TverskyLoss

Bases: WeightedBaseLoss

apply_spectral_decouple

apply_spectral_decouple(loss_matrix: Tensor, yhat: Tensor, lam: float = 0.01) -> torch.Tensor

Apply spectral decoupling L2 norm after the loss.

https://arxiv.org/abs/2011.09468

Parameters:

Name	Type	Description	Default
loss_matrix	Tensor	Pixelwise losses. A tensor of shape (B, H, W).	required
yhat	Tensor	The pixel predictions of the model. Shape (B, C, H, W).	required
lam	float, default=0.01	Lambda constant.	0.01

Returns:

Type	Description
Tensor	torch.Tensor: SD-regularized loss matrix. Same shape as input.

apply_ls_to_target

apply_ls_to_target(target: Tensor, n_classes: int, label_smoothing: float = 0.1) -> torch.Tensor

Apply regular label smoothing to the target map.

https://arxiv.org/abs/1512.00567

Parameters:

Name	Type	Description	Default
target	Tensor	The target one hot tensor. Shape (B, C, H, W). Dtype: Int64.	required
n_classes	int	Number of classes in the data.	required
label_smoothing	float, default=0.1	The smoothing coeff alpha.	0.1

Retrurns

Torch.Tensor: Label smoothed target. Same shape as input.

apply_svls_to_target

apply_svls_to_target(target: Tensor, n_classes: int, kernel_size: int = 5, sigma: int = 3, **kwargs) -> torch.Tensor

Apply spatially varying label smoothihng to target map.

https://arxiv.org/abs/2104.05788

Parameters:

Name	Type	Description	Default
target	Tensor	The target one hot tensor. Shape (B, C, H, W). Dtype: Int64.	required
n_classes	int	Number of classes in the data.	required
kernel_size	int, default=3	Size of a square kernel.	5
sigma	int, default=3	The std of the gaussian.	3

Retrurns

Torch.Tensor: Label smoothed target. Same shape as input.

apply_class_weights

apply_class_weights(loss_matrix: Tensor, target: Tensor) -> torch.Tensor

Multiply pixelwise loss matrix by the class weights.

Note

Does not apply normalization

Parameters:

Name	Type	Description	Default
loss_matrix	Tensor	Pixelwise losses. A tensor of shape (B, H, W).	required
target	Tensor	The target mask. Shape (B, H, W).	required

Returns:

Type	Description
Tensor	torch.Tensor: The loss matrix scaled with the weight matrix. Shape (B, H, W).

apply_edge_weights

apply_edge_weights(loss_matrix: Tensor, weight_map: Tensor) -> torch.Tensor

Apply weights to the object boundaries.

Basically just computes edge_weight**weight_map.

Parameters:

Name	Type	Description	Default
loss_matrix	Tensor	Pixelwise losses. A tensor of shape (B, H, W).	required
weight_map	Tensor	Map that points to the pixels that will be weighted. Shape (B, H, W).	required

Returns:

Type	Description
Tensor	torch.Tensor: The loss matrix scaled with the nuclear boundary weights. Shape (B, H, W).

apply_mask_weight

apply_mask_weight(loss_matrix: Tensor, mask: Tensor, norm: bool = True) -> torch.Tensor

Apply a mask to the loss matrix.

Parameters:

Name	Type	Description	Default
loss_matrix	Tensor	Pixelwise losses. A tensor of shape (B, H, W).	required
mask	Tensor	The mask. Shape (B, H, W).	required
norm	bool, default=True	If True, the loss matrix will be normalized by the mean of the mask.	True

Returns:

Type	Description
Tensor	torch.Tensor: The loss matrix scaled with the mask. Shape (B, H, W).

extra_repr

extra_repr() -> str

Add info to print.

__init__

__init__(alpha: float = 0.7, beta: float = 0.3, apply_sd: bool = False, apply_ls: bool = False, apply_svls: bool = False, apply_mask: bool = False, edge_weight: float = None, class_weights: Tensor = None, **kwargs) -> None

Tversky loss.

https://arxiv.org/abs/1706.05721

Parameters:

Name	Type	Description	Default
alpha	float, default=0.7	False positive dice coefficient.	0.7
beta	float, default=0.3	False negative tanimoto coefficient.	0.3
apply_sd	bool, default=False	If True, Spectral decoupling regularization will be applied to the loss matrix.	False
apply_ls	bool, default=False	If True, Label smoothing will be applied to the target.	False
apply_svls	bool, default=False	If True, spatially varying label smoothing will be applied to the target	False
apply_mask	bool, default=False	If True, a mask will be applied to the loss matrix. Mask shape: (B, H, W)	False
edge_weight	float, default=none	Weight that is added to object borders.	None
class_weights	torch.Tensor, default=None	Class weights. A tensor of shape (n_classes,).	None

forward

forward(yhat: Tensor, target: Tensor, target_weight: Tensor = None, mask: Tensor = None, **kwargs) -> torch.Tensor

Compute the Tversky loss.

Parameters:

Name	Type	Description	Default
yhat	Tensor	The prediction map. Shape (B, C, H, W).	required
target	Tensor	the ground truth annotations. Shape (B, H, W).	required
target_weight	torch.Tensor, default=None	The edge weight map. Shape (B, H, W).	None
mask	torch.Tensor, default=None	The mask map. Shape (B, H, W).	None

Returns:

Type	Description
Tensor	torch.Tensor: Computed Tversky loss (scalar).