44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
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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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