feat(model): Add INP-Former finished

src/anomalib/models/__init__.py

@@ -76,6 +76,7 @@
     Fre,
     Ganomaly,
     GeneralAD,
+    InpFormer,
     Padim,
     Patchcore,
     Patchflow,
@@ -119,6 +120,7 @@ class UnknownModelError(ModuleNotFoundError):
     "Fuvas",
     "Ganomaly",
     "GeneralAD",
+    "InpFormer",
     "L2BT",
     "Padim",
     "Patchcore",

src/anomalib/models/image/__init__.py

@@ -64,6 +64,7 @@
 from .fre import Fre
 from .ganomaly import Ganomaly
 from .general_ad import GeneralAD
+from .inp_former import InpFormer
 from .l2bt import L2BT
 from .padim import Padim
 from .patchcore import Patchcore
@@ -91,6 +92,7 @@
     "Fre",
     "Ganomaly",
     "GeneralAD",
+    "InpFormer",
     "L2BT",
     "Padim",
     "Patchcore",

src/anomalib/models/image/inp_former/LICENSE

@@ -0,0 +1,29 @@
+Copyright (c) 2026 Intel Corporation
+SPDX-License-Identifier: Apache-2.0
+
+Some files in this folder are based on the original INP-Former implementation by Wei Luo
+
+Original license
+----------------
+
+    MIT License
+
+    Copyright (c) 2025 Wei Luo
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE.

src/anomalib/models/image/inp_former/README.md

@@ -0,0 +1,5 @@
+# Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
+
+This is the implementation of the [INP-Former](https://arxiv.org/abs/2503.02424) paper.
+
+Model Type: Segmentation

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

@@ -0,0 +1,43 @@
+# Copyright (C) 2026 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""INP-Former: Anomaly Detection via Intrinsic Normal Prototypes.
+
+The INP-Former model implements an encoder-decoder architecture for anomaly detection
+that extracts Intrinsic Normal Prototypes (INPs) directly from each test image, rather
+than relying on prototypes stored from the training set. Features from a frozen
+pre-trained Vision Transformer encoder are aggregated by an INP Extractor (cross-attention
+with learnable query tokens) into a small set of INPs per image, fused through a
+bottleneck, and reconstructed by an INP-Guided Decoder that uses the INPs as keys and
+values to constrain its output to normal patterns.
+
+Anomaly detection is performed by computing the per-token discrepancy between encoder
+and decoder features at multiple scales. Because the INPs are derived from the test
+image itself, the normality reference is naturally aligned with the input, making the
+model effective across single-class, multi-class, and few-shot anomaly detection
+settings.
+
+Example:
+    >>> from anomalib.models.image import InpFormer
+    >>> model = InpFormer()
+
+The model can be used with any of the supported datasets and task modes in
+anomalib. It combines pre-trained Vision Transformer features with image-specific
+prototype extraction for robust, well-aligned anomaly detection.
+
+Notes:
+    - Uses a frozen pre-trained Vision Transformer as the backbone encoder
+    - Intrinsic Normal Prototypes are extracted dynamically from each image
+    - INP Coherence Loss ensures prototypes faithfully represent normal features
+    - Soft Mining Loss upweights hard-to-reconstruct tokens during training
+    - Supports both anomaly detection and localization tasks
+    - Requires significant GPU memory due to Vision Transformer architecture
+
+See Also:
+    :class:`anomalib.models.image.inp_former.lightning_model.InpFormer`:
+        Lightning implementation of the INP-Former model.
+"""
+
+from anomalib.models.image.inp_former.lightning_model import InpFormer
+
+__all__ = ["InpFormer"]

src/anomalib/models/image/inp_former/components/__init__.py

@@ -0,0 +1,17 @@
+# Copyright (C) 2026 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""Components module for INP-Former model.
+
+This module provides all the necessary components for the INP-Former
+architecture.
+"""
+
+# Layer components
+from .layers import AggregationBlock, PrototypeBlock
+
+__all__ = [
+    # Layers
+    "AggregationBlock",
+    "PrototypeBlock",
+]