93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import mmcv
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import torch.nn as nn
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from ..builder import LOSSES
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from .utils import weighted_loss
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@mmcv.jit(derivate=True, coderize=True)
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@weighted_loss
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def gaussian_focal_loss(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
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@LOSSES.register_module()
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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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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 = (
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reduction_override if reduction_override else self.reduction)
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loss_reg = self.loss_weight * gaussian_focal_loss(
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pred,
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target,
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weight,
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alpha=self.alpha,
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gamma=self.gamma,
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reduction=reduction,
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avg_factor=avg_factor)
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return loss_reg
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