Address compatibility issues with OTE, that are caused by the legacy code.

anomalib/core/model/inference.py

@@ -137,7 +137,9 @@ def pre_process(self, image: np.ndarray) -> Tensor:
         Returns:
             Tensor: pre-processed image.
         """
-        pre_processor = PreProcessor(config=self.config.transform, to_tensor=True)
+        config = self.config.transform if "transform" in self.config.keys() else None
+        image_size = tuple(self.config.dataset.image_size)
+        pre_processor = PreProcessor(config, image_size)
         processed_image = pre_processor(image=image)["image"]
 
         if len(processed_image) == 3:
@@ -236,7 +238,9 @@ def pre_process(self, image: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: pre-processed image.
         """
-        pre_processor = PreProcessor(config=self.config.transform, to_tensor=False)
+        config = self.config.transform if "transform" in self.config.keys() else None
+        image_size = tuple(self.config.dataset.image_size)
+        pre_processor = PreProcessor(config, image_size)
         processed_image = pre_processor(image=image)["image"]
 
         if len(processed_image.shape) == 3:

anomalib/data/mvtec.py

@@ -50,11 +50,14 @@
 
 
 def split_normal_images_in_train_set(samples: DataFrame, split_ratio: float = 0.1, seed: int = 0) -> DataFrame:
-    """This function splits the normal images in training set and assigns the values to the test set.
+    """Split normal images in train set.
 
-    This is particularly useful especially when the test set does not contain any normal images.
-    This is important because when the test set doesn't have any normal images,
-    AUC computation fails due to having single class.
+        This function splits the normal images in training set and assigns the
+        values to the test set. This is particularly useful especially when the
+        test set does not contain any normal images.
+
+        This is important because when the test set doesn't have any normal images,
+        AUC computation fails due to having single class.
 
     Args:
         samples (DataFrame): Dataframe containing dataset info such as filenames, splits etc.
@@ -64,7 +67,9 @@ def split_normal_images_in_train_set(samples: DataFrame, split_ratio: float = 0.
     Returns:
         DataFrame: Output dataframe where the part of the training set is assigned to test set.
     """
-    random.seed(seed)
+
+    if seed > 0:
+        random.seed(seed)
 
     normal_train_image_indices = samples.index[(samples.split == "train") & (samples.label == "good")].to_list()
     num_normal_train_images = len(normal_train_image_indices)
@@ -76,7 +81,44 @@ def split_normal_images_in_train_set(samples: DataFrame, split_ratio: float = 0.
     return samples
 
 
-def make_mvtec_dataset(path: Path, split: str = "train", split_ratio: float = 0.1, seed: int = 0) -> DataFrame:
+def create_validation_set_from_test_set(samples: DataFrame, seed: int = 0) -> DataFrame:
+    """Craete Validation Set from Test Set.
+
+    This function creates a validation set from test set by splitting both
+    normal and abnormal samples to two.
+
+    Args:
+        samples (DataFrame): Dataframe containing dataset info such as filenames, splits etc.
+        seed (int, optional): Random seed to ensure reproducibility. Defaults to 0.
+    """
+
+    if seed > 0:
+        random.seed(seed)
+
+    # Split normal images.
+    normal_test_image_indices = samples.index[(samples.split == "test") & (samples.label == "good")].to_list()
+    num_normal_valid_images = len(normal_test_image_indices) // 2
+
+    indices_to_sample = random.sample(population=normal_test_image_indices, k=num_normal_valid_images)
+    samples.loc[indices_to_sample, "split"] = "val"
+
+    # Split abnormal images.
+    abnormal_test_image_indices = samples.index[(samples.split == "test") & (samples.label != "good")].to_list()
+    num_abnormal_valid_images = len(abnormal_test_image_indices) // 2
+
+    indices_to_sample = random.sample(population=abnormal_test_image_indices, k=num_abnormal_valid_images)
+    samples.loc[indices_to_sample, "split"] = "val"
+
+    return samples
+
+
+def make_mvtec_dataset(
+    path: Path,
+    split: Optional[str] = None,
+    split_ratio: float = 0.1,
+    seed: int = 0,
+    create_validation_set: bool = False,
+) -> DataFrame:
     """Create MVTec samples by parsing the MVTec data file structure.
 
     The files are expected to follow the structure:
@@ -92,11 +134,14 @@ def make_mvtec_dataset(path: Path, split: str = "train", split_ratio: float = 0.
 
     Args:
         path (Path): Path to dataset
-        split (str, optional): Dataset split (ie., either train or test). Defaults to "train".
+        split (str, optional): Dataset split (ie., either train or test). Defaults to None.
         split_ratio (float, optional): Ratio to split normal training images and add to the
-                                       test set in case test set doesn't contain any normal images.
-                                       Defaults to 0.1.
+            test set in case test set doesn't contain any normal images.
+            Defaults to 0.1.
         seed (int, optional): Random seed to ensure reproducibility when splitting. Defaults to 0.
+        create_validation_set (bool, optional): Boolean to create a validation set from the test set.
+            MVTec dataset does not contain a validation set. Those wanting to create a validation set
+            could set this flag to ``True``.
 
     Example:
         The following example shows how to get training samples from MVTec bottle category:
@@ -153,9 +198,13 @@ def make_mvtec_dataset(path: Path, split: str = "train", split_ratio: float = 0.
     samples.loc[(samples.label != "good"), "label_index"] = 1
     samples.label_index = samples.label_index.astype(int)
 
+    if create_validation_set:
+        samples = create_validation_set_from_test_set(samples)
+
     # Get the data frame for the split.
-    samples = samples[samples.split == split]
-    samples = samples.reset_index(drop=True)
+    if split is not None and split in ["train", "test"]:
+        samples = samples[samples.split == split]
+        samples = samples.reset_index(drop=True)
 
     return samples
 

anomalib/data/transforms/pre_process.py

@@ -78,7 +78,7 @@ class PreProcessor:
     def __init__(
         self,
         config: Optional[Union[str, A.Compose]] = None,
-        image_size: Optional[Union[int, Tuple[int, int]]] = None,
+        image_size: Optional[Union[int, Tuple]] = None,
         to_tensor: bool = True,
     ) -> None:
         self.config = config