from typing import Literal
from torch import nn
from torch.optim import Optimizer
from torch_uncertainty.models import mc_dropout
from torch_uncertainty.models.classification import (
packed_vgg,
vgg,
)
from torch_uncertainty.ood_criteria import TUOODCriterion
from torch_uncertainty.routines.classification import ClassificationRoutine
from torch_uncertainty.transforms import RepeatTarget
ENSEMBLE_METHODS = ["mc-dropout", "packed"]
[docs]
class VGGBaseline(ClassificationRoutine):
versions = {
"std": vgg,
"mc-dropout": vgg,
"packed": packed_vgg,
}
archs = [11, 13, 16, 19]
def __init__(
self,
num_classes: int,
in_channels: int,
loss: nn.Module,
version: Literal["std", "mc-dropout", "packed"],
arch: int,
style: str = "imagenet",
num_estimators: int = 1,
dropout_rate: float = 0.0,
last_layer_dropout: bool = False,
optim_recipe: dict | Optimizer | None = None,
mixup_params: dict | None = None,
groups: int = 1,
alpha: int | None = None,
gamma: int = 1,
ood_criterion: type[TUOODCriterion] | str = "msp",
log_plots: bool = False,
save_in_csv: bool = False,
eval_ood: bool = False,
eval_shift: bool = False,
eval_grouping_loss: bool = False,
) -> None:
r"""VGG backbone baseline for classification providing support for
various versions and architectures.
Args:
num_classes (int): Number of classes to predict.
in_channels (int): Number of input channels.
loss (nn.Module): Training loss.
optim_recipe (Any): optimization recipe, corresponds to
what expect the `LightningModule.configure_optimizers()
<https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#configure-optimizers>`_
method.
version (str): Determines which VGG version to use:
- ``"std"``: original VGG
- ``"mc-dropout"``: Monte Carlo Dropout VGG
- ``"packed"``: Packed-Ensembles VGG
arch (int): Determines which VGG architecture to use:
- ``11``: VGG-11
- ``13``: VGG-13
- ``16``: VGG-16
- ``19``: VGG-19
style (str, optional): Which VGG style to use. Defaults to ``imagenet``.
num_estimators (int, optional): Number of estimators in the ensemble.
Only used if :attr:`version` is either ``"packed"``, ``"batched"``
or ``"masked"`` Defaults to ``None``.
dropout_rate (float, optional): Dropout rate. Defaults to ``0.0``.
mixup_params (dict, optional): Mixup parameters. Can include mixtype,
mixmode, dist_sim, kernel_tau_max, kernel_tau_std,
mixup_alpha, and cutmix_alpha. If None, no augmentations.
Defaults to ``None``.
last_layer_dropout (bool): whether to apply dropout to the last layer only.
groups (int, optional): Number of groups in convolutions. Defaults to
``1``.
alpha (float, optional): Expansion factor affecting the width of the
estimators. Only used if :attr:`version` is ``"packed"``. Defaults
to ``None``.
gamma (int, optional): Number of groups within each estimator. Only
used if :attr:`version` is ``"packed"`` and scales with
:attr:`groups`. Defaults to ``1s``.
ood_criterion (TUOODCriterion, optional): Criterion for the binary OOD detection task.
Defaults to None which amounts to the maximum softmax probability score (MSP).
log_plots (bool, optional): Indicates whether to log the plots or not.
Defaults to ``False``.
save_in_csv (bool, optional): Indicates whether to save the results in
a csv file or not. Defaults to ``False``.
eval_ood (bool, optional): Indicates whether to evaluate the
OOD detection or not. Defaults to ``False``.
eval_shift (bool): Whether to evaluate on shifted data. Defaults to
``False``.
eval_grouping_loss (bool, optional): Indicates whether to evaluate the
grouping loss or not. Defaults to ``False``.
Raises:
ValueError: If :attr:`version` is not either ``"std"``,
``"packed"``, ``"batched"`` or ``"masked"``.
Returns:
LightningModule: VGG baseline ready for training and evaluation.
"""
params = {
"dropout_rate": dropout_rate,
"in_channels": in_channels,
"num_classes": num_classes,
"style": style,
"groups": groups,
"arch": arch,
}
if version not in self.versions:
raise ValueError(f"Unknown version: {version}")
format_batch_fn = nn.Identity()
if version == "std":
params |= {
"dropout_rate": dropout_rate,
}
elif version == "mc-dropout":
params |= {
"dropout_rate": dropout_rate,
"num_estimators": num_estimators,
}
if version in ENSEMBLE_METHODS:
params |= {
"num_estimators": num_estimators,
}
if version != "mc-dropout":
format_batch_fn = RepeatTarget(num_repeats=num_estimators)
if version == "packed":
params |= {
"alpha": alpha,
"style": style,
"gamma": gamma,
}
if version == "mc-dropout": # std VGGs don't have `num_estimators`
del params["num_estimators"]
model = self.versions[version](**params)
if version == "mc-dropout":
model = mc_dropout(
model=model,
num_estimators=num_estimators,
last_layer=last_layer_dropout,
)
super().__init__(
num_classes=num_classes,
model=model,
loss=loss,
is_ensemble=version in ENSEMBLE_METHODS,
format_batch_fn=format_batch_fn,
optim_recipe=optim_recipe,
mixup_params=mixup_params,
eval_ood=eval_ood,
eval_shift=eval_shift,
ood_criterion=ood_criterion,
log_plots=log_plots,
save_in_csv=save_in_csv,
eval_grouping_loss=eval_grouping_loss,
)
self.save_hyperparameters(ignore=["loss"])
