rename feature_extractor param to as_feature_extractor

fiftyone/utils/torch.py

@@ -252,14 +252,14 @@ class TorchEmbeddingsMixin(fom.EmbeddingsMixin):
         layer_name (None): the name of the embeddings layer whose output to
             save, or ``None`` if this model instance should not expose
             embeddings. Prepend ``"<"`` to save the input tensor instead
-        feature_extractor (False): whether this model instance should
+        as_feature_extractor (False): whether this model instance should
             operate as a feature extractor, in which case ``layer_name`` if provided
             is used to create a feature extractor. If ``layer_name`` is not provided,
             the model is used as-is for feature extraction.
     """
 
-    def __init__(self, model, layer_name=None, feature_extractor=False):
-        if feature_extractor:
+    def __init__(self, model, layer_name=None, as_feature_extractor=False):
+        if as_feature_extractor:
             if layer_name:
                 # create a torchvision feature extractor
                 self._model = create_feature_extractor(
@@ -272,7 +272,7 @@ def __init__(self, model, layer_name=None, feature_extractor=False):
             embeddings_layer = None
 
         self._embeddings_layer = embeddings_layer
-        self._as_feature_extractor = feature_extractor
+        self._as_feature_extractor = as_feature_extractor
 
     @property
     def has_embeddings(self):
@@ -418,7 +418,7 @@ def predict_all(imgs):
             inputs that are lists of Tensors
         embeddings_layer (None): the name of a layer whose output to expose as
             embeddings. Prepend ``"<"`` to save the input tensor instead
-        feature_extractor (False): whether this model instance should be
+        as_feature_extractor (False): whether this model instance should be
             treated as a feature extractor. If embedding_layer is provided,
             then a feature extractor is created using torchvision, otherwise
             the model itself is treated as a feature extractor.
@@ -488,8 +488,8 @@ def __init__(self, d):
         self.embeddings_layer = self.parse_string(
             d, "embeddings_layer", default=None
         )
-        self.feature_extractor = self.parse_bool(
-            d, "feature_extractor", default=False
+        self.as_feature_extractor = self.parse_bool(
+            d, "as_feature_extractor", default=False
         )
         self.use_half_precision = self.parse_bool(
             d, "use_half_precision", default=None
@@ -548,7 +548,7 @@ def __init__(self, config):
             self,
             self._model,
             layer_name=self.config.embeddings_layer,
-            feature_extractor=self.config.feature_extractor,
+            as_feature_extractor=self.config.as_feature_extractor,
         )
 
     def __enter__(self):

tests/intensive/model_tests.py

@@ -58,7 +58,7 @@ def test_torch_hub_feature_extractor():
     model = fout.load_torch_hub_image_model(
         "facebookresearch/dino:main",
         "dino_vits16",
-        feature_extractor=True,
+        as_feature_extractor=True,
         image_size=[224, 224],
     )