Merge pull request #1605 from AntonioCarta/newdocs

Newdocs

README.md

@@ -1,4 +1,4 @@
-<div align="center">
+div align="center">
 
 # Avalanche: an End-to-End Library for Continual Learning
 **[Avalanche Website](https://avalanche.continualai.org)** | **[Getting Started](https://avalanche.continualai.org/getting-started)** | **[Examples](https://avalanche.continualai.org/examples)** | **[Tutorial](https://avalanche.continualai.org/from-zero-to-hero-tutorial)** | **[API Doc](https://avalanche-api.continualai.org)** | **[Paper](https://arxiv.org/abs/2104.00405)** | **[Twitter](https://twitter.com/AvalancheLib)**

avalanche/benchmarks/scenarios/__init__.py

@@ -8,4 +8,5 @@
 from .dataset_scenario import *
 from .exmodel_scenario import *
 from .online import *
+from .supervised import *
 from .validation_scenario import *

avalanche/benchmarks/scenarios/dataset_scenario.py

@@ -28,7 +28,7 @@
     Dict,
 )
 
-from .generic_scenario import EagerCLStream, CLScenario, CLExperience, make_stream
+from .generic_scenario import EagerCLStream, CLScenario, CLExperience
 from ..utils import TaskAwareSupervisedClassificationDataset
 
 
@@ -257,4 +257,5 @@ def __iter__(
     "benchmark_from_datasets",
     "DatasetExperience",
     "split_validation_random",
+    "split_validation_class_balanced",
 ]

avalanche/benchmarks/scenarios/supervised.py

@@ -417,3 +417,10 @@ def _decorate_stream(obj: CLStream):
         raise ValueError(
             "Unsupported object type: must be one of {CLScenario, CLStream}"
         )
+
+
+__all__ = [
+    "class_incremental_benchmark",
+    "new_instances_benchmark",
+    "with_classes_timeline",
+]

avalanche/benchmarks/scenarios/validation_scenario.py

@@ -1,20 +1,13 @@
 from typing import (
     Callable,
-    Generator,
-    Generic,
-    List,
-    Sequence,
-    TypeVar,
     Union,
     Tuple,
     Optional,
-    Iterable,
-    Dict,
 )
 
 import random
 from avalanche.benchmarks.utils.data import AvalancheDataset
-from .generic_scenario import EagerCLStream, CLScenario, CLExperience, make_stream
+from .generic_scenario import EagerCLStream, CLScenario, make_stream
 from .dataset_scenario import (
     LazyTrainValSplitter,
     DatasetExperience,

docs/benchmarks.rst

@@ -20,7 +20,7 @@ avalanche.benchmarks
 .. currentmodule:: avalanche.benchmarks.scenarios
 
 Continual Learning Scenarios
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+----------------------------------------
 
 | Generic definitions for scenarios, streams and experiences. All the continual learning benchmarks are specific instantiations of these concepts.
 
@@ -50,7 +50,7 @@ Streams
     ClassificationStream
 
 Experiences
-"""""""""
+"""""""""""""
 
 .. autosummary::
     :toctree: generated
@@ -210,7 +210,7 @@ Benchmarks for learning from pretrained models or multi-agent continual learning
 .. currentmodule:: avalanche.benchmarks.datasets
 
 Datasets
-^^^^^^^^^^^^^^^^^^^^^^^^^^
+----------------------------------------
 
 | The **datasets** sub-module provides PyTorch dataset implementations for datasets missing from the torchvision/audio/* libraries. These datasets can also be used in a standalone way!
 
@@ -236,52 +236,54 @@ Datasets
     torchaudio_wrapper.SpeechCommands
 
 
-.. currentmodule:: avalanche.benchmarks.scenarios.deprecated.generators
+.. currentmodule:: avalanche.benchmarks
 
 Benchmark Generators
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-| The **generators** sub-module provides a lot of functions that can be used to create a new benchmark.
-| This set of functions tries to cover most common use cases (Class/Task-Incremental, Domain-Incremental, ...) but it also allows for the creation of entirely custom benchmarks (based on lists of tensors, on file lists, ...).
-
+----------------------------------------
 
-Generators for Class/Task/Domain-incremental benchmarks
-........................................................
+| The **generators** sub-module provides a lot of functions that can be used to create a new benchmark.
+| This set of functions tries to cover most common use cases (Class/Task-Incremental, Domain-Incremental, ...) but it also allows for the creation of entirely custom benchmarks from AvalancheDatasets.
 
 .. autosummary::
     :toctree: generated
 
-    nc_benchmark
-    ni_benchmark
-
+    benchmark_from_datasets
+    class_incremental_benchmark
+    new_instances_benchmark
+    task_incremental_benchmark
 
-Starting from tensor lists, file lists, PyTorch datasets
-..........................................................
+If you want to add attributes to experiences (such as `classes_in_this_experiences` or `task_labels`) you can use the generic decorators:
 
 .. autosummary::
     :toctree: generated
 
-    dataset_benchmark
-    filelist_benchmark
-    paths_benchmark
-    tensors_benchmark
+    with_classes_timeline
+    with_task_labels
 
+Online streams where experiences are made of small minibatches:
+
+.. autosummary::
+    :toctree: generated
 
-Misc (make data-incremental, add a validation stream, ...)
-..............................................................
+    split_online_stream
+    split_continuous_linear_decay_stream
 
-| Avalanche offers utilities to adapt a previously instantiated benchmark object.
-| More utilities to come!
+Train/Validation splits for streams:
 
 .. autosummary::
     :toctree: generated
 
-    data_incremental_benchmark
+    benchmark_with_validation_stream
+    split_validation_class_balanced
+    split_validation_random
+
 
 .. currentmodule:: avalanche.benchmarks.utils
 
 
 Utils (Data Loading and AvalancheDataset)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+--------------------------------------------------------------------------------
+
 | The custom dataset and dataloader implementations contained in this sub-module are described in more detailed in the How-Tos about `"data loading and replay" <https://avalanche.continualai.org/how-tos/dataloading_buffers_replay>` and `"Avalanche Dataset" <https://avalanche.continualai.org/how-tos/avalanchedataset>`.
 
 

docs/gitbook/from-zero-to-hero-tutorial/03_benchmarks.md

@@ -186,10 +186,9 @@ print(f"{bm.test_stream.name} - len {len(bm.test_stream)}")
 
 we can also split a validation stream from the training stream
 
-
 ```python
-from avalanche.benchmarks.scenarios.dataset_scenario import benchmark_with_validation_stream
 
+from avalanche.benchmarks.scenarios.validation_scenario import benchmark_with_validation_stream
 
 print(f"original training samples = {len(bm.train_stream[0].dataset)}")
 

docs/models.rst

@@ -14,7 +14,7 @@ models
 .. currentmodule:: avalanche.models
 
 Dynamic Modules
-^^^^^^^^^^^^^^^
+------------------
 
 Dynamic Modules are Pytorch modules that can be incrementally expanded
 to allow architectural modifications (multi-head classifiers, progressive
@@ -28,9 +28,22 @@ networks, ...).
     IncrementalClassifier
     MultiHeadClassifier
 
+Progressive Neural Networks
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Modules that implement progressive neural networks models, layers, and adapters.
+
+.. autosummary::
+    :toctree: generated
+
+    PNN
+    PNNLayer
+    PNNColumn
+    LinearAdapter
+    MLPAdapter
+
 
 Models
-^^^^^^^^^^^^^^^^^^^^^^^
+------------------
 
 | Neural network architectures that can be used as backbones for CL experiments.
 
@@ -57,22 +70,8 @@ Models
     ExpertGate
 
 
-Progressive Neural Networks
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Modules that implement progressive neural networks models, layers, and adapters.
-
-.. autosummary::
-    :toctree: generated
-
-    PNN
-    PNNLayer
-    PNNColumn    
-    LinearAdapter
-    MLPAdapter
-
-
 Model Wrappers and Utilities
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+------------------------------------
 Wrappers and functions that add utility support to your models.
 
 .. autosummary::
@@ -87,7 +86,7 @@ Wrappers and functions that add utility support to your models.
     pytorchcv_wrapper.get_model
 
 Dynamic optimizer utilities
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+------------------------------------
 
 Utilities to handle optimizer's update when using dynamic architectures.
 Dynamic Modules (e.g. multi-head) can change their parameters dynamically