Add DSR model

README.md

@@ -106,6 +106,7 @@ where the currently available models are:
 - [DFKDE](src/anomalib/models/dfkde)
 - [DFM](src/anomalib/models/dfm)
 - [DRAEM](src/anomalib/models/draem)
+- [DSR](src/anomalib/models/dsr)
 - [EfficientAd](src/anomalib/models/efficient_ad)
 - [FastFlow](src/anomalib/models/fastflow)
 - [GANomaly](src/anomalib/models/ganomaly)

docs/source/reference_guide/algorithms/dsr.rst

@@ -0,0 +1,45 @@
+Dsr
+------
+
+This is the implementation of the `DSR <https://link.springer.com/chapter/10.1007/978-3-031-19821-2_31>`_ paper.
+
+Model Type: Segmentation
+
+Description
+***********
+
+DSR is a quantized-feature based algorithm that consists of an autoencoder with one encoder and two decoders, coupled with an anomaly detection module. DSR learns a codebook of quantized representations on ImageNet, which are then used to encode input images. These quantized representations also serve to sample near-in-distribution anomalies, since they do not rely on external datasets. Training takes place in three phases. The encoder and "general object decoder", as well as the codebook, are pretrained on ImageNet. Defects are then generated at the feature level using the codebook on the quantized representations, and are used to train the object-specific decoder as well as the anomaly detection module. In the final phase of training, the upsampling module is trained on simulated image-level smudges in order to output more robust anomaly maps.
+
+Architecture
+************
+
+.. image:: https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/dsr/architecture.png
+    :alt: DSR Architecture
+
+Usage
+*****
+
+.. code-block:: bash
+
+    $ python tools/train.py --model dsr
+
+
+.. automodule:: anomalib.models.dsr.torch_model
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: anomalib.models.dsr.lightning_model
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: anomalib.models.dsr.anomaly_generator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: anomalib.models.dsr.loss
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/reference_guide/algorithms/index.rst

@@ -11,6 +11,7 @@ Algorithms
    dfkde
    dfm
    draem
+   dsr
    fastflow
    ganomaly
    padim

src/anomalib/models/__init__.py

@@ -21,6 +21,7 @@
 from anomalib.models.dfkde import Dfkde
 from anomalib.models.dfm import Dfm
 from anomalib.models.draem import Draem
+from anomalib.models.dsr import Dsr
 from anomalib.models.efficient_ad import EfficientAd
 from anomalib.models.fastflow import Fastflow
 from anomalib.models.ganomaly import Ganomaly
@@ -37,6 +38,7 @@
     "Dfkde",
     "Dfm",
     "Draem",
+    "Dsr",
     "Fastflow",
     "Ganomaly",
     "Padim",

src/anomalib/models/dsr/LICENSE

@@ -0,0 +1,209 @@
+Copyright (c) 2023 Intel Corporation
+SPDX-License-Identifier: Apache-2.0
+
+Some files in this folder are based on the original DSR implementation by VitjanZ
+
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src/anomalib/models/dsr/README.md

@@ -0,0 +1,21 @@
+# DSR – A Dual Subspace Re-Projection Network for Surface Anomaly Detection
+
+This is the implementation of the [DSR](https://link.springer.com/chapter/10.1007/978-3-031-19821-2_31) paper.
+
+Model Type: Segmentation
+
+## Description
+
+DSR is a quantized-feature based algorithm that consists of an autoencoder with one encoder and two decoders, coupled with an anomaly detection module. DSR learns a codebook of quantized representations on ImageNet, which are then used to encode input images. These quantized representations also serve to sample near-in-distribution anomalies, since they do not rely on external datasets. Training takes place in three phases. The encoder and "general object decoder", as well as the codebook, are pretrained on ImageNet. Defects are then generated at the feature level using the codebook on the quantized representations, and are used to train the object-specific decoder as well as the anomaly detection module. In the final phase of training, the upsampling module is trained on simulated image-level smudges in order to output more robust anomaly maps.
+
+## Architecture
+
+![DSR Architecture](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/dsr/architecture.png "DSR Architecture")
+
+## Usage
+
+`python tools/train.py --model dsr`
+
+## Benchmark
+
+Benchmarking results are not yet available for this algorithm. Please check again later.

src/anomalib/models/dsr/__init__.py

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