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🔨 Refactor Engine.predict method #1772

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merged 6 commits into from
Feb 27, 2024
Merged

🔨 Refactor Engine.predict method #1772

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26e6570
Refactor Engine.predict method
ashwinvaidya17 Feb 23, 2024
fedc851
change import location
ashwinvaidya17 Feb 27, 2024
58a6b3b
Merge branch 'main' of github.com:openvinotoolkit/anomalib into fix/p…
ashwinvaidya17 Feb 27, 2024
1230713
rename variable
ashwinvaidya17 Feb 27, 2024
e00d72c
Merge branch 'main' of github.com:openvinotoolkit/anomalib into fix/p…
ashwinvaidya17 Feb 27, 2024
810ff03
modify args for other entrypoints
ashwinvaidya17 Feb 27, 2024
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4 changes: 4 additions & 0 deletions src/anomalib/cli/cli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
_LIGHTNING_AVAILABLE = True
try:
from lightning.pytorch import Trainer
from lightning.pytorch.core.datamodule import LightningDataModule
from torch.utils.data import DataLoader, Dataset

from anomalib.data import AnomalibDataModule, AnomalibDataset
Expand Down Expand Up @@ -296,6 +297,9 @@ def instantiate_classes(self) -> None:
# the minor change here is that engine is instantiated instead of trainer
self.config_init = self.parser.instantiate_classes(self.config)
self.datamodule = self._get(self.config_init, "data")
if isinstance(self.datamodule, Dataset):
kwargs = {f"{self.config.subcommand}_dataset": self.datamodule}
self.datamodule = LightningDataModule.from_datasets(**kwargs)
self.model = self._get(self.config_init, "model")
self._configure_optimizers_method_to_model()
self.instantiate_engine()
Expand Down
37 changes: 19 additions & 18 deletions src/anomalib/engine/engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -308,7 +308,7 @@ def _setup_anomalib_callbacks(self) -> None:
def _should_run_validation(
self,
model: AnomalyModule,
dataloaders: EVAL_DATALOADERS | AnomalibDataModule | None,
dataloaders: EVAL_DATALOADERS | None,
datamodule: AnomalibDataModule | None,
ckpt_path: str | None,
) -> bool:
Expand All @@ -326,7 +326,7 @@ def _should_run_validation(

Args:
model (AnomalyModule): Model passed to the entrypoint.
dataloaders (EVAL_DATALOADERS | AnomalibDataModule | None): Dataloaders passed to the entrypoint.
dataloaders (EVAL_DATALOADERS | None): Dataloaders passed to the entrypoint.
datamodule (AnomalibDataModule | None): Lightning datamodule passed to the entrypoint.
ckpt_path (str | None): Checkpoint path passed to the entrypoint.

Expand All @@ -348,7 +348,7 @@ def _should_run_validation(
def fit(
self,
model: AnomalyModule,
train_dataloaders: TRAIN_DATALOADERS | AnomalibDataModule | None = None,
train_dataloaders: TRAIN_DATALOADERS | None = None,
val_dataloaders: EVAL_DATALOADERS | None = None,
datamodule: AnomalibDataModule | None = None,
ckpt_path: str | None = None,
Expand All @@ -357,7 +357,7 @@ def fit(

Args:
model (AnomalyModule): Model to be trained.
train_dataloaders (TRAIN_DATALOADERS | AnomalibDataModule | None, optional): Train dataloaders.
train_dataloaders (TRAIN_DATALOADERS | None, optional): Train dataloaders.
Defaults to None.
val_dataloaders (EVAL_DATALOADERS | None, optional): Validation dataloaders.
Defaults to None.
Expand Down Expand Up @@ -392,7 +392,7 @@ def fit(
def validate(
self,
model: AnomalyModule | None = None,
dataloaders: EVAL_DATALOADERS | AnomalibDataModule | None = None,
dataloaders: EVAL_DATALOADERS | None = None,
ckpt_path: str | None = None,
verbose: bool = True,
datamodule: AnomalibDataModule | None = None,
Expand All @@ -402,7 +402,7 @@ def validate(
Args:
model (AnomalyModule | None, optional): Model to be validated.
Defaults to None.
dataloaders (EVAL_DATALOADERS | AnomalibDataModule | None, optional): Dataloaders to be used for
dataloaders (EVAL_DATALOADERS | None, optional): Dataloaders to be used for
validation.
Defaults to None.
ckpt_path (str | None, optional): Checkpoint path. If provided, the model will be loaded from this path.
Expand Down Expand Up @@ -439,7 +439,7 @@ def validate(
def test(
self,
model: AnomalyModule | None = None,
dataloaders: EVAL_DATALOADERS | AnomalibDataModule | None = None,
dataloaders: EVAL_DATALOADERS | None = None,
ckpt_path: str | None = None,
verbose: bool = True,
datamodule: AnomalibDataModule | None = None,
Expand All @@ -453,7 +453,7 @@ def test(
model (AnomalyModule | None, optional):
The model to be tested.
Defaults to None.
dataloaders (EVAL_DATALOADERS | AnomalibDataModule | None, optional):
dataloaders (EVAL_DATALOADERS | None, optional):
An iterable or collection of iterables specifying test samples.
Defaults to None.
ckpt_path (str | None, optional):
Expand Down Expand Up @@ -526,13 +526,12 @@ def test(
self.trainer.validate(model, dataloaders, None, verbose=False, datamodule=datamodule)
return self.trainer.test(model, dataloaders, ckpt_path, verbose, datamodule)

# TODO(ashwinvaidya17): revisit typing of data args
# https://github.com/openvinotoolkit/anomalib/issues/1638
def predict(
self,
model: AnomalyModule | None = None,
dataloaders: EVAL_DATALOADERS | AnomalibDataModule | None = None,
datamodule: AnomalibDataModule | Dataset | PredictDataset | None = None,
dataloaders: EVAL_DATALOADERS | None = None,
datamodule: AnomalibDataModule | None = None,
dataset: Dataset | PredictDataset | None = None,
return_predictions: bool | None = None,
ckpt_path: str | None = None,
) -> _PREDICT_OUTPUT | None:
Expand All @@ -545,14 +544,17 @@ def predict(
model (AnomalyModule | None, optional):
Model to be used for prediction.
Defaults to None.
dataloaders (EVAL_DATALOADERS | AnomalibDataModule | None, optional):
dataloaders (EVAL_DATALOADERS | None, optional):
An iterable or collection of iterables specifying predict samples.
Defaults to None.
datamodule (AnomalibDataModule | None, optional):
A :class:`~lightning.pytorch.core.datamodule.AnomalibDataModule` that defines
the :class:`~lightning.pytorch.core.hooks.DataHooks.predict_dataloader` hook.
The datamodule can also be a dataset that will be wrapped in a torch Dataloader.
Defaults to None.
dataset (Dataset | PredictDataset | None, optional):
A :class:`~torch.utils.data.Dataset` or :class:`~anomalib.data.PredictDataset` that will be used
to create a dataloader. Defaults to None.
return_predictions (bool | None, optional):
Whether to return predictions.
``True`` by default except when an accelerator that spawns processes is used (not supported).
Expand Down Expand Up @@ -598,9 +600,8 @@ def predict(
logger.warning("ckpt_path is not provided. Model weights will not be loaded.")

# Handle the instance when a dataset is passed to the predict method
if datamodule is not None and isinstance(datamodule, Dataset):
dataloader = DataLoader(datamodule)
datamodule = None
if dataset is not None:
dataloader = DataLoader(dataset)
if dataloaders is None:
dataloaders = dataloader
elif isinstance(dataloaders, DataLoader):
Expand Down Expand Up @@ -628,7 +629,7 @@ def predict(
def train(
self,
model: AnomalyModule,
train_dataloaders: TRAIN_DATALOADERS | AnomalibDataModule | None = None,
train_dataloaders: TRAIN_DATALOADERS | None = None,
val_dataloaders: EVAL_DATALOADERS | None = None,
test_dataloaders: EVAL_DATALOADERS | None = None,
datamodule: AnomalibDataModule | None = None,
Expand All @@ -638,7 +639,7 @@ def train(

Args:
model (AnomalyModule): Model to be trained.
train_dataloaders (TRAIN_DATALOADERS | AnomalibDataModule | None, optional): Train dataloaders.
train_dataloaders (TRAIN_DATALOADERS | None, optional): Train dataloaders.
Defaults to None.
val_dataloaders (EVAL_DATALOADERS | None, optional): Validation dataloaders.
Defaults to None.
Expand Down
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