model.py: properly encoding text

model.py

@@ -184,10 +184,19 @@ def __init__(self, hparams):
                             batch_first=True, bidirectional=True)
 
     def forward(self, x, input_lengths):
-        for conv in self.convolutions:
-            x = F.dropout(F.relu(conv(x)), drop_rate, self.training)
-
-        x = x.transpose(1, 2)
+        if x.size()[0] > 1:
+            print("here")
+            x_embedded = []
+            for b_ind in range(x.size()[0]):  # TODO: Speed up
+                curr_x = x[b_ind:b_ind+1, :, :input_lengths[b_ind]].clone()
+                for conv in self.convolutions:
+                    curr_x = F.dropout(F.relu(conv(curr_x)), drop_rate, self.training)
+                x_embedded.append(curr_x[0].transpose(0, 1))
+            x = torch.nn.utils.rnn.pad_sequence(x_embedded, batch_first=True)
+        else:
+            for conv in self.convolutions:
+                x = F.dropout(F.relu(conv(x)), drop_rate, self.training)
+            x = x.transpose(1, 2)
 
         # pytorch tensor are not reversible, hence the conversion
         input_lengths = input_lengths.cpu().numpy()