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41 lines
1.3 KiB
41 lines
1.3 KiB
# 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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@LOSSES.register_module()
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class BCELoss(nn.Module):
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"""Binary Cross Entropy loss."""
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def __init__(self, use_target_weight=False, loss_weight=1.):
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super().__init__()
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self.criterion = F.binary_cross_entropy
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self.use_target_weight = use_target_weight
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self.loss_weight = loss_weight
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def forward(self, output, target, target_weight=None):
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"""Forward function.
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Note:
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- batch_size: N
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- num_labels: K
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Args:
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output (torch.Tensor[N, K]): Output classification.
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target (torch.Tensor[N, K]): Target classification.
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target_weight (torch.Tensor[N, K] or torch.Tensor[N]):
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Weights across different labels.
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"""
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if self.use_target_weight:
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assert target_weight is not None
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loss = self.criterion(output, target, reduction='none')
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if target_weight.dim() == 1:
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target_weight = target_weight[:, None]
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loss = (loss * target_weight).mean()
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else:
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loss = self.criterion(output, target)
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return loss * self.loss_weight
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