[Draft] Llm on

src/anomalib/__init__.py

@@ -22,3 +22,4 @@ class TaskType(str, Enum):
     CLASSIFICATION = "classification"
     DETECTION = "detection"
     SEGMENTATION = "segmentation"
+    EXPLANATION = "explanation"

src/anomalib/callbacks/metrics.py

@@ -75,10 +75,10 @@ def setup(
         pixel_metric_names: list[str] | dict[str, dict[str, Any]]
         if self.pixel_metric_names is None:
             pixel_metric_names = []
-        elif self.task == TaskType.CLASSIFICATION:
+        elif self.task in (TaskType.CLASSIFICATION, TaskType.EXPLANATION):
             pixel_metric_names = []
             logger.warning(
-                "Cannot perform pixel-level evaluation when task type is classification. "
+                "Cannot perform pixel-level evaluation when task type is classification or explanation. "
                 "Ignoring the following pixel-level metrics: %s",
                 self.pixel_metric_names,
             )

src/anomalib/data/base/dataset.py

@@ -23,6 +23,7 @@
 _EXPECTED_COLUMNS_SEGMENTATION = [*_EXPECTED_COLUMNS_CLASSIFICATION, "mask_path"]
 _EXPECTED_COLUMNS_PERTASK = {
     "classification": _EXPECTED_COLUMNS_CLASSIFICATION,
+    "explanation": _EXPECTED_COLUMNS_CLASSIFICATION,
     "segmentation": _EXPECTED_COLUMNS_SEGMENTATION,
     "detection": _EXPECTED_COLUMNS_SEGMENTATION,
 }
@@ -169,7 +170,7 @@ def __getitem__(self, index: int) -> dict[str, str | torch.Tensor]:
         image = read_image(image_path, as_tensor=True)
         item = {"image_path": image_path, "label": label_index}
 
-        if self.task == TaskType.CLASSIFICATION:
+        if self.task in (TaskType.CLASSIFICATION, TaskType.EXPLANATION):
             item["image"] = self.transform(image) if self.transform else image
         elif self.task in {TaskType.DETECTION, TaskType.SEGMENTATION}:
             # Only Anomalous (1) images have masks in anomaly datasets

src/anomalib/data/base/depth.py

@@ -48,7 +48,7 @@ def __getitem__(self, index: int) -> dict[str, str | torch.Tensor]:
         depth_image = to_tensor(read_depth_image(depth_path))
         item = {"image_path": image_path, "depth_path": depth_path, "label": label_index}
 
-        if self.task == TaskType.CLASSIFICATION:
+        if self.task in (TaskType.CLASSIFICATION, TaskType.EXPLANATION):
             item["image"], item["depth_image"] = (
                 self.transform(image, depth_image) if self.transform else (image, depth_image)
             )

src/anomalib/deploy/inferencers/openvino_inferencer.py

@@ -278,7 +278,7 @@ def post_process(self, predictions: np.ndarray, metadata: dict | DictConfig | No
             pred_idx = pred_score >= metadata["image_threshold"]
             pred_label = LabelName.ABNORMAL if pred_idx else LabelName.NORMAL
 
-        if task == TaskType.CLASSIFICATION:
+        if task in (TaskType.CLASSIFICATION, TaskType.EXPLANATION):
             _, pred_score = self._normalize(pred_scores=pred_score, metadata=metadata)
         elif task in {TaskType.SEGMENTATION, TaskType.DETECTION}:
             if "pixel_threshold" in metadata:

src/anomalib/models/__init__.py

@@ -24,6 +24,10 @@
     Fastflow,
     Fre,
     Ganomaly,
+    Llava,
+    Llavanext,
+    Llm,
+    Llmollama,
     Padim,
     Patchcore,
     ReverseDistillation,
@@ -59,6 +63,10 @@ class UnknownModelError(ModuleNotFoundError):
     "Uflow",
     "AiVad",
     "WinClip",
+    "Llm",
+    "Llmollama",
+    "Llava",
+    "Llavanext",
 ]
 
 logger = logging.getLogger(__name__)

src/anomalib/models/image/__init__.py

@@ -14,6 +14,10 @@
 from .fastflow import Fastflow
 from .fre import Fre
 from .ganomaly import Ganomaly
+from .llava import Llava
+from .llava_next import Llavanext
+from .llm import Llm
+from .llm_ollama import Llmollama
 from .padim import Padim
 from .patchcore import Patchcore
 from .reverse_distillation import ReverseDistillation
@@ -41,4 +45,8 @@
     "Stfpm",
     "Uflow",
     "WinClip",
+    "Llm",
+    "Llmollama",
+    "Llava",
+    "Llavanext",
 ]

src/anomalib/models/image/llava/__init__.py

@@ -0,0 +1,8 @@
+"""Llm model."""
+
+# Copyright (C) 2023-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from .lightning_model import Llava
+
+__all__ = ["Llava"]

src/anomalib/models/image/llava/constants.py

@@ -0,0 +1,13 @@
+CONTROLLER_HEART_BEAT_EXPIRATION = 30
+WORKER_HEART_BEAT_INTERVAL = 15
+
+LOGDIR = "."
+
+# Model Constants
+IGNORE_INDEX = -100
+IMAGE_TOKEN_INDEX = -200
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_IM_END_TOKEN = "<im_end>"
+IMAGE_PLACEHOLDER = "<image-placeholder>"