Updatable objects

avalanche/_annotations.py

@@ -81,13 +81,12 @@ def decorator(func):
 
         @functools.wraps(func)
         def wrapper(*args, **kwargs):
-            warnings.simplefilter("always", DeprecationWarning)
+            warnings.simplefilter("once", DeprecationWarning)
             warnings.warn(
                 msg.format(name=func.__name__, version=version, reason=reason),
                 category=DeprecationWarning,
                 stacklevel=2,
             )
-            warnings.simplefilter("default", DeprecationWarning)
             return func(*args, **kwargs)
 
         return wrapper

avalanche/benchmarks/scenarios/online.py

@@ -167,6 +167,7 @@ def __init__(
         shuffle: bool = True,
         drop_last: bool = False,
         access_task_boundaries: bool = False,
+        seed: int = None,
     ) -> None:
         """Returns a lazy stream generated by splitting an experience into
         smaller ones.
@@ -181,7 +182,8 @@ def __init__(
         :param experience_size: The experience size (number of instances).
         :param shuffle: If True, instances will be shuffled before splitting.
         :param drop_last: If True, the last mini-experience will be dropped if
-            not of size `experience_size`
+            not of size `experience_size`.
+        :param seed: random seed for shuffling the data if `shuffle == True`.
         :return: The list of datasets that will be used to create the
             mini-experiences.
         """
@@ -190,10 +192,12 @@ def __init__(
         self.shuffle = shuffle
         self.drop_last = drop_last
         self.access_task_boundaries = access_task_boundaries
+        self.seed = seed
 
         # we need to fix the seed because repeated calls to the generator
         # must return the same order every time.
-        self.seed = random.randint(0, 2**32 - 1)
+        if seed is None:
+            self.seed = random.randint(0, 2**32 - 1)
 
     def __iter__(self) -> Generator[OnlineCLExperience, None, None]:
         exp_dataset = self.experience.dataset
@@ -250,13 +254,15 @@ def _default_online_split(
     access_task_boundaries: bool,
     exp: DatasetExperience,
     size: int,
+    seed: int,
 ):
     return FixedSizeExperienceSplitter(
         experience=exp,
         experience_size=size,
         shuffle=shuffle,
         drop_last=drop_last,
         access_task_boundaries=access_task_boundaries,
+        seed=seed,
     )
 
 
@@ -272,6 +278,7 @@ def split_online_stream(
         ]
     ] = None,
     access_task_boundaries: bool = False,
+    seed: int = None,
 ) -> CLStream[DatasetExperience[TCLDataset]]:
     """Split a stream of large batches to create an online stream of small
     mini-batches.
@@ -300,6 +307,7 @@ def split_online_stream(
         A good starting to understand the mechanism is to look at the
         implementation of the standard splitting function
         :func:`fixed_size_experience_split`.
+    :param seed: random seed used for shuffling by the default splitter.
     :return: A lazy online stream with experiences of size `experience_size`.
     """
 
@@ -308,7 +316,7 @@ def split_online_stream(
         # However, MyPy does not understand what a partial is -_-
         def default_online_split_wrapper(e, e_sz):
             return _default_online_split(
-                shuffle, drop_last, access_task_boundaries, e, e_sz
+                shuffle, drop_last, access_task_boundaries, e, e_sz, seed=seed
             )
 
         split_strategy = default_online_split_wrapper

avalanche/benchmarks/scenarios/validation_scenario.py

@@ -96,6 +96,7 @@ def random_validation_split_strategy_wrapper(data):
 
     # don't drop classes-timeline for compatibility with old API
     e0 = next(iter(train_stream))
+
     if hasattr(e0, "dataset") and hasattr(e0.dataset, "targets"):
         train_stream = with_classes_timeline(train_stream)
         valid_stream = with_classes_timeline(valid_stream)

avalanche/core.py

@@ -1,13 +1,153 @@
+# TODO: doc
 from abc import ABC
-from typing import Any, TypeVar, Generic
+from typing import Any, TypeVar, Generic, Protocol, runtime_checkable
 from typing import TYPE_CHECKING
 
+from avalanche.benchmarks import CLExperience
+
 if TYPE_CHECKING:
     from avalanche.training.templates.base import BaseTemplate
 
 Template = TypeVar("Template", bound="BaseTemplate")
 
 
+class Agent:
+    """Avalanche Continual Learning Agent.
+
+    The agent stores the state needed by continual learning training methods,
+    such as optimizers, models, regularization losses.
+    You can add any objects as attributes dynamically:
+
+    .. code-block::
+
+        agent = Agent()
+        agent.replay = ReservoirSamplingBuffer(max_size=200)
+        agent.loss = MaskedCrossEntropy()
+        agent.reg_loss = LearningWithoutForgetting(alpha=1, temperature=2)
+        agent.model = my_model
+        agent.opt = SGD(agent.model.parameters(), lr=0.001)
+        agent.scheduler = ExponentialLR(agent.opt, gamma=0.999)
+
+    Many CL objects will need to perform some operation before or
+    after training on each experience. This is supported via the `Adaptable`
+    Protocol, which requires the `pre_adapt` and `post_adapt` methods.
+    To call the pre/post adaptation you can implement your training loop
+    like in the following example:
+
+    .. code-block::
+
+        def train(agent, exp):
+            agent.pre_adapt(exp)
+            # do training here
+            agent.post_update(exp)
+
+    Objects that implement the `Adaptable` Protocol will be called by the Agent.
+
+    You can also add additional functionality to the adaptation phases with
+    hooks. For example:
+
+    .. code-block::
+        agent.add_pre_hooks(lambda a, e: update_optimizer(a.opt, new_params={}, optimized_params=dict(a.model.named_parameters())))
+        # we update the lr scheduler after each experience (not every epoch!)
+        agent.add_post_hooks(lambda a, e: a.scheduler.step())
+
+
+    """
+
+    def __init__(self, verbose=False):
+        """Init.
+
+        :param verbose: If True, print every time an adaptable object or hook
+            is called during the adaptation. Useful for debugging.
+        """
+        self._updatable_objects = []
+        self.verbose = verbose
+        self._pre_hooks = []
+        self._post_hooks = []
+
+    def __setattr__(self, name, value):
+        super().__setattr__(name, value)
+        if hasattr(value, "pre_adapt") or hasattr(value, "post_adapt"):
+            self._updatable_objects.append(value)
+            if self.verbose:
+                print("Added updatable object ", value)
+
+    def pre_adapt(self, exp):
+        """Pre-adaptation.
+
+        Remember to call this before training on a new experience.
+
+        :param exp: current experience
+        """
+        for uo in self._updatable_objects:
+            if hasattr(uo, "pre_adapt"):
+                uo.pre_adapt(self, exp)
+                if self.verbose:
+                    print("pre_adapt ", uo)
+        for foo in self._pre_hooks:
+            if self.verbose:
+                print("pre_adapt hook ", foo)
+            foo(self, exp)
+
+    def post_adapt(self, exp):
+        """Post-adaptation.
+
+        Remember to call this after training on a new experience.
+
+        :param exp: current experience
+        """
+        for uo in self._updatable_objects:
+            if hasattr(uo, "post_adapt"):
+                uo.post_adapt(self, exp)
+                if self.verbose:
+                    print("post_adapt ", uo)
+        for foo in self._post_hooks:
+            if self.verbose:
+                print("post_adapt hook ", foo)
+            foo(self, exp)
+
+    def add_pre_hooks(self, foo):
+        """Add a pre-adaptation hooks
+
+        Hooks take two arguments: `<agent, experience>`.
+
+        :param foo: the hook function
+        """
+        self._pre_hooks.append(foo)
+
+    def add_post_hooks(self, foo):
+        """Add a post-adaptation hooks
+
+        Hooks take two arguments: `<agent, experience>`.
+
+        :param foo: the hook function
+        """
+        self._post_hooks.append(foo)
+
+
+class Adaptable(Protocol):
+    """Adaptable objects Protocol.
+
+    These class documents the Adaptable objects API but it is not necessary
+    for an object to inherit from it since the `Agent` will search for the methods
+    dynamically.
+
+    Adaptable objects are objects that require to run their `pre_adapt` and
+    `post_adapt` methods before (and after, respectively) training on each
+    experience.
+
+    Adaptable objects can implement only the method that they need since the
+    `Agent` will look for the methods dynamically and call it only if it is
+    implemented.
+    """
+
+    def pre_adapt(self, agent: Agent, exp: CLExperience):
+        pass
+
+    def post_adapt(self, agent: Agent, exp: CLExperience):
+        pass
+
+
 class BasePlugin(Generic[Template], ABC):
     """ABC for BaseTemplate plugins.