deeplabcut.pose_estimation_pytorch.models.criterions.dekr

Loss criterions for DEKR models.

Classes:

Name	Description
DEKRHeatmapLoss	DEKR Heatmap loss.
DEKROffsetLoss	DEKR Offset loss.

DEKRHeatmapLoss

Bases: BaseCriterion

DEKR Heatmap loss.

Methods:

Name	Description
forward	Args:

Source code in deeplabcut/pose_estimation_pytorch/models/criterions/dekr.py

@CRITERIONS.register_module
class DEKRHeatmapLoss(BaseCriterion):
    """DEKR Heatmap loss."""

    def forward(
        self,
        output: torch.Tensor,
        target: torch.Tensor,
        weights: torch.Tensor | float = 1.0,
        **kwargs,
    ) -> torch.Tensor:
        """
        Args:
            output: the output from which to compute the loss
            target: the target for the loss
            weights: the weights for the loss

        Returns:
            the DEKR offset loss
        """
        assert output.size() == target.size()
        loss = ((output - target) ** 2) * weights
        return loss.mean(dim=3).mean(dim=2).mean(dim=1).mean(dim=0)

forward

forward(output: Tensor, target: Tensor, weights: Tensor | float = 1.0, **kwargs) -> torch.Tensor

Parameters:

Name	Type	Description	Default
output	Tensor	the output from which to compute the loss	required
target	Tensor	the target for the loss	required
weights	Tensor | float	the weights for the loss	1.0

Returns:

Type	Description
Tensor	the DEKR offset loss

Source code in deeplabcut/pose_estimation_pytorch/models/criterions/dekr.py

def forward(
    self,
    output: torch.Tensor,
    target: torch.Tensor,
    weights: torch.Tensor | float = 1.0,
    **kwargs,
) -> torch.Tensor:
    """
    Args:
        output: the output from which to compute the loss
        target: the target for the loss
        weights: the weights for the loss

    Returns:
        the DEKR offset loss
    """
    assert output.size() == target.size()
    loss = ((output - target) ** 2) * weights
    return loss.mean(dim=3).mean(dim=2).mean(dim=1).mean(dim=0)

DEKROffsetLoss

Bases: BaseCriterion

DEKR Offset loss.

Methods:

Name	Description
forward	Args:

Source code in deeplabcut/pose_estimation_pytorch/models/criterions/dekr.py

@CRITERIONS.register_module
class DEKROffsetLoss(BaseCriterion):
    """DEKR Offset loss."""

    def __init__(self, beta: float = 1 / 9):
        super().__init__()
        self.beta = beta

    def smooth_l1_loss(self, pred, gt):
        l1_loss = torch.abs(pred - gt)
        return torch.where(
            l1_loss < self.beta,
            0.5 * l1_loss**2 / self.beta,
            l1_loss - 0.5 * self.beta,
        )

    def forward(
        self,
        output: torch.Tensor,
        target: torch.Tensor,
        weights: torch.Tensor | float = 1.0,
        **kwargs,
    ) -> torch.Tensor:
        """
        Args:
            output: the output from which to compute the loss
            target: the target for the loss
            weights: the weights for the loss

        Returns:
            the DEKR offset loss
        """
        assert output.size() == target.size()
        num_pos = torch.nonzero(weights > 0).size()[0]
        loss = self.smooth_l1_loss(output, target) * weights
        if num_pos == 0:
            num_pos = 1.0
        loss = loss.sum() / num_pos
        return loss

forward

forward(output: Tensor, target: Tensor, weights: Tensor | float = 1.0, **kwargs) -> torch.Tensor

Parameters:

Name	Type	Description	Default
output	Tensor	the output from which to compute the loss	required
target	Tensor	the target for the loss	required
weights	Tensor | float	the weights for the loss	1.0

Returns:

Type	Description
Tensor	the DEKR offset loss

Source code in deeplabcut/pose_estimation_pytorch/models/criterions/dekr.py

def forward(
    self,
    output: torch.Tensor,
    target: torch.Tensor,
    weights: torch.Tensor | float = 1.0,
    **kwargs,
) -> torch.Tensor:
    """
    Args:
        output: the output from which to compute the loss
        target: the target for the loss
        weights: the weights for the loss

    Returns:
        the DEKR offset loss
    """
    assert output.size() == target.size()
    num_pos = torch.nonzero(weights > 0).size()[0]
    loss = self.smooth_l1_loss(output, target) * weights
    if num_pos == 0:
        num_pos = 1.0
    loss = loss.sum() / num_pos
    return loss