Remove an old commented segment

combo/predictors/predictor_model.py

@@ -24,6 +24,7 @@ from combo.data.dataset_readers.dataset_reader import DatasetReader
 from combo.data.instance import JsonDict, Instance
 from combo.modules.model import Model
 from combo.nn import utils
+from combo.nn.utils import move_to_device
 
 logger = logging.getLogger(__name__)
 
@@ -135,7 +136,7 @@ class PredictorModule(pl.LightningModule, FromParameters):
 
         dataset = Batch(instances)
         dataset.index_instances(self._model.vocab)
-        dataset_tensor_dict = util.move_to_device(dataset.as_tensor_dict(), self.cuda_device)
+        dataset_tensor_dict = move_to_device(dataset.as_tensor_dict(), self.cuda_device)
         # To bypass "RuntimeError: cudnn RNN backward can only be called in training mode"
         with backends.cudnn.flags(enabled=False):
             outputs = self._model.make_output_human_readable(
@@ -331,96 +332,3 @@ class PredictorModule(pl.LightningModule, FromParameters):
         for json_dict in json_dicts:
             instances.append(self._json_to_instance(json_dict))
         return instances
-    #
-    # @classmethod
-    # def from_path(
-    #     cls,
-    #     archive_path: Union[str, Path],
-    #     predictor_name: str = None,
-    #     cuda_device: int = -1,
-    #     dataset_reader_to_load: str = "validation",
-    #     frozen: bool = True,
-    #     import_plugins: bool = True,
-    #     overrides: Union[str, Dict[str, Any]] = "",
-    # ) -> "Predictor":
-    #     """
-    #     Instantiate a `Predictor` from an archive path.
-    #
-    #     If you need more detailed configuration options, such as overrides,
-    #     please use `from_archive`.
-    #
-    #     # Parameters
-    #
-    #     archive_path : `Union[str, Path]`
-    #         The path to the archive.
-    #     predictor_name : `str`, optional (default=`None`)
-    #         Name that the predictor is registered as, or None to use the
-    #         predictor associated with the model.
-    #     cuda_device : `int`, optional (default=`-1`)
-    #         If `cuda_device` is >= 0, the model will be loaded onto the
-    #         corresponding GPU. Otherwise it will be loaded onto the CPU.
-    #     dataset_reader_to_load : `str`, optional (default=`"validation"`)
-    #         Which dataset reader to load from the archive, either "train" or
-    #         "validation".
-    #     frozen : `bool`, optional (default=`True`)
-    #         If we should call `model.eval()` when building the predictor.
-    #     import_plugins : `bool`, optional (default=`True`)
-    #         If `True`, we attempt to import plugins before loading the predictor.
-    #         This comes with additional overhead, but means you don't need to explicitly
-    #         import the modules that your predictor depends on as long as those modules
-    #         can be found by `allennlp.common.plugins.import_plugins()`.
-    #     overrides : `Union[str, Dict[str, Any]]`, optional (default = `""`)
-    #         JSON overrides to apply to the unarchived `Params` object.
-    #
-    #     # Returns
-    #
-    #     `Predictor`
-    #         A Predictor instance.
-    #     """
-    #     if import_plugins:
-    #         plugins.import_plugins()
-    #     return Predictor.from_archive(
-    #         load_archive(archive_path, cuda_device=cuda_device, overrides=overrides),
-    #         predictor_name,
-    #         dataset_reader_to_load=dataset_reader_to_load,
-    #         frozen=frozen,
-    #     )
-    #
-    # @classmethod
-    # def from_archive(
-    #     cls,
-    #     archive: Archive,
-    #     predictor_name: str = None,
-    #     dataset_reader_to_load: str = "validation",
-    #     frozen: bool = True,
-    # ) -> "Predictor":
-    #     """
-    #     Instantiate a `Predictor` from an [`Archive`](../models/archival.md);
-    #     that is, from the result of training a model. Optionally specify which `Predictor`
-    #     subclass; otherwise, we try to find a corresponding predictor in `DEFAULT_PREDICTORS`, or if
-    #     one is not found, the base class (i.e. `Predictor`) will be used. Optionally specify
-    #     which [`DatasetReader`](../data/dataset_readers/dataset_reader.md) should be loaded;
-    #     otherwise, the validation one will be used if it exists followed by the training dataset reader.
-    #     Optionally specify if the loaded model should be frozen, meaning `model.eval()` will be called.
-    #     """
-    #     # Duplicate the config so that the config inside the archive doesn't get consumed
-    #     config = archive.config.duplicate()
-    #
-    #     if not predictor_name:
-    #         model_type = config.get("model").get("type")
-    #         model_class, _ = Model.resolve_class_name(model_type)
-    #         predictor_name = model_class.default_predictor
-    #     predictor_class: Type[Predictor] = (
-    #         Predictor.by_name(predictor_name) if predictor_name is not None else cls  # type: ignore
-    #     )
-    #
-    #     if dataset_reader_to_load == "validation":
-    #         dataset_reader = archive.validation_dataset_reader
-    #     else:
-    #         dataset_reader = archive.dataset_reader
-    #
-    #     model = archive.model
-    #     if frozen:
-    #         model.eval()
-    #
-    #     return predictor_class(model, dataset_reader)