fix n_layers

docs/guides/finetuning.rst

@@ -60,16 +60,16 @@ These files are called checkpoints (like video game save files - computer scient
     model = finetune.FinetuneableZoobotClassifier(
       name='hf_hub:mwalmsley/zoobot-encoder-convnext_nano',  # which pretrained model to download
       num_classes=2,
-      n_layers=0
+      n_blocks=0
     )
 
 You can see the list of pretrained models at :doc:`/pretrained_models`.
 
 What about the other arguments?
 When loading the checkpoint, FinetuneableZoobotClassifier will automatically change the head layer to suit a classification problem (hence, ``Classifier``).
 ``num_classes=2`` specifies how many classes we have, Here, two classes (a.k.a. binary classification).
-``n_layers=0`` specifies how many layers (other than the output layer) we want to finetune.
-0 indicates no other layers, so we will only be changing the weights of the output layer.
+``n_blocks=0`` specifies how many inner blocks (groups of layers, excluding the output layer) we want to finetune.
+0 indicates no other blocks, so we will only be changing the weights of the output layer.
 
 
 Prepare Galaxy Data

zoobot/pytorch/examples/finetuning/finetune_binary_classification.py

@@ -44,7 +44,7 @@
     model = finetune.FinetuneableZoobotClassifier(
       name='hf_hub:mwalmsley/zoobot-encoder-convnext_nano',
       num_classes=2,
-      n_layers=0  # only updating the head weights. Set e.g. 1, 2 to finetune deeper. 
+      n_blocks=0  # only updating the head weights. Set e.g. 1, 2 to finetune deeper. 
     )
     # under the hood, this does:
     # encoder = finetune.load_pretrained_encoder(checkpoint_loc)

zoobot/pytorch/training/finetune.py

@@ -68,7 +68,6 @@ class FinetuneableZoobotAbstract(pl.LightningModule):
         prog_bar (bool, optional): Print progress bar during finetuning. Defaults to True.
         visualize_images (bool, optional): Upload example images to WandB. Good for debugging but slow. Defaults to False.
         seed (int, optional): random seed to use. Defaults to 42.
-        n_layers: No effect, deprecated. Use n_blocks instead.
     """
 
     def __init__(
@@ -90,7 +89,6 @@ def __init__(
         learning_rate=1e-4,  # 10x lower than typical, you may like to experiment
         dropout_prob=0.5,
         always_train_batchnorm=False,  # temporarily deprecated
-        # n_layers=0,  # for backward compat., n_blocks preferred. Now removed in v2.
         # these args are for the optional learning rate scheduler, best not to use unless you've tuned everything else already
         cosine_schedule=False,
         warmup_epochs=0,
@@ -101,6 +99,7 @@ def __init__(
         # debugging utils
         prog_bar=True,
         visualize_images=False,  # upload examples to wandb, good for debugging
+        n_layers=0, # deprecated (no effect) but can't remove yet as is an arg in some saved checkpoints
         seed=42,
     ):
         super().__init__()