Merge pull request #1019 from AntonioCarta/docs_v2

apidoc improvements

avalanche/benchmarks/classic/cinaturalist.py

@@ -55,8 +55,8 @@ def SplitInaturalist(
     eval_transform: Optional[Any] = _default_eval_transform,
     dataset_root: Union[str, Path] = None
 ):
-    """
-    Creates a CL benchmark using the iNaturalist2018 dataset.
+    """Creates a CL benchmark using the iNaturalist2018 dataset.
+
     A selection of supercategories (by default 10) define the experiences.
     Note that the supercategories are highly imbalanced in the number of classes
     and the amount of data available.
@@ -66,9 +66,8 @@ def SplitInaturalist(
     (120Gtrain/val).
 
     To parse the dataset jsons you need to install an additional dependency:
-    "pycocotools". You can install it like this:
-
-        "conda install -c conda-forge pycocotools"
+    "pycocotools". You can install it with the command
+    ``conda install -c conda-forge pycocotools``
 
     Implementation is based on the CL survey
     (https://ieeexplore.ieee.org/document/9349197) but differs slightly.
@@ -98,10 +97,10 @@ def SplitInaturalist(
     which contains usage examples ranging from "basic" to "advanced".
 
     :param super_categories: The list of supercategories which define the
-    tasks, i.e. each task consists of all classes in a super-category.
+        tasks, i.e. each task consists of all classes in a super-category.
     :param download: If true and the dataset is not present in the computer,
-    this method will automatically download and store it. This will take 120G
-    for the train/val set.
+        this method will automatically download and store it. This will take
+        120G for the train/val set.
     :param return_task_id: if True, a progressive task id is returned for every
         experience. If False, all experiences will have a task ID of 0.
     :param seed: A valid int used to initialize the random number generator.

avalanche/benchmarks/classic/clear.py

@@ -27,12 +27,9 @@
 from typing import Union, Any, Optional
 from typing_extensions import Literal
 
-from avalanche.benchmarks.classic.classic_benchmarks_utils import (
-    check_vision_benchmark,
-)
 from avalanche.benchmarks.datasets.clear import (
-    CLEARImage,
-    CLEARFeature,
+    _CLEARImage,
+    _CLEARFeature,
     SEED_LIST,
     CLEAR_FEATURE_TYPES,
 )
@@ -135,15 +132,15 @@ def CLEAR(
         raise NotImplementedError()
 
     if feature_type is None:
-        clear_dataset_train = CLEARImage(
+        clear_dataset_train = _CLEARImage(
             root=dataset_root,
             data_name=data_name,
             download=True,
             split=train_split,
             seed=seed,
             transform=train_transform,
         )
-        clear_dataset_test = CLEARImage(
+        clear_dataset_test = _CLEARImage(
             root=dataset_root,
             data_name=data_name,
             download=True,
@@ -159,15 +156,15 @@ def CLEAR(
         )
         benchmark_generator = create_generic_benchmark_from_paths
     else:
-        clear_dataset_train = CLEARFeature(
+        clear_dataset_train = _CLEARFeature(
             root=dataset_root,
             data_name=data_name,
             download=True,
             feature_type=feature_type,
             split=train_split,
             seed=seed,
         )
-        clear_dataset_test = CLEARFeature(
+        clear_dataset_test = _CLEARFeature(
             root=dataset_root,
             data_name=data_name,
             download=True,

avalanche/benchmarks/classic/endless_cl_sim.py

@@ -48,15 +48,14 @@ def EndlessCLSim(
     dataset_root: Union[str, Path] = None,
     semseg=False
 ):
-    """
-    Creates a CL scenario for the Endless-Continual-Learning Simulator's
-    derived datasets, which are available at:
-    https://zenodo.org/record/4899267, or custom datasets created from
-    the Endless-Continual-Learning-Simulator's standalone application,
-    available at: https://zenodo.org/record/4899294.
+    """Creates a CL scenario for the Endless-Continual-Learning Simulator's
+    derived `datasets <https://zenodo.org/record/4899267>`__, or custom
+    datasets created from
+    the Endless-Continual-Learning-Simulator's `standalone application <
+    https://zenodo.org/record/4899294>`__.
     Both are part of the publication of `A Procedural World Generation
     Framework for Systematic Evaluation of Continual Learning
-    (https://arxiv.org/abs/2106.02585).
+    <https://arxiv.org/abs/2106.02585>`__.
 
     If the dataset is not present in the computer, this method will
     automatically download and store it.

avalanche/benchmarks/datasets/__init__.py

@@ -12,3 +12,4 @@
 from .inaturalist import *
 from .lvis_dataset import *
 from .penn_fudan import *
+from .clear import *

avalanche/benchmarks/datasets/clear/clear.py

@@ -163,7 +163,7 @@ def __len__(self):
         return len(self.samples)
 
 
-class CLEARImage(CLEARDataset):
+class _CLEARImage(CLEARDataset):
     """CLEAR Image Dataset (base class for CLEAR10Image)"""
 
     def __init__(
@@ -222,12 +222,12 @@ def __init__(
         aligned with target index.
         """
 
-        super(CLEARImage, self).__init__(
+        super(_CLEARImage, self).__init__(
             root, data_name=data_name, download=download, verbose=True
         )
 
     def _load_metadata(self) -> bool:
-        if not super(CLEARImage, self)._load_metadata():
+        if not super(_CLEARImage, self)._load_metadata():
             print("CLEAR has not yet been downloaded")
             return False
 
@@ -290,7 +290,7 @@ def __len__(self):
         return len(self.targets)
 
 
-class CLEARFeature(CLEARDataset):
+class _CLEARFeature(CLEARDataset):
     """CLEAR Feature Dataset (base class for CLEAR10Feature)"""
 
     def __init__(
@@ -347,12 +347,12 @@ def __init__(
         assert feature_type in CLEAR_FEATURE_TYPES[data_name]
         self.target_transform = target_transform
 
-        super(CLEARFeature, self).__init__(
+        super(_CLEARFeature, self).__init__(
             root, data_name=data_name, download=download, verbose=True
         )
 
     def _load_metadata(self) -> bool:
-        if not super(CLEARFeature, self)._load_metadata():
+        if not super(_CLEARFeature, self)._load_metadata():
             print("CLEAR has not yet been downloaded")
             return False
 
@@ -433,23 +433,23 @@ def __len__(self):
     root = f"../avalanche_datasets/{data_name}"
     if not os.path.exists(root):
         Path(root).mkdir(parents=True)
-    clear_dataset_all = CLEARImage(
+    clear_dataset_all = _CLEARImage(
         root=root,
         data_name=data_name,
         download=True,
         split="all",
         seed=None,
         transform=transform,
     )
-    clear_dataset_train = CLEARImage(
+    clear_dataset_train = _CLEARImage(
         root=root,
         data_name=data_name,
         download=True,
         split="train",
         seed=0,
         transform=transform,
     )
-    clear_dataset_test = CLEARImage(
+    clear_dataset_test = _CLEARImage(
         root=root,
         data_name=data_name,
         download=True,
@@ -461,23 +461,23 @@ def __len__(self):
     print("clear10 size (train): ", len(clear_dataset_train))
     print("clear10 size (test): ", len(clear_dataset_test))
 
-    clear_dataset_all_feature = CLEARFeature(
+    clear_dataset_all_feature = _CLEARFeature(
         root=root,
         data_name=data_name,
         download=True,
         feature_type="moco_b0",
         split="all",
         seed=None,
     )
-    clear_dataset_train_feature = CLEARFeature(
+    clear_dataset_train_feature = _CLEARFeature(
         root=root,
         data_name=data_name,
         download=True,
         feature_type="moco_b0",
         split="train",
         seed=0,
     )
-    clear_dataset_test_feature = CLEARFeature(
+    clear_dataset_test_feature = _CLEARFeature(
         root=root,
         data_name=data_name,
         download=True,
@@ -499,4 +499,5 @@ def __len__(self):
         print(len(y))
         break
 
-__all__ = ["CLEARFeature", "CLEARImage", "SEED_LIST", "CLEAR_FEATURE_TYPES"]
+__all__ = ["CLEARDataset", "_CLEARFeature", "_CLEARImage", "SEED_LIST",
+           "CLEAR_FEATURE_TYPES"]

avalanche/benchmarks/datasets/core50/core50.py

@@ -45,11 +45,10 @@ def __init__(
         object_level=True,
     ):
 
-        """
-        Creates an instance of the CORe50 dataset.
+        """Creates an instance of the CORe50 dataset.
 
-        :param root: root for the datasets data. Defaults to None, which means
-        that the default location for 'core50' will be used.
+        :param root: root for the datasets data. Defaults to None, which 
+            means that the default location for 'core50' will be used.
         :param train: train or test split.
         :param transform: eventual transformations to be applied.
         :param target_transform: eventual transformation to be applied to the

avalanche/benchmarks/datasets/downloadable_dataset.py

@@ -28,8 +28,7 @@
 
 
 class DownloadableDataset(Dataset[T_co], ABC):
-    """
-    Base class for a downloadable dataset.
+    """Base class for a downloadable dataset.
 
     It is recommended to extend this class if a dataset can be downloaded from
     the internet. This implementation codes the recommended behaviour for
@@ -71,8 +70,7 @@ def __init__(
         download: bool = True,
         verbose: bool = False,
     ):
-        """
-        Creates an instance of a downloadable dataset.
+        """Creates an instance of a downloadable dataset.
 
         Consider looking at the class documentation for the precise details on
         how to extend this class.