43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import torch
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import torch.nn as nn
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class LossFunctionSelector:
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def __init__(self):
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self.available_losses = {
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'mse': '均方误差',
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'l1': '平均绝对误差',
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'cross_entropy': '交叉熵',
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'bce': '二分类交叉熵',
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'smooth_l1': '平滑L1',
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'kl_div': 'KL散度',
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'hinge': '合页损失',
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'triplet': '三元组损失'
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}
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def get_loss(self, name, **kwargs):
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"""获取配置好的损失函数实例"""
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if name == 'mse':
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return nn.MSELoss(**kwargs)
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elif name == 'l1':
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return nn.L1Loss(**kwargs)
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elif name == 'cross_entropy':
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return nn.CrossEntropyLoss(**kwargs)
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elif name == 'bce':
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return nn.BCELoss(**kwargs)
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elif name == 'smooth_l1':
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return nn.SmoothL1Loss(**kwargs)
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elif name == 'kl_div':
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return nn.KLDivLoss(**kwargs)
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elif name == 'hinge':
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return nn.HingeEmbeddingLoss(**kwargs)
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elif name == 'triplet':
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return nn.TripletMarginLoss(**kwargs)
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else:
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raise ValueError(f"不支持的损失函数类型，可选: {list(self.available_losses.keys())}")
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def print_available(self):
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"""打印支持的损失函数列表"""
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print("可用损失函数：")
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for k, v in self.available_losses.items():
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print(f"{k.ljust(15)} -> {v}") |