silly typo

zoobot/pytorch/estimators/define_model.py

@@ -131,7 +131,6 @@ class ZoobotTree(GenericLightningModule):
         question_index_groups (List): Mapping of which label indices are part of the same question. See :ref:`training_on_vote_counts`.
         architecture_name (str, optional): Architecture to use. Passed to timm. Must be in timm.list_models(). Defaults to "efficientnet_b0".
         channels (int, optional): Num. input channels. Probably 3 or 1. Defaults to 1.
-        use_imagenet_weights (bool, optional): Load weights pretrained on ImageNet (NOT galaxies!). Defaults to False.
         test_time_dropout (bool, optional): Apply dropout at test time, to pretend to be Bayesian. Defaults to True.
         timm_kwargs (dict, optional): passed to timm.create_model e.g. drop_path_rate=0.2 for effnet. Defaults to {}.
         learning_rate (float, optional): AdamW learning rate. Defaults to 1e-3.
@@ -297,7 +296,7 @@ def get_encoder_dim(encoder, input_size, channels):
 def get_pytorch_encoder(
     architecture_name='efficientnet_b0',
     channels=1,
-    use_imagenet_weights=False,
+    # use_imagenet_weights=False,
     **timm_kwargs
     ) -> nn.Module:
     """
@@ -333,7 +332,7 @@ def get_pytorch_encoder(
     if architecture_name == 'efficientnet':
         logging.warning('efficientnet variant not specified - please set architecture_name=efficientnet_b0 (or similar)')
         architecture_name = 'efficientnet_b0'
-    return timm.create_model(architecture_name, in_chans=channels, num_classes=0, pretrained=use_imagenet_weights, **timm_kwargs)
+    return timm.create_model(architecture_name, in_chans=channels, num_classes=0, **timm_kwargs)
 
 
 def get_pytorch_dirichlet_head(encoder_dim: int, output_dim: int, test_time_dropout: bool, dropout_rate: float) -> torch.nn.Sequential: