Add DSR model (#1142)

* added license, init.py and draft readme * added draft DSR files * minor comment update * Implemented dsr model + comments * added dsr discrete model * added defect generation in torch model + dsr to list of existing methods in init.py * fixed torch model, started implementing lightning model, implemented anomaly generator * added loss file for DSR * Added loss, improved lightning module * Finished up global implementation of DSR second phase * minor fixes * Bugfixes * Fixed DSR loss calculation * on_training_start -> on_train_start * pre-commit run * updated DSR documentation * reset config file * added automatic pretraining weight download * testing pretrained weights. fixed embedding size in upsampling module and image recon module, to be fixed in original branch * successful testing on pretrained dsr weights * checked test quality with pretrained weights, fixed anomaly score calculation * training is functional * Fixed training procedure * test still working * working upsampling module training and testing * fixed minor bugs * updated documentation * added tests and doc * adapted learning schedule to steps * Update src/anomalib/models/dsr/anomaly_generator.py Co-authored-by: Ashwin Vaidya <ashwinnitinvaidya@gmail.com> * Apply suggestions from code review Co-authored-by: Samet Akcay <samet.akcay@intel.com> Co-authored-by: Ashwin Vaidya <ashwinnitinvaidya@gmail.com> * refactored outputs into dicts * remove super() args * changed downloading weights from anomalib releases + minor fixes * pre commit hooks + minor fixes * removed configurable ckpt path refs + default iteration nb from paper * cleaned up dsr.rst and turned exceptions into RuntimeErrors * Added upsampling ratio parameter to set third training phase epochs * Added batched evalaution + minor code simplification * pre commit hooks * squeeze output image score tensor * readded new path check in efficient ad * fixed double step count with manual optimization * fixed trailing whitespace * Fix black issues * Apply suggestions from code review Co-authored-by: Samet Akcay <samet.akcay@intel.com> * review suggestions * updated architecture image links * Address mypy * changed output types for dsr model * readded dict outputs, adapted to TorchInferencer * fixed error in output dict * removed default imagenet norm --------- Co-authored-by: Samet Akcay <samet.akcay@intel.com> Co-authored-by: Ashwin Vaidya <ashwinnitinvaidya@gmail.com>
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diff --git a/README.md b/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)

diff --git a/docs/source/images/dsr/architecture.png b/docs/source/images/dsr/architecture.png
diff --git a/docs/source/reference_guide/algorithms/dsr.rst b/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:
diff --git a/docs/source/reference_guide/algorithms/index.rst b/docs/source/reference_guide/algorithms/index.rst
@@ -11,6 +11,7 @@ Algorithms
    dfkde
    dfm
    draem
+   dsr
    fastflow
    ganomaly
    padim

diff --git a/src/anomalib/models/__init__.py b/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",

diff --git a/src/anomalib/models/dsr/LICENSE b/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
+
+Original license:
+----------------
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diff --git a/src/anomalib/models/dsr/README.md b/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.
diff --git a/src/anomalib/models/dsr/__init__.py b/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"]