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43 lines
1.3 KiB
43 lines
1.3 KiB
import paddle |
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import paddle.nn as nn |
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import paddle.nn.functional as F |
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class MultiLabelLoss(nn.Layer): |
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""" |
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Multi-label loss |
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""" |
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def __init__(self, epsilon=None): |
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super().__init__() |
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if epsilon is not None and (epsilon <= 0 or epsilon >= 1): |
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epsilon = None |
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self.epsilon = epsilon |
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def _labelsmoothing(self, target, class_num): |
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if target.ndim == 1 or target.shape[-1] != class_num: |
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one_hot_target = F.one_hot(target, class_num) |
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else: |
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one_hot_target = target |
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soft_target = F.label_smooth(one_hot_target, epsilon=self.epsilon) |
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soft_target = paddle.reshape(soft_target, shape=[-1, class_num]) |
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return soft_target |
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def _binary_crossentropy(self, input, target, class_num): |
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if self.epsilon is not None: |
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target = self._labelsmoothing(target, class_num) |
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cost = F.binary_cross_entropy_with_logits( |
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logit=input, label=target) |
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else: |
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cost = F.binary_cross_entropy_with_logits( |
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logit=input, label=target) |
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return cost |
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def forward(self, x, target): |
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if isinstance(x, dict): |
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x = x["logits"] |
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class_num = x.shape[-1] |
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loss = self._binary_crossentropy(x, target, class_num) |
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loss = loss.mean() |
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return {"MultiLabelLoss": loss}
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