dice

Dice

Bases: TensorOp

Calculate Element-Wise Dice Score.

Args:
    inputs: A tuple or list of keys representing prediction and ground truth, like: ("y_pred", "y_true").
    outputs: The key under which to save the output.
    mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
        regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
        like "!infer" or "!train".
    ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
        ds_ids except for a particular one, you can pass an argument like "!ds1".
    soft_dice: Whether to square elements. If True, square of elements is added.
    sample_average: Whether to average the element-wise dice score.
    channel_average: Whether to average the channel wise dice score.
    negate: Whether to negate dice score.
    epsilon: A small value to prevent numeric instability in the division.

Returns:
    The dice loss between `y_pred` and `y_true`. A scalar if `average_sample` is True, else a
    tensor with the shape (Batch).

Raises:
    AssertionError: If `y_true` or `y_pred` are unacceptable data types.

Source code in fastestimator\fastestimator\op\tensorop\dice.py

class Dice(TensorOp):
    """
    Calculate Element-Wise Dice Score.

        Args:
            inputs: A tuple or list of keys representing prediction and ground truth, like: ("y_pred", "y_true").
            outputs: The key under which to save the output.
            mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
                regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
                like "!infer" or "!train".
            ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
                ds_ids except for a particular one, you can pass an argument like "!ds1".
            soft_dice: Whether to square elements. If True, square of elements is added.
            sample_average: Whether to average the element-wise dice score.
            channel_average: Whether to average the channel wise dice score.
            negate: Whether to negate dice score.
            epsilon: A small value to prevent numeric instability in the division.

        Returns:
            The dice loss between `y_pred` and `y_true`. A scalar if `average_sample` is True, else a
            tensor with the shape (Batch).

        Raises:
            AssertionError: If `y_true` or `y_pred` are unacceptable data types.
    """

    def __init__(self,
                 inputs: Union[Tuple[str, str], List[str]],
                 outputs: str,
                 mode: Union[None, str, Iterable[str]] = "!infer",
                 ds_id: Union[None, str, Iterable[str]] = None,
                 soft_dice: bool = False,
                 sample_average: bool = False,
                 channel_average: bool = False,
                 negate: bool = False,
                 epsilon: float = 1e-6):
        super().__init__(inputs=inputs, outputs=outputs, mode=mode, ds_id=ds_id)
        self.channel_average = channel_average
        self.soft_dice = soft_dice
        self.epsilon = epsilon
        self.sample_average = sample_average
        self.negate = negate

    def forward(self, data: List[Tensor], state: Dict[str, Any]) -> Tensor:
        y_pred, y_true = data
        dice = dice_score(y_pred, y_true, self.soft_dice, self.sample_average, self.channel_average, self.epsilon)
        return -dice if self.negate else dice