Update csu_model.py

csu_model.py

@@ -133,146 +133,7 @@ def __init__(self, sigma_M, sigma_B, sigma_W, **kwargs):
 class LSTM_w_M_Config(LSTMBaseConfig):
     def __init__(self, beta, **kwargs):
         super(LSTM_w_M_Config, self).__init__(beta=beta, m=True, **kwargs)
-
-
-"""
-Hierarchical ConvNet
-"""
-
-
-class ConvNetEncoder(nn.Module):
-    def __init__(self, config):
-        super(ConvNetEncoder, self).__init__()
-
-        self.word_emb_dim = config['word_emb_dim']
-        self.enc_lstm_dim = config['enc_lstm_dim']
-
-        self.convnet1 = nn.Sequential(
-            nn.Conv1d(self.word_emb_dim, 2 * self.enc_lstm_dim, kernel_size=3,
-                      stride=1, padding=1),
-            nn.ReLU(inplace=True),
-        )
-        self.convnet2 = nn.Sequential(
-            nn.Conv1d(2 * self.enc_lstm_dim, 2 * self.enc_lstm_dim, kernel_size=3,
-                      stride=1, padding=1),
-            nn.ReLU(inplace=True),
-        )
-        self.convnet3 = nn.Sequential(
-            nn.Conv1d(2 * self.enc_lstm_dim, 2 * self.enc_lstm_dim, kernel_size=3,
-                      stride=1, padding=1),
-            nn.ReLU(inplace=True),
-        )
-        self.convnet4 = nn.Sequential(
-            nn.Conv1d(2 * self.enc_lstm_dim, 2 * self.enc_lstm_dim, kernel_size=3,
-                      stride=1, padding=1),
-            nn.ReLU(inplace=True),
-        )
-
-    def forward(self, sent_tuple):
-        # sent_len: [max_len, ..., min_len] (batch)
-        # sent: Variable(seqlen x batch x worddim)
-
-        sent, sent_len = sent_tuple
-
-        sent = sent.transpose(0, 1).transpose(1, 2).contiguous()
-        # batch, nhid, seqlen)
-
-        sent = self.convnet1(sent)
-        u1 = torch.max(sent, 2)[0]
-
-        sent = self.convnet2(sent)
-        u2 = torch.max(sent, 2)[0]
-
-        sent = self.convnet3(sent)
-        u3 = torch.max(sent, 2)[0]
-
-        sent = self.convnet4(sent)
-        u4 = torch.max(sent, 2)[0]
-
-        emb = torch.cat((u1, u2, u3, u4), 1)
-
-        return emb
-
-
-"""
-Normal ConvNet
-"""
-class NormalConvNetEncoder(nn.Module):
-    def __init__(self, config):
-        super(NormalConvNetEncoder, self).__init__()
-        self.word_emb_dim = config['word_emb_dim']
-        self.enc_lstm_dim = config['enc_lstm_dim']
-        self.conv = nn.Conv2d(in_channels=1, out_channels=self.enc_lstm_dim, kernel_size=(3, self.word_emb_dim), stride=(1, self.word_emb_dim))
-
-    def encode(self, inputs):
-        output = inputs.transpose(0, 1).unsqueeze(1) # [batch_size, in_kernel, seq_length, embed_dim]
-        output = F.relu(self.conv(output)) # conv -> [batch_size, out_kernel, seq_length, 1]
-        output = output.squeeze(3).max(2)[0] # max_pool -> [batch_size, out_kernel]
-        return output
-
-    def forward(self, sent_tuple):
-        # sent_len: [max_len, ..., min_len] (batch)
-        # sent: Variable(seqlen x batch x worddim)
-        sent, sent_len = sent_tuple
-        emb = self.encode(sent)
-        return emb
-
-"""
-https://github.com/Shawn1993/cnn-text-classification-pytorch/blob/master/model.py
-352 stars
-"""
-class CNN_Text_Encoder(nn.Module):
-    def __init__(self, config):
-        super(CNN_Text_Encoder, self).__init__()
-
-        self.word_emb_dim = config['word_emb_dim']
-
-        # V = args.embed_num
-        # D = args.embed_dim
-        # C = args.class_num
-        Ci = 1
-        Co = config['kernel_num']  # 100
-        Ks = config['kernel_sizes'] # '3,4,5'
-        # len(Ks)*Co
-
-        # self.convs1 = [nn.Conv2d(Ci, Co, (K, D)) for K in Ks]
-        self.convs1 = nn.ModuleList([nn.Conv2d(Ci, Co, (K, self.word_emb_dim)) for K in Ks])
-        '''
-        self.conv13 = nn.Conv2d(Ci, Co, (3, D))
-        self.conv14 = nn.Conv2d(Ci, Co, (4, D))
-        self.conv15 = nn.Conv2d(Ci, Co, (5, D))
-        '''
-        # self.dropout = nn.Dropout(args.dropout)
-        # self.fc1 = nn.Linear(len(Ks) * Co, C)
-
-    def conv_and_pool(self, x, conv):
-        x = F.relu(conv(x)).squeeze(3)  # (N, Co, W)
-        x = F.max_pool1d(x, x.size(2)).squeeze(2)
-        return x
-
-    def forward(self, x):
-        # x = self.embed(x)  # (N, W, D)
-
-        x = x[0].transpose(0, 1).unsqueeze(1)
-        # x = x.unsqueeze(1)  # (N, Ci, W, D)
-
-        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)
-        # logit = self.fc1(x)  # (N, C)
-        return x
-
-
+
 
 class Classifier(nn.Module):
     def __init__(self, vocab, config):