# Source code for catalyst.utils.metrics.dice

"""
Dice metric.
"""
import torch
from catalyst.utils import get_activation_fn
[docs]def dice(
outputs: torch.Tensor,
targets: torch.Tensor,
eps: float = 1e-7,
threshold: float = None,
activation: str = "Sigmoid",
):
"""Computes the dice metric.
Args:
outputs (list): a list of predicted elements
targets (list): a list of elements that are to be predicted
eps (float): epsilon
threshold (float): threshold for outputs binarization
activation (str): An torch.nn activation applied to the outputs.
Must be one of ["none", "Sigmoid", "Softmax2d"]
Returns:
double: Dice score
"""
activation_fn = get_activation_fn(activation)
outputs = activation_fn(outputs)
if threshold is not None:
outputs = (outputs > threshold).float()
intersection = torch.sum(targets * outputs)
union = torch.sum(targets) + torch.sum(outputs)
# this looks a bit awkward but `eps * (union == 0)` term
# makes sure that if I and U are both 0, than Dice == 1
# and if U != 0 and I == 0 the eps term in numerator is zeroed out
# i.e. (0 + eps) / (U - 0 + eps) doesn't happen
dice = (2 * intersection + eps * (union == 0)) / (union + eps)
return dice
__all__ = ["dice"]