add more losses

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 setup(
     name='deepke',  # 打包后的包文件名
-    version='0.2.91',    #版本号
+    version='0.2.92',    #版本号
     keywords=["pip", "RE","NER","AE"],    # 关键字
     description='DeepKE 是基于 Pytorch 的深度学习中文关系抽取处理套件。',  # 说明
     long_description="client",  #详细说明

src/deepke/relation_extraction/standard/tools/loss.py

@@ -1,6 +1,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.autograd import Variable
 
 class LabelSmoothSoftmaxCEV1(nn.Module):
     def __init__(self, lb_smooth=0.1, reduction='mean', ignore_index=-100):
@@ -68,3 +69,38 @@ class TaylorCrossEntropyLossV1(nn.Module):
         loss = F.nll_loss(log_probs, labels, reduction=self.reduction,
                 ignore_index=self.ignore_index)
         return loss
+
+class FocalLoss(nn.Module):
+    def __init__(self, gamma=0, alpha=None, size_average=True):
+        super(FocalLoss, self).__init__()
+        self.gamma = gamma
+        self.alpha = alpha
+        if isinstance(alpha,(float,int)):
+            self.alpha = torch.Tensor([alpha,1-alpha])
+        if isinstance(alpha,list): 
+            self.alpha = torch.Tensor(alpha)
+        self.size_average = size_average
+
+    def forward(self, input, target):
+        if input.dim()>2:
+            input = input.view(input.size(0),input.size(1),-1)  # N,C,H,W => N,C,H*W
+            input = input.transpose(1,2)    # N,C,H*W => N,H*W,C
+            input = input.contiguous().view(-1,input.size(2))   # N,H*W,C => N*H*W,C
+        target = target.view(-1,1)
+
+        logpt = F.log_softmax(input,dim=1)
+        logpt = logpt.gather(1,target)
+        logpt = logpt.view(-1)
+        pt = Variable(logpt.data.exp())
+
+        if self.alpha is not None:
+            if self.alpha.type()!=input.data.type():
+                self.alpha = self.alpha.type_as(input.data)
+            at = self.alpha.gather(0,target.data.view(-1))
+            logpt = logpt * Variable(at)
+
+        loss = -1 * (1-pt)**self.gamma * logpt
+        if self.size_average: 
+            return loss.mean()
+        else: 
+            return loss.sum()