Remove transforms from datasets

Signed-off-by: Samet Akcay <samet.akcay@intel.com>

src/anomalib/data/datasets/base/depth.py

@@ -9,7 +9,6 @@
 import torch
 from PIL import Image
 from torchvision.transforms.functional import to_tensor
-from torchvision.transforms.v2 import Transform
 from torchvision.tv_tensors import Mask
 
 from anomalib import TaskType
@@ -24,14 +23,10 @@ class AnomalibDepthDataset(AnomalibDataset, ABC):
 
     Args:
         task (str): Task type, either 'classification' or 'segmentation'
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
     """
 
-    def __init__(self, task: TaskType, transform: Transform | None = None) -> None:
-        super().__init__(task, transform)
-
-        self.transform = transform
+    def __init__(self, task: TaskType) -> None:
+        super().__init__(task)
 
     def __getitem__(self, index: int) -> DepthItem:
         """Return rgb image, depth image and mask.
@@ -52,9 +47,7 @@ def __getitem__(self, index: int) -> DepthItem:
         item = {"image_path": image_path, "depth_path": depth_path, "label": label_index}
 
         if self.task == TaskType.CLASSIFICATION:
-            item["image"], item["depth_image"] = (
-                self.transform(image, depth_image) if self.transform else (image, depth_image)
-            )
+            item["image"], item["depth_image"] = image, depth_image
         elif self.task == TaskType.SEGMENTATION:
             # Only Anomalous (1) images have masks in anomaly datasets
             # Therefore, create empty mask for Normal (0) images.
@@ -63,11 +56,8 @@ def __getitem__(self, index: int) -> DepthItem:
                 if label_index == LabelName.NORMAL
                 else Mask(to_tensor(Image.open(mask_path)).squeeze())
             )
-            item["image"], item["depth_image"], item["mask"] = (
-                self.transform(image, depth_image, mask) if self.transform else (image, depth_image, mask)
-            )
+            item["image"], item["depth_image"], item["mask"] = image, depth_image, mask
             item["mask_path"] = mask_path
-
         else:
             msg = f"Unknown task type: {self.task}"
             raise ValueError(msg)

src/anomalib/data/datasets/base/image.py

@@ -13,7 +13,6 @@
 import torch
 from pandas import DataFrame
 from torch.utils.data import Dataset
-from torchvision.transforms.v2 import Transform
 from torchvision.tv_tensors import Mask
 
 from anomalib import TaskType
@@ -58,14 +57,11 @@ class AnomalibDataset(Dataset, ABC):
 
     Args:
         task (str): Task type, either 'classification' or 'segmentation'
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
     """
 
-    def __init__(self, task: TaskType | str, transform: Transform | None = None) -> None:
+    def __init__(self, task: TaskType | str) -> None:
         super().__init__()
         self.task = TaskType(task)
-        self.transform = transform
         self._samples: DataFrame | None = None
         self._category: str | None = None
 
@@ -170,7 +166,7 @@ def __getitem__(self, index: int) -> DatasetItem:
         item = {"image_path": image_path, "gt_label": label_index}
 
         if self.task == TaskType.CLASSIFICATION:
-            item["image"] = self.transform(image) if self.transform else image
+            item["image"] = image
         elif self.task == TaskType.SEGMENTATION:
             # Only Anomalous (1) images have masks in anomaly datasets
             # Therefore, create empty mask for Normal (0) images.

src/anomalib/data/datasets/base/video.py

@@ -9,9 +9,7 @@
 
 import torch
 from pandas import DataFrame
-from torchvision.transforms.v2 import Transform
 from torchvision.transforms.v2.functional import to_dtype, to_dtype_video
-from torchvision.tv_tensors import Mask
 
 from anomalib import TaskType
 from anomalib.data.dataclasses import VideoBatch, VideoItem
@@ -39,8 +37,6 @@ class AnomalibVideoDataset(AnomalibDataset, ABC):
         task (str): Task type, either 'classification' or 'segmentation'
         clip_length_in_frames (int): Number of video frames in each clip.
         frames_between_clips (int): Number of frames between each consecutive video clip.
-        transform (Transform, optional): Transforms that should be applied to the input clips.
-            Defaults to ``None``.
         target_frame (VideoTargetFrame): Specifies the target frame in the video clip, used for ground truth retrieval.
             Defaults to ``VideoTargetFrame.LAST``.
     """
@@ -50,14 +46,12 @@ def __init__(
         task: TaskType,
         clip_length_in_frames: int,
         frames_between_clips: int,
-        transform: Transform | None = None,
         target_frame: VideoTargetFrame = VideoTargetFrame.LAST,
     ) -> None:
-        super().__init__(task, transform)
+        super().__init__(task)
 
         self.clip_length_in_frames = clip_length_in_frames
         self.frames_between_clips = frames_between_clips
-        self.transform = transform
 
         self.indexer: ClipsIndexer | None = None
         self.indexer_cls: Callable | None = None
@@ -153,13 +147,8 @@ def __getitem__(self, index: int) -> VideoItem:
         # include the untransformed image for visualization
         item.original_image = to_dtype(item.image, torch.uint8, scale=True)
 
-        # apply transforms
         if item.gt_mask is not None:
-            if self.transform:
-                item.image, item.gt_mask = self.transform(item.image, Mask(item.gt_mask))
             item.gt_label = torch.Tensor([1 in frame for frame in item.gt_mask]).int().squeeze(0)
-        elif self.transform:
-            item.image = self.transform(item.image)
 
         # squeeze temporal dimensions in case clip length is 1
         item.image = item.image.squeeze(0)

src/anomalib/data/datasets/depth/folder_3d.py

@@ -9,7 +9,6 @@
 from pathlib import Path
 
 from pandas import DataFrame, isna
-from torchvision.transforms.v2 import Transform
 
 from anomalib import TaskType
 from anomalib.data.datasets.base.depth import AnomalibDepthDataset
@@ -24,7 +23,6 @@ class Folder3DDataset(AnomalibDepthDataset):
     Args:
         name (str): Name of the dataset.
         task (TaskType): Task type. (``classification``, ``detection`` or ``segmentation``).
-        transform (Transform): Transforms that should be applied to the input images.
         normal_dir (str | Path): Path to the directory containing normal images.
         root (str | Path | None): Root folder of the dataset.
             Defaults to ``None``.
@@ -45,8 +43,6 @@ class Folder3DDataset(AnomalibDepthDataset):
         normal_test_depth_dir (str | Path | None, optional): Path to the directory containing
             normal depth images for the test dataset. Normal test images will be a split of `normal_dir` if `None`.
             Defaults to ``None``.
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
         split (str | Split | None): Fixed subset split that follows from folder structure on file system.
             Choose from [Split.FULL, Split.TRAIN, Split.TEST]
             Defaults to ``None``.
@@ -70,11 +66,10 @@ def __init__(
         normal_depth_dir: str | Path | None = None,
         abnormal_depth_dir: str | Path | None = None,
         normal_test_depth_dir: str | Path | None = None,
-        transform: Transform | None = None,
         split: str | Split | None = None,
         extensions: tuple[str, ...] | None = None,
     ) -> None:
-        super().__init__(task, transform)
+        super().__init__(task)
 
         self._name = name
         self.split = split

src/anomalib/data/datasets/depth/mvtec_3d.py

@@ -23,7 +23,6 @@
 from pathlib import Path
 
 from pandas import DataFrame
-from torchvision.transforms.v2 import Transform
 
 from anomalib import TaskType
 from anomalib.data.datasets.base.depth import AnomalibDepthDataset
@@ -43,8 +42,6 @@ class MVTec3DDataset(AnomalibDepthDataset):
             Defaults to ``"./datasets/MVTec3D"``.
         category (str): Sub-category of the dataset, e.g. 'bagel'
             Defaults to ``"bagel"``.
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
         split (str | Split | None): Split of the dataset, usually Split.TRAIN or Split.TEST
             Defaults to ``None``.
     """
@@ -54,10 +51,9 @@ def __init__(
         task: TaskType,
         root: Path | str = "./datasets/MVTec3D",
         category: str = "bagel",
-        transform: Transform | None = None,
         split: str | Split | None = None,
     ) -> None:
-        super().__init__(task=task, transform=transform)
+        super().__init__(task)
 
         self.root_category = Path(root) / Path(category)
         self.split = split

src/anomalib/data/datasets/image/btech.py

@@ -13,7 +13,6 @@
 
 import pandas as pd
 from pandas.core.frame import DataFrame
-from torchvision.transforms.v2 import Transform
 
 from anomalib import TaskType
 from anomalib.data.datasets.base.image import AnomalibDataset
@@ -28,19 +27,14 @@ class BTechDataset(AnomalibDataset):
     Args:
         root: Path to the BTech dataset
         category: Name of the BTech category.
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
         split: 'train', 'val' or 'test'
         task: ``classification``, ``detection`` or ``segmentation``
         create_validation_set: Create a validation subset in addition to the train and test subsets
 
     Examples:
         >>> from anomalib.data.image.btech import BTechDataset
-        >>> from anomalib.data.utils.transforms import get_transforms
-        >>> transform = get_transforms(image_size=256)
         >>> dataset = BTechDataset(
         ...     task="classification",
-        ...     transform=transform,
         ...     root='./datasets/BTech',
         ...     category='01',
         ... )
@@ -69,11 +63,10 @@ def __init__(
         self,
         root: str | Path,
         category: str,
-        transform: Transform | None = None,
         split: str | Split | None = None,
         task: TaskType | str = TaskType.SEGMENTATION,
     ) -> None:
-        super().__init__(task, transform)
+        super().__init__(task)
 
         self.root_category = Path(root) / category
         self.split = split

src/anomalib/data/datasets/image/folder.py

@@ -10,7 +10,6 @@
 from pathlib import Path
 
 from pandas import DataFrame
-from torchvision.transforms.v2 import Transform
 
 from anomalib import TaskType
 from anomalib.data.datasets.base.image import AnomalibDataset
@@ -27,8 +26,6 @@ class FolderDataset(AnomalibDataset):
     Args:
         name (str): Name of the dataset. This is used to name the datamodule, especially when logging/saving.
         task (TaskType): Task type. (``classification``, ``detection`` or ``segmentation``).
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
         normal_dir (str | Path | Sequence): Path to the directory containing normal images.
         root (str | Path | None): Root folder of the dataset.
             Defaults to ``None``.
@@ -52,20 +49,14 @@ class FolderDataset(AnomalibDataset):
 
     Examples:
         Assume that we would like to use this ``FolderDataset`` to create a dataset from a folder for a classification
-        task. We could first create the transforms,
-
-        >>> from anomalib.data.utils import InputNormalizationMethod, get_transforms
-        >>> transform = get_transforms(image_size=256, normalization=InputNormalizationMethod.NONE)
-
-        We could then create the dataset as follows,
+        task.
 
         .. code-block:: python
 
             folder_dataset_classification_train = FolderDataset(
                 normal_dir=dataset_root / "good",
                 abnormal_dir=dataset_root / "crack",
                 split="train",
-                transform=transform,
                 task=TaskType.CLASSIFICATION,
             )
 
@@ -76,15 +67,14 @@ def __init__(
         name: str,
         task: TaskType,
         normal_dir: str | Path | Sequence[str | Path],
-        transform: Transform | None = None,
         root: str | Path | None = None,
         abnormal_dir: str | Path | Sequence[str | Path] | None = None,
         normal_test_dir: str | Path | Sequence[str | Path] | None = None,
         mask_dir: str | Path | Sequence[str | Path] | None = None,
         split: str | Split | None = None,
         extensions: tuple[str, ...] | None = None,
     ) -> None:
-        super().__init__(task, transform)
+        super().__init__(task)
 
         self._name = name
         self.split = split

src/anomalib/data/datasets/image/kolektor.py

@@ -23,7 +23,6 @@
 from cv2 import imread
 from pandas import DataFrame
 from sklearn.model_selection import train_test_split
-from torchvision.transforms.v2 import Transform
 
 from anomalib import TaskType
 from anomalib.data.datasets import AnomalibDataset
@@ -38,8 +37,6 @@ class KolektorDataset(AnomalibDataset):
         task (TaskType): Task type, ``classification``, ``detection`` or ``segmentation``
         root (Path | str): Path to the root of the dataset
             Defaults to ``./datasets/kolektor``.
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
         split (str | Split | None): Split of the dataset, usually Split.TRAIN or Split.TEST
             Defaults to ``None``.
     """
@@ -48,10 +45,9 @@ def __init__(
         self,
         task: TaskType,
         root: Path | str = "./datasets/kolektor",
-        transform: Transform | None = None,
         split: str | Split | None = None,
     ) -> None:
-        super().__init__(task=task, transform=transform)
+        super().__init__(task)
 
         self.root = root
         self.split = split

src/anomalib/data/datasets/image/mvtec.py

@@ -29,7 +29,6 @@
 from pathlib import Path
 
 from pandas import DataFrame
-from torchvision.transforms.v2 import Transform
 
 from anomalib import TaskType
 from anomalib.data.datasets.base import AnomalibDataset
@@ -65,21 +64,16 @@ class MVTecDataset(AnomalibDataset):
             Defaults to ``./datasets/MVTec``.
         category (str): Sub-category of the dataset, e.g. 'bottle'
             Defaults to ``bottle``.
-        transform (Transform, optional): Transforms that should be applied to the input images.
-            Defaults to ``None``.
         split (str | Split | None): Split of the dataset, usually Split.TRAIN or Split.TEST
             Defaults to ``None``.
 
     Examples:
         .. code-block:: python
 
             from anomalib.data.image.mvtec import MVTecDataset
-            from anomalib.data.utils.transforms import get_transforms
 
-            transform = get_transforms(image_size=256)
             dataset = MVTecDataset(
                 task="classification",
-                transform=transform,
                 root='./datasets/MVTec',
                 category='zipper',
             )
@@ -110,10 +104,9 @@ def __init__(
         task: TaskType,
         root: Path | str = "./datasets/MVTec",
         category: str = "bottle",
-        transform: Transform | None = None,
         split: str | Split | None = None,
     ) -> None:
-        super().__init__(task=task, transform=transform)
+        super().__init__(task)
 
         self.root_category = Path(root) / Path(category)
         self.category = category