init repository

.gitignore

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+*.zip
+*.pth
+*.jpg
+*.jpeg
+*.tiff
+*.png
+*.PNG
+*.JPG
+*.JPEG
+*.TIFF
+.vscode
+.mypy*
+__pycache__
+.ipynb_checkpoints
+.eggs/*
+*.log
+*.txt
+*.egg*
+tb_logger/*
+*.tiff
+*.png
+*.jpg
+*:Zone.Identifier
+**.pth
+**.tiff
+**:Zone.Identifier
+**.png
+**.mdb
+events.**
+**/events.**
+**.state
+*.mat

pyproject.toml

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+[tool.poetry]
+name = "pytorch-env"
+version = "0.1.0"
+description = ""
+authors = ["Alejandro Amar <alejandroamar66@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+torch = {version="^2.2.1", source="torch"}
+torchaudio = { version = "^2.2.0", source="torch"}
+torchvision = { version = "^0.17", source="torch"}
+matplotlib = "^3.8.3"
+scikit-learn = "^1.4.1.post1"
+scikit-image = "^0.22.0"
+opencv-python = "^4.9.0.80"
+yacs = "^0.1.8"
+joblib = "^1.3.2"
+natsort = "^8.4.0"
+h5py = "^3.10.0"
+tqdm = "^4.66.2"
+einops = "^0.7.0"
+gdown = "^5.1.0"
+addict = "^2.4.0"
+future = "^1.0.0"
+lmdb = "^1.4.1"
+numpy = "^1.26.4"
+pyyaml = "^6.0.1"
+requests = "^2.31.0"
+scipy = "^1.12.0"
+tb-nightly = "^2.17.0a20240318"
+yapf = "^0.40.2"
+lpips = "^0.1.4"
+icecream = "^2.1.3"
+mypy = "^1.9.0"
+pandas = "^2.2.2"
+prettytable = "^3.10.0"
+torchsummary = "^1.5.1"
+linformer = "^0.2.3"
+timm = "^0.9.16"
+ptflops = "^0.7.2.2"
+tornado = "^6.4"
+jupyterlab = "^4.2.0"
+
+[[tool.poetry.source]]
+
+name = "torch"
+url = "https://download.pytorch.org/whl/cu121"
+priority  = "supplemental"
+
+
+
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.29.3"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

pytorch_env/transformers_ruben/Uformer/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/

pytorch_env/transformers_ruben/Uformer/LICENSE

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+MIT License
+
+Copyright (c) 2022 Zhendong Wang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

pytorch_env/transformers_ruben/Uformer/README.md

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+# Uformer: A General U-Shaped Transformer for Image Restoration (CVPR 2022)
+<b>Zhendong Wang, <a href='https://vinthony.github.io'>Xiaodong Cun</a>, <a href='https://jianminbao.github.io/'>Jianmin Bao</a>, <a href='http://staff.ustc.edu.cn/~zhwg/'>Wengang Zhou</a>, <a href='http://people.ucas.ac.cn/~jzliu?language=en'>Jianzhuang Liu</a>, <a href='http://staff.ustc.edu.cn/~lihq/en/'>Houqiang Li </a> </b>
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/deblurring-on-realblur-j-trained-on-gopro)](https://paperswithcode.com/sota/deblurring-on-realblur-j-trained-on-gopro?p=uformer-a-general-u-shaped-transformer-for) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/deblurring-on-realblur-r-trained-on-gopro)](https://paperswithcode.com/sota/deblurring-on-realblur-r-trained-on-gopro?p=uformer-a-general-u-shaped-transformer-for)	
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/image-denoising-on-dnd)](https://paperswithcode.com/sota/image-denoising-on-dnd?p=uformer-a-general-u-shaped-transformer-for) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/image-denoising-on-sidd)](https://paperswithcode.com/sota/image-denoising-on-sidd?p=uformer-a-general-u-shaped-transformer-for)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/deblurring-on-gopro)](https://paperswithcode.com/sota/deblurring-on-gopro?p=uformer-a-general-u-shaped-transformer-for) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/deblurring-on-hide-trained-on-gopro)](https://paperswithcode.com/sota/deblurring-on-hide-trained-on-gopro?p=uformer-a-general-u-shaped-transformer-for)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/image-defocus-deblurring-on-dpd)](https://paperswithcode.com/sota/image-defocus-deblurring-on-dpd?p=uformer-a-general-u-shaped-transformer-for)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/uformer-a-general-u-shaped-transformer-for/image-enhancement-on-tip-2018)](https://paperswithcode.com/sota/image-enhancement-on-tip-2018?p=uformer-a-general-u-shaped-transformer-for)
+
+Paper link: [[Arxiv]](https://arxiv.org/abs/2106.03106) [[CVPR]](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Uformer_A_General_U-Shaped_Transformer_for_Image_Restoration_CVPR_2022_paper.pdf)
+
+
+### Update:
+* **2022.07.06** Upload new codes and models for our Uformer. 
+* **2022.04.09** Upload results of Uformer on denoising (SIDD, DND), motion deblurring (GoPro, HIDE, RealBlur-J/-R), and defocus deblurring (DPDD). 
+* **2022.03.02** Uformer has been accepted by CVPR 2022! :fire:
+* **2021.11.30** Update Uformer in [Arxiv link](https://arxiv.org/abs/2106.03106). The new code, models and results will be uploaded.
+* **2021.10.28** Release the results of Uformer32 on SIDD and DND.
+* **2021.09.30** Release pre-trained Uformer16 for SIDD denoising.
+* **2021.08.19** Release a pre-trained model(Uformer32)! Add a script for FLOP/GMAC calculation.
+* **2021.07.29** Add a script for testing the pre-trained model on the arbitrary-resolution images.
+
+<hr>
+<i>In this paper, we present Uformer, an effective and efficient Transformer-based architecture, in which we build a hierarchical encoder-decoder network using the Transformer block for image restoration. Uformer has two core designs to make it suitable for this task. The first key element is a local-enhanced window Transformer block, where we use non-overlapping window-based self-attention to reduce the computational requirement and employ the depth-wise convolution in the feed-forward network to further improve its potential for capturing local context. The second key element is that we explore three skip-connection schemes to effectively deliver information from the encoder to the decoder. Powered by these two designs, Uformer enjoys a high capability for capturing useful dependencies for image restoration. Extensive experiments on several image restoration tasks demonstrate the superiority of Uformer, including image denoising, deraining, deblurring and demoireing. We expect that our work will encourage further research to explore Transformer-based architectures for low-level vision tasks.</i>
+
+![Uformer](fig/Uformer.png)
+
+## Package dependencies
+The project is built with PyTorch 1.9.0, Python3.7, CUDA11.1. For package dependencies, you can install them by:
+```bash
+pip install -r requirements.txt
+```
+
+## Pretrained model
+- Uformer_B: [SIDD](https://mailustceducn-my.sharepoint.com/:u:/g/personal/zhendongwang_mail_ustc_edu_cn/Ea7hMP82A0xFlOKPlQnBJy0B9gVP-1MJL75mR4QKBMGc2w?e=iOz0zz) |
+[GoPro](https://mailustceducn-my.sharepoint.com/:u:/g/personal/zhendongwang_mail_ustc_edu_cn/EfCPoTSEKJRAshoE6EAC_3YB7oNkbLUX6AUgWSCwoJe0oA?e=jai90x)
+
+## Results from the pretrained model
+- Uformer_B: [SIDD](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/EtcRYRDGWhBIlQa3EYBp4FYBao7ZZT2dPc5k1Qe-CdPh3A?e=PjBMub) | [DND](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/Ekv3A5ic_4RChFa9XXquF_MB8M8tFd7spyHGJi_8obycnA?e=W7xeHe) | [GoPro](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/ElFalK0Qb8NHnyvhkSe1APgB5D0urGRMLnu2nBXJhtzdIw?e=D2XBhS) | [HIDE](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/Eh4p1_kZ95xIopXDNyhl-Q0B65xX6C3J_fL-TQDbgvofqQ?e=8766eT) | [RealBlur-J](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/EpHFC9FauEpHhJDsFruEmmQBJ4_ZZaMgjaO9SHmB_vocaA?e=3a4b8A) | [RealBlur-R](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/Eo2EC8rmkapNu9CxcYLwFpYBD8tX8pvfX_60jJIP8TGgGQ?e=yGbkQ8) | [DPDD](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/EvVAI84ZvlNChWsZA6QY4IkBc201zdTAs_g2Ufd5l0FgIQ?e=2DTlah)
+
+
+## Data preparation 
+### Denoising
+For training data of SIDD, you can download the SIDD-Medium dataset from the [official url](https://www.eecs.yorku.ca/~kamel/sidd/dataset.php).
+Then generate training patches for training by:
+```python
+python3 generate_patches_SIDD.py --src_dir ../SIDD_Medium_Srgb/Data --tar_dir ../datasets/denoising/sidd/train
+```
+
+For evaluation on SIDD and DND, you can download data from [here](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/Ev832uKaw2JJhwROKqiXGfMBttyFko_zrDVzfSbFFDoi4Q?e=S3p5hQ).
+
+
+### Deblurring
+For training on GoPro, and evaluation on GoPro, HIDE, RealBlur-J and RealBlur-R, you can download data from [here](https://mailustceducn-my.sharepoint.com/:f:/g/personal/zhendongwang_mail_ustc_edu_cn/Ev832uKaw2JJhwROKqiXGfMBttyFko_zrDVzfSbFFDoi4Q?e=S3p5hQ).
+
+
+Then put all the denoising data into `../datasets/denoising`, and all the deblurring data into `../datasets/deblurring`.
+
+## Training
+### Denoising
+To train Uformer on SIDD, you can begin the training by:
+
+```sh
+sh script/train_denoise.sh
+```
+### Deblurring
+To train Uformer on GoPro, you can begin the training by:
+
+```sh
+sh script/train_motiondeblur.sh
+```
+
+
+## Evaluation
+To evaluate Uformer, you can run:
+
+```sh
+sh script/test.sh
+```
+For evaluate on each dataset, you should uncomment corresponding line.
+
+## Computational Cost
+
+We provide a simple script to calculate the flops by ourselves, a simple script has been added in `model.py`. You can change the configuration and run:
+
+```python
+python3 model.py
+```
+
+> The manual calculation of GMacs in this repo differs slightly from the main paper, but they do not influence the conclusion. We will correct the paper later.
+
+
+## Citation
+If you find this project useful in your research, please consider citing:
+
+```
+@InProceedings{Wang_2022_CVPR,
+    author    = {Wang, Zhendong and Cun, Xiaodong and Bao, Jianmin and Zhou, Wengang and Liu, Jianzhuang and Li, Houqiang},
+    title     = {Uformer: A General U-Shaped Transformer for Image Restoration},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2022},
+    pages     = {17683-17693}
+}
+```
+
+## Acknowledgement
+
+This code borrows heavily from [MIRNet](https://github.com/swz30/MIRNet) and [SwinTransformer](https://github.com/microsoft/Swin-Transformer).
+
+
+## Contact
+Please contact us if there is any question or suggestion(Zhendong Wang ZhendongWang6@outlook.com, Xiaodong Cun vinthony@gmail.com).