codestyle

Author: voorhs

autointent/_wrappers/base_torch_module.py

@@ -33,10 +33,9 @@ def load(cls, path: Path, device: str | None = None) -> Self:
             device: torch notation for CPU, CUDA, MPS, etc. By default, it is inferred automatically.
         """
 
-
     @property
     def device(self) -> torch.device:
         """Torch device object where this module resides."""
         if not hasattr(self, "_device"):
-            self._device =  next(self.parameters()).device
+            self._device = next(self.parameters()).device
         return self._device

autointent/modules/scoring/_cnn/textcnn.py

@@ -23,7 +23,7 @@ class TextCNNDumpMetadata(TypedDict):
     padding_idx: int
     vocab: dict[str, int]
     max_seq_length: int
-    vocab: dict[str, int]
+    max_vocab_size: int | None
 
 
 class TextCNN(BaseTorchModule):
@@ -115,7 +115,7 @@ def text_to_indices(self, utterances: list[str]) -> list[list[int]]:
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Forward pass of the model."""
-        if self._vocab is None:
+        if not hasattr(self, "_vocab"):
             msg = "Model not initialized. Call build_vocab() first."
             raise ValueError(msg)
 

tests/modules/scoring/test_cnn.py

@@ -100,8 +100,7 @@ def test_cnn_scorer_dump_load(dataset):
         scorer.dump(str(temp_dir_path))
 
         # Create a new scorer and load saved model
-        scorer_loaded = CNNScorer(max_seq_length=50, num_train_epochs=1, batch_size=8, learning_rate=5e-5)
-        scorer_loaded = scorer_loaded.load(str(temp_dir_path))
+        scorer_loaded = CNNScorer.load(str(temp_dir_path))
 
         # Verify model is loaded
         assert hasattr(scorer_loaded, "_model")