Transformer seems to be running

language_interpolation/utils.py

@@ -110,21 +110,17 @@ def generate_transformer_text(
         just = ((len(text) // characters_per_feature) + 1) * characters_per_feature
         text_list[index] = text.rjust(just)
 
-    print("text_list", text_list)
-    print("text lengths", [len(text) for text in text_list])
     results = []
     for text_in in text_list:
         for i in range(output_size):
             encoding, text_used = encode_input_from_text(
                 text_in=text_in, features=max_characters
             )
-            print("encoding length", len(encoding), encoding.shape)
             encoding = (
                 ascii_to_float(encoding)
                 .to(model._device)
                 .reshape(1, -1, characters_per_feature)
             )
-            print("encoding", encoding)
             model.eval()
             output = model(encoding)
             values, indices, ascii = decode_output_to_text(
@@ -153,7 +149,7 @@ def __init__(self, cfg):
     def on_train_epoch_end(self, trainer, pl_module, outputs=None):
         with torch.no_grad():
             for topk in range(1, self._cfg.topk + 1):
-                if self._cfg.model_type == "high_order_transformer":
+                if self._cfg.net.model_type == "high_order_transformer":
                     predictions = generate_transformer_text(
                         pl_module,
                         characters_per_feature=self._cfg.data.characters_per_feature,