base_ml/base_loss.py

# -*- coding: utf-8 -*-
# Loss functions (PyTorch and own defined)
#
# Own defined loss functions:
# xentropy_loss, dice_loss, mse_loss and msge_loss (https://github.com/vqdang/hover_net)
# WeightedBaseLoss, MAEWeighted, MSEWeighted, BCEWeighted, CEWeighted (https://github.com/okunator/cellseg_models.pytorch)
# @ Fabian Hörst, [email protected]
# Institute for Artifical Intelligence in Medicine,
# University Medicine Essen


import torch
import torch.nn.functional as F
from typing import List, Tuple
from torch import nn
from torch.nn.modules.loss import _Loss
from base_ml.base_utils import filter2D, gaussian_kernel2d


class XentropyLoss(_Loss):
    """Cross entropy loss"""

    def __init__(self, reduction: str = "mean") -> None:
        super().__init__(size_average=None, reduce=None, reduction=reduction)

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Assumes NCHW shape of array, must be torch.float32 dtype

        Args:
            input (torch.Tensor): Ground truth array with shape (N, C, H, W) with N being the batch-size, H the height, W the width and C the number of classes
            target (torch.Tensor): Prediction array with shape (N, C, H, W) with N being the batch-size, H the height, W the width and C the number of classes

        Returns:
            torch.Tensor: Cross entropy loss, with shape () [scalar], grad_fn = MeanBackward0
        """
        # reshape
        input = input.permute(0, 2, 3, 1)
        target = target.permute(0, 2, 3, 1)

        epsilon = 10e-8
        # scale preds so that the class probs of each sample sum to 1
        pred = input / torch.sum(input, -1, keepdim=True)
        # manual computation of crossentropy
        pred = torch.clamp(pred, epsilon, 1.0 - epsilon)
        loss = -torch.sum((target * torch.log(pred)), -1, keepdim=True)
        loss = loss.mean() if self.reduction == "mean" else loss.sum()

        return loss


class DiceLoss(_Loss):
    """Dice loss

    Args:
        smooth (float, optional): Smoothing value. Defaults to 1e-3.
    """

    def __init__(self, smooth: float = 1e-3) -> None:
        super().__init__(size_average=None, reduce=None, reduction="mean")
        self.smooth = smooth

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Assumes NCHW shape of array, must be torch.float32 dtype

        `pred` and `true` must be of torch.float32. Assuming of shape NxHxWxC.

        Args:
            input (torch.Tensor): Prediction array with shape (N, C, H, W) with N being the batch-size, H the height, W the width and C the number of classes
            target (torch.Tensor): Ground truth array with shape (N, C, H, W) with N being the batch-size, H the height, W the width and C the number of classes

        Returns:
            torch.Tensor: Dice loss, with shape () [scalar], grad_fn=SumBackward0
        """
        input = input.permute(0, 2, 3, 1)
        target = target.permute(0, 2, 3, 1)
        inse = torch.sum(input * target, (0, 1, 2))
        l = torch.sum(input, (0, 1, 2))
        r = torch.sum(target, (0, 1, 2))
        loss = 1.0 - (2.0 * inse + self.smooth) / (l + r + self.smooth)
        loss = torch.sum(loss)

        return loss


class MSELossMaps(_Loss):
    """Calculate mean squared error loss for combined horizontal and vertical maps of segmentation tasks."""

    def __init__(self) -> None:
        super().__init__(size_average=None, reduce=None, reduction="mean")

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Loss calculation

        Args:
            input (torch.Tensor): Prediction of combined horizontal and vertical maps
                with shape (N, 2, H, W), channel 0 is vertical and channel 1 is horizontal
            target (torch.Tensor): Ground truth of combined horizontal and vertical maps
                with shape (N, 2, H, W), channel 0 is vertical and channel 1 is horizontal

        Returns:
            torch.Tensor: Mean squared error per pixel with shape (N, 2, H, W), grad_fn=SubBackward0

        """
        # reshape
        loss = input - target
        loss = (loss * loss).mean()
        return loss


class MSGELossMaps(_Loss):
    def __init__(self) -> None:
        super().__init__(size_average=None, reduce=None, reduction="mean")

    def get_sobel_kernel(
        self, size: int, device: str
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Get sobel kernel with a given size.

        Args:
            size (int): Kernel site
            device (str): Cuda device

        Returns:
            Tuple[torch.Tensor, torch.Tensor]: Horizontal and vertical sobel kernel, each with shape (size, size)
        """
        assert size % 2 == 1, "Must be odd, get size=%d" % size

        h_range = torch.arange(
            -size // 2 + 1,
            size // 2 + 1,
            dtype=torch.float32,
            device=device,
            requires_grad=False,
        )
        v_range = torch.arange(
            -size // 2 + 1,
            size // 2 + 1,
            dtype=torch.float32,
            device=device,
            requires_grad=False,
        )
        h, v = torch.meshgrid(h_range, v_range, indexing="ij")
        kernel_h = h / (h * h + v * v + 1.0e-15)
        kernel_v = v / (h * h + v * v + 1.0e-15)
        return kernel_h, kernel_v

    def get_gradient_hv(self, hv: torch.Tensor, device: str) -> torch.Tensor:
        """For calculating gradient of horizontal and vertical prediction map


        Args:
            hv (torch.Tensor): horizontal and vertical map
            device (str): CUDA device

        Returns:
            torch.Tensor: Gradient with same shape as input
        """
        kernel_h, kernel_v = self.get_sobel_kernel(5, device=device)
        kernel_h = kernel_h.view(1, 1, 5, 5)  # constant
        kernel_v = kernel_v.view(1, 1, 5, 5)  # constant

        h_ch = hv[..., 0].unsqueeze(1)  # Nx1xHxW
        v_ch = hv[..., 1].unsqueeze(1)  # Nx1xHxW

        # can only apply in NCHW mode
        h_dh_ch = F.conv2d(h_ch, kernel_h, padding=2)
        v_dv_ch = F.conv2d(v_ch, kernel_v, padding=2)
        dhv = torch.cat([h_dh_ch, v_dv_ch], dim=1)
        dhv = dhv.permute(0, 2, 3, 1).contiguous()  # to NHWC
        return dhv

    def forward(
        self,
        input: torch.Tensor,
        target: torch.Tensor,
        focus: torch.Tensor,
        device: str,
    ) -> torch.Tensor:
        """MSGE (Gradient of MSE) loss

        Args:
            input (torch.Tensor): Input with shape (B, C, H, W)
            target (torch.Tensor): Target with shape (B, C, H, W)
            focus (torch.Tensor): Focus, type of masking (B, C, W, W)
            device (str): CUDA device to work with.

        Returns:
            torch.Tensor: MSGE loss
        """
        input = input.permute(0, 2, 3, 1)
        target = target.permute(0, 2, 3, 1)
        focus = focus.permute(0, 2, 3, 1)
        focus = focus[..., 1]

        focus = (focus[..., None]).float()  # assume input NHW
        focus = torch.cat([focus, focus], axis=-1).to(device)
        true_grad = self.get_gradient_hv(target, device)
        pred_grad = self.get_gradient_hv(input, device)
        loss = pred_grad - true_grad
        loss = focus * (loss * loss)
        # artificial reduce_mean with focused region
        loss = loss.sum() / (focus.sum() + 1.0e-8)
        return loss


class FocalTverskyLoss(nn.Module):
    """FocalTverskyLoss

    PyTorch implementation of the Focal Tversky Loss Function for multiple classes
    doi: 10.1109/ISBI.2019.8759329
    Abraham, N., & Khan, N. M. (2019).
    A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation.
    In International Symposium on Biomedical Imaging. https://doi.org/10.1109/isbi.2019.8759329

    @ Fabian Hörst, [email protected]
    Institute for Artifical Intelligence in Medicine,
    University Medicine Essen

    Args:
        alpha_t (float, optional): Alpha parameter for tversky loss (multiplied with false-negatives). Defaults to 0.7.
        beta_t (float, optional): Beta parameter for tversky loss (multiplied with false-positives). Defaults to 0.3.
        gamma_f (float, optional): Gamma Focal parameter. Defaults to 4/3.
        smooth (float, optional): Smooting factor. Defaults to 0.000001.
    """

    def __init__(
        self,
        alpha_t: float = 0.7,
        beta_t: float = 0.3,
        gamma_f: float = 4 / 3,
        smooth: float = 1e-6,
    ) -> None:
        super().__init__()
        self.alpha_t = alpha_t
        self.beta_t = beta_t
        self.gamma_f = gamma_f
        self.smooth = smooth
        self.num_classes = 2

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Loss calculation

        Args:
            input (torch.Tensor): Predictions, logits (without Softmax). Shape: (B, C, H, W)
            target (torch.Tensor): Targets, either flattened (Shape: (C, H, W) or as one-hot encoded (Shape: (batch-size, C, H, W)).

        Raises:
            ValueError: Error if there is a shape missmatch

        Returns:
            torch.Tensor: FocalTverskyLoss (weighted)
        """
        input = input.permute(0, 2, 3, 1)
        if input.shape[-1] != self.num_classes:
            raise ValueError(
                "Predictions must be a logit tensor with the last dimension shape beeing equal to the number of classes"
            )
        if len(target.shape) != len(input.shape):
            # convert the targets to onehot
            target = F.one_hot(target, num_classes=self.num_classes)

        # flatten
        target = target.permute(0, 2, 3, 1)
        target = target.view(-1)
        input = torch.softmax(input, dim=-1).view(-1)

        # calculate true positives, false positives and false negatives
        tp = (input * target).sum()
        fp = ((1 - target) * input).sum()
        fn = (target * (1 - input)).sum()

        Tversky = (tp + self.smooth) / (
            tp + self.alpha_t * fn + self.beta_t * fp + self.smooth
        )
        FocalTversky = (1 - Tversky) ** self.gamma_f

        return FocalTversky


class MCFocalTverskyLoss(FocalTverskyLoss):
    """Multiclass FocalTverskyLoss

    PyTorch implementation of the Focal Tversky Loss Function for multiple classes
    doi: 10.1109/ISBI.2019.8759329
    Abraham, N., & Khan, N. M. (2019).
    A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation.
    In International Symposium on Biomedical Imaging. https://doi.org/10.1109/isbi.2019.8759329

    @ Fabian Hörst, [email protected]
    Institute for Artifical Intelligence in Medicine,
    University Medicine Essen

    Args:
        alpha_t (float, optional): Alpha parameter for tversky loss (multiplied with false-negatives). Defaults to 0.7.
        beta_t (float, optional): Beta parameter for tversky loss (multiplied with false-positives). Defaults to 0.3.
        gamma_f (float, optional): Gamma Focal parameter. Defaults to 4/3.
        smooth (float, optional): Smooting factor. Defaults to 0.000001.
        num_classes (int, optional): Number of output classes. For binary segmentation, prefer FocalTverskyLoss (speed optimized). Defaults to 2.
        class_weights (List[int], optional): Weights for each class. If not provided, equal weight. Length must be equal to num_classes. Defaults to None.
    """

    def __init__(
        self,
        alpha_t: float = 0.7,
        beta_t: float = 0.3,
        gamma_f: float = 4 / 3,
        smooth: float = 0.000001,
        num_classes: int = 2,
        class_weights: List[int] = None,
    ) -> None:
        super().__init__(alpha_t, beta_t, gamma_f, smooth)
        self.num_classes = num_classes
        if class_weights is None:
            self.class_weights = [1 for i in range(self.num_classes)]
        else:
            assert (
                len(class_weights) == self.num_classes
            ), "Please provide matching weights"
            self.class_weights = class_weights
        self.class_weights = torch.Tensor(self.class_weights)

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        """Loss calculation

        Args:
            input (torch.Tensor): Predictions, logits (without Softmax). Shape: (B, num_classes, H, W)
            target (torch.Tensor): Targets, either flattened (Shape: (B, H, W) or as one-hot encoded (Shape: (B, num_classes, H, W)).

        Raises:
            ValueError: Error if there is a shape missmatch

        Returns:
            torch.Tensor: FocalTverskyLoss (weighted)
        """
        input = input.permute(0, 2, 3, 1)
        if input.shape[-1] != self.num_classes:
            raise ValueError(
                "Predictions must be a logit tensor with the last dimension shape beeing equal to the number of classes"
            )
        if len(target.shape) != len(input.shape):
            # convert the targets to onehot
            target = F.one_hot(target, num_classes=self.num_classes)

        target = target.permute(0, 2, 3, 1)
        # Softmax
        input = torch.softmax(input, dim=-1)

        # Reshape
        input = torch.permute(input, (3, 1, 2, 0))
        target = torch.permute(target, (3, 1, 2, 0))

        input = torch.flatten(input, start_dim=1)
        target = torch.flatten(target, start_dim=1)

        tp = torch.sum(input * target, 1)
        fp = torch.sum((1 - target) * input, 1)
        fn = torch.sum(target * (1 - input), 1)

        Tversky = (tp + self.smooth) / (
            tp + self.alpha_t * fn + self.beta_t * fp + self.smooth
        )
        FocalTversky = (1 - Tversky) ** self.gamma_f

        self.class_weights = self.class_weights.to(FocalTversky.device)
        return torch.sum(self.class_weights * FocalTversky)


class WeightedBaseLoss(nn.Module):
    """Init a base class for weighted cross entropy based losses.

    Enables weighting for object instance edges and classes.

    Adapted/Copied from: https://github.com/okunator/cellseg_models.pytorch (10.5281/zenodo.7064617)

    Args:
        apply_sd (bool, optional): If True, Spectral decoupling regularization will be applied to the
            loss matrix. Defaults to False.
        apply_ls (bool, optional): If True, Label smoothing will be applied to the target.. Defaults to False.
        apply_svls (bool, optional): If True, spatially varying label smoothing will be applied to the target. Defaults to False.
        apply_mask (bool, optional): If True, a mask will be applied to the loss matrix. Mask shape: (B, H, W). Defaults to False.
        class_weights (torch.Tensor, optional): Class weights. A tensor of shape (C, ). Defaults to None.
        edge_weight (float, optional): Weight for the object instance border pixels. Defaults to None.
    """

    def __init__(
        self,
        apply_sd: bool = False,
        apply_ls: bool = False,
        apply_svls: bool = False,
        apply_mask: bool = False,
        class_weights: torch.Tensor = None,
        edge_weight: float = None,
        **kwargs,
    ) -> None:
        super().__init__()
        self.apply_sd = apply_sd
        self.apply_ls = apply_ls
        self.apply_svls = apply_svls
        self.apply_mask = apply_mask
        self.class_weights = class_weights
        self.edge_weight = edge_weight

    def apply_spectral_decouple(
        self, loss_matrix: torch.Tensor, yhat: torch.Tensor, lam: float = 0.01
    ) -> torch.Tensor:
        """Apply spectral decoupling L2 norm after the loss.

        https://arxiv.org/abs/2011.09468

        Args:
            loss_matrix (torch.Tensor): Pixelwise losses. A tensor of shape (B, H, W).
            yhat (torch.Tensor): The pixel predictions of the model. Shape (B, C, H, W).
            lam (float, optional): Lambda constant.. Defaults to 0.01.

        Returns:
            torch.Tensor: SD-regularized loss matrix. Same shape as input.
        """
        return loss_matrix + (lam / 2) * (yhat**2).mean(axis=1)

    def apply_ls_to_target(
        self,
        target: torch.Tensor,
        num_classes: int,
        label_smoothing: float = 0.1,
    ) -> torch.Tensor:
        """_summary_

        Args:
            target (torch.Tensor): Number of classes in the data.
            num_classes (int): The target one hot tensor. Shape (B, C, H, W)
            label_smoothing (float, optional):  The smoothing coeff alpha. Defaults to 0.1.

        Returns:
            torch.Tensor: Label smoothed target. Same shape as input.
        """
        return target * (1 - label_smoothing) + label_smoothing / num_classes

    def apply_svls_to_target(
        self,
        target: torch.Tensor,
        num_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

        Args:
            target (torch.Tensor): The target one hot tensor. Shape (B, C, H, W).
            num_classes (int):  Number of classes in the data.
            kernel_size (int, optional): Size of a square kernel.. Defaults to 5.
            sigma (int, optional): The std of the gaussian. Defaults to 3.

        Returns:
            torch.Tensor: Label smoothed target. Same shape as input.
        """
        my, mx = kernel_size // 2, kernel_size // 2
        gaussian_kernel = gaussian_kernel2d(
            kernel_size, sigma, num_classes, device=target.device
        )
        neighborsum = (1 - gaussian_kernel[..., my, mx]) + 1e-16
        gaussian_kernel = gaussian_kernel.clone()
        gaussian_kernel[..., my, mx] = neighborsum
        svls_kernel = gaussian_kernel / neighborsum[0]

        return filter2D(target.float(), svls_kernel) / svls_kernel[0].sum()

    def apply_class_weights(
        self, loss_matrix: torch.Tensor, target: torch.Tensor
    ) -> torch.Tensor:
        """Multiply pixelwise loss matrix by the class weights.

        NOTE: No normalization

        Args:
            loss_matrix (torch.Tensor): Pixelwise losses. A tensor of shape (B, H, W).
            target (torch.Tensor): The target mask. Shape (B, H, W).

        Returns:
            torch.Tensor: The loss matrix scaled with the weight matrix. Shape (B, H, W).
        """
        weight_mat = self.class_weights[target.long()].to(target.device)  # to (B, H, W)
        loss = loss_matrix * weight_mat

        return loss

    def apply_edge_weights(
        self, loss_matrix: torch.Tensor, weight_map: torch.Tensor
    ) -> torch.Tensor:
        """Apply weights to the object boundaries.

        Basically just computes `edge_weight`**`weight_map`.

        Args:
            loss_matrix (torch.Tensor): Pixelwise losses. A tensor of shape (B, H, W).
            weight_map (torch.Tensor): Map that points to the pixels that will be weighted. Shape (B, H, W).

        Returns:
            torch.Tensor: The loss matrix scaled with the nuclear boundary weights. Shape (B, H, W).
        """
        return loss_matrix * self.edge_weight**weight_map

    def apply_mask_weight(
        self, loss_matrix: torch.Tensor, mask: torch.Tensor, norm: bool = True
    ) -> torch.Tensor:
        """Apply a mask to the loss matrix.

        Args:
            loss_matrix (torch.Tensor): Pixelwise losses. A tensor of shape (B, H, W).
            mask (torch.Tensor): The mask. Shape (B, H, W).
            norm (bool, optional): If True, the loss matrix will be normalized by the mean of the mask. Defaults to True.

        Returns:
            torch.Tensor: The loss matrix scaled with the mask. Shape (B, H, W).
        """
        loss_matrix *= mask
        if norm:
            norm_mask = torch.mean(mask.float()) + 1e-7
            loss_matrix /= norm_mask

        return loss_matrix

    def extra_repr(self) -> str:
        """Add info to print."""
        s = "apply_sd={apply_sd}, apply_ls={apply_ls}, apply_svls={apply_svls}, apply_mask={apply_mask}, class_weights={class_weights}, edge_weight={edge_weight}"  # noqa
        return s.format(**self.__dict__)


class MAEWeighted(WeightedBaseLoss):
    """Compute the MAE loss. Used in the stardist method.

    Stardist:
    https://arxiv.org/pdf/1806.03535.pdf
    Adapted/Copied from: https://github.com/okunator/cellseg_models.pytorch (10.5281/zenodo.7064617)

    NOTE: We have added the option to apply spectral decoupling and edge weights
    to the loss matrix.

    Args:
        alpha (float, optional): Weight regulizer b/w [0,1]. In stardist repo, this is the
        'train_background_reg' parameter. Defaults to 1e-4.
        apply_sd (bool, optional): If True, Spectral decoupling regularization will be applied  to the
        loss matrix. Defaults to False.
        apply_mask (bool, optional): f True, a mask will be applied to the loss matrix. Mask shape: (B, H, W). Defaults to False.
        edge_weight (float, optional): Weight that is added to object borders. Defaults to None.
    """

    def __init__(
        self,
        alpha: float = 1e-4,
        apply_sd: bool = False,
        apply_mask: bool = False,
        edge_weight: float = None,
        **kwargs,
    ) -> None:
        super().__init__(apply_sd, False, False, apply_mask, False, edge_weight)
        self.alpha = alpha
        self.eps = 1e-7

    def forward(
        self,
        input: torch.Tensor,
        target: torch.Tensor,
        target_weight: torch.Tensor = None,
        mask: torch.Tensor = None,
        **kwargs,
    ) -> torch.Tensor:
        """Compute the masked MAE loss.

        Args:
            input (torch.Tensor): The prediction map. Shape (B, C, H, W).
            target (torch.Tensor): The ground truth annotations. Shape (B, H, W).
            target_weight (torch.Tensor, optional): The edge weight map. Shape (B, H, W). Defaults to None.
            mask (torch.Tensor, optional): The mask map. Shape (B, H, W). Defaults to None.

        Raises:
            ValueError: Pred and target shapes must match.

        Returns:
            torch.Tensor: Computed MAE loss (scalar).
        """
        yhat = input
        n_classes = yhat.shape[1]
        if target.size() != yhat.size():
            target = target.unsqueeze(1).repeat_interleave(n_classes, dim=1)

        if not yhat.shape == target.shape:
            raise ValueError(
                f"Pred and target shapes must match. Got: {yhat.shape}, {target.shape}"
            )

        # compute the MAE loss with alpha as weight
        mae_loss = torch.mean(torch.abs(target - yhat), axis=1)  # (B, H, W)

        if self.apply_mask and mask is not None:
            mae_loss = self.apply_mask_weight(mae_loss, mask, norm=True)  # (B, H, W)

            # add the background regularization
            if self.alpha > 0:
                reg = torch.mean(((1 - mask).unsqueeze(1)) * torch.abs(yhat), axis=1)
                mae_loss += self.alpha * reg

        if self.apply_sd:
            mae_loss = self.apply_spectral_decouple(mae_loss, yhat)

        if self.edge_weight is not None:
            mae_loss = self.apply_edge_weights(mae_loss, target_weight)

        return mae_loss.mean()


class MSEWeighted(WeightedBaseLoss):
    """MSE-loss.

    Args:
        apply_sd (bool, optional): If True, Spectral decoupling regularization will be applied  to the
            loss matrix. Defaults to False.
        apply_ls (bool, optional): If True, Label smoothing will be applied to the target. Defaults to False.
        apply_svls (bool, optional): If True, spatially varying label smoothing will be applied to the target. Defaults to False.
        apply_mask (bool, optional): If True, a mask will be applied to the loss matrix. Mask shape: (B, H, W). Defaults to False.
        edge_weight (float, optional): Weight that is added to object borders. Defaults to None.
        class_weights (torch.Tensor, optional): Class weights. A tensor of shape (n_classes,). Defaults to None.
    """

    def __init__(
        self,
        apply_sd: bool = False,
        apply_ls: bool = False,
        apply_svls: bool = False,
        apply_mask: bool = False,
        edge_weight: float = None,
        class_weights: torch.Tensor = None,
        **kwargs,
    ) -> None:
        super().__init__(
            apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight
        )

    @staticmethod
    def tensor_one_hot(type_map: torch.Tensor, n_classes: int) -> torch.Tensor:
        """Convert a segmentation mask into one-hot-format.

        I.e. Takes in a segmentation mask of shape (B, H, W) and reshapes it
        into a tensor of shape (B, C, H, W).

        Args:
            type_map (torch.Tensor):  Multi-label Segmentation mask. Shape (B, H, W).
            n_classes (int): Number of classes. (Zero-class included.)

        Raises:
            TypeError: Input `type_map` should have dtype: torch.int64.

        Returns:
            torch.Tensor: A one hot tensor. Shape: (B, C, H, W). Dtype: torch.FloatTensor.
        """
        if not type_map.dtype == torch.int64:
            raise TypeError(
                f"""
                Input `type_map` should have dtype: torch.int64. Got: {type_map.dtype}."""
            )

        one_hot = torch.zeros(
            type_map.shape[0],
            n_classes,
            *type_map.shape[1:],
            device=type_map.device,
            dtype=type_map.dtype,
        )

        return one_hot.scatter_(dim=1, index=type_map.unsqueeze(1), value=1.0) + 1e-7

    def forward(
        self,
        input: torch.Tensor,
        target: torch.Tensor,
        target_weight: torch.Tensor = None,
        mask: torch.Tensor = None,
        **kwargs,
    ) -> torch.Tensor:
        """Compute the MSE-loss.

        Args:
            input (torch.Tensor): The prediction map. Shape (B, C, H, W, C).
            target (torch.Tensor): The ground truth annotations. Shape (B, H, W).
            target_weight (torch.Tensor, optional):  The edge weight map. Shape (B, H, W). Defaults to None.
            mask (torch.Tensor, optional): The mask map. Shape (B, H, W). Defaults to None.

        Returns:
            torch.Tensor: Computed MSE loss (scalar).
        """
        yhat = input
        target_one_hot = target
        num_classes = yhat.shape[1]

        if target.size() != yhat.size():
            if target.dtype == torch.float32:
                target_one_hot = target.unsqueeze(1)
            else:
                target_one_hot = MSEWeighted.tensor_one_hot(target, num_classes)

        if self.apply_svls:
            target_one_hot = self.apply_svls_to_target(
                target_one_hot, num_classes, **kwargs
            )

        if self.apply_ls:
            target_one_hot = self.apply_ls_to_target(
                target_one_hot, num_classes, **kwargs
            )

        mse = F.mse_loss(yhat, target_one_hot, reduction="none")  # (B, C, H, W)
        mse = torch.mean(mse, dim=1)  # to (B, H, W)

        if self.apply_mask and mask is not None:
            mse = self.apply_mask_weight(mse, mask, norm=False)  # (B, H, W)

        if self.apply_sd:
            mse = self.apply_spectral_decouple(mse, yhat)

        if self.class_weights is not None:
            mse = self.apply_class_weights(mse, target)

        if self.edge_weight is not None:
            mse = self.apply_edge_weights(mse, target_weight)

        return torch.mean(mse)


class BCEWeighted(WeightedBaseLoss):
    def __init__(
        self,
        apply_sd: bool = False,
        apply_ls: bool = False,
        apply_svls: bool = False,
        apply_mask: bool = False,
        edge_weight: float = None,
        class_weights: torch.Tensor = None,
        **kwargs,
    ) -> None:
        """Binary cross entropy loss with weighting and other tricks.

        Parameters
        ----------
        apply_sd : bool, default=False
            If True, Spectral decoupling regularization will be applied  to the
            loss matrix.
        apply_ls : bool, default=False
            If True, Label smoothing will be applied to the target.
        apply_svls : bool, default=False
            If True, spatially varying label smoothing will be applied to the target
        apply_mask : bool, default=False
            If True, a mask will be applied to the loss matrix. Mask shape: (B, H, W)
        edge_weight : float, default=None
            Weight that is added to object borders.
        class_weights : torch.Tensor, default=None
            Class weights. A tensor of shape (n_classes,).
        """
        super().__init__(
            apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight
        )
        self.eps = 1e-8

    def forward(
        self,
        input: torch.Tensor,
        target: torch.Tensor,
        target_weight: torch.Tensor = None,
        mask: torch.Tensor = None,
        **kwargs,
    ) -> torch.Tensor:
        """Compute binary cross entropy loss.

        Parameters
        ----------
            yhat : torch.Tensor
                The prediction map. Shape (B, C, H, W).
            target : torch.Tensor
                the ground truth annotations. Shape (B, H, W).
            target_weight : torch.Tensor, default=None
                The edge weight map. Shape (B, H, W).
            mask : torch.Tensor, default=None
                The mask map. Shape (B, H, W).

        Returns
        -------
            torch.Tensor:
                Computed BCE loss (scalar).
        """
        # Logits input
        yhat = input
        num_classes = yhat.shape[1]
        yhat = torch.clip(yhat, self.eps, 1.0 - self.eps)

        if target.size() != yhat.size():
            target = target.unsqueeze(1).repeat_interleave(num_classes, dim=1)

        if self.apply_svls:
            target = self.apply_svls_to_target(target, num_classes, **kwargs)

        if self.apply_ls:
            target = self.apply_ls_to_target(target, num_classes, **kwargs)

        bce = F.binary_cross_entropy_with_logits(
            yhat.float(), target.float(), reduction="none"
        )  # (B, C, H, W)
        bce = torch.mean(bce, dim=1)  # (B, H, W)

        if self.apply_mask and mask is not None:
            bce = self.apply_mask_weight(bce, mask, norm=False)  # (B, H, W)

        if self.apply_sd:
            bce = self.apply_spectral_decouple(bce, yhat)

        if self.class_weights is not None:
            bce = self.apply_class_weights(bce, target)

        if self.edge_weight is not None:
            bce = self.apply_edge_weights(bce, target_weight)

        return torch.mean(bce)


# class BCEWeighted(WeightedBaseLoss):
#     """Binary cross entropy loss with weighting and other tricks.
#     Adapted/Copied from: https://github.com/okunator/cellseg_models.pytorch (10.5281/zenodo.7064617)

#     Args:
#         apply_sd (bool, optional): If True, Spectral decoupling regularization will be applied  to the
#             loss matrix. Defaults to False.
#         apply_ls (bool, optional): If True, Label smoothing will be applied to the target. Defaults to False.
#         apply_svls (bool, optional): If True, spatially varying label smoothing will be applied to the target. Defaults to False.
#         apply_mask (bool, optional): If True, a mask will be applied to the loss matrix. Mask shape: (B, H, W). Defaults to False.
#         edge_weight (float, optional):  Weight that is added to object borders. Defaults to None.
#         class_weights (torch.Tensor, optional): Class weights. A tensor of shape (n_classes,). Defaults to None.
#     """

#     def __init__(
#         self,
#         apply_sd: bool = False,
#         apply_ls: bool = False,
#         apply_svls: bool = False,
#         apply_mask: bool = False,
#         edge_weight: float = None,
#         class_weights: torch.Tensor = None,
#         **kwargs,
#     ) -> None:
#         super().__init__(
#             apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight
#         )
#         self.eps = 1e-8

#     def forward(
#         self,
#         input: torch.Tensor,
#         target: torch.Tensor,
#         target_weight: torch.Tensor = None,
#         mask: torch.Tensor = None,
#         **kwargs,
#     ) -> torch.Tensor:
#         """Compute binary cross entropy loss.

#         Args:
#             input (torch.Tensor): The prediction map. We internally convert back via logit function. Shape (B, C, H, W).
#             target (torch.Tensor): the ground truth annotations. Shape (B, H, W).
#             target_weight (torch.Tensor, optional): The edge weight map. Shape (B, H, W). Defaults to None.
#             mask (torch.Tensor, optional): The mask map. Shape (B, H, W). Defaults to None.

#         Returns:
#             torch.Tensor: Computed BCE loss (scalar).
#         """
#         yhat = input
#         yhat = torch.special.logit(yhat)
#         num_classes = yhat.shape[1]
#         yhat = torch.clip(yhat, self.eps, 1.0 - self.eps)

#         if target.size() != yhat.size():
#             target = target.unsqueeze(1).repeat_interleave(num_classes, dim=1)

#         if self.apply_svls:
#             target = self.apply_svls_to_target(target, num_classes, **kwargs)

#         if self.apply_ls:
#             target = self.apply_ls_to_target(target, num_classes, **kwargs)

#         bce = F.binary_cross_entropy_with_logits(
#             yhat.float(), target.float(), reduction="none"
#         )  # (B, C, H, W)
#         bce = torch.mean(bce, dim=1)  # (B, H, W)

#         if self.apply_mask and mask is not None:
#             bce = self.apply_mask_weight(bce, mask, norm=False)  # (B, H, W)

#         if self.apply_sd:
#             bce = self.apply_spectral_decouple(bce, yhat)

#         if self.class_weights is not None:
#             bce = self.apply_class_weights(bce, target)

#         if self.edge_weight is not None:
#             bce = self.apply_edge_weights(bce, target_weight)

#         return torch.mean(bce)


class CEWeighted(WeightedBaseLoss):
    def __init__(
        self,
        apply_sd: bool = False,
        apply_ls: bool = False,
        apply_svls: bool = False,
        apply_mask: bool = False,
        edge_weight: float = None,
        class_weights: torch.Tensor = None,
        **kwargs,
    ) -> None:
        """Cross-Entropy loss with weighting.

        Parameters
        ----------
        apply_sd : bool, default=False
            If True, Spectral decoupling regularization will be applied  to the
            loss matrix.
        apply_ls : bool, default=False
            If True, Label smoothing will be applied to the target.
        apply_svls : bool, default=False
            If True, spatially varying label smoothing will be applied to the target
        apply_mask : bool, default=False
            If True, a mask will be applied to the loss matrix. Mask shape: (B, H, W)
        edge_weight : float, default=None
            Weight that is added to object borders.
        class_weights : torch.Tensor, default=None
            Class weights. A tensor of shape (n_classes,).
        """
        super().__init__(
            apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight
        )
        self.eps = 1e-8

    def forward(
        self,
        input: torch.Tensor,
        target: torch.Tensor,
        target_weight: torch.Tensor = None,
        mask: torch.Tensor = None,
        **kwargs,
    ) -> torch.Tensor:
        """Compute the cross entropy loss.

        Parameters
        ----------
            yhat : torch.Tensor
                The prediction map. Shape (B, C, H, W).
            target : torch.Tensor
                the ground truth annotations. Shape (B, H, W).
            target_weight : torch.Tensor, default=None
                The edge weight map. Shape (B, H, W).
            mask : torch.Tensor, default=None
                The mask map. Shape (B, H, W).

        Returns
        -------
            torch.Tensor:
                Computed CE loss (scalar).
        """
        yhat = input  # TODO: remove doubled Softmax -> this function needs logits instead of softmax output
        input_soft = F.softmax(yhat, dim=1) + self.eps  # (B, C, H, W)
        num_classes = yhat.shape[1]
        if len(target.shape) != len(yhat.shape) and target.shape[1] != num_classes:
            target_one_hot = MSEWeighted.tensor_one_hot(
                target, num_classes
            )  # (B, C, H, W)
        else:
            target_one_hot = target
            target = torch.argmax(target, dim=1)
        assert target_one_hot.shape == yhat.shape

        if self.apply_svls:
            target_one_hot = self.apply_svls_to_target(
                target_one_hot, num_classes, **kwargs
            )

        if self.apply_ls:
            target_one_hot = self.apply_ls_to_target(
                target_one_hot, num_classes, **kwargs
            )

        loss = -torch.sum(target_one_hot * torch.log(input_soft), dim=1)  # (B, H, W)

        if self.apply_mask and mask is not None:
            loss = self.apply_mask_weight(loss, mask, norm=False)  # (B, H, W)

        if self.apply_sd:
            loss = self.apply_spectral_decouple(loss, yhat)

        if self.class_weights is not None:
            loss = self.apply_class_weights(loss, target)

        if self.edge_weight is not None:
            loss = self.apply_edge_weights(loss, target_weight)

        return loss.mean()


# class CEWeighted(WeightedBaseLoss):
#     """Cross-Entropy loss with weighting.
#     Adapted/Copied from: https://github.com/okunator/cellseg_models.pytorch (10.5281/zenodo.7064617)

#     Args:
#         apply_sd (bool, optional): If True, Spectral decoupling regularization will be applied to the loss matrix. Defaults to False.
#         apply_ls (bool, optional): If True, Label smoothing will be applied to the target. Defaults to False.
#         apply_svls (bool, optional): If True, spatially varying label smoothing will be applied to the target. Defaults to False.
#         apply_mask (bool, optional): If True, a mask will be applied to the loss matrix. Mask shape: (B, H, W). Defaults to False.
#         edge_weight (float, optional): Weight that is added to object borders. Defaults to None.
#         class_weights (torch.Tensor, optional): Class weights. A tensor of shape (n_classes,). Defaults to None.
#         logits (bool, optional): If work on logit values. Defaults to False. Defaults to False.
#     """

#     def __init__(
#         self,
#         apply_sd: bool = False,
#         apply_ls: bool = False,
#         apply_svls: bool = False,
#         apply_mask: bool = False,
#         edge_weight: float = None,
#         class_weights: torch.Tensor = None,
#         logits: bool = False,
#         **kwargs,
#     ) -> None:
#         super().__init__(
#             apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight
#         )
#         self.eps = 1e-8
#         self.logits = logits

#     def forward(
#         self,
#         input: torch.Tensor,
#         target: torch.Tensor,
#         target_weight: torch.Tensor = None,
#         mask: torch.Tensor = None,
#         **kwargs,
#     ) -> torch.Tensor:
#         """Compute the cross entropy loss.

#         Args:
#             input (torch.Tensor): The prediction map. Shape (B, C, H, W).
#             target (torch.Tensor): The ground truth annotations. Shape (B, H, W).
#             target_weight (torch.Tensor, optional): The edge weight map. Shape (B, H, W). Defaults to None.
#             mask (torch.Tensor, optional): The mask map. Shape (B, H, W). Defaults to None.

#         Returns:
#             torch.Tensor: Computed CE loss (scalar).
#         """
#         yhat = input
#         if self.logits:
#             input_soft = (
#                 F.softmax(yhat, dim=1) + self.eps
#             )  # (B, C, H, W) # check if doubled softmax
#         else:
#             input_soft = input

#         num_classes = yhat.shape[1]
#         if len(target.shape) != len(yhat.shape) and target.shape[1] != num_classes:
#             target_one_hot = MSEWeighted.tensor_one_hot(
#                 target, num_classes
#             )  # (B, C, H, W)
#         else:
#             target_one_hot = target
#             target = torch.argmax(target, dim=1)
#         assert target_one_hot.shape == yhat.shape

#         if self.apply_svls:
#             target_one_hot = self.apply_svls_to_target(
#                 target_one_hot, num_classes, **kwargs
#             )

#         if self.apply_ls:
#             target_one_hot = self.apply_ls_to_target(
#                 target_one_hot, num_classes, **kwargs
#             )

#         loss = -torch.sum(target_one_hot * torch.log(input_soft), dim=1)  # (B, H, W)

#         if self.apply_mask and mask is not None:
#             loss = self.apply_mask_weight(loss, mask, norm=False)  # (B, H, W)

#         if self.apply_sd:
#             loss = self.apply_spectral_decouple(loss, yhat)

#         if self.class_weights is not None:
#             loss = self.apply_class_weights(loss, target)

#         if self.edge_weight is not None:
#             loss = self.apply_edge_weights(loss, target_weight)

#         return loss.mean()


### Stardist loss functions
class L1LossWeighted(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(
        self,
        input: torch.Tensor,
        target: torch.Tensor,
        target_weight: torch.Tensor = None,
    ) -> torch.Tensor:
        l1loss = F.l1_loss(input, target, size_average=True, reduce=False)
        l1loss = torch.mean(l1loss, dim=1)
        if target_weight is not None:
            l1loss = torch.mean(target_weight * l1loss)
        else:
            l1loss = torch.mean(l1loss)
        return l1loss


def retrieve_loss_fn(loss_name: dict, **kwargs) -> _Loss:
    """Return the loss function with given name defined in the LOSS_DICT and initialize with kwargs

    kwargs must match with the parameters defined in the initialization method of the selected loss object

    Args:
        loss_name (dict): Name of the loss function

    Returns:
        _Loss: Loss
    """
    loss_fn = LOSS_DICT[loss_name]
    loss_fn = loss_fn(**kwargs)

    return loss_fn


LOSS_DICT = {
    "xentropy_loss": XentropyLoss,
    "dice_loss": DiceLoss,
    "mse_loss_maps": MSELossMaps,
    "msge_loss_maps": MSGELossMaps,
    "FocalTverskyLoss": FocalTverskyLoss,
    "MCFocalTverskyLoss": MCFocalTverskyLoss,
    "CrossEntropyLoss": nn.CrossEntropyLoss,  # input logits, targets
    "L1Loss": nn.L1Loss,
    "MSELoss": nn.MSELoss,
    "CTCLoss": nn.CTCLoss,  # probability
    "NLLLoss": nn.NLLLoss,  # log-probabilities of each class
    "PoissonNLLLoss": nn.PoissonNLLLoss,
    "GaussianNLLLoss": nn.GaussianNLLLoss,
    "KLDivLoss": nn.KLDivLoss,  # argument input in log-space
    "BCELoss": nn.BCELoss,  # probabilities
    "BCEWithLogitsLoss": nn.BCEWithLogitsLoss,  # logits
    "MarginRankingLoss": nn.MarginRankingLoss,
    "HingeEmbeddingLoss": nn.HingeEmbeddingLoss,
    "MultiLabelMarginLoss": nn.MultiLabelMarginLoss,
    "HuberLoss": nn.HuberLoss,
    "SmoothL1Loss": nn.SmoothL1Loss,
    "SoftMarginLoss": nn.SoftMarginLoss,  # logits
    "MultiLabelSoftMarginLoss": nn.MultiLabelSoftMarginLoss,
    "CosineEmbeddingLoss": nn.CosineEmbeddingLoss,
    "MultiMarginLoss": nn.MultiMarginLoss,
    "TripletMarginLoss": nn.TripletMarginLoss,
    "TripletMarginWithDistanceLoss": nn.TripletMarginWithDistanceLoss,
    "MAEWeighted": MAEWeighted,
    "MSEWeighted": MSEWeighted,
    "BCEWeighted": BCEWeighted,  # logits
    "CEWeighted": CEWeighted,  # logits
    "L1LossWeighted": L1LossWeighted,
}