Remove Bayesian models

docs/q_and_a.md

@@ -19,7 +19,6 @@ To get insipiration, you can look at the source code of
 ## How can I change the loss function?
 If you are using pre-made GW architecture
 ([`GlobalWorkspace`](https://ruflab.github.io/shimmer/latest/shimmer/modules/global_workspace.html#GlobalWorkspace),
-[`GlobalWorkspaceBayesian`](https://ruflab.github.io/shimmer/latest/shimmer/modules/global_workspace.html#GlobalWorkspaceBayesian),
 [`GlobalWorkspaceFusion`](https://ruflab.github.io/shimmer/latest/shimmer/modules/global_workspace.html#GlobalWorkspaceFusion)) and want to update the loss
 used for demi-cycles, cycles, translations or broadcast, you can do so directly from
 your definition of the

shimmer/__init__.py

@@ -14,7 +14,6 @@
 from shimmer.modules.global_workspace import (
     GlobalWorkspace2Domains,
     GlobalWorkspaceBase,
-    GlobalWorkspaceBayesian,
     SchedulerArgs,
     batch_broadcasts,
     batch_cycles,
@@ -28,7 +27,6 @@
     GWEncoderLinear,
     GWModule,
     GWModuleBase,
-    GWModuleBayesian,
     GWModulePrediction,
     broadcast,
     broadcast_cycles,
@@ -39,7 +37,6 @@
     BroadcastLossCoefs,
     GWLosses2Domains,
     GWLossesBase,
-    GWLossesBayesian,
     LossCoefs,
 )
 from shimmer.modules.selection import (
@@ -75,7 +72,6 @@
     "SchedulerArgs",
     "GlobalWorkspaceBase",
     "GlobalWorkspace2Domains",
-    "GlobalWorkspaceBayesian",
     "pretrained_global_workspace",
     "LossOutput",
     "DomainModule",
@@ -84,7 +80,6 @@
     "GWEncoderLinear",
     "GWModuleBase",
     "GWModule",
-    "GWModuleBayesian",
     "GWModulePrediction",
     "ContrastiveLossType",
     "contrastive_loss",
@@ -93,7 +88,6 @@
     "BroadcastLossCoefs",
     "GWLossesBase",
     "GWLosses2Domains",
-    "GWLossesBayesian",
     "RepeatedDataset",
     "batch_cycles",
     "batch_demi_cycles",

shimmer/modules/__init__.py

@@ -1,15 +1,13 @@
 from shimmer.data.dataset import RepeatedDataset
 from shimmer.modules.contrastive_loss import (
     ContrastiveLoss,
-    ContrastiveLossBayesianType,
     ContrastiveLossType,
     contrastive_loss,
 )
 from shimmer.modules.domain import DomainModule, LossOutput
 from shimmer.modules.global_workspace import (
     GlobalWorkspace2Domains,
     GlobalWorkspaceBase,
-    GlobalWorkspaceBayesian,
     SchedulerArgs,
     batch_broadcasts,
     batch_cycles,
@@ -23,7 +21,6 @@
     GWEncoderLinear,
     GWModule,
     GWModuleBase,
-    GWModuleBayesian,
     GWModulePrediction,
     broadcast,
     broadcast_cycles,
@@ -34,7 +31,6 @@
     BroadcastLossCoefs,
     GWLosses2Domains,
     GWLossesBase,
-    GWLossesBayesian,
     LossCoefs,
 )
 from shimmer.modules.selection import (
@@ -55,7 +51,6 @@
     "SchedulerArgs",
     "GlobalWorkspaceBase",
     "GlobalWorkspace2Domains",
-    "GlobalWorkspaceBayesian",
     "pretrained_global_workspace",
     "LossOutput",
     "DomainModule",
@@ -64,17 +59,14 @@
     "GWEncoderLinear",
     "GWModuleBase",
     "GWModule",
-    "GWModuleBayesian",
     "GWModulePrediction",
     "ContrastiveLossType",
-    "ContrastiveLossBayesianType",
     "contrastive_loss",
     "ContrastiveLoss",
     "LossCoefs",
     "BroadcastLossCoefs",
     "GWLossesBase",
     "GWLosses2Domains",
-    "GWLossesBayesian",
     "RepeatedDataset",
     "reparameterize",
     "kl_divergence_loss",

shimmer/modules/contrastive_loss.py

@@ -15,16 +15,6 @@
 A function taking the prediction and targets and returning a LossOutput.
 """
 
-ContrastiveLossBayesianType = Callable[
-    [torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor], LossOutput
-]
-"""
-Contrastive loss function type for GlobalWorkspaceBayesian.
-
-A function taking the prediction mean, prediction std, target mean and target std and
-    returns a LossOutput.
-"""
-
 
 def info_nce(
     x: torch.Tensor,

shimmer/modules/global_workspace.py

@@ -15,7 +15,6 @@
 from shimmer.modules.gw_module import (
     GWModule,
     GWModuleBase,
-    GWModuleBayesian,
     GWModulePrediction,
     broadcast_cycles,
     cycle,
@@ -26,11 +25,9 @@
     GWLosses,
     GWLosses2Domains,
     GWLossesBase,
-    GWLossesBayesian,
     LossCoefs,
 )
 from shimmer.modules.selection import (
-    FixedSharedSelection,
     RandomSelection,
     SelectionBase,
     SingleDomainSelection,
@@ -793,107 +790,6 @@ def __init__(
         )
 
 
-class GlobalWorkspaceBayesian(
-    GlobalWorkspaceBase[GWModuleBayesian, FixedSharedSelection, GWLossesBayesian]
-):
-    """
-    A simple 2-domains max GlobalWorkspaceBase with a Bayesian base uncertainty
-    prediction.
-
-    This is used to simplify a Global Workspace instanciation and only overrides the
-    `__init__` method.
-    """
-
-    def __init__(
-        self,
-        domain_mods: Mapping[str, DomainModule],
-        gw_encoders: Mapping[str, Module],
-        gw_decoders: Mapping[str, Module],
-        workspace_dim: int,
-        loss_coefs: BroadcastLossCoefs,
-        sensitivity_selection: float = 1,
-        sensitivity_precision: float = 1,
-        optim_lr: float = 1e-3,
-        optim_weight_decay: float = 0.0,
-        scheduler_args: SchedulerArgs | None = None,
-        learn_logit_scale: bool = False,
-        use_normalized_constrastive: bool = True,
-        contrastive_loss: ContrastiveLossType | None = None,
-        precision_softmax_temp: float = 0.01,
-        scheduler: LRScheduler
-        | None
-        | OneCycleSchedulerSentinel = OneCycleSchedulerSentinel.DEFAULT,
-    ) -> None:
-        """
-        Initializes a Global Workspace
-
-        Args:
-            domain_mods (`Mapping[str, DomainModule]`): mapping of the domains
-                connected to the GW. Keys are domain names, values are the
-                `DomainModule`.
-            gw_encoders (`Mapping[str, torch.nn.Module]`): mapping for each domain
-                name to a `torch.nn.Module` class which role is to encode a
-                unimodal latent representations into a GW representation (pre fusion).
-            gw_decoders (`Mapping[str, torch.nn.Module]`): mapping for each domain
-                name to a `torch.nn.Module` class which role is to decode a
-                GW representation into a unimodal latent representations.
-            workspace_dim (`int`): dimension of the GW.
-            loss_coefs (`LossCoefs`): loss coefficients
-            sensitivity_selection (`float`): sensivity coef $c'_1$
-            sensitivity_precision (`float`): sensitivity coef $c'_2$
-            optim_lr (`float`): learning rate
-            optim_weight_decay (`float`): weight decay
-            scheduler_args (`SchedulerArgs | None`): optimization scheduler's arguments
-            learn_logit_scale (`bool`): whether to learn the contrastive learning
-                contrastive loss when using the default contrastive loss.
-            use_normalized_constrastive (`bool`): whether to use the normalized cont
-                loss by the precision coefs
-            contrastive_loss (`ContrastiveLossType | None`): a contrastive loss
-                function used for alignment. `learn_logit_scale` will not affect custom
-                contrastive losses.
-            precision_softmax_temp (`float`): temperature to use in softmax of
-                precision
-            scheduler: The scheduler to use for traning. If None is explicitely given,
-                no scheduler will be used. Defaults to use OneCycleScheduler
-        """
-        domain_mods = freeze_domain_modules(domain_mods)
-
-        gw_mod = GWModuleBayesian(
-            domain_mods,
-            workspace_dim,
-            gw_encoders,
-            gw_decoders,
-            sensitivity_selection,
-            sensitivity_precision,
-            precision_softmax_temp,
-        )
-
-        selection_mod = FixedSharedSelection()
-
-        contrastive_loss = ContrastiveLoss(
-            torch.tensor([1]).log(), "mean", learn_logit_scale
-        )
-
-        loss_mod = GWLossesBayesian(
-            gw_mod,
-            selection_mod,
-            domain_mods,
-            loss_coefs,
-            contrastive_loss,
-            use_normalized_constrastive,
-        )
-
-        super().__init__(
-            gw_mod,
-            selection_mod,
-            loss_mod,
-            optim_lr,
-            optim_weight_decay,
-            scheduler_args,
-            scheduler,
-        )
-
-
 def pretrained_global_workspace(
     checkpoint_path: str | Path,
     domain_mods: Mapping[str, DomainModule],