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import re |
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import torch.nn as nn |
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class BaseObject(nn.Module): |
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def __init__(self, name=None): |
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super().__init__() |
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self._name = name |
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@property |
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def __name__(self): |
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if self._name is None: |
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name = self.__class__.__name__ |
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s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name) |
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return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower() |
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else: |
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return self._name |
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class Metric(BaseObject): |
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pass |
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class Loss(BaseObject): |
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def __add__(self, other): |
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if isinstance(other, Loss): |
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return SumOfLosses(self, other) |
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else: |
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raise ValueError("Loss should be inherited from `Loss` class") |
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def __radd__(self, other): |
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return self.__add__(other) |
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def __mul__(self, value): |
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if isinstance(value, (int, float)): |
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return MultipliedLoss(self, value) |
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else: |
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raise ValueError("Loss should be inherited from `BaseLoss` class") |
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def __rmul__(self, other): |
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return self.__mul__(other) |
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class SumOfLosses(Loss): |
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def __init__(self, l1, l2): |
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name = "{} + {}".format(l1.__name__, l2.__name__) |
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super().__init__(name=name) |
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self.l1 = l1 |
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self.l2 = l2 |
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def __call__(self, *inputs): |
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return self.l1.forward(*inputs) + self.l2.forward(*inputs) |
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class MultipliedLoss(Loss): |
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def __init__(self, loss, multiplier): |
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if len(loss.__name__.split("+")) > 1: |
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name = "{} * ({})".format(multiplier, loss.__name__) |
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else: |
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name = "{} * {}".format(multiplier, loss.__name__) |
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super().__init__(name=name) |
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self.loss = loss |
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self.multiplier = multiplier |
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def __call__(self, *inputs): |
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return self.multiplier * self.loss.forward(*inputs) |
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