Updated docstring for lightly/embedding subpackage (#1682)

lightly/embedding/callbacks.py

@@ -25,6 +25,9 @@ def create_checkpoint_callback(
  dirpath:
  Where to save the checkpoint.
 
+ Returns:
+ ModelCheckpoint: The initialized checkpoint callback.
+
  """
  return ModelCheckpoint(
  dirpath=os.getcwd() if dirpath is None else dirpath,
@@ -39,7 +42,18 @@ def create_checkpoint_callback(
 def create_summary_callback(
  summary_callback_config: DictConfig, trainer_config: DictConfig
 ) -> ModelSummary:
- """Creates a summary callback."""
+ """Creates a model summary callback based on the configuration.
+
+ Args:
+ summary_callback_config:
+ Configuration dictionary for the summary callback.
+ trainer_config:
+ Trainer configuration dictionary, which may include deprecated `weights_summary`.
+
+ Returns:
+ ModelSummary: The model summary callback.
+
+ """
  # TODO: Drop support for the "weights_summary" argument.
  weights_summary = trainer_config.get("weights_summary", None)
  if weights_summary not in [None, "None"]:
@@ -56,6 +70,10 @@ def _create_summary_callback(max_depth: int) -> ModelSummary:
  Args:
  max_depth:
  The maximum depth of layer nesting that the summary will include.
+
+ Returns:
+ ModelSummary: The initialized model summary callback.
+
  """
  return ModelSummary(max_depth=max_depth)
 
@@ -66,6 +84,17 @@ def _create_summary_callback_deprecated(weights_summary: str) -> ModelSummary:
  The ``weights_summary`` trainer argument was deprecated with the release
  of pytorch lightning 1.7 in 08/2022. Support for this will be removed
  in the future.
+
+ Args:
+ weights_summary:
+ The deprecated `weights_summary` argument value ("top" or "full").
+
+ Returns:
+ ModelSummary: The initialized model summary callback based on the `weights_summary` argument.
+
+ Raises:
+ ValueError: If an invalid value is provided for `weights_summary`.
+
  """
  print_as_warning(
  "The configuration parameter 'trainer.weights_summary' is deprecated."

lightly/embedding/embedding.py

@@ -103,7 +103,6 @@ def embed(
  filenames:
  The filenames from dataloader.dataset.get_filenames().
 
-
  Examples:
  >>> # embed images in vector space
  >>> embeddings, labels, fnames = encoder.embed(dataloader)