Merge pull request #28 from chcwww/fix_wf

Author: chcwww

libmultilabel/nn/attentionxml.py

@@ -287,7 +287,7 @@ def fit(self, datasets):
             logger.info(f"Finish training level 0")
 
         logger.info(f"Best model loaded from {best_model_path}")
-        model_0 = Model.load_from_checkpoint(best_model_path)
+        model_0 = Model.load_from_checkpoint(best_model_path, weights_only=False)
 
         logger.info(
             f"Predicting clusters by level-0 model. We then select {self.beam_width} clusters for each instance and "
@@ -422,11 +422,13 @@ def test(self, dataset):
         model_0 = Model.load_from_checkpoint(
             self.get_best_model_path(level=0),
             save_k_predictions=self.beam_width,
+            weights_only=False,
         )
         model_1 = PLTModel.load_from_checkpoint(
             self.get_best_model_path(level=1),
             save_k_predictions=self.save_k_predictions,
             metrics=self.metrics,
+            weights_only=False,
         )
 
         word_dict_path = os.path.join(os.path.dirname(self.get_best_model_path(level=1)), self.WORD_DICT_NAME)

libmultilabel/nn/networks/bert.py

@@ -34,7 +34,6 @@ def __init__(
             hidden_dropout_prob=encoder_hidden_dropout,
             attention_probs_dropout_prob=encoder_attention_dropout,
             classifier_dropout=post_encoder_dropout,
-            torchscript=True,
         )
 
     def forward(self, input):

libmultilabel/nn/networks/bert_attention.py

@@ -40,7 +40,6 @@ def __init__(
 
         self.lm = AutoModel.from_pretrained(
             lm_weight,
-            torchscript=True,
             hidden_dropout_prob=encoder_hidden_dropout,
             attention_probs_dropout_prob=encoder_attention_dropout,
         )

torch_trainer.py

@@ -150,7 +150,7 @@ def _setup_model(
 
         if checkpoint_path is not None:
             logging.info(f"Loading model from `{checkpoint_path}` with the previously saved hyper-parameter...")
-            self.model = Model.load_from_checkpoint(checkpoint_path, log_path=log_path)
+            self.model = Model.load_from_checkpoint(checkpoint_path, log_path=log_path, weights_only=False)
             word_dict_path = os.path.join(os.path.dirname(checkpoint_path), self.WORD_DICT_NAME)
             if os.path.exists(word_dict_path):
                 with open(word_dict_path, "rb") as f: