Update modeling_textcnn.py

modeling_textcnn.py

@@ -61,14 +61,17 @@ class TextCNNModel(TextCNNPreTrainedModel):
         
     def forward(self, input_ids):
         # input_ids.shape == (bsz, seq_len)
-        x = self.embeder(input_ids).unsqueeze(1) # add channel dim
         # x.shape == (bsz, 1, seq_len, emb_dim)
-        convs = [torch.relu(conv(x)).squeeze(3) for conv in self.convs]
-        # convs[i].shape == (bsz, n_filter[i], ngram_seq_len)
-        pools = [torch.max_pool1d(conv, conv.size(2)).squeeze(2) for conv in convs]
-        # pools[i].shape == (bsz, n_filter[i])
-        outputs = torch.cat(pools, 1)
+        x = self.embeder(input_ids).unsqueeze(1) # add channel dim
+        outputs = []
+        for conv in self.convs:
+            # conv_output.shape == (bsz, n_filter[i], ngram_seq_len)
+            conv_output = torch.relu(conv(x)).squeeze(3)
+            # output.shape == (bsz, n_filter[i])
+            output = torch.max_pool1d(conv_output, conv_output.size(2)).squeeze(2)
+            outputs.append(output)
         # outputs.shape == (bsz, feature_dim)
+        outputs = torch.cat(outputs, dim=1)
         
         return TextCNNModelOutput(
             last_hidden_states=outputs,