[Datamodules] Merge feature branch

CHANGELOG.md

@@ -8,6 +8,11 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Add Synthetic anomalous dataset for validation and testing (https://github.com/openvinotoolkit/anomalib/pull/822)
+- Add Detection task type support (https://github.com/openvinotoolkit/anomalib/pull/822)
+- Add UCSDped and Avenue dataset implementation (https://github.com/openvinotoolkit/anomalib/pull/822)
+- Add base classes for video dataset and video datamodule (https://github.com/openvinotoolkit/anomalib/pull/822)
+- Add base classes for image dataset and image dataModule (https://github.com/openvinotoolkit/anomalib/pull/822)
 - ✨ Add CSFlow model (<https://github.com/openvinotoolkit/anomalib/pull/657>)
 - Log loss for existing trainable models (<https://github.com/openvinotoolkit/anomalib/pull/804>)
 - Add section for community project (<https://github.com/openvinotoolkit/anomalib/pull/768>)
@@ -19,6 +24,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Make input image normalization and center cropping configurable from config (https://github.com/openvinotoolkit/anomalib/pull/822)
+- Improve flexibility and configurability of subset splitting (https://github.com/openvinotoolkit/anomalib/pull/822)
+- Switch to new datamodules design (https://github.com/openvinotoolkit/anomalib/pull/822)
+- Make normalization and center cropping configurable through config (<https://github.com/openvinotoolkit/anomalib/pull/795>)
 - Switch to new [changelog format](https://keepachangelog.com/en/1.0.0/). (<https://github.com/openvinotoolkit/anomalib/pull/777>)
 - Rename feature to task (<https://github.com/openvinotoolkit/anomalib/pull/769>)
 - make device configurable in OpenVINO inference (<https://github.com/openvinotoolkit/anomalib/pull/755>)
@@ -32,6 +41,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ### Deprecated
 
+- Deprecated PreProcessor class (<https://github.com/openvinotoolkit/anomalib/pull/795>)
 - Deprecate OptimalF1 metric in favor of AnomalyScoreThreshold and F1Score (<https://github.com/openvinotoolkit/anomalib/pull/796>)
 
 ### Fixed

anomalib/config/config.py

@@ -14,6 +14,8 @@
 
 from omegaconf import DictConfig, ListConfig, OmegaConf
 
+from anomalib.data.utils import TestSplitMode, ValSplitMode
+
 
 def _get_now_str(timestamp: float) -> str:
     """Standard format for datetimes is defined here."""
@@ -32,11 +34,27 @@ def update_input_size_config(config: Union[DictConfig, ListConfig]) -> Union[Dic
     Returns:
         Union[DictConfig, ListConfig]: Configurable parameters with updated values
     """
-    # handle image size
-    if isinstance(config.dataset.image_size, int):
-        config.dataset.image_size = (config.dataset.image_size,) * 2
-
-    config.model.input_size = config.dataset.image_size
+    # Image size: Ensure value is in the form [height, width]
+    image_size = config.dataset.get("image_size")
+    if isinstance(image_size, int):
+        config.dataset.image_size = (image_size,) * 2
+    elif isinstance(image_size, ListConfig):
+        assert len(image_size) == 2, "image_size must be a single integer or tuple of length 2 for width and height."
+    else:
+        raise ValueError(f"image_size must be either int or ListConfig, got {type(image_size)}")
+
+    # Center crop: Ensure value is in the form [height, width], and update input_size
+    center_crop = config.dataset.get("center_crop")
+    if center_crop is None:
+        config.model.input_size = config.dataset.image_size
+    elif isinstance(center_crop, int):
+        config.dataset.center_crop = (center_crop,) * 2
+        config.model.input_size = config.dataset.center_crop
+    elif isinstance(center_crop, ListConfig):
+        assert len(center_crop) == 2, "center_crop must be a single integer or tuple of length 2 for width and height."
+        config.model.input_size = center_crop
+    else:
+        raise ValueError(f"center_crop must be either int or ListConfig, got {type(center_crop)}")
 
     if "tiling" in config.dataset.keys() and config.dataset.tiling.apply:
         if isinstance(config.dataset.tiling.tile_size, int):
@@ -109,6 +127,78 @@ def update_multi_gpu_training_config(config: Union[DictConfig, ListConfig]) -> U
     return config
 
 
+def update_datasets_config(config: Union[DictConfig, ListConfig]) -> Union[DictConfig, ListConfig]:
+    """Updates the dataset section of the config.
+
+    Args:
+        config (Union[DictConfig, ListConfig]): Configurable parameters for the current run.
+
+    Returns:
+        Union[DictConfig, ListConfig]: Updated config
+    """
+    if "format" not in config.dataset.keys():
+        config.dataset.format = "mvtec"
+
+    if "create_validation_set" in config.dataset.keys():
+        warn(
+            DeprecationWarning(
+                "The 'create_validation_set' parameter is deprecated and will be removed in a future release. Please "
+                "use 'validation_split_mode' instead."
+            )
+        )
+        config.dataset.val_split_mode = "from_test" if config.dataset.create_validation_set else "same_as_test"
+
+    if "test_batch_size" in config.dataset.keys():
+        warn(
+            DeprecationWarning(
+                "The 'test_batch_size' parameter is deprecated and will be removed in a future release. Please use "
+                "'eval_batch_size' instead."
+            )
+        )
+        config.dataset.eval_batch_size = config.dataset.test_batch_size
+
+    if "transform_config" in config.dataset.keys() and "val" in config.dataset.transform_config.keys():
+        warn(
+            DeprecationWarning(
+                "The 'transform_config.val' parameter is deprecated and will be removed in a future release. Please "
+                "use 'transform_config.eval' instead."
+            )
+        )
+        config.dataset.transform_config.eval = config.dataset.transform_config.val
+
+    config = update_input_size_config(config)
+
+    if "clip_length_in_frames" in config.dataset.keys() and config.dataset.clip_length_in_frames > 1:
+        warn(
+            "Anomalib's models and visualizer are currently not compatible with video datasets with a clip length > 1. "
+            "Custom changes to these modules will be needed to prevent errors and/or unpredictable behaviour."
+        )
+
+    if config.dataset.format == "folder" and "split_ratio" in config.dataset.keys():
+        warn(
+            DeprecationWarning(
+                "The 'split_ratio' parameter is deprecated and will be removed in a future release. Please use "
+                "'test_split_ratio' instead."
+            )
+        )
+        config.dataset.test_split_ratio = config.dataset.split_ratio
+
+    if config.dataset.get("test_split_mode") == TestSplitMode.NONE and config.dataset.get("val_split_mode") in [
+        ValSplitMode.SAME_AS_TEST,
+        ValSplitMode.FROM_TEST,
+    ]:
+        warn(
+            f"val_split_mode {config.dataset.val_split_mode} not allowed for test_split_mode = 'none'. "
+            "Setting val_split_mode to 'none'."
+        )
+        config.dataset.val_split_mode = ValSplitMode.NONE
+
+    if config.dataset.get("val_split_mode") == ValSplitMode.NONE and config.trainer.limit_val_batches != 0.0:
+        warn("Running without validation set. Setting trainer.limit_val_batches to 0.")
+        config.trainer.limit_val_batches = 0.0
+    return config
+
+
 def get_configurable_parameters(
     model_name: Optional[str] = None,
     config_path: Optional[Union[Path, str]] = None,
@@ -151,15 +241,24 @@ def get_configurable_parameters(
             "(`null` in the YAML file) or remove the `seed` key from the YAML file."
         )
 
-    # Dataset Configs
-    if "format" not in config.dataset.keys():
-        config.dataset.format = "mvtec"
-
+    config = update_datasets_config(config)
     config = update_input_size_config(config)
 
     # Project Configs
     project_path = Path(config.project.path) / config.model.name / config.dataset.name
 
+    if config.dataset.format == "folder":
+        if "mask" in config.dataset:
+            warn(
+                DeprecationWarning(
+                    "mask will be deprecated in favor of mask_dir in config.dataset in a future release."
+                )
+            )
+            config.dataset.mask_dir = config.dataset.mask
+        if "path" in config.dataset:
+            warn(DeprecationWarning("path will be deprecated in favor of root in config.dataset in a future release."))
+            config.dataset.root = config.dataset.path
+
     # add category subfolder if needed
     if config.dataset.format.lower() in ("btech", "mvtec"):
         project_path = project_path / config.dataset.category
@@ -196,15 +295,29 @@ def get_configurable_parameters(
     if "metrics" in config.keys():
         # NOTE: Deprecate this after v0.4.0.
         if "adaptive" in config.metrics.threshold.keys():
-            warn("adaptive will be deprecated in favor of method in config.metrics.threshold in v0.4.0.")
+            warn(
+                DeprecationWarning(
+                    "adaptive will be deprecated in favor of method in config.metrics.threshold in a future release"
+                )
+            )
             config.metrics.threshold.method = "adaptive" if config.metrics.threshold.adaptive else "manual"
         if "image_default" in config.metrics.threshold.keys():
-            warn("image_default will be deprecated in favor of manual_image in config.metrics.threshold in v0.4.0.")
+            warn(
+                DeprecationWarning(
+                    "image_default will be deprecated in favor of manual_image in config.metrics.threshold in a future "
+                    "release."
+                )
+            )
             config.metrics.threshold.manual_image = (
                 None if config.metrics.threshold.adaptive else config.metrics.threshold.image_default
             )
         if "pixel_default" in config.metrics.threshold.keys():
-            warn("pixel_default will be deprecated in favor of manual_pixel in config.metrics.threshold in v0.4.0.")
+            warn(
+                DeprecationWarning(
+                    "pixel_default will be deprecated in favor of manual_pixel in config.metrics.threshold in a future "
+                    "release."
+                )
+            )
             config.metrics.threshold.manual_pixel = (
                 None if config.metrics.threshold.adaptive else config.metrics.threshold.pixel_default
             )

anomalib/data/__init__.py

@@ -7,17 +7,20 @@
 from typing import Union
 
 from omegaconf import DictConfig, ListConfig
-from pytorch_lightning import LightningDataModule
 
+from .avenue import Avenue
+from .base import AnomalibDataModule, AnomalibDataset
 from .btech import BTech
 from .folder import Folder
 from .inference import InferenceDataset
 from .mvtec import MVTec
+from .task_type import TaskType
+from .ucsd_ped import UCSDped
 
 logger = logging.getLogger(__name__)
 
 
-def get_datamodule(config: Union[DictConfig, ListConfig]) -> LightningDataModule:
+def get_datamodule(config: Union[DictConfig, ListConfig]) -> AnomalibDataModule:
     """Get Anomaly Datamodule.
 
     Args:
@@ -28,56 +31,106 @@ def get_datamodule(config: Union[DictConfig, ListConfig]) -> LightningDataModule
     """
     logger.info("Loading the datamodule")
 
-    datamodule: LightningDataModule
+    datamodule: AnomalibDataModule
+
+    # convert center crop to tuple
+    center_crop = config.dataset.get("center_crop")
+    if center_crop is not None:
+        center_crop = (center_crop[0], center_crop[1])
 
     if config.dataset.format.lower() == "mvtec":
         datamodule = MVTec(
-            # TODO: Remove config values. IAAALD-211
             root=config.dataset.path,
             category=config.dataset.category,
             image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
+            center_crop=center_crop,
+            normalization=config.dataset.normalization,
             train_batch_size=config.dataset.train_batch_size,
-            test_batch_size=config.dataset.test_batch_size,
+            eval_batch_size=config.dataset.eval_batch_size,
             num_workers=config.dataset.num_workers,
-            seed=config.project.seed,
             task=config.dataset.task,
             transform_config_train=config.dataset.transform_config.train,
-            transform_config_val=config.dataset.transform_config.val,
-            create_validation_set=config.dataset.create_validation_set,
+            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,
         )
     elif config.dataset.format.lower() == "btech":
         datamodule = BTech(
-            # TODO: Remove config values. IAAALD-211
             root=config.dataset.path,
             category=config.dataset.category,
             image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
+            center_crop=center_crop,
+            normalization=config.dataset.normalization,
             train_batch_size=config.dataset.train_batch_size,
-            test_batch_size=config.dataset.test_batch_size,
+            eval_batch_size=config.dataset.eval_batch_size,
             num_workers=config.dataset.num_workers,
-            seed=config.project.seed,
             task=config.dataset.task,
             transform_config_train=config.dataset.transform_config.train,
-            transform_config_val=config.dataset.transform_config.val,
-            create_validation_set=config.dataset.create_validation_set,
+            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,
         )
     elif config.dataset.format.lower() == "folder":
         datamodule = Folder(
-            root=config.dataset.path,
+            root=config.dataset.root,
             normal_dir=config.dataset.normal_dir,
             abnormal_dir=config.dataset.abnormal_dir,
             task=config.dataset.task,
             normal_test_dir=config.dataset.normal_test_dir,
-            mask_dir=config.dataset.mask,
+            mask_dir=config.dataset.mask_dir,
             extensions=config.dataset.extensions,
-            split_ratio=config.dataset.split_ratio,
-            seed=config.project.seed,
             image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
+            center_crop=center_crop,
+            normalization=config.dataset.normalization,
             train_batch_size=config.dataset.train_batch_size,
-            test_batch_size=config.dataset.test_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_val=config.dataset.transform_config.val,
-            create_validation_set=config.dataset.create_validation_set,
+            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,
+        )
+    elif config.dataset.format.lower() == "ucsdped":
+        datamodule = UCSDped(
+            root=config.dataset.path,
+            category=config.dataset.category,
+            task=config.dataset.task,
+            clip_length_in_frames=config.dataset.clip_length_in_frames,
+            frames_between_clips=config.dataset.frames_between_clips,
+            image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
+            center_crop=center_crop,
+            normalization=config.dataset.normalization,
+            transform_config_train=config.dataset.transform_config.train,
+            transform_config_eval=config.dataset.transform_config.eval,
+            train_batch_size=config.dataset.train_batch_size,
+            eval_batch_size=config.dataset.eval_batch_size,
+            num_workers=config.dataset.num_workers,
+            val_split_mode=config.dataset.val_split_mode,
+            val_split_ratio=config.dataset.val_split_ratio,
+        )
+    elif config.dataset.format.lower() == "avenue":
+        datamodule = Avenue(
+            root=config.dataset.path,
+            gt_dir=config.dataset.gt_dir,
+            task=config.dataset.task,
+            clip_length_in_frames=config.dataset.clip_length_in_frames,
+            frames_between_clips=config.dataset.frames_between_clips,
+            image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
+            center_crop=center_crop,
+            normalization=config.dataset.normalization,
+            transform_config_train=config.dataset.transform_config.train,
+            transform_config_eval=config.dataset.transform_config.eval,
+            train_batch_size=config.dataset.train_batch_size,
+            eval_batch_size=config.dataset.eval_batch_size,
+            num_workers=config.dataset.num_workers,
+            val_split_mode=config.dataset.val_split_mode,
+            val_split_ratio=config.dataset.val_split_ratio,
         )
     else:
         raise ValueError(
@@ -90,9 +143,14 @@ def get_datamodule(config: Union[DictConfig, ListConfig]) -> LightningDataModule
 
 
 __all__ = [
+    "AnomalibDataset",
+    "AnomalibDataModule",
     "get_datamodule",
     "BTech",
     "Folder",
     "InferenceDataset",
     "MVTec",
+    "Avenue",
+    "UCSDped",
+    "TaskType",
 ]