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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+output_video.mp4 filter=lfs diff=lfs merge=lfs -text
+resources/github_readme/teaser.gif filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 MBZUAI-Metaverse
+
+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
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+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
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

LICENSES/LICENSE

@@ -0,0 +1,20 @@
+Copyright (c) 2024 MBZUAI Metaverse Center
+
+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
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+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.

LICENSES/LICENSE_EG3D

@@ -0,0 +1,99 @@
+Copyright (c) 2021-2022, NVIDIA Corporation & affiliates. All rights
+reserved.
+
+
+NVIDIA Source Code License for EG3D
+
+
+=======================================================================
+
+1. Definitions
+
+"Licensor" means any person or entity that distributes its Work.
+
+"Software" means the original work of authorship made available under
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+"Work" means the Software and any additions to or derivative works of
+the Software that are made available under this License.
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+The terms "reproduce," "reproduction," "derivative works," and
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+link (or bind by name) to the interfaces of, the Work.
+
+Works, including the Software, are "made available" under this License
+by including in or with the Work either (a) a copyright notice
+referencing the applicability of this License to the Work, or (b) a
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+2. License Grants
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+    2.1 Copyright Grant. Subject to the terms and conditions of this
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+3. Limitations
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+    3.1 Redistribution. You may reproduce or distribute the Work only
+    if (a) you do so under this License, (b) you include a complete
+    copy of this License with your distribution, and (c) you retain
+    without modification any copyright, patent, trademark, or
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+
+    3.2 Derivative Works. You may specify that additional or different
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+    3.4 Patent Claims. If you bring or threaten to bring a patent claim
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+    counterclaim in a lawsuit) to enforce any patents that you allege
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+    3.5 Trademarks. This License does not grant any rights to use any
+    Licensor’s or its affiliates’ names, logos, or trademarks, except
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+    3.6 Termination. If you violate any term of this License, then your
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+
+EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
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+
+=======================================================================

LICENSES/LICENSE_GFPGAN

@@ -0,0 +1,351 @@
+Tencent is pleased to support the open source community by making GFPGAN available.
+
+Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
+
+GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
+
+
+Terms of the Apache License Version 2.0:
+---------------------------------------------
+Apache License
+
+Version 2.0, January 2004
+
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+1. Definitions.
+
+“License” shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
+“Licensor” shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
+
+“Legal Entity” shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, “control” means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
+
+“You” (or “Your”) shall mean an individual or Legal Entity exercising permissions granted by this License.
+
+“Source” form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
+
+“Object” form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
+
+“Work” shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
+
+“Derivative Works” shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
+
+“Contribution” shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, “submitted” means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as “Not a Contribution.”
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+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
+
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+
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+
+9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
+
+
+
+Other  dependencies and licenses:
+
+
+Open Source Software licensed under the Apache 2.0 license and Other Licenses of the Third-Party Components therein:
+---------------------------------------------
+1. basicsr
+Copyright 2018-2020 BasicSR Authors
+
+
+This BasicSR project is released under the Apache 2.0 license.
+
+A copy of Apache 2.0 is included in this file.
+
+StyleGAN2
+The codes are modified from the repository stylegan2-pytorch. Many thanks to the author - Kim Seonghyeon 😊 for translating from the official TensorFlow codes to PyTorch ones. Here is the license of stylegan2-pytorch.
+The official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.
+DFDNet
+The codes are largely modified from the repository DFDNet. Their license is Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
+
+Terms of the Nvidia License:
+---------------------------------------------
+
+1. Definitions
+
+"Licensor" means any person or entity that distributes its Work.
+
+"Software" means the original work of authorship made available under
+this License.
+
+"Work" means the Software and any additions to or derivative works of
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+
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+application-specific integrated circuit (ASIC) or any combination
+thereof designed, made, sold, or provided by Nvidia or its affiliates.
+
+The terms "reproduce," "reproduction," "derivative works," and
+"distribution" have the meaning as provided under U.S. copyright law;
+provided, however, that for the purposes of this License, derivative
+works shall not include works that remain separable from, or merely
+link (or bind by name) to the interfaces of, the Work.
+
+Works, including the Software, are "made available" under this License
+by including in or with the Work either (a) a copyright notice
+referencing the applicability of this License to the Work, or (b) a
+copy of this License.
+
+2. License Grants
+
+    2.1 Copyright Grant. Subject to the terms and conditions of this
+    License, each Licensor grants to you a perpetual, worldwide,
+    non-exclusive, royalty-free, copyright license to reproduce,
+    prepare derivative works of, publicly display, publicly perform,
+    sublicense and distribute its Work and any resulting derivative
+    works in any form.
+
+3. Limitations
+
+    3.1 Redistribution. You may reproduce or distribute the Work only
+    if (a) you do so under this License, (b) you include a complete
+    copy of this License with your distribution, and (c) you retain
+    without modification any copyright, patent, trademark, or
+    attribution notices that are present in the Work.
+
+    3.2 Derivative Works. You may specify that additional or different
+    terms apply to the use, reproduction, and distribution of your
+    derivative works of the Work ("Your Terms") only if (a) Your Terms
+    provide that the use limitation in Section 3.3 applies to your
+    derivative works, and (b) you identify the specific derivative
+    works that are subject to Your Terms. Notwithstanding Your Terms,
+    this License (including the redistribution requirements in Section
+    3.1) will continue to apply to the Work itself.
+
+    3.3 Use Limitation. The Work and any derivative works thereof only
+    may be used or intended for use non-commercially. The Work or
+    derivative works thereof may be used or intended for use by Nvidia
+    or its affiliates commercially or non-commercially. As used herein,
+    "non-commercially" means for research or evaluation purposes only.
+
+    3.4 Patent Claims. If you bring or threaten to bring a patent claim
+    against any Licensor (including any claim, cross-claim or
+    counterclaim in a lawsuit) to enforce any patents that you allege
+    are infringed by any Work, then your rights under this License from
+    such Licensor (including the grants in Sections 2.1 and 2.2) will
+    terminate immediately.
+
+    3.5 Trademarks. This License does not grant any rights to use any
+    Licensor's or its affiliates' names, logos, or trademarks, except
+    as necessary to reproduce the notices described in this License.
+
+    3.6 Termination. If you violate any term of this License, then your
+    rights under this License (including the grants in Sections 2.1 and
+    2.2) will terminate immediately.
+
+4. Disclaimer of Warranty.
+
+THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
+NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
+THIS LICENSE.
+
+5. Limitation of Liability.
+
+EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
+THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
+SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
+INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
+OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
+(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
+LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
+COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGES.
+
+MIT License
+
+Copyright (c) 2019 Kim Seonghyeon
+
+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.
+
+
+
+Open Source Software licensed under the BSD 3-Clause license:
+---------------------------------------------
+1. torchvision
+Copyright (c) Soumith Chintala 2016,
+All rights reserved.
+
+2. torch
+Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+
+Terms of the BSD 3-Clause License:
+---------------------------------------------
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+
+Open Source Software licensed under the BSD 3-Clause License and Other Licenses of the Third-Party Components therein:
+---------------------------------------------
+1. numpy
+Copyright (c) 2005-2020, NumPy Developers.
+All rights reserved.
+
+A copy of BSD 3-Clause License is included in this file.
+
+The NumPy repository and source distributions bundle several libraries that are
+compatibly licensed.  We list these here.
+
+Name: Numpydoc
+Files: doc/sphinxext/numpydoc/*
+License: BSD-2-Clause
+  For details, see doc/sphinxext/LICENSE.txt
+
+Name: scipy-sphinx-theme
+Files: doc/scipy-sphinx-theme/*
+License: BSD-3-Clause AND PSF-2.0 AND Apache-2.0
+  For details, see doc/scipy-sphinx-theme/LICENSE.txt
+
+Name: lapack-lite
+Files: numpy/linalg/lapack_lite/*
+License: BSD-3-Clause
+  For details, see numpy/linalg/lapack_lite/LICENSE.txt
+
+Name: tempita
+Files: tools/npy_tempita/*
+License: MIT
+  For details, see tools/npy_tempita/license.txt
+
+Name: dragon4
+Files: numpy/core/src/multiarray/dragon4.c
+License: MIT
+  For license text, see numpy/core/src/multiarray/dragon4.c
+
+
+
+Open Source Software licensed under the MIT license:
+---------------------------------------------
+1. facexlib
+Copyright (c) 2020 Xintao Wang
+
+2. opencv-python
+Copyright (c) Olli-Pekka Heinisuo
+Please note that only files in cv2 package are used.
+
+
+Terms of the MIT License:
+---------------------------------------------
+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.
+
+
+
+Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:
+---------------------------------------------
+1. tqdm
+Copyright (c) 2013 noamraph
+
+`tqdm` is a product of collaborative work.
+Unless otherwise stated, all authors (see commit logs) retain copyright
+for their respective work, and release the work under the MIT licence
+(text below).
+
+Exceptions or notable authors are listed below
+in reverse chronological order:
+
+* files: *
+  MPLv2.0 2015-2020 (c) Casper da Costa-Luis
+  [casperdcl](https://github.com/casperdcl).
+* files: tqdm/_tqdm.py
+  MIT 2016 (c) [PR #96] on behalf of Google Inc.
+* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore
+  MIT 2013 (c) Noam Yorav-Raphael, original author.
+
+[PR #96]: https://github.com/tqdm/tqdm/pull/96
+
+
+Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
+-----------------------------------------------
+
+This Source Code Form is subject to the terms of the
+Mozilla Public License, v. 2.0.
+If a copy of the MPL was not distributed with this file,
+You can obtain one at https://mozilla.org/MPL/2.0/.
+
+
+MIT License (MIT)
+-----------------
+
+Copyright (c) 2013 noamraph
+
+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.

LICENSES/LICENSE_MODNET

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
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README.md

@@ -0,0 +1,100 @@
+# [CVPR 2024] VOODOO 3D: <ins>Vo</ins>lumetric P<ins>o</ins>rtrait <ins>D</ins>isentanglement f<ins>o</ins>r <ins>O</ins>ne-Shot 3D Head Reenactment
+
+[![arXiv](https://img.shields.io/badge/arXiv-2312.04651-red?logo=arxiv&logoColor=red)](https://arxiv.org/abs/2312.04651)
+[![youtube](https://img.shields.io/badge/video-Youtube-white?logo=youtube&logoColor=red)](https://arxiv.org/abs/2312.04651)
+[![homepage](https://img.shields.io/badge/project-Homepage-orange?logo=Homepage&logoColor=orange)](https://arxiv.org/abs/2312.04651)
+[![LICENSE](https://img.shields.io/badge/license-MIT-blue?logo=C&logoColor=blue)](https://github.com/MBZUAI-Metaverse/VOODOO3D-official/LICENSE)
+
+![teaser](./resources/github_readme/teaser.gif)
+
+## Overview
+This is the official implementation of VOODOO 3D: a high-fidelity 3D-aware one-shot head reenactment technique. Our method transfers the expression of a driver to a source and produces view consistent renderings for holographic displays.
+
+For more details of the method and experimental results of the project, please checkout our [paper](https://arxiv.org/abs/2312.04651), [youtube video](https://www.youtube.com/watch?v=Gu3oPG0_BaE), or the [project page](https://p0lyfish.github.io/voodoo3d/).
+
+## Installation
+First, clone the project:
+```
+git clone https://github.com/MBZUAI-Metaverse/VOODOO3D-official
+```
+The implementation only requires standard libraries. You can install all the dependencies using conda and pip:
+```
+conda create -n voodoo3d python=3.10 pytorch=2.3.0 torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
+
+pip install -r requirements.txt
+```
+
+Next, prepare the pretrained weights and put them into `./pretrained_models`:
+- Foreground Extractor: Donwload weights provided by [MODNet](https://github.com/ZHKKKe/MODNet) using [this link](https://drive.google.com/file/d/1mcr7ALciuAsHCpLnrtG_eop5-EYhbCmz/view?usp=drive_link)
+- Pose estimation: Download weights provided by [Deep3DFaceRecon_pytorch](https://github.com/sicxu/Deep3DFaceRecon_pytorch) using [this link](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/the_tran_mbzuai_ac_ae/EXlLGrp1Km1EkhObscL8r18BwI39MEq-4QLHb5MQMN0egw?e=gNfQI9)
+- [Our pretrained weights](https://mbzuaiac-my.sharepoint.com/:u:/g/personal/the_tran_mbzuai_ac_ae/ETxx3EQF6QFPkviUD9ivk6EBmdVrE8_0j8qtIi59ThkBBQ?e=UkSCh2)
+
+## Inference
+### 3D Head Reenactment
+Use the following command to test the model:
+```
+python test_voodoo3d.py --source_root <IMAGE_FOLDERS / IMAGE_PATH> \
+                    --driver_root <IMAGE_FOLDERS / IMAGE_PATH> \
+                    --config_path configs/voodoo3d.yml \
+                    --model_path pretrained_models/voodoo3d.pth \
+                    --save_root <SAVE_ROOT> \
+```
+Where `source_root` and `driver_root` are either image folders or image paths of the sources and drivers respectively. `save_root` is the folder root that you want to save the results. This script will generate pairwise reenactment results of the sources and drivers in the input folders / paths. For example, to test with our provided images:
+```
+python test_voodoo3d.py --source_root resources/images/sources \
+                    --driver_root resources/images/drivers \
+                    --config_path configs/voodoo3d.yml \
+                    --model_path pretrained_models/voodoo3d.pth \
+                    --save_root results/voodoo3d_test \
+```
+### Fine-tuned Lp3D for 3D Reconstruction
+[Lp3D](https://research.nvidia.com/labs/nxp/lp3d/) is the state-of-the-art 3D Portrait Reconstruction model. As mentioned in the VOODOO 3D paper, we had a reimplementation of this model but fine-tuned on in-the-wild data. To evaluate this model, use the following script:
+```
+python test_lp3d.py --source_root <IMAGE_FOLDERS / IMAGE_PATH> \
+                    --config_path configs/lp3d.yml \
+                    --model_path pretrained_models/voodoo3d.pth \
+                    --save_root <SAVE_ROOT> \
+                    --cam_batch_size <BATCH_SIZE>
+```
+where `source_root` is either an image folder or an image path of the images that will be reconstructed in 3D. `SAVE_ROOT` is the destination of the results. `BATCH_SIZE` is the testing batch size (the higher, the faster). For each image in the input folder, the model will generate a rendered video of its corresponding 3D head using a fixed camera trajectory. Here is an example using our provided images:
+```
+python test_lp3d.py --source_root resources/images/sources \
+                    --config_path configs/lp3d.yml \
+                    --model_path pretrained_models/voodoo3d.pth \
+                    --save_root results/lp3d_test \
+                    --cam_batch_size 2
+```
+
+## License
+
+Our implementation uses modified versions of other projects that has different licenses. Specifically:
+- GPFGAN and MODNet,  is distributed under Apache License version 2.0.
+- EG3D and SegFormer is distributed under NVIDIA Source Code License.
+
+Other code if not stated otherwise is licensed under the MIT License. See the [LICENSES](LICENSES) file for details.
+
+## Acknowledgements
+This work would not be possible without the following projects:
+
+- [eg3d](https://github.com/NVlabs/eg3d): We used portions of the data preprocessing and the generative model code to synthesize the data during training.
+- [Deep3DFaceRecon_pytorch](https://github.com/sicxu/Deep3DFaceRecon_pytorch): We used portions of this code to predict the camera pose and process the data.
+- [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch): We used portions of DeepLabV3 implementation from this project.
+- [MODNet](https://github.com/ZHKKKe/MODNet): We used portions of the foreground extraction code from this project.
+- [SegFormer](https://github.com/NVlabs/SegFormer): We used portions of the transformer blocks from this project.
+- [GFPGAN](https://github.com/TencentARC/GFPGAN): We used portions of GFPGAN as our super-resolution module
+
+If you see your code used in this implementation but haven't properly acknowledged, please contact me via [[email protected]]([email protected]).
+
+## BibTeX
+If our code is useful for your research or application, please cite our paper:
+```
+@inproceedings{tran2023voodoo,
+	title = {VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment},
+	author = {Tran, Phong and Zakharov, Egor and Ho, Long-Nhat and Tran, Anh Tuan and Hu, Liwen and Li, Hao},
+	year = 2024,
+	booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}
+}
+```
+
+## Contact
+For any questions or issues, please open an issue or contact [[email protected]](mailto:[email protected]).

additional_modules/deep3dfacerecon/BFM/std_exp.txt

@@ -0,0 +1 @@
+453980 257264 263068 211890 135873 184721 47055.6 72732 62787.4 106226 56708.5 51439.8 34887.1 44378.7 51813.4 31030.7 23354.9 23128.1 19400 21827.6 22767.7 22057.4 19894.3 16172.8 17142.7 10035.3 14727.5 12972.5 10763.8 8953.93 8682.62 8941.81 6342.3 5205.3 7065.65 6083.35 6678.88 4666.63 5082.89 5134.76 4908.16 3964.93 3739.95 3180.09 2470.45 1866.62 1624.71 2423.74 1668.53 1471.65 1194.52 782.102 815.044 835.782 834.937 744.496 575.146 633.76 705.685 753.409 620.306 673.326 766.189 619.866 559.93 357.264 396.472 556.849 455.048 460.592 400.735 326.702 279.428 291.535 326.584 305.664 287.816 283.642 276.19

additional_modules/deep3dfacerecon/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Sicheng Xu
+
+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.

additional_modules/deep3dfacerecon/README.md

@@ -0,0 +1,268 @@
+## Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set —— PyTorch implementation ##
+
+<p align="center"> 
+<img src="images/example.gif">
+</p>
+
+This is an unofficial official pytorch implementation of the following paper:
+
+Y. Deng, J. Yang, S. Xu, D. Chen, Y. Jia, and X. Tong, [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://arxiv.org/abs/1903.08527), IEEE Computer Vision and Pattern Recognition Workshop (CVPRW) on Analysis and Modeling of Faces and Gestures (AMFG), 2019. (**_Best Paper Award!_**)
+
+The method enforces a hybrid-level weakly-supervised training for CNN-based 3D face reconstruction. It is fast, accurate, and robust to pose and occlussions. It achieves state-of-the-art performance on multiple datasets such as FaceWarehouse, MICC Florence and NoW Challenge.
+
+
+For the original tensorflow implementation, check this [repo](https://github.com/microsoft/Deep3DFaceReconstruction).
+
+This implementation is written by S. Xu.
+## 04/25/2023 Update
+We updated a new model to improve the results on "closed eye" images. We collected ~2K facial images with closed eyes and included them in the training data. The updated model has similar reconstruction accuracy as the previous one on the benchmarks, but has better results for faces with closed eyes (see below). Here's the [link (google drive)](https://drive.google.com/drive/folders/1grs8J4vu7gOhEClyKjWU-SNxfonGue5F?usp=share_link) to the new model.
+### ● Reconstruction accuracy
+
+|Method|FaceWareHouse|MICC Florence     
+|:----:|:-----------:|:-----------:|
+|Deep3DFace_PyTorch_20230425|1.60±0.44|1.54±0.49|
+
+### ● Visual quality
+<p align="center"> 
+<img src="images/20230425_compare.png">
+</p>
+
+## Performance
+
+### ● Reconstruction accuracy
+
+The pytorch implementation achieves lower shape reconstruction error (9% improvement) compare to the [original tensorflow implementation](https://github.com/microsoft/Deep3DFaceReconstruction). Quantitative evaluation (average shape errors in mm) on several benchmarks is as follows:
+
+|Method|FaceWareHouse|MICC Florence     | NoW Challenge |
+|:----:|:-----------:|:-----------:|:-----------:|
+|Deep3DFace Tensorflow |  1.81±0.50  |  1.67±0.50  | 1.54±1.29 |
+|**Deep3DFace PyTorch** |**1.64±0.50**|**1.53±0.45**| **1.41±1.21** |
+
+The comparison result with state-of-the-art public 3D face reconstruction methods on the NoW face benchmark is as follows:
+|Rank|Method|Median(mm)    | Mean(mm) | Std(mm) |
+|:----:|:-----------:|:-----------:|:-----------:|:-----------:|
+| 1. | [DECA\[Feng et al., SIGGRAPH 2021\]](https://github.com/YadiraF/DECA)|1.09|1.38|1.18|
+| **2.** | **Deep3DFace PyTorch**|**1.11**|**1.41**|**1.21**|
+| 3. | 	[RingNet [Sanyal et al., CVPR 2019]](https://github.com/soubhiksanyal/RingNet) | 1.21 | 1.53 | 1.31 |
+| 4. | [Deep3DFace [Deng et al., CVPRW 2019]](https://github.com/microsoft/Deep3DFaceReconstruction) | 1.23 | 1.54 | 1.29 |
+| 5. | [3DDFA-V2 [Guo et al., ECCV 2020]](https://github.com/cleardusk/3DDFA_V2) | 1.23 | 1.57 | 1.39 |
+| 6. | [MGCNet [Shang et al., ECCV 2020]](https://github.com/jiaxiangshang/MGCNet) | 1.31 | 1.87 | 2.63 |
+| 7. | [PRNet [Feng et al., ECCV 2018]](https://github.com/YadiraF/PRNet) | 1.50 | 1.98 | 1.88 |
+| 8. | [3DMM-CNN [Tran et al., CVPR 2017]](https://github.com/anhttran/3dmm_cnn) | 1.84 | 2.33 | 2.05 |
+
+For more details about the evaluation, check [Now Challenge](https://ringnet.is.tue.mpg.de/challenge.html) website.
+
+**_A recent benchmark [REALY](https://www.realy3dface.com/) indicates that our method still has the SOTA performance! You can check their paper and website for more details._**
+
+### ● Visual quality
+The pytorch implementation achieves better visual consistency with the input images compare to the original tensorflow version.
+
+<p align="center"> 
+<img src="images/compare.png">
+</p>
+
+### ● Speed
+The training speed is on par with the original tensorflow implementation. For more information, see [here](https://github.com/sicxu/Deep3DFaceRecon_pytorch#train-the-face-reconstruction-network).
+
+## Major changes
+
+### ● Differentiable renderer
+
+We use [Nvdiffrast](https://nvlabs.github.io/nvdiffrast/) which is a pytorch library that provides high-performance primitive operations for rasterization-based differentiable rendering. The original tensorflow implementation used [tf_mesh_renderer](https://github.com/google/tf_mesh_renderer) instead.
+
+### ● Face recognition model
+
+We use [Arcface](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch), a state-of-the-art face recognition model, for perceptual loss computation. By contrast, the original tensorflow implementation used [Facenet](https://github.com/davidsandberg/facenet).
+
+### ● Training configuration
+
+Data augmentation is used in the training process which contains random image shifting, scaling, rotation, and flipping. We also enlarge the training batchsize from 5 to 32 to stablize the training process. 
+
+### ● Training data
+
+We use an extra high quality face image dataset [FFHQ](https://github.com/NVlabs/ffhq-dataset) to increase the diversity of training data.
+
+## Requirements
+**This implementation is only tested under Ubuntu environment with Nvidia GPUs and CUDA installed.** But it should also work on Windows with proper lib configures.
+
+## Installation
+1. Clone the repository and set up a conda environment with all dependencies as follows:
+```
+git clone https://github.com/sicxu/Deep3DFaceRecon_pytorch.git
+cd Deep3DFaceRecon_pytorch
+conda env create -f environment.yml
+source activate deep3d_pytorch
+```
+
+2. Install Nvdiffrast library:
+```
+git clone https://github.com/NVlabs/nvdiffrast
+cd nvdiffrast    # ./Deep3DFaceRecon_pytorch/nvdiffrast
+pip install .
+```
+
+3. Install Arcface Pytorch:
+```
+cd ..    # ./Deep3DFaceRecon_pytorch
+git clone https://github.com/deepinsight/insightface.git
+cp -r ./insightface/recognition/arcface_torch ./models/
+```
+## Inference with a pre-trained model
+
+### Prepare prerequisite models
+1. Our method uses [Basel Face Model 2009 (BFM09)](https://faces.dmi.unibas.ch/bfm/main.php?nav=1-0&id=basel_face_model) to represent 3d faces. Get access to BFM09 using this [link](https://faces.dmi.unibas.ch/bfm/main.php?nav=1-2&id=downloads). After getting the access, download "01_MorphableModel.mat". In addition, we use an Expression Basis provided by [Guo et al.](https://github.com/Juyong/3DFace). Download the Expression Basis (Exp_Pca.bin) using this [link (google drive)](https://drive.google.com/file/d/1bw5Xf8C12pWmcMhNEu6PtsYVZkVucEN6/view?usp=sharing). Organize all files into the following structure:
+```
+Deep3DFaceRecon_pytorch
+│
+└─── BFM
+    │
+    └─── 01_MorphableModel.mat
+    │
+    └─── Exp_Pca.bin
+    |
+    └─── ...
+```
+2. We provide a model trained on a combination of [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html), 
+[LFW](http://vis-www.cs.umass.edu/lfw/), [300WLP](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm),
+[IJB-A](https://www.nist.gov/programs-projects/face-challenges), [LS3D-W](https://www.adrianbulat.com/face-alignment), and [FFHQ](https://github.com/NVlabs/ffhq-dataset) datasets. Download the pre-trained model using this [link (google drive)](https://drive.google.com/drive/folders/1liaIxn9smpudjjqMaWWRpP0mXRW_qRPP?usp=sharing) and organize the directory into the following structure:
+```
+Deep3DFaceRecon_pytorch
+│
+└─── checkpoints
+    │
+    └─── <model_name>
+        │
+        └─── epoch_20.pth
+
+```
+
+### Test with custom images
+To reconstruct 3d faces from test images, organize the test image folder as follows:
+```
+Deep3DFaceRecon_pytorch
+│
+└─── <folder_to_test_images>
+    │
+    └─── *.jpg/*.png
+    |
+    └─── detections
+        |
+	└─── *.txt
+```
+The \*.jpg/\*.png files are test images. The \*.txt files are detected 5 facial landmarks with a shape of 5x2, and have the same name as the corresponding images. Check [./datasets/examples](datasets/examples) for a reference.
+
+Then, run the test script:
+```
+# get reconstruction results of your custom images
+python test.py --name=<model_name> --epoch=20 --img_folder=<folder_to_test_images>
+
+# get reconstruction results of example images
+python test.py --name=<model_name> --epoch=20 --img_folder=./datasets/examples
+```
+**_Following [#108](https://github.com/sicxu/Deep3DFaceRecon_pytorch/issues/108), if you don't have OpenGL environment, you can simply add "--use_opengl False" to use CUDA context. Make sure you have updated the nvdiffrast to the latest version._**
+
+Results will be saved into ./checkpoints/<model_name>/results/<folder_to_test_images>, which contain the following files:
+| \*.png | A combination of cropped input image, reconstructed image, and visualization of projected landmarks.
+|:----|:-----------|
+| \*.obj | Reconstructed 3d face mesh with predicted color (texture+illumination) in the world coordinate space. Best viewed in Meshlab. |
+| \*.mat | Predicted 257-dimensional coefficients and 68 projected 2d facial landmarks. Best viewed in Matlab.
+
+## Training a model from scratch
+### Prepare prerequisite models
+1. We rely on [Arcface](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch) to extract identity features for loss computation. Download the pre-trained model from Arcface using this [link](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch#ms1mv3). By default, we use the resnet50 backbone ([ms1mv3_arcface_r50_fp16](https://onedrive.live.com/?authkey=%21AFZjr283nwZHqbA&id=4A83B6B633B029CC%215583&cid=4A83B6B633B029CC)), organize the download files into the following structure:
+```
+Deep3DFaceRecon_pytorch
+│
+└─── checkpoints
+    │
+    └─── recog_model
+        │
+        └─── ms1mv3_arcface_r50_fp16
+	    |
+	    └─── backbone.pth
+```
+2. We initialize R-Net using the weights trained on [ImageNet](https://image-net.org/). Download the weights provided by PyTorch using this [link](https://download.pytorch.org/models/resnet50-0676ba61.pth), and organize the file as the following structure:
+```
+Deep3DFaceRecon_pytorch
+│
+└─── checkpoints
+    │
+    └─── init_model
+        │
+        └─── resnet50-0676ba61.pth
+```
+3. We provide a landmark detector (tensorflow model) to extract 68 facial landmarks for loss computation. The detector is trained on [300WLP](http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/main.htm), [LFW](http://vis-www.cs.umass.edu/lfw/), and [LS3D-W](https://www.adrianbulat.com/face-alignment) datasets. Download the trained model using this [link (google drive)](https://drive.google.com/file/d/1Jl1yy2v7lIJLTRVIpgg2wvxYITI8Dkmw/view?usp=sharing) and organize the file as follows:
+```
+Deep3DFaceRecon_pytorch
+│
+└─── checkpoints
+    │
+    └─── lm_model
+        │
+        └─── 68lm_detector.pb
+```
+### Data preparation
+1. To train a model with custom images，5 facial landmarks of each image are needed in advance for an image pre-alignment process. We recommend using [dlib](http://dlib.net/) or [MTCNN](https://github.com/ipazc/mtcnn) to detect these landmarks. Then, organize all files into the following structure:
+```
+Deep3DFaceRecon_pytorch
+│
+└─── datasets
+    │
+    └─── <folder_to_training_images>
+        │
+        └─── *.png/*.jpg
+	|
+	└─── detections
+            |
+	    └─── *.txt
+```
+The \*.txt files contain 5 facial landmarks with a shape of 5x2, and should have the same name with their corresponding images.
+
+2. Generate 68 landmarks and skin attention mask for images using the following script:
+```
+# preprocess training images
+python data_preparation.py --img_folder <folder_to_training_images>
+
+# alternatively, you can preprocess multiple image folders simultaneously
+python data_preparation.py --img_folder <folder_to_training_images1> <folder_to_training_images2> <folder_to_training_images3>
+
+# preprocess validation images
+python data_preparation.py --img_folder <folder_to_validation_images> --mode=val
+```
+The script will generate files of landmarks and skin masks, and save them into ./datasets/<folder_to_training_images>. In addition, it also generates a file containing the path of all training data into ./datalist which will then be used in the training script.
+
+### Train the face reconstruction network
+Run the following script to train a face reconstruction model using the pre-processed data:
+```
+# train with single GPU
+python train.py --name=<custom_experiment_name> --gpu_ids=0
+
+# train with multiple GPUs
+python train.py --name=<custom_experiment_name> --gpu_ids=0,1
+
+# train with other custom settings
+python train.py --name=<custom_experiment_name> --gpu_ids=0 --batch_size=32 --n_epochs=20
+```
+Training logs and model parameters will be saved into ./checkpoints/<custom_experiment_name>. 
+
+By default, the script uses a batchsize of 32 and will train the model with 20 epochs. For reference, the pre-trained model in this repo is trained with the default setting on a image collection of 300k images. A single iteration takes 0.8~0.9s on a single Tesla M40 GPU. The total training process takes around two days.
+
+To use a trained model, see [Inference](https://github.com/sicxu/Deep3DFaceRecon_pytorch#inference-with-a-pre-trained-model) section.
+## Contact
+If you have any questions, please contact the paper authors.
+
+## Citation
+
+Please cite the following paper if this model helps your research:
+
+	@inproceedings{deng2019accurate,
+	    title={Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set},
+	    author={Yu Deng and Jiaolong Yang and Sicheng Xu and Dong Chen and Yunde Jia and Xin Tong},
+	    booktitle={IEEE Computer Vision and Pattern Recognition Workshops},
+	    year={2019}
+	}
+##
+The face images on this page are from the public [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset released by MMLab, CUHK.
+
+Part of the code in this implementation takes [CUT](https://github.com/taesungp/contrastive-unpaired-translation) as a reference.
+

additional_modules/deep3dfacerecon/__init__.py

@@ -0,0 +1,3 @@
+"""
+Modified from https://github.com/sicxu/Deep3DFaceRecon_pytorch
+"""

additional_modules/deep3dfacerecon/data/__init__.py

@@ -0,0 +1,116 @@
+"""This package includes all the modules related to data loading and preprocessing
+
+ To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
+ You need to implement four functions:
+    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
+    -- <__len__>:                       return the size of dataset.
+    -- <__getitem__>:                   get a data point from data loader.
+    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
+
+Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
+See our template dataset class 'template_dataset.py' for more details.
+"""
+import numpy as np
+import importlib
+import torch.utils.data
+from data.base_dataset import BaseDataset
+
+
+def find_dataset_using_name(dataset_name):
+    """Import the module "data/[dataset_name]_dataset.py".
+
+    In the file, the class called DatasetNameDataset() will
+    be instantiated. It has to be a subclass of BaseDataset,
+    and it is case-insensitive.
+    """
+    dataset_filename = "data." + dataset_name + "_dataset"
+    datasetlib = importlib.import_module(dataset_filename)
+
+    dataset = None
+    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
+    for name, cls in datasetlib.__dict__.items():
+        if name.lower() == target_dataset_name.lower() \
+           and issubclass(cls, BaseDataset):
+            dataset = cls
+
+    if dataset is None:
+        raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
+
+    return dataset
+
+
+def get_option_setter(dataset_name):
+    """Return the static method <modify_commandline_options> of the dataset class."""
+    dataset_class = find_dataset_using_name(dataset_name)
+    return dataset_class.modify_commandline_options
+
+
+def create_dataset(opt, rank=0):
+    """Create a dataset given the option.
+
+    This function wraps the class CustomDatasetDataLoader.
+        This is the main interface between this package and 'train.py'/'test.py'
+
+    Example:
+        >>> from data import create_dataset
+        >>> dataset = create_dataset(opt)
+    """
+    data_loader = CustomDatasetDataLoader(opt, rank=rank)
+    dataset = data_loader.load_data()
+    return dataset
+
+class CustomDatasetDataLoader():
+    """Wrapper class of Dataset class that performs multi-threaded data loading"""
+
+    def __init__(self, opt, rank=0):
+        """Initialize this class
+
+        Step 1: create a dataset instance given the name [dataset_mode]
+        Step 2: create a multi-threaded data loader.
+        """
+        self.opt = opt
+        dataset_class = find_dataset_using_name(opt.dataset_mode)
+        self.dataset = dataset_class(opt)
+        self.sampler = None
+        print("rank %d %s dataset [%s] was created" % (rank, self.dataset.name, type(self.dataset).__name__))
+        if opt.use_ddp and opt.isTrain:
+            world_size = opt.world_size
+            self.sampler = torch.utils.data.distributed.DistributedSampler(
+                    self.dataset,
+                    num_replicas=world_size,
+                    rank=rank,
+                    shuffle=not opt.serial_batches
+                )
+            self.dataloader = torch.utils.data.DataLoader(
+                        self.dataset,
+                        sampler=self.sampler,
+                        num_workers=int(opt.num_threads / world_size), 
+                        batch_size=int(opt.batch_size / world_size), 
+                        drop_last=True)
+        else:
+            self.dataloader = torch.utils.data.DataLoader(
+                self.dataset,
+                batch_size=opt.batch_size,
+                shuffle=(not opt.serial_batches) and opt.isTrain,
+                num_workers=int(opt.num_threads),
+                drop_last=True
+            )
+
+    def set_epoch(self, epoch):
+        self.dataset.current_epoch = epoch
+        if self.sampler is not None:
+            self.sampler.set_epoch(epoch)
+
+    def load_data(self):
+        return self
+
+    def __len__(self):
+        """Return the number of data in the dataset"""
+        return min(len(self.dataset), self.opt.max_dataset_size)
+
+    def __iter__(self):
+        """Return a batch of data"""
+        for i, data in enumerate(self.dataloader):
+            if i * self.opt.batch_size >= self.opt.max_dataset_size:
+                break
+            yield data

additional_modules/deep3dfacerecon/data/base_dataset.py

@@ -0,0 +1,131 @@
+"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
+
+It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
+"""
+import random
+import numpy as np
+import torch.utils.data as data
+from PIL import Image
+try:
+    from PIL.Image import Resampling
+    RESAMPLING_METHOD = Resampling.BICUBIC
+except ImportError:
+    from PIL.Image import BICUBIC
+    RESAMPLING_METHOD = BICUBIC
+import torchvision.transforms as transforms
+from abc import ABC, abstractmethod
+
+
+class BaseDataset(data.Dataset, ABC):
+    """This class is an abstract base class (ABC) for datasets.
+
+    To create a subclass, you need to implement the following four functions:
+    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
+    -- <__len__>:                       return the size of dataset.
+    -- <__getitem__>:                   get a data point.
+    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
+    """
+
+    def __init__(self, opt):
+        """Initialize the class; save the options in the class
+
+        Parameters:
+            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
+        """
+        self.opt = opt
+        # self.root = opt.dataroot
+        self.current_epoch = 0
+
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        """Add new dataset-specific options, and rewrite default values for existing options.
+
+        Parameters:
+            parser          -- original option parser
+            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
+
+        Returns:
+            the modified parser.
+        """
+        return parser
+
+    @abstractmethod
+    def __len__(self):
+        """Return the total number of images in the dataset."""
+        return 0
+
+    @abstractmethod
+    def __getitem__(self, index):
+        """Return a data point and its metadata information.
+
+        Parameters:
+            index - - a random integer for data indexing
+
+        Returns:
+            a dictionary of data with their names. It ususally contains the data itself and its metadata information.
+        """
+        pass
+
+
+def get_transform(grayscale=False):
+    transform_list = []
+    if grayscale:
+        transform_list.append(transforms.Grayscale(1))
+    transform_list += [transforms.ToTensor()]
+    return transforms.Compose(transform_list)
+
+def get_affine_mat(opt, size):
+    shift_x, shift_y, scale, rot_angle, flip = 0., 0., 1., 0., False
+    w, h = size
+
+    if 'shift' in opt.preprocess:
+        shift_pixs = int(opt.shift_pixs)
+        shift_x = random.randint(-shift_pixs, shift_pixs)
+        shift_y = random.randint(-shift_pixs, shift_pixs)
+    if 'scale' in opt.preprocess:
+        scale = 1 + opt.scale_delta * (2 * random.random() - 1)
+    if 'rot' in opt.preprocess:
+        rot_angle = opt.rot_angle * (2 * random.random() - 1)
+        rot_rad = -rot_angle * np.pi/180
+    if 'flip' in opt.preprocess:
+        flip = random.random() > 0.5
+
+    shift_to_origin = np.array([1, 0, -w//2, 0, 1, -h//2, 0, 0, 1]).reshape([3, 3])
+    flip_mat = np.array([-1 if flip else 1, 0, 0, 0, 1, 0, 0, 0, 1]).reshape([3, 3])
+    shift_mat = np.array([1, 0, shift_x, 0, 1, shift_y, 0, 0, 1]).reshape([3, 3])
+    rot_mat = np.array([np.cos(rot_rad), np.sin(rot_rad), 0, -np.sin(rot_rad), np.cos(rot_rad), 0, 0, 0, 1]).reshape([3, 3])
+    scale_mat = np.array([scale, 0, 0, 0, scale, 0, 0, 0, 1]).reshape([3, 3])
+    shift_to_center = np.array([1, 0, w//2, 0, 1, h//2, 0, 0, 1]).reshape([3, 3])
+    
+    affine = shift_to_center @ scale_mat @ rot_mat @ shift_mat @ flip_mat @ shift_to_origin    
+    affine_inv = np.linalg.inv(affine)
+    return affine, affine_inv, flip
+
+def apply_img_affine(img, affine_inv, method=RESAMPLING_METHOD):
+    return img.transform(img.size, Image.AFFINE, data=affine_inv.flatten()[:6], resample=RESAMPLING_METHOD)
+
+def apply_lm_affine(landmark, affine, flip, size):
+    _, h = size
+    lm = landmark.copy()
+    lm[:, 1] = h - 1 - lm[:, 1]
+    lm = np.concatenate((lm, np.ones([lm.shape[0], 1])), -1)
+    lm = lm @ np.transpose(affine)
+    lm[:, :2] = lm[:, :2] / lm[:, 2:]
+    lm = lm[:, :2]
+    lm[:, 1] = h - 1 - lm[:, 1]
+    if flip:
+        lm_ = lm.copy()
+        lm_[:17] = lm[16::-1]
+        lm_[17:22] = lm[26:21:-1]
+        lm_[22:27] = lm[21:16:-1]
+        lm_[31:36] = lm[35:30:-1]
+        lm_[36:40] = lm[45:41:-1]
+        lm_[40:42] = lm[47:45:-1]
+        lm_[42:46] = lm[39:35:-1]
+        lm_[46:48] = lm[41:39:-1]
+        lm_[48:55] = lm[54:47:-1]
+        lm_[55:60] = lm[59:54:-1]
+        lm_[60:65] = lm[64:59:-1]
+        lm_[65:68] = lm[67:64:-1]
+        lm = lm_
+    return lm

additional_modules/deep3dfacerecon/data/flist_dataset.py

@@ -0,0 +1,125 @@
+"""This script defines the custom dataset for Deep3DFaceRecon_pytorch
+"""
+
+import os.path
+from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
+from data.image_folder import make_dataset
+from PIL import Image
+import random
+import util.util as util
+import numpy as np
+import json
+import torch
+from scipy.io import loadmat, savemat
+import pickle
+from util.preprocess import align_img, estimate_norm
+from util.load_mats import load_lm3d
+
+
+def default_flist_reader(flist):
+    """
+    flist format: impath label\nimpath label\n ...(same to caffe's filelist)
+    """
+    imlist = []
+    with open(flist, 'r') as rf:
+        for line in rf.readlines():
+            impath = line.strip()
+            imlist.append(impath)
+
+    return imlist
+
+def jason_flist_reader(flist):
+    with open(flist, 'r') as fp:
+        info = json.load(fp)
+    return info
+
+def parse_label(label):
+    return torch.tensor(np.array(label).astype(np.float32))
+
+
+class FlistDataset(BaseDataset):
+    """
+    It requires one directories to host training images '/path/to/data/train'
+    You can train the model with the dataset flag '--dataroot /path/to/data'.
+    """
+
+    def __init__(self, opt):
+        """Initialize this dataset class.
+
+        Parameters:
+            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
+        """
+        BaseDataset.__init__(self, opt)
+        
+        self.lm3d_std = load_lm3d(opt.bfm_folder)
+        
+        msk_names = default_flist_reader(opt.flist)
+        self.msk_paths = [os.path.join(opt.data_root, i) for i in msk_names]
+
+        self.size = len(self.msk_paths) 
+        self.opt = opt
+        
+        self.name = 'train' if opt.isTrain else 'val'
+        if '_' in opt.flist:
+            self.name += '_' + opt.flist.split(os.sep)[-1].split('_')[0]
+        
+
+    def __getitem__(self, index):
+        """Return a data point and its metadata information.
+
+        Parameters:
+            index (int)      -- a random integer for data indexing
+
+        Returns a dictionary that contains A, B, A_paths and B_paths
+            img (tensor)       -- an image in the input domain
+            msk (tensor)       -- its corresponding attention mask
+            lm  (tensor)       -- its corresponding 3d landmarks
+            im_paths (str)     -- image paths
+            aug_flag (bool)    -- a flag used to tell whether its raw or augmented
+        """
+        msk_path = self.msk_paths[index % self.size]  # make sure index is within then range
+        img_path = msk_path.replace('mask/', '')
+        lm_path = '.'.join(msk_path.replace('mask', 'landmarks').split('.')[:-1]) + '.txt'
+
+        raw_img = Image.open(img_path).convert('RGB')
+        raw_msk = Image.open(msk_path).convert('RGB')
+        raw_lm = np.loadtxt(lm_path).astype(np.float32)
+
+        _, img, lm, msk = align_img(raw_img, raw_lm, self.lm3d_std, raw_msk)
+        
+        aug_flag = self.opt.use_aug and self.opt.isTrain
+        if aug_flag:
+            img, lm, msk = self._augmentation(img, lm, self.opt, msk)
+        
+        _, H = img.size
+        M = estimate_norm(lm, H)
+        transform = get_transform()
+        img_tensor = transform(img)
+        msk_tensor = transform(msk)[:1, ...]
+        lm_tensor = parse_label(lm)
+        M_tensor = parse_label(M)
+
+
+        return {'imgs': img_tensor, 
+                'lms': lm_tensor, 
+                'msks': msk_tensor, 
+                'M': M_tensor,
+                'im_paths': img_path, 
+                'aug_flag': aug_flag,
+                'dataset': self.name}
+
+    def _augmentation(self, img, lm, opt, msk=None):
+        affine, affine_inv, flip = get_affine_mat(opt, img.size)
+        img = apply_img_affine(img, affine_inv)
+        lm = apply_lm_affine(lm, affine, flip, img.size)
+        if msk is not None:
+            msk = apply_img_affine(msk, affine_inv, method=Image.BILINEAR)
+        return img, lm, msk
+    
+
+
+
+    def __len__(self):
+        """Return the total number of images in the dataset.
+        """
+        return self.size

additional_modules/deep3dfacerecon/data/image_folder.py

@@ -0,0 +1,66 @@
+"""A modified image folder class
+
+We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
+so that this class can load images from both current directory and its subdirectories.
+"""
+import numpy as np
+import torch.utils.data as data
+
+from PIL import Image
+import os
+import os.path
+
+IMG_EXTENSIONS = [
+    '.jpg', '.JPG', '.jpeg', '.JPEG',
+    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
+    '.tif', '.TIF', '.tiff', '.TIFF',
+]
+
+
+def is_image_file(filename):
+    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+def make_dataset(dir, max_dataset_size=float("inf")):
+    images = []
+    assert os.path.isdir(dir) or os.path.islink(dir), '%s is not a valid directory' % dir
+
+    for root, _, fnames in sorted(os.walk(dir, followlinks=True)):
+        for fname in fnames:
+            if is_image_file(fname):
+                path = os.path.join(root, fname)
+                images.append(path)
+    return images[:min(max_dataset_size, len(images))]
+
+
+def default_loader(path):
+    return Image.open(path).convert('RGB')
+
+
+class ImageFolder(data.Dataset):
+
+    def __init__(self, root, transform=None, return_paths=False,
+                 loader=default_loader):
+        imgs = make_dataset(root)
+        if len(imgs) == 0:
+            raise(RuntimeError("Found 0 images in: " + root + "\n"
+                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
+
+        self.root = root
+        self.imgs = imgs
+        self.transform = transform
+        self.return_paths = return_paths
+        self.loader = loader
+
+    def __getitem__(self, index):
+        path = self.imgs[index]
+        img = self.loader(path)
+        if self.transform is not None:
+            img = self.transform(img)
+        if self.return_paths:
+            return img, path
+        else:
+            return img
+
+    def __len__(self):
+        return len(self.imgs)

additional_modules/deep3dfacerecon/data/template_dataset.py

@@ -0,0 +1,75 @@
+"""Dataset class template
+
+This module provides a template for users to implement custom datasets.
+You can specify '--dataset_mode template' to use this dataset.
+The class name should be consistent with both the filename and its dataset_mode option.
+The filename should be <dataset_mode>_dataset.py
+The class name should be <Dataset_mode>Dataset.py
+You need to implement the following functions:
+    -- <modify_commandline_options>:　Add dataset-specific options and rewrite default values for existing options.
+    -- <__init__>: Initialize this dataset class.
+    -- <__getitem__>: Return a data point and its metadata information.
+    -- <__len__>: Return the number of images.
+"""
+from data.base_dataset import BaseDataset, get_transform
+# from data.image_folder import make_dataset
+# from PIL import Image
+
+
+class TemplateDataset(BaseDataset):
+    """A template dataset class for you to implement custom datasets."""
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        """Add new dataset-specific options, and rewrite default values for existing options.
+
+        Parameters:
+            parser          -- original option parser
+            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
+
+        Returns:
+            the modified parser.
+        """
+        parser.add_argument('--new_dataset_option', type=float, default=1.0, help='new dataset option')
+        parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0)  # specify dataset-specific default values
+        return parser
+
+    def __init__(self, opt):
+        """Initialize this dataset class.
+
+        Parameters:
+            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
+
+        A few things can be done here.
+        - save the options (have been done in BaseDataset)
+        - get image paths and meta information of the dataset.
+        - define the image transformation.
+        """
+        # save the option and dataset root
+        BaseDataset.__init__(self, opt)
+        # get the image paths of your dataset;
+        self.image_paths = []  # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
+        # define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
+        self.transform = get_transform(opt)
+
+    def __getitem__(self, index):
+        """Return a data point and its metadata information.
+
+        Parameters:
+            index -- a random integer for data indexing
+
+        Returns:
+            a dictionary of data with their names. It usually contains the data itself and its metadata information.
+
+        Step 1: get a random image path: e.g., path = self.image_paths[index]
+        Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB').
+        Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image)
+        Step 4: return a data point as a dictionary.
+        """
+        path = 'temp'    # needs to be a string
+        data_A = None    # needs to be a tensor
+        data_B = None    # needs to be a tensor
+        return {'data_A': data_A, 'data_B': data_B, 'path': path}
+
+    def __len__(self):
+        """Return the total number of images."""
+        return len(self.image_paths)

additional_modules/deep3dfacerecon/data_preparation.py

@@ -0,0 +1,45 @@
+"""This script is the data preparation script for Deep3DFaceRecon_pytorch
+"""
+
+import os 
+import numpy as np
+import argparse
+from util.detect_lm68 import detect_68p,load_lm_graph
+from util.skin_mask import get_skin_mask
+from util.generate_list import check_list, write_list
+import warnings
+warnings.filterwarnings("ignore") 
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--data_root', type=str, default='datasets', help='root directory for training data')
+parser.add_argument('--img_folder', nargs="+", required=True, help='folders of training images')
+parser.add_argument('--mode', type=str, default='train', help='train or val')
+opt = parser.parse_args()
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+def data_prepare(folder_list,mode):
+
+    lm_sess,input_op,output_op = load_lm_graph('./checkpoints/lm_model/68lm_detector.pb') # load a tensorflow version 68-landmark detector
+
+    for img_folder in folder_list:
+        detect_68p(img_folder,lm_sess,input_op,output_op) # detect landmarks for images
+        get_skin_mask(img_folder) # generate skin attention mask for images
+
+    # create files that record path to all training data
+    msks_list = []
+    for img_folder in folder_list:
+        path = os.path.join(img_folder, 'mask')
+        msks_list += ['/'.join([img_folder, 'mask', i]) for i in sorted(os.listdir(path)) if 'jpg' in i or 
+                                                    'png' in i or 'jpeg' in i or 'PNG' in i]
+
+    imgs_list = [i.replace('mask/', '') for i in msks_list]
+    lms_list = [i.replace('mask', 'landmarks') for i in msks_list]
+    lms_list = ['.'.join(i.split('.')[:-1]) + '.txt' for i in lms_list]
+    
+    lms_list_final, imgs_list_final, msks_list_final = check_list(lms_list, imgs_list, msks_list) # check if the path is valid
+    write_list(lms_list_final, imgs_list_final, msks_list_final, mode=mode) # save files
+
+if __name__ == '__main__':
+    print('Datasets:',opt.img_folder)
+    data_prepare([os.path.join(opt.data_root,folder) for folder in opt.img_folder],opt.mode)

additional_modules/deep3dfacerecon/environment.yml

@@ -0,0 +1,24 @@
+name: deep3d_pytorch
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.6
+  - pytorch=1.6.0
+  - torchvision=0.7.0
+  - numpy=1.18.1
+  - scikit-image=0.16.2
+  - scipy=1.4.1
+  - pillow=6.2.1
+  - pip=20.0.2
+  - ipython=7.13.0
+  - yaml=0.1.7
+  - pip:
+    - matplotlib==2.2.5
+    - opencv-python==3.4.9.33
+    - tensorboard==1.15.0
+    - tensorflow==1.15.0
+    - kornia==0.5.5
+    - dominate==2.6.0
+    - trimesh==3.9.20

additional_modules/deep3dfacerecon/models/arcface_torch/README.md

@@ -0,0 +1,218 @@
+# Distributed Arcface Training in Pytorch
+
+The "arcface_torch" repository is the official implementation of the ArcFace algorithm. It supports distributed and sparse training with multiple distributed training examples, including several memory-saving techniques such as mixed precision training and gradient checkpointing. It also supports training for ViT models and datasets including WebFace42M and Glint360K, two of the largest open-source datasets. Additionally, the repository comes with a built-in tool for converting to ONNX format, making it easy to submit to MFR evaluation systems.
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-ijb-c)](https://paperswithcode.com/sota/face-verification-on-ijb-c?p=killing-two-birds-with-one-stone-efficient)  
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-ijb-b)](https://paperswithcode.com/sota/face-verification-on-ijb-b?p=killing-two-birds-with-one-stone-efficient)  
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-agedb-30)](https://paperswithcode.com/sota/face-verification-on-agedb-30?p=killing-two-birds-with-one-stone-efficient)  
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/killing-two-birds-with-one-stone-efficient/face-verification-on-cfp-fp)](https://paperswithcode.com/sota/face-verification-on-cfp-fp?p=killing-two-birds-with-one-stone-efficient)
+
+## Requirements
+
+To avail the latest features of PyTorch, we have upgraded to version 1.12.0.
+
+- Install [PyTorch](https://pytorch.org/get-started/previous-versions/) (torch>=1.12.0).
+- (Optional) Install [DALI](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/), our doc for [install_dali.md](docs/install_dali.md).
+- `pip install -r requirement.txt`.
+  
+## How to Training
+
+To train a model, execute the `train_v2.py` script with the path to the configuration files. The sample commands provided below demonstrate the process of conducting distributed training.
+
+### 1. To run on one GPU:
+
+```shell
+python train_v2.py configs/ms1mv3_r50_onegpu
+```
+
+Note:   
+It is not recommended to use a single GPU for training, as this may result in longer training times and suboptimal performance. For best results, we suggest using multiple GPUs or a GPU cluster.  
+
+
+### 2. To run on a machine with 8 GPUs:
+
+```shell
+torchrun --nproc_per_node=8 train_v2.py configs/ms1mv3_r50
+```
+
+### 3. To run on 2 machines with 8 GPUs each:
+
+Node 0:
+
+```shell
+torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=12581 train_v2.py configs/wf42m_pfc02_16gpus_r100
+```
+
+Node 1:
+  
+```shell
+torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=12581 train_v2.py configs/wf42m_pfc02_16gpus_r100
+```
+
+### 4. Run ViT-B on a machine with 24k batchsize:
+
+```shell
+torchrun --nproc_per_node=8 train_v2.py configs/wf42m_pfc03_40epoch_8gpu_vit_b
+```
+
+
+## Download Datasets or Prepare Datasets  
+- [MS1MV2](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-arcface-85k-ids58m-images-57) (87k IDs, 5.8M images)
+- [MS1MV3](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_#ms1m-retinaface) (93k IDs, 5.2M images)
+- [Glint360K](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc#4-download) (360k IDs, 17.1M images)
+- [WebFace42M](docs/prepare_webface42m.md) (2M IDs, 42.5M images)
+- [Your Dataset, Click Here!](docs/prepare_custom_dataset.md)
+
+Note: 
+If you want to use DALI for data reading, please use the script 'scripts/shuffle_rec.py' to shuffle the InsightFace style rec before using it.  
+Example:
+
+`python scripts/shuffle_rec.py ms1m-retinaface-t1`
+
+You will get the "shuffled_ms1m-retinaface-t1" folder, where the samples in the "train.rec" file are shuffled.
+
+
+## Model Zoo
+
+- The models are available for non-commercial research purposes only.  
+- All models can be found in here.  
+- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw  
+- [OneDrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d)
+
+### Performance on IJB-C and [**ICCV2021-MFR**](https://github.com/deepinsight/insightface/blob/master/challenges/mfr/README.md)
+
+ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face 
+recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. 
+As the result, we can evaluate the FAIR performance for different algorithms.  
+
+For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The 
+globalised multi-racial testset contains 242,143 identities and 1,624,305 images. 
+
+
+#### 1. Training on Single-Host GPU
+
+| Datasets       | Backbone            | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | log                                                                                                                                 |
+|:---------------|:--------------------|:------------|:------------|:------------|:------------------------------------------------------------------------------------------------------------------------------------|
+| MS1MV2         | mobilefacenet-0.45G | 62.07       | 93.61       | 90.28       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_mbf/training.log)                     |
+| MS1MV2         | r50                 | 75.13       | 95.97       | 94.07       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_r50/training.log)                     |
+| MS1MV2         | r100                | 78.12       | 96.37       | 94.27       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv2_r100/training.log)                    |
+| MS1MV3         | mobilefacenet-0.45G | 63.78       | 94.23       | 91.33       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_mbf/training.log)                     |
+| MS1MV3         | r50                 | 79.14       | 96.37       | 94.47       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r50/training.log)                     |
+| MS1MV3         | r100                | 81.97       | 96.85       | 95.02       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_r100/training.log)                    |
+| Glint360K      | mobilefacenet-0.45G | 70.18       | 95.04       | 92.62       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_mbf/training.log)                  |
+| Glint360K      | r50                 | 86.34       | 97.16       | 95.81       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r50/training.log)                  |
+| Glint360k      | r100                | 89.52       | 97.55       | 96.38       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_r100/training.log)                 |
+| WF4M           | r100                | 89.87       | 97.19       | 95.48       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf4m_r100/training.log)                      |
+| WF12M-PFC-0.2  | r100                | 94.75       | 97.60       | 95.90       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_pfc02_r100/training.log)               |
+| WF12M-PFC-0.3  | r100                | 94.71       | 97.64       | 96.01       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_pfc03_r100/training.log)               |
+| WF12M          | r100                | 94.69       | 97.59       | 95.97       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf12m_r100/training.log)                     |
+| WF42M-PFC-0.2  | r100                | 96.27       | 97.70       | 96.31       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf42m_pfc02_r100/training.log)               |
+| WF42M-PFC-0.2  | ViT-T-1.5G          | 92.04       | 97.27       | 95.68       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/wf42m_pfc02_40epoch_8gpu_vit_t/training.log) |
+| WF42M-PFC-0.3  | ViT-B-11G           | 97.16       | 97.91       | 97.05       | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_b_8gpu/training.log)         |
+
+#### 2. Training on Multi-Host GPU
+
+| Datasets         | Backbone(bs*gpus) | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Throughout | log                                                                                                                                        |
+|:-----------------|:------------------|:------------|:------------|:------------|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------|
+| WF42M-PFC-0.2    | r50(512*8)        | 93.83       | 97.53       | 96.16       | ~5900      | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_bs4k_pfc02/training.log)             |
+| WF42M-PFC-0.2    | r50(512*16)       | 93.96       | 97.46       | 96.12       | ~11000     | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r50_lr01_pfc02_bs8k_16gpus/training.log) |
+| WF42M-PFC-0.2    | r50(128*32)       | 94.04       | 97.48       | 95.94       | ~17000     | click me                                                                                                                                   |
+| WF42M-PFC-0.2    | r100(128*16)      | 96.28       | 97.80       | 96.57       | ~5200      | click me                                                                                                                                   |
+| WF42M-PFC-0.2    | r100(256*16)      | 96.69       | 97.85       | 96.63       | ~5200      | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/webface42m_r100_bs4k_pfc02/training.log)            |
+| WF42M-PFC-0.0018 | r100(512*32)      | 93.08       | 97.51       | 95.88       | ~10000     | click me                                                                                                                                   |
+| WF42M-PFC-0.2    | r100(128*32)      | 96.57       | 97.83       | 96.50       | ~9800      | click me                                                                                                                                   |
+
+`r100(128*32)` means backbone is r100, batchsize per gpu is 128, the number of gpus is 32.
+
+
+
+#### 3. ViT For Face Recognition
+
+| Datasets      | Backbone(bs)  | FLOPs | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | Throughout | log                                                                                                                          |
+|:--------------|:--------------|:------|:------------|:------------|:------------|:-----------|:-----------------------------------------------------------------------------------------------------------------------------|
+| WF42M-PFC-0.3 | r18(128*32)   | 2.6   | 79.13       | 95.77       | 93.36       | -          | click me                                                                                                                     |
+| WF42M-PFC-0.3 | r50(128*32)   | 6.3   | 94.03       | 97.48       | 95.94       | -          | click me                                                                                                                     |
+| WF42M-PFC-0.3 | r100(128*32)  | 12.1  | 96.69       | 97.82       | 96.45       | -          | click me                                                                                                                     |
+| WF42M-PFC-0.3 | r200(128*32)  | 23.5  | 97.70       | 97.97       | 96.93       | -          | click me                                                                                                                     |
+| WF42M-PFC-0.3 | VIT-T(384*64) | 1.5   | 92.24       | 97.31       | 95.97       | ~35000     | click me                                                                                                                     |
+| WF42M-PFC-0.3 | VIT-S(384*64) | 5.7   | 95.87       | 97.73       | 96.57       | ~25000     | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_s_64gpu/training.log) |
+| WF42M-PFC-0.3 | VIT-B(384*64) | 11.4  | 97.42       | 97.90       | 97.04       | ~13800     | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_b_64gpu/training.log) |
+| WF42M-PFC-0.3 | VIT-L(384*64) | 25.3  | 97.85       | 98.00       | 97.23       | ~9406      | [click me](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/pfc03_wf42m_vit_l_64gpu/training.log) |
+
+`WF42M` means WebFace42M, `PFC-0.3` means negivate class centers sample rate is 0.3.
+
+#### 4. Noisy Datasets
+  
+| Datasets                 | Backbone | **MFR-ALL** | IJB-C(1E-4) | IJB-C(1E-5) | log      |
+|:-------------------------|:---------|:------------|:------------|:------------|:---------|
+| WF12M-Flip(40%)          | r50      | 43.87       | 88.35       | 80.78       | click me |
+| WF12M-Flip(40%)-PFC-0.1* | r50      | 80.20       | 96.11       | 93.79       | click me |
+| WF12M-Conflict           | r50      | 79.93       | 95.30       | 91.56       | click me |
+| WF12M-Conflict-PFC-0.3*  | r50      | 91.68       | 97.28       | 95.75       | click me |
+
+`WF12M` means WebFace12M, `+PFC-0.1*` denotes additional abnormal inter-class filtering.
+
+
+
+## Speed Benchmark
+<div><img src="https://github.com/anxiangsir/insightface_arcface_log/blob/master/pfc_exp.png" width = "90%" /></div>
+
+
+**Arcface-Torch** is an efficient tool for training large-scale face recognition training sets. When the number of classes in the training sets exceeds one million, the partial FC sampling strategy maintains the same accuracy while providing several times faster training performance and lower GPU memory utilization. The partial FC is a sparse variant of the model parallel architecture for large-scale face recognition, utilizing a sparse softmax that dynamically samples a subset of class centers for each training batch. During each iteration, only a sparse portion of the parameters are updated, leading to a significant reduction in GPU memory requirements and computational demands. With the partial FC approach, it is possible to train sets with up to 29 million identities, the largest to date. Furthermore, the partial FC method supports multi-machine distributed training and mixed precision training.
+
+
+
+More details see 
+[speed_benchmark.md](docs/speed_benchmark.md) in docs.
+
+> 1. Training Speed of Various Parallel Techniques (Samples per Second) on a Tesla V100 32GB x 8 System (Higher is Optimal)
+
+`-` means training failed because of gpu memory limitations.
+
+| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
+|:--------------------------------|:--------------|:---------------|:---------------|
+| 125000                          | 4681          | 4824           | 5004           |
+| 1400000                         | **1672**      | 3043           | 4738           |
+| 5500000                         | **-**         | **1389**       | 3975           |
+| 8000000                         | **-**         | **-**          | 3565           |
+| 16000000                        | **-**         | **-**          | 2679           |
+| 29000000                        | **-**         | **-**          | **1855**       |
+
+> 2. GPU Memory Utilization of Various Parallel Techniques (MB per GPU) on a Tesla V100 32GB x 8 System (Lower is Optimal)
+
+| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
+|:--------------------------------|:--------------|:---------------|:---------------|
+| 125000                          | 7358          | 5306           | 4868           |
+| 1400000                         | 32252         | 11178          | 6056           |
+| 5500000                         | **-**         | 32188          | 9854           |
+| 8000000                         | **-**         | **-**          | 12310          |
+| 16000000                        | **-**         | **-**          | 19950          |
+| 29000000                        | **-**         | **-**          | 32324          |
+
+
+## Citations
+
+```
+@inproceedings{deng2019arcface,
+  title={Arcface: Additive angular margin loss for deep face recognition},
+  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={4690--4699},
+  year={2019}
+}
+@inproceedings{An_2022_CVPR,
+    author={An, Xiang and Deng, Jiankang and Guo, Jia and Feng, Ziyong and Zhu, XuHan and Yang, Jing and Liu, Tongliang},
+    title={Killing Two Birds With One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month={June},
+    year={2022},
+    pages={4042-4051}
+}
+@inproceedings{zhu2021webface260m,
+  title={Webface260m: A benchmark unveiling the power of million-scale deep face recognition},
+  author={Zhu, Zheng and Huang, Guan and Deng, Jiankang and Ye, Yun and Huang, Junjie and Chen, Xinze and Zhu, Jiagang and Yang, Tian and Lu, Jiwen and Du, Dalong and Zhou, Jie},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={10492--10502},
+  year={2021}
+}
+```

additional_modules/deep3dfacerecon/models/arcface_torch/backbones/__init__.py

@@ -0,0 +1,92 @@
+from .iresnet import iresnet18, iresnet34, iresnet50, iresnet100, iresnet200
+from .mobilefacenet import get_mbf
+
+
+def get_model(name, **kwargs):
+    # resnet
+    if name == "r18":
+        return iresnet18(False, **kwargs)
+    elif name == "r34":
+        return iresnet34(False, **kwargs)
+    elif name == "r50":
+        return iresnet50(False, **kwargs)
+    elif name == "r100":
+        return iresnet100(False, **kwargs)
+    elif name == "r200":
+        return iresnet200(False, **kwargs)
+    elif name == "r2060":
+        from .iresnet2060 import iresnet2060
+        return iresnet2060(False, **kwargs)
+
+    elif name == "mbf":
+        fp16 = kwargs.get("fp16", False)
+        num_features = kwargs.get("num_features", 512)
+        return get_mbf(fp16=fp16, num_features=num_features)
+
+    elif name == "mbf_large":
+        from .mobilefacenet import get_mbf_large
+        fp16 = kwargs.get("fp16", False)
+        num_features = kwargs.get("num_features", 512)
+        return get_mbf_large(fp16=fp16, num_features=num_features)
+
+    elif name == "vit_t":
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=256, depth=12,
+            num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1)
+
+    elif name == "vit_t_dp005_mask0": # For WebFace42M
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=256, depth=12,
+            num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.0)
+
+    elif name == "vit_s":
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=12,
+            num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1)
+    
+    elif name == "vit_s_dp005_mask_0":  # For WebFace42M
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=12,
+            num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.0)
+    
+    elif name == "vit_b":
+        # this is a feature
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=24,
+            num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0.1, using_checkpoint=True)
+
+    elif name == "vit_b_dp005_mask_005":  # For WebFace42M
+        # this is a feature
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=512, depth=24,
+            num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.05, using_checkpoint=True)
+
+    elif name == "vit_l_dp005_mask_005":  # For WebFace42M
+        # this is a feature
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=768, depth=24,
+            num_heads=8, drop_path_rate=0.05, norm_layer="ln", mask_ratio=0.05, using_checkpoint=True)
+        
+    elif name == "vit_h":  # For WebFace42M
+        num_features = kwargs.get("num_features", 512)
+        from .vit import VisionTransformer
+        return VisionTransformer(
+            img_size=112, patch_size=9, num_classes=num_features, embed_dim=1024, depth=48,
+            num_heads=8, drop_path_rate=0.1, norm_layer="ln", mask_ratio=0, using_checkpoint=True)
+
+    else:
+        raise ValueError()

additional_modules/deep3dfacerecon/models/arcface_torch/backbones/iresnet.py

@@ -0,0 +1,194 @@
+import torch
+from torch import nn
+from torch.utils.checkpoint import checkpoint
+
+__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
+using_ckpt = False
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=3,
+                     stride=stride,
+                     padding=dilation,
+                     groups=groups,
+                     bias=False,
+                     dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=1,
+                     stride=stride,
+                     bias=False)
+
+
+class IBasicBlock(nn.Module):
+    expansion = 1
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 groups=1, base_width=64, dilation=1):
+        super(IBasicBlock, self).__init__()
+        if groups != 1 or base_width != 64:
+            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
+        self.conv1 = conv3x3(inplanes, planes)
+        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
+        self.prelu = nn.PReLU(planes)
+        self.conv2 = conv3x3(planes, planes, stride)
+        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward_impl(self, x):
+        identity = x
+        out = self.bn1(x)
+        out = self.conv1(out)
+        out = self.bn2(out)
+        out = self.prelu(out)
+        out = self.conv2(out)
+        out = self.bn3(out)
+        if self.downsample is not None:
+            identity = self.downsample(x)
+        out += identity
+        return out        
+
+    def forward(self, x):
+        if self.training and using_ckpt:
+            return checkpoint(self.forward_impl, x)
+        else:
+            return self.forward_impl(x)
+
+
+class IResNet(nn.Module):
+    fc_scale = 7 * 7
+    def __init__(self,
+                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
+                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
+        super(IResNet, self).__init__()
+        self.extra_gflops = 0.0
+        self.fp16 = fp16
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError("replace_stride_with_dilation should be None "
+                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
+        self.prelu = nn.PReLU(self.inplanes)
+        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
+        self.layer2 = self._make_layer(block,
+                                       128,
+                                       layers[1],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[0])
+        self.layer3 = self._make_layer(block,
+                                       256,
+                                       layers[2],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[1])
+        self.layer4 = self._make_layer(block,
+                                       512,
+                                       layers[3],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[2])
+        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
+        self.dropout = nn.Dropout(p=dropout, inplace=True)
+        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
+        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
+        nn.init.constant_(self.features.weight, 1.0)
+        self.features.weight.requires_grad = False
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.normal_(m.weight, 0, 0.1)
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, IBasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
+            )
+        layers = []
+        layers.append(
+            block(self.inplanes, planes, stride, downsample, self.groups,
+                  self.base_width, previous_dilation))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(self.inplanes,
+                      planes,
+                      groups=self.groups,
+                      base_width=self.base_width,
+                      dilation=self.dilation))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        with torch.cuda.amp.autocast(self.fp16):
+            x = self.conv1(x)
+            x = self.bn1(x)
+            x = self.prelu(x)
+            x = self.layer1(x)
+            x = self.layer2(x)
+            x = self.layer3(x)
+            x = self.layer4(x)
+            x = self.bn2(x)
+            x = torch.flatten(x, 1)
+            x = self.dropout(x)
+        x = self.fc(x.float() if self.fp16 else x)
+        x = self.features(x)
+        return x
+
+
+def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
+    model = IResNet(block, layers, **kwargs)
+    if pretrained:
+        raise ValueError()
+    return model
+
+
+def iresnet18(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet34(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet50(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet100(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
+                    progress, **kwargs)
+
+
+def iresnet200(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
+                    progress, **kwargs)

additional_modules/deep3dfacerecon/models/arcface_torch/backbones/iresnet2060.py

@@ -0,0 +1,176 @@
+import torch
+from torch import nn
+
+assert torch.__version__ >= "1.8.1"
+from torch.utils.checkpoint import checkpoint_sequential
+
+__all__ = ['iresnet2060']
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=3,
+                     stride=stride,
+                     padding=dilation,
+                     groups=groups,
+                     bias=False,
+                     dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes,
+                     out_planes,
+                     kernel_size=1,
+                     stride=stride,
+                     bias=False)
+
+
+class IBasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None,
+                 groups=1, base_width=64, dilation=1):
+        super(IBasicBlock, self).__init__()
+        if groups != 1 or base_width != 64:
+            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, )
+        self.conv1 = conv3x3(inplanes, planes)
+        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05, )
+        self.prelu = nn.PReLU(planes)
+        self.conv2 = conv3x3(planes, planes, stride)
+        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05, )
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+        out = self.bn1(x)
+        out = self.conv1(out)
+        out = self.bn2(out)
+        out = self.prelu(out)
+        out = self.conv2(out)
+        out = self.bn3(out)
+        if self.downsample is not None:
+            identity = self.downsample(x)
+        out += identity
+        return out
+
+
+class IResNet(nn.Module):
+    fc_scale = 7 * 7
+
+    def __init__(self,
+                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
+                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
+        super(IResNet, self).__init__()
+        self.fp16 = fp16
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError("replace_stride_with_dilation should be None "
+                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
+        self.prelu = nn.PReLU(self.inplanes)
+        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
+        self.layer2 = self._make_layer(block,
+                                       128,
+                                       layers[1],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[0])
+        self.layer3 = self._make_layer(block,
+                                       256,
+                                       layers[2],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[1])
+        self.layer4 = self._make_layer(block,
+                                       512,
+                                       layers[3],
+                                       stride=2,
+                                       dilate=replace_stride_with_dilation[2])
+        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05, )
+        self.dropout = nn.Dropout(p=dropout, inplace=True)
+        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
+        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
+        nn.init.constant_(self.features.weight, 1.0)
+        self.features.weight.requires_grad = False
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.normal_(m.weight, 0, 0.1)
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, IBasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
+            )
+        layers = []
+        layers.append(
+            block(self.inplanes, planes, stride, downsample, self.groups,
+                  self.base_width, previous_dilation))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(self.inplanes,
+                      planes,
+                      groups=self.groups,
+                      base_width=self.base_width,
+                      dilation=self.dilation))
+
+        return nn.Sequential(*layers)
+
+    def checkpoint(self, func, num_seg, x):
+        if self.training:
+            return checkpoint_sequential(func, num_seg, x)
+        else:
+            return func(x)
+
+    def forward(self, x):
+        with torch.cuda.amp.autocast(self.fp16):
+            x = self.conv1(x)
+            x = self.bn1(x)
+            x = self.prelu(x)
+            x = self.layer1(x)
+            x = self.checkpoint(self.layer2, 20, x)
+            x = self.checkpoint(self.layer3, 100, x)
+            x = self.layer4(x)
+            x = self.bn2(x)
+            x = torch.flatten(x, 1)
+            x = self.dropout(x)
+        x = self.fc(x.float() if self.fp16 else x)
+        x = self.features(x)
+        return x
+
+
+def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
+    model = IResNet(block, layers, **kwargs)
+    if pretrained:
+        raise ValueError()
+    return model
+
+
+def iresnet2060(pretrained=False, progress=True, **kwargs):
+    return _iresnet('iresnet2060', IBasicBlock, [3, 128, 1024 - 128, 3], pretrained, progress, **kwargs)

additional_modules/deep3dfacerecon/models/arcface_torch/backbones/mobilefacenet.py

@@ -0,0 +1,147 @@
+'''
+Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py
+Original author cavalleria
+'''
+
+import torch.nn as nn
+from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
+import torch
+
+
+class Flatten(Module):
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+
+class ConvBlock(Module):
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(ConvBlock, self).__init__()
+        self.layers = nn.Sequential(
+            Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),
+            BatchNorm2d(num_features=out_c),
+            PReLU(num_parameters=out_c)
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class LinearBlock(Module):
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(LinearBlock, self).__init__()
+        self.layers = nn.Sequential(
+            Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),
+            BatchNorm2d(num_features=out_c)
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class DepthWise(Module):
+    def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
+        super(DepthWise, self).__init__()
+        self.residual = residual
+        self.layers = nn.Sequential(
+            ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
+            ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride),
+            LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
+        )
+
+    def forward(self, x):
+        short_cut = None
+        if self.residual:
+            short_cut = x
+        x = self.layers(x)
+        if self.residual:
+            output = short_cut + x
+        else:
+            output = x
+        return output
+
+
+class Residual(Module):
+    def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
+        super(Residual, self).__init__()
+        modules = []
+        for _ in range(num_block):
+            modules.append(DepthWise(c, c, True, kernel, stride, padding, groups))
+        self.layers = Sequential(*modules)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class GDC(Module):
+    def __init__(self, embedding_size):
+        super(GDC, self).__init__()
+        self.layers = nn.Sequential(
+            LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)),
+            Flatten(),
+            Linear(512, embedding_size, bias=False),
+            BatchNorm1d(embedding_size))
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class MobileFaceNet(Module):
+    def __init__(self, fp16=False, num_features=512, blocks=(1, 4, 6, 2), scale=2):
+        super(MobileFaceNet, self).__init__()
+        self.scale = scale
+        self.fp16 = fp16
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            ConvBlock(3, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
+        )
+        if blocks[0] == 1:
+            self.layers.append(
+                ConvBlock(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
+            )
+        else:
+            self.layers.append(
+                Residual(64 * self.scale, num_block=blocks[0], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
+            )
+        
+        self.layers.extend(
+        [
+            DepthWise(64 * self.scale, 64 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128),
+            Residual(64 * self.scale, num_block=blocks[1], groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
+            DepthWise(64 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256),
+            Residual(128 * self.scale, num_block=blocks[2], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
+            DepthWise(128 * self.scale, 128 * self.scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512),
+            Residual(128 * self.scale, num_block=blocks[3], groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
+        ])
+
+        self.conv_sep = ConvBlock(128 * self.scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
+        self.features = GDC(num_features)
+        self._initialize_weights()
+
+    def _initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+    def forward(self, x):
+        with torch.cuda.amp.autocast(self.fp16):
+            for func in self.layers:
+                x = func(x)
+        x = self.conv_sep(x.float() if self.fp16 else x)
+        x = self.features(x)
+        return x
+
+
+def get_mbf(fp16, num_features, blocks=(1, 4, 6, 2), scale=2):
+    return MobileFaceNet(fp16, num_features, blocks, scale=scale)
+
+def get_mbf_large(fp16, num_features, blocks=(2, 8, 12, 4), scale=4):
+    return MobileFaceNet(fp16, num_features, blocks, scale=scale)

additional_modules/deep3dfacerecon/models/arcface_torch/backbones/vit.py

@@ -0,0 +1,280 @@
+import torch
+import torch.nn as nn
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+from typing import Optional, Callable
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class VITBatchNorm(nn.Module):
+    def __init__(self, num_features):
+        super().__init__()
+        self.num_features = num_features
+        self.bn = nn.BatchNorm1d(num_features=num_features)
+
+    def forward(self, x):
+        return self.bn(x)
+
+
+class Attention(nn.Module):
+    def __init__(self,
+                 dim: int,
+                 num_heads: int = 8,
+                 qkv_bias: bool = False,
+                 qk_scale: Optional[None] = None,
+                 attn_drop: float = 0.,
+                 proj_drop: float = 0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        
+        with torch.cuda.amp.autocast(True):
+            batch_size, num_token, embed_dim = x.shape
+            #qkv is [3,batch_size,num_heads,num_token, embed_dim//num_heads]
+            qkv = self.qkv(x).reshape(
+                batch_size, num_token, 3, self.num_heads, embed_dim // self.num_heads).permute(2, 0, 3, 1, 4)
+        with torch.cuda.amp.autocast(False):
+            q, k, v = qkv[0].float(), qkv[1].float(), qkv[2].float()
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = (attn @ v).transpose(1, 2).reshape(batch_size, num_token, embed_dim)
+        with torch.cuda.amp.autocast(True):
+            x = self.proj(x)
+            x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self,
+                 dim: int,
+                 num_heads: int,
+                 num_patches: int,
+                 mlp_ratio: float = 4.,
+                 qkv_bias: bool = False,
+                 qk_scale: Optional[None] = None,
+                 drop: float = 0.,
+                 attn_drop: float = 0.,
+                 drop_path: float = 0.,
+                 act_layer: Callable = nn.ReLU6,
+                 norm_layer: str = "ln", 
+                 patch_n: int = 144):
+        super().__init__()
+
+        if norm_layer == "bn":
+            self.norm1 = VITBatchNorm(num_features=num_patches)
+            self.norm2 = VITBatchNorm(num_features=num_patches)
+        elif norm_layer == "ln":
+            self.norm1 = nn.LayerNorm(dim)
+            self.norm2 = nn.LayerNorm(dim)
+
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
+                       act_layer=act_layer, drop=drop)
+        self.extra_gflops = (num_heads * patch_n * (dim//num_heads)*patch_n * 2) / (1000**3)
+
+    def forward(self, x):
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        with torch.cuda.amp.autocast(True):
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+
+class PatchEmbed(nn.Module):
+    def __init__(self, img_size=108, patch_size=9, in_channels=3, embed_dim=768):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches = (img_size[1] // patch_size[1]) * \
+            (img_size[0] // patch_size[0])
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+        self.proj = nn.Conv2d(in_channels, embed_dim,
+                              kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x):
+        batch_size, channels, height, width = x.shape
+        assert height == self.img_size[0] and width == self.img_size[1], \
+            f"Input image size ({height}*{width}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+class VisionTransformer(nn.Module):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+
+    def __init__(self,
+                 img_size: int = 112,
+                 patch_size: int = 16,
+                 in_channels: int = 3,
+                 num_classes: int = 1000,
+                 embed_dim: int = 768,
+                 depth: int = 12,
+                 num_heads: int = 12,
+                 mlp_ratio: float = 4.,
+                 qkv_bias: bool = False,
+                 qk_scale: Optional[None] = None,
+                 drop_rate: float = 0.,
+                 attn_drop_rate: float = 0.,
+                 drop_path_rate: float = 0.,
+                 hybrid_backbone: Optional[None] = None,
+                 norm_layer: str = "ln",
+                 mask_ratio = 0.1,
+                 using_checkpoint = False,
+                 ):
+        super().__init__()
+        self.num_classes = num_classes
+        # num_features for consistency with other models
+        self.num_features = self.embed_dim = embed_dim
+
+        if hybrid_backbone is not None:
+            raise ValueError
+        else:
+            self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim)
+        self.mask_ratio = mask_ratio
+        self.using_checkpoint = using_checkpoint
+        num_patches = self.patch_embed.num_patches
+        self.num_patches = num_patches
+
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
+        patch_n = (img_size//patch_size)**2
+        self.blocks = nn.ModuleList(
+            [
+                Block(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                      drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
+                      num_patches=num_patches, patch_n=patch_n)
+                for i in range(depth)]
+        )
+        self.extra_gflops = 0.0
+        for _block in self.blocks:
+            self.extra_gflops += _block.extra_gflops
+
+        if norm_layer == "ln":
+            self.norm = nn.LayerNorm(embed_dim)
+        elif norm_layer == "bn":
+            self.norm = VITBatchNorm(self.num_patches)
+
+        # features head
+        self.feature = nn.Sequential(
+            nn.Linear(in_features=embed_dim * num_patches, out_features=embed_dim, bias=False),
+            nn.BatchNorm1d(num_features=embed_dim, eps=2e-5),
+            nn.Linear(in_features=embed_dim, out_features=num_classes, bias=False),
+            nn.BatchNorm1d(num_features=num_classes, eps=2e-5)
+        )
+
+        self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        torch.nn.init.normal_(self.mask_token, std=.02)
+        trunc_normal_(self.pos_embed, std=.02)
+        # trunc_normal_(self.cls_token, std=.02)
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    @torch.jit.ignore
+    def no_weight_decay(self):
+        return {'pos_embed', 'cls_token'}
+
+    def get_classifier(self):
+        return self.head
+    
+    def random_masking(self, x, mask_ratio=0.1):
+        """
+        Perform per-sample random masking by per-sample shuffling.
+        Per-sample shuffling is done by argsort random noise.
+        x: [N, L, D], sequence
+        """
+        N, L, D = x.size()  # batch, length, dim
+        len_keep = int(L * (1 - mask_ratio))
+
+        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]
+
+        # sort noise for each sample
+        # ascend: small is keep, large is remove
+        ids_shuffle = torch.argsort(noise, dim=1)
+        ids_restore = torch.argsort(ids_shuffle, dim=1)
+
+        # keep the first subset
+        ids_keep = ids_shuffle[:, :len_keep]
+        x_masked = torch.gather(
+            x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
+
+        # generate the binary mask: 0 is keep, 1 is remove
+        mask = torch.ones([N, L], device=x.device)
+        mask[:, :len_keep] = 0
+        # unshuffle to get the binary mask
+        mask = torch.gather(mask, dim=1, index=ids_restore)
+
+        return x_masked, mask, ids_restore
+
+    def forward_features(self, x):
+        B = x.shape[0]
+        x = self.patch_embed(x)
+        x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        if self.training and self.mask_ratio > 0:
+            x, _, ids_restore = self.random_masking(x)
+
+        for func in self.blocks:
+            if self.using_checkpoint and self.training:
+                from torch.utils.checkpoint import checkpoint
+                x = checkpoint(func, x)
+            else:
+                x = func(x)
+        x = self.norm(x.float())
+        
+        if self.training and self.mask_ratio > 0:
+            mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] - x.shape[1], 1)
+            x_ = torch.cat([x[:, :, :], mask_tokens], dim=1)  # no cls token
+            x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
+            x = x_
+        return torch.reshape(x, (B, self.num_patches * self.embed_dim))
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.feature(x)
+        return x

additional_modules/deep3dfacerecon/models/arcface_torch/configs/3millions.py

@@ -0,0 +1,23 @@
+from easydict import EasyDict as edict
+
+# configs for test speed
+
+config = edict()
+config.margin_list = (1.0, 0.0, 0.4)
+config.network = "mbf"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 0.1
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 5e-4
+config.batch_size = 512 # total_batch_size = batch_size * num_gpus
+config.lr = 0.1  # batch size is 512
+
+config.rec = "synthetic"
+config.num_classes = 30 * 10000
+config.num_image = 100000
+config.num_epoch = 30
+config.warmup_epoch = -1
+config.val_targets = []

additional_modules/deep3dfacerecon/models/arcface_torch/configs/base.py

@@ -0,0 +1,60 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+
+# Margin Base Softmax
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "r50"
+config.resume = False
+config.save_all_states = False
+config.output = "ms1mv3_arcface_r50"
+
+config.embedding_size = 512
+
+# Partial FC
+config.sample_rate = 1
+config.interclass_filtering_threshold = 0
+
+config.fp16 = False
+config.batch_size = 128
+
+# For SGD 
+config.optimizer = "sgd"
+config.lr = 0.1
+config.momentum = 0.9
+config.weight_decay = 5e-4
+
+# For AdamW
+# config.optimizer = "adamw"
+# config.lr = 0.001
+# config.weight_decay = 0.1
+
+config.verbose = 2000
+config.frequent = 10
+
+# For Large Sacle Dataset, such as WebFace42M
+config.dali = False 
+config.dali_aug = False
+
+# Gradient ACC
+config.gradient_acc = 1
+
+# setup seed
+config.seed = 2048
+
+# dataload numworkers
+config.num_workers = 2
+
+# WandB Logger
+config.wandb_key = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
+config.suffix_run_name = None
+config.using_wandb = False
+config.wandb_entity = "entity"
+config.wandb_project = "project"
+config.wandb_log_all = True
+config.save_artifacts = False
+config.wandb_resume = False # resume wandb run: Only if the you wand t resume the last run that it was interrupted

additional_modules/deep3dfacerecon/models/arcface_torch/configs/glint360k_mbf.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.0, 0.4)
+config.network = "mbf"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 1e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/glint360k"
+config.num_classes = 360232
+config.num_image = 17091657
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/glint360k_r100.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.0, 0.4)
+config.network = "r100"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 1e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/glint360k"
+config.num_classes = 360232
+config.num_image = 17091657
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/glint360k_r50.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.0, 0.4)
+config.network = "r50"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 1e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/glint360k"
+config.num_classes = 360232
+config.num_image = 17091657
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/ms1mv2_mbf.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "mbf"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 1e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/faces_emore"
+config.num_classes = 85742
+config.num_image = 5822653
+config.num_epoch = 40
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/ms1mv2_r100.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "r100"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 5e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/faces_emore"
+config.num_classes = 85742
+config.num_image = 5822653
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/ms1mv2_r50.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "r50"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 5e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/faces_emore"
+config.num_classes = 85742
+config.num_image = 5822653
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/ms1mv3_mbf.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "mbf"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 1e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/ms1m-retinaface-t1"
+config.num_classes = 93431
+config.num_image = 5179510
+config.num_epoch = 40
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/ms1mv3_r100.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "r100"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 5e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/ms1m-retinaface-t1"
+config.num_classes = 93431
+config.num_image = 5179510
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/ms1mv3_r50.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "r50"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 5e-4
+config.batch_size = 128
+config.lr = 0.1
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/ms1m-retinaface-t1"
+config.num_classes = 93431
+config.num_image = 5179510
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]

additional_modules/deep3dfacerecon/models/arcface_torch/configs/ms1mv3_r50_onegpu.py

@@ -0,0 +1,27 @@
+from easydict import EasyDict as edict
+
+# make training faster
+# our RAM is 256G
+# mount -t tmpfs -o size=140G  tmpfs /train_tmp
+
+config = edict()
+config.margin_list = (1.0, 0.5, 0.0)
+config.network = "r50"
+config.resume = False
+config.output = None
+config.embedding_size = 512
+config.sample_rate = 1.0
+config.fp16 = True
+config.momentum = 0.9
+config.weight_decay = 5e-4
+config.batch_size = 128
+config.lr = 0.02
+config.verbose = 2000
+config.dali = False
+
+config.rec = "/train_tmp/ms1m-retinaface-t1"
+config.num_classes = 93431
+config.num_image = 5179510
+config.num_epoch = 20
+config.warmup_epoch = 0
+config.val_targets = ['lfw', 'cfp_fp', "agedb_30"]