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+ .vscode
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+
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+ # ignored files
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+ version.py
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+
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+ # ignored files with suffix
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+ *.html
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+ *.png
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+ *.jpeg
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+ *.jpg
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+ *.gif
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+ *.pth
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+ *.zip
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+
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+ # template
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+
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
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+ # C extensions
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+ *.so
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+ # Distribution / packaging
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+ .Python
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+ dist/
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+ .eggs/
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+ lib64/
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+ parts/
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+ var/
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+ wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # PyInstaller
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+ # Usually these files are written by a python script from a template
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+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
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+ .coverage.*
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+ .cache
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+ nosetests.xml
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+ *.cover
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+ .hypothesis/
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+
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+ # Translations
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+ # PyBuilder
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+ # Jupyter Notebook
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+ # pyenv
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+ repos:
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+ # flake8
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+ - repo: https://github.com/PyCQA/flake8
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+ rev: 3.8.3
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+ hooks:
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+ - id: flake8
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+ args: ["--config=setup.cfg", "--ignore=W504, W503"]
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+
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+ # modify known_third_party
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+ - repo: https://github.com/asottile/seed-isort-config
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+ rev: v2.2.0
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+ hooks:
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+ - id: seed-isort-config
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+
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+ # isort
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+ - repo: https://github.com/timothycrosley/isort
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+ rev: 5.2.2
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+ hooks:
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+ - id: isort
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+
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+ # yapf
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+ - repo: https://github.com/pre-commit/mirrors-yapf
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+ rev: v0.30.0
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+ hooks:
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+ - id: yapf
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+
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+ # pre-commit-hooks
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+ - repo: https://github.com/pre-commit/pre-commit-hooks
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+ rev: v3.2.0
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+ hooks:
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+ - id: trailing-whitespace # Trim trailing whitespace
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+ - id: check-yaml # Attempt to load all yaml files to verify syntax
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+ - id: check-merge-conflict # Check for files that contain merge conflict strings
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+ - id: double-quote-string-fixer # Replace double quoted strings with single quoted strings
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+ - id: end-of-file-fixer # Make sure files end in a newline and only a newline
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+ - id: requirements-txt-fixer # Sort entries in requirements.txt and remove incorrect entry for pkg-resources==0.0.0
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+ - id: fix-encoding-pragma # Remove the coding pragma: # -*- coding: utf-8 -*-
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+ args: ["--remove"]
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+ - id: mixed-line-ending # Replace or check mixed line ending
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+ args: ["--fix=lf"]
License_GFPGAN.txt ADDED
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+ Tencent is pleased to support the open source community by making GFPGAN available.
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+ Name: tempita
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+ Open Source Software licensed under the MIT license:
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+ 1. facexlib
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+ Copyright (c) 2020 Xintao Wang
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+ 2. opencv-python
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+ Copyright (c) Olli-Pekka Heinisuo
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+ Please note that only files in cv2 package are used.
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287
+
288
+ Terms of the MIT License:
289
+ ---------------------------------------------
290
+ 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:
291
+
292
+ The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
293
+
294
+ 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.
295
+
296
+
297
+
298
+ Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:
299
+ ---------------------------------------------
300
+ 1. tqdm
301
+ Copyright (c) 2013 noamraph
302
+
303
+ `tqdm` is a product of collaborative work.
304
+ Unless otherwise stated, all authors (see commit logs) retain copyright
305
+ for their respective work, and release the work under the MIT licence
306
+ (text below).
307
+
308
+ Exceptions or notable authors are listed below
309
+ in reverse chronological order:
310
+
311
+ * files: *
312
+ MPLv2.0 2015-2020 (c) Casper da Costa-Luis
313
+ [casperdcl](https://github.com/casperdcl).
314
+ * files: tqdm/_tqdm.py
315
+ MIT 2016 (c) [PR #96] on behalf of Google Inc.
316
+ * files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore
317
+ MIT 2013 (c) Noam Yorav-Raphael, original author.
318
+
319
+ [PR #96]: https://github.com/tqdm/tqdm/pull/96
320
+
321
+
322
+ Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
323
+ -----------------------------------------------
324
+
325
+ This Source Code Form is subject to the terms of the
326
+ Mozilla Public License, v. 2.0.
327
+ If a copy of the MPL was not distributed with this file,
328
+ You can obtain one at https://mozilla.org/MPL/2.0/.
329
+
330
+
331
+ MIT License (MIT)
332
+ -----------------
333
+
334
+ Copyright (c) 2013 noamraph
335
+
336
+ Permission is hereby granted, free of charge, to any person obtaining a copy of
337
+ this software and associated documentation files (the "Software"), to deal in
338
+ the Software without restriction, including without limitation the rights to
339
+ use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
340
+ the Software, and to permit persons to whom the Software is furnished to do so,
341
+ subject to the following conditions:
342
+
343
+ The above copyright notice and this permission notice shall be included in all
344
+ copies or substantial portions of the Software.
345
+
346
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
347
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
348
+ FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
349
+ COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
350
+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
351
+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
README.md ADDED
@@ -0,0 +1,103 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # GFPGAN (CVPR 2021)
2
+
3
+ [**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md)
4
+
5
+ GFPGAN is a blind face restoration algorithm towards real-world face images.
6
+
7
+ <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
8
+ [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
9
+
10
+ ### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
11
+ > [[Paper](https://arxiv.org/abs/2101.04061)] &emsp; [[Project Page](https://xinntao.github.io/projects/gfpgan)] &emsp; [Demo] <br>
12
+ > [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
13
+ > Applied Research Center (ARC), Tencent PCG
14
+
15
+ #### Abstract
16
+
17
+ Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
18
+
19
+ #### BibTeX
20
+
21
+ @InProceedings{wang2021gfpgan,
22
+ author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
23
+ title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
24
+ booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
25
+ year = {2021}
26
+ }
27
+
28
+ <p align="center">
29
+ <img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
30
+ </p>
31
+
32
+ ---
33
+
34
+ ## :wrench: Dependencies and Installation
35
+
36
+ - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
37
+ - [PyTorch >= 1.7](https://pytorch.org/)
38
+ - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
39
+
40
+ ### Installation
41
+
42
+ 1. Clone repo
43
+
44
+ ```bash
45
+ git clone https://github.com/xinntao/GFPGAN.git
46
+ cd GFPGAN
47
+ ```
48
+
49
+ 1. Install dependent packages
50
+
51
+ ```bash
52
+ # Install basicsr - https://github.com/xinntao/BasicSR
53
+ # We use BasicSR for both training and inference
54
+ # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
55
+ BASICSR_EXT=True pip install basicsr
56
+
57
+ # Install facexlib - https://github.com/xinntao/facexlib
58
+ # We use face detection and face restoration helper in the facexlib package
59
+ pip install facexlib
60
+
61
+ pip install -r requirements.txt
62
+ ```
63
+
64
+ ## :zap: Quick Inference
65
+
66
+ Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
67
+
68
+ ```bash
69
+ wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
70
+ ```
71
+
72
+ ```bash
73
+ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
74
+
75
+ # for aligned images
76
+ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
77
+ ```
78
+
79
+ ## :computer: Training
80
+
81
+ We provide complete training codes for GFPGAN. <br>
82
+ You could improve it according to your own needs.
83
+
84
+ 1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
85
+
86
+ 1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
87
+ 1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
88
+ 1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
89
+ 1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
90
+
91
+ 1. Modify the configuration file `train_gfpgan_v1.yml` accordingly.
92
+
93
+ 1. Training
94
+
95
+ > python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
96
+
97
+ ## :scroll: License and Acknowledgement
98
+
99
+ GFPGAN is realeased under Apache License Version 2.0.
100
+
101
+ ## :e-mail: Contact
102
+
103
+ If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
README_CN.md ADDED
@@ -0,0 +1,103 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # GFPGAN (CVPR 2021)
2
+
3
+ [**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan) &emsp;&emsp; [English](README.md) **|** [简体中文](README_CN.md)
4
+
5
+ GFPGAN is a blind face restoration algorithm towards real-world face images.
6
+
7
+ <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
8
+ [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
9
+
10
+ ### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
11
+ > [[Paper](https://arxiv.org/abs/2101.04061)] &emsp; [[Project Page](https://xinntao.github.io/projects/gfpgan)] &emsp; [Demo] <br>
12
+ > [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
13
+ > Applied Research Center (ARC), Tencent PCG
14
+
15
+ #### Abstract
16
+
17
+ Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
18
+
19
+ #### BibTeX
20
+
21
+ @InProceedings{wang2021gfpgan,
22
+ author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
23
+ title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
24
+ booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
25
+ year = {2021}
26
+ }
27
+
28
+ <p align="center">
29
+ <img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
30
+ </p>
31
+
32
+ ---
33
+
34
+ ## :wrench: Dependencies and Installation
35
+
36
+ - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
37
+ - [PyTorch >= 1.7](https://pytorch.org/)
38
+ - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
39
+
40
+ ### Installation
41
+
42
+ 1. Clone repo
43
+
44
+ ```bash
45
+ git clone https://github.com/xinntao/GFPGAN.git
46
+ cd GFPGAN
47
+ ```
48
+
49
+ 1. Install dependent packages
50
+
51
+ ```bash
52
+ # Install basicsr - https://github.com/xinntao/BasicSR
53
+ # We use BasicSR for both training and inference
54
+ # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
55
+ BASICSR_EXT=True pip install basicsr
56
+
57
+ # Install facexlib - https://github.com/xinntao/facexlib
58
+ # We use face detection and face restoration helper in the facexlib package
59
+ pip install facexlib
60
+
61
+ pip install -r requirements.txt
62
+ ```
63
+
64
+ ## :zap: Quick Inference
65
+
66
+ Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
67
+
68
+ ```bash
69
+ wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
70
+ ```
71
+
72
+ ```bash
73
+ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
74
+
75
+ # for aligned images
76
+ python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
77
+ ```
78
+
79
+ ## :computer: Training
80
+
81
+ We provide complete training codes for GFPGAN. <br>
82
+ You could improve it according to your own needs.
83
+
84
+ 1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
85
+
86
+ 1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
87
+ 1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
88
+ 1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
89
+ 1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
90
+
91
+ 1. Modify the configuration file `train_gfpgan_v1.yml` accordingly.
92
+
93
+ 1. Training
94
+
95
+ > python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
96
+
97
+ ## :scroll: License and Acknowledgement
98
+
99
+ GFPGAN is realeased under Apache License Version 2.0.
100
+
101
+ ## :e-mail: Contact
102
+
103
+ If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
archs/__init__.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import importlib
2
+ from os import path as osp
3
+
4
+ from basicsr.utils import scandir
5
+
6
+ # automatically scan and import arch modules for registry
7
+ # scan all the files under the 'archs' folder and collect files ending with
8
+ # '_arch.py'
9
+ arch_folder = osp.dirname(osp.abspath(__file__))
10
+ arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')]
11
+ # import all the arch modules
12
+ _arch_modules = [importlib.import_module(f'archs.{file_name}') for file_name in arch_filenames]
archs/arcface_arch.py ADDED
@@ -0,0 +1,198 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch.nn as nn
2
+
3
+ from basicsr.utils.registry import ARCH_REGISTRY
4
+
5
+
6
+ def conv3x3(in_planes, out_planes, stride=1):
7
+ """3x3 convolution with padding"""
8
+ return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
9
+
10
+
11
+ class BasicBlock(nn.Module):
12
+ expansion = 1
13
+
14
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
15
+ super(BasicBlock, self).__init__()
16
+ self.conv1 = conv3x3(inplanes, planes, stride)
17
+ self.bn1 = nn.BatchNorm2d(planes)
18
+ self.relu = nn.ReLU(inplace=True)
19
+ self.conv2 = conv3x3(planes, planes)
20
+ self.bn2 = nn.BatchNorm2d(planes)
21
+ self.downsample = downsample
22
+ self.stride = stride
23
+
24
+ def forward(self, x):
25
+ residual = x
26
+
27
+ out = self.conv1(x)
28
+ out = self.bn1(out)
29
+ out = self.relu(out)
30
+
31
+ out = self.conv2(out)
32
+ out = self.bn2(out)
33
+
34
+ if self.downsample is not None:
35
+ residual = self.downsample(x)
36
+
37
+ out += residual
38
+ out = self.relu(out)
39
+
40
+ return out
41
+
42
+
43
+ class IRBlock(nn.Module):
44
+ expansion = 1
45
+
46
+ def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
47
+ super(IRBlock, self).__init__()
48
+ self.bn0 = nn.BatchNorm2d(inplanes)
49
+ self.conv1 = conv3x3(inplanes, inplanes)
50
+ self.bn1 = nn.BatchNorm2d(inplanes)
51
+ self.prelu = nn.PReLU()
52
+ self.conv2 = conv3x3(inplanes, planes, stride)
53
+ self.bn2 = nn.BatchNorm2d(planes)
54
+ self.downsample = downsample
55
+ self.stride = stride
56
+ self.use_se = use_se
57
+ if self.use_se:
58
+ self.se = SEBlock(planes)
59
+
60
+ def forward(self, x):
61
+ residual = x
62
+ out = self.bn0(x)
63
+ out = self.conv1(out)
64
+ out = self.bn1(out)
65
+ out = self.prelu(out)
66
+
67
+ out = self.conv2(out)
68
+ out = self.bn2(out)
69
+ if self.use_se:
70
+ out = self.se(out)
71
+
72
+ if self.downsample is not None:
73
+ residual = self.downsample(x)
74
+
75
+ out += residual
76
+ out = self.prelu(out)
77
+
78
+ return out
79
+
80
+
81
+ class Bottleneck(nn.Module):
82
+ expansion = 4
83
+
84
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
85
+ super(Bottleneck, self).__init__()
86
+ self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
87
+ self.bn1 = nn.BatchNorm2d(planes)
88
+ self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
89
+ self.bn2 = nn.BatchNorm2d(planes)
90
+ self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
91
+ self.bn3 = nn.BatchNorm2d(planes * self.expansion)
92
+ self.relu = nn.ReLU(inplace=True)
93
+ self.downsample = downsample
94
+ self.stride = stride
95
+
96
+ def forward(self, x):
97
+ residual = x
98
+
99
+ out = self.conv1(x)
100
+ out = self.bn1(out)
101
+ out = self.relu(out)
102
+
103
+ out = self.conv2(out)
104
+ out = self.bn2(out)
105
+ out = self.relu(out)
106
+
107
+ out = self.conv3(out)
108
+ out = self.bn3(out)
109
+
110
+ if self.downsample is not None:
111
+ residual = self.downsample(x)
112
+
113
+ out += residual
114
+ out = self.relu(out)
115
+
116
+ return out
117
+
118
+
119
+ class SEBlock(nn.Module):
120
+
121
+ def __init__(self, channel, reduction=16):
122
+ super(SEBlock, self).__init__()
123
+ self.avg_pool = nn.AdaptiveAvgPool2d(1)
124
+ self.fc = nn.Sequential(
125
+ nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel),
126
+ nn.Sigmoid())
127
+
128
+ def forward(self, x):
129
+ b, c, _, _ = x.size()
130
+ y = self.avg_pool(x).view(b, c)
131
+ y = self.fc(y).view(b, c, 1, 1)
132
+ return x * y
133
+
134
+
135
+ @ARCH_REGISTRY.register()
136
+ class ResNetArcFace(nn.Module):
137
+
138
+ def __init__(self, block, layers, use_se=True):
139
+ if block == 'IRBlock':
140
+ block = IRBlock
141
+ self.inplanes = 64
142
+ self.use_se = use_se
143
+ super(ResNetArcFace, self).__init__()
144
+ self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
145
+ self.bn1 = nn.BatchNorm2d(64)
146
+ self.prelu = nn.PReLU()
147
+ self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
148
+ self.layer1 = self._make_layer(block, 64, layers[0])
149
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
150
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
151
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
152
+ self.bn4 = nn.BatchNorm2d(512)
153
+ self.dropout = nn.Dropout()
154
+ self.fc5 = nn.Linear(512 * 8 * 8, 512)
155
+ self.bn5 = nn.BatchNorm1d(512)
156
+
157
+ for m in self.modules():
158
+ if isinstance(m, nn.Conv2d):
159
+ nn.init.xavier_normal_(m.weight)
160
+ elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
161
+ nn.init.constant_(m.weight, 1)
162
+ nn.init.constant_(m.bias, 0)
163
+ elif isinstance(m, nn.Linear):
164
+ nn.init.xavier_normal_(m.weight)
165
+ nn.init.constant_(m.bias, 0)
166
+
167
+ def _make_layer(self, block, planes, blocks, stride=1):
168
+ downsample = None
169
+ if stride != 1 or self.inplanes != planes * block.expansion:
170
+ downsample = nn.Sequential(
171
+ nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
172
+ nn.BatchNorm2d(planes * block.expansion),
173
+ )
174
+ layers = []
175
+ layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
176
+ self.inplanes = planes
177
+ for _ in range(1, blocks):
178
+ layers.append(block(self.inplanes, planes, use_se=self.use_se))
179
+
180
+ return nn.Sequential(*layers)
181
+
182
+ def forward(self, x):
183
+ x = self.conv1(x)
184
+ x = self.bn1(x)
185
+ x = self.prelu(x)
186
+ x = self.maxpool(x)
187
+
188
+ x = self.layer1(x)
189
+ x = self.layer2(x)
190
+ x = self.layer3(x)
191
+ x = self.layer4(x)
192
+ x = self.bn4(x)
193
+ x = self.dropout(x)
194
+ x = x.view(x.size(0), -1)
195
+ x = self.fc5(x)
196
+ x = self.bn5(x)
197
+
198
+ return x
archs/gfpganv1_arch.py ADDED
@@ -0,0 +1,418 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import random
3
+ import torch
4
+ from torch import nn
5
+ from torch.nn import functional as F
6
+
7
+ from basicsr.archs.stylegan2_arch import (ConvLayer, EqualConv2d, EqualLinear, ResBlock, ScaledLeakyReLU,
8
+ StyleGAN2Generator)
9
+ from basicsr.ops.fused_act import FusedLeakyReLU
10
+ from basicsr.utils.registry import ARCH_REGISTRY
11
+
12
+
13
+ class StyleGAN2GeneratorSFT(StyleGAN2Generator):
14
+ """StyleGAN2 Generator.
15
+
16
+ Args:
17
+ out_size (int): The spatial size of outputs.
18
+ num_style_feat (int): Channel number of style features. Default: 512.
19
+ num_mlp (int): Layer number of MLP style layers. Default: 8.
20
+ channel_multiplier (int): Channel multiplier for large networks of
21
+ StyleGAN2. Default: 2.
22
+ resample_kernel (list[int]): A list indicating the 1D resample kernel
23
+ magnitude. A cross production will be applied to extent 1D resample
24
+ kenrel to 2D resample kernel. Default: [1, 3, 3, 1].
25
+ lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
26
+ """
27
+
28
+ def __init__(self,
29
+ out_size,
30
+ num_style_feat=512,
31
+ num_mlp=8,
32
+ channel_multiplier=2,
33
+ resample_kernel=(1, 3, 3, 1),
34
+ lr_mlp=0.01,
35
+ narrow=1,
36
+ sft_half=False):
37
+ super(StyleGAN2GeneratorSFT, self).__init__(
38
+ out_size,
39
+ num_style_feat=num_style_feat,
40
+ num_mlp=num_mlp,
41
+ channel_multiplier=channel_multiplier,
42
+ resample_kernel=resample_kernel,
43
+ lr_mlp=lr_mlp,
44
+ narrow=narrow)
45
+ self.sft_half = sft_half
46
+
47
+ def forward(self,
48
+ styles,
49
+ conditions,
50
+ input_is_latent=False,
51
+ noise=None,
52
+ randomize_noise=True,
53
+ truncation=1,
54
+ truncation_latent=None,
55
+ inject_index=None,
56
+ return_latents=False):
57
+ """Forward function for StyleGAN2Generator.
58
+
59
+ Args:
60
+ styles (list[Tensor]): Sample codes of styles.
61
+ input_is_latent (bool): Whether input is latent style.
62
+ Default: False.
63
+ noise (Tensor | None): Input noise or None. Default: None.
64
+ randomize_noise (bool): Randomize noise, used when 'noise' is
65
+ False. Default: True.
66
+ truncation (float): TODO. Default: 1.
67
+ truncation_latent (Tensor | None): TODO. Default: None.
68
+ inject_index (int | None): The injection index for mixing noise.
69
+ Default: None.
70
+ return_latents (bool): Whether to return style latents.
71
+ Default: False.
72
+ """
73
+ # style codes -> latents with Style MLP layer
74
+ if not input_is_latent:
75
+ styles = [self.style_mlp(s) for s in styles]
76
+ # noises
77
+ if noise is None:
78
+ if randomize_noise:
79
+ noise = [None] * self.num_layers # for each style conv layer
80
+ else: # use the stored noise
81
+ noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)]
82
+ # style truncation
83
+ if truncation < 1:
84
+ style_truncation = []
85
+ for style in styles:
86
+ style_truncation.append(truncation_latent + truncation * (style - truncation_latent))
87
+ styles = style_truncation
88
+ # get style latent with injection
89
+ if len(styles) == 1:
90
+ inject_index = self.num_latent
91
+
92
+ if styles[0].ndim < 3:
93
+ # repeat latent code for all the layers
94
+ latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
95
+ else: # used for encoder with different latent code for each layer
96
+ latent = styles[0]
97
+ elif len(styles) == 2: # mixing noises
98
+ if inject_index is None:
99
+ inject_index = random.randint(1, self.num_latent - 1)
100
+ latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
101
+ latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
102
+ latent = torch.cat([latent1, latent2], 1)
103
+
104
+ # main generation
105
+ out = self.constant_input(latent.shape[0])
106
+ out = self.style_conv1(out, latent[:, 0], noise=noise[0])
107
+ skip = self.to_rgb1(out, latent[:, 1])
108
+
109
+ i = 1
110
+ for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2],
111
+ noise[2::2], self.to_rgbs):
112
+ out = conv1(out, latent[:, i], noise=noise1)
113
+
114
+ # the conditions may have fewer levels
115
+ if i < len(conditions):
116
+ # SFT part to combine the conditions
117
+ if self.sft_half:
118
+ out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
119
+ out_sft = out_sft * conditions[i - 1] + conditions[i]
120
+ out = torch.cat([out_same, out_sft], dim=1)
121
+ else:
122
+ out = out * conditions[i - 1] + conditions[i]
123
+
124
+ out = conv2(out, latent[:, i + 1], noise=noise2)
125
+ skip = to_rgb(out, latent[:, i + 2], skip)
126
+ i += 2
127
+
128
+ image = skip
129
+
130
+ if return_latents:
131
+ return image, latent
132
+ else:
133
+ return image, None
134
+
135
+
136
+ class ConvUpLayer(nn.Module):
137
+ """Conv Up Layer. Bilinear upsample + Conv.
138
+
139
+ Args:
140
+ in_channels (int): Channel number of the input.
141
+ out_channels (int): Channel number of the output.
142
+ kernel_size (int): Size of the convolving kernel.
143
+ stride (int): Stride of the convolution. Default: 1
144
+ padding (int): Zero-padding added to both sides of the input.
145
+ Default: 0.
146
+ bias (bool): If ``True``, adds a learnable bias to the output.
147
+ Default: ``True``.
148
+ bias_init_val (float): Bias initialized value. Default: 0.
149
+ activate (bool): Whether use activateion. Default: True.
150
+ """
151
+
152
+ def __init__(self,
153
+ in_channels,
154
+ out_channels,
155
+ kernel_size,
156
+ stride=1,
157
+ padding=0,
158
+ bias=True,
159
+ bias_init_val=0,
160
+ activate=True):
161
+ super(ConvUpLayer, self).__init__()
162
+ self.in_channels = in_channels
163
+ self.out_channels = out_channels
164
+ self.kernel_size = kernel_size
165
+ self.stride = stride
166
+ self.padding = padding
167
+ self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
168
+
169
+ self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size))
170
+
171
+ if bias and not activate:
172
+ self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
173
+ else:
174
+ self.register_parameter('bias', None)
175
+
176
+ # activation
177
+ if activate:
178
+ if bias:
179
+ self.activation = FusedLeakyReLU(out_channels)
180
+ else:
181
+ self.activation = ScaledLeakyReLU(0.2)
182
+ else:
183
+ self.activation = None
184
+
185
+ def forward(self, x):
186
+ # bilinear upsample
187
+ out = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
188
+ # conv
189
+ out = F.conv2d(
190
+ out,
191
+ self.weight * self.scale,
192
+ bias=self.bias,
193
+ stride=self.stride,
194
+ padding=self.padding,
195
+ )
196
+ # activation
197
+ if self.activation is not None:
198
+ out = self.activation(out)
199
+ return out
200
+
201
+
202
+ class ResUpBlock(nn.Module):
203
+ """Residual block with upsampling.
204
+
205
+ Args:
206
+ in_channels (int): Channel number of the input.
207
+ out_channels (int): Channel number of the output.
208
+ """
209
+
210
+ def __init__(self, in_channels, out_channels):
211
+ super(ResUpBlock, self).__init__()
212
+
213
+ self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
214
+ self.conv2 = ConvUpLayer(in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True)
215
+ self.skip = ConvUpLayer(in_channels, out_channels, 1, bias=False, activate=False)
216
+
217
+ def forward(self, x):
218
+ out = self.conv1(x)
219
+ out = self.conv2(out)
220
+ skip = self.skip(x)
221
+ out = (out + skip) / math.sqrt(2)
222
+ return out
223
+
224
+
225
+ @ARCH_REGISTRY.register()
226
+ class GFPGANv1(nn.Module):
227
+ """Unet + StyleGAN2 decoder with SFT."""
228
+
229
+ def __init__(
230
+ self,
231
+ out_size,
232
+ num_style_feat=512,
233
+ channel_multiplier=1,
234
+ resample_kernel=(1, 3, 3, 1),
235
+ decoder_load_path=None,
236
+ fix_decoder=True,
237
+ # for stylegan decoder
238
+ num_mlp=8,
239
+ lr_mlp=0.01,
240
+ input_is_latent=False,
241
+ different_w=False,
242
+ narrow=1,
243
+ sft_half=False):
244
+
245
+ super(GFPGANv1, self).__init__()
246
+ self.input_is_latent = input_is_latent
247
+ self.different_w = different_w
248
+ self.num_style_feat = num_style_feat
249
+
250
+ unet_narrow = narrow * 0.5
251
+ channels = {
252
+ '4': int(512 * unet_narrow),
253
+ '8': int(512 * unet_narrow),
254
+ '16': int(512 * unet_narrow),
255
+ '32': int(512 * unet_narrow),
256
+ '64': int(256 * channel_multiplier * unet_narrow),
257
+ '128': int(128 * channel_multiplier * unet_narrow),
258
+ '256': int(64 * channel_multiplier * unet_narrow),
259
+ '512': int(32 * channel_multiplier * unet_narrow),
260
+ '1024': int(16 * channel_multiplier * unet_narrow)
261
+ }
262
+
263
+ self.log_size = int(math.log(out_size, 2))
264
+ first_out_size = 2**(int(math.log(out_size, 2)))
265
+
266
+ self.conv_body_first = ConvLayer(3, channels[f'{first_out_size}'], 1, bias=True, activate=True)
267
+
268
+ # downsample
269
+ in_channels = channels[f'{first_out_size}']
270
+ self.conv_body_down = nn.ModuleList()
271
+ for i in range(self.log_size, 2, -1):
272
+ out_channels = channels[f'{2**(i - 1)}']
273
+ self.conv_body_down.append(ResBlock(in_channels, out_channels, resample_kernel))
274
+ in_channels = out_channels
275
+
276
+ self.final_conv = ConvLayer(in_channels, channels['4'], 3, bias=True, activate=True)
277
+
278
+ # upsample
279
+ in_channels = channels['4']
280
+ self.conv_body_up = nn.ModuleList()
281
+ for i in range(3, self.log_size + 1):
282
+ out_channels = channels[f'{2**i}']
283
+ self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
284
+ in_channels = out_channels
285
+
286
+ # to RGB
287
+ self.toRGB = nn.ModuleList()
288
+ for i in range(3, self.log_size + 1):
289
+ self.toRGB.append(EqualConv2d(channels[f'{2**i}'], 3, 1, stride=1, padding=0, bias=True, bias_init_val=0))
290
+
291
+ if different_w:
292
+ linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
293
+ else:
294
+ linear_out_channel = num_style_feat
295
+
296
+ self.final_linear = EqualLinear(
297
+ channels['4'] * 4 * 4, linear_out_channel, bias=True, bias_init_val=0, lr_mul=1, activation=None)
298
+
299
+ self.stylegan_decoder = StyleGAN2GeneratorSFT(
300
+ out_size=out_size,
301
+ num_style_feat=num_style_feat,
302
+ num_mlp=num_mlp,
303
+ channel_multiplier=channel_multiplier,
304
+ resample_kernel=resample_kernel,
305
+ lr_mlp=lr_mlp,
306
+ narrow=narrow,
307
+ sft_half=sft_half)
308
+
309
+ if decoder_load_path:
310
+ self.stylegan_decoder.load_state_dict(
311
+ torch.load(decoder_load_path, map_location=lambda storage, loc: storage)['params_ema'])
312
+ if fix_decoder:
313
+ for _, param in self.stylegan_decoder.named_parameters():
314
+ param.requires_grad = False
315
+
316
+ # for SFT
317
+ self.condition_scale = nn.ModuleList()
318
+ self.condition_shift = nn.ModuleList()
319
+ for i in range(3, self.log_size + 1):
320
+ out_channels = channels[f'{2**i}']
321
+ if sft_half:
322
+ sft_out_channels = out_channels
323
+ else:
324
+ sft_out_channels = out_channels * 2
325
+ self.condition_scale.append(
326
+ nn.Sequential(
327
+ EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
328
+ ScaledLeakyReLU(0.2),
329
+ EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=1)))
330
+ self.condition_shift.append(
331
+ nn.Sequential(
332
+ EqualConv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0),
333
+ ScaledLeakyReLU(0.2),
334
+ EqualConv2d(out_channels, sft_out_channels, 3, stride=1, padding=1, bias=True, bias_init_val=0)))
335
+
336
+ def forward(self,
337
+ x,
338
+ return_latents=False,
339
+ save_feat_path=None,
340
+ load_feat_path=None,
341
+ return_rgb=True,
342
+ randomize_noise=True):
343
+ conditions = []
344
+ unet_skips = []
345
+ out_rgbs = []
346
+
347
+ # encoder
348
+ feat = self.conv_body_first(x)
349
+ for i in range(self.log_size - 2):
350
+ feat = self.conv_body_down[i](feat)
351
+ unet_skips.insert(0, feat)
352
+
353
+ feat = self.final_conv(feat)
354
+
355
+ # style code
356
+ style_code = self.final_linear(feat.view(feat.size(0), -1))
357
+ if self.different_w:
358
+ style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
359
+
360
+ # decode
361
+ for i in range(self.log_size - 2):
362
+ # add unet skip
363
+ feat = feat + unet_skips[i]
364
+ # ResUpLayer
365
+ feat = self.conv_body_up[i](feat)
366
+ # generate scale and shift for SFT layer
367
+ scale = self.condition_scale[i](feat)
368
+ conditions.append(scale.clone())
369
+ shift = self.condition_shift[i](feat)
370
+ conditions.append(shift.clone())
371
+ # generate rgb images
372
+ if return_rgb:
373
+ out_rgbs.append(self.toRGB[i](feat))
374
+
375
+ if save_feat_path is not None:
376
+ torch.save(conditions, save_feat_path)
377
+ if load_feat_path is not None:
378
+ conditions = torch.load(load_feat_path)
379
+ conditions = [v.cuda() for v in conditions]
380
+
381
+ # decoder
382
+ image, _ = self.stylegan_decoder([style_code],
383
+ conditions,
384
+ return_latents=return_latents,
385
+ input_is_latent=self.input_is_latent,
386
+ randomize_noise=randomize_noise)
387
+
388
+ return image, out_rgbs
389
+
390
+
391
+ @ARCH_REGISTRY.register()
392
+ class FacialComponentDiscriminator(nn.Module):
393
+
394
+ def __init__(self):
395
+ super(FacialComponentDiscriminator, self).__init__()
396
+
397
+ self.conv1 = ConvLayer(3, 64, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
398
+ self.conv2 = ConvLayer(64, 128, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
399
+ self.conv3 = ConvLayer(128, 128, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
400
+ self.conv4 = ConvLayer(128, 256, 3, downsample=True, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
401
+ self.conv5 = ConvLayer(256, 256, 3, downsample=False, resample_kernel=(1, 3, 3, 1), bias=True, activate=True)
402
+ self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False)
403
+
404
+ def forward(self, x, return_feats=False):
405
+ feat = self.conv1(x)
406
+ feat = self.conv3(self.conv2(feat))
407
+ rlt_feats = []
408
+ if return_feats:
409
+ rlt_feats.append(feat.clone())
410
+ feat = self.conv5(self.conv4(feat))
411
+ if return_feats:
412
+ rlt_feats.append(feat.clone())
413
+ out = self.final_conv(feat)
414
+
415
+ if return_feats:
416
+ return out, rlt_feats
417
+ else:
418
+ return out, None
data/__init__.py ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ import importlib
2
+ from os import path as osp
3
+
4
+ from basicsr.utils import scandir
5
+
6
+ # automatically scan and import dataset modules for registry
7
+ # scan all the files under the data folder with '_dataset' in file names
8
+ data_folder = osp.dirname(osp.abspath(__file__))
9
+ dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')]
10
+ # import all the dataset modules
11
+ _dataset_modules = [importlib.import_module(f'data.{file_name}') for file_name in dataset_filenames]
data/ffhq_degradation_dataset.py ADDED
@@ -0,0 +1,213 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import cv2
2
+ import math
3
+ import numpy as np
4
+ import os.path as osp
5
+ import torch
6
+ import torch.utils.data as data
7
+ from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation,
8
+ normalize)
9
+
10
+ from basicsr.data import degradations as degradations
11
+ from basicsr.data.data_util import paths_from_folder
12
+ from basicsr.data.transforms import augment
13
+ from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
14
+ from basicsr.utils.registry import DATASET_REGISTRY
15
+
16
+
17
+ @DATASET_REGISTRY.register()
18
+ class FFHQDegradationDataset(data.Dataset):
19
+
20
+ def __init__(self, opt):
21
+ super(FFHQDegradationDataset, self).__init__()
22
+ self.opt = opt
23
+ # file client (io backend)
24
+ self.file_client = None
25
+ self.io_backend_opt = opt['io_backend']
26
+
27
+ self.gt_folder = opt['dataroot_gt']
28
+ self.mean = opt['mean']
29
+ self.std = opt['std']
30
+ self.out_size = opt['out_size']
31
+
32
+ self.crop_components = opt.get('crop_components', False) # facial components
33
+ self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1)
34
+
35
+ if self.crop_components:
36
+ self.components_list = torch.load(opt.get('component_path'))
37
+
38
+ if self.io_backend_opt['type'] == 'lmdb':
39
+ self.io_backend_opt['db_paths'] = self.gt_folder
40
+ if not self.gt_folder.endswith('.lmdb'):
41
+ raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}")
42
+ with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin:
43
+ self.paths = [line.split('.')[0] for line in fin]
44
+ else:
45
+ self.paths = paths_from_folder(self.gt_folder)
46
+
47
+ # degradations
48
+ self.blur_kernel_size = opt['blur_kernel_size']
49
+ self.kernel_list = opt['kernel_list']
50
+ self.kernel_prob = opt['kernel_prob']
51
+ self.blur_sigma = opt['blur_sigma']
52
+ self.downsample_range = opt['downsample_range']
53
+ self.noise_range = opt['noise_range']
54
+ self.jpeg_range = opt['jpeg_range']
55
+
56
+ # color jitter
57
+ self.color_jitter_prob = opt.get('color_jitter_prob')
58
+ self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob')
59
+ self.color_jitter_shift = opt.get('color_jitter_shift', 20)
60
+ # to gray
61
+ self.gray_prob = opt.get('gray_prob')
62
+
63
+ logger = get_root_logger()
64
+ logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, '
65
+ f'sigma: [{", ".join(map(str, self.blur_sigma))}]')
66
+ logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]')
67
+ logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]')
68
+ logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]')
69
+
70
+ if self.color_jitter_prob is not None:
71
+ logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, '
72
+ f'shift: {self.color_jitter_shift}')
73
+ if self.gray_prob is not None:
74
+ logger.info(f'Use random gray. Prob: {self.gray_prob}')
75
+
76
+ self.color_jitter_shift /= 255.
77
+
78
+ @staticmethod
79
+ def color_jitter(img, shift):
80
+ jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32)
81
+ img = img + jitter_val
82
+ img = np.clip(img, 0, 1)
83
+ return img
84
+
85
+ @staticmethod
86
+ def color_jitter_pt(img, brightness, contrast, saturation, hue):
87
+ fn_idx = torch.randperm(4)
88
+ for fn_id in fn_idx:
89
+ if fn_id == 0 and brightness is not None:
90
+ brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item()
91
+ img = adjust_brightness(img, brightness_factor)
92
+
93
+ if fn_id == 1 and contrast is not None:
94
+ contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item()
95
+ img = adjust_contrast(img, contrast_factor)
96
+
97
+ if fn_id == 2 and saturation is not None:
98
+ saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item()
99
+ img = adjust_saturation(img, saturation_factor)
100
+
101
+ if fn_id == 3 and hue is not None:
102
+ hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item()
103
+ img = adjust_hue(img, hue_factor)
104
+ return img
105
+
106
+ def get_component_coordinates(self, index, status):
107
+ components_bbox = self.components_list[f'{index:08d}']
108
+ if status[0]: # hflip
109
+ # exchange right and left eye
110
+ tmp = components_bbox['left_eye']
111
+ components_bbox['left_eye'] = components_bbox['right_eye']
112
+ components_bbox['right_eye'] = tmp
113
+ # modify the width coordinate
114
+ components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0]
115
+ components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0]
116
+ components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0]
117
+
118
+ # get coordinates
119
+ locations = []
120
+ for part in ['left_eye', 'right_eye', 'mouth']:
121
+ mean = components_bbox[part][0:2]
122
+ half_len = components_bbox[part][2]
123
+ if 'eye' in part:
124
+ half_len *= self.eye_enlarge_ratio
125
+ loc = np.hstack((mean - half_len + 1, mean + half_len))
126
+ loc = torch.from_numpy(loc).float()
127
+ locations.append(loc)
128
+ return locations
129
+
130
+ def __getitem__(self, index):
131
+ if self.file_client is None:
132
+ self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
133
+
134
+ # load gt image
135
+ gt_path = self.paths[index]
136
+ img_bytes = self.file_client.get(gt_path)
137
+ img_gt = imfrombytes(img_bytes, float32=True)
138
+
139
+ # random horizontal flip
140
+ img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True)
141
+ h, w, _ = img_gt.shape
142
+
143
+ if self.crop_components:
144
+ locations = self.get_component_coordinates(index, status)
145
+ loc_left_eye, loc_right_eye, loc_mouth = locations
146
+
147
+ # ------------------------ generate lq image ------------------------ #
148
+ # blur
149
+ kernel = degradations.random_mixed_kernels(
150
+ self.kernel_list,
151
+ self.kernel_prob,
152
+ self.blur_kernel_size,
153
+ self.blur_sigma,
154
+ self.blur_sigma, [-math.pi, math.pi],
155
+ noise_range=None)
156
+ img_lq = cv2.filter2D(img_gt, -1, kernel)
157
+ # downsample
158
+ scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1])
159
+ img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR)
160
+ # noise
161
+ if self.noise_range is not None:
162
+ img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range)
163
+ # jpeg compression
164
+ if self.jpeg_range is not None:
165
+ img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range)
166
+
167
+ # resize to original size
168
+ img_lq = cv2.resize(img_lq, (w, h), interpolation=cv2.INTER_LINEAR)
169
+
170
+ # random color jitter (only for lq)
171
+ if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob):
172
+ img_lq = self.color_jitter(img_lq, self.color_jitter_shift)
173
+ # random to gray (only for lq)
174
+ if self.gray_prob and np.random.uniform() < self.gray_prob:
175
+ img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY)
176
+ img_lq = np.tile(img_lq[:, :, None], [1, 1, 3])
177
+ if self.opt.get('gt_gray'):
178
+ img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY)
179
+ img_gt = np.tile(img_gt[:, :, None], [1, 1, 3])
180
+
181
+ # BGR to RGB, HWC to CHW, numpy to tensor
182
+ img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
183
+
184
+ # random color jitter (pytorch version) (only for lq)
185
+ if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob):
186
+ brightness = self.opt.get('brightness', (0.5, 1.5))
187
+ contrast = self.opt.get('contrast', (0.5, 1.5))
188
+ saturation = self.opt.get('saturation', (0, 1.5))
189
+ hue = self.opt.get('hue', (-0.1, 0.1))
190
+ img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue)
191
+
192
+ # round and clip
193
+ img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255.
194
+
195
+ # normalize
196
+ normalize(img_gt, self.mean, self.std, inplace=True)
197
+ normalize(img_lq, self.mean, self.std, inplace=True)
198
+
199
+ if self.crop_components:
200
+ return_dict = {
201
+ 'lq': img_lq,
202
+ 'gt': img_gt,
203
+ 'gt_path': gt_path,
204
+ 'loc_left_eye': loc_left_eye,
205
+ 'loc_right_eye': loc_right_eye,
206
+ 'loc_mouth': loc_mouth
207
+ }
208
+ return return_dict
209
+ else:
210
+ return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path}
211
+
212
+ def __len__(self):
213
+ return len(self.paths)
experiments/pretrained_models/README.md ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
1
+ # Pre-trained Models and Other Data
2
+
3
+ Download pre-trained models and other data. Put them in this folder.
4
+
5
+ 1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
6
+ 1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
7
+ 1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
inference_gfpgan_full.py ADDED
@@ -0,0 +1,130 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import cv2
3
+ import glob
4
+ import numpy as np
5
+ import os
6
+ import torch
7
+ from facexlib.utils.face_restoration_helper import FaceRestoreHelper
8
+ from torchvision.transforms.functional import normalize
9
+
10
+ from archs.gfpganv1_arch import GFPGANv1
11
+ from basicsr.utils import img2tensor, imwrite, tensor2img
12
+
13
+
14
+ def restoration(gfpgan,
15
+ face_helper,
16
+ img_path,
17
+ save_root,
18
+ has_aligned=False,
19
+ only_center_face=True,
20
+ suffix=None,
21
+ paste_back=False):
22
+ # read image
23
+ img_name = os.path.basename(img_path)
24
+ print(f'Processing {img_name} ...')
25
+ basename, _ = os.path.splitext(img_name)
26
+ input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
27
+ face_helper.clean_all()
28
+
29
+ if has_aligned:
30
+ input_img = cv2.resize(input_img, (512, 512))
31
+ face_helper.cropped_faces = [input_img]
32
+ else:
33
+ face_helper.read_image(input_img)
34
+ # get face landmarks for each face
35
+ face_helper.get_face_landmarks_5(only_center_face=only_center_face, pad_blur=False)
36
+ # align and warp each face
37
+ save_crop_path = os.path.join(save_root, 'cropped_faces', img_name)
38
+ face_helper.align_warp_face(save_crop_path)
39
+
40
+ # face restoration
41
+ for idx, cropped_face in enumerate(face_helper.cropped_faces):
42
+ # prepare data
43
+ cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
44
+ normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
45
+ cropped_face_t = cropped_face_t.unsqueeze(0).to('cuda')
46
+
47
+ try:
48
+ with torch.no_grad():
49
+ output = gfpgan(cropped_face_t, return_rgb=False)[0]
50
+ # convert to image
51
+ restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
52
+ except RuntimeError as error:
53
+ print(f'\tFailed inference for GFPGAN: {error}.')
54
+ restored_face = cropped_face
55
+
56
+ restored_face = restored_face.astype('uint8')
57
+ face_helper.add_restored_face(restored_face)
58
+
59
+ if suffix is not None:
60
+ save_face_name = f'{basename}_{idx:02d}_{suffix}.png'
61
+ else:
62
+ save_face_name = f'{basename}_{idx:02d}.png'
63
+ save_restore_path = os.path.join(save_root, 'restored_faces', save_face_name)
64
+ imwrite(restored_face, save_restore_path)
65
+
66
+ # save cmp image
67
+ cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
68
+ imwrite(cmp_img, os.path.join(save_root, 'cmp', f'{basename}_{idx:02d}.png'))
69
+
70
+ if not has_aligned and paste_back:
71
+ face_helper.get_inverse_affine(None)
72
+ save_restore_path = os.path.join(save_root, 'restored_imgs', img_name)
73
+ # paste each restored face to the input image
74
+ face_helper.paste_faces_to_input_image(save_restore_path)
75
+
76
+
77
+ if __name__ == '__main__':
78
+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
79
+ parser = argparse.ArgumentParser()
80
+
81
+ parser.add_argument('--upscale_factor', type=int, default=1)
82
+ parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.pth')
83
+ parser.add_argument('--test_path', type=str, default='inputs/whole_imgs')
84
+ parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
85
+ parser.add_argument('--only_center_face', action='store_true')
86
+ parser.add_argument('--aligned', action='store_true')
87
+ parser.add_argument('--paste_back', action='store_true')
88
+
89
+ args = parser.parse_args()
90
+ if args.test_path.endswith('/'):
91
+ args.test_path = args.test_path[:-1]
92
+ save_root = 'results/'
93
+ os.makedirs(save_root, exist_ok=True)
94
+
95
+ # initialize the GFP-GAN
96
+ gfpgan = GFPGANv1(
97
+ out_size=512,
98
+ num_style_feat=512,
99
+ channel_multiplier=1,
100
+ decoder_load_path=None,
101
+ fix_decoder=True,
102
+ # for stylegan decoder
103
+ num_mlp=8,
104
+ input_is_latent=True,
105
+ different_w=True,
106
+ narrow=1,
107
+ sft_half=True)
108
+
109
+ gfpgan.to(device)
110
+ checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage)
111
+ gfpgan.load_state_dict(checkpoint['params_ema'])
112
+ gfpgan.eval()
113
+
114
+ # initialize face helper
115
+ face_helper = FaceRestoreHelper(
116
+ args.upscale_factor, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png')
117
+
118
+ img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
119
+ for img_path in img_list:
120
+ restoration(
121
+ gfpgan,
122
+ face_helper,
123
+ img_path,
124
+ save_root,
125
+ has_aligned=args.aligned,
126
+ only_center_face=args.only_center_face,
127
+ suffix=args.suffix,
128
+ paste_back=args.paste_back)
129
+
130
+ print('Results are in the <results> folder.')
models/__init__.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import importlib
2
+ from os import path as osp
3
+
4
+ from basicsr.utils import scandir
5
+
6
+ # automatically scan and import model modules for registry
7
+ # scan all the files under the 'models' folder and collect files ending with
8
+ # '_model.py'
9
+ model_folder = osp.dirname(osp.abspath(__file__))
10
+ model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')]
11
+ # import all the model modules
12
+ _model_modules = [importlib.import_module(f'models.{file_name}') for file_name in model_filenames]
models/gfpgan_model.py ADDED
@@ -0,0 +1,562 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ import os.path as osp
3
+ import torch
4
+ from collections import OrderedDict
5
+ from torch.nn import functional as F
6
+ from torchvision.ops import roi_align
7
+ from tqdm import tqdm
8
+
9
+ from basicsr.archs import build_network
10
+ from basicsr.losses import build_loss
11
+ from basicsr.losses.losses import r1_penalty
12
+ from basicsr.metrics import calculate_metric
13
+ from basicsr.models.base_model import BaseModel
14
+ from basicsr.utils import get_root_logger, imwrite, tensor2img
15
+ from basicsr.utils.registry import MODEL_REGISTRY
16
+
17
+
18
+ @MODEL_REGISTRY.register()
19
+ class GFPGANModel(BaseModel):
20
+ """GFPGAN model for <Towards real-world blind face restoratin with generative facial prior>"""
21
+
22
+ def __init__(self, opt):
23
+ super(GFPGANModel, self).__init__(opt)
24
+ self.idx = 0
25
+
26
+ # define network
27
+ self.net_g = build_network(opt['network_g'])
28
+ self.net_g = self.model_to_device(self.net_g)
29
+ self.print_network(self.net_g)
30
+
31
+ # load pretrained model
32
+ load_path = self.opt['path'].get('pretrain_network_g', None)
33
+ if load_path is not None:
34
+ param_key = self.opt['path'].get('param_key_g', 'params')
35
+ self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
36
+
37
+ self.log_size = int(math.log(self.opt['network_g']['out_size'], 2))
38
+
39
+ if self.is_train:
40
+ self.init_training_settings()
41
+
42
+ def init_training_settings(self):
43
+ train_opt = self.opt['train']
44
+
45
+ # ----------- define net_d ----------- #
46
+ self.net_d = build_network(self.opt['network_d'])
47
+ self.net_d = self.model_to_device(self.net_d)
48
+ self.print_network(self.net_d)
49
+ # load pretrained model
50
+ load_path = self.opt['path'].get('pretrain_network_d', None)
51
+ if load_path is not None:
52
+ self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True))
53
+
54
+ # ----------- define net_g with Exponential Moving Average (EMA) ----------- #
55
+ # net_g_ema only used for testing on one GPU and saving
56
+ # There is no need to wrap with DistributedDataParallel
57
+ self.net_g_ema = build_network(self.opt['network_g']).to(self.device)
58
+ # load pretrained model
59
+ load_path = self.opt['path'].get('pretrain_network_g', None)
60
+ if load_path is not None:
61
+ self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema')
62
+ else:
63
+ self.model_ema(0) # copy net_g weight
64
+
65
+ self.net_g.train()
66
+ self.net_d.train()
67
+ self.net_g_ema.eval()
68
+
69
+ # ----------- facial components networks ----------- #
70
+ if ('network_d_left_eye' in self.opt and 'network_d_right_eye' in self.opt and 'network_d_mouth' in self.opt):
71
+ self.use_facial_disc = True
72
+ else:
73
+ self.use_facial_disc = False
74
+
75
+ if self.use_facial_disc:
76
+ # left eye
77
+ self.net_d_left_eye = build_network(self.opt['network_d_left_eye'])
78
+ self.net_d_left_eye = self.model_to_device(self.net_d_left_eye)
79
+ self.print_network(self.net_d_left_eye)
80
+ load_path = self.opt['path'].get('pretrain_network_d_left_eye')
81
+ if load_path is not None:
82
+ self.load_network(self.net_d_left_eye, load_path, True, 'params')
83
+ # right eye
84
+ self.net_d_right_eye = build_network(self.opt['network_d_right_eye'])
85
+ self.net_d_right_eye = self.model_to_device(self.net_d_right_eye)
86
+ self.print_network(self.net_d_right_eye)
87
+ load_path = self.opt['path'].get('pretrain_network_d_right_eye')
88
+ if load_path is not None:
89
+ self.load_network(self.net_d_right_eye, load_path, True, 'params')
90
+ # mouth
91
+ self.net_d_mouth = build_network(self.opt['network_d_mouth'])
92
+ self.net_d_mouth = self.model_to_device(self.net_d_mouth)
93
+ self.print_network(self.net_d_mouth)
94
+ load_path = self.opt['path'].get('pretrain_network_d_mouth')
95
+ if load_path is not None:
96
+ self.load_network(self.net_d_mouth, load_path, True, 'params')
97
+
98
+ self.net_d_left_eye.train()
99
+ self.net_d_right_eye.train()
100
+ self.net_d_mouth.train()
101
+
102
+ # ----------- define facial component gan loss ----------- #
103
+ self.cri_component = build_loss(train_opt['gan_component_opt']).to(self.device)
104
+
105
+ # ----------- define losses ----------- #
106
+ if train_opt.get('pixel_opt'):
107
+ self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device)
108
+ else:
109
+ self.cri_pix = None
110
+
111
+ if train_opt.get('perceptual_opt'):
112
+ self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device)
113
+ else:
114
+ self.cri_perceptual = None
115
+
116
+ # L1 loss used in pyramid loss, component style loss and identity loss
117
+ self.cri_l1 = build_loss(train_opt['L1_opt']).to(self.device)
118
+
119
+ # gan loss (wgan)
120
+ self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device)
121
+
122
+ # ----------- define identity loss ----------- #
123
+ if 'network_identity' in self.opt:
124
+ self.use_identity = True
125
+ else:
126
+ self.use_identity = False
127
+
128
+ if self.use_identity:
129
+ # define identity network
130
+ self.network_identity = build_network(self.opt['network_identity'])
131
+ self.network_identity = self.model_to_device(self.network_identity)
132
+ self.print_network(self.network_identity)
133
+ load_path = self.opt['path'].get('pretrain_network_identity')
134
+ if load_path is not None:
135
+ self.load_network(self.network_identity, load_path, True, None)
136
+ self.network_identity.eval()
137
+ for param in self.network_identity.parameters():
138
+ param.requires_grad = False
139
+
140
+ # regularization weights
141
+ self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator
142
+ self.net_d_iters = train_opt.get('net_d_iters', 1)
143
+ self.net_d_init_iters = train_opt.get('net_d_init_iters', 0)
144
+ self.net_d_reg_every = train_opt['net_d_reg_every']
145
+
146
+ # set up optimizers and schedulers
147
+ self.setup_optimizers()
148
+ self.setup_schedulers()
149
+
150
+ def setup_optimizers(self):
151
+ train_opt = self.opt['train']
152
+
153
+ # ----------- optimizer g ----------- #
154
+ net_g_reg_ratio = 1
155
+ normal_params = []
156
+ for _, param in self.net_g.named_parameters():
157
+ normal_params.append(param)
158
+ optim_params_g = [{ # add normal params first
159
+ 'params': normal_params,
160
+ 'lr': train_opt['optim_g']['lr']
161
+ }]
162
+ optim_type = train_opt['optim_g'].pop('type')
163
+ lr = train_opt['optim_g']['lr'] * net_g_reg_ratio
164
+ betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio)
165
+ self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas)
166
+ self.optimizers.append(self.optimizer_g)
167
+
168
+ # ----------- optimizer d ----------- #
169
+ net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1)
170
+ normal_params = []
171
+ for _, param in self.net_d.named_parameters():
172
+ normal_params.append(param)
173
+ optim_params_d = [{ # add normal params first
174
+ 'params': normal_params,
175
+ 'lr': train_opt['optim_d']['lr']
176
+ }]
177
+ optim_type = train_opt['optim_d'].pop('type')
178
+ lr = train_opt['optim_d']['lr'] * net_d_reg_ratio
179
+ betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio)
180
+ self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas)
181
+ self.optimizers.append(self.optimizer_d)
182
+
183
+ if self.use_facial_disc:
184
+ # setup optimizers for facial component discriminators
185
+ optim_type = train_opt['optim_component'].pop('type')
186
+ lr = train_opt['optim_component']['lr']
187
+ # left eye
188
+ self.optimizer_d_left_eye = self.get_optimizer(
189
+ optim_type, self.net_d_left_eye.parameters(), lr, betas=(0.9, 0.99))
190
+ self.optimizers.append(self.optimizer_d_left_eye)
191
+ # right eye
192
+ self.optimizer_d_right_eye = self.get_optimizer(
193
+ optim_type, self.net_d_right_eye.parameters(), lr, betas=(0.9, 0.99))
194
+ self.optimizers.append(self.optimizer_d_right_eye)
195
+ # mouth
196
+ self.optimizer_d_mouth = self.get_optimizer(
197
+ optim_type, self.net_d_mouth.parameters(), lr, betas=(0.9, 0.99))
198
+ self.optimizers.append(self.optimizer_d_mouth)
199
+
200
+ def feed_data(self, data):
201
+ self.lq = data['lq'].to(self.device)
202
+ if 'gt' in data:
203
+ self.gt = data['gt'].to(self.device)
204
+
205
+ if 'loc_left_eye' in data:
206
+ # get facial component locations, shape (batch, 4)
207
+ self.loc_left_eyes = data['loc_left_eye']
208
+ self.loc_right_eyes = data['loc_right_eye']
209
+ self.loc_mouths = data['loc_mouth']
210
+
211
+ # uncomment to check data
212
+ # import torchvision
213
+ # if self.opt['rank'] == 0:
214
+ # import os
215
+ # os.makedirs('tmp/gt', exist_ok=True)
216
+ # os.makedirs('tmp/lq', exist_ok=True)
217
+ # print(self.idx)
218
+ # torchvision.utils.save_image(
219
+ # self.gt, f'tmp/gt/gt_{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
220
+ # torchvision.utils.save_image(
221
+ # self.lq, f'tmp/lq/lq{self.idx}.png', nrow=4, padding=2, normalize=True, range=(-1, 1))
222
+ # self.idx = self.idx + 1
223
+
224
+ def construct_img_pyramid(self):
225
+ pyramid_gt = [self.gt]
226
+ down_img = self.gt
227
+ for _ in range(0, self.log_size - 3):
228
+ down_img = F.interpolate(down_img, scale_factor=0.5, mode='bilinear', align_corners=False)
229
+ pyramid_gt.insert(0, down_img)
230
+ return pyramid_gt
231
+
232
+ def get_roi_regions(self, eye_out_size=80, mouth_out_size=120):
233
+ # hard code
234
+ face_ratio = int(self.opt['network_g']['out_size'] / 512)
235
+ eye_out_size *= face_ratio
236
+ mouth_out_size *= face_ratio
237
+
238
+ rois_eyes = []
239
+ rois_mouths = []
240
+ for b in range(self.loc_left_eyes.size(0)): # loop for batch size
241
+ # left eye and right eye
242
+ img_inds = self.loc_left_eyes.new_full((2, 1), b)
243
+ bbox = torch.stack([self.loc_left_eyes[b, :], self.loc_right_eyes[b, :]], dim=0) # shape: (2, 4)
244
+ rois = torch.cat([img_inds, bbox], dim=-1) # shape: (2, 5)
245
+ rois_eyes.append(rois)
246
+ # mouse
247
+ img_inds = self.loc_left_eyes.new_full((1, 1), b)
248
+ rois = torch.cat([img_inds, self.loc_mouths[b:b + 1, :]], dim=-1) # shape: (1, 5)
249
+ rois_mouths.append(rois)
250
+
251
+ rois_eyes = torch.cat(rois_eyes, 0).to(self.device)
252
+ rois_mouths = torch.cat(rois_mouths, 0).to(self.device)
253
+
254
+ # real images
255
+ all_eyes = roi_align(self.gt, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
256
+ self.left_eyes_gt = all_eyes[0::2, :, :, :]
257
+ self.right_eyes_gt = all_eyes[1::2, :, :, :]
258
+ self.mouths_gt = roi_align(self.gt, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio
259
+ # output
260
+ all_eyes = roi_align(self.output, boxes=rois_eyes, output_size=eye_out_size) * face_ratio
261
+ self.left_eyes = all_eyes[0::2, :, :, :]
262
+ self.right_eyes = all_eyes[1::2, :, :, :]
263
+ self.mouths = roi_align(self.output, boxes=rois_mouths, output_size=mouth_out_size) * face_ratio
264
+
265
+ def _gram_mat(self, x):
266
+ """Calculate Gram matrix.
267
+
268
+ Args:
269
+ x (torch.Tensor): Tensor with shape of (n, c, h, w).
270
+
271
+ Returns:
272
+ torch.Tensor: Gram matrix.
273
+ """
274
+ n, c, h, w = x.size()
275
+ features = x.view(n, c, w * h)
276
+ features_t = features.transpose(1, 2)
277
+ gram = features.bmm(features_t) / (c * h * w)
278
+ return gram
279
+
280
+ def gray_resize_for_identity(self, out, size=128):
281
+ out_gray = (0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :])
282
+ out_gray = out_gray.unsqueeze(1)
283
+ out_gray = F.interpolate(out_gray, (size, size), mode='bilinear', align_corners=False)
284
+ return out_gray
285
+
286
+ def optimize_parameters(self, current_iter):
287
+ # optimize net_g
288
+ for p in self.net_d.parameters():
289
+ p.requires_grad = False
290
+ self.optimizer_g.zero_grad()
291
+
292
+ if self.use_facial_disc:
293
+ for p in self.net_d_left_eye.parameters():
294
+ p.requires_grad = False
295
+ for p in self.net_d_right_eye.parameters():
296
+ p.requires_grad = False
297
+ for p in self.net_d_mouth.parameters():
298
+ p.requires_grad = False
299
+
300
+ # image pyramid loss weight
301
+ if current_iter < self.opt['train'].get('remove_pyramid_loss', float('inf')):
302
+ pyramid_loss_weight = self.opt['train'].get('pyramid_loss_weight', 1)
303
+ else:
304
+ pyramid_loss_weight = 1e-12 # very small loss
305
+ if pyramid_loss_weight > 0:
306
+ self.output, out_rgbs = self.net_g(self.lq, return_rgb=True)
307
+ pyramid_gt = self.construct_img_pyramid()
308
+ else:
309
+ self.output, out_rgbs = self.net_g(self.lq, return_rgb=False)
310
+
311
+ # get roi-align regions
312
+ if self.use_facial_disc:
313
+ self.get_roi_regions(eye_out_size=80, mouth_out_size=120)
314
+
315
+ l_g_total = 0
316
+ loss_dict = OrderedDict()
317
+ if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters):
318
+ # pixel loss
319
+ if self.cri_pix:
320
+ l_g_pix = self.cri_pix(self.output, self.gt)
321
+ l_g_total += l_g_pix
322
+ loss_dict['l_g_pix'] = l_g_pix
323
+
324
+ # image pyramid loss
325
+ if pyramid_loss_weight > 0:
326
+ for i in range(0, self.log_size - 2):
327
+ l_pyramid = self.cri_l1(out_rgbs[i], pyramid_gt[i]) * pyramid_loss_weight
328
+ l_g_total += l_pyramid
329
+ loss_dict[f'l_p_{2**(i+3)}'] = l_pyramid
330
+
331
+ # perceptual loss
332
+ if self.cri_perceptual:
333
+ l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt)
334
+ if l_g_percep is not None:
335
+ l_g_total += l_g_percep
336
+ loss_dict['l_g_percep'] = l_g_percep
337
+ if l_g_style is not None:
338
+ l_g_total += l_g_style
339
+ loss_dict['l_g_style'] = l_g_style
340
+
341
+ # gan loss
342
+ fake_g_pred = self.net_d(self.output)
343
+ l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False)
344
+ l_g_total += l_g_gan
345
+ loss_dict['l_g_gan'] = l_g_gan
346
+
347
+ # facial component loss
348
+ if self.use_facial_disc:
349
+ # left eye
350
+ fake_left_eye, fake_left_eye_feats = self.net_d_left_eye(self.left_eyes, return_feats=True)
351
+ l_g_gan = self.cri_component(fake_left_eye, True, is_disc=False)
352
+ l_g_total += l_g_gan
353
+ loss_dict['l_g_gan_left_eye'] = l_g_gan
354
+ # right eye
355
+ fake_right_eye, fake_right_eye_feats = self.net_d_right_eye(self.right_eyes, return_feats=True)
356
+ l_g_gan = self.cri_component(fake_right_eye, True, is_disc=False)
357
+ l_g_total += l_g_gan
358
+ loss_dict['l_g_gan_right_eye'] = l_g_gan
359
+ # mouth
360
+ fake_mouth, fake_mouth_feats = self.net_d_mouth(self.mouths, return_feats=True)
361
+ l_g_gan = self.cri_component(fake_mouth, True, is_disc=False)
362
+ l_g_total += l_g_gan
363
+ loss_dict['l_g_gan_mouth'] = l_g_gan
364
+
365
+ if self.opt['train'].get('comp_style_weight', 0) > 0:
366
+ # get gt feat
367
+ _, real_left_eye_feats = self.net_d_left_eye(self.left_eyes_gt, return_feats=True)
368
+ _, real_right_eye_feats = self.net_d_right_eye(self.right_eyes_gt, return_feats=True)
369
+ _, real_mouth_feats = self.net_d_mouth(self.mouths_gt, return_feats=True)
370
+
371
+ def _comp_style(feat, feat_gt, criterion):
372
+ return criterion(self._gram_mat(feat[0]), self._gram_mat(
373
+ feat_gt[0].detach())) * 0.5 + criterion(
374
+ self._gram_mat(feat[1]), self._gram_mat(feat_gt[1].detach()))
375
+
376
+ # facial component style loss
377
+ comp_style_loss = 0
378
+ comp_style_loss += _comp_style(fake_left_eye_feats, real_left_eye_feats, self.cri_l1)
379
+ comp_style_loss += _comp_style(fake_right_eye_feats, real_right_eye_feats, self.cri_l1)
380
+ comp_style_loss += _comp_style(fake_mouth_feats, real_mouth_feats, self.cri_l1)
381
+ comp_style_loss = comp_style_loss * self.opt['train']['comp_style_weight']
382
+ l_g_total += comp_style_loss
383
+ loss_dict['l_g_comp_style_loss'] = comp_style_loss
384
+
385
+ # identity loss
386
+ if self.use_identity:
387
+ identity_weight = self.opt['train']['identity_weight']
388
+ # get gray images and resize
389
+ out_gray = self.gray_resize_for_identity(self.output)
390
+ gt_gray = self.gray_resize_for_identity(self.gt)
391
+
392
+ identity_gt = self.network_identity(gt_gray).detach()
393
+ identity_out = self.network_identity(out_gray)
394
+ l_identity = self.cri_l1(identity_out, identity_gt) * identity_weight
395
+ l_g_total += l_identity
396
+ loss_dict['l_identity'] = l_identity
397
+
398
+ l_g_total.backward()
399
+ self.optimizer_g.step()
400
+
401
+ # EMA
402
+ self.model_ema(decay=0.5**(32 / (10 * 1000)))
403
+
404
+ # ----------- optimize net_d ----------- #
405
+ for p in self.net_d.parameters():
406
+ p.requires_grad = True
407
+ self.optimizer_d.zero_grad()
408
+ if self.use_facial_disc:
409
+ for p in self.net_d_left_eye.parameters():
410
+ p.requires_grad = True
411
+ for p in self.net_d_right_eye.parameters():
412
+ p.requires_grad = True
413
+ for p in self.net_d_mouth.parameters():
414
+ p.requires_grad = True
415
+ self.optimizer_d_left_eye.zero_grad()
416
+ self.optimizer_d_right_eye.zero_grad()
417
+ self.optimizer_d_mouth.zero_grad()
418
+
419
+ fake_d_pred = self.net_d(self.output.detach())
420
+ real_d_pred = self.net_d(self.gt)
421
+ l_d = self.cri_gan(real_d_pred, True, is_disc=True) + self.cri_gan(fake_d_pred, False, is_disc=True)
422
+ loss_dict['l_d'] = l_d
423
+ # In wgan, real_score should be positive and fake_score should benegative
424
+ loss_dict['real_score'] = real_d_pred.detach().mean()
425
+ loss_dict['fake_score'] = fake_d_pred.detach().mean()
426
+ l_d.backward()
427
+
428
+ if current_iter % self.net_d_reg_every == 0:
429
+ self.gt.requires_grad = True
430
+ real_pred = self.net_d(self.gt)
431
+ l_d_r1 = r1_penalty(real_pred, self.gt)
432
+ l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0])
433
+ loss_dict['l_d_r1'] = l_d_r1.detach().mean()
434
+ l_d_r1.backward()
435
+
436
+ self.optimizer_d.step()
437
+
438
+ if self.use_facial_disc:
439
+ # lefe eye
440
+ fake_d_pred, _ = self.net_d_left_eye(self.left_eyes.detach())
441
+ real_d_pred, _ = self.net_d_left_eye(self.left_eyes_gt)
442
+ l_d_left_eye = self.cri_component(
443
+ real_d_pred, True, is_disc=True) + self.cri_gan(
444
+ fake_d_pred, False, is_disc=True)
445
+ loss_dict['l_d_left_eye'] = l_d_left_eye
446
+ l_d_left_eye.backward()
447
+ # right eye
448
+ fake_d_pred, _ = self.net_d_right_eye(self.right_eyes.detach())
449
+ real_d_pred, _ = self.net_d_right_eye(self.right_eyes_gt)
450
+ l_d_right_eye = self.cri_component(
451
+ real_d_pred, True, is_disc=True) + self.cri_gan(
452
+ fake_d_pred, False, is_disc=True)
453
+ loss_dict['l_d_right_eye'] = l_d_right_eye
454
+ l_d_right_eye.backward()
455
+ # mouth
456
+ fake_d_pred, _ = self.net_d_mouth(self.mouths.detach())
457
+ real_d_pred, _ = self.net_d_mouth(self.mouths_gt)
458
+ l_d_mouth = self.cri_component(
459
+ real_d_pred, True, is_disc=True) + self.cri_gan(
460
+ fake_d_pred, False, is_disc=True)
461
+ loss_dict['l_d_mouth'] = l_d_mouth
462
+ l_d_mouth.backward()
463
+
464
+ self.optimizer_d_left_eye.step()
465
+ self.optimizer_d_right_eye.step()
466
+ self.optimizer_d_mouth.step()
467
+
468
+ self.log_dict = self.reduce_loss_dict(loss_dict)
469
+
470
+ def test(self):
471
+ with torch.no_grad():
472
+ if hasattr(self, 'net_g_ema'):
473
+ self.net_g_ema.eval()
474
+ self.output, _ = self.net_g_ema(self.lq)
475
+ else:
476
+ logger = get_root_logger()
477
+ logger.warning('Do not have self.net_g_ema, use self.net_g.')
478
+ self.net_g.eval()
479
+ self.output, _ = self.net_g(self.lq)
480
+ self.net_g.train()
481
+
482
+ def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
483
+ if self.opt['rank'] == 0:
484
+ self.nondist_validation(dataloader, current_iter, tb_logger, save_img)
485
+
486
+ def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
487
+ dataset_name = dataloader.dataset.opt['name']
488
+ with_metrics = self.opt['val'].get('metrics') is not None
489
+ if with_metrics:
490
+ self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
491
+ pbar = tqdm(total=len(dataloader), unit='image')
492
+
493
+ for idx, val_data in enumerate(dataloader):
494
+ img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0]
495
+ self.feed_data(val_data)
496
+ self.test()
497
+
498
+ visuals = self.get_current_visuals()
499
+ sr_img = tensor2img([visuals['sr']], min_max=(-1, 1))
500
+ gt_img = tensor2img([visuals['gt']], min_max=(-1, 1))
501
+
502
+ if 'gt' in visuals:
503
+ gt_img = tensor2img([visuals['gt']], min_max=(-1, 1))
504
+ del self.gt
505
+ # tentative for out of GPU memory
506
+ del self.lq
507
+ del self.output
508
+ torch.cuda.empty_cache()
509
+
510
+ if save_img:
511
+ if self.opt['is_train']:
512
+ save_img_path = osp.join(self.opt['path']['visualization'], img_name,
513
+ f'{img_name}_{current_iter}.png')
514
+ else:
515
+ if self.opt['val']['suffix']:
516
+ save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
517
+ f'{img_name}_{self.opt["val"]["suffix"]}.png')
518
+ else:
519
+ save_img_path = osp.join(self.opt['path']['visualization'], dataset_name,
520
+ f'{img_name}_{self.opt["name"]}.png')
521
+ imwrite(sr_img, save_img_path)
522
+
523
+ if with_metrics:
524
+ # calculate metrics
525
+ for name, opt_ in self.opt['val']['metrics'].items():
526
+ metric_data = dict(img1=sr_img, img2=gt_img)
527
+ self.metric_results[name] += calculate_metric(metric_data, opt_)
528
+ pbar.update(1)
529
+ pbar.set_description(f'Test {img_name}')
530
+ pbar.close()
531
+
532
+ if with_metrics:
533
+ for metric in self.metric_results.keys():
534
+ self.metric_results[metric] /= (idx + 1)
535
+
536
+ self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
537
+
538
+ def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
539
+ log_str = f'Validation {dataset_name}\n'
540
+ for metric, value in self.metric_results.items():
541
+ log_str += f'\t # {metric}: {value:.4f}\n'
542
+ logger = get_root_logger()
543
+ logger.info(log_str)
544
+ if tb_logger:
545
+ for metric, value in self.metric_results.items():
546
+ tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)
547
+
548
+ def get_current_visuals(self):
549
+ out_dict = OrderedDict()
550
+ out_dict['gt'] = self.gt.detach().cpu()
551
+ out_dict['sr'] = self.output.detach().cpu()
552
+ return out_dict
553
+
554
+ def save(self, epoch, current_iter):
555
+ self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema'])
556
+ self.save_network(self.net_d, 'net_d', current_iter)
557
+ # save component discriminators
558
+ if self.use_facial_disc:
559
+ self.save_network(self.net_d_left_eye, 'net_d_left_eye', current_iter)
560
+ self.save_network(self.net_d_right_eye, 'net_d_right_eye', current_iter)
561
+ self.save_network(self.net_d_mouth, 'net_d_mouth', current_iter)
562
+ self.save_training_state(epoch, current_iter)
requirements.txt ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
1
+ facexlib
2
+ lmdb
3
+ numpy
4
+ opencv-python
5
+ pyyaml
6
+ tb-nightly
7
+ torch>=1.7
8
+ torchvision
9
+ tqdm
10
+ yapf
setup.cfg ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [flake8]
2
+ ignore =
3
+ # line break before binary operator (W503)
4
+ W503,
5
+ # line break after binary operator (W504)
6
+ W504,
7
+ max-line-length=120
8
+
9
+ [yapf]
10
+ based_on_style = pep8
11
+ column_limit = 120
12
+ blank_line_before_nested_class_or_def = true
13
+ split_before_expression_after_opening_paren = true
14
+
15
+ [isort]
16
+ line_length = 120
17
+ multi_line_output = 0
18
+ known_standard_library = pkg_resources,setuptools
19
+ known_first_party = basicsr
20
+ known_third_party = cv2,facexlib,numpy,torch,torchvision,tqdm
21
+ no_lines_before = STDLIB,LOCALFOLDER
22
+ default_section = THIRDPARTY
train.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
1
+ import os.path as osp
2
+
3
+ import archs # noqa: F401
4
+ import data # noqa: F401
5
+ import models # noqa: F401
6
+ from basicsr.train import train_pipeline
7
+
8
+ if __name__ == '__main__':
9
+ root_path = osp.abspath(osp.join(__file__, osp.pardir))
10
+ train_pipeline(root_path)
train_gfpgan_v1.yml ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # general settings
2
+ name: train_GFPGANv1_512
3
+ model_type: GFPGANModel
4
+ num_gpu: 4
5
+ manual_seed: 0
6
+
7
+ # dataset and data loader settings
8
+ datasets:
9
+ train:
10
+ name: FFHQ
11
+ type: FFHQDegradationDataset
12
+ # dataroot_gt: datasets/ffhq/ffhq_512.lmdb
13
+ dataroot_gt: datasets/ffhq/ffhq_512
14
+ io_backend:
15
+ # type: lmdb
16
+ type: disk
17
+
18
+ use_hflip: true
19
+ mean: [0.5, 0.5, 0.5]
20
+ std: [0.5, 0.5, 0.5]
21
+ out_size: 512
22
+
23
+ blur_kernel_size: 41
24
+ kernel_list: ['iso', 'aniso']
25
+ kernel_prob: [0.5, 0.5]
26
+ blur_sigma: [0.1, 10]
27
+ downsample_range: [0.8, 8]
28
+ noise_range: [0, 20]
29
+ jpeg_range: [60, 100]
30
+
31
+ # color jitter and gray
32
+ color_jitter_prob: 0.3
33
+ color_jitter_shift: 20
34
+ color_jitter_pt_prob: 0.3
35
+ gray_prob: 0.01
36
+
37
+ crop_components: true
38
+ component_path: experiments/pretrained_models/FFHQ_eye_mouth_landmarks_512.pth
39
+ eye_enlarge_ratio: 1.4
40
+
41
+ # data loader
42
+ use_shuffle: true
43
+ num_worker_per_gpu: 6
44
+ batch_size_per_gpu: 3
45
+ dataset_enlarge_ratio: 100
46
+ prefetch_mode: ~
47
+
48
+ val:
49
+ # Please modify accordingly to use your own validation
50
+ # Or comment the val block if do not need validation during training
51
+ name: validation
52
+ type: PairedImageDataset
53
+ dataroot_lq: datasets/faces/validation/input
54
+ dataroot_gt: datasets/faces/validation/reference
55
+ io_backend:
56
+ type: disk
57
+ mean: [0.5, 0.5, 0.5]
58
+ std: [0.5, 0.5, 0.5]
59
+ scale: 1
60
+
61
+ # network structures
62
+ network_g:
63
+ type: GFPGANv1
64
+ out_size: 512
65
+ num_style_feat: 512
66
+ channel_multiplier: 1
67
+ resample_kernel: [1, 3, 3, 1]
68
+ decoder_load_path: experiments/pretrained_models/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth
69
+ fix_decoder: true
70
+ num_mlp: 8
71
+ lr_mlp: 0.01
72
+ input_is_latent: true
73
+ different_w: true
74
+ narrow: 1
75
+ sft_half: true
76
+
77
+ network_d:
78
+ type: StyleGAN2Discriminator
79
+ out_size: 512
80
+ channel_multiplier: 1
81
+ resample_kernel: [1, 3, 3, 1]
82
+
83
+ network_d_left_eye:
84
+ type: FacialComponentDiscriminator
85
+
86
+ network_d_right_eye:
87
+ type: FacialComponentDiscriminator
88
+
89
+ network_d_mouth:
90
+ type: FacialComponentDiscriminator
91
+
92
+ network_identity:
93
+ type: ResNetArcFace
94
+ block: IRBlock
95
+ layers: [2, 2, 2, 2]
96
+ use_se: False
97
+
98
+ # path
99
+ path:
100
+ pretrain_network_g: ~
101
+ param_key_g: params_ema
102
+ strict_load_g: ~
103
+ pretrain_network_d: ~
104
+ pretrain_network_d_left_eye: ~
105
+ pretrain_network_d_right_eye: ~
106
+ pretrain_network_d_mouth: ~
107
+ pretrain_network_identity: experiments/pretrained_models/arcface_resnet18.pth
108
+ # resume
109
+ resume_state: ~
110
+ ignore_resume_networks: ['network_identity']
111
+
112
+ # training settings
113
+ train:
114
+ optim_g:
115
+ type: Adam
116
+ lr: !!float 2e-3
117
+ optim_d:
118
+ type: Adam
119
+ lr: !!float 2e-3
120
+ optim_component:
121
+ type: Adam
122
+ lr: !!float 2e-3
123
+
124
+ scheduler:
125
+ type: MultiStepLR
126
+ milestones: [600000, 700000]
127
+ gamma: 0.5
128
+
129
+ total_iter: 800000
130
+ warmup_iter: -1 # no warm up
131
+
132
+ # losses
133
+ # pixel loss
134
+ pixel_opt:
135
+ type: L1Loss
136
+ loss_weight: !!float 1e-1
137
+ reduction: mean
138
+ # L1 loss used in pyramid loss, component style loss and identity loss
139
+ L1_opt:
140
+ type: L1Loss
141
+ loss_weight: 1
142
+ reduction: mean
143
+
144
+ # image pyramid loss
145
+ pyramid_loss_weight: 1
146
+ remove_pyramid_loss: 50000
147
+ # perceptual loss (content and style losses)
148
+ perceptual_opt:
149
+ type: PerceptualLoss
150
+ layer_weights:
151
+ # before relu
152
+ 'conv1_2': 0.1
153
+ 'conv2_2': 0.1
154
+ 'conv3_4': 1
155
+ 'conv4_4': 1
156
+ 'conv5_4': 1
157
+ vgg_type: vgg19
158
+ use_input_norm: true
159
+ perceptual_weight: !!float 1
160
+ style_weight: 50
161
+ range_norm: true
162
+ criterion: l1
163
+ # gan loss
164
+ gan_opt:
165
+ type: GANLoss
166
+ gan_type: wgan_softplus
167
+ loss_weight: !!float 1e-1
168
+ # r1 regularization for discriminator
169
+ r1_reg_weight: 10
170
+ # facial component loss
171
+ gan_component_opt:
172
+ type: GANLoss
173
+ gan_type: vanilla
174
+ real_label_val: 1.0
175
+ fake_label_val: 0.0
176
+ loss_weight: !!float 1
177
+ comp_style_weight: 200
178
+ # identity loss
179
+ identity_weight: 10
180
+
181
+ net_d_iters: 1
182
+ net_d_init_iters: 0
183
+ net_d_reg_every: 16
184
+
185
+ # validation settings
186
+ val:
187
+ val_freq: !!float 5e3
188
+ save_img: true
189
+
190
+ metrics:
191
+ psnr: # metric name, can be arbitrary
192
+ type: calculate_psnr
193
+ crop_border: 0
194
+ test_y_channel: false
195
+
196
+ # logging settings
197
+ logger:
198
+ print_freq: 100
199
+ save_checkpoint_freq: !!float 5e3
200
+ use_tb_logger: true
201
+ wandb:
202
+ project: ~
203
+ resume_id: ~
204
+
205
+ # dist training settings
206
+ dist_params:
207
+ backend: nccl
208
+ port: 29500
209
+
210
+ find_unused_parameters: true