Source code for catalyst.utils.criterion.iou
from typing import List, Union # isort:skip
from functools import partial
import torch
from catalyst.utils import get_activation_fn
[docs]def iou(
outputs: torch.Tensor,
targets: torch.Tensor,
# values are discarded, only None check
# used for compatibility with MultiMetricCallback
classes: List[str] = None,
eps: float = 1e-7,
threshold: float = None,
activation: str = "Sigmoid"
) -> Union[float, List[float]]:
"""
Args:
outputs (torch.Tensor): A list of predicted elements
targets (torch.Tensor): A list of elements that are to be predicted
eps (float): epsilon to avoid zero division
threshold (float): threshold for outputs binarization
activation (str): An torch.nn activation applied to the outputs.
Must be one of ["none", "Sigmoid", "Softmax2d"]
Returns:
Union[float, List[float]]: IoU (Jaccard) score(s)
"""
activation_fn = get_activation_fn(activation)
outputs = activation_fn(outputs)
if threshold is not None:
outputs = (outputs > threshold).float()
# ! fix backward compatibility
if classes is not None:
# if classes are specified we reduce across all dims except channels
_sum = partial(torch.sum, dim=[0, 2, 3])
else:
_sum = torch.sum
intersection = _sum(targets * outputs)
union = _sum(targets) + _sum(outputs)
# this looks a bit awkward but `eps * (union == 0)` term
# makes sure that if I and U are both 0, than IoU == 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
iou = (intersection + eps * (union == 0)) / (union - intersection + eps)
return iou
jaccard = iou
__all__ = ["iou", "jaccard"]