Support null seed

anomalib/data/btech.py

@@ -22,6 +22,7 @@
 
 import logging
 import shutil
+import warnings
 import zipfile
 from pathlib import Path
 from typing import Dict, Optional, Tuple, Union
@@ -56,7 +57,7 @@ def make_btech_dataset(
     path: Path,
     split: Optional[str] = None,
     split_ratio: float = 0.1,
-    seed: int = 0,
+    seed: Optional[int] = None,
     create_validation_set: bool = False,
 ) -> DataFrame:
     """Create BTech samples by parsing the BTech data file structure.
@@ -152,7 +153,7 @@ def __init__(
         pre_process: PreProcessor,
         split: str,
         task: str = "segmentation",
-        seed: int = 0,
+        seed: Optional[int] = None,
         create_validation_set: bool = False,
     ) -> None:
         """Btech Dataset class.
@@ -197,6 +198,14 @@ def __init__(
             (torch.Size([3, 256, 256]), torch.Size([256, 256]))
         """
         super().__init__(root)
+
+        if seed is None:
+            warnings.warn(
+                "seed is None."
+                " When seed is not set, images from the normal directory are split between training and test dir."
+                " This will lead to inconsistency between runs."
+            )
+
         self.root = Path(root) if isinstance(root, str) else root
         self.category: str = category
         self.split = split
@@ -274,7 +283,7 @@ def __init__(
         task: str = "segmentation",
         transform_config_train: Optional[Union[str, A.Compose]] = None,
         transform_config_val: Optional[Union[str, A.Compose]] = None,
-        seed: int = 0,
+        seed: Optional[int] = None,
         create_validation_set: bool = False,
     ) -> None:
         """Instantiate BTech Lightning Data Module.

anomalib/data/folder.py

@@ -95,7 +95,7 @@ def make_dataset(
     mask_dir: Optional[Union[str, Path]] = None,
     split: Optional[str] = None,
     split_ratio: float = 0.2,
-    seed: int = 0,
+    seed: Optional[int] = None,
     create_validation_set: bool = True,
     extensions: Optional[Tuple[str, ...]] = None,
 ):
@@ -191,7 +191,7 @@ def __init__(
         mask_dir: Optional[Union[Path, str]] = None,
         extensions: Optional[Tuple[str, ...]] = None,
         task: Optional[str] = None,
-        seed: int = 0,
+        seed: Optional[int] = None,
         create_validation_set: bool = False,
     ) -> None:
         """Create Folder Folder Dataset.
@@ -316,7 +316,7 @@ def __init__(
         mask_dir: Optional[Union[Path, str]] = None,
         extensions: Optional[Tuple[str, ...]] = None,
         split_ratio: float = 0.2,
-        seed: int = 0,
+        seed: Optional[int] = None,
         image_size: Optional[Union[int, Tuple[int, int]]] = None,
         train_batch_size: int = 32,
         test_batch_size: int = 32,
@@ -425,6 +425,13 @@ def __init__(
         """
         super().__init__()
 
+        if seed is None and normal_test_dir is None:
+            raise ValueError(
+                "Both seed and normal_test_dir cannot be None."
+                " When seed is not set, images from the normal directory are split between training and test dir."
+                " This will lead to inconsistency between runs."
+            )
+
         self.root = _check_and_convert_path(root)
         self.normal_dir = self.root / normal_dir
         self.abnormal_dir = self.root / abnormal_dir

anomalib/data/mvtec.py

@@ -40,6 +40,7 @@
 
 import logging
 import tarfile
+import warnings
 from pathlib import Path
 from typing import Dict, Optional, Tuple, Union
 from urllib.request import urlretrieve
@@ -72,7 +73,7 @@ def make_mvtec_dataset(
     path: Path,
     split: Optional[str] = None,
     split_ratio: float = 0.1,
-    seed: int = 0,
+    seed: Optional[int] = None,
     create_validation_set: bool = False,
 ) -> DataFrame:
     """Create MVTec AD samples by parsing the MVTec AD data file structure.
@@ -175,7 +176,7 @@ def __init__(
         pre_process: PreProcessor,
         split: str,
         task: str = "segmentation",
-        seed: int = 0,
+        seed: Optional[int] = None,
         create_validation_set: bool = False,
     ) -> None:
         """Mvtec AD Dataset class.
@@ -220,6 +221,14 @@ def __init__(
             (torch.Size([3, 256, 256]), torch.Size([256, 256]))
         """
         super().__init__(root)
+
+        if seed is None:
+            warnings.warn(
+                "seed is None."
+                " When seed is not set, images from the normal directory are split between training and test dir."
+                " This will lead to inconsistency between runs."
+            )
+
         self.root = Path(root) if isinstance(root, str) else root
         self.category: str = category
         self.split = split
@@ -297,7 +306,7 @@ def __init__(
         task: str = "segmentation",
         transform_config_train: Optional[Union[str, A.Compose]] = None,
         transform_config_val: Optional[Union[str, A.Compose]] = None,
-        seed: int = 0,
+        seed: Optional[int] = None,
         create_validation_set: bool = False,
     ) -> None:
         """Mvtec AD Lightning Data Module.

anomalib/data/utils/split.py

@@ -23,12 +23,13 @@
 # and limitations under the License.
 
 import random
+from typing import Optional
 
 from pandas.core.frame import DataFrame
 
 
 def split_normal_images_in_train_set(
-    samples: DataFrame, split_ratio: float = 0.1, seed: int = 0, normal_label: str = "good"
+    samples: DataFrame, split_ratio: float = 0.1, seed: Optional[int] = None, normal_label: str = "good"
 ) -> DataFrame:
     """Split normal images in train set.
 
@@ -49,7 +50,7 @@ def split_normal_images_in_train_set(
         DataFrame: Output dataframe where the part of the training set is assigned to test set.
     """
 
-    if seed >= 0:
+    if seed:
         random.seed(seed)
 
     normal_train_image_indices = samples.index[(samples.split == "train") & (samples.label == normal_label)].to_list()
@@ -62,7 +63,9 @@ def split_normal_images_in_train_set(
     return samples
 
 
-def create_validation_set_from_test_set(samples: DataFrame, seed: int = 0, normal_label: str = "good") -> DataFrame:
+def create_validation_set_from_test_set(
+    samples: DataFrame, seed: Optional[int] = None, normal_label: str = "good"
+) -> DataFrame:
     """Craete Validation Set from Test Set.
 
     This function creates a validation set from test set by splitting both
@@ -74,7 +77,7 @@ def create_validation_set_from_test_set(samples: DataFrame, seed: int = 0, norma
         normal_label (str): Name of the normal label. For MVTec AD, for instance, this is normal_label.
     """
 
-    if seed >= 0:
+    if seed:
         random.seed(seed)
 
     # Split normal images.

tests/helpers/dataset.py

@@ -177,7 +177,7 @@ def __init__(
         max_size: Optional[int] = 10,
         train_shapes: List[str] = ["triangle", "rectangle"],
         test_shapes: List[str] = ["star", "hexagon"],
-        seed: int = 0,
+        seed: Optional[int] = None,
     ) -> None:
         self.root_dir = mkdtemp()
         self.num_train = num_train
@@ -244,7 +244,7 @@ def _generate_dataset(self):
 
     def __enter__(self):
         """Creates the dataset in temp folder."""
-        if self.seed > 0:
+        if self.seed:
             np.random.seed(self.seed)
         self._generate_dataset()
         return self.root_dir

tests/pre_merge/datasets/test_dataset.py

@@ -92,6 +92,17 @@ def test_val_and_test_dataloaders_has_mask_and_gt(self, mvtec_data_module):
         assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(val_data.keys())
         assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(test_data.keys())
 
+    def test_non_overlapping_splits(self, mvtec_data_module):
+        """This test ensures that the train and test splits generated are non-overlapping."""
+        assert (
+            len(
+                set(mvtec_data_module.test_data.samples["image_path"].values).intersection(
+                    set(mvtec_data_module.train_data.samples["image_path"].values)
+                )
+            )
+            == 0
+        ), "Found train and test split contamination"
+
 
 class TestBTechDataModule:
     """Test BTech Data Module."""
@@ -111,6 +122,17 @@ def test_val_and_test_dataloaders_has_mask_and_gt(self, btech_data_module):
         assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(val_data.keys())
         assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(test_data.keys())
 
+    def test_non_overlapping_splits(self, btech_data_module):
+        """This test ensures that the train and test splits generated are non-overlapping."""
+        assert (
+            len(
+                set(btech_data_module.test_data.samples["image_path"].values).intersection(
+                    set(btech_data_module.train_data.samples["image_path"].values)
+                )
+            )
+            == 0
+        ), "Found train and test split contamination"
+
 
 class TestFolderDataModule:
     """Test Folder Data Module."""
@@ -130,6 +152,17 @@ def test_val_and_test_dataloaders_has_mask_and_gt(self, folder_data_module):
         assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(val_data.keys())
         assert sorted(["image_path", "mask_path", "image", "label", "mask"]) == sorted(test_data.keys())
 
+    def test_non_overlapping_splits(self, folder_data_module):
+        """This test ensures that the train and test splits generated are non-overlapping."""
+        assert (
+            len(
+                set(folder_data_module.test_data.samples["image_path"].values).intersection(
+                    set(folder_data_module.train_data.samples["image_path"].values)
+                )
+            )
+            == 0
+        ), "Found train and test split contamination"
+
 
 class TestDenormalize:
     """Test Denormalize Util."""

tools/train.py

@@ -58,7 +58,7 @@ def train():
         warnings.filterwarnings("ignore")
 
     config = get_configurable_parameters(model_name=args.model, config_path=args.config)
-    if config.project.seed != 0:
+    if config.project.seed:
         seed_everything(config.project.seed)
 
     datamodule = get_datamodule(config)