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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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