Fix documentation

baal/active/active_loop.py

@@ -11,7 +11,7 @@
 from . import heuristics
 from .dataset import ActiveLearningDataset
 
-log = structlog.get_logger('baal')
+log = structlog.get_logger("baal")
 pjoin = os.path.join
 
 

baal/active/heuristics/stochastics.py

@@ -7,7 +7,7 @@
 
 from baal.active.heuristics import AbstractHeuristic, Sequence
 
-log = structlog.get_logger('baal')
+log = structlog.get_logger("baal")
 EPSILON = 1e-8
 
 

baal/experiments/base.py

@@ -24,7 +24,7 @@
  TransformersAdapter = None # type: ignore
  TRANSFORMERS_AVAILABLE = False
 
-log = structlog.get_logger('baal')
+log = structlog.get_logger("baal")
 
 
 class ActiveLearningExperiment:
@@ -78,10 +78,12 @@ def start(self):
  "No item labelled in the training set."
  " Did you run `ActiveLearningDataset.label_randomly`?"
  )
- for _ in tqdm(itertools.count(start=0), # Infinite counter to rely on Criterion
- desc="Active Experiment",
- # Upper bound estimation.
- total=np.round(self.al_dataset.n_unlabelled // self.query_size)):
+ for _ in tqdm(
+ itertools.count(start=0), # Infinite counter to rely on Criterion
+ desc="Active Experiment",
+ # Upper bound estimation.
+ total=np.round(self.al_dataset.n_unlabelled // self.query_size),
+ ):
  self.adapter.reset_weights()
  train_metrics = self.adapter.train(self.al_dataset)
  eval_metrics = self.adapter.evaluate(

baal/modelwrapper.py

@@ -53,8 +53,7 @@ class ModelWrapper(MetricMixin):
 
  Args:
  model (nn.Module): The model to optimize.
- criterion (Callable): A loss function.
- replicate_in_memory (bool): Replicate in memory optional.
+ args (TrainingArgs): Model arguments for training/predicting.
  """
 
  def __init__(self, model, args: TrainingArgs):
@@ -149,7 +148,6 @@ def train_and_test_on_datasets(
  Args:
  train_dataset (Dataset): Dataset to train on.
  test_dataset (Dataset): Dataset to evaluate on.
- optimizer (Optimizer): Optimizer to use during training.
  return_best_weights (bool): If True, will keep the best weights and return them.
  patience (Optional[int]): If provided, will use early stopping to stop after
  `patience` epoch without improvement.

docs/index.md

@@ -12,6 +12,10 @@ To know more on what is Bayesian active learning, see our [User guide](user_guid
 
 We are a member of Pytorch's ecosystem, and we welcome contributions from the community.
 
+!!! tip "Baal 2.0 !"
+ Baal is now version 2.0! We made a lot of changes to make everyone life easier!
+ See our [Release Note](https://github.com/baal-org/baal/releases/tag/v2.0.0) for details.
+
 ::cards:: cols=2
 - title: User Guide
  content: |
@@ -46,4 +50,3 @@ Baal is available as a package on PyPI:
  `baal[nlp]` installs needed dependencies for HuggingFace support.
 
  `baal[vision]` installs dependencies for our Lightning-Flash integration.
-

docs/learn/index.md

@@ -58,7 +58,7 @@ We hope that work in this area continues so that we can better understand the im
 
 **Resources**
 
-* [Literature review](/literature/index.md)
+* [Literature review](./literature/index.md)
 * [Active learning dataset and training loop classes](../notebooks/fundamentals/active-learning)
 * [Methods for approximating bayesian posteriors](../notebooks/fundamentals/posteriors)
 * [Full active learning example](../notebooks/active_learning_process)

experiments/pytorch_lightning/lightning_flash_example.py

@@ -29,7 +29,7 @@
 
 from baal.active import get_heuristic
 
-log = structlog.get_logger('baal')
+log = structlog.get_logger("baal")
 
 IMG_SIZE = 128