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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class MSELoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.criterion = nn.MSELoss(reduction='none')
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def forward(self, output, target, mask=None):
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loss = self.criterion(output, target)
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if mask is not None:
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loss = (loss * mask).mean()
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else:
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loss = (loss).mean()
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return loss
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class KLDivLoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.criterion = nn.KLDivLoss(reduction='batchmean')
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def forward(self, output, target, mask=None):
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if mask is not None:
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output_masked = output * mask
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target_masked = target * mask
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loss = self.criterion(F.log_softmax(output_masked), target_masked)
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else:
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loss = self.criterion(F.log_softmax(output), target)
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return loss
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class HeatmapWeightingMSELoss(nn.Module):
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def __init__(self):
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super().__init__()
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self.criterion = nn.MSELoss(reduction='none')
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def forward(self, output, target, mask=None):
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"""Forward function."""
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batch_size = output.size(0)
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num_joints = output.size(1)
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heatmaps_pred = output.reshape(
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(batch_size, num_joints, -1)).split(1, 1)
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heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1)
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loss = 0.
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for idx in range(num_joints):
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heatmap_pred = heatmaps_pred[idx].squeeze(1)
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heatmap_gt = heatmaps_gt[idx].squeeze(1)
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"""
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Set different weight generation functions.
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weight = heatmap_gt + 1
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weight = heatmap_gt * 2 + 1
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weight = heatmap_gt * heatmap_gt + 1
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weight = torch.exp(heatmap_gt + 1)
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"""
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if mask is not None:
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weight = torch.exp(heatmap_gt * mask[:, idx] + 1)
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loss += torch.mean(self.criterion(heatmap_pred * mask[:, idx],
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heatmap_gt * mask[:, idx]) * weight)
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else:
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weight = heatmap_gt + 1
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loss += torch.mean(self.criterion(heatmap_pred, heatmap_gt) * weight)
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return loss / (num_joints+1)
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class CombMSEAW(nn.Module):
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def __init__(self, lambda1=1, lambda2=1, alpha=2.1, omega=14, epsilon=1, theta=0.5):
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super().__init__()
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self.lambda1 = lambda1
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self.lambda2 = lambda2
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self.criterion1 = nn.MSELoss(reduction='none')
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self.alpha = alpha
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self.omega = omega
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self.epsilon = epsilon
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self.theta = theta
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def forward(self, pred, target, mask=None):
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loss = 0
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if mask is not None:
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pred_masked, target_masked = pred * mask, target * mask
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loss += self.lambda1 * self.criterion1(pred_masked, target_masked)
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loss += self.lambda2 * self.adaptive_wing(pred_masked, target_masked)
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else:
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loss += self.lambda1 * self.criterion1(pred, target)
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loss += self.lambda2 * self.adaptive_wing(pred, target)
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return torch.mean(loss)
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def adaptive_wing(self, pred, target):
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delta = (target - pred).abs()
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alpha_t = self.alpha - target
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A = self.omega * (
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1 / (1 + torch.pow(self.theta / self.epsilon,
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alpha_t))) * alpha_t \
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* (torch.pow(self.theta / self.epsilon,
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self.alpha - target - 1)) * (1 / self.epsilon)
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C = self.theta * A - self.omega * torch.log(
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1 + torch.pow(self.theta / self.epsilon, alpha_t))
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losses = torch.where(delta < self.theta,
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self.omega * torch.log(
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1 + torch.pow(delta / self.epsilon, alpha_t)),
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A * delta - C)
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return losses
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class AdaptiveWingLoss(nn.Module):
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def __init__(self, alpha=2.1, omega=14, epsilon=1, theta=0.5):
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super().__init__()
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self.alpha = alpha
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self.omega = omega
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self.epsilon = epsilon
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self.theta = theta
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def forward(self, pred, target, mask=None):
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if mask is not None:
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pred_masked, target_masked = pred * mask, target * mask
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loss = self.adaptive_wing(pred_masked, target_masked)
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else:
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loss = self.adaptive_wing(pred, target)
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return loss
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def adaptive_wing(self, pred, target):
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delta = (target - pred).abs()
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alpha_t = self.alpha - target
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A = self.omega * (
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1 / (1 + torch.pow(self.theta / self.epsilon,
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alpha_t))) * alpha_t \
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* (torch.pow(self.theta / self.epsilon,
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self.alpha - target - 1)) * (1 / self.epsilon)
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C = self.theta * A - self.omega * torch.log(
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1 + torch.pow(self.theta / self.epsilon, alpha_t))
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losses = torch.where(delta < self.theta,
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self.omega * torch.log(
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1 + torch.pow(delta / self.epsilon, alpha_t)),
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A * delta - C)
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return torch.mean(losses)
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class GaussianFocalLoss(nn.Module):
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"""GaussianFocalLoss is a variant of focal loss.
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More details can be found in the `paper
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<https://arxiv.org/abs/1808.01244>`_
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Code is modified from `kp_utils.py
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<https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_ # noqa: E501
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Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
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not 0/1 binary target.
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Args:
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alpha (float): Power of prediction.
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gamma (float): Power of target for negative samples.
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reduction (str): Options are "none", "mean" and "sum".
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loss_weight (float): Loss weight of current loss.
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"""
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def __init__(self,
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alpha=2.0,
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gamma=4.0,
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reduction='mean',
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loss_weight=1.0):
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super(GaussianFocalLoss, self).__init__()
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self.alpha = alpha
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self.gamma = gamma
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self.reduction = reduction
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self.loss_weight = loss_weight
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def forward(self,
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pred,
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target,
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mask=None,
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weight=None,
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avg_factor=None,
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reduction_override=None):
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"""Forward function.
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Args:
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pred (torch.Tensor): The prediction.
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target (torch.Tensor): The learning target of the prediction
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in gaussian distribution.
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weight (torch.Tensor, optional): The weight of loss for each
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prediction. Defaults to None.
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avg_factor (int, optional): Average factor that is used to average
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the loss. Defaults to None.
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reduction_override (str, optional): The reduction method used to
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override the original reduction method of the loss.
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Defaults to None.
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"""
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assert reduction_override in (None, 'none', 'mean', 'sum')
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reduction = (reduction_override if reduction_override else self.reduction)
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if mask is not None:
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pred_masked, target_masked = pred * mask, target * mask
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loss_reg = self.loss_weight * self.gaussian_focal_loss(pred_masked, target_masked, alpha=self.alpha,
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gamma=self.gamma)
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else:
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loss_reg = self.loss_weight * self.gaussian_focal_loss(pred, target, alpha=self.alpha, gamma=self.gamma)
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return loss_reg.mean()
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def gaussian_focal_loss(self, pred, gaussian_target, alpha=2.0, gamma=4.0):
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"""`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
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distribution.
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Args:
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pred (torch.Tensor): The prediction.
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gaussian_target (torch.Tensor): The learning target of the prediction
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in gaussian distribution.
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alpha (float, optional): A balanced form for Focal Loss.
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Defaults to 2.0.
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gamma (float, optional): The gamma for calculating the modulating
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factor. Defaults to 4.0.
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"""
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eps = 1e-12
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pos_weights = gaussian_target.eq(1)
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neg_weights = (1 - gaussian_target).pow(gamma)
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pos_loss = -(pred + eps).log() * (1 - pred).pow(alpha) * pos_weights
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neg_loss = -(1 - pred + eps).log() * pred.pow(alpha) * neg_weights
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return pos_loss + neg_loss |
