Synthetic anomaly for testing and validation

anomalib/data/__init__.py

@@ -44,6 +44,8 @@ def get_datamodule(config: Union[DictConfig, ListConfig]) -> AnomalibDataModule:
             task=config.dataset.task,
             transform_config_train=config.dataset.transform_config.train,
             transform_config_eval=config.dataset.transform_config.eval,
+            test_split_mode=config.dataset.test_split_mode,
+            test_split_ratio=config.dataset.test_split_ratio,
             val_split_mode=config.dataset.val_split_mode,
             val_split_ratio=config.dataset.val_split_ratio,
         )
@@ -58,6 +60,8 @@ def get_datamodule(config: Union[DictConfig, ListConfig]) -> AnomalibDataModule:
             task=config.dataset.task,
             transform_config_train=config.dataset.transform_config.train,
             transform_config_eval=config.dataset.transform_config.eval,
+            test_split_mode=config.dataset.test_split_mode,
+            test_split_ratio=config.dataset.test_split_ratio,
             val_split_mode=config.dataset.val_split_mode,
             val_split_ratio=config.dataset.val_split_ratio,
         )
@@ -70,13 +74,14 @@ def get_datamodule(config: Union[DictConfig, ListConfig]) -> AnomalibDataModule:
             normal_test_dir=config.dataset.normal_test_dir,
             mask_dir=config.dataset.mask,
             extensions=config.dataset.extensions,
-            normal_split_ratio=config.dataset.normal_split_ratio,
             image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
             train_batch_size=config.dataset.train_batch_size,
             eval_batch_size=config.dataset.eval_batch_size,
             num_workers=config.dataset.num_workers,
             transform_config_train=config.dataset.transform_config.train,
             transform_config_eval=config.dataset.transform_config.eval,
+            test_split_mode=config.dataset.test_split_mode,
+            test_split_ratio=config.dataset.test_split_ratio,
             val_split_mode=config.dataset.val_split_mode,
             val_split_ratio=config.dataset.val_split_ratio,
         )

anomalib/data/avenue.py

@@ -26,7 +26,7 @@
 from pandas import DataFrame
 from torch import Tensor
 
-from anomalib.data.base import AnomalibDataModule, VideoAnomalibDataset
+from anomalib.data.base import AnomalibVideoDataModule, AnomalibVideoDataset
 from anomalib.data.task_type import TaskType
 from anomalib.data.utils import DownloadProgressBar, Split, ValSplitMode, hash_check
 from anomalib.data.utils.video import ClipsIndexer
@@ -121,7 +121,7 @@ def get_mask(self, idx) -> Optional[Tensor]:
         return masks
 
 
-class AvenueDataset(VideoAnomalibDataset):
+class AvenueDataset(AnomalibVideoDataset):
     """Avenue Dataset class.
 
     Args:
@@ -156,7 +156,7 @@ def _setup(self):
         self.samples = make_avenue_dataset(self.root, self.gt_dir, self.split)
 
 
-class Avenue(AnomalibDataModule):
+class Avenue(AnomalibVideoDataModule):
     """Avenue DataModule class.
 
     Args:
@@ -177,6 +177,8 @@ class Avenue(AnomalibDataModule):
             during validation.
             Defaults to None.
         val_split_mode (ValSplitMode): Setting that determines how the validation subset is obtained.
+        val_split_ratio (float): Fraction of train or test images that will be reserved for validation.
+        seed (Optional[int], optional): Seed which may be set to a fixed value for reproducibility.
     """
 
     def __init__(

anomalib/data/base/__init__.py

@@ -6,6 +6,6 @@
 
 from .datamodule import AnomalibDataModule
 from .dataset import AnomalibDataset
-from .video import VideoAnomalibDataset
+from .video import AnomalibVideoDataModule, AnomalibVideoDataset
 
-__all__ = ["AnomalibDataset", "AnomalibDataModule", "VideoAnomalibDataset"]
+__all__ = ["AnomalibDataset", "AnomalibDataModule", "AnomalibVideoDataset", "AnomalibVideoDataModule"]

anomalib/data/base/datamodule.py

@@ -15,7 +15,13 @@
 from torch.utils.data import DataLoader, default_collate
 
 from anomalib.data.base.dataset import AnomalibDataset
-from anomalib.data.utils import ValSplitMode, random_split
+from anomalib.data.synthetic import SyntheticAnomalyDataset
+from anomalib.data.utils import (
+    TestSplitMode,
+    ValSplitMode,
+    random_split,
+    split_by_label,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -60,12 +66,16 @@ def __init__(
         num_workers: int,
         val_split_mode: ValSplitMode,
         val_split_ratio: float,
+        test_split_mode: Optional[TestSplitMode] = None,
+        test_split_ratio: Optional[float] = None,
         seed: Optional[int] = None,
     ):
         super().__init__()
         self.train_batch_size = train_batch_size
         self.eval_batch_size = eval_batch_size
         self.num_workers = num_workers
+        self.test_split_mode = test_split_mode
+        self.test_split_ratio = test_split_ratio
         self.val_split_mode = val_split_mode
         self.val_split_ratio = val_split_ratio
         self.seed = seed
@@ -101,12 +111,44 @@ def _setup(self, _stage: Optional[str] = None) -> None:
 
         self.train_data.setup()
         self.test_data.setup()
+
+        self._create_test_split()
+        self._create_val_split()
+
+    def _create_test_split(self):
+        """Obtain the test set based on the settings in the config."""
+        if self.test_data.has_normal:
+            # split the test data into normal and anomalous so these can be processed separately
+            normal_test_data, self.test_data = split_by_label(self.test_data)
+        else:
+            # when the user did not provide any normal images for testing, we sample some from the training set
+            logger.info(
+                "No normal test images found. Sampling from training set using a split ratio of %d",
+                self.test_split_ratio,
+            )
+            self.train_data, normal_test_data = random_split(self.train_data, self.test_split_ratio)
+
+        if self.test_split_mode == TestSplitMode.FROM_DIR:
+            self.test_data += normal_test_data
+        elif self.test_split_mode == TestSplitMode.SYNTHETIC:
+            self.test_data = SyntheticAnomalyDataset.from_dataset(normal_test_data)
+        else:
+            raise ValueError(f"Unsupported Test Split Mode: {self.test_split_mode}")
+
+    def _create_val_split(self):
+        """Obtain the validation set based on the settings in the config."""
         if self.val_split_mode == ValSplitMode.FROM_TEST:
+            # randomly sampled from test set
             self.test_data, self.val_data = random_split(
                 self.test_data, self.val_split_ratio, label_aware=True, seed=self.seed
             )
         elif self.val_split_mode == ValSplitMode.SAME_AS_TEST:
+            # equal to test set
             self.val_data = self.test_data
+        elif self.val_split_mode == ValSplitMode.SYNTHETIC:
+            # converted from random training sample
+            self.train_data, normal_val_data = random_split(self.train_data, self.val_split_ratio)
+            self.val_data = SyntheticAnomalyDataset.from_dataset(normal_val_data)
         elif self.val_split_mode != ValSplitMode.NONE:
             raise ValueError(f"Unknown validation split mode: {self.val_split_mode}")
 

anomalib/data/base/video.py

@@ -6,14 +6,15 @@
 import torch
 from torch import Tensor
 
+from anomalib.data.base.datamodule import AnomalibDataModule
 from anomalib.data.base.dataset import AnomalibDataset
 from anomalib.data.task_type import TaskType
-from anomalib.data.utils import masks_to_boxes
+from anomalib.data.utils import ValSplitMode, masks_to_boxes
 from anomalib.data.utils.video import ClipsIndexer
 from anomalib.pre_processing import PreProcessor
 
 
-class VideoAnomalibDataset(AnomalibDataset, ABC):
+class AnomalibVideoDataset(AnomalibDataset, ABC):
     """Base video anomalib dataset class.
 
     Args:
@@ -48,7 +49,7 @@ def samples(self):
     @samples.setter
     def samples(self, samples):
         """Overwrite samples and re-index subvideos."""
-        super(VideoAnomalibDataset, self.__class__).samples.fset(self, samples)
+        super(AnomalibVideoDataset, self.__class__).samples.fset(self, samples)
         self._setup_clips()
 
     def _setup_clips(self) -> None:
@@ -93,3 +94,25 @@ def __getitem__(self, index: int) -> Dict[str, Union[str, Tensor]]:
             item.pop("mask")
 
         return item
+
+
+class AnomalibVideoDataModule(AnomalibDataModule):
+    """Base class for video data modules."""
+
+    def _setup(self, _stage: Optional[str] = None) -> None:
+        """Set up the datasets and perform dynamic subset splitting.
+
+        This method may be overridden in subclass for custom splitting behaviour.
+
+        Video datamodules are not compatible with synthetic anomaly generation.
+        """
+        assert self.train_data is not None
+        assert self.test_data is not None
+
+        self.train_data.setup()
+        self.test_data.setup()
+
+        if self.val_split_mode == ValSplitMode.SYNTHETIC:
+            raise ValueError(f"Val split mode {self.test_split_mode} not supported for video datasets.")
+
+        self._create_val_split()

anomalib/data/btech.py

@@ -25,7 +25,13 @@
 
 from anomalib.data.base import AnomalibDataModule, AnomalibDataset
 from anomalib.data.task_type import TaskType
-from anomalib.data.utils import DownloadProgressBar, Split, ValSplitMode, hash_check
+from anomalib.data.utils import (
+    DownloadProgressBar,
+    Split,
+    TestSplitMode,
+    ValSplitMode,
+    hash_check,
+)
 from anomalib.pre_processing import PreProcessor
 
 logger = logging.getLogger(__name__)
@@ -181,6 +187,8 @@ def __init__(
         task: TaskType = TaskType.SEGMENTATION,
         transform_config_train: Optional[Union[str, A.Compose]] = None,
         transform_config_eval: Optional[Union[str, A.Compose]] = None,
+        test_split_mode: TestSplitMode = TestSplitMode.FROM_DIR,
+        test_split_ratio: float = 0.2,
         val_split_mode: ValSplitMode = ValSplitMode.SAME_AS_TEST,
         val_split_ratio: float = 0.5,
         seed: Optional[int] = None,
@@ -199,6 +207,11 @@ def __init__(
             transform_config_val: Config for pre-processing during validation.
             create_validation_set: Create a validation subset in addition to the train and test subsets
             seed (Optional[int], optional): Seed used during random subset splitting.
+            test_split_mode (TestSplitMode): Setting that determines how the testing subset is obtained.
+            test_split_ratio (float): Fraction of images from the train set that will be reserved for testing.
+            val_split_mode (ValSplitMode): Setting that determines how the validation subset is obtained.
+            val_split_ratio (float): Fraction of train or test images that will be reserved for validation.
+            seed (Optional[int], optional): Seed which may be set to a fixed value for reproducibility.
 
         Examples:
             >>> from anomalib.data import BTech
@@ -230,6 +243,8 @@ def __init__(
             train_batch_size=train_batch_size,
             eval_batch_size=eval_batch_size,
             num_workers=num_workers,
+            test_split_mode=test_split_mode,
+            test_split_ratio=test_split_ratio,
             val_split_mode=val_split_mode,
             val_split_ratio=val_split_ratio,
             seed=seed,

anomalib/data/folder.py

@@ -15,7 +15,7 @@
 
 from anomalib.data.base import AnomalibDataModule, AnomalibDataset
 from anomalib.data.task_type import TaskType
-from anomalib.data.utils import Split, ValSplitMode, random_split
+from anomalib.data.utils import Split, TestSplitMode, ValSplitMode
 from anomalib.pre_processing.pre_process import PreProcessor
 
 
@@ -237,7 +237,10 @@ class Folder(AnomalibDataModule):
         transform_config_val (Optional[Union[str, A.Compose]], optional): Config for pre-processing
             during validation.
             Defaults to None.
+        test_split_mode (TestSplitMode): Setting that determines how the testing subset is obtained.
+        test_split_ratio (float): Fraction of images from the train set that will be reserved for testing.
         val_split_mode (ValSplitMode): Setting that determines how the validation subset is obtained.
+        val_split_ratio (float): Fraction of train or test images that will be reserved for validation.
         seed (Optional[int], optional): Seed used during random subset splitting.
     """
 
@@ -258,6 +261,8 @@ def __init__(
         task: TaskType = TaskType.SEGMENTATION,
         transform_config_train: Optional[Union[str, A.Compose]] = None,
         transform_config_eval: Optional[Union[str, A.Compose]] = None,
+        test_split_mode: TestSplitMode = TestSplitMode.FROM_DIR,
+        test_split_ratio: float = 0.2,
         val_split_mode: ValSplitMode = ValSplitMode.FROM_TEST,
         val_split_ratio: float = 0.5,
         seed: Optional[int] = None,
@@ -266,6 +271,8 @@ def __init__(
             train_batch_size=train_batch_size,
             eval_batch_size=eval_batch_size,
             num_workers=num_workers,
+            test_split_mode=test_split_mode,
+            test_split_ratio=test_split_ratio,
             val_split_mode=val_split_mode,
             val_split_ratio=val_split_ratio,
             seed=seed,
@@ -299,18 +306,3 @@ def __init__(
             mask_dir=mask_dir,
             extensions=extensions,
         )
-
-    def _setup(self, _stage: Optional[str] = None):
-        """Set up the datasets for the Folder Data Module."""
-        assert self.train_data is not None
-        assert self.test_data is not None
-
-        self.train_data.setup()
-        self.test_data.setup()
-
-        # add some normal images to the test set
-        if not self.test_data.has_normal:
-            self.train_data, normal_test_data = random_split(self.train_data, self.normal_split_ratio, seed=self.seed)
-            self.test_data += normal_test_data
-
-        super()._setup()

anomalib/data/mvtec.py

@@ -34,7 +34,13 @@
 
 from anomalib.data.base import AnomalibDataModule, AnomalibDataset
 from anomalib.data.task_type import TaskType
-from anomalib.data.utils import DownloadProgressBar, Split, ValSplitMode, hash_check
+from anomalib.data.utils import (
+    DownloadProgressBar,
+    Split,
+    TestSplitMode,
+    ValSplitMode,
+    hash_check,
+)
 from anomalib.pre_processing import PreProcessor
 
 logger = logging.getLogger(__name__)
@@ -149,7 +155,29 @@ def _setup(self):
 
 
 class MVTec(AnomalibDataModule):
-    """MVTec Datamodule."""
+    """MVTec Datamodule.
+
+    Args:
+        root (str): Path to the root of the dataset
+        category (str): Category of the MVTec dataset (e.g. "bottle" or "cable").
+        image_size (Optional[Union[int, Tuple[int, int]]], optional): Size of the input image.
+            Defaults to None.
+        train_batch_size (int, optional): Training batch size. Defaults to 32.
+        eval_batch_size (int, optional): Test batch size. Defaults to 32.
+        num_workers (int, optional): Number of workers. Defaults to 8.
+        task TaskType): Task type, 'classification', 'detection' or 'segmentation'
+        transform_config_train (Optional[Union[str, A.Compose]], optional): Config for pre-processing
+            during training.
+            Defaults to None.
+        transform_config_val (Optional[Union[str, A.Compose]], optional): Config for pre-processing
+            during validation.
+            Defaults to None.
+        test_split_mode (TestSplitMode): Setting that determines how the testing subset is obtained.
+        test_split_ratio (float): Fraction of images from the train set that will be reserved for testing.
+        val_split_mode (ValSplitMode): Setting that determines how the validation subset is obtained.
+        val_split_ratio (float): Fraction of train or test images that will be reserved for validation.
+        seed (Optional[int], optional): Seed which may be set to a fixed value for reproducibility.
+    """
 
     def __init__(
         self,
@@ -162,6 +190,8 @@ def __init__(
         task: TaskType = TaskType.SEGMENTATION,
         transform_config_train: Optional[Union[str, A.Compose]] = None,
         transform_config_eval: Optional[Union[str, A.Compose]] = None,
+        test_split_mode: TestSplitMode = TestSplitMode.FROM_DIR,
+        test_split_ratio: float = 0.2,
         val_split_mode: ValSplitMode = ValSplitMode.SAME_AS_TEST,
         val_split_ratio: float = 0.5,
         seed: Optional[int] = None,
@@ -170,6 +200,8 @@ def __init__(
             train_batch_size=train_batch_size,
             eval_batch_size=eval_batch_size,
             num_workers=num_workers,
+            test_split_mode=test_split_mode,
+            test_split_ratio=test_split_ratio,
             val_split_mode=val_split_mode,
             val_split_ratio=val_split_ratio,
             seed=seed,