data[idx][1],
                data_table_ref.data[idx][2],
                pred,
            )


model.fit(
    x,
    y,
    epochs=2,
    validation_data=(x, y),
    callbacks=[
        WandbClfEvalCallback(
            validation_data=(x, y),
            data_table_columns=["idx", "image", "label"],
            pred_table_columns=["epoch", "idx", "image", "label", "pred"],
        )
    ],
)

@abc.abstractmethod
add_ground_truth(
    logs: Optional[Dict[str, float]] = None
) -> None

for idx, data in enumerate(dataloader):
    self.data_table.add_data(idx, data)

@abc.abstractmethod
add_model_predictions(
    epoch: int,
    logs: Optional[Dict[str, float]] = None
) -> None

# 데이터 로더가 샘플을 섞지 않는다고 가정합니다.
for idx, data in enumerate(dataloader):
    preds = model.predict(data)
    self.pred_table.add_data(
        self.data_table_ref.data[idx][0],
        self.data_table_ref.data[idx][1],
        preds,
    )

init_data_table(
    column_names: List[str]
) -> None

init_pred_table(
    column_names: List[str]
) -> None

log_data_table(
    name: str = "val",
    type: str = "dataset",
    table_name: str = "val_data"
) -> None

log_pred_table(
    type: str = "evaluation",
    table_name: str = "eval_data",
    aliases: Optional[List[str]] = None
) -> None

set_model(
    model
)

set_params(
    params
)