58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch.nn as nn
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import torch.nn.functional as F
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from ..builder import LOSSES
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from .utils import weighted_loss
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@weighted_loss
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def mse_loss(pred, target):
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"""Warpper of mse loss."""
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return F.mse_loss(pred, target, reduction='none')
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@LOSSES.register_module()
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class MSELoss(nn.Module):
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"""MSELoss.
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Args:
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reduction (str, optional): The method that reduces the loss to a
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scalar. Options are "none", "mean" and "sum".
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loss_weight (float, optional): The weight of the loss. Defaults to 1.0
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"""
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def __init__(self, reduction='mean', loss_weight=1.0):
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super().__init__()
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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 of loss.
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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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weight (torch.Tensor, optional): Weight of the 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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Returns:
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torch.Tensor: The calculated loss
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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 = self.loss_weight * mse_loss(
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pred, target, weight, reduction=reduction, avg_factor=avg_factor)
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return loss
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