model_CNN_MUI.py 文件源码

def forward(self, x):
        # print("aaaaa")
        x_no_static = self.embed_no_static(x)
        # x_no_static = self.dropout(x_no_static)
        x_static = self.embed_static(x)
        # fix the embedding
        # x_static = Variable(x_static.data)
        # x_static = self.dropout(x_static)
        x = torch.stack([x_static, x_no_static], 1)
        # x = x.unsqueeze(1) # (N,Ci,W,D)
        x = self.dropout(x)
        if self.args.batch_normalizations is True:
            x = [F.relu(self.convs1_bn(conv(x))).squeeze(3) for conv in self.convs1] #[(N,Co,W), ...]*len(Ks)
            x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x] #[(N,Co), ...]*len(Ks)
        else:
            x = [F.relu(conv(x)).squeeze(3) for conv in self.convs1] #[(N,Co,W), ...]*len(Ks)
            x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in x]  # [(N,Co), ...]*len(Ks)
        x = torch.cat(x, 1)
        '''
        x1 = self.conv_and_pool(x,self.conv13) #(N,Co)
        x2 = self.conv_and_pool(x,self.conv14) #(N,Co)
        x3 = self.conv_and_pool(x,self.conv15) #(N,Co)
        x = torch.cat((x1, x2, x3), 1) # (N,len(Ks)*Co)
        '''
        x = self.dropout(x)  # (N,len(Ks)*Co)

        if self.args.batch_normalizations is True:
            x = self.fc1(x)
            logit = self.fc2(F.relu(x))
        else:
            x = self.fc1(x)
            logit = self.fc2(F.relu(x))
        return logit