# Source code for segmentation_models_pytorch.losses.jaccard

```
from typing import Optional, List
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from ._functional import soft_jaccard_score, to_tensor
from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE
__all__ = ["JaccardLoss"]
[docs]class JaccardLoss(_Loss):
def __init__(
self,
mode: str,
classes: Optional[List[int]] = None,
log_loss: bool = False,
from_logits: bool = True,
smooth: float = 0.,
eps: float = 1e-7,
):
"""Implementation of Jaccard loss for image segmentation task.
It supports binary, multiclass and multilabel cases
Args:
mode: Loss mode 'binary', 'multiclass' or 'multilabel'
classes: List of classes that contribute in loss computation. By default, all channels are included.
log_loss: If True, loss computed as `- log(jaccard_coeff)`, otherwise `1 - jaccard_coeff`
from_logits: If True, assumes input is raw logits
smooth: Smoothness constant for dice coefficient
ignore_index: Label that indicates ignored pixels (does not contribute to loss)
eps: A small epsilon for numerical stability to avoid zero division error
(denominator will be always greater or equal to eps)
Shape
- **y_pred** - torch.Tensor of shape (N, C, H, W)
- **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W)
Reference
https://github.com/BloodAxe/pytorch-toolbelt
"""
assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE}
super(JaccardLoss, self).__init__()
self.mode = mode
if classes is not None:
assert mode != BINARY_MODE, "Masking classes is not supported with mode=binary"
classes = to_tensor(classes, dtype=torch.long)
self.classes = classes
self.from_logits = from_logits
self.smooth = smooth
self.eps = eps
self.log_loss = log_loss
def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
assert y_true.size(0) == y_pred.size(0)
if self.from_logits:
# Apply activations to get [0..1] class probabilities
# Using Log-Exp as this gives more numerically stable result and does not cause vanishing gradient on
# extreme values 0 and 1
if self.mode == MULTICLASS_MODE:
y_pred = y_pred.log_softmax(dim=1).exp()
else:
y_pred = F.logsigmoid(y_pred).exp()
bs = y_true.size(0)
num_classes = y_pred.size(1)
dims = (0, 2)
if self.mode == BINARY_MODE:
y_true = y_true.view(bs, 1, -1)
y_pred = y_pred.view(bs, 1, -1)
if self.mode == MULTICLASS_MODE:
y_true = y_true.view(bs, -1)
y_pred = y_pred.view(bs, num_classes, -1)
y_true = F.one_hot(y_true, num_classes) # N,H*W -> N,H*W, C
y_true = y_true.permute(0, 2, 1) # H, C, H*W
if self.mode == MULTILABEL_MODE:
y_true = y_true.view(bs, num_classes, -1)
y_pred = y_pred.view(bs, num_classes, -1)
scores = soft_jaccard_score(y_pred, y_true.type(y_pred.dtype), smooth=self.smooth, eps=self.eps, dims=dims)
if self.log_loss:
loss = -torch.log(scores.clamp_min(self.eps))
else:
loss = 1.0 - scores
# IoU loss is defined for non-empty classes
# So we zero contribution of channel that does not have true pixels
# NOTE: A better workaround would be to use loss term `mean(y_pred)`
# for this case, however it will be a modified jaccard loss
mask = y_true.sum(dims) > 0
loss *= mask.float()
if self.classes is not None:
loss = loss[self.classes]
return loss.mean()
```