切换git用户重新进行项目首次归档

FC_ML_Loss_Function/Loss_Function_Selector.py

@@ -0,0 +1,43 @@
+import torch
+import torch.nn as nn
+
+
+class LossFunctionSelector:
+    def __init__(self):
+        self.available_losses = {
+            'mse': '均方误差',
+            'l1': '平均绝对误差',
+            'cross_entropy': '交叉熵',
+            'bce': '二分类交叉熵',
+            'smooth_l1': '平滑L1',
+            'kl_div': 'KL散度',
+            'hinge': '合页损失',
+            'triplet': '三元组损失'
+        }
+
+    def get_loss(self, name, **kwargs):
+        """获取配置好的损失函数实例"""
+        if name == 'mse':
+            return nn.MSELoss(**kwargs)
+        elif name == 'l1':
+            return nn.L1Loss(**kwargs)
+        elif name == 'cross_entropy':
+            return nn.CrossEntropyLoss(**kwargs)
+        elif name == 'bce':
+            return nn.BCELoss(**kwargs)
+        elif name == 'smooth_l1':
+            return nn.SmoothL1Loss(**kwargs)
+        elif name == 'kl_div':
+            return nn.KLDivLoss(**kwargs)
+        elif name == 'hinge':
+            return nn.HingeEmbeddingLoss(**kwargs)
+        elif name == 'triplet':
+            return nn.TripletMarginLoss(**kwargs)
+        else:
+            raise ValueError(f"不支持的损失函数类型，可选: {list(self.available_losses.keys())}")
+
+    def print_available(self):
+        """打印支持的损失函数列表"""
+        print("可用损失函数：")
+        for k, v in self.available_losses.items():
+            print(f"{k.ljust(15)} -> {v}")